Estimation of volumetric breast density for breast cancer risk prediction
Pawluczyk, Olga; Yaffe, Martin J.; Boyd, Norman F.; Jong, Roberta A.
2000-04-01
Mammographic density (MD) has been shown to be a strong risk predictor for breast cancer. Compared to subjective assessment by a radiologist, computer-aided analysis of digitized mammograms provides a quantitative and more reproducible method for assessing breast density. However, the current methods of estimating breast density based on the area of bright signal in a mammogram do not reflect the true, volumetric quantity of dense tissue in the breast. A computerized method to estimate the amount of radiographically dense tissue in the overall volume of the breast has been developed to provide an automatic, user-independent tool for breast cancer risk assessment. The procedure for volumetric density estimation consists of first correcting the image for inhomogeneity, then performing a volume density calculation. First, optical sensitometry is used to convert all images to the logarithm of relative exposure (LRE), in order to simplify the image correction operations. The field non-uniformity correction, which takes into account heel effect, inverse square law, path obliquity and intrinsic field and grid non- uniformity is obtained by imaging a spherical section PMMA phantom. The processed LRE image of the phantom is then used as a correction offset for actual mammograms. From information about the thickness and placement of the breast, as well as the parameters of a breast-like calibration step wedge placed in the mammogram, MD of the breast is calculated. Post processing and a simple calibration phantom enable user- independent, reliable and repeatable volumetric estimation of density in breast-equivalent phantoms. Initial results obtained on known density phantoms show the estimation to vary less than 5% in MD from the actual value. This can be compared to estimated mammographic density differences of 30% between the true and non-corrected values. Since a more simplistic breast density measurement based on the projected area has been shown to be a strong indicator
Mammography density estimation with automated volumetic breast density measurement
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Ko, Su Yeon; Kim, Eun Kyung; Kim, Min Jung; Moon, Hee Jung [Dept. of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul (Korea, Republic of)
2014-06-15
To compare automated volumetric breast density measurement (VBDM) with radiologists' evaluations based on the Breast Imaging Reporting and Data System (BI-RADS), and to identify the factors associated with technical failure of VBDM. In this study, 1129 women aged 19-82 years who underwent mammography from December 2011 to January 2012 were included. Breast density evaluations by radiologists based on BI-RADS and by VBDM (Volpara Version 1.5.1) were compared. The agreement in interpreting breast density between radiologists and VBDM was determined based on four density grades (D1, D2, D3, and D4) and a binary classification of fatty (D1-2) vs. dense (D3-4) breast using kappa statistics. The association between technical failure of VBDM and patient age, total breast volume, fibroglandular tissue volume, history of partial mastectomy, the frequency of mass > 3 cm, and breast density was analyzed. The agreement between breast density evaluations by radiologists and VBDM was fair (k value = 0.26) when the four density grades (D1/D2/D3/D4) were used and moderate (k value = 0.47) for the binary classification (D1-2/D3-4). Twenty-seven women (2.4%) showed failure of VBDM. Small total breast volume, history of partial mastectomy, and high breast density were significantly associated with technical failure of VBDM (p 0.001 to 0.015). There is fair or moderate agreement in breast density evaluation between radiologists and VBDM. Technical failure of VBDM may be related to small total breast volume, a history of partial mastectomy, and high breast density.
Chen, Biao; Ruth, Chris; Jing, Zhenxue; Ren, Baorui; Smith, Andrew; Kshirsagar, Ashwini
2014-03-01
Breast density has been identified to be a risk factor of developing breast cancer and an indicator of lesion diagnostic obstruction due to masking effect. Volumetric density measurement evaluates fibro-glandular volume, breast volume, and breast volume density measures that have potential advantages over area density measurement in risk assessment. One class of volume density computing methods is based on the finding of the relative fibro-glandular tissue attenuation with regards to the reference fat tissue, and the estimation of the effective x-ray tissue attenuation differences between the fibro-glandular and fat tissue is key to volumetric breast density computing. We have modeled the effective attenuation difference as a function of actual x-ray skin entrance spectrum, breast thickness, fibro-glandular tissue thickness distribution, and detector efficiency. Compared to other approaches, our method has threefold advantages: (1) avoids the system calibration-based creation of effective attenuation differences which may introduce tedious calibrations for each imaging system and may not reflect the spectrum change and scatter induced overestimation or underestimation of breast density; (2) obtains the system specific separate and differential attenuation values of fibroglandular and fat for each mammographic image; and (3) further reduces the impact of breast thickness accuracy to volumetric breast density. A quantitative breast volume phantom with a set of equivalent fibro-glandular thicknesses has been used to evaluate the volume breast density measurement with the proposed method. The experimental results have shown that the method has significantly improved the accuracy of estimating breast density.
Comparison of breast percent density estimation from raw versus processed digital mammograms
Li, Diane; Gavenonis, Sara; Conant, Emily; Kontos, Despina
2011-03-01
We compared breast percent density (PD%) measures obtained from raw and post-processed digital mammographic (DM) images. Bilateral raw and post-processed medio-lateral oblique (MLO) images from 81 screening studies were retrospectively analyzed. Image acquisition was performed with a GE Healthcare DS full-field DM system. Image post-processing was performed using the PremiumViewTM algorithm (GE Healthcare). Area-based breast PD% was estimated by a radiologist using a semi-automated image thresholding technique (Cumulus, Univ. Toronto). Comparison of breast PD% between raw and post-processed DM images was performed using the Pearson correlation (r), linear regression, and Student's t-test. Intra-reader variability was assessed with a repeat read on the same data-set. Our results show that breast PD% measurements from raw and post-processed DM images have a high correlation (r=0.98, R2=0.95, ptest comparison of breast PD% between the raw and the post-processed images showed a statistically significant difference equal to 1.2% (p = 0.006). Our results suggest that the relatively small magnitude of the absolute difference in PD% between raw and post-processed DM images is unlikely to be clinically significant in breast cancer risk stratification. Therefore, it may be feasible to use post-processed DM images for breast PD% estimation in clinical settings. Since most breast imaging clinics routinely use and store only the post-processed DM images, breast PD% estimation from post-processed data may accelerate the integration of breast density in breast cancer risk assessment models used in clinical practice.
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Tagliafico, Alberto [University of Genoa, Institute of Anatomy, Department of Experimental Medicine, Genoa (Italy); Tagliafico, Giulio [University of Genoa, Dipartimento di Chimica e Chimica Industriale (DCCI), Genoa (Italy); Astengo, Davide [University of Genoa, Radiology Department, Genoa, Genova (Italy); Cavagnetto, Francesca; Rosasco, Raffaella [IRCCS Azienda Ospedaliera Universitaria San Martino, Department of Medical Physics, Ist Istituto Nazionale per la Ricerca sul Cancro, Genoa, Genova (Italy); Rescinito, Giuseppe; Monetti, Francesco; Calabrese, Massimo [IRCCS Azienda Ospedaliera Universitaria San Martino, Department of Diagnostic Senology, Ist Istituto Nazionale per la Ricerca sul Cancro, Genoa (Italy)
2012-06-15
To compare breast density on digital mammography and digital breast tomosynthesis using fully automated software. Following institutional approval and written informed consent from all participating women, both digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) were obtained. Breast percentage density was calculated with software on DBT and FFDM. Fifty consecutive patients (mean age, 51 years; range, 35-83 years) underwent both FFDM and DBT. Using a method based on the integral curve, breast density showed higher results on FFDM (68.1 {+-} 12.1 for FFDM and 51.9 {+-} 6.5 for DBT). FFDM overestimated breast density in 16.2% (P < 0.0001). Using a method based on maximum entropy thresholding, breast density showed higher results on FFDM (68.1 {+-} 12.1 for FFDM and 51.9 {+-} 6.5 for DBT). FFDM overestimated breast density in 11.4% (P < 0.0001). There was a good correlation among BI-RADS categories on a four-grade scale and the density evaluated with DBT and FFDM (r = 0.54, P < 0.01 and r = 0.44, P < 0.01). Breast density appeared to be significantly underestimated on digital breast tomosynthesis. Breast density is considered to be an independent risk factor for cancer Density can be assessed on full-field digital mammography and digital breast tomosynthesis Objective automated estimation of breast density eliminates subjectivity Automated estimation is more accurate than BI-RADS quantitative evaluation Breast density may be significantly underestimated on digital breast tomosynthesis. (orig.)
Robust estimation of mammographic breast density: a patient-based approach
Heese, Harald S.; Erhard, Klaus; Gooßen, Andre; Bulow, Thomas
2012-02-01
Breast density has become an established risk indicator for developing breast cancer. Current clinical practice reflects this by grading mammograms patient-wise as entirely fat, scattered fibroglandular, heterogeneously dense, or extremely dense based on visual perception. Existing (semi-) automated methods work on a per-image basis and mimic clinical practice by calculating an area fraction of fibroglandular tissue (mammographic percent density). We suggest a method that follows clinical practice more strictly by segmenting the fibroglandular tissue portion directly from the joint data of all four available mammographic views (cranio-caudal and medio-lateral oblique, left and right), and by subsequently calculating a consistently patient-based mammographic percent density estimate. In particular, each mammographic view is first processed separately to determine a region of interest (ROI) for segmentation into fibroglandular and adipose tissue. ROI determination includes breast outline detection via edge-based methods, peripheral tissue suppression via geometric breast height modeling, and - for medio-lateral oblique views only - pectoral muscle outline detection based on optimizing a three-parameter analytic curve with respect to local appearance. Intensity harmonization based on separately acquired calibration data is performed with respect to compression height and tube voltage to facilitate joint segmentation of available mammographic views. A Gaussian mixture model (GMM) on the joint histogram data with a posteriori calibration guided plausibility correction is finally employed for tissue separation. The proposed method was tested on patient data from 82 subjects. Results show excellent correlation (r = 0.86) to radiologist's grading with deviations ranging between -28%, (q = 0.025) and +16%, (q = 0.975).
Chen, Lin; Ray, Shonket; Keller, Brad M; Pertuz, Said; McDonald, Elizabeth S; Conant, Emily F; Kontos, Despina
2016-09-01
Purpose To investigate the impact of radiation dose on breast density estimation in digital mammography. Materials and Methods With institutional review board approval and Health Insurance Portability and Accountability Act compliance under waiver of consent, a cohort of women from the American College of Radiology Imaging Network Pennsylvania 4006 trial was retrospectively analyzed. All patients underwent breast screening with a combination of dose protocols, including standard full-field digital mammography, low-dose digital mammography, and digital breast tomosynthesis. A total of 5832 images from 486 women were analyzed with previously validated, fully automated software for quantitative estimation of density. Clinical Breast Imaging Reporting and Data System (BI-RADS) density assessment results were also available from the trial reports. The influence of image acquisition radiation dose on quantitative breast density estimation was investigated with analysis of variance and linear regression. Pairwise comparisons of density estimations at different dose levels were performed with Student t test. Agreement of estimation was evaluated with quartile-weighted Cohen kappa values and Bland-Altman limits of agreement. Results Radiation dose of image acquisition did not significantly affect quantitative density measurements (analysis of variance, P = .37 to P = .75), with percent density demonstrating a high overall correlation between protocols (r = 0.88-0.95; weighted κ = 0.83-0.90). However, differences in breast percent density (1.04% and 3.84%, P digital mammography are not substantially affected by variations in radiation dose; thus, the use of low-dose techniques for the purpose of density estimation may be feasible. (©) RSNA, 2016 Online supplemental material is available for this article. PMID:27002418
Breast Density and Your Breast Mammogram Report
Breast Density and Your Mammogram Report Regular mammograms are the best way to find breast cancer early. But if ... But in some women, there’s little change. Breast density is very common, and is not abnormal. How ...
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Purpose: High breast density, as measured by mammography, is associated with increased breast cancer risk, but standard methods of assessment have limitations including 2D representation of breast tissue, distortion due to breast compression, and use of ionizing radiation. Ultrasound tomography (UST) is a novel imaging method that averts these limitations and uses sound speed measures rather than x-ray imaging to estimate breast density. The authors evaluated the reproducibility of measures of speed of sound and changes in this parameter using UST. Methods: One experienced and five newly trained raters measured sound speed in serial UST scans for 22 women (two scans per person) to assess inter-rater reliability. Intrarater reliability was assessed for four raters. A random effects model was used to calculate the percent variation in sound speed and change in sound speed attributable to subject, scan, rater, and repeat reads. The authors estimated the intraclass correlation coefficients (ICCs) for these measures based on data from the authors’ experienced rater. Results: Median (range) time between baseline and follow-up UST scans was five (1–13) months. Contributions of factors to sound speed variance were differences between subjects (86.0%), baseline versus follow-up scans (7.5%), inter-rater evaluations (1.1%), and intrarater reproducibility (∼0%). When evaluating change in sound speed between scans, 2.7% and ∼0% of variation were attributed to inter- and intrarater variation, respectively. For the experienced rater’s repeat reads, agreement for sound speed was excellent (ICC = 93.4%) and for change in sound speed substantial (ICC = 70.4%), indicating very good reproducibility of these measures. Conclusions: UST provided highly reproducible sound speed measurements, which reflect breast density, suggesting that UST has utility in sensitively assessing change in density
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Khodr, Zeina G.; Pfeiffer, Ruth M.; Gierach, Gretchen L., E-mail: GierachG@mail.nih.gov [Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive MSC 9774, Bethesda, Maryland 20892 (United States); Sak, Mark A.; Bey-Knight, Lisa [Karmanos Cancer Institute, Wayne State University, 4100 John R, Detroit, Michigan 48201 (United States); Duric, Nebojsa; Littrup, Peter [Karmanos Cancer Institute, Wayne State University, 4100 John R, Detroit, Michigan 48201 and Delphinus Medical Technologies, 46701 Commerce Center Drive, Plymouth, Michigan 48170 (United States); Ali, Haythem; Vallieres, Patricia [Henry Ford Health System, 2799 W Grand Boulevard, Detroit, Michigan 48202 (United States); Sherman, Mark E. [Division of Cancer Prevention, National Cancer Institute, Department of Health and Human Services, 9609 Medical Center Drive MSC 9774, Bethesda, Maryland 20892 (United States)
2015-10-15
Purpose: High breast density, as measured by mammography, is associated with increased breast cancer risk, but standard methods of assessment have limitations including 2D representation of breast tissue, distortion due to breast compression, and use of ionizing radiation. Ultrasound tomography (UST) is a novel imaging method that averts these limitations and uses sound speed measures rather than x-ray imaging to estimate breast density. The authors evaluated the reproducibility of measures of speed of sound and changes in this parameter using UST. Methods: One experienced and five newly trained raters measured sound speed in serial UST scans for 22 women (two scans per person) to assess inter-rater reliability. Intrarater reliability was assessed for four raters. A random effects model was used to calculate the percent variation in sound speed and change in sound speed attributable to subject, scan, rater, and repeat reads. The authors estimated the intraclass correlation coefficients (ICCs) for these measures based on data from the authors’ experienced rater. Results: Median (range) time between baseline and follow-up UST scans was five (1–13) months. Contributions of factors to sound speed variance were differences between subjects (86.0%), baseline versus follow-up scans (7.5%), inter-rater evaluations (1.1%), and intrarater reproducibility (∼0%). When evaluating change in sound speed between scans, 2.7% and ∼0% of variation were attributed to inter- and intrarater variation, respectively. For the experienced rater’s repeat reads, agreement for sound speed was excellent (ICC = 93.4%) and for change in sound speed substantial (ICC = 70.4%), indicating very good reproducibility of these measures. Conclusions: UST provided highly reproducible sound speed measurements, which reflect breast density, suggesting that UST has utility in sensitively assessing change in density.
Comparing measurements of breast density
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Highnam, R [Highnam Associates Limited, Wellington (New Zealand); Jeffreys, M [Massey University, Wellington (New Zealand); McCormack, V [Department of Epidemiology and Population Health, London School of Hygiene and Medicine, London (United Kingdom); Warren, R [Addenbrooke' s Hospital, Cambridge (United Kingdom); Smith, G Davey [Department of Clinical Epidemiology, University of Bristol, Bristol (United Kingdom); Brady, M [Department of Engineering Science, University of Oxford, Oxford (United Kingdom)
2007-09-21
Breast density measurements can be made from mammograms using either area-based methods, such as the six category classification (SCC), or volumetric based methods, such as the standard mammogram form (SMF). Previously, we have shown how both types of methods generate breast density estimates which are generally close. In this paper, we switch our attention to the question of why, for certain cases, they provide widely differing estimates. First, we show how the underlying physical models of the breast employed in the methods need to be consistent, and how area-based methods are susceptible to projection effects. We then analyse a set of patients whose mammograms show large differences between their SCC and SMF assessments. More precisely, 12% of 657 patients were found to fall into this category. Of these, 2.7% were attributable to errors either in the SMF segmentation algorithms, human error in SCC categorization or poor image exposure. More importantly, 9.3% of the cases appear to be due to fundamental differences between the area- and volume-based techniques. We conclude by suggesting how we might remove half of those discrepancies by introducing a new categorization of the SMF estimates based on the breast thickness. We note however, that this still leaves 6% of patients with large differences between SMF and SCC estimates. We discuss why it might not be appropriate to assume SMF (or any volume measure) has a similar breast cancer risk prediction capability to SCC.
Imaging Breast Density: Established and Emerging Modalities
Directory of Open Access Journals (Sweden)
Jeon-Hor Chen
2015-12-01
Full Text Available Mammographic density has been proven as an independent risk factor for breast cancer. Women with dense breast tissue visible on a mammogram have a much higher cancer risk than women with little density. A great research effort has been devoted to incorporate breast density into risk prediction models to better estimate each individual’s cancer risk. In recent years, the passage of breast density notification legislation in many states in USA requires that every mammography report should provide information regarding the patient’s breast density. Accurate definition and measurement of breast density are thus important, which may allow all the potential clinical applications of breast density to be implemented. Because the two-dimensional mammography-based measurement is subject to tissue overlapping and thus not able to provide volumetric information, there is an urgent need to develop reliable quantitative measurements of breast density. Various new imaging technologies are being developed. Among these new modalities, volumetric mammographic density methods and three-dimensional magnetic resonance imaging are the most well studied. Besides, emerging modalities, including different x-ray–based, optical imaging, and ultrasound-based methods, have also been investigated. All these modalities may either overcome some fundamental problems related to mammographic density or provide additional density and/or compositional information. The present review article aimed to summarize the current established and emerging imaging techniques for the measurement of breast density and the evidence of the clinical use of these density methods from the literature.
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Keller, Brad M.; Nathan, Diane L.; Wang Yan; Zheng Yuanjie; Gee, James C.; Conant, Emily F.; Kontos, Despina [Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States)
2012-08-15
Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., 'FOR PROCESSING') and vendor postprocessed (i.e., 'FOR PRESENTATION'), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely
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Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., “FOR PROCESSING”) and vendor postprocessed (i.e., “FOR PRESENTATION”), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which
Directory of Open Access Journals (Sweden)
Abbas Cheddad
Full Text Available INTRODUCTION: Mammographic density, the white radiolucent part of a mammogram, is a marker of breast cancer risk and mammographic sensitivity. There are several means of measuring mammographic density, among which are area-based and volumetric-based approaches. Current volumetric methods use only unprocessed, raw mammograms, which is a problematic restriction since such raw mammograms are normally not stored. We describe fully automated methods for measuring both area and volumetric mammographic density from processed images. METHODS: The data set used in this study comprises raw and processed images of the same view from 1462 women. We developed two algorithms for processed images, an automated area-based approach (CASAM-Area and a volumetric-based approach (CASAM-Vol. The latter method was based on training a random forest prediction model with image statistical features as predictors, against a volumetric measure, Volpara, for corresponding raw images. We contrast the three methods, CASAM-Area, CASAM-Vol and Volpara directly and in terms of association with breast cancer risk and a known genetic variant for mammographic density and breast cancer, rs10995190 in the gene ZNF365. Associations with breast cancer risk were evaluated using images from 47 breast cancer cases and 1011 control subjects. The genetic association analysis was based on 1011 control subjects. RESULTS: All three measures of mammographic density were associated with breast cancer risk and rs10995190 (p0.10 for risk, p>0.03 for rs10995190. CONCLUSIONS: Our results show that it is possible to obtain reliable automated measures of volumetric and area mammographic density from processed digital images. Area and volumetric measures of density on processed digital images performed similar in terms of risk and genetic association.
Direct Density Derivative Estimation.
Sasaki, Hiroaki; Noh, Yung-Kyun; Niu, Gang; Sugiyama, Masashi
2016-06-01
Estimating the derivatives of probability density functions is an essential step in statistical data analysis. A naive approach to estimate the derivatives is to first perform density estimation and then compute its derivatives. However, this approach can be unreliable because a good density estimator does not necessarily mean a good density derivative estimator. To cope with this problem, in this letter, we propose a novel method that directly estimates density derivatives without going through density estimation. The proposed method provides computationally efficient estimation for the derivatives of any order on multidimensional data with a hyperparameter tuning method and achieves the optimal parametric convergence rate. We further discuss an extension of the proposed method by applying regularized multitask learning and a general framework for density derivative estimation based on Bregman divergences. Applications of the proposed method to nonparametric Kullback-Leibler divergence approximation and bandwidth matrix selection in kernel density estimation are also explored. PMID:27140943
Keller, Brad M.; Nathan, Diane L.; Conant, Emily F.; Kontos, Despina
2012-03-01
Breast percent density (PD%), as measured mammographically, is one of the strongest known risk factors for breast cancer. While the majority of studies to date have focused on PD% assessment from digitized film mammograms, digital mammography (DM) is becoming increasingly common, and allows for direct PD% assessment at the time of imaging. This work investigates the accuracy of a generalized linear model-based (GLM) estimation of PD% from raw and postprocessed digital mammograms, utilizing image acquisition physics, patient characteristics and gray-level intensity features of the specific image. The model is trained in a leave-one-woman-out fashion on a series of 81 cases for which bilateral, mediolateral-oblique DM images were available in both raw and post-processed format. Baseline continuous and categorical density estimates were provided by a trained breast-imaging radiologist. Regression analysis is performed and Pearson's correlation, r, and Cohen's kappa, κ, are computed. The GLM PD% estimation model performed well on both processed (r=0.89, pbreast PD% could allow for a reproducible estimation of breast density, providing a rapid risk assessment tool for clinical practice.
Information Geometric Density Estimation
Sun, Ke; Marchand-Maillet, Stéphane
2014-01-01
We investigate kernel density estimation where the kernel function varies from point to point. Density estimation in the input space means to find a set of coordinates on a statistical manifold. This novel perspective helps to combine efforts from information geometry and machine learning to spawn a family of density estimators. We present example models with simulations. We discuss the principle and theory of such density estimation.
Adaptive kernel density estimation
Philippe Van Kerm
2003-01-01
This insert describes the module akdensity. akdensity extends the official kdensity that estimates density functions by the kernel method. The extensions are of two types: akdensity allows the use of an "adaptive kernel" approach with varying, rather than fixed, bandwidths; and akdensity estimates pointwise variability bands around the estimated density functions. Copyright 2003 by Stata Corporation.
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Purpose: Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment. Methods: In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's pairedt-test, and Dice's similarity coefficients (DSC). Results: The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers’ manual segmentation, the proposed FCM-Atlas method achieves a correlation ofr = 0
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Wu, Shandong; Weinstein, Susan P.; Conant, Emily F.; Kontos, Despina, E-mail: despina.kontos@uphs.upenn.edu [Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States)
2013-12-15
Purpose: Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment. Methods: In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's pairedt-test, and Dice's similarity coefficients (DSC). Results: The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers’ manual segmentation, the proposed FCM-Atlas method achieves a
Automatic breast density classification using neural network
Arefan, D.; Talebpour, A.; Ahmadinejhad, N.; Kamali Asl, A.
2015-12-01
According to studies, the risk of breast cancer directly associated with breast density. Many researches are done on automatic diagnosis of breast density using mammography. In the current study, artifacts of mammograms are removed by using image processing techniques and by using the method presented in this study, including the diagnosis of points of the pectoral muscle edges and estimating them using regression techniques, pectoral muscle is detected with high accuracy in mammography and breast tissue is fully automatically extracted. In order to classify mammography images into three categories: Fatty, Glandular, Dense, a feature based on difference of gray-levels of hard tissue and soft tissue in mammograms has been used addition to the statistical features and a neural network classifier with a hidden layer. Image database used in this research is the mini-MIAS database and the maximum accuracy of system in classifying images has been reported 97.66% with 8 hidden layers in neural network.
Automatic breast density classification using neural network
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According to studies, the risk of breast cancer directly associated with breast density. Many researches are done on automatic diagnosis of breast density using mammography. In the current study, artifacts of mammograms are removed by using image processing techniques and by using the method presented in this study, including the diagnosis of points of the pectoral muscle edges and estimating them using regression techniques, pectoral muscle is detected with high accuracy in mammography and breast tissue is fully automatically extracted. In order to classify mammography images into three categories: Fatty, Glandular, Dense, a feature based on difference of gray-levels of hard tissue and soft tissue in mammograms has been used addition to the statistical features and a neural network classifier with a hidden layer. Image database used in this research is the mini-MIAS database and the maximum accuracy of system in classifying images has been reported 97.66% with 8 hidden layers in neural network
Association between mammographic density and basal-like and luminal A breast cancer subtypes
Razzaghi, Hilda; Troester, Melissa A.; Gierach, Gretchen L.; Olshan, Andrew F.; Yankaskas, Bonnie C.; Millikan, Robert C.
2013-01-01
Introduction Mammographic density is a strong risk factor for breast cancer overall, but few studies have examined the association between mammographic density and specific subtypes of breast cancer, especially aggressive basal-like breast cancers. Because basal-like breast cancers are less frequently screen-detected, it is important to understand how mammographic density relates to risk of basal-like breast cancer. Methods We estimated associations between mammographic density and breast can...
Postmortem validation of breast density using dual-energy mammography
International Nuclear Information System (INIS)
Purpose: Mammographic density has been shown to be an indicator of breast cancer risk and also reduces the sensitivity of screening mammography. Currently, there is no accepted standard for measuring breast density. Dual energy mammography has been proposed as a technique for accurate measurement of breast density. The purpose of this study is to validate its accuracy in postmortem breasts and compare it with other existing techniques. Methods: Forty postmortem breasts were imaged using a dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decomposition was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition. Results: Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively. Conclusions: The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer
Postmortem validation of breast density using dual-energy mammography
Energy Technology Data Exchange (ETDEWEB)
Molloi, Sabee, E-mail: symolloi@uci.edu; Ducote, Justin L.; Ding, Huanjun; Feig, Stephen A. [Department of Radiological Sciences, University of California, Irvine, California 92697 (United States)
2014-08-15
Purpose: Mammographic density has been shown to be an indicator of breast cancer risk and also reduces the sensitivity of screening mammography. Currently, there is no accepted standard for measuring breast density. Dual energy mammography has been proposed as a technique for accurate measurement of breast density. The purpose of this study is to validate its accuracy in postmortem breasts and compare it with other existing techniques. Methods: Forty postmortem breasts were imaged using a dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decomposition was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition. Results: Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively. Conclusions: The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer.
Endocrine determinants of breast density and breast cancer
Verheus, M.
2007-01-01
Worldwide, breast cancer is the most common malignancy among females. The total breast area on a mammogram can be dived in a radiologicaly dense area (glandular and stromal tissue) and a non-dense area (mainly fat tissue). Women with a high proportion of dense breast tissue (percent breast density)
Contingent kernel density estimation.
Directory of Open Access Journals (Sweden)
Scott Fortmann-Roe
Full Text Available Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points. Such error can be the result of imprecise measurements or observation bias. Often this error is negligible and may be disregarded in analysis. In cases where the error is non-negligible, estimation methods should be adjusted to reduce resulting bias. Several modifications of kernel density estimation have been developed to address specific forms of errors. One form of error that has not yet been addressed is the case where observations are nominally placed at the centers of areas from which the points are assumed to have been drawn, where these areas are of varying sizes. In this scenario, the bias arises because the size of the error can vary among points and some subset of points can be known to have smaller error than another subset or the form of the error may change among points. This paper proposes a "contingent kernel density estimation" technique to address this form of error. This new technique adjusts the standard kernel on a point-by-point basis in an adaptive response to changing structure and magnitude of error. In this paper, equations for our contingent kernel technique are derived, the technique is validated using numerical simulations, and an example using the geographic locations of social networking users is worked to demonstrate the utility of the method.
A novel ultrasonic method for measuring breast density and breast cancer risk
Glide-Hurst, Carri K.; Duric, Neb; Littrup, Peter J.
2008-03-01
Women with high mammographic breast density are at 4- to 6-fold increased risk of developing breast cancer compared to women with fatty breasts. However, current breast density estimations rely on mammography, which cannot provide accurate volumetric breast representation. Therefore, we explored two techniques of breast density evaluation via ultrasound tomography. A sample of 93 patients was imaged with our clinical prototype; each dataset contained 45-75 tomograms ranging from near the chest wall through the nipple. Whole breast acoustic velocity was determined by creating image stacks and evaluating the sound speed frequency distribution. Ultrasound percent density (USPD) was determined by segmenting high sound speed areas from each tomogram using k-means clustering, integrating over the entire breast, and dividing by total breast area. Both techniques were independently evaluated using two mammographic density measures: (1) qualitative, determined by a radiologist's visual assessment using BI-RADS Categories, and (2) quantitative, via semi-automatic segmentation to calculate mammographic percent density (MPD) for craniocaudal and medio-lateral oblique mammograms. ~140 m/s difference in acoustic velocity was observed between fatty and dense BI-RADS Categories. Increased sound speed was found with increased BI-RADS Category and quantitative MPD. Furthermore, strong positive associations between USPD, BI-RADS Category, and calculated MPD were observed. These results confirm that utilizing sound speed, both for whole-breast evaluation and segmenting locally, can be implemented to evaluate breast density.
Breast density and mode of detection in relation to breast cancer specific survival: a cohort study
International Nuclear Information System (INIS)
The aim of this study was to examine breast density in relation to breast cancer specific survival and to assess if this potential association was modified by mode of detection. An additional aim was to study whether the established association between mode of detection and survival is modified by breast density. The study included 619 cases from a prospective cohort, The Malmö Diet and Cancer Study. Breast density estimated qualitatively, was analyzed in relation to breast cancer death, in non-symptomatic and symptomatic women, using Cox regression calculating hazard ratios (HR) with 95% confidence intervals. Adjustments were made in several steps for; diagnostic age, tumour size, axillary lymph node involvement, grade, hormone receptor status, body mass index (baseline), diagnostic period, use of hormone replacement therapy at diagnosis and mode of detection. Detection mode in relation to survival was analyzed stratified for breast density. Differences in HR following different adjustments were analyzed by Freedmans%. After adjustment for age and other prognostic factors, women with dense, as compared to fatty breasts, had an increased risk of breast cancer death, HR 2.56:1.07-6.11, with a statistically significant trend over density categories, p = 0.04. In the stratified analysis, the effect was less pronounced in non-symptomatic women, HR 2.04:0.49-8.49 as compared to symptomatic, HR 3.40:1.06-10.90. In the unadjusted model, symptomatic women had a higher risk of breast cancer death, regardless of breast density. Analyzed by Freedmans%, age, tumour size, lymph nodes, grade, diagnostic period, ER and PgR explained 55.5% of the observed differences in mortality between non-symptomatic and symptomatic cases. Additional adjustment for breast density caused only a minor change. High breast density at diagnosis may be associated with decreased breast cancer survival. This association appears to be stronger in women with symptomatic cancers but breast density could
A new look at breast density and breast cancer risk
Haars, G.
2008-01-01
Breast density, as visible on mammograms, comprises connective and epithelial tissue and can be seen to represent the glandular target tissue for breast cancer, whereas the non-dense tissue mainly comprises fat. High percentages of density are established to be one of the strongest risk factors of b
The Estimation of Conditional Densities
Xiaohong Chen; Oliver Linton; Robinson, Peter M.
2001-01-01
We discuss a number of issues in the smoothed nonparametric estimation of kernel conditional probability density functions for stationary processes. The kernel conditional density estimate is a ratio of joint and marginal density estimates. We point out the different implications of leading choices of bandwidths in numerator and denominator for the ability of the estimate to integrate to one and to have finite moments. Again bearing in mind different bandwidth possibilities, we discuss asympt...
Density Estimation with Mercer Kernels
Macready, William G.
2003-01-01
We present a new method for density estimation based on Mercer kernels. The density estimate can be understood as the density induced on a data manifold by a mixture of Gaussians fit in a feature space. As is usual, the feature space and data manifold are defined with any suitable positive-definite kernel function. We modify the standard EM algorithm for mixtures of Gaussians to infer the parameters of the density. One benefit of the approach is it's conceptual simplicity, and uniform applicability over many different types of data. Preliminary results are presented for a number of simple problems.
A Review of Segmentation of Mammographic Images Based on Breast Density
Directory of Open Access Journals (Sweden)
Anusree Mohan
2015-03-01
Full Text Available Breast cancer is one of the leading causes of fatality in women. Mammogram is the effectual modality for early detection of breast cancer. Increased mammographic breast density is a moderate independent risk factor for breast cancer, Radiologists have estimated breast density using four broad categories (BI-RADS swearing on visual assessment of mammograms. The aim of this paper is to review approaches for segmentation of breast regions in mammograms according to breast density. Studies based on density have been undertaken because of the relationship between breast cancer and density. Breast cancer usually occurs in the fibroglandular area of breast tissue, which appears bright on mammograms and is described as breast density. Most of the studies are focused on the classification methods for glandular tissue detection. Others highlighted on the segmentation methods for fibroglandular tissue, while few researchers performed segmentation of the breast anatomical regions based on density. There have also been works on the segmentation of other specific parts of breast regions such as either detection of nipple position, skin-air interface or pectoral muscles. The problems on the evaluation performance of the segmentation results in relation to ground truth are also discussed in this paper.
Cumulative sum quality control for calibrated breast density measurements
Energy Technology Data Exchange (ETDEWEB)
Heine, John J.; Cao Ke; Beam, Craig [Cancer Prevention and Control Division, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612 (United States); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 W. Taylor St., Chicago, Illinois 60612 (United States)
2009-12-15
Purpose: Breast density is a significant breast cancer risk factor. Although various methods are used to estimate breast density, there is no standard measurement for this important factor. The authors are developing a breast density standardization method for use in full field digital mammography (FFDM). The approach calibrates for interpatient acquisition technique differences. The calibration produces a normalized breast density pixel value scale. The method relies on first generating a baseline (BL) calibration dataset, which required extensive phantom imaging. Standardizing prospective mammograms with calibration data generated in the past could introduce unanticipated error in the standardized output if the calibration dataset is no longer valid. Methods: Sample points from the BL calibration dataset were imaged approximately biweekly over an extended timeframe. These serial samples were used to evaluate the BL dataset reproducibility and quantify the serial calibration accuracy. The cumulative sum (Cusum) quality control method was used to evaluate the serial sampling. Results: There is considerable drift in the serial sample points from the BL calibration dataset that is x-ray beam dependent. Systematic deviation from the BL dataset caused significant calibration errors. This system drift was not captured with routine system quality control measures. Cusum analysis indicated that the drift is a sign of system wear and eventual x-ray tube failure. Conclusions: The BL calibration dataset must be monitored and periodically updated, when necessary, to account for sustained system variations to maintain the calibration accuracy.
Mueller-Schimpfle, M P; Brandenbusch, V C; Degenhardt, F; Duda, V; Madjar, H; Mundinger, A; Rathmann, R; Hahn, M
2016-04-01
Mammographic breast density correlates with breast cancer risk and also with the number of false-negative calls. In the USA these facts lead to the "Breast Density and Mammography Reporting Act" of 2011. In the case of mammographically dense breasts, the Working Group on Breast Ultrasound in Germany recommends explaining the advantages of adjunct imaging to women, depending on the individual breast cancer risk. Due to the particular structure of German healthcare, quality-assured breast ultrasound would be the first choice. Possible overdiagnosis, costs, potentially increased emotional stress should be addressed. In high familial breast cancer risk, genetic counselling and an intensified early detection program should be performed. PMID:26882482
Breast Cancer Risk Estimation Using Parenchymal Texture Analysis in Digital Breast Tomosynthesis
Ikejimba, Lynda C.; Kontos, Despina; Maidment, Andrew D. A.
2010-10-01
Mammographic parenchymal texture has been shown to correlate with genetic markers of developing breast cancer. Digital breast tomosynthesis (DBT) is a novel x-ray imaging technique in which tomographic images of the breast are reconstructed from multiple source projections acquired at different angles of the x-ray tube. Compared to digital mammography (DM), DBT eliminates breast tissue overlap, offering superior parenchymal tissue visualization. We hypothesize that texture analysis in DBT could potentially provide a better assessment of parenchymal texture and ultimately result in more accurate assessment of breast cancer risk. As a first step towards validating this hypothesis, we investigated the association between DBT parenchymal texture and breast percent density (PD), a known breast cancer risk factor, and compared it to DM. Bilateral DBT and DM images from 71 women participating in a breast cancer screening trial were analyzed. Filtered-backprojection was used to reconstruct DBT tomographic planes in 1 mm increments with 0.22 mm in-plane resolution. Corresponding DM images were acquired at 0.1 mm pixel resolution. Retroareolar regions of interest (ROIs) equivalent to 2.5 cm3 were segmented from the DBT images and corresponding 2.5 cm2 ROIs were segmented from the DM images. Breast PD was mammographically estimated using the Cumulus scale. Overall, DBT texture features demonstrated a stronger correlation than DM to PD. The Pearson correlation coefficients for DBT were r = 0.40 (pbreast cancer risk assessment in the future.
Changes in mammographic density and breast cancer risk
Lokate, A.J.M.
2012-01-01
Breast cancer is the most frequently occurring cancer among women worldwide. One of the most important risk factors for breast cancer is high mammographic density. Mammographic density represents the amount of fibroglandular tissue relative to the fat tissue in the breast. Women with >75% of their b
Variability of breast density assessment in short-term reimaging with digital mammography
Energy Technology Data Exchange (ETDEWEB)
Kim, Won Hwa [Department of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of); Moon, Woo Kyung, E-mail: moonwk@snu.ac.kr [Department of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of); Kim, Sun Mi [Department of Radiology, Seoul National University Bundang Hospital, Gyeonggi-do (Korea, Republic of); Yi, Ann [Department of Radiology, Seoul Metropolitan Government Seoul National University, Boramae Medical Center, Seoul (Korea, Republic of); Chang, Jung Min; Koo, Hye Ryoung; Lee, Su Hyun; Cho, Nariya [Department of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of)
2013-10-01
Objective: To evaluate the variability of breast density assessments in short-term reimaging with digital mammography. Materials and methods: In 186 women, short term (mean interval, 27.6 days) serial digital mammograms including CC and MLO views were obtained without any treatment. Mammographic density assessments were performed by three blinded radiologists for Breast Imaging Report and Data System (BI-RADS, grades 1–4) and visual percentage density (PD) estimation, and by one radiologist for computer-aided PD estimation. The variability of assessments was analyzed according to the age, breast density, and mammography types by multivariate logistic regression. Results: In BI-RADS assessments, 29% (161 of 558) of breast density categories were assessed differently after short-term reimaging and the mean absolute difference in PD for CC and MLO view was 7.6% and 8.1% for visual assessments, and 7.4% and 6.4% for computer-aided assessments, respectively. Among all computer-aided assessments, 29% (54 of 186) of CC view and 22% (41 of 186) of MLO view assessments had discrepancy over 10% in PD. Younger age (<50), greater breast density (grades 3 and 4), and different mammography types were significantly associated with the variability. Conclusion: Considerable variability in breast density assessments occurred in short-term reimaging with digital mammography, particularly in women with younger age and greater breast density and when examined using different types of mammography.
Variability of breast density assessment in short-term reimaging with digital mammography
International Nuclear Information System (INIS)
Objective: To evaluate the variability of breast density assessments in short-term reimaging with digital mammography. Materials and methods: In 186 women, short term (mean interval, 27.6 days) serial digital mammograms including CC and MLO views were obtained without any treatment. Mammographic density assessments were performed by three blinded radiologists for Breast Imaging Report and Data System (BI-RADS, grades 1–4) and visual percentage density (PD) estimation, and by one radiologist for computer-aided PD estimation. The variability of assessments was analyzed according to the age, breast density, and mammography types by multivariate logistic regression. Results: In BI-RADS assessments, 29% (161 of 558) of breast density categories were assessed differently after short-term reimaging and the mean absolute difference in PD for CC and MLO view was 7.6% and 8.1% for visual assessments, and 7.4% and 6.4% for computer-aided assessments, respectively. Among all computer-aided assessments, 29% (54 of 186) of CC view and 22% (41 of 186) of MLO view assessments had discrepancy over 10% in PD. Younger age (<50), greater breast density (grades 3 and 4), and different mammography types were significantly associated with the variability. Conclusion: Considerable variability in breast density assessments occurred in short-term reimaging with digital mammography, particularly in women with younger age and greater breast density and when examined using different types of mammography
Diffuse optical tomography with structured-light patterns to quantify breast density
Kwong, Jessica; Nouizi, Farouk; Cho, Jaedu; Zheng, Jie; Li, Yifan; Chen, Jeon-hor; Su, Min-Ying; Gulsen, Gultekin
2016-02-01
Breast density is an independent risk factor for breast cancer, where women with denser breasts are more likely to develop cancer. By identifying women at higher risk, healthcare providers can suggest screening at a younger age to effectively diagnose and treat breast cancer in its earlier stages. Clinical risk assessment models currently do not incorporate breast density, despite its strong correlation with breast cancer. Current methods to measure breast density rely on mammography and MRI, both of which may be difficult to use as a routine risk assessment tool. We propose to use diffuse optical tomography with structured-light to measure the dense, fibroglandular (FGT) tissue volume, which has a different chromophore signature than the surrounding adipose tissue. To test the ability of this technique, we performed simulations by creating numerical breast phantoms from segmented breast MR images. We looked at two different cases, one with a centralized FGT distribution and one with a dispersed distribution. As expected, the water and lipid volumes segmented at half-maximum were overestimated for the dispersed case. However, it was noticed that the recovered water and lipid concentrations were lower and higher, respectively, than the centralized case. This information may provide insight into the morphological distribution of the FGT and can be a correction in estimating the breast density.
Mammographic breast density as a biomarker of effects of isoflavones on the female breast
Atkinson, Charlotte; Bingham, Sheila A.
2001-01-01
Isoflavones possess both estrogenic and anti-estrogenic actions, and are hypothesized to protect against breast cancer. However, two intervention studies of markers of proliferation on breast tissue have raised concerns that soy isoflavones may have an estrogenic effect on breast tissue. Increased mammographic breast density is associated with an elevated risk of breast cancer, although the mechanism underlying this relationship has not been explained. Estrogens increase and anti-estrogens de...
Vision 20/20: Mammographic breast density and its clinical applications
Energy Technology Data Exchange (ETDEWEB)
Ng, Kwan-Hoong, E-mail: ngkh@ummc.edu.my; Lau, Susie [Department of Biomedical Imaging and University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur (Malaysia)
2015-12-15
Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are “dense” and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believe there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.
Correlations between female breast density and biochemical markers
Kim, Ji-Hye; Lee, Hae-Kag; Cho, Jae-Hwan; Park, Hyong-Keun; Yang, Han-Jun
2015-01-01
[Purpose] The aim of this study was to identify biochemical markers related to breast density. The study was performed with 200 patients who received mammography and biochemical marker testing between March 1, 2014 to October 1, 2014. [Subjects and Methods] Following the American College of Radiology, Breast Imaging Reporting and Data System (ACR BI-RADS), breast parenchymal pattern density from mammography was categorized into four grades: grade 1, almost entirely fat; grade 2, fibroglandula...
Reference state estimation of breast computed tomography for registration with digital mammography
Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Ning, Ruola; Cha, Kenny; Helvie, Mark A.
2016-03-01
Understanding the deformation of the breast is a fundamental aspect to lesion localization in multi-view and multimodality imaging. Finite element methods (FEMs) are commonly used to model the deformation process of the breast. In FEM, ideally a reference state of the breast with no loading conditions is available as a starting point and then appropriate imaging-modality-based loading conditions for a specific application can be applied to the breast in the reference state. We propose an iterative method to estimate the reference state configuration between a gravity loaded uncompressed breast computed tomography (BCT) volume and a compressed breast using the corresponding digital mammograms (DM) as a guide. The reference state breast model is compressed between two plates similar to mammographic imaging. A DM-like image is generated by forward ray-tracing. The iterative method applies pressure in the anterior-to-posterior direction of the breast and uses information from the DM geometry and measurements to converge on a reference state of the breast. The process of reference state estimation and breast compression was studied using BCT cases from small to large breast sizes and breast densities consisting of scattered, heterogeneous and extremely dense categories. The breasts were assumed to be composed of non-linear materials based on Mooney-Rivlin models. The effects of the material properties on the estimation process were analyzed. The Fréchet distance between the edges of the DM-like image and the DM image was used as a performance measure.
Yaghjyan, Lusine; Colditz, Graham A; Rosner, Bernard; Tamimi, Rulla M
2015-02-01
We investigated if associations of breast density and breast cancer differ according to the level of other known breast cancer risk factors, including body mass index (BMI), age at menarche, parity, age at first child's birth, age at menopause, alcohol consumption, a family history of breast cancer, a history of benign breast disease, and physical activity. This study included 1,044 postmenopausal incident breast cancer cases diagnosed within the Nurses' Health Study cohort and 1,794 matched controls. Percent breast density, absolute dense, and non-dense areas were measured from digitized film images with computerized techniques. Information on breast cancer risk factors was obtained prospectively from biennial questionnaires. Percent breast density was more strongly associated with breast cancer risk in current postmenopausal hormone users (≥50 vs. 10 %: OR 5.34, 95 % CI 3.36-8.49) as compared to women with past (OR 2.69, 95 % CI 1.32-5.49) or no hormone history (OR 2.57, 95 % CI 1.18-5.60, p-interaction = 0.03). Non-dense area was inversely associated with breast cancer risk in parous women, but not in women without children (p-interaction = 0.03). Associations of density with breast cancer risk did not differ by the levels of BMI, age at menarche, parity, age at first child's birth, age at menopause, alcohol consumption, a family history of breast cancer, a history of benign breast disease, and physical activity. Women with dense breasts, who currently use menopausal hormone therapy are at a particularly high risk of breast cancer. Most breast cancer risk factors do not modify the association between mammographic breast density and breast cancer risk.
Varying kernel density estimation on ℝ+
Mnatsakanov, Robert; Sarkisian, Khachatur
2015-01-01
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is proposed. This method is natural when estimating an unknown density function of a positive random variable. The rates of Mean Squared Error, Mean Integrated Squared Error, and the L1-consistency are investigated. Simulation studies are conducted to compare a new estimator and its modified version with traditional kernel density construction. PMID:26740729
Maskarinec, Gertraud; Morimoto, Yukiko; Laguana, Michelle B; Novotny, Rachel; Leon Guerrero, Rachael T
2016-01-01
Although high mammographic density is one of the strongest predictors of breast cancer risk, X-ray based mammography cannot be performed before the recommended screening age, especially not in adolescents and young women. Therefore, new techniques for breast density measurement are of interest. In this pilot study in Guam and Hawaii, we evaluated a radiation-free, bioimpedance device called Electrical Breast DensitometerTM (EBD; senoSENSE Medical Systems, Inc., Ontario, Canada) for measuring breast density in 95 women aged 31-82 years and 41 girls aged 8-18 years. Percent density (PD) was estimated in the women's most recent mammogram using a computer-assisted method. Correlation coefficients and linear regression were applied for statistical analysis. In adult women, mean EBD and PD values of the left and right breasts were 230±52 and 226±50 Ω and 23.7±15.1 and 24.2±15.2%, respectively. The EBD measurements were inversely correlated with PD (rSpearman=-0.52, passessment of breast cancer risk early in life and in populations without access to mammography. PMID:26838256
Density Estimation Trees in High Energy Physics
Anderlini, Lucio
2015-01-01
Density Estimation Trees can play an important role in exploratory data analysis for multidimensional, multi-modal data models of large samples. I briefly discuss the algorithm, a self-optimization technique based on kernel density estimation, and some applications in High Energy Physics.
Bayesian multivariate mixed-scale density estimation
Canale, Antonio
2011-01-01
Although univariate continuous density estimation has received abundant attention in the Bayesian nonparametrics literature, there is essentially no theory on multivariate mixed scale density estimation. In this article, we consider a general framework to jointly model continuous, count and categorical variables under a nonparametric prior, which is induced through rounding latent variables having an unknown density with respect to Lesbesgue measure. For the proposed class of priors, we provide sufficient conditions for large support, strong consistency and rates of posterior contraction. These conditions, which primarily relate to the prior on the latent variable density and heaviness of the tails for the observed continuous variables, allow one to convert sufficient conditions obtained in the setting of multivariate continuous density estimation to the mixed scale case. We provide new results in the multivariate continuous density estimation case, showing the Kullback-Leibler property and strong consistency...
Breast density and outcome of mammography screening: a cohort study
DEFF Research Database (Denmark)
Olsen, A H; Bihrmann, K; Jensen, M-B;
2009-01-01
The purpose of this study was to investigate the effect of breast density on breast cancer (BC) mortality in a mammography screening programme. The cohort included 48 052 women participating in mammography screening in Copenhagen, Denmark, where biennial screening is offered to women aged 50...
DEFF Research Database (Denmark)
Andersen, Zorana Jovanovic; Baker, Jennifer Lyn; Bihrmann, Kristine;
2014-01-01
High breast density, a strong predictor of breast cancer may be determined early in life. Childhood anthropometric factors have been related to breast cancer and breast density, but rarely simultaneously. We examined whether mammographic density (MD) mediates an association of birth weight......, childhood body mass index (BMI), and height with the risk of breast cancer....
Sparse Density Estimation on the Multinomial Manifold.
Hong, Xia; Gao, Junbin; Chen, Sheng; Zia, Tanveer
2015-11-01
A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion for the finite mixture model. Since the constraint on the mixing coefficients of the finite mixture model is on the multinomial manifold, we use the well-known Riemannian trust-region (RTR) algorithm for solving this problem. The first- and second-order Riemannian geometry of the multinomial manifold are derived and utilized in the RTR algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with an accuracy competitive with those of existing kernel density estimators. PMID:25647665
Nonparametric Estimation of Risk-Neutral Densities
Grith, Maria; Härdle, Wolfgang Karl; Schienle, Melanie
2010-01-01
This chapter deals with nonparametric estimation of the risk neutral density. We present three different approaches which do not require parametric functional assumptions on the underlying asset price dynamics nor on the distributional form of the risk neutral density. The first estimator is a kernel smoother of the second derivative of call prices, while the second procedure applies kernel type smoothing in the implied volatility domain. In the conceptually different third approach we assume...
ADN* Density log estimation Using Rockcell*
International Nuclear Information System (INIS)
This work is intended to inform on the possibilities of estimating good density data in zones associated with sliding in a reservoir with ADN* tool with or without ADOS in string in cases where repeat sections were not done, possibly due to hole stability or directional concerns. This procedure has been equally used to obtain a better density data in corkscrew holes. Density data (ROBB) was recomputed using neural network in RockCell* to estimate the density over zones of interest. RockCell* is a Schlumberger software that has neural network functionally which can be used to estimate missing logs using the combination of the responses of other log curves and intervals that are not affected by sliding. In this work, an interval was selected and within this interval twelve litho zones were defined using the unsupervised neural network. From this a training set was selected based on intervals of very good log responses outside the sliding zones. This training set was used to train and run the neural network for a specific lithostratigraphic interval. The results matched the known good density curve. Then after this, an estimation of the density curve was done using the supervised neural network. The output from this estimation matched very closely in the good portions of the log, thus providing some density measurements in the sliding zone. This methodology provides a scientific solution to missing data during the process of Formation evaluation
Graph Bundling by Kernel Density Estimation
Hurter, C.; Ersoy, O.; Telea, A.
2012-01-01
We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved
Assessment of two mammographic density related features in predicting near-term breast cancer risk
Zheng, Bin; Sumkin, Jules H.; Zuley, Margarita L.; Wang, Xingwei; Klym, Amy H.; Gur, David
2012-02-01
In order to establish a personalized breast cancer screening program, it is important to develop risk models that have high discriminatory power in predicting the likelihood of a woman developing an imaging detectable breast cancer in near-term (e.g., breast cancer risk models, mammographic density is considered the second highest breast cancer risk factor (second to woman's age). In this study we explored a new feature, namely bilateral mammographic density asymmetry, and investigated the feasibility of predicting near-term screening outcome. The database consisted of 343 negative examinations, of which 187 depicted cancers that were detected during the subsequent screening examination and 155 that remained negative. We computed the average pixel value of the segmented breast areas depicted on each cranio-caudal view of the initial negative examinations. We then computed the mean and difference mammographic density for paired bilateral images. Using woman's age, subjectively rated density (BIRADS), and computed mammographic density related features we compared classification performance in estimating the likelihood of detecting cancer during the subsequent examination using areas under the ROC curves (AUC). The AUCs were 0.63+/-0.03, 0.54+/-0.04, 0.57+/-0.03, 0.68+/-0.03 when using woman's age, BIRADS rating, computed mean density and difference in computed bilateral mammographic density, respectively. Performance increased to 0.62+/-0.03 and 0.72+/-0.03 when we fused mean and difference in density with woman's age. The results suggest that, in this study, bilateral mammographic tissue density is a significantly stronger (p<0.01) risk indicator than both woman's age and mean breast density.
Parametric density estimation by minimizing nonextensive entropy
Davide Ferrari
2008-01-01
In this paper, we consider parametric density estimation based on minimizing the Havrda-Charvat-Tsallis nonextensive entropy. The resulting estimator, called the Maximum Lq-Likelihood estimator (MLqE), is indexed by a single distortion parameter q, which controls the trade-off between bias and variance. The method has two notable special cases. If q tends to 1, the MLqE is the Maximum Likelihood Estimator (MLE). When q = 1=2, the MLqE is a minimum Hellinger distance type of estimator with the...
Parametric Return Density Estimation for Reinforcement Learning
Morimura, Tetsuro; Kashima, Hisashi; Hachiya, Hirotaka; Tanaka, Toshiyuki
2012-01-01
Most conventional Reinforcement Learning (RL) algorithms aim to optimize decision- making rules in terms of the expected re- turns. However, especially for risk man- agement purposes, other risk-sensitive crite- ria such as the value-at-risk or the expected shortfall are sometimes preferred in real ap- plications. Here, we describe a parametric method for estimating density of the returns, which allows us to handle various criteria in a unified manner. We first extend the Bellman equation for the conditional expected return to cover a conditional probability density of the returns. Then we derive an extension of the TD-learning algorithm for estimating the return densities in an unknown environment. As test instances, several parametric density estimation algorithms are presented for the Gaussian, Laplace, and skewed Laplace dis- tributions. We show that these algorithms lead to risk-sensitive as well as robust RL paradigms through numerical experiments.
Directory of Open Access Journals (Sweden)
Hori,Keisuke
2013-08-01
Full Text Available A relatively large number of women in their 40s with high-density breasts, in which it can be difficult to detect lesions, are encountered in mammography cancer screenings in Japan. Here, we retrospectively investigated factors related to breast density. Two hundred women (40-49 years old were examined at the screening center in our hospital. Multivariate analysis showed that factors such as small abdominal circumference, high HDL cholesterol, and no history of childbirth were related to high breast density in women in their 40s undergoing mammography. Other non-mammographic screening methods should be considered in women with abdominal circumferences ＜76cm, HDL-C >53mg/dl, and no history of childbirth, as there is a strong possibility of these women having high-density breasts that can make lesion detection difficult.
Implementation of several mathematical algorithms to breast tissue density classification
Quintana, C.; Redondo, M.; Tirao, G.
2014-02-01
The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories.
Estimating stellar mean density through seismic inversions
Reese, D R; Goupil, M J; Thompson, M J; Deheuvels, S
2012-01-01
Determining the mass of stars is crucial both to improving stellar evolution theory and to characterising exoplanetary systems. Asteroseismology offers a promising way to estimate stellar mean density. When combined with accurate radii determinations, such as is expected from GAIA, this yields accurate stellar masses. The main difficulty is finding the best way to extract the mean density from a set of observed frequencies. We seek to establish a new method for estimating stellar mean density, which combines the simplicity of a scaling law while providing the accuracy of an inversion technique. We provide a framework in which to construct and evaluate kernel-based linear inversions which yield directly the mean density of a star. We then describe three different inversion techniques (SOLA and two scaling laws) and apply them to the sun, several test cases and three stars. The SOLA approach and the scaling law based on the surface correcting technique described by Kjeldsen et al. (2008) yield comparable result...
Local mammographic density as a predictor of breast cancer
Otsuka, Mayu; Harkness, Elaine F.; Chen, Xin; Moschidis, Emmanouil; Bydder, Megan; Gadde, Soujanya; Lim, Yit Y.; Maxwell, Anthony J.; Evans, Gareth D.; Howell, Anthony; Stavrinos, Paula; Wilson, Mary; Astley, Susan M.
2015-03-01
High overall mammographic density is associated with both an increased risk of developing breast cancer and the risk of cancer being masked. We compared local density at cancer sites in diagnostic images with corresponding previous screening mammograms (priors), and matched controls. VolparaTM density maps were obtained for 54 mammograms showing unilateral breast cancer and their priors which had been previously read as normal. These were each matched to 3 controls on age, menopausal status, hormone replacement therapy usage, body mass index and year of prior. Local percent density was computed in 15mm square regions at lesion sites and similar locations in the corresponding images. Conditional logistic regression was used to predict case-control status. In diagnostic and prior images, local density was increased at the lesion site compared with the opposite breast (medians 21.58%, 9.18%, pcancer than those in the lowest tertile (OR 42.09, 95% CI 5.37-329.94). Those in the highest tertile of VolparaTM gland volume were also more likely to develop cancer (OR 2.89, 95% CI 1.30-6.42). Local density is increased where cancer will develop compared with corresponding regions in the opposite breast and matched controls, and its measurement could enhance computer-aided mammography.
Density Estimation in Infinite Dimensional Exponential Families
Sriperumbudur, Bharath; Fukumizu, Kenji; Kumar, Revant; Gretton, Arthur; Hyvärinen, Aapo
2013-01-01
In this paper, we consider an infinite dimensional exponential family, $\\mathcal{P}$ of probability densities, which are parametrized by functions in a reproducing kernel Hilbert space, $H$ and show it to be quite rich in the sense that a broad class of densities on $\\mathbb{R}^d$ can be approximated arbitrarily well in Kullback-Leibler (KL) divergence by elements in $\\mathcal{P}$. The main goal of the paper is to estimate an unknown density, $p_0$ through an element in $\\mathcal{P}$. Standar...
Particle Size Estimation Based on Edge Density
Institute of Scientific and Technical Information of China (English)
WANG Wei-xing
2005-01-01
Given image sequences of closely packed particles, the underlying aim is to estimate diameters without explicit segmentation. In a way, this is similar to the task of counting objects without directly counting them. Such calculations may, for example, be useful fast estimation of particle size in different application areas. The topic is that of estimating average size (=average diameter) of packed particles, from formulas involving edge density, and the edges from moment-based thresholding are used. An average shape factor is involved in the calculations, obtained for some frames from crude partial segmentation. Measurement results from about 80 frames have been analyzed.
Nonparametric methods for volatility density estimation
B. van Es; P. Spreij; H. van Zanten
2011-01-01
Stochastic volatility modeling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on discrete
Anisotropic Density Estimation in Global Illumination
DEFF Research Database (Denmark)
Schjøth, Lars
2009-01-01
Density estimation employed in multi-pass global illumination algorithms gives cause to a trade-off problem between bias and noise. The problem is seen most evident as blurring of strong illumination features. This thesis addresses the problem, presenting four methods that reduce both noise...
Conditional Density Estimation in Measurement Error Problems.
Wang, Xiao-Feng; Ye, Deping
2015-01-01
This paper is motivated by a wide range of background correction problems in gene array data analysis, where the raw gene expression intensities are measured with error. Estimating a conditional density function from the contaminated expression data is a key aspect of statistical inference and visualization in these studies. We propose re-weighted deconvolution kernel methods to estimate the conditional density function in an additive error model, when the error distribution is known as well as when it is unknown. Theoretical properties of the proposed estimators are investigated with respect to the mean absolute error from a "double asymptotic" view. Practical rules are developed for the selection of smoothing-parameters. Simulated examples and an application to an Illumina bead microarray study are presented to illustrate the viability of the methods. PMID:25284902
Visually assessed breast density, breast cancer risk and the importance of the craniocaudal view.
Duffy, S.W.; Nagtegaal, I.D.; Astley, S.M.; Gillan, M.G.; McGee, M.A.; Boggis, C.R.; Wilson, M.; Beetles, U.M.; Griffiths, M.A.; Jain, A.K.; Johnson, J.; Roberts, R.; Deans, H.; Duncan, K.A.; Iyengar, G.; Griffiths, P.M.; Warwick, J.; Cuzick, J.; Gilbert, F.J.
2008-01-01
INTRODUCTION: Mammographic density is known to be a strong risk factor for breast cancer. A particularly strong association with risk has been observed when density is measured using interactive threshold software. This, however, is a labour-intensive process for large-scale studies. METHODS: Our ai
Automatic breast density segmentation: an integration of different approaches
International Nuclear Information System (INIS)
Mammographic breast density has been found to be a strong risk factor for breast cancer. In most studies, it is assessed with a user-assisted threshold method, which is time consuming and subjective. In this study, we develop a breast density segmentation method that is fully automatic. The method is based on pixel classification in which different approaches known in the literature to segment breast density are integrated and extended. In addition, the method incorporates the knowledge of a trained observer, by using segmentations obtained by the user-assisted threshold method as training data. The method is trained and tested using 1300 digitized film mammographic images acquired with a variety of systems. Results show a high correspondence between the automated method and the user-assisted threshold method. Pearson's correlation coefficient between our method and the user-assisted method is R = 0.911 for percent density and R = 0.895 for dense area, which is substantially higher than the best correlation found in the literature (R = 0.70, R = 0.68). The area under the receiver operating characteristic curve obtained when discriminating between fatty and dense pixels is 0.987. A combination of segmentation strategies outperforms the application of single segmentation techniques.
International Nuclear Information System (INIS)
High mammographic density might mask breast tumors, resulting in delayed diagnosis or missed cancers. To investigate the association between mammographic density and histopathologic tumor characteristics (histologic type, size, grade, and lymph node status) among women screened in the Norwegian Breast Cancer Screening Program. Information about 1760 screen-detected ductal carcinoma in situ (DCIS) and 7366 invasive breast cancers diagnosed among women aged 50–69 years, 1996–2010, was analyzed. The screening mammograms were classified subjectively according to the amount of fibroglandular tissue into fatty, medium dense, and dense by breast radiologists. Chi-square test was used to compare the distribution of tumor characteristics by mammographic density. Odds ratio (OR) of tumor characteristics by density was estimated by means of logistic regression, adjusting for screening mode (screen-film and full-field digital mammography), and age. Mean and median tumor size of invasive breast cancers was 13.8 and 12 mm, respectively, for women with fatty breasts, and 16.2 and 14 mm for those with dense breasts. Lymph node positive tumors were identified among 20.6% of women with fatty breasts compared with 27.2% of those with dense breasts (P < 0.001). The proportion of DCIS was significantly lower for women with fatty (15.8%) compared with dense breasts (22.0%). Women with dense breasts had an increased risk of large (OR, 1.44; 95% CI, 1.18–1.73) and lymph node positive tumors (OR, 1.26; 95% CI, 1.05–1.51) compared with women with fatty and medium dense breasts. High mammographic density was positively associated with tumor size and lymph node positive tumors
International Nuclear Information System (INIS)
Digital breast tomosynthesis (DBT) is a screening and diagnostic modality that acquires images of the breast at multiple angles during a short scan. The Selenia Dimensions (Hologic, Bedford, Mass) DBT system can perform both full-field digital mammography and DBT. The system acquires 15 projections over a 15 deg. angular range (from -7.5 deg. to +7.5 deg.). An important factor in determining the optimal imaging technique for breast tomosynthesis is the radiation dose. In breast imaging, the radiation dose of concern is that deposited in the glandular tissue of the breast because this is the tissue that has a risk of developing cancer. The concept of the normalised mean glandular dose (DgN) has been introduced as the metric for the dose in breast imaging. The DgN is difficult to measure. The Monte Carlo techniques offer an alternative method for a realistic estimation of the radiation dose. The purpose of this work was to use the Monte Carlo code MCNPX technique to generate monoenergetic glandular dose data for estimating the breast tissue dose in tomosynthesis for arbitrary spectra as well as to observe the deposited radiation dose by projection on the glandular portion of the breast in a Selenia Dimensions DBT system. A Monte Carlo simulation of the system was developed to compute the DgN in a craniocaudal view. Monoenergetic X-ray beams from 10 to 49 keV in 1-keV increments were used. The simulation utilised the assumption of a homogeneous breast composition and three compositions (0 % glandular, 50 % glandular and 100 % glandular). The glandular and adipose tissue compositions were specified according ICRU Report 44. A skin layer of 4 mm was assumed to encapsulate the breast on all surfaces. The breast size was varied using the chest wall-to-nipple distance (CND) and compressed breast thickness (t). In this work, the authors assumed a CND of 5 cm and the thicknesses ranged from 2 to 8 cm, in steps of 2 cm. The fractional energy absorption increases (up to 44
Breast Cancer Patients with High Density Mammograms Do Not Have Increased Risk of Death
... density mammograms do not have increased risk of death High mammographic breast density, which is a marker ... does not seem to increase the risk of death among breast cancer patients, according to a study ...
Breast cancer research output, 1945-2008: a bibliometric and density-equalizing analysis
LENUS (Irish Health Repository)
Glynn, Ronan W
2010-12-22
Abstract Introduction Breast cancer is the most common form of cancer among women, with an estimated 194,280 new cases diagnosed in the United States in 2009 alone. The primary aim of this work was to provide an in-depth evaluation of research yield in breast cancer from 1945 to 2008, using large-scale data analysis, the employment of bibliometric indicators of production and quality, and density-equalizing mapping. Methods Data were retrieved from the Web of Science (WOS) Science Citation Expanded database; this was searched using the Boolean operator, \\'OR\\
A detailed breast model and its application for Chinese female breast dose estimation in mammography
International Nuclear Information System (INIS)
The IAEA protocol for breast dosimetry is based on the calculations of Dance which used very simple models of the breast based on the western female data. In order to estimate the dose more precisely in the mammographic examination for the Chinese female, a 3D detailed breast model was constructed. This model contains the detailed structures in the breast, including skin, subcutaneous fat, Cooper's ligaments, and fibro-glandular region containing intra-glandular fat, gland, ductal system, and terminal ductal lobular units. And the size of this breast model was adjusted to represent the characteristic of Chinese female. The glandular tissue conversion coefficients were calculated using this detailed breast model with Monte Carlo code, Geant4. The comparison between the data obtained in this study and the data from Dance was carried out. The discrepancy may come from both the structure and size of the breast models
Sandberg, Maria EC; Li, Jingmei; Hall, Per; Hartman, Mikael; dos-Santos-Silva, Isabel; Humphreys, Keith; Czene, Kamila
2013-01-01
Introduction Mammographic density is a strong risk factor for breast cancer, but it is unknown whether density at first breast cancer diagnosis and changes during follow-up influences risk of non-simultaneous contralateral breast cancer (CBC). Methods We collected mammograms for CBC-patients (cases, N = 211) and unilateral breast cancer patients (controls, N = 211), individually matched on age and calendar period of first breast cancer diagnosis, type of adjuvant therapy and length of follow-...
Height, adiposity and body fat distribution and breast density in young women
Dorgan, Joanne F; Klifa, Catherine; John A. Shepherd; Egleston, Brian L.; Kwiterovich, Peter O.; Himes, John H.; Gabriel, Kelley; Van Horn, Linda; Snetselaar, Linda G; Stevens, Victor J.; Barton, Bruce A; Robson, Alan M.; Lasser, Norman L.; Deshmukh, Snehal; Hylton, Nola M.
2012-01-01
Abstract Introduction Breast density is one of the strongest risk factors for breast cancer, but determinants of breast density in young women remain largely unknown. Methods Associations of height, adiposity and body fat distribution with percentage dense breast volume (%DBV) and absolute dense breast volume (ADBV) were evaluated in a cross-sectional study of 174 healthy women, 25 to 29 years old. Adiposity and body fat distribution were measured by anthropometry and dual-energy...
Directory of Open Access Journals (Sweden)
Mary Mockus
2015-06-01
Conclusions and general significance: While reproductive characteristics, in particular parity, generally demonstrated independent associations with postmenopausal breast density and E, P and DHEA levels, T levels showed concordant inverse associations with age-at-first birth and breast density. These findings suggest that reproductive effects and later life salivary sex steroid hormone levels may have independent effects on later life breast density and cancer risk.
Conroy, Shannon M; Koga, Karin; Woolcott, Christy G.; Dahl, Timothy; Byrne, Celia; Nagata, Chisato; Ursin, Giske; Yaffe, Martin J.; Vachon, Celine M.; Maskarinec, Gertraud
2012-01-01
Alcohol consumption and mammographic density are established risk factors for breast cancer. This study examined whether the association of mammographic density with breast cancer varies by alcohol intake. Mammographic density was assessed in digitized images for 1,207 cases and 1,663 controls from three populations (Japan, Hawaii, California) using a computer-assisted method. Associations were estimated by logistic regression. When comparing ever to never drinking, mean density was similar a...
Directory of Open Access Journals (Sweden)
Thomas Jerry A
2010-11-01
Full Text Available Abstract Background Calibrating mammograms to produce a standardized breast density measurement for breast cancer risk analysis requires an accurate spatial measure of the compressed breast thickness. Thickness inaccuracies due to the nominal system readout value and compression paddle orientation induce unacceptable errors in the calibration. Method A thickness correction was developed and evaluated using a fully specified two-component surrogate breast model. A previously developed calibration approach based on effective radiation attenuation coefficient measurements was used in the analysis. Water and oil were used to construct phantoms to replicate the deformable properties of the breast. Phantoms consisting of measured proportions of water and oil were used to estimate calibration errors without correction, evaluate the thickness correction, and investigate the reproducibility of the various calibration representations under compression thickness variations. Results The average thickness uncertainty due to compression paddle warp was characterized to within 0.5 mm. The relative calibration error was reduced to 7% from 48-68% with the correction. The normalized effective radiation attenuation coefficient (planar representation was reproducible under intra-sample compression thickness variations compared with calibrated volume measures. Conclusion Incorporating this thickness correction into the rigid breast tissue equivalent calibration method should improve the calibration accuracy of mammograms for risk assessments using the reproducible planar calibration measure.
Multivariate density estimation theory, practice, and visualization
Scott, David W
2015-01-01
David W. Scott, PhD, is Noah Harding Professor in the Department of Statistics at Rice University. The author of over 100 published articles, papers, and book chapters, Dr. Scott is also Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics. He is recipient of the ASA Founder's Award and the Army Wilks Award. His research interests include computational statistics, data visualization, and density estimation. Dr. Scott is also Coeditor of Wiley Interdisciplinary Reviews: Computational Statistics and previous Editor of the Journal of Computational and
Cox, B.; Ballard-Barbash, R.; Broeders, M.J.M.; Dowling, E.; Malila, N.; Shumak, R.; Taplin, S.; Buist, D.; Miglioretti, D.
2010-01-01
Breast density and the use of hormone therapy (HT) for menopausal symptoms alter the risk of breast cancer and both factors influence screening mammography performance. The International Cancer Screening Network (ICSN) surveyed its 29 member countries and found that few programs record breast densit
Mammographic density and breast cancer risk in White and African American Women
Razzaghi, Hilda; Troester, Melissa A.; Gierach, Gretchen L.; Olshan, Andrew F.; Yankaskas, Bonnie C.; Millikan, Robert C.
2012-01-01
Mammographic density is a strong risk factor for breast cancer, but limited data are available in African American (AA) women. We examined the association between mammographic density and breast cancer risk in AA and white women. Cases (n = 491) and controls (n = 528) were from the Carolina Breast Cancer Study (CBCS) who also had mammograms recorded in the Carolina Mammography Registry (CMR). Mammographic density was reported to CMR using Breast Imaging Reporting and Data System (BI-RADS) cat...
Mammographic Breast Density Patterns in Asymptomatic Mexican Women
International Nuclear Information System (INIS)
Breast density (BD) is a risk factor for breast cancer. Aims. To describe BD patterns in asymptomatic Mexican women and the pathological mammographic findings. Methods and Material. Prospective, descriptive, and comparative study. Women answered a questionnaire and their mammograms were analyzed according to BI-RADS. Univariate (X2) and conditional logistic regression analyses were performed. Results. In 300 women studied the BD patterns were fat 56.7% (170), fibroglandular 29% (87), heterogeneously dense 5.7% (17), and dense pattern 8.6% (26). Prevalence of fat pattern was significantly different in women under 50 years (37.6%, 44/117) and older than 50 (68.8%, 126/183). Patterns of high breast density (BD) (dense + heterogeneously dense) were observed in 25.6% (30/117) of women ≤50 years and 7.1% (13/183) of women >50. Asymmetry in BD was observed in 22% (66/300). Compression cone ruled out underlying disease in 56 cases. In the remaining 10, biopsy revealed one fibroadenoma, one complex cyst, and 6 invasive and 2 intraductal carcinomas. 2.6% (8/300) of patients had non-palpable carcinomas. Benign lesions were observed in 63.3% (190/300) of cases, vascular calcification in 150 cases (78.9%), and fat necrosis in 38 cases (20%). Conclusions. Mexican women have a low percentage of high-density patterns
Mammographic Breast Density Patterns in Asymptomatic Mexican Women
Calderón-Garcidueñas, Ana Laura; Sanabria-Mondragón, Mónica; Hernández-Beltrán, Lourdes; López-Amador, Noé; Cerda-Flores, Ricardo M.
2012-01-01
Breast density (BD) is a risk factor for breast cancer. Aims. To describe BD patterns in asymptomatic Mexican women and the pathological mammographic findings. Methods and Material. Prospective, descriptive, and comparative study. Women answered a questionnaire and their mammograms were analyzed according to BI-RADS. Univariate (χ2) and conditional logistic regression analyses were performed. Results. In 300 women studied the BD patterns were fat 56.7% (170), fibroglandular 29% (87), heterogeneously dense 5.7% (17), and dense pattern 8.6% (26). Prevalence of fat pattern was significantly different in women under 50 years (37.6%, 44/117) and older than 50 (68.8%, 126/183). Patterns of high breast density (BD) (dense + heterogeneously dense) were observed in 25.6% (30/117) of women ≤50 years and 7.1% (13/183) of women >50. Asymmetry in BD was observed in 22% (66/300). Compression cone ruled out underlying disease in 56 cases. In the remaining 10, biopsy revealed one fibroadenoma, one complex cyst, and 6 invasive and 2 intraductal carcinomas. 2.6% (8/300) of patients had non-palpable carcinomas. Benign lesions were observed in 63.3% (190/300) of cases, vascular calcification in 150 cases (78.9%), and fat necrosis in 38 cases (20%). Conclusions. Mexican women have a low percentage of high-density patterns. PMID:23346398
Mammographic Breast Density Patterns in Asymptomatic Mexican Women
Directory of Open Access Journals (Sweden)
Ana Laura Calderón-Garcidueñas
2012-01-01
Full Text Available Breast density (BD is a risk factor for breast cancer. Aims. To describe BD patterns in asymptomatic Mexican women and the pathological mammographic findings. Methods and Material. Prospective, descriptive, and comparative study. Women answered a questionnaire and their mammograms were analyzed according to BI-RADS. Univariate ( and conditional logistic regression analyses were performed. Results. In 300 women studied the BD patterns were fat 56.7% (170, fibroglandular 29% (87, heterogeneously dense 5.7% (17, and dense pattern 8.6% (26. Prevalence of fat pattern was significantly different in women under 50 years (37.6%, 44/117 and older than 50 (68.8%, 126/183. Patterns of high breast density (BD (dense + heterogeneously dense were observed in 25.6% (30/117 of women ≤50 years and 7.1% (13/183 of women >50. Asymmetry in BD was observed in 22% (66/300. Compression cone ruled out underlying disease in 56 cases. In the remaining 10, biopsy revealed one fibroadenoma, one complex cyst, and 6 invasive and 2 intraductal carcinomas. 2.6% (8/300 of patients had non-palpable carcinomas. Benign lesions were observed in 63.3% (190/300 of cases, vascular calcification in 150 cases (78.9%, and fat necrosis in 38 cases (20%. Conclusions. Mexican women have a low percentage of high-density patterns.
Linton, Linda; Martin, Lisa J.; Li, Qing; Huszti, Ella; Minkin, Salomon; John, Esther M.; Rommens, Johanna; Paterson, Andrew D.; Boyd, Norman F
2013-01-01
Introduction Percent mammographic density (PMD) is a strong and highly heritable risk factor for breast cancer. Studies of the role of PMD in familial breast cancer may require controls, such as the sisters of cases, selected from the same 'risk set' as the cases. The use of sister controls would allow control for factors that have been shown to influence risk of breast cancer such as race/ethnicity, socioeconomic status and a family history of breast cancer, but may introduce 'overmatching' ...
Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation.
Yamane, Ikko; Sasaki, Hiroaki; Sugiyama, Masashi
2016-07-01
Log-density gradient estimation is a fundamental statistical problem and possesses various practical applications such as clustering and measuring nongaussianity. A naive two-step approach of first estimating the density and then taking its log gradient is unreliable because an accurate density estimate does not necessarily lead to an accurate log-density gradient estimate. To cope with this problem, a method to directly estimate the log-density gradient without density estimation has been explored and demonstrated to work much better than the two-step method. The objective of this letter is to improve the performance of this direct method in multidimensional cases. Our idea is to regard the problem of log-density gradient estimation in each dimension as a task and apply regularized multitask learning to the direct log-density gradient estimator. We experimentally demonstrate the usefulness of the proposed multitask method in log-density gradient estimation and mode-seeking clustering.
Multivariate kernel density estimation with a parametric support
Directory of Open Access Journals (Sweden)
Jolanta Jarnicka
2009-01-01
Full Text Available We consider kernel density estimation in the multivariate case, focusing on the use of some elements of parametric estimation. We present a two-step method, based on a modification of the EM algorithm and the generalized kernel density estimator, and compare this method with a couple of well known multivariate kernel density estimation methods.
Density Estimations in Laboratory Debris Flow Experiments
Queiroz de Oliveira, Gustavo; Kulisch, Helmut; Malcherek, Andreas; Fischer, Jan-Thomas; Pudasaini, Shiva P.
2016-04-01
Bulk density and its variation is an important physical quantity to estimate the solid-liquid fractions in two-phase debris flows. Here we present mass and flow depth measurements for experiments performed in a large-scale laboratory set up. Once the mixture is released and it moves down the inclined channel, measurements allow us to determine the bulk density evolution throughout the debris flow. Flow depths are determined by ultrasonic pulse reflection, and the mass is measured with a total normal force sensor. The data were obtained at 50 Hz. The initial two phase material was composed of 350 kg debris with water content of 40%. A very fine pebble with mean particle diameter of 3 mm, particle density of 2760 kg/m³ and bulk density of 1400 kg/m³ in dry condition was chosen as the solid material. Measurements reveal that the debris bulk density remains high from the head to the middle of the debris body whereas it drops substantially at the tail. This indicates lower water content at the tail, compared to the head and the middle portion of the debris body. This means that the solid and fluid fractions are varying strongly in a non-linear manner along the flow path, and from the head to the tail of the debris mass. Importantly, this spatial-temporal density variation plays a crucial role in determining the impact forces associated with the dynamics of the flow. Our setup allows for investigating different two phase material compositions, including large fluid fractions, with high resolutions. The considered experimental set up may enable us to transfer the observed phenomena to natural large-scale events. Furthermore, the measurement data allows evaluating results of numerical two-phase mass flow simulations. These experiments are parts of the project avaflow.org that intends to develop a GIS-based open source computational tool to describe wide spectrum of rapid geophysical mass flows, including avalanches and real two-phase debris flows down complex natural
Colin, Catherine; Prince, Violaine
2009-01-01
High breast density (HBD) tends to be seen as a significant and independent risk factor for breast cancer. This article describes a methodological and quantitative study of the variables selected by the large DMIST study, i.e., age, hormonal status and breast density, in correlation with cancer occurrence frequency. The statistical analysis of cancer rates in every patient subgroup of a study involving more than 42,000 women in screening, shows that HBD, when isolated from other variables, do...
Kernel density estimation using graphical processing unit
Sunarko, Su'ud, Zaki
2015-09-01
Kernel density estimation for particles distributed over a 2-dimensional space is calculated using a single graphical processing unit (GTX 660Ti GPU) and CUDA-C language. Parallel calculations are done for particles having bivariate normal distribution and by assigning calculations for equally-spaced node points to each scalar processor in the GPU. The number of particles, blocks and threads are varied to identify favorable configuration. Comparisons are obtained by performing the same calculation using 1, 2 and 4 processors on a 3.0 GHz CPU using MPICH 2.0 routines. Speedups attained with the GPU are in the range of 88 to 349 times compared the multiprocessor CPU. Blocks of 128 threads are found to be the optimum configuration for this case.
Association between body mass index and breast density using digital mammography
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Kim, Mi Young [Dept. of Diagnostic Radiology, Dankook University Hospital, Cheonan (Korea, Republic of); Kim, Hwa Sun [Dept. of Radiological Technology, Ansan University, Ansan (Korea, Republic of)
2014-12-15
It is well known that low body mass index and younger age are associated with high breast density. Mammographic dense breast has been reported both as a cause of false-negative findings on mammography and as an indicator of increased breast cancer risk. The purpose of this study was to evaluate the association between breast density and body mass index. Furthermore, we considered proper screening method of breast cancer in Korean women. The study was performed on 496 women who underwent health checkup in a university hospital. Age and body mass index were negatively associated with breast density respectively. In postmenopausal women, age and body mass index showed statistically significant association with breast density. Therefore, we should consider sensitive additional method for breast cancer screening especially in younger age and underweight women.
Kyoo il Kim
2006-01-01
This paper studies the uniform convergence rate of the turncated SNP (semi-nonparametric) density estimator. Using the uniform convergence rate result we obtain, we propose a test statistic testing the equivalence of two unknown densities where two densities are estimated using the SNP estimator and supports of densities are possibly unbounded.
Taghipour, S.; Banjevic, D; Miller, A.B.; Montgomery, N; A K S Jardine; Harvey, B. J.
2013-01-01
Background: The aim of screening is to detect a cancer in the preclinical state. However, a false-positive or a false-negative test result is a real possibility. Methods: We describe invasive breast cancer progression in the Canadian National Breast Screening Study and construct progression models with and without covariates. The effect of risk factors on transition intensities and false-negative probability is investigated. We estimate the transition rates, the sojourn time and sensitivity o...
Lokate, M.; Kallenberg, M.G.J.; Karssemeijer, N.; Bosch, M.H.J. van den; Peeters, P.H.M.; Gils, C.H. van
2010-01-01
INTRODUCTION: Breast density, a strong breast cancer risk factor, is usually measured on the projected breast area from film screen mammograms. This is far from ideal, as breast thickness and technical characteristics are not taken into account. We investigated whether volumetric density measurement
Energy Technology Data Exchange (ETDEWEB)
Baek, Ji Eun [Department of Radiology, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea (Korea, Republic of); Kim, Sung Hun, E-mail: rad-ksh@catholic.ac.kr [Department of Radiology, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea (Korea, Republic of); Lee, Ah Won [Department of Hospital Pathology, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea (Korea, Republic of)
2014-08-15
Objective: To evaluate whether the degree of background parenchymal enhancement affects the accuracy of tumor size estimation based on breast MRI. Methods: Three hundred and twenty-two patients who had known breast cancer and underwent breast MRIs were recruited in our study. The total number of breast cancer cases was 339. All images were assessed retrospectively for the level of background parenchymal enhancement based on the BI-RADS criteria. Maximal lesion diameters were measured on the MRIs, and tumor types (mass vs. non-mass) were assessed. Tumor size differences between the MRI-based estimates and estimates based on pathological examinations were analyzed. The relationship between accuracy and tumor types and clinicopathologic features were also evaluated. Results: The cases included minimal (47.5%), mild (28.9%), moderate (12.4%) and marked background parenchymal enhancement (11.2%). The tumors of patients with minimal or mild background parenchymal enhancement were more accurately estimated than those of patients with moderate or marked enhancement (72.1% vs. 56.8%; p = 0.003). The tumors of women with mass type lesions were significantly more accurately estimated than those of the women with non-mass type lesions (81.6% vs. 28.6%; p < 0.001). The tumor of women negative for HER2 was more accurately estimated than those of women positive for HER2 (72.2% vs. 51.6%; p = 0.047). Conclusion: Moderate and marked background parenchymal enhancement is related to the inaccurate estimation of tumor size based on MRI. Non-mass type breast cancer and HER2-positive breast cancer are other factors that may cause inaccurate assessment of tumor size.
Seiji Hashimoto; Syuji Yoshiki; Ryoko Saeki; Yasuhiro Mimura; Ryosuke Ando; Shutaro Nanba
2016-01-01
Traffic accident frequency has been decreasing in Japan in recent years. Nevertheless, many accidents still occur on residential roads. Area-wide traffic calming measures including Zone 30, which discourages traffic by setting a speed limit of 30 km/h in residential areas, have been implemented. However, no objective implementation method has been established. Development of a model for traffic accident density estimation explained by GIS data can enable the determination of dangerous areas o...
Zakariyah, N.; Pathy, N. B.; Taib, N. A. M.; Rahmat, K.; Judy, C. W.; Fadzil, F.; Lau, S.; Ng, K. H.
2016-03-01
It has been shown that breast density and obesity are related to breast cancer risk. The aim of this study is to investigate the relationships of breast volume, breast dense volume and volumetric breast density (VBD) with body mass index (BMI) and body fat mass (BFM) for the three ethnic groups (Chinese, Malay and Indian) in Malaysia. We collected raw digital mammograms from 2450 women acquired on three digital mammography systems. The mammograms were analysed using Volpara software to obtain breast volume, breast dense volume and VBD. Body weight, BMI and BFM of the women were measured using a body composition analyser. Multivariable logistic regression was used to determine the independent predictors of increased overall breast volume, breast dense volume and VBD. Indians have highest breast volume and breast dense volume followed by Malays and Chinese. While Chinese are highest in VBD, followed by Malay and Indian. Multivariable analysis showed that increasing BMI and BFM were independent predictors of increased overall breast volume and dense volume. Moreover, BMI and BFM were independently and inversely related to VBD.
Concrete density estimation by rebound hammer method
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Ismail, Mohamad Pauzi bin, E-mail: pauzi@nm.gov.my; Masenwat, Noor Azreen bin; Sani, Suhairy bin; Mohd, Shukri [NDT Group, Nuclear Malaysia, Bangi, Kajang, Selangor (Malaysia); Jefri, Muhamad Hafizie Bin; Abdullah, Mahadzir Bin [Material Technology Program, Faculty of Applied Sciences, UiTM, Shah Alam, Selangor (Malaysia); Isa, Nasharuddin bin; Mahmud, Mohamad Haniza bin [Pusat Penyelidikan Mineral, Jabatan Mineral dan Geosains, Ipoh, Perak (Malaysia)
2016-01-22
Concrete is the most common and cheap material for radiation shielding. Compressive strength is the main parameter checked for determining concrete quality. However, for shielding purposes density is the parameter that needs to be considered. X- and -gamma radiations are effectively absorbed by a material with high atomic number and high density such as concrete. The high strength normally implies to higher density in concrete but this is not always true. This paper explains and discusses the correlation between rebound hammer testing and density for concrete containing hematite aggregates. A comparison is also made with normal concrete i.e. concrete containing crushed granite.
Sprague, Brian L.; Trentham-Dietz, Amy; Terry, Mary Beth; Nichols, Hazel B.; Bersch, Andy J.; Buist, Diana S. M.
2008-01-01
The widespread use of ovulation-inducing drugs to enhance fertility has raised concerns regarding potential effects on breast cancer risk, as ovarian stimulation is associated with increases in estrogen and progesterone levels. We investigated the short-term relation between fertility drug use and mammographic breast density, a strong marker of breast cancer risk, among participants in the Group Health Breast Cancer Screening Program. Data linkage with Group Health’s automated pharmacy record...
Percent Mammographic Density and Dense Area as Risk Factors for Breast Cancer
Rauh, C.; Hack, C. C.; Häberle, L.; Hein, A.; Engel, A.; Schrauder, M. G.; Fasching, P. A.; Jud, S. M.; Ekici, A.B.; Loehberg, C. R.; Meier-Meitinger, M.; Ozan, S.; Schulz-Wendtland, R.; Uder, M.; A. Hartmann
2012-01-01
Purpose: Mammographic characteristics are known to be correlated to breast cancer risk. Percent mammographic density (PMD), as assessed by computer-assisted methods, is an established risk factor for breast cancer. Along with this assessment the absolute dense area (DA) of the breast is reported as well. Aim of this study was to assess the predictive value of DA concerning breast cancer risk in addition to other risk factors and in addition to PMD. Methods: We conducted a case control study w...
Efficient estimation of analytic density under random censorship
Belitser, E.
2001-01-01
The nonparametric minimax estimation of an analytic density at a given point, under random censorship, is considered. Although the problem of estimating density is known to be irregular in a certain sense, we make some connections relating this problem to the problem of estimating smooth functionals
Using ultrasound tomography to identify the distributions of density throughout the breast
Sak, Mark; Duric, Neb; Littrup, Peter; Sherman, Mark E.; Gierach, Gretchen L.
2016-04-01
Women with high breast density are at increased risk of developing breast cancer. Breast density has usually been defined using mammography as the ratio of fibroglandular tissue to total breast area. Ultrasound tomography (UST) is an emerging modality that can also be used to measure breast density. UST creates tomographic sound speed images of the patient's breast which is useful as sound speed is directly proportional to tissue density. Furthermore, the volumetric and quantitative information contained in the sound speed images can be used to describe the distribution of breast density. The work presented here measures the UST sound speed density distributions of 165 women with negative screening mammography. Frequency distributions of the sound speed voxel information were examined for each patient. In a preliminary analysis, the UST sound speed distributions were averaged across patients and grouped by various patient and density-related factors (e.g., age, body mass index, menopausal status, average mammographic breast density). It was found that differences in the distribution of density could be easily visualized for different patient groupings. Furthermore, findings suggest that the shape of the distributions may be used to identify participants with varying amounts of dense and non-dense tissue.
Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina
2014-03-01
Accurate assessment of a woman's risk to develop specific subtypes of breast cancer is critical for appropriate utilization of chemopreventative measures, such as with tamoxifen in preventing estrogen-receptor positive breast cancer. In this context, we investigate quantitative measures of breast density and parenchymal texture, measures of glandular tissue content and tissue structure, as risk factors for estrogen-receptor positive (ER+) breast cancer. Mediolateral oblique (MLO) view digital mammograms of the contralateral breast from 106 women with unilateral invasive breast cancer were retrospectively analyzed. Breast density and parenchymal texture were analyzed via fully-automated software. Logistic regression with feature selection and was performed to predict ER+ versus ER- cancer status. A combined model considering all imaging measures extracted was compared to baseline models consisting of density-alone and texture-alone features. Area under the curve (AUC) of the receiver operating characteristic (ROC) and Delong's test were used to compare the models' discriminatory capacity for receptor status. The density-alone model had a discriminatory capacity of 0.62 AUC (p=0.05). The texture-alone model had a higher discriminatory capacity of 0.70 AUC (p=0.001), which was not significantly different compared to the density-alone model (p=0.37). In contrast the combined density-texture logistic regression model had a discriminatory capacity of 0.82 AUC (pbreast density and texture measures may have the potential to identify women specifically at risk for estrogen-receptor positive breast cancer and could be useful in triaging women into appropriate risk-reduction strategies.
Statistical Analysis of Distance Estimators with Density Differences and Density Ratios
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Takafumi Kanamori
2014-02-01
Full Text Available Estimating a discrepancy between two probability distributions from samples is an important task in statistics and machine learning. There are mainly two classes of discrepancy measures: distance measures based on the density difference, such as the Lp-distances, and divergence measures based on the density ratio, such as the Φ-divergences. The intersection of these two classes is the L1-distance measure, and thus, it can be estimated either based on the density difference or the density ratio. In this paper, we first show that the Bregman scores, which are widely employed for the estimation of probability densities in statistical data analysis, allows us to estimate the density difference and the density ratio directly without separately estimating each probability distribution. We then theoretically elucidate the robustness of these estimators and present numerical experiments.
Breast density measurements using ultrasound tomography for patients undergoing tamoxifen treatment
Sak, Mark; Duric, Neb; Littrup, Peter; Li, Cuiping; Bey-Knight, Lisa; Sherman, Mark; Boyd, Norman; Gierach, Gretchen
2013-03-01
Women with high breast density have an increased risk of developing breast cancer. Women treated with the selective estrogen receptor modulator tamoxifen for estrogen receptor positive breast cancer experience a 50% reduction in risk of contralateral breast cancer and overall reduction of similar magnitude has been identified among high-risk women receiving the drug for prevention. Tamoxifen has been shown to reduce mammographic density, and in the IBIS-1 chemoprevention trial, risk reduction and decline in density were significantly associated. Ultrasound tomography (UST) is an imaging modality that can create tomographic sound speed images of the breast. These sound speed images are useful because breast density is proportional to sound speed. The aim of this work is to examine the relationship between USTmeasured breast density and the use of tamoxifen. So far, preliminary results for a small number of patients have been observed and are promising. Correlations between the UST-measured density and mammographic density are strong and positive, while relationships between UST density with some patient specific risk factors behave as expected. Initial results of UST examinations of tamoxifen treated patients show that approximately 45% of the patients have a decrease in density in the contralateral breast after only several months of treatment. The true effect of tamoxifen on UST-measured density cannot yet be fully determined until more data are collected. However, these promising results suggest that UST can be used to reliably assess quantitative changes in breast density over short intervals and therefore suggest that UST may enable rapid assessment of density changes associated with therapeutic and preventative interventions.
A kernel type nonparametric density estimator for decompounding
A.J. van Es; S. Gugushvili; P.J.C. Spreij
2007-01-01
Abstract Given a sample from a discretely observed compound Poisson process, we consider estimation of the density of the jump sizes. We propose a kernel type nonparametric density estimator and study its asymptotic properties. An order bound for the bias and an asymptotic expansion of the variance
Nonparametric volatility density estimation for discrete time models.
A.J. van Es; P.J.C. Spreij; J.H. van Zanten
2005-01-01
Abstract: We consider discrete time models for asset prices with a stationary volatility process. We aim at estimating the multivariate density of this process at a set of consecutive time instants. A Fourier type deconvolution kernel density estimator based on the logarithm of the squared process i
International Nuclear Information System (INIS)
Isoflavones are hypothesized to protect against breast cancer, but it is not clear whether they act as oestrogens or anti-oestrogens in breast tissue. Our aim was to determine the effects of taking a red clover-derived isoflavone supplement daily for 1 year on mammographic breast density. Effects on oestradiol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), lymphocyte tyrosine kinase activity and menopausal symptoms were also assessed. A total of 205 women (age range 49–65 years) with Wolfe P2 or DY mammographic breast patterns were randomly assigned to receive either a red clover-derived isoflavone tablet (26 mg biochanin A, 16 mg formononetin, 1 mg genistein and 0.5 mg daidzein) or placebo. Change in mammographic breast density, serum oestradiol, FSH, LH, menopausal symptoms and lymphocyte tyrosine kinase activity from baseline to 12 months were assessed. A total of 177 women completed the trial. Mammographic breast density decreased in both groups but the difference between the treatment and placebo was not statistically significant. There was a significant interaction between treatment group and oestrogen receptor (ESR1) PvuII polymorphism for the change in estimated percentage breast density (mean ± standard deviation): TT isoflavone 1.4 ± 12.3% and TT placebo -9.6 ± 14.2%; CT isoflavone -5.2 ± 12.0% and CT placebo -2.8 ± 10.3%; and CC isoflavone -3.4 ± 9.7% and CC placebo -1.1 ± 9.5%. There were no statistically significant treatment effects on oestradiol, FSH, or LH (assessed only in postmenopausal women), or on lymphocyte tyrosine kinase activity. Baseline levels of menopausal symptoms were low, and there were no statistically significant treatment effects on frequency of hot flushes or other menopausal symptoms. In contrast to studies showing that conventional hormone replacement therapies increase mammographic breast density, the isoflavone supplement did not increase mammographic breast density in this population of women
Current Source Density Estimation for Single Neurons
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Dorottya Cserpán
2014-03-01
Full Text Available Recent developments of multielectrode technology made it possible to measure the extracellular potential generated in the neural tissue with spatial precision on the order of tens of micrometers and on submillisecond time scale. Combining such measurements with imaging of single neurons within the studied tissue opens up new experimental possibilities for estimating distribution of current sources along a dendritic tree. In this work we show that if we are able to relate part of the recording of extracellular potential to a specific cell of known morphology we can estimate the spatiotemporal distribution of transmembrane currents along it. We present here an extension of the kernel CSD method (Potworowski et al., 2012 applicable in such case. We test it on several model neurons of progressively complicated morphologies from ball-and-stick to realistic, up to analysis of simulated neuron activity embedded in a substantial working network (Traub et al, 2005. We discuss the caveats and possibilities of this new approach.
Quantitative comparison of estimations for the density within pedestrian streams
Tordeux, Antoine; Zhang, Jun; Steffen, Bernhard; Seyfried, Armin
2015-06-01
In this work, the precision of estimators for the density within unidirectional pedestrian streams is evaluated. The analysis is done in controllable systems where the density is homogeneous and all the characteristics are known. The objectives are to estimate the global density with local measurements or density profile at high spatial resolution with no bias and low fluctuations. The classical estimation using discrete numbers of observed pedestrians is compared to continuous estimators using spacing distance, Voronoi diagram, Gaussian kernel as well as maximum likelihood. Mean squared error and bias of the estimators are calculated from empirical data and Monte Carlo experiments. The results show quantitatively how continuous approaches improve the precision of the estimations.
Quantification of breast density with dual energy mammography: An experimental feasibility study
International Nuclear Information System (INIS)
Purpose: Breast density, the percentage of glandular breast tissue, has been shown to be a strong indicator of breast cancer risk. A quantitative method to measure breast density with dual energy mammography was investigated using physical phantoms. Methods: The dual energy mammography system used a tungsten anode x-ray tube with a 50 μm rhodium beam filter for low energy images and a 300 μm copper beam filter for high energy images. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Four different phantom studies were used to evaluate the technique. The first study consisted of phantoms with thicknesses of 2.5-8.5 cm in 0.5 cm steps with variable densities centered at a mean of 28%. The second study consisted of phantoms at a fixed thickness of 4.0 cm, which ranged in densities from 0% to 100% in increments of 12.5%. The third study consisted of 4.0 cm thick phantoms at densities of 25%, 50% and 75% each imaged at three areal sizes, approximately 62.5, 125, and 250 cm2, in order to assess the effect of breast size on density measurement. The fourth study consisted of step phantoms designed to more closely mimic the shape of a female breast with maximal thicknesses from 3.0 to 7.0 cm at a fixed density of 50%. All images were corrected for x-ray scatter. Results: The RMS errors in breast density measurements were 0.44% for the variable thickness phantoms, 0.64% for the variable density phantoms, 2.87% for the phantoms of different areal sizes, and 4.63% for step phantoms designed to closely resemble the shape of a breast. Conclusions: The results of the phantom studies indicate that dual energy mammography can be used to measure breast density with an RMS error of approximately 5%.
Quantification of breast density with dual energy mammography: An experimental feasibility study
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Ducote, Justin L.; Molloi, Sabee [Department of Radiological Sciences, University of California, Irvine, California 92697 (United States)
2010-02-15
Purpose: Breast density, the percentage of glandular breast tissue, has been shown to be a strong indicator of breast cancer risk. A quantitative method to measure breast density with dual energy mammography was investigated using physical phantoms. Methods: The dual energy mammography system used a tungsten anode x-ray tube with a 50 {mu}m rhodium beam filter for low energy images and a 300 {mu}m copper beam filter for high energy images. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Four different phantom studies were used to evaluate the technique. The first study consisted of phantoms with thicknesses of 2.5-8.5 cm in 0.5 cm steps with variable densities centered at a mean of 28%. The second study consisted of phantoms at a fixed thickness of 4.0 cm, which ranged in densities from 0% to 100% in increments of 12.5%. The third study consisted of 4.0 cm thick phantoms at densities of 25%, 50% and 75% each imaged at three areal sizes, approximately 62.5, 125, and 250 cm{sup 2}, in order to assess the effect of breast size on density measurement. The fourth study consisted of step phantoms designed to more closely mimic the shape of a female breast with maximal thicknesses from 3.0 to 7.0 cm at a fixed density of 50%. All images were corrected for x-ray scatter. Results: The RMS errors in breast density measurements were 0.44% for the variable thickness phantoms, 0.64% for the variable density phantoms, 2.87% for the phantoms of different areal sizes, and 4.63% for step phantoms designed to closely resemble the shape of a breast. Conclusions: The results of the phantom studies indicate that dual energy mammography can be used to measure breast density with an RMS error of approximately 5%.
Angiogenesis in Paget's Disease of the Vulva and the Breast: Correlation with Microvessel Density
Crow, Julie C.; Perrett, Christopher W.; Patricia E. Ellis; Wong Te Fong, L. F.; MacLean, Allan B.
2012-01-01
Our understanding of the pathogenesis of Paget's disease of the vulva and the breast remains limited. Current evidence supports the fact that angiogenesis plays an important role in the pathogenesis of several diseases. Therefore, we sought to define its role, as correlated with microvessel density, in Paget's disease of the vulva and the breast. Microvessels were analysed using anti-von Willebrand factor antibody in 105 cases of Paget's disease of the vulva and the breast comprising 71 cases...
Directory of Open Access Journals (Sweden)
Kristina M Blackmore
Full Text Available Mammographic density (MD, associated with higher water and lower fat content in the breast, is strongly related to breast cancer risk. Optical attenuation spectroscopy (OS is a non-imaging method of evaluating breast tissue composition by red and near-infrared light transmitted through the breast that, unlike mammography, does not involve radiation. OS provides information on wavelength dependent light scattering of tissue and on absorption by water, lipid, oxy-, deoxy-hemoglobin. We propose that OS could be an alternative marker of breast cancer risk and that OS breast tissue measures will be associated with MD. In the present analysis, we developed an algorithm to estimate breast tissue composition and light scattering parameters using a spectrally constrained global fitting procedure employing a diffuse light transport model. OS measurements were obtained from 202 pre- and post-menopausal women with normal mammograms. Percent density (PD and dense area (DA were measured using Cumulus. The association between OS tissue composition and PD and DA was analyzed using linear regression adjusted for body mass index. Among pre-menopausal women, lipid content was significantly inversely associated with square root transformed PD (β = -0.05, p = 0.0002 and DA (β = -0.05, p = 0.019; water content was significantly positively associated with PD (β = 0.06, p = 0.008. Tissue oxygen saturation was marginally inversely associated with PD (β = -0.03, p = 0.057 but significantly inversely associated with DA (β = -0.10, p = 0.002. Among post-menopausal women lipid and water content were significantly associated (negatively and positively, respectively with PD (β lipid = -0.08, β water = 0.14, both p<0.0001 and DA (β lipid = -0.10, p<0.0001; β water = 0.11, p = 0.001. The association between OS breast content and PD and DA is consistent with more proliferation in dense tissue of younger women, greater lipid content in low density tissue and higher water
Sample size re-estimation in a breast cancer trial
Hade, Erinn; Jarjoura, David; Wei, Lai
2016-01-01
Background During the recruitment phase of a randomized breast cancer trial, investigating the time to recurrence, we found evidence that the failure probabilities used at the design stage were too high. Since most of the methodological research involving sample size re-estimation has focused on normal or binary outcomes, we developed a method which preserves blinding to re-estimate sample size in our time to event trial. Purpose A mistakenly high estimate of the failure rate at the design stage may reduce the power unacceptably for a clinically important hazard ratio. We describe an ongoing trial and an application of a sample size re-estimation method that combines current trial data with prior trial data or assumes a parametric model to re-estimate failure probabilities in a blinded fashion. Methods Using our current blinded trial data and additional information from prior studies, we re-estimate the failure probabilities to be used in sample size re-calculation. We employ bootstrap resampling to quantify uncertainty in the re-estimated sample sizes. Results At the time of re-estimation data from 278 patients was available, averaging 1.2 years of follow up. Using either method, we estimated an increase of 0 for the hazard ratio proposed at the design stage. We show that our method of blinded sample size re-estimation preserves the Type I error rate. We show that when the initial guess of the failure probabilities are correct; the median increase in sample size is zero. Limitations Either some prior knowledge of an appropriate survival distribution shape or prior data is needed for re-estimation. Conclusions In trials when the accrual period is lengthy, blinded sample size re-estimation near the end of the planned accrual period should be considered. In our examples, when assumptions about failure probabilities and HRs are correct the methods usually do not increase sample size or otherwise increase it by very little. PMID:20392786
Estimating cosmic velocity fields from density fields and tidal tensors
Kitaura, Francisco-Shu; Hoffman, Yehuda; Gottl"ober, Stefan
2011-01-01
In this work we investigate the nonlinear and nonlocal relation between cosmological density and peculiar velocity fields. Our goal is to provide an algorithm for the reconstruction of the nonlinear velocity field from the fully nonlinear density. We find that including the gravitational tidal field tensor using second order Lagrangian perturbation theory (2LPT) based upon an estimate of the linear component of the nonlinear density field significantly improves the estimate of the cosmic flow in comparison to linear theory not only in the low density, but also and more dramatically in the high density regions. In particular we test two estimates of the linear component: the lognormal model and the iterative Lagrangian linearisation. The present approach relies on a rigorous higher order Lagrangian perturbation theory analysis which incorporates a nonlocal relation. It does not require additional fitting from simulations being in this sense parameter free, it is independent of statistical-geometrical optimisat...
Estimators of integrals of powers of density derivatives
Rodney C Wolff; Peter Hall
2006-01-01
Simple kernel-type estimators of integrals of general powers of general derivatives of probability densities are proposed. They are based on two simple properties, and in many circumstances enjoy optimal convergence rate.
Nonparametric density estimation of continuous part of a mixed measure
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Rachid Sabre
2007-10-01
Full Text Available We consider a pair of random variables(X,Y whose probability measure is the sum of an absolutely continuous measure, a discrete measure and a finite number of absolutely continuous measures on several lines(1. An asymptotically unbiased and consistent estimate, at all points, of the density of the continuous part is given as well as its rate of convergence. We also estimate the amplitude of the discrete measure and the densities on several lines.
Evaluating parasite densities and estimation of parameters in transmission systems
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Heinzmann D.
2008-09-01
Full Text Available Mathematical modelling of parasite transmission systems can provide useful information about host parasite interactions and biology and parasite population dynamics. In addition good predictive models may assist in designing control programmes to reduce the burden of human and animal disease. Model building is only the first part of the process. These models then need to be confronted with data to obtain parameter estimates and the accuracy of these estimates has to be evaluated. Estimation of parasite densities is central to this. Parasite density estimates can include the proportion of hosts infected with parasites (prevalence or estimates of the parasite biomass within the host population (abundance or intensity estimates. Parasite density estimation is often complicated by highly aggregated distributions of parasites within the hosts. This causes additional challenges when calculating transmission parameters. Using Echinococcus spp. as a model organism, this manuscript gives a brief overview of the types of descriptors of parasite densities, how to estimate them and on the use of these estimates in a transmission model.
Comparison of density estimation methods for astronomical datasets
Ferdosi, B.J.; Buddelmeijer, H.; Trager, S.C.; Wilkinson, M.H.F.; Roerdink, J.B.T.M.
2011-01-01
Context. Galaxies are strongly influenced by their environment. Quantifying the galaxy density is a difficult but critical step in studying the properties of galaxies. Aims. We aim to determine differences in density estimation methods and their applicability in astronomical problems. We study the p
Estimating maritime snow density from seasonal climate variables
Bormann, K. J.; Evans, J. P.; Westra, S.; McCabe, M. F.; Painter, T. H.
2013-12-01
Snow density is a complex parameter that influences thermal, optical and mechanical snow properties and processes. Depth-integrated properties of snowpacks, including snow density, remain very difficult to obtain remotely. Observations of snow density are therefore limited to in-situ point locations. In maritime snowfields such as those in Australia and in parts of the western US, snow densification rates are enhanced and inter-annual variability is high compared to continental snow regions. In-situ snow observation networks in maritime climates often cannot characterise the variability in snowpack properties at spatial and temporal resolutions required for many modelling and observations-based applications. Regionalised density-time curves are commonly used to approximate snow densities over broad areas. However, these relationships have limited spatial applicability and do not allow for interannual variability in densification rates, which are important in maritime environments. Physically-based density models are relatively complex and rely on empirical algorithms derived from limited observations, which may not represent the variability observed in maritime snow. In this study, seasonal climate factors were used to estimate late season snow densities using multiple linear regressions. Daily snow density estimates were then obtained by projecting linearly to fresh snow densities at the start of the season. When applied spatially, the daily snow density fields compare well to in-situ observations across multiple sites in Australia, and provide a new method for extrapolating existing snow density datasets in maritime snow environments. While the relatively simple algorithm for estimating snow densities has been used in this study to constrain snowmelt rates in a temperature-index model, the estimates may also be used to incorporate variability in snow depth to snow water equivalent conversion.
Darker, Iain T.; Chen, Yan; Gale, Alastair G.
2011-03-01
Higher breast density is associated with a greater chance of developing breast cancer. Additionally, it is well known that higher mammographic breast density is associated with increased difficulty in accurately identifying breast cancer. However, comparatively little is known of the reliability of breast density judgements. All UK breast screeners (primarily radiologists and technologists) annually participate in the PERFORMS self-assessment scheme where they make several judgements about series of challenging recent screening cases of known outcomes. As part of this process, for each case, they provide a radiological assessment of the likelihood of cancer on a confidence scale, alongside an assessment of case density using a three point scale. Analysis of the data from two years of the scheme found that the degree of agreement on case density was significantly greater than no agreement (p < .001). However, only a moderate degree of inter-rater reliability was exhibited (κ = .44) with significant differences between the occupational groups. The reasons for differences between the occupational groups and the relationship between agreement on density rating and case reading ability are explored.
Mean thermospheric density estimation derived from satellite constellations
Li, Alan; Close, Sigrid
2015-10-01
This paper defines a method to estimate the mean neutral density of the thermosphere given many satellites of the same form factor travelling in similar regions of space. A priori information to the estimation scheme include ranging measurements and a general knowledge of the onboard ADACS, although precise measurements are not required for the latter. The estimation procedure seeks to utilize order statistics to estimate the probability of the minimum drag coefficient achievable, and amalgamating all measurements across multiple time periods allows estimation of the probability density of the ballistic factor itself. The model does not depend on prior models of the atmosphere; instead we require estimation of the minimum achievable drag coefficient which is based upon physics models of simple shapes in free molecular flow. From the statistics of the minimum, error statistics on the estimated atmospheric density can be calculated. Barring measurement errors from the ranging procedure itself, it is shown that with a constellation of 10 satellites, we can achieve a standard deviation of roughly 4% on the estimated mean neutral density. As more satellites are added to the constellation, the result converges towards the lower limit of the achievable drag coefficient, and accuracy becomes limited by the quality of the ranging measurements and the probability of the accommodation coefficient. Comparisons are made to existing atmospheric models such as NRLMSISE-00 and JB2006.
Quantitative assessment of breast density from digitized mammograms into Tabar's patterns
Energy Technology Data Exchange (ETDEWEB)
Jamal, N [Medical Technology Division, Malaysian Institute for Nuclear Technology Research (MINT) 43000 Kajang (Malaysia); Ng, K-H [Department of Radiology, University of Malaya, 50603 Kuala Lumpur (Malaysia); Looi, L-M [Department of Pathology, University of Malaya, 50603 Kuala Lumpur (Malaysia); McLean, D [Medical Physics Department, Westmead Hospital, Sydney, NSW 2145 (Australia); Zulfiqar, A [Department of Radiology, Hospital Universiti Kebangsaan Malaysia, 56000 Malaysia, Kuala Lumpur, Malaysia (Malaysia); Tan, S-P [Department of Radiology, Hospital Universiti Kebangsaan Malaysia, 56000 Malaysia, Kuala Lumpur, Malaysia (Malaysia); Liew, W-F [Department of Radiology, Hospital Universiti Kebangsaan Malaysia, 56000 Malaysia, Kuala Lumpur, Malaysia (Malaysia); Shantini, A [Department of Radiology, Kuala Lumpur Hospital, 50586 Kuala Lumpur (Malaysia); Ranganathan, S [Department of Radiology, University of Malaya, 50603 Kuala Lumpur (Malaysia)
2006-11-21
We describe a semi-automated technique for the quantitative assessment of breast density from digitized mammograms in comparison with patterns suggested by Tabar. It was developed using the MATLAB-based graphical user interface applications. It is based on an interactive thresholding method, after a short automated method that shows the fibroglandular tissue area, breast area and breast density each time new thresholds are placed on the image. The breast density is taken as a percentage of the fibroglandular tissue to the breast tissue areas. It was tested in four different ways, namely by examining: (i) correlation of the quantitative assessment results with subjective classification, (ii) classification performance using the quantitative assessment technique, (iii) interobserver agreement and (iv) intraobserver agreement. The results of the quantitative assessment correlated well (r{sup 2} = 0.92) with the subjective Tabar patterns classified by the radiologist (correctly classified 83% of digitized mammograms). The average kappa coefficient for the agreement between the readers was 0.63. This indicated moderate agreement between the three observers in classifying breast density using the quantitative assessment technique. The kappa coefficient of 0.75 for intraobserver agreement reflected good agreement between two sets of readings. The technique may be useful as a supplement to the radiologist's assessment in classifying mammograms into Tabar's pattern associated with breast cancer risk.
Quantification of Regional Breast Density in Four Quadrants Using 3D MRI—A Pilot Study
Directory of Open Access Journals (Sweden)
Peter T. Fwu
2015-08-01
Full Text Available PURPOSE: This study presented a three-dimensional magnetic resonance (MR–based method to separate a breast into four quadrants for quantitative measurements of the quadrant breast volume (BV and density. METHODS: Breast MR images from 58 healthy women were studied. The breast and the fibroglandular tissue were segmented by using a computer-based algorithm. A breast was divided into four quadrants using two perpendicular planes intersecting at the nipple or the nipple-centroid line. After the separation, the BV, the fibroglandular tissue volume, and the percent density (PD were calculated. The symmetry of the quadrant BV in the left and right breasts separated by using the nipple alone, or the nipple-centroid line, was compared. RESULTS: The quadrant separation made on the basis of the nipple-centroid line showed closer BVs in four quadrants than using the nipple alone. The correlation and agreement for the BV in corresponding quadrants of the left and the right breasts were improved after the nipple-centroid reorientation. Among the four quadrants, PD was the highest in the lower outer and the lowest in the upper outer (significant than the other three quadrants (P < .05. CONCLUSIONS: We presented a quantitative method to divide a breast into four quadrants. The reorientation based on the nipple-centroid line improved the left to right quadrant symmetry, and this may provide a better standardized method to measure quantitative quadrant density. The cancer occurrence rates are known to vary in different sites of a breast, and our method may provide a tool for investigating its association with the quantitative breast density.
Law, Yan Nei; Lieng, Monica Keiko; Li, Jingmei; Khoo, David Aik-Aun
2014-03-01
Breast cancer is the most common cancer and second leading cause of cancer death among women in the US. The relative survival rate is lower among women with a more advanced stage at diagnosis. Early detection through screening is vital. Mammography is the most widely used and only proven screening method for reliably and effectively detecting abnormal breast tissues. In particular, mammographic density is one of the strongest breast cancer risk factors, after age and gender, and can be used to assess the future risk of disease before individuals become symptomatic. A reliable method for automatic density assessment would be beneficial and could assist radiologists in the evaluation of mammograms. To address this problem, we propose a density classification method which uses statistical features from different parts of the breast. Our method is composed of three parts: breast region identification, feature extraction and building ensemble classifiers for density assessment. It explores the potential of the features extracted from second and higher order statistical information for mammographic density classification. We further investigate the registration of bilateral pairs and time-series of mammograms. The experimental results on 322 mammograms demonstrate that (1) a classifier using features from dense regions has higher discriminative power than a classifier using only features from the whole breast region; (2) these high-order features can be effectively combined to boost the classification accuracy; (3) a classifier using these statistical features from dense regions achieves 75% accuracy, which is a significant improvement from 70% accuracy obtained by the existing approaches.
Energy Technology Data Exchange (ETDEWEB)
Poellinger, Alexander, E-mail: alexander.poellinger@charite.de [Charité, Universitätsmedizin Berlin, Department of Radiology, Augustenburger Platz 1, 13353 Berlin (Germany); El-Ghannam, Sahra; Diekmann, Susanne; Fischer, Thomas [Charité, Universitätsmedizin Berlin, Department of Radiology, Augustenburger Platz 1, 13353 Berlin (Germany); Kristiansen, Glen [Universitätsklinikum Bonn, Department of Pathology, Sigmund-Freud-Str. 25, D-53127 Bonn (Germany); Fritzsche, Florian [Institut für Histologie und Zytologie, Bahnhofplatz 11, Postfach, 9101 Herisau (Switzerland); Fallenberg, Eva [Charité, Universitätsmedizin Berlin, Department of Radiology, Augustenburger Platz 1, 13353 Berlin (Germany); Morawietz, Lars [Diagnostik Ernst von Bergmann GmbH, Charlottenstr. 72, 14467 Potsdam (Germany); Diekmann, Felix [Charité, Universitätsmedizin Berlin, Department of Radiology, Augustenburger Platz 1, 13353 Berlin (Germany)
2014-12-15
Highlights: • We correlate capillary density of breast lesions with MRM. • Capillary density correlates with tumor enhancement for all lesions. • However no such correlation exists for the malignant or benign groups separately. • Mean vessel number of lymphatic vessels do not correlate with tumor enhancement.These results might be of help in the workup of MR-guided breast biopsies. • These results might be of help in the workup of MR-guided breast biopsies. - Abstract: Objective: To correlate capillary density of breast lesions using the markers D2-40, CD31, and CD34 with early and late enhancement of magnetic resonance mammography (MRM). Materials and methods: The local ethics committee approved this study, and informed consent was available from all patients. The study included 64 women with 66 histologically proven breast lesions (41 malignant, 25 benign). MR-enhancement 1 min after contrast medium administration was determined in the tumor (I{sub t1}/I{sub t0} ratio) and in comparison to the surrounding tissue (I{sub t1}/I{sub t1-fat} ratio). Capillary density was quantified based on immunohistological staining with D2-40, CD31, and CD34 in breast tumors and surrounding breast tissue. Mean capillary densities were correlated with contrast enhancement in the tumor and surrounding breast tissue. The Kruskal–Wallis test was used to test whether lesions with different MR enhancement patterns differed in terms of capillary density. Results: For CD34, there was statistically significant correlation between capillary density and tumor enhancement (r = 0.329, p = 0.012), however not for the malignant or benign groups separately. Mean vessel number identified by staining with D2-40 and CD31 did not correlate significantly with tumor enhancement (D2-40: r = −0.188, p = 0.130; CD31: r = 0.095, p = 0.448). There were no statistically significant differences in capillary density between breast lesions with delayed enhancement or a plateau and lesions showing
International Nuclear Information System (INIS)
Highlights: • We correlate capillary density of breast lesions with MRM. • Capillary density correlates with tumor enhancement for all lesions. • However no such correlation exists for the malignant or benign groups separately. • Mean vessel number of lymphatic vessels do not correlate with tumor enhancement.These results might be of help in the workup of MR-guided breast biopsies. • These results might be of help in the workup of MR-guided breast biopsies. - Abstract: Objective: To correlate capillary density of breast lesions using the markers D2-40, CD31, and CD34 with early and late enhancement of magnetic resonance mammography (MRM). Materials and methods: The local ethics committee approved this study, and informed consent was available from all patients. The study included 64 women with 66 histologically proven breast lesions (41 malignant, 25 benign). MR-enhancement 1 min after contrast medium administration was determined in the tumor (It1/It0 ratio) and in comparison to the surrounding tissue (It1/It1-fat ratio). Capillary density was quantified based on immunohistological staining with D2-40, CD31, and CD34 in breast tumors and surrounding breast tissue. Mean capillary densities were correlated with contrast enhancement in the tumor and surrounding breast tissue. The Kruskal–Wallis test was used to test whether lesions with different MR enhancement patterns differed in terms of capillary density. Results: For CD34, there was statistically significant correlation between capillary density and tumor enhancement (r = 0.329, p = 0.012), however not for the malignant or benign groups separately. Mean vessel number identified by staining with D2-40 and CD31 did not correlate significantly with tumor enhancement (D2-40: r = −0.188, p = 0.130; CD31: r = 0.095, p = 0.448). There were no statistically significant differences in capillary density between breast lesions with delayed enhancement or a plateau and lesions showing washout (Kruskal
Angiogenesis in Paget's Disease of the Vulva and the Breast: Correlation with Microvessel Density
Directory of Open Access Journals (Sweden)
Patricia E. Ellis
2012-01-01
Full Text Available Our understanding of the pathogenesis of Paget's disease of the vulva and the breast remains limited. Current evidence supports the fact that angiogenesis plays an important role in the pathogenesis of several diseases. Therefore, we sought to define its role, as correlated with microvessel density, in Paget's disease of the vulva and the breast. Microvessels were analysed using anti-von Willebrand factor antibody in 105 cases of Paget's disease of the vulva and the breast comprising 71 cases of Paget's disease of the vulva, including 8 cases with invasive disease, and 34 cases of Paget's disease of the breast. The latter included 12 cases with DCIS, 5 cases with both DCIS and invasive carcinoma, and 6 with carcinoma alone. Eleven cases had no underlying tumour identified. Increased microvessel density was demonstrated in Paget's disease of the breast with DCIS and with carcinoma alone compared to Paget's disease of the breast alone, <0.08 and <0.013, respectively. There were no significant differences in microvessel density in the vulval cases. Neovascularisation is an important process in the development of Paget's disease of the breast. Other biological and molecular processes are more involved in the pathogenesis of Paget's disease of the vulva.
Open-cluster density profiles derived using a kernel estimator
Seleznev, Anton F.
2016-03-01
Surface and spatial radial density profiles in open clusters are derived using a kernel estimator method. Formulae are obtained for the contribution of every star into the spatial density profile. The evaluation of spatial density profiles is tested against open-cluster models from N-body experiments with N = 500. Surface density profiles are derived for seven open clusters (NGC 1502, 1960, 2287, 2516, 2682, 6819 and 6939) using Two-Micron All-Sky Survey data and for different limiting magnitudes. The selection of an optimal kernel half-width is discussed. It is shown that open-cluster radius estimates hardly depend on the kernel half-width. Hints of stellar mass segregation and structural features indicating cluster non-stationarity in the regular force field are found. A comparison with other investigations shows that the data on open-cluster sizes are often underestimated. The existence of an extended corona around the open cluster NGC 6939 was confirmed. A combined function composed of the King density profile for the cluster core and the uniform sphere for the cluster corona is shown to be a better approximation of the surface radial density profile.The King function alone does not reproduce surface density profiles of sample clusters properly. The number of stars, the cluster masses and the tidal radii in the Galactic gravitational field for the sample clusters are estimated. It is shown that NGC 6819 and 6939 are extended beyond their tidal surfaces.
Institute of Scientific and Technical Information of China (English)
Lukui Yang; Long Li; Xiangyu Cui; Dalei Yang
2015-01-01
Objective The purpose of this study was to evaluate the distribution of vascular endothelial growth factor (VEGF) and CD105-microvessel density (MVD) in invasive breast carcinomas. We also aimed to analyze the relationship between VEGF and MVD expression with other standard prognostic parameters associated with invasive breast cancer, such as size, grade, stage of the cancer, metastases, and tumor recurrence. Methods Immunohistochemistry via the Ultra SensitiveTM S-P method was used to detect VEGF and MVD expression in 128 cases of invasive breast carcinoma. Specimens were evaluated for CD105 expres-sion. Positively stained microvessels were counted in dense vascular foci under 400× magnification. MVD in the peripheral area adjacent to the lesion and in the central area within the lesion in invasive breast carcinomas and benign leisions groups were also assessed. Fifty cases of benign breast disease tissue were selected as the control group. Results Results showed that 64.1% of invasive breast cancer samples were VEGF-positive, higher than in benign breast disease tissue (22.0%, P 0.05). MVD of the peripheral area adja-cent to the lesion was significantly higher than those central area within the lesion in both invasive breast cancer and benign breast disease groups (P 50 years) or the two tumor diameter groups (≤2 cm vs.>2 cm), P > 0.05. Conclusion Overexpression of VEGF and MVD may be important biological markers for invasion and lymph node and distant metastases of invasive breast cancer. Combined detection of the two tumor mark-ers could provide better prognostic monitoring for disease recurrence and metastasis, as wel as aid with clinical staging of breast tumors. Prediction of the risk for metastasis and recurrence, as wel as recurrence patterns based on VEGF and MVD post-surgery, could aid design of better fol ow-up regimens and appro-priate treatment strategies for breast cancer patients.
Kernel density estimation of a multidimensional efficiency profile
Poluektov, Anton
2014-01-01
Kernel density estimation is a convenient way to estimate the probability density of a distribution given the sample of data points. However, it has certain drawbacks: proper description of the density using narrow kernels needs large data samples, whereas if the kernel width is large, boundaries and narrow structures tend to be smeared. Here, an approach to correct for such effects, is proposed that uses an approximate density to describe narrow structures and boundaries. The approach is shown to be well suited for the description of the efficiency shape over a multidimensional phase space in a typical particle physics analysis. An example is given for the five-dimensional phase space of the $\\Lambda_b^0\\to D^0p\\pi$ decay.
A morpho-density approach to estimating neural connectivity.
Directory of Open Access Journals (Sweden)
Michael P McAssey
Full Text Available Neuronal signal integration and information processing in cortical neuronal networks critically depend on the organization of synaptic connectivity. Because of the challenges involved in measuring a large number of neurons, synaptic connectivity is difficult to determine experimentally. Current computational methods for estimating connectivity typically rely on the juxtaposition of experimentally available neurons and applying mathematical techniques to compute estimates of neural connectivity. However, since the number of available neurons is very limited, these connectivity estimates may be subject to large uncertainties. We use a morpho-density field approach applied to a vast ensemble of model-generated neurons. A morpho-density field (MDF describes the distribution of neural mass in the space around the neural soma. The estimated axonal and dendritic MDFs are derived from 100,000 model neurons that are generated by a stochastic phenomenological model of neurite outgrowth. These MDFs are then used to estimate the connectivity between pairs of neurons as a function of their inter-soma displacement. Compared with other density-field methods, our approach to estimating synaptic connectivity uses fewer restricting assumptions and produces connectivity estimates with a lower standard deviation. An important requirement is that the model-generated neurons reflect accurately the morphology and variation in morphology of the experimental neurons used for optimizing the model parameters. As such, the method remains subject to the uncertainties caused by the limited number of neurons in the experimental data set and by the quality of the model and the assumptions used in creating the MDFs and in calculating estimating connectivity. In summary, MDFs are a powerful tool for visualizing the spatial distribution of axonal and dendritic densities, for estimating the number of potential synapses between neurons with low standard deviation, and for obtaining
Nonparametric probability density estimation by optimization theoretic techniques
Scott, D. W.
1976-01-01
Two nonparametric probability density estimators are considered. The first is the kernel estimator. The problem of choosing the kernel scaling factor based solely on a random sample is addressed. An interactive mode is discussed and an algorithm proposed to choose the scaling factor automatically. The second nonparametric probability estimate uses penalty function techniques with the maximum likelihood criterion. A discrete maximum penalized likelihood estimator is proposed and is shown to be consistent in the mean square error. A numerical implementation technique for the discrete solution is discussed and examples displayed. An extensive simulation study compares the integrated mean square error of the discrete and kernel estimators. The robustness of the discrete estimator is demonstrated graphically.
Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring
DEFF Research Database (Denmark)
Kallenberg, Michiel Gijsbertus J; Petersen, Peter Kersten; Nielsen, Mads;
2016-01-01
Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used...... as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime...... and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy...
Comparison of bone mineral density in young patients with breast cancer and healthy women
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Sousan Kolahi
2014-05-01
Full Text Available BACKGROUND: Almost 1 in 8 women will have breast cancer during their lifetime. Several risk factors were identified; however, 70% of females with breast cancer have no risk factors. Many risk factors are associated with sex steroid hormones. Some studies have been focused on identification of the indices of cumulative exposures to estrogen during the patients’ life. One of these indicators is bone mineral density (BMD. Our aim was the comparison of BMD in young patients with and without breast cancer, and finding a relationship between breast cancer and bone density. METHODS: In this case-control study, 120 people were enrolled; 40 patients with breast cancer and 80 normal healthy persons as control group. Measurement of BMD was performed in both groups and compared. RESULTS: Both groups were matched in age, weight, age at menarche, age at first marriage and first pregnancy, number of pregnancies over 32 weeks and lactation period, and taking supplemental calcium and vitamin D. However, there was a significant difference between the two groups in terms of estrogen intake, family history of breast cancer, and history of breast masses (P = 0.03, P = 0.03, P ≤ 0.01, respectively. A significant difference was found between BMD, bone mineral content (BMC, and t-scores of lumbar spine of the two groups; they were higher in the control group (P = 0.08, P ≤ 0.01, P = 0.06, respectively. CONCLUSIONS: This study shows that bone mineral density of young patients with breast cancer is not higher than normal similar age females; thus, BMD is not directly a risk factor for breast cancer.
Forecasting Electricity Smart Meter Data Using Conditional Kernel Density Estimation
Arora, Siddharth; Taylor, James W.
2014-01-01
The recent advent of smart meters has led to large micro-level datasets. For the first time, the electricity consumption at individual sites is available on a near real-time basis. Efficient management of energy resources, electric utilities, and transmission grids, can be greatly facilitated by harnessing the potential of this data. The aim of this study is to generate probability density estimates for consumption recorded by individual smart meters. Such estimates can assist decision making...
Density Estimation in Several Populations With Uncertain Population Membership
Ma, Yanyuan
2011-09-01
We devise methods to estimate probability density functions of several populations using observations with uncertain population membership, meaning from which population an observation comes is unknown. The probability of an observation being sampled from any given population can be calculated. We develop general estimation procedures and bandwidth selection methods for our setting. We establish large-sample properties and study finite-sample performance using simulation studies. We illustrate our methods with data from a nutrition study.
Green's function based density estimation
Energy Technology Data Exchange (ETDEWEB)
Kovesarki, Peter; Brock, Ian C.; Nuncio Quiroz, Adriana Elizabeth [Physikalisches Institut, Universitaet Bonn (Germany)
2012-07-01
A method was developed based on Green's function identities to estimate probability densities. This can be used for likelihood estimations and for binary classifications. It offers several advantages over neural networks, boosted decision trees and other, regression based classifiers. For example, it is less prone to overtraining, and it is much easier to combine several samples. Some capabilities are demonstrated using ATLAS data.
A new approach for estimating the density of liquids.
Sakagami, T; Fuchizaki, K; Ohara, K
2016-10-01
We propose a novel approach with which to estimate the density of liquids. The approach is based on the assumption that the systems would be structurally similar when viewed at around the length scale (inverse wavenumber) of the first peak of the structure factor, unless their thermodynamic states differ significantly. The assumption was implemented via a similarity transformation to the radial distribution function to extract the density from the structure factor of a reference state with a known density. The method was first tested using two model liquids, and could predict the densities within an error of several percent unless the state in question differed significantly from the reference state. The method was then applied to related real liquids, and satisfactory results were obtained for predicted densities. The possibility of applying the method to amorphous materials is discussed. PMID:27494268
A new approach for estimating the density of liquids
Sakagami, T.; Fuchizaki, K.; Ohara, K.
2016-10-01
We propose a novel approach with which to estimate the density of liquids. The approach is based on the assumption that the systems would be structurally similar when viewed at around the length scale (inverse wavenumber) of the first peak of the structure factor, unless their thermodynamic states differ significantly. The assumption was implemented via a similarity transformation to the radial distribution function to extract the density from the structure factor of a reference state with a known density. The method was first tested using two model liquids, and could predict the densities within an error of several percent unless the state in question differed significantly from the reference state. The method was then applied to related real liquids, and satisfactory results were obtained for predicted densities. The possibility of applying the method to amorphous materials is discussed.
An Infrastructureless Approach to Estimate Vehicular Density in Urban Environments
Sanguesa, Julio A.; Fogue, Manuel; Garrido, Piedad; Martinez, Francisco J.; Cano, Juan-Carlos; Calafate, Carlos T.; Manzoni, Pietro
2013-01-01
In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes. PMID:23435054
An Infrastructureless Approach to Estimate Vehicular Density in Urban Environments
Directory of Open Access Journals (Sweden)
Juan-Carlos Cano
2013-02-01
Full Text Available In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes.
An infrastructureless approach to estimate vehicular density in urban environments.
Sanguesa, Julio A; Fogue, Manuel; Garrido, Piedad; Martinez, Francisco J; Cano, Juan-Carlos; Calafate, Carlos T; Manzoni, Pietro
2013-01-01
In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes. PMID:23435054
Comparison of bone mineral density in young patients with breast cancer and healthy women
Sousan Kolahi; Hamid Noshad; Jamal Eivazi Ziaei; Alireza Nikanfar; Parvin Shakori Partovi; Iraj Asvadi Kermani; Farid Panahi; Nassim Mahmoudzade
2014-01-01
BACKGROUND: Almost 1 in 8 women will have breast cancer during their lifetime. Several risk factors were identified; however, 70% of females with breast cancer have no risk factors. Many risk factors are associated with sex steroid hormones. Some studies have been focused on identification of the indices of cumulative exposures to estrogen during the patients’ life. One of these indicators is bone mineral density (BMD). Our aim was the comparison of BMD in young patients with and without brea...
Estimating neuronal connectivity from axonal and dendritic density fields
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Jaap evan Pelt
2013-11-01
Full Text Available Neurons innervate space by extending axonal and dendritic arborizations. When axons and dendrites come in close proximity of each other, synapses between neurons can be formed. Neurons vary greatly in their morphologies and synaptic connections with other neurons. The size and shape of the arborizations determine the way neurons innervate space. A neuron may therefore be characterized by the spatial distribution of its axonal and dendritic 'mass'. A population mean 'mass' density field of a particular neuron type can be obtained by averaging over the individual variations in neuron geometries. Connectivity in terms of candidate synaptic contacts between neurons can be determined directly on the basis of their arborizations but also indirectly on the basis of their density fields. To decide when a candidate synapse can be formed, we previously developed a criterion defining that axonal and dendritic line pieces should cross in 3D and have an orthogonal distance less than a threshold value. In this paper, we developed new methodology for applying this criterion to density fields. We show that estimates of the number of contacts between neuron pairs calculated from their density fields are fully consistent with the number of contacts calculated from the actual arborizations. However, the estimation of the connection probability and the expected number of contacts per connection cannot be calculated directly from density fields, because density fields do not carry anymore the correlative structure in the spatial distribution of synaptic contacts. Alternatively, these two connectivity measures can be estimated from the expected number of contacts by using empirical mapping functions. The neurons used for the validation studies were generated by our neuron simulator NETMORPH. An example is given of the estimation of average connectivity and Euclidean pre- and postsynaptic distance distributions in a network of neurons represented by their population
Collagen I fiber density increases in lymph node positive breast cancers: pilot study
Kakkad, Samata M.; Solaiyappan, Meiyappan; Argani, Pedram; Sukumar, Saraswati; Jacobs, Lisa K.; Leibfritz, Dieter; Bhujwalla, Zaver M.; Glunde, Kristine
2012-11-01
Collagen I (Col1) fibers are a major structural component in the extracellular matrix of human breast cancers. In a preliminary pilot study, we explored the link between Col1 fiber density in primary human breast cancers and the occurrence of lymph node metastasis. Col1 fibers were detected by second harmonic generation (SHG) microscopy in primary human breast cancers from patients presenting with lymph node metastasis (LN+) versus those without lymph node metastasis (LN-). Col1 fiber density, which was quantified using our in-house SHG image analysis software, was significantly higher in the primary human breast cancers of LN+ (fiber volume=29.22%±4.72%, inter-fiber distance=2.25±0.45 μm) versus LN- (fiber volume=20.33%±5.56%, inter-fiber distance=2.88±1.07 μm) patients. Texture analysis by evaluating the co-occurrence matrix and the Fourier transform of the Col1 fibers proved to be significantly different for the parameters of co-relation and energy, as well as aspect ratio and eccentricity, for LN+ versus LN- cases. We also demonstrated that tissue fixation and paraffin embedding had negligible effect on SHG Col1 fiber detection and quantification. High Col1 fiber density in primary breast tumors is associated with breast cancer metastasis and may serve as an imaging biomarker of metastasis.
Face Value: Towards Robust Estimates of Snow Leopard Densities.
Directory of Open Access Journals (Sweden)
Justine S Alexander
Full Text Available When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems. Understanding the population status of such species remains a major challenge as they occur in low densities and their ranges are wide. This paper describes the use of non-invasive data collection techniques combined with recent spatial capture-recapture methods to estimate the density of snow leopards Panthera uncia. It also investigates the influence of environmental and human activity indicators on their spatial distribution. A total of 60 camera traps were systematically set up during a three-month period over a 480 km2 study area in Qilianshan National Nature Reserve, Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trap-days, representing an average capture success of 2.62 captures/100 trap-days. We identified a total number of 20 unique individuals from photographs and estimated snow leopard density at 3.31 (SE = 1.01 individuals per 100 km2. Results of our simulation exercise indicate that our estimates from the Spatial Capture Recapture models were not optimal to respect to bias and precision (RMSEs for density parameters less or equal to 0.87. Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures. Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality.
State of the Art in Photon-Density Estimation
DEFF Research Database (Denmark)
Hachisuka, Toshiya; Jarosz, Wojciech; Georgiev, Iliyan;
2013-01-01
Photon-density estimation techniques are a popular choice for simulating light transport in scenes with complicated geometry and materials. This class of algorithms can be used to accurately simulate inter-reflections, caustics, color bleeding, scattering in participating media, and subsurface sc...
Estimation of a multivariate stochastic volatility density by kernel deconvolution
B. van Es; P. Spreij
2011-01-01
We consider a continuous time stochastic volatility model. The model contains a stationary volatility process. We aim to estimate the multivariate density of the finite-dimensional distributions of this process. We assume that we observe the process at discrete equidistant instants of time. The dist
Density estimates for phase transitions with a trace
Sire, Yannick
2010-01-01
We consider a functional obtained by adding a trace term to the Allen-Cahn phase segregation model and we prove some density estimates for the level sets of the interfaces. We treat in a unified way also the cases of possible degeneracy and singularity of the ellipticity of the model and the quasiminimal case.
Corruption clubs: empirical evidence from kernel density estimates
Herzfeld, T.; Weiss, Ch.
2007-01-01
A common finding of many analytical models is the existence of multiple equilibria of corruption. Countries characterized by the same economic, social and cultural background do not necessarily experience the same levels of corruption. In this article, we use Kernel Density Estimation techniques to
State of the Art in Photon Density Estimation
DEFF Research Database (Denmark)
Hachisuka, Toshiya; Jarosz, Wojciech; Bouchard, Guillaume;
2012-01-01
Photon-density estimation techniques are a popular choice for simulating light transport in scenes with complicated geometry and materials. This class of algorithms can be used to accurately simulate inter-reflections, caustics, color bleeding, scattering in participating media, and subsurface sc...
Fu, Wanyi; Tian, Xiaoyu; Sturgeon, Gregory; Agasthya, Greeshma; Segars, William Paul; Goodsitt, Mitchell M.; Kazerooni, Ella A.; Samei, Ehsan
2016-04-01
In thoracic CT, organ-based tube current modulation (OTCM) reduces breast dose by lowering the tube current in the 120° anterior dose reduction zone of patients. However, in practice the breasts usually expand to an angle larger than the dose reduction zone. This work aims to simulate a breast positioning technique (BPT) to constrain the breast tissue to within the dose reduction zone for OTCM and to evaluate the corresponding potential reduction in breast dose. Thirteen female anthropomorphic computational phantoms were studied (age range: 27-65 y.o., weight range: 52-105.8 kg). Each phantom was modeled in the supine position with and without application of the BPT. Attenuation-based tube current (ATCM, reference mA) was generated by a ray-tracing program, taking into account the patient attenuation change in the longitudinal and angular plane (CAREDose4D, Siemens Healthcare). OTCM was generated by reducing the mA to 20% between +/- 60° anterior of the patient and increasing the mA in the remaining projections correspondingly (X-CARE, Siemens Healthcare) to maintain the mean tube current. Breast tissue dose was estimated using a validated Monte Carlo program for a commercial scanner (SOMATOM Definition Flash, Siemens Healthcare). Compared to standard tube current modulation, breast dose was significantly reduced using OTCM by 19.8+/-4.7%. With the BPT, breast dose was reduced by an additional 20.4+/-6.5% to 37.1+/-6.9%, using the same CTDIvol. BPT was more effective for phantoms simulating women with larger breasts with the average breast dose reduction of 30.2%, 39.2%, and 49.2% from OTCMBP to ATCM, using the same CTDIvol for phantoms with 0.5, 1.5, and 2.5 kg breasts, respectively. This study shows that a specially designed BPT improves the effectiveness of OTCM.
Regularized Regression and Density Estimation based on Optimal Transport
Burger, M.
2012-03-11
The aim of this paper is to investigate a novel nonparametric approach for estimating and smoothing density functions as well as probability densities from discrete samples based on a variational regularization method with the Wasserstein metric as a data fidelity. The approach allows a unified treatment of discrete and continuous probability measures and is hence attractive for various tasks. In particular, the variational model for special regularization functionals yields a natural method for estimating densities and for preserving edges in the case of total variation regularization. In order to compute solutions of the variational problems, a regularized optimal transport problem needs to be solved, for which we discuss several formulations and provide a detailed analysis. Moreover, we compute special self-similar solutions for standard regularization functionals and we discuss several computational approaches and results. © 2012 The Author(s).
Density estimates for deep-sea gastropod assemblages
Rex, Michael A.; Etter, Ron J.; Nimeskern, Phillip W.
1990-04-01
Extensive boxcore sampling in the Atlantic Continental Slope and Rise study permitted the first precise measurement of gastropod density in the bathyal region of the deep sea. Gastropod density decreases significantly and exponentially with depth (250-3494 m), and density-depth regression lines do not differ significantly in either slope or elevatiob over horizontal scales of approximately 1000 km. The subclasses Prosobranchia and Opisthobranchia both show significant decreases in density with depth. Predatory taxa (neogastropods and opisthobranchs) exhibit significantly steeper declines in density with depth than do taxa dominated by deposit feeders (archaeogastropods and mesogastropods). Members of upper trophic levels may be more sensitive to the reduction in nutrient input with increased depth because of the energy loss between trophic levels in the food chain. A comparison of density estimates of gastropods from boxcore, grab and anchor-dredge samples taken in the same region revealed no significant differences in density-depth relationships among the sampling methods. A synthesis of data from 777 boxcore samples collected from the Atlantic, Caribbean and Pacific over a depth range of 250-7298 m indicates that the decline in gastropod density with depth is a global trend with only moderate inter-regional variation.
International Nuclear Information System (INIS)
To compare the conspicuity of different histopathological types of breast cancer according to breast density and mammographic imaging in patients with screen-detected breast cancers undergoing both full-field digital mammography (FFDM) and screen-film mammography (SFM) in the United Kingdom National Health Service Breast Screening Programme (NHSBSP). 185 patients underwent routine screening with SFM followed by further imaging using FFDM with consequent diagnosis of breast cancer. All SFM and soft-copy FFDM images were evaluated by two readers in an independent, retrospective review. The visualisation and conspicuity of the mammographic abnormality were recorded and graded using a four-level scale. Conspicuity of breast cancer was qualitatively evaluated. Breast density and conspicuity were correlated with histopathological diagnosis and inter-observer correlation was calculated. Mixed Model ANOVA demonstrated significant differences between FFDM and SFM (p < 0.001) and breast densities (p = 0.009): conspicuity of the mammographic abnormality (p < 0.001) and visualisation of the dominant mammographic feature (p < 0.001) were significantly greater with FFDM than SFM. This held true for both readers and for all histopathological tumour types with no significant differences between each tumour type. FFDM is significantly superior to SFM for conspicuity of screen-detected breast cancers for all histopathological types and breast densities. (orig.)
SVM for density estimation and application to medical image segmentation
Institute of Scientific and Technical Information of China (English)
ZHANG Zhao; ZHANG Su; ZHANG Chen-xi; CHEN Ya-zhu
2006-01-01
A method of medical image segmentation based on support vector machine (SVM) for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process.Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.
Evaluating lidar point densities for effective estimation of aboveground biomass
Wu, Zhuoting; Dye, Dennis G.; Stoker, Jason; Vogel, John M.; Velasco, Miguel G.; Middleton, Barry R.
2016-01-01
The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. High point density airborne lidar data, were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m2, corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m2. Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m2, and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m2, our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.
Estimation of Enceladus Plume Density Using Cassini Flight Data
Wang, Eric K.; Lee, Allan Y.
2011-01-01
The Cassini spacecraft was launched on October 15, 1997 by a Titan 4B launch vehicle. After an interplanetary cruise of almost seven years, it arrived at Saturn on June 30, 2004. In 2005, Cassini completed three flybys of Enceladus, a small, icy satellite of Saturn. Observations made during these flybys confirmed the existence of water vapor plumes in the south polar region of Enceladus. Five additional low-altitude flybys of Enceladus were successfully executed in 2008-9 to better characterize these watery plumes. During some of these Enceladus flybys, the spacecraft attitude was controlled by a set of three reaction wheels. When the disturbance torque imparted on the spacecraft was predicted to exceed the control authority of the reaction wheels, thrusters were used to control the spacecraft attitude. Using telemetry data of reaction wheel rates or thruster on-times collected from four low-altitude Enceladus flybys (in 2008-10), one can reconstruct the time histories of the Enceladus plume jet density. The 1 sigma uncertainty of the estimated density is 5.9-6.7% (depending on the density estimation methodology employed). These plume density estimates could be used to confirm measurements made by other onboard science instruments and to support the modeling of Enceladus plume jets.
Open-cluster density profiles derived using a kernel estimator
Seleznev, Anton F
2016-01-01
Surface and spatial radial density profiles in open clusters are derived using a kernel estimator method. Formulae are obtained for the contribution of every star into the spatial density profile. The evaluation of spatial density profiles is tested against open-cluster models from N-body experiments with N = 500. Surface density profiles are derived for seven open clusters (NGC 1502, 1960, 2287, 2516, 2682, 6819 and 6939) using Two-Micron All-Sky Survey data and for different limiting magnitudes. The selection of an optimal kernel half-width is discussed. It is shown that open-cluster radius estimates hardly depend on the kernel half-width. Hints of stellar mass segregation and structural features indicating cluster non-stationarity in the regular force field are found. A comparison with other investigations shows that the data on open-cluster sizes are often underestimated. The existence of an extended corona around the open cluster NGC 6939 was confirmed. A combined function composed of the King density pr...
Zheng, Yuanjie; Wang, Yan; Keller, Brad M.; Conant, Emily; Gee, James C.; Kontos, Despina
2013-02-01
Estimating a woman's risk of breast cancer is becoming increasingly important in clinical practice. Mammographic density, estimated as the percent of dense (PD) tissue area within the breast, has been shown to be a strong risk factor. Studies also support a relationship between mammographic texture and breast cancer risk. We have developed a fullyautomated software pipeline for computerized analysis of digital mammography parenchymal patterns by quantitatively measuring both breast density and texture properties. Our pipeline combines advanced computer algorithms of pattern recognition, computer vision, and machine learning and offers a standardized tool for breast cancer risk assessment studies. Different from many existing methods performing parenchymal texture analysis within specific breast subregions, our pipeline extracts texture descriptors for points on a spatial regular lattice and from a surrounding window of each lattice point, to characterize the local mammographic appearance throughout the whole breast. To demonstrate the utility of our pipeline, and optimize its parameters, we perform a case-control study by retrospectively analyzing a total of 472 digital mammography studies. Specifically, we investigate the window size, which is a lattice related parameter, and compare the performance of texture features to that of breast PD in classifying case-control status. Our results suggest that different window sizes may be optimal for raw (12.7mm2) versus vendor post-processed images (6.3mm2). We also show that the combination of PD and texture features outperforms PD alone. The improvement is significant (p=0.03) when raw images and window size of 12.7mm2 are used, having an ROC AUC of 0.66. The combination of PD and our texture features computed from post-processed images with a window size of 6.3 mm2 achieves an ROC AUC of 0.75.
Can modeling improve estimation of desert tortoise population densities?
Nussear, K.E.; Tracy, C.R.
2007-01-01
The federally listed desert tortoise (Gopherus agassizii) is currently monitored using distance sampling to estimate population densities. Distance sampling, as with many other techniques for estimating population density, assumes that it is possible to quantify the proportion of animals available to be counted in any census. Because desert tortoises spend much of their life in burrows, and the proportion of tortoises in burrows at any time can be extremely variable, this assumption is difficult to meet. This proportion of animals available to be counted is used as a correction factor (g0) in distance sampling and has been estimated from daily censuses of small populations of tortoises (6-12 individuals). These censuses are costly and produce imprecise estimates of g0 due to small sample sizes. We used data on tortoise activity from a large (N = 150) experimental population to model activity as a function of the biophysical attributes of the environment, but these models did not improve the precision of estimates from the focal populations. Thus, to evaluate how much of the variance in tortoise activity is apparently not predictable, we assessed whether activity on any particular day can predict activity on subsequent days with essentially identical environmental conditions. Tortoise activity was only weakly correlated on consecutive days, indicating that behavior was not repeatable or consistent among days with similar physical environments. ?? 2007 by the Ecological Society of America.
Directory of Open Access Journals (Sweden)
Svjetlana Mujagić
2011-11-01
Full Text Available Objective. Th e aim of this study was to analyse the sensitivity and specificity of ultrasound and mammography according to breast densityand determine which of these diagnostic imagings is a more accuratetest for diagnosis of breast cancer. Patients and methods. By meansof a cross-sectional study, ultrasound and mammographic examinationsof 148 women with breast disease symptoms were analysed.All women underwent surgery and all lesions were examined by histological examination which revealed the presence of 63 breast cancers,and 85 benign lesions. Histological examination was used as the “goldstandard”. In relation to breast density, the women were separated into two groups, group A: women with “fatty breast” (ACR BI-RADS density categories 1 and 2 and group B: women with “dense breast”(categories 3 and 4. Ultrasound and mammographic findings were classified on the BI-RADS categorical scale of 1-5. For statistical data processing, the logistic regression analysis and the McNemar chi-square test for paired proportions was used. Th e differences on the level of p<0.05 were considered statistically significant. Results. In the group of women with breast density categories 1 and 2 the difference in the sensitivities (p=1 as well as in the specificities (p=0.11 of the two imaging tests was not statistically significant. In the group of women with breast density categories 3 and 4 the ultrasound sensitivity was significantly higher than the mammographic sensitivity (p=0.03 without a statistically significant difference in specificity (p=0.26. Sensitivity of mammography was (linearly – ex; linearity exists between breast density and the logarithm of odds for a positive result associated with breast density (likelihood ratio χ2 =15.99, p =0.0001. Th e odds ratio for (the probability of – ex a positive mammographic result was 0.25 (95% CI, 0.11-0.58. Thesensitivity of ultrasound and specificity of each test were not (linearly - ex
Bayesian error estimation in density-functional theory
DEFF Research Database (Denmark)
Mortensen, Jens Jørgen; Kaasbjerg, Kristen; Frederiksen, Søren Lund;
2005-01-01
We present a practical scheme for performing error estimates for density-functional theory calculations. The approach, which is based on ideas from Bayesian statistics, involves creating an ensemble of exchange-correlation functionals by comparing with an experimental database of binding energies...... for molecules and solids. Fluctuations within the ensemble can then be used to estimate errors relative to experiment on calculated quantities such as binding energies, bond lengths, and vibrational frequencies. It is demonstrated that the error bars on energy differences may vary by orders of magnitude...
Photo-z Estimation: An Example of Nonparametric Conditional Density Estimation under Selection Bias
Izbicki, Rafael; Freeman, Peter E
2016-01-01
Redshift is a key quantity for inferring cosmological model parameters. In photometric redshift estimation, cosmologists use the coarse data collected from the vast majority of galaxies to predict the redshift of individual galaxies. To properly quantify the uncertainty in the predictions, however, one needs to go beyond standard regression and instead estimate the full conditional density f(z|x) of a galaxy's redshift z given its photometric covariates x. The problem is further complicated by selection bias: usually only the rarest and brightest galaxies have known redshifts, and these galaxies have characteristics and measured covariates that do not necessarily match those of more numerous and dimmer galaxies of unknown redshift. Unfortunately, there is not much research on how to best estimate complex multivariate densities in such settings. Here we describe a general framework for properly constructing and assessing nonparametric conditional density estimators under selection bias, and for combining two o...
Instantaneous Spectrum Estimation from Event-Based Densities
Directory of Open Access Journals (Sweden)
Lorenzo Galleani
2002-01-01
Full Text Available We present a method for obtaining a time-varying spectrum that is particularly suited when the data are in event-based form. This form arises in many areas of science and engineering, and especially in astronomy, where one has photon counting detectors. The method presented consists of three procedures. First, estimating the density using the kernel method; second, highpass filtering the manifestly positive density; finally, obtaining the time-frequency distribution with a modified WelchÃ¢Â€Â²s method. For the sake of validation event-based data are generated from a given distribution and the proposed method is used to construct the time-frequency spectrum and is compared to the original density. The results demonstrate the effectiveness of the method.
Some Bayesian statistical techniques useful in estimating frequency and density
Johnson, D.H.
1977-01-01
This paper presents some elementary applications of Bayesian statistics to problems faced by wildlife biologists. Bayesian confidence limits for frequency of occurrence are shown to be generally superior to classical confidence limits. Population density can be estimated from frequency data if the species is sparsely distributed relative to the size of the sample plot. For other situations, limits are developed based on the normal distribution and prior knowledge that the density is non-negative, which insures that the lower confidence limit is non-negative. Conditions are described under which Bayesian confidence limits are superior to those calculated with classical methods; examples are also given on how prior knowledge of the density can be used to sharpen inferences drawn from a new sample.
Estimating black bear density using DNA data from hair snares
Gardner, B.; Royle, J. Andrew; Wegan, M.T.; Rainbolt, R.E.; Curtis, P.D.
2010-01-01
DNA-based mark-recapture has become a methodological cornerstone of research focused on bear species. The objective of such studies is often to estimate population size; however, doing so is frequently complicated by movement of individual bears. Movement affects the probability of detection and the assumption of closure of the population required in most models. To mitigate the bias caused by movement of individuals, population size and density estimates are often adjusted using ad hoc methods, including buffering the minimum polygon of the trapping array. We used a hierarchical, spatial capturerecapture model that contains explicit components for the spatial-point process that governs the distribution of individuals and their exposure to (via movement), and detection by, traps. We modeled detection probability as a function of each individual's distance to the trap and an indicator variable for previous capture to account for possible behavioral responses. We applied our model to a 2006 hair-snare study of a black bear (Ursus americanus) population in northern New York, USA. Based on the microsatellite marker analysis of collected hair samples, 47 individuals were identified. We estimated mean density at 0.20 bears/km2. A positive estimate of the indicator variable suggests that bears are attracted to baited sites; therefore, including a trap-dependence covariate is important when using bait to attract individuals. Bayesian analysis of the model was implemented in WinBUGS, and we provide the model specification. The model can be applied to any spatially organized trapping array (hair snares, camera traps, mist nests, etc.) to estimate density and can also account for heterogeneity and covariate information at the trap or individual level. ?? The Wildlife Society.
International Nuclear Information System (INIS)
Preventive oncology is in need of a risk assessment technique that can identify individuals at high risk for breast cancer and has the ability to monitor the efficacy of a risk reducing intervention. Optical transillumination spectroscopy (OTS) gives information about breast tissue composition and tissue density. OTS is noninvasive and in contrast to mammography, uses nonionizing radiation. It is safe and can be used frequently on younger women, potentially permitting early risk detection and thus increasing the time available for risk reduction interventions to assert their influence. Before OTS can be used as a risk assessment and/or monitoring technique, its predictive ability needs to be demonstrated and maximized through the construction of various mathematical models relating OTS and breast tissue density, and hence, risk. To establish a correlation between OTS and mammographic density principal components analysis (PCA), using risk classification, is calculated. The PCA scores are presented in three-dimensional cluster plots and a plane of differentiation that separates the high and low tissue densities is used to calculate the predictive value. Stratification of PCA for measurement position on the breast in cranial-caudal projection is introduced. Analysis of PCA scores as a function of the volunteer's age and body mass index (BMI) is examined. A small but significant correlation between the component scores and age or BMI is noted but the correlation is dependent on the tissue density category examined. Correction of the component scores for age and BMI is not recommended, since a priori knowledge of a woman's breast tissue density is required. Stratification for the center and distal measurement positions provide a predictive value for OTS above 96%
Accurate photometric redshift probability density estimation - method comparison and application
Rau, Markus Michael; Brimioulle, Fabrice; Frank, Eibe; Friedrich, Oliver; Gruen, Daniel; Hoyle, Ben
2015-01-01
We introduce an ordinal classification algorithm for photometric redshift estimation, which vastly improves the reconstruction of photometric redshift probability density functions (PDFs) for individual galaxies and galaxy samples. As a use case we apply our method to CFHTLS galaxies. The ordinal classification algorithm treats distinct redshift bins as ordered values, which improves the quality of photometric redshift PDFs, compared with non-ordinal classification architectures. We also propose a new single value point estimate of the galaxy redshift, that can be used to estimate the full redshift PDF of a galaxy sample. This method is competitive in terms of accuracy with contemporary algorithms, which stack the full redshift PDFs of all galaxies in the sample, but requires orders of magnitudes less storage space. The methods described in this paper greatly improve the log-likelihood of individual object redshift PDFs, when compared with a popular Neural Network code (ANNz). In our use case, this improvemen...
Kernel density estimation on the Siegel space applied to radar processing
Chevallier, Emmanuel; Barbaresco, Frédéric; Angulo, Jesus
2016-01-01
Main techniques of probability density estimation on Riemannian manifolds are reviewed in the case of the Siegel space. For computational reasons we chose to focus on the kernel density estimation. The main result of the paper is the expression of Pelletier's kernel density estimator. The method is applied to density estimation of reflection coefficients from radar observations.
Combining Ratio Estimation for Low Density Parity Check (LDPC) Coding
Mahmoud, Saad; Hi, Jianjun
2012-01-01
The Low Density Parity Check (LDPC) Code decoding algorithm make use of a scaled receive signal derived from maximizing the log-likelihood ratio of the received signal. The scaling factor (often called the combining ratio) in an AWGN channel is a ratio between signal amplitude and noise variance. Accurately estimating this ratio has shown as much as 0.6 dB decoding performance gain. This presentation briefly describes three methods for estimating the combining ratio: a Pilot-Guided estimation method, a Blind estimation method, and a Simulation-Based Look-Up table. The Pilot Guided Estimation method has shown that the maximum likelihood estimates of signal amplitude is the mean inner product of the received sequence and the known sequence, the attached synchronization marker (ASM) , and signal variance is the difference of the mean of the squared received sequence and the square of the signal amplitude. This method has the advantage of simplicity at the expense of latency since several frames worth of ASMs. The Blind estimation method s maximum likelihood estimator is the average of the product of the received signal with the hyperbolic tangent of the product combining ratio and the received signal. The root of this equation can be determined by an iterative binary search between 0 and 1 after normalizing the received sequence. This method has the benefit of requiring one frame of data to estimate the combining ratio which is good for faster changing channels compared to the previous method, however it is computationally expensive. The final method uses a look-up table based on prior simulated results to determine signal amplitude and noise variance. In this method the received mean signal strength is controlled to a constant soft decision value. The magnitude of the deviation is averaged over a predetermined number of samples. This value is referenced in a look up table to determine the combining ratio that prior simulation associated with the average magnitude of
A projection and density estimation method for knowledge discovery.
Directory of Open Access Journals (Sweden)
Adam Stanski
Full Text Available A key ingredient to modern data analysis is probability density estimation. However, it is well known that the curse of dimensionality prevents a proper estimation of densities in high dimensions. The problem is typically circumvented by using a fixed set of assumptions about the data, e.g., by assuming partial independence of features, data on a manifold or a customized kernel. These fixed assumptions limit the applicability of a method. In this paper we propose a framework that uses a flexible set of assumptions instead. It allows to tailor a model to various problems by means of 1d-decompositions. The approach achieves a fast runtime and is not limited by the curse of dimensionality as all estimations are performed in 1d-space. The wide range of applications is demonstrated at two very different real world examples. The first is a data mining software that allows the fully automatic discovery of patterns. The software is publicly available for evaluation. As a second example an image segmentation method is realized. It achieves state of the art performance on a benchmark dataset although it uses only a fraction of the training data and very simple features.
Time of flight estimation for breast cancer margin thickness using embedded tumors
Bowman, Tyler; El-Shenawee, Magda; Campbell, Lucas
2016-03-01
This work aims to enact a quick and reasonable estimation of breast cancer margin thickness using time of flight analysis of embedded breast cancer tissue. A pulsed terahertz system is used to obtain reflection imaging scans from breast cancer tumors that are formalin-fixed and embedded in paraffin blocks. Time of flight analysis is then used to compare the reflection patterns seen within the block to pathology sections and paraffin-embedded sections that are taken throughout the depth of the tumor in order to estimate the three-dimensional boundaries of the tumor.
Parameter estimation in stochastic mammogram model by heuristic optimization techniques.
Selvan, S.E.; Xavier, C.C.; Karssemeijer, N.; Sequeira, J.; Cherian, R.A.; Dhala, B.Y.
2006-01-01
The appearance of disproportionately large amounts of high-density breast parenchyma in mammograms has been found to be a strong indicator of the risk of developing breast cancer. Hence, the breast density model is popular for risk estimation or for monitoring breast density change in prevention or
Sun, Wei; Jiang, Yi-Zhou; Liu, Yi-Rong; Ma, Ding; Shao, Zhi-Ming
2016-04-12
Luminal breast cancer constitutes a group of highly heterogeneous diseases with a sustained high risk of late recurrence. We aimed to develop comprehensive and practical nomograms to better estimate the long-term survival of luminal breast cancer.Patients with luminal breast cancer diagnosed between 1990 and 2006 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into the training (n = 87,867) and validation (n = 88,215) cohorts. The cumulative incidence function (CIF) and a competing-risks model were used to estimate the probability of breast cancer-specific survival (BCSS) and death from other causes. We integrated significant prognostic factors to build nomograms and subjected the nomograms to bootstrap internal validation and to external validation.We screened 176,082 luminal breast cancer cases. The 5- and 10-year probabilities of overall death were 0.089 and 0.202, respectively. The 5- and 10-year probabilities of breast cancer-specific mortality (BCSM) were 0.053 and 0.112, respectively. Nine independent prognostic factors for both OS and BCSS were integrated to construct the nomograms. The calibration curves for the probabilities of 5- and 10-year OS and BCSS showed excellent agreement between the nomogram prediction and actual observation. The C-indexes of the nomograms were high in both internal validation (0.732 for OS and 0.800 for BCSS) and external validation (0.731 for OS and 0.794 for BCSS).We established nomograms that accurately predict OS and BCSS for patients with luminal breast cancer. The nomograms can identify patients with higher risk of late overall mortality and BCSM, helping physicians in facilitating individualized treatment. PMID:26967253
Image Representation Using EPANECHNIKOV Density Feature Points Estimator
Directory of Open Access Journals (Sweden)
Tranos Zuva
2013-02-01
Full Text Available In image retrieval most of the existing visual content based representation methods are usually application dependent or non robust, making them not suitable for generic applications. These representation methods use visual contents such as colour, texture, shape, size etc. Human image recognition is largely based on shape, thus making it very appealing for image repr esentation algorithms in computer vision. In this paper we propose a generic image representation algorithm using Epanechnikov Density Feature Points Estimator (EDFPE. It is invariant to rotation, scale and translation. The image density feature points within defined rectangular rings around the gravitational centre of the image are obtained in the form of a vector. The EDFPE is applied to the vecto r representation of the image. The Cosine Angle Distance (CAD algorithm is used to measure similarity of the images in the database. Quantitative evaluation of the performance of the system and comparison with other algorithms was done.
A Concept of Approximated Densities for Efficient Nonlinear Estimation
Directory of Open Access Journals (Sweden)
Virginie F. Ruiz
2002-10-01
Full Text Available This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD. The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques, the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy principle subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes theorem. Through simulation on a generic exponential model, the proposed nonlinear filter is implemented and the results prove to be superior to that of the extended Kalman filter and a class of nonlinear filters based on partitioning algorithms.
Adaptive kernel estimation of the L\\'evy density
Bec, Mélina
2012-01-01
This paper is concerned with adaptive kernel estimation of the L\\'evy density $N(x)$ for pure jump L\\'evy processes. The sample path is observed at $n$ discrete instants in the "high frequency" context ($ \\Delta $ = $ \\Delta_n $ tends to zero while $n \\Delta_n $ tends to infinity). We construct a collection of kernel estimators of the function $g(x)=xN(x)$ and propose a method of local adaptive selection of the bandwidth. We provide an oracle inequality and a rate of convergence for the quadratic pointwise risk. This rate is proved to be the optimal minimax rate. We give examples and simulation results for processes fitting in our framework.
Acoustic Signal based Traffic Density State Estimation using SVM
Directory of Open Access Journals (Sweden)
Prashant Borkar
2013-06-01
Full Text Available Based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone, this paper considers the problem of vehicular traffic density state estimation. The occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc are determined by the prevalent traffic density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using MFCC (Mel-Frequency Cepstral Coefficients. Support Vector Machines (SVM is used as classifier is used to model the traffic density state as Low (40 Km/h and above, Medium (20-40 Km/h, and Heavy (0-20 Km/h. For the developing geographies where the traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors are inapplicable. SVM classifier with different kernels are used to classify the acoustic signal segments spanning duration of 20–40 s, which results in average classification accuracy of 96.67% for Quadratic kernel function and 98.33% for polynomial kernel function, when entire frames are considered for classification.
Atkinson, Charlotte; Warren, Ruth M. L.; Sala, Evis; Dowsett, Mitch; Dunning, Alison M; Healey, Catherine S.; Runswick, Shirley; Day, Nicholas E; Bingham, Sheila
2004-01-01
Abstract Introduction Isoflavones are hypothesized to protect against breast cancer, but it is not clear whether they act as oestrogens or anti-oestrogens in breast tissue. Our aim was to determine the effects of taking a red clover-derived isoflavone supplement daily for 1 year on mammographic breast density. Effects on oestradiol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), lymphocyte tyrosine kinase activity and menopausal symptoms were also assessed. Methods A total of 2...
Probability Density and CFAR Threshold Estimation for Hyperspectral Imaging
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Clark, G A
2004-09-21
The work reported here shows the proof of principle (using a small data set) for a suite of algorithms designed to estimate the probability density function of hyperspectral background data and compute the appropriate Constant False Alarm Rate (CFAR) matched filter decision threshold for a chemical plume detector. Future work will provide a thorough demonstration of the algorithms and their performance with a large data set. The LASI (Large Aperture Search Initiative) Project involves instrumentation and image processing for hyperspectral images of chemical plumes in the atmosphere. The work reported here involves research and development on algorithms for reducing the false alarm rate in chemical plume detection and identification algorithms operating on hyperspectral image cubes. The chemical plume detection algorithms to date have used matched filters designed using generalized maximum likelihood ratio hypothesis testing algorithms [1, 2, 5, 6, 7, 12, 10, 11, 13]. One of the key challenges in hyperspectral imaging research is the high false alarm rate that often results from the plume detector [1, 2]. The overall goal of this work is to extend the classical matched filter detector to apply Constant False Alarm Rate (CFAR) methods to reduce the false alarm rate, or Probability of False Alarm P{sub FA} of the matched filter [4, 8, 9, 12]. A detector designer is interested in minimizing the probability of false alarm while simultaneously maximizing the probability of detection P{sub D}. This is summarized by the Receiver Operating Characteristic Curve (ROC) [10, 11], which is actually a family of curves depicting P{sub D} vs. P{sub FA}parameterized by varying levels of signal to noise (or clutter) ratio (SNR or SCR). Often, it is advantageous to be able to specify a desired P{sub FA} and develop a ROC curve (P{sub D} vs. decision threshold r{sub 0}) for that case. That is the purpose of this work. Specifically, this work develops a set of algorithms and MATLAB
Change-in-ratio density estimator for feral pigs is less biased than closed mark-recapture estimates
Hanson, L.B.; Grand, J.B.; Mitchell, M.S.; Jolley, D.B.; Sparklin, B.D.; Ditchkoff, S.S.
2008-01-01
Closed-population capture-mark-recapture (CMR) methods can produce biased density estimates for species with low or heterogeneous detection probabilities. In an attempt to address such biases, we developed a density-estimation method based on the change in ratio (CIR) of survival between two populations where survival, calculated using an open-population CMR model, is known to differ. We used our method to estimate density for a feral pig (Sus scrofa) population on Fort Benning, Georgia, USA. To assess its validity, we compared it to an estimate of the minimum density of pigs known to be alive and two estimates based on closed-population CMR models. Comparison of the density estimates revealed that the CIR estimator produced a density estimate with low precision that was reasonable with respect to minimum known density. By contrast, density point estimates using the closed-population CMR models were less than the minimum known density, consistent with biases created by low and heterogeneous capture probabilities for species like feral pigs that may occur in low density or are difficult to capture. Our CIR density estimator may be useful for tracking broad-scale, long-term changes in species, such as large cats, for which closed CMR models are unlikely to work. ?? CSIRO 2008.
Bilateral mammographic density asymmetry and breast cancer risk: A preliminary assessment
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Zheng, Bin, E-mail: zhengb@upmc.edu [Department of Radiology, University of Pittsburgh, 3362 Fifth Ave, Pittsburgh, PA 15213 (United States); Sumkin, Jules H., E-mail: jsumkin@mail.magee.edu [Department of Radiology, Magee Womens Hospital, 300 Halket Street, Pittsburgh, PA 15213 (United States); Zuley, Margarita L., E-mail: zuleyml@upmc.edu [Department of Radiology, Magee Womens Hospital, 300 Halket Street, Pittsburgh, PA 15213 (United States); Wang, Xingwei, E-mail: wangx6@upmc.edu [Department of Radiology, University of Pittsburgh, 3362 Fifth Ave, Pittsburgh, PA 15213 (United States); Klym, Amy H., E-mail: klymah@upmc.edu [Department of Radiology, University of Pittsburgh, 3362 Fifth Ave, Pittsburgh, PA 15213 (United States); Gur, David, E-mail: gurd@upmc.edu [Department of Radiology, University of Pittsburgh, 3362 Fifth Ave, Pittsburgh, PA 15213 (United States)
2012-11-15
To improve efficacy of breast cancer screening and prevention programs, it requires a risk assessment model with high discriminatory power. This study aimed to assess classification performance of using computed bilateral mammographic density asymmetry to predict risk of individual women developing breast cancer in near-term. The database includes 451 cases with multiple screening mammography examinations. The first (baseline) examinations of all case were interpreted negative. In the next sequential examinations, 187 cases developed cancer or surgically excised high-risk lesions, 155 remained negative (not-recalled), and 109 were recalled benign cases. From each of two bilateral cranio-caudal view images acquired from the baseline examination, we computed two features of average pixel value and local pixel value fluctuation. We then computed mean and difference of each feature computed from two images. When applying the computed features and other two risk factors (woman's age and subjectively rated mammographic density) to predict risk of cancer development, areas under receiver operating characteristic curves (AUC) were computed to evaluate the discriminatory/classification performance. The AUCs are 0.633 {+-} 0.030, 0.535 {+-} 0.036, 0.567 {+-} 0.031, and 0.719 {+-} 0.027 when using woman's age, subjectively rated, computed mean and asymmetry of mammographic density, to classify between two groups of cancer-verified and negative cases, respectively. When using an equal-weighted fusion method to combine woman's age and computed density asymmetry, AUC increased to 0.761 {+-} 0.025 (p < 0.05). The study demonstrated that bilateral mammographic density asymmetry could be a significantly stronger risk factor associated to the risk of women developing breast cancer in near-term than woman's age and assessed mean mammographic density.
Bilateral mammographic density asymmetry and breast cancer risk: A preliminary assessment
International Nuclear Information System (INIS)
To improve efficacy of breast cancer screening and prevention programs, it requires a risk assessment model with high discriminatory power. This study aimed to assess classification performance of using computed bilateral mammographic density asymmetry to predict risk of individual women developing breast cancer in near-term. The database includes 451 cases with multiple screening mammography examinations. The first (baseline) examinations of all case were interpreted negative. In the next sequential examinations, 187 cases developed cancer or surgically excised high-risk lesions, 155 remained negative (not-recalled), and 109 were recalled benign cases. From each of two bilateral cranio-caudal view images acquired from the baseline examination, we computed two features of average pixel value and local pixel value fluctuation. We then computed mean and difference of each feature computed from two images. When applying the computed features and other two risk factors (woman's age and subjectively rated mammographic density) to predict risk of cancer development, areas under receiver operating characteristic curves (AUC) were computed to evaluate the discriminatory/classification performance. The AUCs are 0.633 ± 0.030, 0.535 ± 0.036, 0.567 ± 0.031, and 0.719 ± 0.027 when using woman's age, subjectively rated, computed mean and asymmetry of mammographic density, to classify between two groups of cancer-verified and negative cases, respectively. When using an equal-weighted fusion method to combine woman's age and computed density asymmetry, AUC increased to 0.761 ± 0.025 (p < 0.05). The study demonstrated that bilateral mammographic density asymmetry could be a significantly stronger risk factor associated to the risk of women developing breast cancer in near-term than woman's age and assessed mean mammographic density.
Cortical cell and neuron density estimates in one chimpanzee hemisphere.
Collins, Christine E; Turner, Emily C; Sawyer, Eva Kille; Reed, Jamie L; Young, Nicole A; Flaherty, David K; Kaas, Jon H
2016-01-19
The density of cells and neurons in the neocortex of many mammals varies across cortical areas and regions. This variability is, perhaps, most pronounced in primates. Nonuniformity in the composition of cortex suggests regions of the cortex have different specializations. Specifically, regions with densely packed neurons contain smaller neurons that are activated by relatively few inputs, thereby preserving information, whereas regions that are less densely packed have larger neurons that have more integrative functions. Here we present the numbers of cells and neurons for 742 discrete locations across the neocortex in a chimpanzee. Using isotropic fractionation and flow fractionation methods for cell and neuron counts, we estimate that neocortex of one hemisphere contains 9.5 billion cells and 3.7 billion neurons. Primary visual cortex occupies 35 cm(2) of surface, 10% of the total, and contains 737 million densely packed neurons, 20% of the total neurons contained within the hemisphere. Other areas of high neuron packing include secondary visual areas, somatosensory cortex, and prefrontal granular cortex. Areas of low levels of neuron packing density include motor and premotor cortex. These values reflect those obtained from more limited samples of cortex in humans and other primates.
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Saadatmand Sepideh
2012-10-01
Full Text Available Abstract Background To reduce mortality, women with a family history of breast cancer often start mammography screening at a younger age than the general population. Breast density is high in over 50% of women younger than 50 years. With high breast density, breast cancer incidence increases, but sensitivity of mammography decreases. Therefore, mammography might not be the optimal method for breast cancer screening in young women. Adding MRI increases sensitivity, but also the risk of false-positive results. The limitation of all previous MRI screening studies is that they do not contain a comparison group; all participants received both MRI and mammography. Therefore, we cannot empirically assess in which stage tumours would have been detected by either test. The aim of the Familial MRI Screening Study (FaMRIsc is to compare the efficacy of MRI screening to mammography for women with a familial risk. Furthermore, we will assess the influence of breast density. Methods/Design This Dutch multicentre, randomized controlled trial, with balanced randomisation (1:1 has a parallel grouped design. Women with a cumulative lifetime risk for breast cancer due to their family history of ≥20%, aged 30–55 years are eligible. Identified BRCA1/2 mutation carriers or women with 50% risk of carrying a mutation are excluded. Group 1 receives yearly mammography and clinical breast examination (n = 1000, and group 2 yearly MRI and clinical breast examination, and mammography biennially (n = 1000. Primary endpoints are the number and stage of the detected breast cancers in each arm. Secondary endpoints are the number of false-positive results in both screening arms. Furthermore, sensitivity and positive predictive value of both screening strategies will be assessed. Cost-effectiveness of both strategies will be assessed. Analyses will also be performed with mammographic density as stratification factor. Discussion Personalized breast cancer screening
Estimating Foreign-Object-Debris Density from Photogrammetry Data
Long, Jason; Metzger, Philip; Lane, John
2013-01-01
Within the first few seconds after launch of STS-124, debris traveling vertically near the vehicle was captured on two 16-mm film cameras surrounding the launch pad. One particular piece of debris caught the attention of engineers investigating the release of the flame trench fire bricks. The question to be answered was if the debris was a fire brick, and if it represented the first bricks that were ejected from the flame trench wall, or was the object one of the pieces of debris normally ejected from the vehicle during launch. If it was typical launch debris, such as SRB throat plug foam, why was it traveling vertically and parallel to the vehicle during launch, instead of following its normal trajectory, flying horizontally toward the north perimeter fence? By utilizing the Runge-Kutta integration method for velocity and the Verlet integration method for position, a method that suppresses trajectory computational instabilities due to noisy position data was obtained. This combination of integration methods provides a means to extract the best estimate of drag force and drag coefficient under the non-ideal conditions of limited position data. This integration strategy leads immediately to the best possible estimate of object density, within the constraints of unknown particle shape. These types of calculations do not exist in readily available off-the-shelf simulation software, especially where photogrammetry data is needed as an input.
Estimates of transition densities and their derivatives for jump L\\'evy processes
Kaleta, Kamil; Sztonyk, Paweł
2013-01-01
We give upper and lower estimates of densities of convolution semigroups of probability measures under explicit assumptions on the corresponding Levy measure and the Levy--Khinchin exponent. We obtain also estimates of derivatives of densities.
Fiora, Alessandro; Cescatti, Alessandro
2006-09-01
Daily and seasonal patterns in radial distribution of sap flux density were monitored in six trees differing in social position in a mixed coniferous stand dominated by silver fir (Abies alba Miller) and Norway spruce (Picea abies (L.) Karst) in the Alps of northeastern Italy. Radial distribution of sap flux was measured with arrays of 1-cm-long Granier probes. The radial profiles were either Gaussian or decreased monotonically toward the tree center, and seemed to be related to social position and crown distribution of the trees. The ratio between sap flux estimated with the most external sensor and the mean flux, weighted with the corresponding annulus areas, was used as a correction factor (CF) to express diurnal and seasonal radial variation in sap flow. During sunny days, the diurnal radial profile of sap flux changed with time and accumulated photosynthetic active radiation (PAR), with an increasing contribution of sap flux in the inner sapwood during the day. Seasonally, the contribution of sap flux in the inner xylem increased with daily cumulative PAR and the variation of CF was proportional to the tree diameter, ranging from 29% for suppressed trees up to 300% for dominant trees. Two models were developed, relating CF with PAR and tree diameter at breast height (DBH), to correct daily and seasonal estimates of whole-tree and stand sap flow obtained by assuming uniform sap flux density over the sapwood. If the variability in the radial profile of sap flux density was not accounted for, total stand transpiration would be overestimated by 32% during sunny days and 40% for the entire season.
The prognostic relevance of estimates of proliferative activity in early breast cancer
DEFF Research Database (Denmark)
Offersen, B V; Sørensen, Flemming Brandt; Knoop, A;
2003-01-01
clinicopathological parameters at diagnosis in early breast cancer patients. MATERIALS AND METHODS: Tumour specimens from 365 consecutively treated breast cancer patients were immunostained for MIB-1 and evaluated under the microscope using systematic random sampling accomplished by the CAST-grid system. RESULTS...... and number of mitoses included in the analysis, MIB-1 estimates showed no independent prognostic impact. CONCLUSIONS: High MIB-1 estimates did not add independent prognostic information at diagnosis when evaluated together with classical prognostic markers of early breast cancer.......AIMS: Immunohistochemical estimates of cell proliferation evaluated with MIB-1 antibody have been suggested as prognostic indicators in different types of carcinoma. This study investigates whether MIB-1 scores add additional prognostic impact when evaluated together with classical...
A Berry-Esseen Type Bound in Kernel Density Estimation for Negatively Associated Censored Data
Qunying Wu; Pingyan Chen
2013-01-01
We discuss the kernel estimation of a density function based on censored data when the survival and the censoring times form the stationary negatively associated (NA) sequences. Under certain regularity conditions, the Berry-Esseen type bounds are derived for the kernel density estimator and the Kaplan-Meier kernel density estimator at a fixed point $x$ .
LIMIT THEOREMS FOR KERNEL DENSITY ESTIMATORS IN SPACES OF ARBITRARY NATURE
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Orlov A. I.
2015-04-01
Full Text Available Some estimators of the probability density function in spaces of arbitrary nature are used for various tasks in statistics of non-numerical data. Systematic exposition of the theory of such estimators had a start in our work [2]. This article is a direct continuation of the article [2]. We will regularly use references to conditions and theorems of the article [2], in which we introduced several types of nonparametric estimators of the probability density. We studied more linear estimators. In this article we consider particular cases - kernel density estimates in spaces of arbitrary nature. When estimating the density of the one-dimensional random variable, kernel estimators become the Parzen-Rosenblatt estimators. Asymptotic behavior of kernel density estimators in the general case of an arbitrary nature spaces are devoted to Theorem 1 - 8. Under different conditions we prove the consistency and asymptotic normality of kernel density estimators. We have studied uniform convergence. We have introduced the concept of "preferred rate differences" and studied nuclear density estimators based on it. We have also introduced and studied natural affinity measures which are used in the analysis of the asymptotic behavior of kernel density estimators. We have found the asymptotic behavior of dispersions of kernel density estimators and considered the examples including kernel density estimators in finite-dimensional spaces and in the space of square-integrable functions
Breast cancer size estimation with MRI in BRCA mutation carriers and other high risk patients
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Mann, R.M., E-mail: r.mann@rad.umcn.nl [Radboud University Nijmegen Medical Centre, Department of Radiology, Nijmegen (Netherlands); Bult, P., E-mail: p.bult@path.umcn.nl [Radboud University Nijmegen Medical Centre, Department of Pathology, Nijmegen (Netherlands); Laarhoven, H.W.M. van, E-mail: h.vanlaarhoven@amc.uva.nl [Academic Medical Centre, University of Amsterdam, Department of Medical Oncology, Amsterdam (Netherlands); Radboud University Nijmegen Medical Centre, Department of Medical Oncology, Nijmegen (Netherlands); Span, P.N., E-mail: p.span@rther.umcn.nl [Radboud University Nijmegen Medical Centre, Department of Radiation Oncology, Nijmegen (Netherlands); Schlooz, M., E-mail: m.schlooz@chir.umcn.nl [Radboud University Nijmegen Medical Centre, Department of Surgery, Nijmegen (Netherlands); Veltman, J., E-mail: j.veltman@zgt.nl [Hospital group Twente (ZGT), Department of Radiology, Almelo (Netherlands); Hoogerbrugge, N., E-mail: n.hoogerbrugge@gen.umcn.nl [Radboud University Nijmegen Medical Centre, Department of Human Genetics, Nijmegen (Netherlands)
2013-09-15
Objective: To assess the value of breast MRI in size assessment of breast cancers in high risk patients, including those with a BRCA 1 or 2 mutation. Guidelines recommend invariably breast MRI screening for these patients and therapy is thus based on these findings. However, the accuracy of breast MRI for staging purposes is only tested in sporadic cancers. Methods: We assessed concordance of radiologic staging using MRI with histopathology in 49 tumors in 46 high risk patients (23 BRCA1, 12 BRCA2 and 11 Non-BRCA patients). The size of the total tumor area (TTA) was compared to pathology. In invasive carcinomas (n = 45) the size of the largest focus (LF) was also addressed. Results: Correlation of MRI measurements with pathology was 0.862 for TTA and 0.793 for LF. TTA was underestimated in 8(16%), overestimated in 5(10%), and correctly measured in 36(73%) cases. LF was underestimated in 4(9%), overestimated in 5(11%), and correctly measured in 36(80%) cases. Impact of BRCA 1 or 2 mutations on the quality of size estimation was not observed. Conclusions: Tumor size estimation using breast MRI in high risk patients is comparable to its performance in sporadic cancers. Therefore, breast MRI can safely be used for treatment planning.
An Infrastructureless Approach to Estimate Vehicular Density in Urban Environments
Juan-Carlos Cano; Carlos T. Calafate; Pietro Manzoni; Martinez, Francisco J.; Piedad Garrido; Manuel Fogue; Sanguesa, Julio A.
2013-01-01
In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take...
González, Francisco Javier
2011-01-01
In this work the metabolic heat generated by breast tumors was estimated indirectly and noninvasively from digital infrared images and numerically simulating a simplified breast model and a cancerous tumor, this parameter can be of clinical importance since it has been related to the doubling volume's time and malignancy for that particular tumor. The results indicate that digital infrared imaging has the potential to estimate in a non-invasive way the malignancy of a tumor by calculating its metabolic heat generation from bioheat thermal transfer models.
Medical image segmentation to estimate HER2 gene status in breast cancer
Palacios-Navarro, Guillermo; Acirón-Pomar, José Manuel; Vilchez-Sorribas, Enrique; Zambrano, Eddie Galarza
2016-02-01
This work deals with the estimation of HER2 Gene status in breast tumour images treated with in situ hybridization techniques (ISH). We propose a simple algorithm to obtain the amplification factor of HER2 gene. The obtained results are very close to those obtained by specialists in a manual way. The developed algorithm is based on colour image segmentation and has been included in a software application tool for breast tumour analysis. The developed tool focus on the estimation of the seriousness of tumours, facilitating the work of pathologists and contributing to a better diagnosis.
Theoretical and Experimental Estimations of Volumetric Inductive Phase Shift in Breast Cancer Tissue
González, C. A.; Lozano, L. M.; Uscanga, M. C.; Silva, J. G.; Polo, S. M.
2013-04-01
Impedance measurements based on magnetic induction for breast cancer detection has been proposed in some studies. This study evaluates theoretical and experimentally the use of a non-invasive technique based on magnetic induction for detection of patho-physiological conditions in breast cancer tissue associated to its volumetric electrical conductivity changes through inductive phase shift measurements. An induction coils-breast 3D pixel model was designed and tested. The model involves two circular coils coaxially centered and a human breast volume centrally placed with respect to the coils. A time-harmonic numerical simulation study addressed the effects of frequency-dependent electrical properties of tumoral tissue on the volumetric inductive phase shift of the breast model measured with the circular coils as inductor and sensor elements. Experimentally; five female volunteer patients with infiltrating ductal carcinoma previously diagnosed by the radiology and oncology departments of the Specialty Clinic for Women of the Mexican Army were measured by an experimental inductive spectrometer and the use of an ergonomic inductor-sensor coil designed to estimate the volumetric inductive phase shift in human breast tissue. Theoretical and experimental inductive phase shift estimations were developed at four frequencies: 0.01, 0.1, 1 and 10 MHz. The theoretical estimations were qualitatively in agreement with the experimental findings. Important increments in volumetric inductive phase shift measurements were evident at 0.01MHz in theoretical and experimental observations. The results suggest that the tested technique has the potential to detect pathological conditions in breast tissue associated to cancer by non-invasive monitoring. Further complementary studies are warranted to confirm the observations.
Probability Distribution Extraction from TEC Estimates based on Kernel Density Estimation
Demir, Uygar; Toker, Cenk; Çenet, Duygu
2016-07-01
Statistical analysis of the ionosphere, specifically the Total Electron Content (TEC), may reveal important information about its temporal and spatial characteristics. One of the core metrics that express the statistical properties of a stochastic process is its Probability Density Function (pdf). Furthermore, statistical parameters such as mean, variance and kurtosis, which can be derived from the pdf, may provide information about the spatial uniformity or clustering of the electron content. For example, the variance differentiates between a quiet ionosphere and a disturbed one, whereas kurtosis differentiates between a geomagnetic storm and an earthquake. Therefore, valuable information about the state of the ionosphere (and the natural phenomena that cause the disturbance) can be obtained by looking at the statistical parameters. In the literature, there are publications which try to fit the histogram of TEC estimates to some well-known pdf.s such as Gaussian, Exponential, etc. However, constraining a histogram to fit to a function with a fixed shape will increase estimation error, and all the information extracted from such pdf will continue to contain this error. In such techniques, it is highly likely to observe some artificial characteristics in the estimated pdf which is not present in the original data. In the present study, we use the Kernel Density Estimation (KDE) technique to estimate the pdf of the TEC. KDE is a non-parametric approach which does not impose a specific form on the TEC. As a result, better pdf estimates that almost perfectly fit to the observed TEC values can be obtained as compared to the techniques mentioned above. KDE is particularly good at representing the tail probabilities, and outliers. We also calculate the mean, variance and kurtosis of the measured TEC values. The technique is applied to the ionosphere over Turkey where the TEC values are estimated from the GNSS measurement from the TNPGN-Active (Turkish National Permanent
Directory of Open Access Journals (Sweden)
Lis Ellison-Loschmann
Full Text Available Breast cancer incidence differs by ethnicity in New Zealand (NZ with Māori (the indigenous people women having the highest rates followed by Pakeha (people primarily of British/European descent, Pacific and Asian women, who experience the lowest rates. The reasons for these differences are unclear. Breast density, an important risk factor for breast cancer, has not previously been studied here. We used an automated system, Volpara™, to measure breast density volume from the medio-lateral oblique view of digital mammograms, by age (≤50 years and >50 years and ethnicity (Pakeha/Māori/Pacific/Asian using routine data from the national screening programme: age; x-ray system and mammography details for 3,091 Pakeha, 716 Māori, 170 Pacific and 662 Asian (total n = 4,239 women. Linear regression of the natural logarithm of absolute and percent density values was used, back-transformed and expressed as the ratio of the geometric means. Covariates were age, x-ray system and, for absolute density, the natural log of the volume of non-dense tissue (a proxy for body mass index. Median age for Pakeha women was 55 years; Māori 53 years; and Pacific and Asian women, 52 years. Compared to Pakeha women (reference, Māori had higher absolute volumetric density (1.09; 95% confidence interval [95% CI] 1.03-1.15 which remained following adjustment (1.06; 95% CI 1.01-1.12 and was stronger for older compared to younger Māori women. Asian women had the greatest risk of high percentage breast density (1.35; 95% CI 1.27-1.43 while Pacific women in both the ≤50 and >50 year age groups (0.78; 95% CI 0.66-0.92 and 0.81; 95% CI 0.71-0.93 respectively had the lowest percentage breast density compared to Pakeha. As well as expected age differences, we found differential patterns of breast density by ethnicity consistent with ethnic differences seen in breast cancer risk. Breast density may be a contributing factor to NZ's well-known, but poorly explained
Gabrielson, Marike; Chiesa, Flaminia; Paulsson, Janna; Strell, Carina; Behmer, Catharina; Rönnow, Katarina; Czene, Kamila; Östman, Arne; Hall, Per
2016-07-01
Following female sex and age, mammographic density is considered one of the strongest risk factors for breast cancer. Despite the association between mammographic density and breast cancer risk, little is known about the underlying histology and biological basis of breast density. To better understand the mechanisms behind mammographic density we assessed morphology, proliferation and hormone receptor status in relation to mammographic density in breast tissues from healthy women. Tissues were obtained from 2012-2013 by ultrasound-guided core needle biopsy from 160 women as part of the Karma (Karolinska mammography project for risk prediction for breast cancer) project. Mammograms were collected through routine mammography screening and mammographic density was calculated using STRATUS. The histological composition, epithelial and stromal proliferation status and hormone receptor status were assessed through immunohistochemical staining. Higher mammographic density was significantly associated with a greater proportion of stromal and epithelial tissue and a lower proportion of adipose tissue. Epithelial expression levels of Ki-67, oestrogen receptor (ER) and progesterone receptor (PR) were not associated with mammographic density. Epithelial Ki-67 was associated with a greater proportion of epithelial tissue, and epithelial PR was associated with a greater proportion of stromal and a lower proportion of adipose tissue. Epithelial ER was not associated with any tissues. In contrast, expression of ER in the stroma was significantly associated with a greater proportion of stroma, and negatively associated with the amount of adipose tissue. High mammographic density is associated with higher amount of stroma and epithelium and less amount of fat, but is not associated with a change in epithelial proliferation or receptor status. Increased expressions of both epithelial PR and stromal ER are associated with a greater proportion of stroma, suggesting hormonal involvement
Estimation of breast implant volumes: error assessment of the subjective judgment method
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Henseler, Helga
2016-01-01
Full Text Available Background: The degree of error in subjective estimation of breast implant volume remains unknown. Methods: Nine silicone breast implants were used by 16 examiners for subjective, blinded, implant volume estimation. The figures on the stamps were taken as the true breast implant volumes for comparison. The examiners were divided into two groups of equal size: experienced examiners (consultant breast surgeons and unexperienced examiners (trainees. Fourteen examiners repeated the tests after about 2 weeks.Results: In both groups of doctors, the implant volumes were underestimated by a mean ± standard deviation of 61.6 ± 58.1 cc (21.9% ± 20.1% in the first test and 37.9 ± 59.1 cc (13.6% ± 20.7% in the second test. The absolute deviation independent of volume overestimation or underestimation was 77.3 ± 36.9 cc (27.2% ± 12.8% in the first test and 58.1 ± 41.3 cc (20.6% ± 14.6% in the second test. In terms of absolute figures, the experienced examiners showed significantly better results than did the unexperienced examiners during the first test. The unexperienced examiners showed increasing deviations with increasing implant volumes. The degree of estimation error was lower in the second test.Conclusions: These blinded subjective estimations of breast implant volume exhibited limited accuracy and reproducibility. Estimation errors were quantified and increased with increasing implant volumes. Experienced examiners performed considerably better than unexperienced examiners, and results of the second test were better in both groups. The subjective volume estimations deviated from the true volumes by around 70 cc (underestimated by around 50 cc.Level of evidence: Level III
Institute of Scientific and Technical Information of China (English)
张路平; 王鲁平; 李飚; 赵明
2015-01-01
In order to improve the performance of the probability hypothesis density (PHD) algorithm based particle filter (PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.
Functional regression approximate Bayesian computation for Gaussian process density estimation
RODRIGUES G.S.; Nott, David J.; Sisson, S. A.
2014-01-01
We propose a novel Bayesian nonparametric method for hierarchical modelling on a set of related density functions, where grouped data in the form of samples from each density function are available. Borrowing strength across the groups is a major challenge in this context. To address this problem, we introduce a hierarchically structured prior, defined over a set of univariate density functions, using convenient transformations of Gaussian processes. Inference is performed through approximate...
Glycemic index, glycemic load and mammographic breast density: the EPIC Florence longitudinal study.
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Giovanna Masala
Full Text Available A few studies have evaluated the association between diet and mammographic breast density (MBD and results are inconsistent. MBD, a well-recognized risk factor for breast cancer, has been proposed as a marker of cumulative exposure to hormones and growth factors. Diets with a high glycemic index (GI or glycemic load (GL may increase breast cancer risk, via an effect on the insulin-like growth factor axis. We have investigated the association between carbohydrate intake, GI, GL and MBD in a prospective study. We identified a large series of women, in the frame of the EPIC-Florence cohort, with a mammogram taken five years after enrolment, when detailed information on dietary and lifestyle habits and anthropometric measurements had been collected. Mammograms have been retrieved (1,668, 83% and MBD assessed according to Wolfe's classification. We compared women with high MBD (P2+DY Wolfe's categories with those with low MBD (N1+P1 through logistic models adjusted for age, education, body mass index, menopause, number of children, breast feeding, physical activity, non-alcohol energy, fibers, saturated fat and alcohol. A direct association between GL and high MBD emerged in the highest quintile of intake in comparison with the lowest quintile (OR = 1.73, 95%CI 1.13-2.67, p for trend = 0.048 while no association with glycemic index was evident. These results were confirmed after exclusion of women reporting to be on a diet or affected with diabetes, and when Hormone Replacement Therapy at the date of mammographic examination used to assess MBD was considered. The effect was particularly evident among leaner women, although no interaction was found. A positive association was suggested for increasing simple sugar and total carbohydrates intakes limited to the highest quintiles. In this Italian population we observed an association between glycemic load, total and rapidly absorbed carbohydrates and high MBD. These novel results warrant further
Lindström, Sara; Thompson, Deborah J.; Paterson, Andrew D.; Li, Jingmei; Gierach, Gretchen L.; Scott, Christopher; Stone, Jennifer; Douglas, Julie A.; dos-Santos-Silva, Isabel; Fernandez-Navarro, Pablo; Verghase, Jajini; Smith, Paula; Brown, Judith; Luben, Robert; Wareham, Nicholas J.; Loos, Ruth J.F.; Heit, John A.; Pankratz, V. Shane; Norman, Aaron; Goode, Ellen L.; Cunningham, Julie M.; deAndrade, Mariza; Vierkant, Robert A.; Czene, Kamila; Fasching, Peter A.; Baglietto, Laura; Southey, Melissa C.; Giles, Graham G.; Shah, Kaanan P.; Chan, Heang-Ping; Helvie, Mark A.; Beck, Andrew H.; Knoblauch, Nicholas W.; Hazra, Aditi; Hunter, David J.; Kraft, Peter; Pollan, Marina; Figueroa, Jonine D.; Couch, Fergus J.; Hopper, John L.; Hall, Per; Easton, Douglas F.; Boyd, Norman F.; Vachon, Celine M.; Tamimi, Rulla M.
2015-01-01
Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5×10−8) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B, SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23, TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease susceptibility loci. PMID:25342443
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Shin, Sung Ui; Chang, Jung Min; Bae, Min Sun; Lee, Su Hyun; Cho, Nariya; Seo, Mirinae; Kim, Won Hwa; Moon, Woo Kyung [Seoul National University Hospital, Department of Radiology, Seoul (Korea, Republic of)
2015-01-15
To compare the average glandular dose (AGD) and diagnostic performance of mediolateral oblique (MLO) digital breast tomosynthesis (DBT) plus cranio-caudal (CC) digital mammography (DM) with two-view DM, and to evaluate the correlation of AGD with breast thickness and density. MLO and CC DM and DBT images of both breasts were obtained in 149 subjects. AGDs of DBT and DM per exposure were recorded, and their correlation with breast thickness and density were evaluated. Paired data of MLO DBT plus CC DM and two-view DM were reviewed for presence of malignancy in a jack-knife alternative free-response ROC (JAFROC) method. The AGDs of both DBT and DM, and differences in AGD between DBT and DM (ΔAGD), were correlated with breast thickness and density. The average JAFROC figure of merit (FOM) was significantly higher on the combined technique than two-view DM (P = 0.005). In dense breasts, the FOM and sensitivity of the combined technique was higher than that of two-view DM (P = 0.003) with small ΔAGD. MLO DBT plus CC DM provided higher diagnostic performance than two-view DM in dense breasts with a small increase in AGD. (orig.)
Pilot study of bone mineral density in breast cancer patients treated with adjuvant chemotherapy
Headley, J. A.; Theriault, R. L.; LeBlanc, A. D.; Vassilopoulou-Sellin, R.; Hortobagyi, G. N.
1998-01-01
The objective of this cross-sectional study was to determine lumbar spine bone mineral density (BMD) in breast cancer patients previously treated with adjuvant chemotherapy. Sixteen of 27 patients who received adjuvant chemotherapy became permanently amenorrheic as a result of chemotherapy. BMD was measured at the lumbar spine using dual energy X-ray absorptiometry (DEXA). Chemotherapy drugs and dosages along with a history of risk factors for reduced bone density including activity level, tobacco and/or alcohol use, metabolic bone disease, family history, and hormone exposure were identified. Results showed that women who became permanently amenorrheic as a result of chemotherapy had BMD 14% lower than women who maintained menses after chemotherapy. Chemotherapy-treated women who maintained ovarian function had normal BMD. This study suggests that women who have premature menopause as a result of chemotherapy for breast cancer are at increased risk of bone loss and may be at risk for early development of osteoporosis. Women who maintain menses do not appear to be at risk for accelerated trabecular bone loss.
Institute of Scientific and Technical Information of China (English)
Xiaojing Guo; Ling Chen; Ronggang Lang; Yu Fan; Li Fu
2006-01-01
OBJECTIVE To investigate the relationship between lymphatic vessel density and lymph node metastasis of invasive micropapillary carcinoma (IMPC) of the breast.METHODS The immunohistochemical study for vascular endothelial growth factor-c (VEGF-C), VEGF Receptor-3 (VEGFR-3) and lymphatic vessel density of 51 cases of IMPC were performed, and lymph node metastases were examined by microscopic analysis of these cases.RESULTS In IMPC, VEGF-C was expressed in the cytoplasm and/or on the membrane of the tumor cells, and the expression of VEGF-C showed a positive correlation with lymph node metastasis (P＜0.01). Lymphatic vessel density was determined by the number of micro-lymphatic vessels with VEGFR-3 positive staining. Lymphatic vessel density was positively correlated with VEGF-C expression (P＜0.01) and lymph node metastasis (P＜0.01). The percentage of IMPC in the tumor was not associated with the incidence of lymph node metastasis. The metastatic foci in lymph nodes were either pure or predominant micropapillary carcinoma.CONCLUSION The results suggested that VEGF-C overexpression stimulated tumor lymphangiogenesis, and the increased lymphatic vessel density may be the key factor that influenced lymph node metastasis of IMPC.
International Nuclear Information System (INIS)
This study retrospectively analyzed the mean glandular dose (MGD) to 133 breasts from 132 subjects, all women, who participated in a clinical trial evaluating dedicated breast CT in a diagnostic population. The clinical trial was conducted in adherence to a protocol approved by institutional review boards and the study participants provided written informed consent. Individual estimates of MGD to each breast from dedicated breast CT was obtained by combining x-ray beam characteristics with estimates of breast dimensions and fibroglandular fraction from volumetric breast CT images, and using normalized glandular dose coefficients. For each study participant and for the breast corresponding to that imaged with breast CT, an estimate of the MGD from diagnostic mammography (including supplemental views) was obtained from the DICOM image headers for comparison. This estimate uses normalized glandular dose coefficients corresponding to a breast with 50% fibroglandular weight fraction. The median fibroglandular weight fraction for the study cohort determined from volumetric breast CT images was 15%. Hence, the MGD from diagnostic mammography was corrected to be representative of the study cohort. Individualized estimates of MGD from breast CT ranged from 5.7 to 27.8 mGy. Corresponding to the breasts imaged with breast CT, the MGD from diagnostic mammography ranged from 2.6 to 31.6 mGy. The mean (± inter-breast SD) and the median MGD (mGy) from dedicated breast CT exam were 13.9 ± 4.6 and 12.6, respectively. For the corresponding breasts, the mean (± inter-breast SD) and the median MGD (mGy) from diagnostic mammography were 12.4 ± 6.3 and 11.1, respectively. Statistical analysis indicated that at the 0.05 level, the distributions of MGD from dedicated breast CT and diagnostic mammography were significantly different (Wilcoxon signed ranks test, p = 0.007). While the interquartile range and the range (maximum–minimum) of MGD from dedicated breast CT was lower than
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Electron densities of 33 samples of normal (adipose and fibroglangular) and neoplastic (benign and malignant) human breast tissues were determined through Compton scattering data using a monochromatic synchrotron radiation source and an energy dispersive detector. The area of Compton peaks was used to determine the electron densities of the samples. Adipose tissue exhibits the lowest values of electron density whereas malignant tissue the highest. The relationship with their histology was discussed. Comparison with previous results showed differences smaller than 4%. - Highlights: ► Electron density of normal and neoplastic breast tissues was measured using Compton scattering. ► Monochromatic synchrotron radiation was used to obtain the Compton scattering data. ► The area of Compton peaks was used to determine the electron densities of samples. ► Adipose tissue shows the lowest electron density values whereas the malignant tissue the highest. ► Comparison with previous results showed differences smaller than 4%.
Spectral density estimation for symmetric stable p-adic processes
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Rachid Sabre
2013-05-01
Full Text Available Applications of p-adic numbers ar beming increasingly important espcially in the field of applied physics. The objective of this work is to study the estimation of the spectral of p-adic stable processes. An estimator formed by a smoothing periodogram is constructed. It is shwon that this estimator is asymptotically unbiased and consistent. Rates of convergences are also examined.
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Tadayyon, Hadi [Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5 (Canada); Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Ontario M5G 2M9 (Canada); Sadeghi-Naini, Ali; Czarnota, Gregory, E-mail: Gregory.Czarnota@sunnybrook.ca [Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5 (Canada); Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Ontario M5G 2M9 (Canada); Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5 (Canada); Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, Ontario M5T 1P5 (Canada); Wirtzfeld, Lauren [Department of Physics, Ryerson University, Toronto, Ontario M5B 2K3 (Canada); Wright, Frances C. [Division of Surgical Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5 (Canada)
2014-01-15
Purpose: Tumor grading is an important part of breast cancer diagnosis and currently requires biopsy as its standard. Here, the authors investigate quantitative ultrasound parameters in locally advanced breast cancers that can potentially separate tumors from normal breast tissue and differentiate tumor grades. Methods: Ultrasound images and radiofrequency data from 42 locally advanced breast cancer patients were acquired and analyzed. Parameters related to the linear regression of the power spectrum—midband fit, slope, and 0-MHz-intercept—were determined from breast tumors and normal breast tissues. Mean scatterer spacing was estimated from the spectral autocorrelation, and the effective scatterer diameter and effective acoustic concentration were estimated from the Gaussian form factor. Parametric maps of each quantitative ultrasound parameter were constructed from the gated radiofrequency segments in tumor and normal tissue regions of interest. In addition to the mean values of the parametric maps, higher order statistical features, computed from gray-level co-occurrence matrices were also determined and used for characterization. Finally, linear and quadratic discriminant analyses were performed using combinations of quantitative ultrasound parameters to classify breast tissues. Results: Quantitative ultrasound parameters were found to be statistically different between tumor and normal tissue (p < 0.05). The combination of effective acoustic concentration and mean scatterer spacing could separate tumor from normal tissue with 82% accuracy, while the addition of effective scatterer diameter to the combination did not provide significant improvement (83% accuracy). Furthermore, the two advanced parameters, including effective scatterer diameter and mean scatterer spacing, were found to be statistically differentiating among grade I, II, and III tumors (p = 0.014 for scatterer spacing, p = 0.035 for effective scatterer diameter). The separation of the tumor
International Nuclear Information System (INIS)
Purpose: Tumor grading is an important part of breast cancer diagnosis and currently requires biopsy as its standard. Here, the authors investigate quantitative ultrasound parameters in locally advanced breast cancers that can potentially separate tumors from normal breast tissue and differentiate tumor grades. Methods: Ultrasound images and radiofrequency data from 42 locally advanced breast cancer patients were acquired and analyzed. Parameters related to the linear regression of the power spectrum—midband fit, slope, and 0-MHz-intercept—were determined from breast tumors and normal breast tissues. Mean scatterer spacing was estimated from the spectral autocorrelation, and the effective scatterer diameter and effective acoustic concentration were estimated from the Gaussian form factor. Parametric maps of each quantitative ultrasound parameter were constructed from the gated radiofrequency segments in tumor and normal tissue regions of interest. In addition to the mean values of the parametric maps, higher order statistical features, computed from gray-level co-occurrence matrices were also determined and used for characterization. Finally, linear and quadratic discriminant analyses were performed using combinations of quantitative ultrasound parameters to classify breast tissues. Results: Quantitative ultrasound parameters were found to be statistically different between tumor and normal tissue (p < 0.05). The combination of effective acoustic concentration and mean scatterer spacing could separate tumor from normal tissue with 82% accuracy, while the addition of effective scatterer diameter to the combination did not provide significant improvement (83% accuracy). Furthermore, the two advanced parameters, including effective scatterer diameter and mean scatterer spacing, were found to be statistically differentiating among grade I, II, and III tumors (p = 0.014 for scatterer spacing, p = 0.035 for effective scatterer diameter). The separation of the tumor
Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator.
Fleming, C H; Fagan, W F; Mueller, T; Olson, K A; Leimgruber, P; Calabrese, J M
2015-05-01
Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identically distributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs, are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology. PMID:26236833
Directory of Open Access Journals (Sweden)
Hendra Gunawan
2014-06-01
Full Text Available http://dx.doi.org/10.17014/ijog.vol3no3.20084The precision of topographic density (Bouguer density estimation by the Nettleton approach is based on a minimum correlation of Bouguer gravity anomaly and topography. The other method, the Parasnis approach, is based on a minimum correlation of Bouguer gravity anomaly and Bouguer correction. The precision of Bouguer density estimates was investigated by both methods on simple 2D syntetic models and under an assumption free-air anomaly consisting of an effect of topography, an effect of intracrustal, and an isostatic compensation. Based on simulation results, Bouguer density estimates were then investigated for a gravity survey of 2005 on La Soufriere Volcano-Guadeloupe area (Antilles Islands. The Bouguer density based on the Parasnis approach is 2.71 g/cm3 for the whole area, except the edifice area where average topography density estimates are 2.21 g/cm3 where Bouguer density estimates from previous gravity survey of 1975 are 2.67 g/cm3. The Bouguer density in La Soufriere Volcano was uncertainly estimated to be 0.1 g/cm3. For the studied area, the density deduced from refraction seismic data is coherent with the recent Bouguer density estimates. New Bouguer anomaly map based on these Bouguer density values allows to a better geological intepretation.
Estimation of current density distribution under electrodes for external defibrillation
Directory of Open Access Journals (Sweden)
Papazov Sava P
2002-12-01
Full Text Available Abstract Background Transthoracic defibrillation is the most common life-saving technique for the restoration of the heart rhythm of cardiac arrest victims. The procedure requires adequate application of large electrodes on the patient chest, to ensure low-resistance electrical contact. The current density distribution under the electrodes is non-uniform, leading to muscle contraction and pain, or risks of burning. The recent introduction of automatic external defibrillators and even wearable defibrillators, presents new demanding requirements for the structure of electrodes. Method and Results Using the pseudo-elliptic differential equation of Laplace type with appropriate boundary conditions and applying finite element method modeling, electrodes of various shapes and structure were studied. The non-uniformity of the current density distribution was shown to be moderately improved by adding a low resistivity layer between the metal and tissue and by a ring around the electrode perimeter. The inclusion of openings in long-term wearable electrodes additionally disturbs the current density profile. However, a number of small-size perforations may result in acceptable current density distribution. Conclusion The current density distribution non-uniformity of circular electrodes is about 30% less than that of square-shaped electrodes. The use of an interface layer of intermediate resistivity, comparable to that of the underlying tissues, and a high-resistivity perimeter ring, can further improve the distribution. The inclusion of skin aeration openings disturbs the current paths, but an appropriate selection of number and size provides a reasonable compromise.
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Rongda Chen
Full Text Available Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody's. However, it has a fatal defect that it can't fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody's new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management.
Estimation of the cost of treatment by chemotherapy for early breast cancer in Morocco
Directory of Open Access Journals (Sweden)
Boutayeb Saber
2010-09-01
Full Text Available Abstract Background Breast cancer is the first cancer in women both in incidence and mortality. The treatment of breast cancer benefited from the progress of chemotherapy and targeted therapies, but there was a parallel increase in treatment costs. Despite a relatively high incidence of many sites of cancer, so far, there is no national register for this disease in Morocco. The main goal of this paper is to estimate the total cost of chemotherapy in the early stages of breast cancer due to its frequency and the chances of patients being cured. This study provides health decision-makers with a first estimate of costs and the opportunity to achieve the optimal use of available data to estimate the needs of antimitotics and trastuzumab in Morocco. Method We start by evaluating the individual cost according to the therapeutic sub-groups, namely: 1. Patients needing chemotherapy with only anthracycline-based therapy. 2. Patients needing chemotherapy with both anthracycline and taxane but without trastuzumab. 3. Patients needing trastuzumab in addition to chemotherapy. For each sub-group, the protocol of treatment is described, and the individual costs per unit, and for the whole cycle, are evaluated. Then we estimate the number of women suffering from breast cancer on the basis of two data bases available in Morocco. Finally, we calculate the total annual cost of treatment of breast cancer in Morocco. Results The total cost of breast cancer in Morocco is given in Moroccan dirhams (MAD, the US dollar at the current exchange rate (MAD 10 = USD 1.30 and in international dollars or purchasing power parity (MAD 10 = PPP 1.95. The cost of a therapy with trastuzumab is 8.4 times the cost of a sequential chemotherapy combining anthracycline and taxane, and nearly 60 times the cost of chemotherapy based on anthracycline alone. Globally, between USD 13.3 million and USD 28.6 million need to be devoted every year by the Moroccan health authorities to treat
Directory of Open Access Journals (Sweden)
Kirsten Knutson, MPH
2008-01-01
Full Text Available Introduction Populations eligible for public health programs are often narrowly defined and, therefore, difficult to describe quantitatively, particularly at the local level, because of lack of data. This information, however, is vital for program planning and evaluation. We demonstrate the application of a statistical method using multiple sources of data to generate county estimates of women eligible for free breast cancer screening and diagnostic services through California’s Cancer Detection Programs: Every Woman Counts.MethodsWe used the small-area estimation method to determine the proportion of eligible women by county and racial/ethnic group. To do so, we included individual and community data in a generalized, linear, mixed-effect model.ResultsOur method yielded widely varied estimated proportions of service-eligible women at the county level. In all counties, the estimated proportion of eligible women was higher for Hispanics than for whites, blacks, Asian/Pacific Islanders, or American Indian/Alaska Natives. Across counties, the estimated proportions of eligible Hispanic women varied more than did those of women of other races.ConclusionThe small-area estimation method is a powerful tool for approximating narrowly defined eligible or target populations that are not represented fully in any one data source. The variability and reliability of the estimates are measurable and meaningful. Public health programs can use this method to estimate the size of local populations eligible for, or in need of, preventive health services and interventions.
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Kim, Eun Kyung; Lee, Yong Hee; Son, Hong Ju; Oh, Ki Keun; Chung, Hyun Cheol; Lee, Kyong Sik [Yonsei University College of Medicine, Seoul (Korea, Republic of)
2000-12-15
To evaluate how breast cancer vascularity detected by power Doppler sonography correlates with microvessel density(MVD) and lymph node involvement. Power Doppler US was performed on 47 patients with pathologically diagnosed invasive ductal carcinoma. According to the presense or absence of vessels, power Doppler findings were categorized regarding total tumors and tumors less than 2 cm. MVD was assessed immunohistochemically using polyclonal antisera against factor VIII. Tumor vascularity was correlated with MVD and lymph node involvement. Among 47 infiltrating ductal carcinomas, 35 (74.5%) cases were vascular and 12 (25.5%) cases were avascular on power Doppler US. The mean MVD of the vascular and avascular carcinomas did not show any statistical difference. However, lymph mode involvement showed significant statistical difference between the two groups (51.4% versus 25.0%, p=0.036), even though in cancers less than 2 cm (40% versus 22.2%, p=0.047). Breast cancer vascularity detected by power Doppler sonography was not correlated with MVD but correlated with lymph node involvement. So we suggest that power Doppler sonography may be useful for predicting lymph node metastasis.
An evidence-based estimate of appropriate radiotherapy utilization rate for breast cancer
International Nuclear Information System (INIS)
Purpose: Current estimates of the proportion of cancer patients who will require radiotherapy (RT) are based almost entirely on expert opinion. We sought to use an evidence-based approach to estimate the proportion of incident cases of breast cancer that will require RT at any point in the evolution of the illness. Methods and Materials: We undertook a systematic review of the literature to identify indications for RT for breast cancer and to ascertain the level of evidence that supported each indication. An epidemiologic approach was then used to estimate the incidence of each indication for RT in a typical North American population of breast cancer patients. The effect of sampling error on the estimated appropriate rate of RT was calculated mathematically, and the effect of systematic error was estimated by sensitivity analysis. Results: It was estimated that 66.4% ± 4.8% of breast cancer patients develop one or more indications for RT at some point in the course of the illness. The plausible range for this rate was 56.3%-72.4% on sensitivity analysis. Of all breast cancer patients, 57.3% ± 4.7% require RT in their initial treatment and 9.1% ± 1.0% do so later for recurrence or progression. The proportion of patients who ever require RT is stage dependent: 39.8% ± 1.1% in ductal carcinoma in situ; 68.6% ± 4.1% in Stage I invasive carcinoma; 81.5% ± 2.3% in Stage II; 95.3% ± 0.3% in Stage III; and 63.7% ± 0.3% in Stage IV. Conclusion: This method provides a rational starting point for the long-term planning of RT services and for the audit of access to RT at the population level. By completing such evaluations in the major cancer sites, it will be possible to estimate the appropriate RT treatment rate for the cancer population as a whole
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In Japan it is considered that mammography should be introduced with physical examination for the mass screening of breast cancer instead of physical examination alone, which is performed at present. Before the introduction of mammography, a mathematical simulation should be performed to show the reduction in breast cancer mortality by mass screening compared with an unscreened population. A mathematical model of cancer screening devised by the authors was used to estimate the number of deaths due to breast cancer (A) in the screened group and those (B) in the unscreened group within the same population. Then the relative risk (RR) and attributable risk (RD) were calculated as (A/B) and (B-A) respectively. Three methods of mass screening were compared: (1) physical examination (1-year interval), (2) mammography with physical examination (1-year interval), (3) mammography with physical examination (2-year interval). The calculated RR values were 0.85 for (1), 0.60 for (2) and 0.69 for (3). Assuming that the incidence of breast cancer was 100/105 person-years, the calculated RD values were 3.0, 8.1 and 6.2 persons/105 person-years for (1), (2) and (3), respectively. The 95% confidence interval of RR for three methods was over 1.0, and thus the reduction of breast cancer mortality was not statistically significant in the present population. In conclusion, mammography with physical examination may reduce breast cancer mortality in comparison with physical examination alone, but a larger number of women must be screened in order to obtain a significant RR value. (author)
Institute of Scientific and Technical Information of China (English)
陈颖; 史春颖; 杨予川; 李睿; 刘源; 崔春玲
2015-01-01
Objective To explore the correlation between breast cancer and breast density in Heilongjiang region .Methods X-ray data and epidemiological data of 487 women from June 2010 to November 2010 in the First Affiliated Hospital of Harbin Medical University were col-lected, including 105 cases of breast cancer and 382 cases of healthy controls ( breast benign disease ) .Breast imaging reporting and data system recommended by americaradiology doctors was the standard for evaluation of breast density , breast density percentage was used to analyze the relationship between different levels of mammary gland photography density and breast canc -er risk factors .Results Based on 487 cases of mammary X-ray data , breast density was statis-tically different between breast cancer groups and healthy controls in Heilongjiang area , the oc-currence of breast cancer was associated with a high breast density , menopausal status did not affect breast density;breast density was associated with menarche age and history of lactation . Conclusion Breast X-ray photography density is associated with breast cancer risk factors .%目的：探讨黑龙江地区乳腺癌与其乳腺密度是否存在相关性。方法收集2010年6月至2010年11月哈尔滨医科大学附属第一医院就诊的487例黑龙江地区女性的乳腺钼靶X线检查资料及流行病学资料，其中105例为乳腺癌，382例为健康对照组，以美国放射医师学会推荐的乳腺影像报告和数据系统（ breast imaging reporting and data system ，BI-RADS）作为评估乳腺密度的标准，以乳腺密度百分比分析不同等级乳腺摄影密度和乳腺癌危险因素的关系。结果乳腺癌组与健康对照组乳腺密度有统计学差异。绝经状态未对乳腺密度产生影响；乳腺密度与初潮年龄、哺乳史相关。结论乳腺X线摄影密度与乳腺癌危险因素均相关联。
Huerga, Iker; Gerrikagoitia, Jon Kepa
2010-01-01
Risk estimation of developing breast cancer poses as the first prevention method for early diagnosis. Furthermore, data integration from different departments involved in the process plays a key role. In order to guarantee patient safety, the whole process should be orchestrated and monitored automatically. Support for the solution will be a linked data cloud, composed by all the departments that take part in the process, combined with rule engines.
High-density SNP arrays improve detection of HER2 amplification and polyploidy in breast tumors
DEFF Research Database (Denmark)
Hansen, Thomas V. O.; Vikesaa, Jonas; Buhl, Sine S;
2015-01-01
BACKGROUND: Human epidermal growth factor receptor-2 (HER2) overexpression and gene amplification are currently established by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), respectively. This study investigates whether high-density single nucleotide polymorphism (SNP......) arrays can provide additional diagnostic power to assess HER2 gene status. METHODS: DNA from 65 breast tumor samples previously diagnosed by HER2 IHC and FISH analysis were blinded and examined for HER2 copy number variation employing SNP array analysis. RESULTS: SNP array analysis identified 24 (37......%) samples with selective amplification or imbalance of the HER2 region in the q-arm of chromosome 17. In contrast, only 15 (23%) tumors were found to have HER2 amplification by IHC and FISH analysis. In total, there was a discrepancy in 19 (29%) samples between SNP array and IHC/FISH analysis. In 12...
Estimation of Ga-67 radiation dose to a breast-fed infant
International Nuclear Information System (INIS)
Full text: The quality assurance programs for the practice of nuclear medicine in Colombia include optimization of public exposure by the radioactive sources used in medical diagnosis and treatment. Precautionary measures notwithstanding, occasionally members of the public receive doses larger than expected; usually there is no direct data to evaluate dose. We briefly describe the process to estimate the radiation dose absorbed by a 5-month-old baby that was inadvertently breast-fed for 48 hours by his 22-year-old mother that was administered 296 MBq of radioactive Ga-67 citrate. After the intravenous administration, the Ga-67 is commonly observed in the intestine, and other organs, and in the breasts of lactating women, thus possibly leading to radiation exposure of the infant's lower intestine. We had 256x256 single photon emission computed tomography (SPECT) images, and whole-body 1024 x 256 planar images, taken 48h and 72 h after Ga-67 intake. The images were obtained with a Siemens E-cam gamma camera, a 20% energy window was placed at the 93, 184 and 296 keV gamma photopeaks. From these images we calculated the concentration of Ga-67 in mother's milk (using Medical Internal Radiation Dose MIRD methodology). Hence, the dose absorbed by the baby. The radioactivity in breast milk per liter was 4% of the injected activity. The dose absorbed by the infant's lower intestine was 11 mGy. The effective dose to the infant was estimated as 7 mSv, which is above the annual limit to the general public (1 mSv/year). Hence, an infant breast-fed by a mother undergoing Ga-67 treatment may easily receive a radiation dose in excess of the annual upper limit. From the viewpoint of radiation protection it is extremely important to convey to the mother the necessity of abiding by the standard medical recommendation of discontinuing breast feeding for at least 2 weeks after Ga-67 intake. (author)
Singh, Harpreet; Arvind; Dorai, Kavita
2016-09-01
Estimation of quantum states is an important step in any quantum information processing experiment. A naive reconstruction of the density matrix from experimental measurements can often give density matrices which are not positive, and hence not physically acceptable. How do we ensure that at all stages of reconstruction, we keep the density matrix positive? Recently a method has been suggested based on maximum likelihood estimation, wherein the density matrix is guaranteed to be positive definite. We experimentally implement this protocol on an NMR quantum information processor. We discuss several examples and compare with the standard method of state estimation.
Samb, Rawane
2012-01-01
This manuscript is a supplemental document providing the omitted material for our paper entitled "Nonparametric kernel estimation of the probability density function of regression errors using estimated residuals" [arXiv:1010.0439]. The paper is submitted to Journal of Nonparametric Statistics.
EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area
DEFF Research Database (Denmark)
Proietti, Tommaso; Marczak, Martyna; Mazzi, Gianluigi
EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom–up approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model...... of a set of coincident time series handling mixed frequencies of observation and ragged–edged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model...
DEFF Research Database (Denmark)
Offersen, Birgitte Vrou; Riisbro, Rikke; Knoop, Ann;
2007-01-01
Plasminogen Activator Inhibitor type-1 (PAI-1) is involved in tumor invasion and progression. High levels of PAI-1 are associated with poor prognosis in breast cancer, and PAI-1 has been shown to play a role in angiogenic processes. Since estimates of tumor angiogenesis may predict poor prognosis...... we studied the relationship between PAI-1 and estimates of angiogenesis in breast cancer. Tumor tissue specimens from 438 breast cancer patients were included. Median follow-up was 10.3 years. Protein levels of PAI-1 were measured using an ELISA. Angiogenesis scores were performed using a Chalkley.......009) were independent markers of death from breast cancer. This study confirms high PAI-1 or high Chalkley counts as markers of poor prognosis in breast cancer patients, and suggests that the prognostic impact of PAI-1 is independent of its supposed involvement in tumor angiogenesis. Udgivelsesdato: 2007...
Lance, Amanda; Yang, Chih-Chao; Swamydas, Muthulekha; Dean, Delphine; Deitch, Sandy; Burg, Karen J L; Dréau, Didier
2016-01-01
The extracellular matrix (ECM) contributes to the generation and dynamic of normal breast tissue, in particular to the generation of polarized acinar and ductal structures. In vitro 3D culture conditions, including variations in the composition of the ECM, have been shown to directly influence the formation and organization of acinus-like and duct-like structures. Furthermore, the density of the ECM appears to also play a role in the normal mammary tissue and tumour formation. Here we show that the density of the ECM directly influences the number, organization and function of breast acini. Briefly, non-malignant human breast MCF10A cells were incubated in increasing densities of a Matrigel®-collagen I matrix. Elastic moduli near and distant to the acinus structures were measured by atomic force microscopy, and the number of acinus structures was determined. Immunochemistry was used to investigate the expression levels of E-cadherin, laminin, matrix metalloproteinase-14 and ß-casein in MCF10A cells. The modulus of the ECM was significantly increased near the acinus structures and the number of acinus structures decreased with the increase in Matrigel-collagen I density. As evaluated by the expression of laminin, the organization of the acinus structures present was altered as the density of the ECM increased. Increases in both E-cadherin and MMP14 expression by MCF10A cells as ECM density increased were also observed. In contrast, MCF10A cells expressed lower ß-casein levels as the ECM density increased. Taken together, these observations highlight the key role of ECM density in modulating the number, organization and function of breast acini.
Carlos B. Kasper; Fábio D. Mazim; José B. G. Soares; Tadeu G. de Oliveira
2015-01-01
ABSTRACT Using camera traps and capture/recapture analyses we recorded the presence and abundance of cat species at Turvo State Park, in southern Brazil. Ocelot [Leopardus pardalis (Linnaeus, 1758)] population density was estimated for two areas of the park, with differing management profiles. Density estimates varied from 0.14 to 0.26 indiv. km2. Another five cat species were recorded at very low frequencies, precluding more accurate analyses. We estimate 24 to 45 ocelots occur in the reserv...
Gamma Kernel Estimators for Density and Hazard Rate of Right-Censored Data
Directory of Open Access Journals (Sweden)
T. Bouezmarni
2011-01-01
Full Text Available The nonparametric estimation for the density and hazard rate functions for right-censored data using the kernel smoothing techniques is considered. The “classical” fixed symmetric kernel type estimator of these functions performs well in the interior region, but it suffers from the problem of bias in the boundary region. Here, we propose new estimators based on the gamma kernels for the density and the hazard rate functions. The estimators are free of bias and achieve the optimal rate of convergence in terms of integrated mean squared error. The mean integrated squared error, the asymptotic normality, and the law of iterated logarithm are studied. A comparison of gamma estimators with the local linear estimator for the density function and with hazard rate estimator proposed by Müller and Wang (1994, which are free from boundary bias, is investigated by simulations.
RADIATION PRESSURE DETECTION AND DENSITY ESTIMATE FOR 2011 MD
Energy Technology Data Exchange (ETDEWEB)
Micheli, Marco; Tholen, David J.; Elliott, Garrett T., E-mail: micheli@ifa.hawaii.edu, E-mail: tholen@ifa.hawaii.edu, E-mail: gte@ifa.hawaii.edu [Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822 (United States)
2014-06-10
We present our astrometric observations of the small near-Earth object 2011 MD (H ∼ 28.0), obtained after its very close fly-by to Earth in 2011 June. Our set of observations extends the observational arc to 73 days, and, together with the published astrometry obtained around the Earth fly-by, allows a direct detection of the effect of radiation pressure on the object, with a confidence of 5σ. The detection can be used to put constraints on the density of the object, pointing to either an unexpectedly low value of ρ=(640±330)kg m{sup −3} (68% confidence interval) if we assume a typical probability distribution for the unknown albedo, or to an unusually high reflectivity of its surface. This result may have important implications both in terms of impact hazard from small objects and in light of a possible retrieval of this target.
Asymptotically local minimax estimation of infinitely smooth density with censored data
Belitser, E.N.; Levit, B.Y.
2001-01-01
The problem of the nonparametric minimax estimation of an innitely smooth density at a given point, under random censorship, is considered. We establish the exact limiting behavior of the local minimax risk and propose the ecient kernel -type estimator based on the Kaplan-Meier estimator.
International Nuclear Information System (INIS)
We aimed to determine whether background 18F-FDG uptake in positron emission mammography (PEM) was related to mammographic density or background parenchymal enhancement in breast MRI. Methods: We studied a total of 52 patients (mean age, 50.9 years, 26 premenopausal, 26 postmenopausal) with newly diagnosed breast cancer who underwent 18F-FDG PEM (positron emission mammography), conventional mammography and breast MRI. The background mean 18F-FDG uptake value on PEM was obtained by drawing a user-defined region of interest (ROI) in a normal area of the contralateral breast. We reviewed the mammography retrospectively for overall breast density of contralateral breast according to the four-point scale (grade 1–4) of the Breast Imaging Reporting and Data System (BI-RADS) classification. The background parenchymal enhancement of breast MRI was classified as minimal, mild, moderate, or marked. All imaging findings were interpreted by two readers in consensus without knowledge of image findings of other modalities. Results: Multiple linear regression analysis revealed a significant correlation between background 18F-FDG uptake on PEM and mammographic density after adjustment for age and menopausal status (P 18F-FDG uptake on PEM and background parenchymal enhancement on MRI. Conclusion: Background 18F-FDG uptake on PEM significantly increases as mammographic density increases. Background parenchymal enhancement in breast MRI was not an independent predictor of the background 18F-FDG uptake on PEM unlike mammographic density
Using mortality data to estimate radiation effects on breast cancer incidence
International Nuclear Information System (INIS)
In this paper we combine Japanese data on radiation exposure and cancer mortality with U.S. data on cancer incidence and lethality to estimate the effects of ionizing radiation on cancer incidence. The analysis is based on the mathematical relationship between the mortality rate and the incidence and lethality rates, as well as on statistical models that relate Japanese incidence rates to U.S. incidence rates and radiation risk factors. Our approach assumes that the risk of death from causes other than the cancer does not depend on whether or not the cancer is present, and among individuals with the cancer, the risk of death attributable to the cancer is the same in Japan and the U.S. and is not affected by radiation exposure. In particular, we focus on the incidence of breast cancer in Japanese women and how this incidence is affected by radiation risk factors. The analysis uses Japanese exposure and mortality data from the Radiation Effects Research Foundation study of atomic bomb survivors and U.S. incidence and lethality data from the Surveillance, Epidemiology, and End Results Registry. Even without Japanese incidence data, we obtain reasonable estimates of the incidence of breast cancer in unexposed Japanese women and identify the radiation risk factors that affect this incidence. Our analysis demonstrates that the age at exposure is an important risk factor, but that the incidence of breast cancer is not affected by the city of residence (Nagasaki versus Hiroshima) or the time since exposure
Performances of Different Algorithms for Tracer Kinetics Parameters Estimation in Breast DCE-MRI
Directory of Open Access Journals (Sweden)
Roberta Fusco
2014-07-01
Full Text Available Objective of this study was to evaluate the performances of different algorithms for tracer kinetics parameters estimation in breast Dynamic Contrast Enhanced-MRI. We considered four algorithms: two non-iterative algorithms based on impulsive and linear approximation of the Arterial Input Function respectively; and two iterative algorithms widely used for non-linear regression (Levenberg-Marquardt, LM and VARiable PROjection, VARPRO. Per each value of the kinetic parameters within a physiological range, we simulated 100 noisy curves and estimated the parameters with all algorithms. Sampling time, total duration and noise level have been chosen as in a typical breast examination. We compared the performances with respect to the Cramer-Rao Lower Bound (CRLB. Moreover, in order to gain further insight we applied the algorithms to a real breast examination. Accuracy of all the methods depends on the specific value of the parameters. The methods are in general biased: however, VARPRO showed small bias in a region of the parameter space larger than the other methods; moreover, VARPRO approached CRLB and the number of iterations were smaller than LM. In the specific conditions analyzed, VARPRO showed better performances with respect to LM and to non-iterative algorithms
Efficient estimation of dynamic density functions with an application to outlier detection
Qahtan, Abdulhakim Ali
2012-01-01
In this paper, we propose a new method to estimate the dynamic density over data streams, named KDE-Track as it is based on a conventional and widely used Kernel Density Estimation (KDE) method. KDE-Track can efficiently estimate the density with linear complexity by using interpolation on a kernel model, which is incrementally updated upon the arrival of streaming data. Both theoretical analysis and experimental validation show that KDE-Track outperforms traditional KDE and a baseline method Cluster-Kernels on estimation accuracy of the complex density structures in data streams, computing time and memory usage. KDE-Track is also demonstrated on timely catching the dynamic density of synthetic and real-world data. In addition, KDE-Track is used to accurately detect outliers in sensor data and compared with two existing methods developed for detecting outliers and cleaning sensor data. © 2012 ACM.
Change in bone mineral density during adjuvant chemotherapy for early-stage breast cancer
DEFF Research Database (Denmark)
Christensen, Carina Ørts; Cronin-Fenton, Deirdre; Frøslev, Trine;
2016-01-01
PURPOSE: Adjuvant chemotherapy has been associated with loss of bone mineral density (BMD) either as a direct effect or due to glucocorticoids used as supportive care medication. A prospective cohort study was conducted to evaluate changes in BMD from baseline to right after completion of chemoth......PURPOSE: Adjuvant chemotherapy has been associated with loss of bone mineral density (BMD) either as a direct effect or due to glucocorticoids used as supportive care medication. A prospective cohort study was conducted to evaluate changes in BMD from baseline to right after completion...... of chemotherapy, i.e., 4 months. METHODS: Dual-imaging X-ray absorptiometry (DXA) was performed at baseline and after completing anthracycline- and taxane-based chemotherapy to measure BMD in the spine, hip, and forearm in early-stage breast cancer patients. High-dose prednisolone was used at three weekly...... were excluded: seven because of initiation of bisphosphonate treatment due to osteoporosis at baseline, and one had non-interpretable DXA. The final cohort included 97 patients with a mean age of 53 years (range 34-72). Mean cumulative prednisolone dose was 1308 mg (95 % CI 1255; 1362). BMD increased 1...
An Efficient Acoustic Density Estimation Method with Human Detectors Applied to Gibbons in Cambodia.
Directory of Open Access Journals (Sweden)
Darren Kidney
Full Text Available Some animal species are hard to see but easy to hear. Standard visual methods for estimating population density for such species are often ineffective or inefficient, but methods based on passive acoustics show more promise. We develop spatially explicit capture-recapture (SECR methods for territorial vocalising species, in which humans act as an acoustic detector array. We use SECR and estimated bearing data from a single-occasion acoustic survey of a gibbon population in northeastern Cambodia to estimate the density of calling groups. The properties of the estimator are assessed using a simulation study, in which a variety of survey designs are also investigated. We then present a new form of the SECR likelihood for multi-occasion data which accounts for the stochastic availability of animals. In the context of gibbon surveys this allows model-based estimation of the proportion of groups that produce territorial vocalisations on a given day, thereby enabling the density of groups, instead of the density of calling groups, to be estimated. We illustrate the performance of this new estimator by simulation. We show that it is possible to estimate density reliably from human acoustic detections of visually cryptic species using SECR methods. For gibbon surveys we also show that incorporating observers' estimates of bearings to detected groups substantially improves estimator performance. Using the new form of the SECR likelihood we demonstrate that estimates of availability, in addition to population density and detection function parameters, can be obtained from multi-occasion data, and that the detection function parameters are not confounded with the availability parameter. This acoustic SECR method provides a means of obtaining reliable density estimates for territorial vocalising species. It is also efficient in terms of data requirements since since it only requires routine survey data. We anticipate that the low-tech field requirements will
DEFF Research Database (Denmark)
Hansen, S; Grabau, D A; Rose, C;
1998-01-01
The purpose of this study was to evaluate the reliability of different methods for estimating neovascularization in breast cancer and to compare them in terms of observer variability. The microvessel endothelium was stained immunohistochemically by antibodies against CD34. The investigated method...
DEFF Research Database (Denmark)
Nielsen, Mads; Pettersen, Paola; Alexandersen, P;
2010-01-01
(1 mg) continuously combined with drospirenone (2 mg) was administered to postmenopausal women for up to 2 years (26 treatment cycles, 28 d/cycle) in a randomized, placebo-controlled trial. This post hoc analysis assessed the changes in breast density measured from digitized images by two radiologist...... mineral density at the spine and femur were also assessed. Results: Breast density assessed by the radiologist-based approaches increased significantly from baseline in the HT group (P < 0.01), with significant divergence from placebo at 2 years (P < 0.01). Heterogeneity examination of radiograph score by...... computer-based technique was unchanged in the HT group and decreased significantly with placebo (P < 0.001) to produce a significant group divergence (P < 0.05). Changes in mammographic markers by radiologist- and computer-based approaches correlated with each other in the HT group (P < 0.01) but not in...
Institute of Scientific and Technical Information of China (English)
付小娜; 王丽; 康欣梅
2015-01-01
Mammographic breast density( BD)is independent risk factor for breast cancer,sex hormones also play a role in the development of breast cancer,but it is unclear whether mammographic breast density associate with sex hormone. In this review,we describe the association of BD and sex hormone with breast cancer.%乳腺密度（ mammographic breast density，BD）是乳腺癌危险性独立的预测指标，性激素也在乳腺癌的发展中扮演重要角色，而乳腺密度与性激素的关系仍不明确。本文将对乳腺密度，性激素，乳腺癌的关系进行综述。
Montserrat Guillen; Jens Perch Nielsen; Catalina Bolance
2000-01-01
In this paper we concentrate on the estimation of loss functions using nonparametric methods. We focus on the parametric transformation approach to kernel smoothing introduced by Wand, Marron and Ruppert (1991) and compare it with the standard kernel estimator and the multiplicative bias correction method (Hjort and Glad, 1995 and Jones, Linton and Nielsen, 1995). We advocate in this paper that the transformation method behaves excellently when it comes to estimating actuarial and financial l...
EnviroAtlas - Des Moines, IA - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Portland, ME - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - New Bedford, MA - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Portland, OR - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Phoenix, AZ - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Fresno, CA - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Pittsburgh, PA - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - New York, NY - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Durham, NC - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Memphis, TN - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Woodbine, IA - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Milwaukee, WI - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Austin, TX - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Paterson, NJ - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas - Tampa, FL - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
EnviroAtlas Estimated Intersection Density of Walkable Roads Web Service
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in each EnviroAtlas community....
EnviroAtlas - Green Bay, WI - Estimated Intersection Density of Walkable Roads
U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...
International Nuclear Information System (INIS)
Indirect estimation of low-dose radiation hazards is possible using the multihit model of carcinogenesis. This model is based on cancer incidence data collected over many decades on tens of millions of people. Available data on human radiation effects can be introduced into the modeling process without the requirement that these data precisely define the model to be used. This reduction in the information demanded from the limited data on human radiation effects allows a more rational approach to estimation of low-dose radiation hazards and helps to focus attention on research directed towards understanding the process of carcinogenesis, rather than on repeating human or animal experiments that cannot provide sufficient data to resolve the low-dose estimation problem. Assessment of the risk of radiation-induced breast cancer provides an excellent example of the utility of multihit modeling procedures
A comparative simulation study of data-driven methods for estimating density level sets
Saavedra-Nieves, Paula; González-Manteiga, Wenceslao; Rodríguez-Casal, Alberto
2014-01-01
Density level sets are mainly estimated using one of three methodologies: plug-in, excess mass, or a hybrid approach. The plug-in methods are based on replacing the unknown density by some nonparametric estimator, usually the kernel. Thus, the bandwidth selection is a fundamental problem from a practical point of view. Recently, specific selectors for level sets have been proposed. However, if some a priori information about the geometry of the level set is available, then excess mass algorit...
Ferreira, P; Baptista, M; Di Maria, S; Vaz, P
2016-05-01
The aim of this work was to estimate the risk of radiation induced cancer following the Portuguese breast screening recommendations for Digital Mammography (DM) when applied to Digital Breast Tomosynthesis (DBT) and to evaluate how the risk to induce cancer could influence the energy used in breast diagnostic exams. The organ doses were calculated by Monte Carlo simulations using a female voxel phantom and considering the acquisition of 25 projection images. Single organ cancer incidence risks were calculated in order to assess the total effective radiation induced cancer risk. The screening strategy techniques considered were: DBT in Cranio-Caudal (CC) view and two-view DM (CC and Mediolateral Oblique (MLO)). The risk of cancer incidence following the Portuguese screening guidelines (screening every two years in the age range of 50-80years) was calculated by assuming a single CC DBT acquisition view as standalone screening strategy and compared with two-view DM. The difference in the total effective risk between DBT and DM is quite low. Nevertheless in DBT an increase of risk for the lung is observed with respect to DM. The lung is also the organ that is mainly affected when non-optimal beam energy (in terms of image quality and absorbed dose) is used instead of an optimal one. The use of non-optimal energies could increase the risk of lung cancer incidence by a factor of about 2. PMID:27133140
The importance of spatial models for estimating the strength of density dependence
DEFF Research Database (Denmark)
Thorson, James T.; Skaug, Hans J.; Kristensen, Kasper;
2014-01-01
Identifying the existence and magnitude of density dependence is one of the oldest concerns in ecology. Ecologists have aimed to estimate density dependence in population and community data by fitting a simple autoregressive (Gompertz) model for density dependence to time series of abundance...... for an entire population. However, it is increasingly recognized that spatial heterogeneity in population densities has implications for population and community dynamics. We therefore adapt the Gompertz model to approximate local densities over continuous space instead of population-wide abundance......, and to allow productivity to vary spatially. Using simulated data generated from a spatial model, we show that the conventional (nonspatial) Gompertz model will result in biased estimates of density dependence, e.g., identifying oscillatory dynamics when not present. By contrast, the spatial Gompertz model...
Helium beam diagnostics for the estimation electron temperature and density in SST-1
International Nuclear Information System (INIS)
Supersonic helium beam Diagnostics is used to estimate edge electron density and temperature in tokamaks. Ratio of line emission intensities from neutral helium is used to estimate electron temperature and density. Temperature is estimated from the ratio of intensities (728.1 nm /706.3 nm) whereas density is estimated from ratio (668.1 nm/728.1 nm). We have designed and tested a supersonic helium beam injector for edge plasma temperature and density for SST-1 tokamak. The system consists of a supersonic injector and an imaging system. The emission is collected by the imaging system and optical fibers and an EMMCD coupled spectrograph is used to record the spectra from various spatial locations. The spatial resolution is around 5 mm. In a recent campaign in SST-1, we tried to estimate these parameters using the residual helium after the helium GDC. The spectrometer and detection system was calibrated and signal was optimized. The spectra were good enough to use these helium lines to estimate electron temperature and density with an integration time of 10 ms. The observed line ratios are compared with the line ratios obtained from CR model/Atomic Data and Analysis Structure (ADAS) to get an estimate of electron temperature and density. The estimated electron density is in the range of 5 x 1011 - 2 x 1012 cm-3 and electron temperature 30-55 eV. The obtained parameters provide reasonable estimates when compared with other diagnostics considering the diffusion and ionization of neutral helium inside the tokamak. (author)
Directory of Open Access Journals (Sweden)
YU Wenhao
2015-01-01
Full Text Available The distribution pattern and the distribution density of urban facility POIs are of great significance in the fields of infrastructure planning and urban spatial analysis. The kernel density estimation, which has been usually utilized for expressing these spatial characteristics, is superior to other density estimation methods (such as Quadrat analysis, Voronoi-based method, for that the Kernel density estimation considers the regional impact based on the first law of geography. However, the traditional kernel density estimation is mainly based on the Euclidean space, ignoring the fact that the service function and interrelation of urban feasibilities is carried out on the network path distance, neither than conventional Euclidean distance. Hence, this research proposed a computational model of network kernel density estimation, and the extension type of model in the case of adding constraints. This work also discussed the impacts of distance attenuation threshold and height extreme to the representation of kernel density. The large-scale actual data experiment for analyzing the different POIs' distribution patterns (random type, sparse type, regional-intensive type, linear-intensive type discusses the POI infrastructure in the city on the spatial distribution of characteristics, influence factors, and service functions.
Anjali; Deepak
2015-01-01
Breast cancer is the most common site specific cancer in women. Lots of etiological factors have been suggested regarding its causation. The risk is influenced by obesity, parity, exogenous and endogenous hormones, exposure to chemicals and radiation and many more. Various studies suggest that as HDL-C seems to be cardio protective, it is also protective for breast cancer. Serum HDL-C levels are found to be low in breast cancer patients. We studied this hypothesis in local population of South...
Novel and simple non-parametric methods of estimating the joint and marginal densities
Alghalith, Moawia
2016-07-01
We introduce very simple non-parametric methods that overcome key limitations of the existing literature on both the joint and marginal density estimation. In doing so, we do not assume any form of the marginal distribution or joint distribution a priori. Furthermore, our method circumvents the bandwidth selection problems. We compare our method to the kernel density method.
Fotin, Sergei V.; Yin, Yin; Haldankar, Hrishikesh; Hoffmeister, Jeffrey W.; Periaswamy, Senthil
2016-03-01
Computer-aided detection (CAD) has been used in screening mammography for many years and is likely to be utilized for digital breast tomosynthesis (DBT). Higher detection performance is desirable as it may have an impact on radiologist's decisions and clinical outcomes. Recently the algorithms based on deep convolutional architectures have been shown to achieve state of the art performance in object classification and detection. Similarly, we trained a deep convolutional neural network directly on patches sampled from two-dimensional mammography and reconstructed DBT volumes and compared its performance to a conventional CAD algorithm that is based on computation and classification of hand-engineered features. The detection performance was evaluated on the independent test set of 344 DBT reconstructions (GE SenoClaire 3D, iterative reconstruction algorithm) containing 328 suspicious and 115 malignant soft tissue densities including masses and architectural distortions. Detection sensitivity was measured on a region of interest (ROI) basis at the rate of five detection marks per volume. Moving from conventional to deep learning approach resulted in increase of ROI sensitivity from 0:832 +/- 0:040 to 0:893 +/- 0:033 for suspicious ROIs; and from 0:852 +/- 0:065 to 0:930 +/- 0:046 for malignant ROIs. These results indicate the high utility of deep feature learning in the analysis of DBT data and high potential of the method for broader medical image analysis tasks.
Albada, A.; Dulmen, S. van; Dijkstra, H.; Wieffer, I.; Witkamp, A.; Ausems, M.G.E.M.
2016-01-01
We studied counselees’ expressed understanding of the risk estimate and surveillance recommendation in the final consultation for breast cancer genetic counseling in relation with their risk perception, worry and cancer surveillance adherence 1 year post-counseling. Consecutive counselees were included from 2008 to 2010. Counselees with an indication for diagnostic DNA-testing for themselves or a breast cancer affected relative were requested to complete online questionnaires before and after...
Autocorrelation-based estimate of particle image density for diffraction limited particle images
International Nuclear Information System (INIS)
In particle image velocimetry (PIV), the number of particle images per interrogation region, or particle image density, impacts the strength of the correlation and, as a result, the number of valid vectors and the measurement uncertainty. For some uncertainty methods, an a priori estimate of the uncertainty of PIV requires knowledge of the particle image density. An autocorrelation-based method for estimating the local, instantaneous, particle image density is presented. The method assumes that the particle images are diffraction limited and thus Gaussian in shape. Synthetic images are used to develop an empirical relationship between the autocorrelation peak magnitude and the particle image density, particle image diameter, particle image intensity, and interrogation region size. This relationship is tested using experimental images. The experimental results are compared to particle image densities obtained through implementing a local maximum method and are found to be more robust. The effect of varying particle image intensities was also investigated and is found to affect the measurement of the particle image density. Knowledge of the particle image density in PIV facilitates uncertainty estimation, and can alert the user that particle image density is too low or too high, even if these conditions are intermittent. This information can be used as a new vector validation criterion for PIV processing. In addition, use of this method is not limited to PIV, but it can be used to determine the density of any image with diffraction limited particle images. (paper)
A bound for the smoothing parameter in certain well-known nonparametric density estimators
Terrell, G. R.
1980-01-01
Two classes of nonparametric density estimators, the histogram and the kernel estimator, both require a choice of smoothing parameter, or 'window width'. The optimum choice of this parameter is in general very difficult. An upper bound to the choices that depends only on the standard deviation of the distribution is described.
An asymptotically unbiased minimum density power divergence estimator for the Pareto-tail index
DEFF Research Database (Denmark)
Dierckx, G.; Goegebeur, Y.; Guillou, A.
2013-01-01
We introduce a robust and asymptotically unbiased estimator for the tail index of Pareto-type distributions. The estimator is obtained by fitting the extended Pareto distribution to the relative excesses over a high threshold with the minimum density power divergence criterion. Consistency...
Energy Technology Data Exchange (ETDEWEB)
Antoniassi, M.; Conceicao, A.L.C. [Departamento de Fisica-Faculdade de Filosofia Ciencias e Letras de Ribeirao Preto-Universidade de Sao Paulo, Ribeirao Preto, Sao Paulo (Brazil); Poletti, M.E., E-mail: poletti@ffclrp.usp.br [Departamento de Fisica-Faculdade de Filosofia Ciencias e Letras de Ribeirao Preto-Universidade de Sao Paulo, Ribeirao Preto, Sao Paulo (Brazil)
2012-07-15
Electron densities of 33 samples of normal (adipose and fibroglangular) and neoplastic (benign and malignant) human breast tissues were determined through Compton scattering data using a monochromatic synchrotron radiation source and an energy dispersive detector. The area of Compton peaks was used to determine the electron densities of the samples. Adipose tissue exhibits the lowest values of electron density whereas malignant tissue the highest. The relationship with their histology was discussed. Comparison with previous results showed differences smaller than 4%. - Highlights: Black-Right-Pointing-Pointer Electron density of normal and neoplastic breast tissues was measured using Compton scattering. Black-Right-Pointing-Pointer Monochromatic synchrotron radiation was used to obtain the Compton scattering data. Black-Right-Pointing-Pointer The area of Compton peaks was used to determine the electron densities of samples. Black-Right-Pointing-Pointer Adipose tissue shows the lowest electron density values whereas the malignant tissue the highest. Black-Right-Pointing-Pointer Comparison with previous results showed differences smaller than 4%.
Nonparametric maximum likelihood estimation of probability densities by penalty function methods
Demontricher, G. F.; Tapia, R. A.; Thompson, J. R.
1974-01-01
When it is known a priori exactly to which finite dimensional manifold the probability density function gives rise to a set of samples, the parametric maximum likelihood estimation procedure leads to poor estimates and is unstable; while the nonparametric maximum likelihood procedure is undefined. A very general theory of maximum penalized likelihood estimation which should avoid many of these difficulties is presented. It is demonstrated that each reproducing kernel Hilbert space leads, in a very natural way, to a maximum penalized likelihood estimator and that a well-known class of reproducing kernel Hilbert spaces gives polynomial splines as the nonparametric maximum penalized likelihood estimates.
Kocovsky, Patrick M.; Rudstam, Lars G.; Yule, Daniel L.; Warner, David M.; Schaner, Ted; Pientka, Bernie; Deller, John W.; Waterfield, Holly A.; Witzel, Larry D.; Sullivan, Patrick J.
2013-01-01
Standardized methods of data collection and analysis ensure quality and facilitate comparisons among systems. We evaluated the importance of three recommendations from the Standard Operating Procedure for hydroacoustics in the Laurentian Great Lakes (GLSOP) on density estimates of target species: noise subtraction; setting volume backscattering strength (Sv) thresholds from user-defined minimum target strength (TS) of interest (TS-based Sv threshold); and calculations of an index for multiple targets (Nv index) to identify and remove biased TS values. Eliminating noise had the predictable effect of decreasing density estimates in most lakes. Using the TS-based Sv threshold decreased fish densities in the middle and lower layers in the deepest lakes with abundant invertebrates (e.g., Mysis diluviana). Correcting for biased in situ TS increased measured density up to 86% in the shallower lakes, which had the highest fish densities. The current recommendations by the GLSOP significantly influence acoustic density estimates, but the degree of importance is lake dependent. Applying GLSOP recommendations, whether in the Laurentian Great Lakes or elsewhere, will improve our ability to compare results among lakes. We recommend further development of standards, including minimum TS and analytical cell size, for reducing the effect of biased in situ TS on density estimates.
Rojas-Lima, J. E.; Domínguez-Pacheco, A.; Hernández-Aguilar, C.; Cruz-Orea, A.
2016-09-01
Considering the necessity of photothermal alternative approaches for characterizing nonhomogeneous materials like maize seeds, the objective of this research work was to analyze statistically the amplitude variations of photopyroelectric signals, by means of nonparametric techniques such as the histogram and the kernel density estimator, and the probability density function of the amplitude variations of two genotypes of maize seeds with different pigmentations and structural components: crystalline and floury. To determine if the probability density function had a known parametric form, the histogram was determined which did not present a known parametric form, so the kernel density estimator using the Gaussian kernel, with an efficiency of 95 % in density estimation, was used to obtain the probability density function. The results obtained indicated that maize seeds could be differentiated in terms of the statistical values for floury and crystalline seeds such as the mean (93.11, 159.21), variance (1.64× 103, 1.48× 103), and standard deviation (40.54, 38.47) obtained from the amplitude variations of photopyroelectric signals in the case of the histogram approach. For the case of the kernel density estimator, seeds can be differentiated in terms of kernel bandwidth or smoothing constant h of 9.85 and 6.09 for floury and crystalline seeds, respectively.
Estimation of breast dose and cancer risk in chest and abdomen CT procedures
International Nuclear Information System (INIS)
The use of CT in medical diagnosis delivers radiation doses to patents that are higher than those from other radiological procedures. Lack of optimized protocols be an additional source of increased dose in developing countries. The aims of this study are first, to measure patient doses during CT chest and abdomen procedures, second, to estimate the radiation dose to the breast, and third to quantify the radiation risks during the procedures. Patient doses from two common CT examinations were obtained from four hospitals in Khartoum.The patient doses were estimated using measurement of CT dose indexes (CTDI), exposure-related parameters, and the IMPACT spreadsheet based on NRPB conversion factors. A large variation of mean organ doses among hospitals was observed for similar CT examinations. These variations largely originated from different CT scanning protocols used in different hospitals and scanner type. The largest range was found for CT of the chest, for which the dose varied from 2.3 to 47 (average 24.7) mSv and for abdomen CT, it was 1.6 to 18.8 (average 10.2) mSv. Radiation dose to the breast ranged from 1.6 to 32.9 mSv for the chest and 1.1 to 13.2 mSv for the abdomen. The radiation risk per procedure was high. The obtained values were mostly higher than the values of organ doses reported from the other studies. It was concluded that current clinical chest and abdomen protocols result in variable radiation doses to the breast. The magnitude of exposure may have implications for imaging strategies.(Author)
Directory of Open Access Journals (Sweden)
Anjali
2015-12-01
Full Text Available Breast cancer is the most common site specific cancer in women. Lots of etiological factors have been suggested regarding its causation. The risk is influenced by obesity, parity, exogenous and endogenous hormones, exposure to chemicals and radiation and many more. Various studies suggest that as HDL-C seems to be cardio protective, it is also protective for breast cancer. Serum HDL-C levels are found to be low in breast cancer patients. We studied this hypothesis in local population of Southern Rajasthan and nearby region to see if low serum HDL-C is associated with increased risk of breast cancer. We studied serum HDL-C level in 50 female patients of breast cancer along with their menopausal status and compared it to their respective controls. We found that breast cancer patients had significantly low level of serum HDL-C and presented in advanced stage of cancer. It supports that low serum HDL-C level is associated with increased risk of breast cancer. So one should think about those dietary and lifestyle measures, which maintain high serum HDL-C level so that it might become preventive measure for breast cancer.
International Nuclear Information System (INIS)
The authors report on a method to calculate radiological risks, applicable to breast screening programs and other controlled medical exposures to ionizing radiation. In particular, it has been applied to make a risk assessment in the Valencian Breast Cancer Early Detection Program (VBCEDP) in Spain. This method is based on a parametric approach, through Markov processes, of hazard functions for radio-induced breast cancer incidence and mortality, with mean glandular breast dose, attained age and age-at-exposure as covariates. Excess relative risk functions of breast cancer mortality have been obtained from two different case-control studies exposed to ionizing radiation, with different follow-up time: the Canadian Fluoroscopy Cohort Study (1950-1987) and the Life Span Study (1950-1985 and 1950-1990), whereas relative risk functions for incidence have been obtained from the Life Span Study (1958-1993), the Massachusetts tuberculosis cohorts (1926-1985 and 1970-1985), the New York post-partum mastitis patients (1930-1981) and the Swedish benign breast disease cohort (1958-1987). Relative risks from these cohorts have been transported to the target population undergoing screening in the Valencian Community, a region in Spain with about four and a half million inhabitants. The SCREENRISK software has been developed to estimate radiological detriments in breast screening. Some hypotheses corresponding to different screening conditions have been considered in order to estimate the total risk associated with a woman who takes part in all screening rounds. In the case of the VBCEDP, the total radio-induced risk probability for fatal breast cancer is in a range between [5 x 10-6, 6 x 10-4] versus the natural rate of dying from breast cancer in the Valencian Community which is 9.2 x 10-3. The results show that these indicators could be included in quality control tests and could be adequate for making comparisons between several screening programs
Energy Technology Data Exchange (ETDEWEB)
Ramos, M [Chemical and Nuclear Engineering Department, Polytechnic University of Valencia, Camino de Vera s/n 46022 Valencia (Spain); Ferrer, S [Chemical and Nuclear Engineering Department, Polytechnic University of Valencia, Camino de Vera s/n 46022 Valencia (Spain); Villaescusa, J I [Radiation Protection Service, Hospital Universitario La Fe, Avda Campanar, 21 46009 Valencia (Spain); Verdu, G [Chemical and Nuclear Engineering Department, Polytechnic University of Valencia, Camino de Vera s/n 46022 Valencia (Spain); Salas, M D [Public Health General Direction, Conselleria de Sanitat de Valencia, C/Micer Masco, 31 46021 Valencia (Spain); Cuevas, M D [Assistential Service General Direction, Conselleria de Sanitat de Valencia, C/Micer Masco, 31 46021 Valencia (Spain)
2005-02-07
The authors report on a method to calculate radiological risks, applicable to breast screening programs and other controlled medical exposures to ionizing radiation. In particular, it has been applied to make a risk assessment in the Valencian Breast Cancer Early Detection Program (VBCEDP) in Spain. This method is based on a parametric approach, through Markov processes, of hazard functions for radio-induced breast cancer incidence and mortality, with mean glandular breast dose, attained age and age-at-exposure as covariates. Excess relative risk functions of breast cancer mortality have been obtained from two different case-control studies exposed to ionizing radiation, with different follow-up time: the Canadian Fluoroscopy Cohort Study (1950-1987) and the Life Span Study (1950-1985 and 1950-1990), whereas relative risk functions for incidence have been obtained from the Life Span Study (1958-1993), the Massachusetts tuberculosis cohorts (1926-1985 and 1970-1985), the New York post-partum mastitis patients (1930-1981) and the Swedish benign breast disease cohort (1958-1987). Relative risks from these cohorts have been transported to the target population undergoing screening in the Valencian Community, a region in Spain with about four and a half million inhabitants. The SCREENRISK software has been developed to estimate radiological detriments in breast screening. Some hypotheses corresponding to different screening conditions have been considered in order to estimate the total risk associated with a woman who takes part in all screening rounds. In the case of the VBCEDP, the total radio-induced risk probability for fatal breast cancer is in a range between [5 x 10{sup -6}, 6 x 10{sup -4}] versus the natural rate of dying from breast cancer in the Valencian Community which is 9.2 x 10{sup -3}. The results show that these indicators could be included in quality control tests and could be adequate for making comparisons between several screening programs.
Pyrethroids in human breast milk: occurrence and nursing daily intake estimation.
Corcellas, Cayo; Feo, Maria Luisa; Torres, Joao Paulo; Malm, Olaf; Ocampo-Duque, William; Eljarrat, Ethel; Barceló, Damià
2012-10-15
There is an assumption that pyrethroid pesticides are converted to non-toxic metabolites by hydrolysis in mammals. However, some recent works have shown their bioaccumulation in human breast milk collected in areas where pyrethroids have been widely used for agriculture or malaria control. In this work, thirteen pyrethroids have been studied in human breast milk samples coming from areas without pyrethroid use for malaria control, such as Brazil, Colombia and Spain. The concentrations of pyrethroids ranged from 1.45 to 24.2 ng g⁻¹ lw. Cypermethrin, λ-cyhalothrin, permethrin and esfenvalerate/fenvalerate were present in all the studied samples. The composition of pyrethroid mixture depended on the country of origin of the samples, bifenthrin being the most abundant in Brazilian samples, λ-cyhalothrin in Colombian and permethrin in Spanish ones. When the pyrethroid concentrations were confronted against the number of gestations, an exponential decay was observed. Moreover, a time trend study was carried out in Brazil, where additional archived pool samples were analyzed, corresponding to years when pyrethroids were applied for dengue epidemic control. In these cases, total pyrethroid levels reached up to 128 ng g⁻¹ lw, and concentrations decreased when massive use was not allowed. Finally, daily intake estimation of nursing infants was calculated in each country and compared to acceptable WHO levels. The estimated daily intakes for nursing infants were always below the acceptable daily intake levels, nevertheless in certain samples the detected concentrations were very close to the maximum acceptable levels. PMID:22717642
Ku, Bon Ki; Evans, Douglas E.
2015-01-01
For nanoparticles with nonspherical morphologies, e.g., open agglomerates or fibrous particles, it is expected that the actual density of agglomerates may be significantly different from the bulk material density. It is further expected that using the material density may upset the relationship between surface area and mass when a method for estimating aerosol surface area from number and mass concentrations (referred to as “Maynard’s estimation method”) is used. Therefore, it is necessary to quantitatively investigate how much the Maynard’s estimation method depends on particle morphology and density. In this study, aerosol surface area estimated from number and mass concentration measurements was evaluated and compared with values from two reference methods: a method proposed by Lall and Friedlander for agglomerates and a mobility based method for compact nonspherical particles using well-defined polydisperse aerosols with known particle densities. Polydisperse silver aerosol particles were generated by an aerosol generation facility. Generated aerosols had a range of morphologies, count median diameters (CMD) between 25 and 50 nm, and geometric standard deviations (GSD) between 1.5 and 1.8. The surface area estimates from number and mass concentration measurements correlated well with the two reference values when gravimetric mass was used. The aerosol surface area estimates from the Maynard’s estimation method were comparable to the reference method for all particle morphologies within the surface area ratios of 3.31 and 0.19 for assumed GSDs 1.5 and 1.8, respectively, when the bulk material density of silver was used. The difference between the Maynard’s estimation method and surface area measured by the reference method for fractal-like agglomerates decreased from 79% to 23% when the measured effective particle density was used, while the difference for nearly spherical particles decreased from 30% to 24%. The results indicate that the use of
Makeev, Andrey; Clajus, Martin; Snyder, Scott; Wang, Xiaolang; Glick, Stephen J
2015-04-01
Semiconductor photon-counting detectors based on high atomic number, high density materials [cadmium zinc telluride (CZT)/cadmium telluride (CdTe)] for x-ray computed tomography (CT) provide advantages over conventional energy-integrating detectors, including reduced electronic and Swank noise, wider dynamic range, capability of spectral CT, and improved signal-to-noise ratio. Certain CT applications require high spatial resolution. In breast CT, for example, visualization of microcalcifications and assessment of tumor microvasculature after contrast enhancement require resolution on the order of [Formula: see text]. A straightforward approach to increasing spatial resolution of pixellated CZT-based radiation detectors by merely decreasing the pixel size leads to two problems: (1) fabricating circuitry with small pixels becomes costly and (2) inter-pixel charge spreading can obviate any improvement in spatial resolution. We have used computer simulations to investigate position estimation algorithms that utilize charge sharing to achieve subpixel position resolution. To study these algorithms, we model a simple detector geometry with a [Formula: see text] array of [Formula: see text] pixels, and use a conditional probability function to model charge transport in CZT. We used COMSOL finite element method software to map the distribution of charge pulses and the Monte Carlo package PENELOPE for simulating fluorescent radiation. Performance of two x-ray interaction position estimation algorithms was evaluated: the method of maximum-likelihood estimation and a fast, practical algorithm that can be implemented in a readout application-specific integrated circuit and allows for identification of a quadrant of the pixel in which the interaction occurred. Both methods demonstrate good subpixel resolution; however, their actual efficiency is limited by the presence of fluorescent [Formula: see text]-escape photons. Current experimental breast CT systems typically use
Estimation of mechanical properties of panels based on modal density and mean mobility measurements
Elie, Benjamin; Gautier, François; David, Bertrand
2013-11-01
The mechanical characteristics of wood panels used by instrument makers are related to numerous factors, including the nature of the wood or characteristic of the wood sample (direction of fibers, micro-structure nature). This leads to variations in Young's modulus, the mass density, and the damping coefficients. Existing methods for estimating these parameters are not suitable for instrument makers, mainly because of the need of expensive experimental setups, or complicated protocols, which are not adapted to a daily practice in a workshop. In this paper, a method for estimating Young's modulus, the mass density, and the modal loss factors of flat panels, requiring a few measurement points and an affordable experimental setup, is presented. It is based on the estimation of two characteristic quantities: the modal density and the mean mobility. The modal density is computed from the values of the modal frequencies estimated by the subspace method ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques), associated with the signal enumeration technique ESTER (ESTimation of ERror). This modal identification technique is proved to be robust in the low- and the mid-frequency domains, i.e. when the modal overlap factor does not exceed 1. The estimation of the modal parameters also enables the computation of the modal loss factor in the low- and the mid-frequency domains. An experimental fit with the theoretical expressions for the modal density and the mean mobility enables an accurate estimation of Young's modulus and the mass density of flat panels. A numerical and an experimental study show that the method is robust, and that it requires solely a few measurement points.
International Nuclear Information System (INIS)
Purpose: Estimates of the need for radiotherapy (RT) using different methods (criterion based benchmarking [CBB] and the Canadian [C-EBEST] and Australian [A-EBEST] epidemiologically based estimates) exist for various cancer sites. We compared these model estimates to actual RT rates for lung, breast, and prostate cancers in British Columbia (BC). Methods and Materials: All cases of lung, breast, and prostate cancers in BC from 1997 to 2004 and all patients receiving RT within 1 year (RT1Y) and within 5 years (RT5Y) of diagnosis were identified. The RT1Y and RT5Y proportions in health regions with a cancer center for the most recent year were then calculated. RT rates were compared with CBB and EBEST estimates of RT needs. Variation was assessed by time and region. Results: The RT1Y in regions with a cancer center for lung, breast, and prostate cancers were 51%, 58%, and 33% compared with 45%, 57%, and 32% for C-EBEST and 41%, 61%, and 37% for CBB models. The RT5Y rates in regions with a cancer center for lung, breast, and prostate cancers were 59%, 61%, and 40% compared with 61%, 66%, and 61% for C-EBEST and 75%, 83%, and 60% for A-EBEST models. The RT1Y rates increased for breast and prostate cancers. Conclusions: C-EBEST and CBB model estimates are closer to the actual RT rates than the A-EBEST estimates. Application of these model estimates by health care decision makers should be undertaken with an understanding of the methods used and the assumptions on which they were based.
Eilertsen, A.L.; Karssemeijer, N.; Skaane, P.; Qvigstad, E.; Sandset, P.M.
2008-01-01
OBJECTIVE: To evaluate impact of different postmenopausal hormone therapy (HT) regimens and raloxifene on mammographic breast density. DESIGN: Open, randomised, comparative clinical trial. SETTING: Women were recruited through local newspapers and posters. They were examined at the Departments of Ha
NUMERICAL EXPERIMENTS FOR THE ESTIMATION OF MEAN DENSITIES OF RANDOM SETS
Directory of Open Access Journals (Sweden)
Federico Camerlenghi
2014-05-01
Full Text Available Many real phenomena may be modelled as random closed sets in ℝd, of different Hausdorff dimensions. The problem of the estimation of pointwise mean densities of absolutely continuous, and spatially inhomogeneous, random sets with Hausdorff dimension n < d, has been the subject of extended mathematical analysis by the authors. In particular, two different kinds of estimators have been recently proposed, the first one is based on the notion of Minkowski content, the second one is a kernel-type estimator generalizing the well-known kernel density estimator for random variables. The specific aim of the present paper is to validate the theoretical results on statistical properties of those estimators by numerical experiments. We provide a set of simulations which illustrates their valuable properties via typical examples of lower dimensional random sets.
Wavelet Optimal Estimations for Density Functions under Severely Ill-Posed Noises
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Rui Li
2013-01-01
Full Text Available Motivated by Lounici and Nickl's work (2011, this paper considers the problem of estimation of a density f based on an independent and identically distributed sample Y1,…,Yn from g=f*φ. We show a wavelet optimal estimation for a density (function over Besov ball Br,qs(L and Lp risk (1≤p<∞ in the presence of severely ill-posed noises. A wavelet linear estimation is firstly presented. Then, we prove a lower bound, which shows our wavelet estimator optimal. In other words, nonlinear wavelet estimations are not needed in that case. It turns out that our results extend some theorems of Pensky and Vidakovic (1999, as well as Fan and Koo (2002.
Estimating population density and connectivity of American mink using spatial capture-recapture.
Fuller, Angela K; Sutherland, Chris S; Royle, J Andrew; Hare, Matthew P
2016-06-01
Estimating the abundance or density of populations is fundamental to the conservation and management of species, and as landscapes become more fragmented, maintaining landscape connectivity has become one of the most important challenges for biodiversity conservation. Yet these two issues have never been formally integrated together in a model that simultaneously models abundance while accounting for connectivity of a landscape. We demonstrate an application of using capture-recapture to develop a model of animal density using a least-cost path model for individual encounter probability that accounts for non-Euclidean connectivity in a highly structured network. We utilized scat detection dogs (Canis lupus familiaris) as a means of collecting non-invasive genetic samples of American mink (Neovison vison) individuals and used spatial capture-recapture models (SCR) to gain inferences about mink population density and connectivity. Density of mink was not constant across the landscape, but rather increased with increasing distance from city, town, or village centers, and mink activity was associated with water. The SCR model allowed us to estimate the density and spatial distribution of individuals across a 388 km² area. The model was used to investigate patterns of space usage and to evaluate covariate effects on encounter probabilities, including differences between sexes. This study provides an application of capture-recapture models based on ecological distance, allowing us to directly estimate landscape connectivity. This approach should be widely applicable to provide simultaneous direct estimates of density, space usage, and landscape connectivity for many species. PMID:27509753
Estimating population density and connectivity of American mink using spatial capture-recapture
Fuller, Angela K.; Sutherland, Christopher S.; Royle, Andy; Hare, Matthew P.
2016-01-01
Estimating the abundance or density of populations is fundamental to the conservation and management of species, and as landscapes become more fragmented, maintaining landscape connectivity has become one of the most important challenges for biodiversity conservation. Yet these two issues have never been formally integrated together in a model that simultaneously models abundance while accounting for connectivity of a landscape. We demonstrate an application of using capture–recapture to develop a model of animal density using a least-cost path model for individual encounter probability that accounts for non-Euclidean connectivity in a highly structured network. We utilized scat detection dogs (Canis lupus familiaris) as a means of collecting non-invasive genetic samples of American mink (Neovison vison) individuals and used spatial capture–recapture models (SCR) to gain inferences about mink population density and connectivity. Density of mink was not constant across the landscape, but rather increased with increasing distance from city, town, or village centers, and mink activity was associated with water. The SCR model allowed us to estimate the density and spatial distribution of individuals across a 388 km2 area. The model was used to investigate patterns of space usage and to evaluate covariate effects on encounter probabilities, including differences between sexes. This study provides an application of capture–recapture models based on ecological distance, allowing us to directly estimate landscape connectivity. This approach should be widely applicable to provide simultaneous direct estimates of density, space usage, and landscape connectivity for many species.
Estimation of heavy metal concentration in FBR reprocessing solvent streams by density measurement
Energy Technology Data Exchange (ETDEWEB)
Brown, M.L.; Savage, D.J.
1986-04-15
The application of density measurement to heavy metal monitoring in the solvent phase is described, including practical experience gained during three fast reactor fuel reprocessing campaigns. An experimental algorithm relating heavy metal concentration and sample density was generated from laboratory-measured density data, for uranyl nitrate dissolved in nitric acid loaded tri-butyl phosphate in odorless kerosene. Differences in odorless kerosene batch densities are mathematically interpolated, and the algorithm can be used to estimate heavy metal concentrations from the density to within +1.5 g/l. An Anton Paar calculating digital densimeter with remote cell operation was used for all density measurements, but the algorithm will give similar accuracy with any density measuring device capable of a precision of better than 0.0005 g/cm/sup 3/. For plant control purposes, the algorithm was simplified using a density referencing system, whereby the density of solvent not yet loaded with heavy metal is subtracted from the sample density. This simplified algorithm compares very favorably with empirical algorithms, derived from numerical analysis of density data and chemically measured uranium and plutonium data obtained during fuel reprocessing campaigns, particularly when differences in the acidity of the solvent are considered before and after loading with heavy metal. This simplified algorithm has been successfully used for plant control of heavy metal loaded solvent during four fast reactor fuel reprocessing campaigns.
Royle, J. Andrew; Chandler, Richard B.; Gazenski, Kimberly D.; Graves, Tabitha A.
2013-01-01
Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture–recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have used encounter probability models based on the Euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. In this paper we devise encounter probability models based on “ecological distance,” i.e., the least-cost path between traps and activity centers, which is a function of both Euclidean distance and animal movement behavior in resistant landscapes. We integrate least-cost path models into a likelihood-based estimation scheme for spatial capture–recapture models in order to estimate population density and parameters of the least-cost encounter probability model. Therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture–recapture data. Furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive SCR model.
van Uytven, Eric Peter
Screening mammography is the current standard in detecting breast cancer. However, its fundamental disadvantage is that it projects a 3D object into a 2D image. Small lesions are difficult to detect when superimposed over layers of normal tissue. Commercial Computed Tomography (CT) produces a true 3D image yet has a limited role in mammography due to relatively low resolution and contrast. With the intent of enhancing mammography and breast CT, we have developed an algorithm which can produce 3D electron density images using a single projection. Imaging an object with x rays produces a characteristic scattered photon spectrum at the detector plane. A known incident beam spectrum, beam shape, and arbitrary 3D matrix of electron density values enable a theoretical scattered photon distribution to be calculated. An iterative minimization algorithm is used to make changes to the electron density voxel matrix to reduce regular differences between the theoretical and the experimentally measured distributions. The object is characterized by the converged electron density image. This technique has been validated in simulation using data produced by the EGSnrc Monte Carlo code system. At both mammographic and CT energies, a scanning polychromatic pencil beam was used to image breast tissue phantoms containing lesion-like inhomogeneities. The resulting Monte Carlo data is processed using a Nelder-Mead iterative algorithm (MATLAB) to produce the 3D matrix of electron density values. Resulting images have confirmed the ability of the algorithm to detect various 1x1x2.5 mm3 lesions with calcification content as low as 0.5% (p<0.005) at a dose comparable to mammography.
DEFF Research Database (Denmark)
Pettersen, Paola; Raundahl, Jakob; Loog, Marco;
2008-01-01
OBJECTIVES: First, to compare the impact of nasally and orally dosed estradiol on breast density; second, to investigate the utility of computer-based automated approaches to the assessment of breast density with reference to traditional methods. METHODS: Digitized images from two 2-year, randomi......OBJECTIVES: First, to compare the impact of nasally and orally dosed estradiol on breast density; second, to investigate the utility of computer-based automated approaches to the assessment of breast density with reference to traditional methods. METHODS: Digitized images from two 2-year......, randomized, placebo-controlled trials formed the basis of the present post hoc analysis. Active treatments were 1 mg estradiol continuously combined with 0.125 mg trimegestone (oral hormone replacement therapy, HRT) or low-dose (150 or 300 microg estradiol) nasal estradiol cyclically combined with 200 mg...... micronized progesterone (nasal HRT). The effects on breast density were assessed by a radiologist, providing the BI-RADS score and the interactive threshold, and by a computer-based approach, providing the measure of stripiness and the HRT-effect specific measure of breast density. RESULTS: In the oral HRT...
Directory of Open Access Journals (Sweden)
Yousri Slaoui
2014-01-01
Full Text Available We propose an automatic selection of the bandwidth of the recursive kernel estimators of a probability density function defined by the stochastic approximation algorithm introduced by Mokkadem et al. (2009a. We showed that, using the selected bandwidth and the stepsize which minimize the MISE (mean integrated squared error of the class of the recursive estimators defined in Mokkadem et al. (2009a, the recursive estimator will be better than the nonrecursive one for small sample setting in terms of estimation error and computational costs. We corroborated these theoretical results through simulation study.
A Greedy Algorithm for Unimodal Kernel Density Estimation by Data Sharpening
Directory of Open Access Journals (Sweden)
Mark A. Wolters
2012-04-01
Full Text Available We consider the problem of nonparametric density estimation where estimates are constrained to be unimodal. Though several methods have been proposed to achieve this end, each of them has its own drawbacks and none of them have readily-available computer codes. The approach of Braun and Hall (2001, where a kernel density estimatoris modified by data sharpening, is one of the most promising options, but optimization difficulties make it hard to use in practice. This paper presents a new algorithm and MATLAB code for finding good unimodal density estimates under the Braun and Hall scheme. The algorithm uses a greedy, feasibility-preserving strategy to ensure that it always returns a unimodal solution. Compared to the incumbent method of optimization, the greedy method is easier to use, runs faster, and produces solutions of comparable quality. It can also be extended to the bivariate case.
LSTA, Rawane Samb
2010-01-01
This thesis deals with the nonparametric estimation of density f of the regression error term E of the model Y=m(X)+E, assuming its independence with the covariate X. The difficulty linked to this study is the fact that the regression error E is not observed. In a such setup, it would be unwise, for estimating f, to use a conditional approach based upon the probability distribution function of Y given X. Indeed, this approach is affected by the curse of dimensionality, so that the resulting estimator of the residual term E would have considerably a slow rate of convergence if the dimension of X is very high. Two approaches are proposed in this thesis to avoid the curse of dimensionality. The first approach uses the estimated residuals, while the second integrates a nonparametric conditional density estimator of Y given X. If proceeding so can circumvent the curse of dimensionality, a challenging issue is to evaluate the impact of the estimated residuals on the final estimator of the density f. We will also at...
[Estimation of Hunan forest carbon density based on spectral mixture analysis of MODIS data].
Yan, En-ping; Lin, Hui; Wang, Guang-xing; Chen, Zhen-xiong
2015-11-01
With the fast development of remote sensing technology, combining forest inventory sample plot data and remotely sensed images has become a widely used method to map forest carbon density. However, the existence of mixed pixels often impedes the improvement of forest carbon density mapping, especially when low spatial resolution images such as MODIS are used. In this study, MODIS images and national forest inventory sample plot data were used to conduct the study of estimation for forest carbon density. Linear spectral mixture analysis with and without constraint, and nonlinear spectral mixture analysis were compared to derive the fractions of different land use and land cover (LULC) types. Then sequential Gaussian co-simulation algorithm with and without the fraction images from spectral mixture analyses were employed to estimate forest carbon density of Hunan Province. Results showed that 1) Linear spectral mixture analysis with constraint, leading to a mean RMSE of 0.002, more accurately estimated the fractions of LULC types than linear spectral and nonlinear spectral mixture analyses; 2) Integrating spectral mixture analysis model and sequential Gaussian co-simulation algorithm increased the estimation accuracy of forest carbon density to 81.5% from 74.1%, and decreased the RMSE to 5.18 from 7.26; and 3) The mean value of forest carbon density for the province was 30.06 t · hm(-2), ranging from 0.00 to 67.35 t · hm(-2). This implied that the spectral mixture analysis provided a great potential to increase the estimation accuracy of forest carbon density on regional and global level. PMID:26915200
[Estimation of Hunan forest carbon density based on spectral mixture analysis of MODIS data].
Yan, En-ping; Lin, Hui; Wang, Guang-xing; Chen, Zhen-xiong
2015-11-01
With the fast development of remote sensing technology, combining forest inventory sample plot data and remotely sensed images has become a widely used method to map forest carbon density. However, the existence of mixed pixels often impedes the improvement of forest carbon density mapping, especially when low spatial resolution images such as MODIS are used. In this study, MODIS images and national forest inventory sample plot data were used to conduct the study of estimation for forest carbon density. Linear spectral mixture analysis with and without constraint, and nonlinear spectral mixture analysis were compared to derive the fractions of different land use and land cover (LULC) types. Then sequential Gaussian co-simulation algorithm with and without the fraction images from spectral mixture analyses were employed to estimate forest carbon density of Hunan Province. Results showed that 1) Linear spectral mixture analysis with constraint, leading to a mean RMSE of 0.002, more accurately estimated the fractions of LULC types than linear spectral and nonlinear spectral mixture analyses; 2) Integrating spectral mixture analysis model and sequential Gaussian co-simulation algorithm increased the estimation accuracy of forest carbon density to 81.5% from 74.1%, and decreased the RMSE to 5.18 from 7.26; and 3) The mean value of forest carbon density for the province was 30.06 t · hm(-2), ranging from 0.00 to 67.35 t · hm(-2). This implied that the spectral mixture analysis provided a great potential to increase the estimation accuracy of forest carbon density on regional and global level.
Fast and accurate probability density estimation in large high dimensional astronomical datasets
Gupta, Pramod; Connolly, Andrew J.; Gardner, Jeffrey P.
2015-01-01
Astronomical surveys will generate measurements of hundreds of attributes (e.g. color, size, shape) on hundreds of millions of sources. Analyzing these large, high dimensional data sets will require efficient algorithms for data analysis. An example of this is probability density estimation that is at the heart of many classification problems such as the separation of stars and quasars based on their colors. Popular density estimation techniques use binning or kernel density estimation. Kernel density estimation has a small memory footprint but often requires large computational resources. Binning has small computational requirements but usually binning is implemented with multi-dimensional arrays which leads to memory requirements which scale exponentially with the number of dimensions. Hence both techniques do not scale well to large data sets in high dimensions. We present an alternative approach of binning implemented with hash tables (BASH tables). This approach uses the sparseness of data in the high dimensional space to ensure that the memory requirements are small. However hashing requires some extra computation so a priori it is not clear if the reduction in memory requirements will lead to increased computational requirements. Through an implementation of BASH tables in C++ we show that the additional computational requirements of hashing are negligible. Hence this approach has small memory and computational requirements. We apply our density estimation technique to photometric selection of quasars using non-parametric Bayesian classification and show that the accuracy of the classification is same as the accuracy of earlier approaches. Since the BASH table approach is one to three orders of magnitude faster than the earlier approaches it may be useful in various other applications of density estimation in astrostatistics.
An automatic iris occlusion estimation method based on high-dimensional density estimation.
Li, Yung-Hui; Savvides, Marios
2013-04-01
Iris masks play an important role in iris recognition. They indicate which part of the iris texture map is useful and which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. The accuracy of the iris mask is extremely important. The performance of the iris recognition system will decrease dramatically when the iris mask is inaccurate, even when the best recognition algorithm is used. Traditionally, people used the rule-based algorithms to estimate iris masks from iris images. However, the accuracy of the iris masks generated this way is questionable. In this work, we propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Experimental results show that the masks generated by the proposed algorithm increase the iris recognition rate on both ICE2 and UBIRIS dataset, verifying the effectiveness and importance of our proposed method for iris occlusion estimation. PMID:22868651
Bulk density estimation using a 3-dimensional image acquisition and analysis system
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Heyduk Adam
2016-01-01
Full Text Available The paper presents a concept of dynamic bulk density estimation of a particulate matter stream using a 3-d image analysis system and a conveyor belt scale. A method of image acquisition should be adjusted to the type of scale. The paper presents some laboratory results of static bulk density measurements using the MS Kinect time-of-flight camera and OpenCV/Matlab software. Measurements were made for several different size classes.
Bignami, C.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia; Ruch, J.; Dipartimento di Scienze, Università Roma Tre, Largo S.L. Murialdo 1, Rome IT-00146, Italy; Chini, M.; Centre de Recherche Public — Gabriel Lippmann, Luxembourg; Neri, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Buongiorno, M. F.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia; Sri, H.; Center of Volcanology and Geological Hazard Mitigation, Bandung, Indonesia; Dewi Sri, S.; Center of Volcanology and Geological Hazard Mitigation, Bandung, Indonesia; Surono, S.; Center of Volcanology and Geological Hazard Mitigation, Bandung, Indonesia
2013-01-01
Pyroclastic density current deposits remobilized by water during periods of heavy rainfall trigger lahars (volcanic mudflows) that affect inhabited areas at considerable distance from volcanoes, even years after an eruption.Here we present an innovative approach to detect and estimate the thickness and volume of pyroclastic density current (PDC) deposits as well as erosional versus depositional environments. We use SAR interferometry to compare an airborne digital surface model (D...
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Aggarwal Himanshu
2007-01-01
Full Text Available The change in expression of apoptotic markers (Bcl-2 and Bax proteins brought about by various chemotherapeutic regimens is being used for its predictive value for assessing response to neoadjuvant chemotherapy (NACT in locally advanced breast carcinoma (LABC. Aims: (1 Estimation of Bcl 2 expression in LABC, (2 Any change in Bcl 2 expression following chemotherapy in LABC, (3 Any relation of Bcl 2 estimation to changes in size of tumor, nodal status, age, and menopausal status. Settings and Design: This was a prospective study of 120 cases of LABC. Materials and Methods: All cases were subjected to biopsy and the tissue was evaluated immunohistochemically for apoptotic marker Bcl-2 family protein. Three cycles of NACT were given at three-weekly intervals. Modified radical mastectomy was performed and the specimens were re-evaluated for any change in the Bcl-2 family protein. The clinical response and immunohistochemical response were correlated and compared. Statistical Analysis: Coefficient of correlation was calculated by Pearson correlation coefficient (P-value. Results: Clinical response, as measured by reduction in the tumor size, was observed in 81 (67.5% patients while immunohistochemical response was observed in 67 (55.8% patients. Correlation between immunohistochemical and clinical response was found to be statistically significant (P = 0.02. Nodal response was seen in 72 (60% patients. There were no patients in the N o group; 22 (53.7% of the N 1 patients were down-staged to N o , while 19 (46.3% remained N 1 . In patients with N 2 disease, 11 (13.9% were down-staged to N o status, 39 (49.4% were down-staged to N 1 status, and 29 (36.7% did not show any response. Immunohistochemical response was observed in 67 (55.8% patients. Correlation between immunohistochemical and nodal responses was also found to be statistically significant (P = 0.03. Conclusions: This significant positive correlation between clinical and immunohistochemical
Susaki, J.
2016-06-01
In this paper, we analyze probability density functions (PDFs) of scatterings derived from fully polarimetric synthetic aperture radar (SAR) images for improving the accuracies of estimated urban density. We have reported a method for estimating urban density that uses an index Tv+c obtained by normalizing the sum of volume and helix scatterings Pv+c. Validation results showed that estimated urban densities have a high correlation with building-to-land ratios (Kajimoto and Susaki, 2013b; Susaki et al., 2014). While the method is found to be effective for estimating urban density, it is not clear why Tv+c is more effective than indices derived from other scatterings, such as surface or double-bounce scatterings, observed in urban areas. In this research, we focus on PDFs of scatterings derived from fully polarimetric SAR images in terms of scattering normalization. First, we introduce a theoretical PDF that assumes that image pixels have scatterers showing random backscattering. We then generate PDFs of scatterings derived from observations of concrete blocks with different orientation angles, and from a satellite-based fully polarimetric SAR image. The analysis of the PDFs and the derived statistics reveals that the curves of the PDFs of Pv+c are the most similar to the normal distribution among all the scatterings derived from fully polarimetric SAR images. It was found that Tv+c works most effectively because of its similarity to the normal distribution.
International Nuclear Information System (INIS)
Ultrasound is not an efficacious screening modality to detect early-stage breast malignancy in a clinically unremarkable population of women. Computed body tomography is similarly not practical for screening because of slice thickness and partial volume averaging, a higher radiation dose than modern mammography, and the lack of availability of such units for such a high throughput requirement. Nevertheless, these two imaging modalities can be very useful in management to guide the least invasive and efficacious treatment of the patient. X-ray mammography remains the principal imaging modality in the search for breast malignancy, but ultrasound is the single most important second study in the diagnostic evaluation of the breast. The combined use of these techniques and the ability to perform guided aspiration and localization procedures can result in a reduction in the surgical removal of benign cysts and reduction in the amount of tissue volume required if excision becomes necessary
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Koo, Hye Ryoung, E-mail: huilings@hanmail.net [Department of Radiology, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110-744 (Korea, Republic of); Moon, Woo Kyung, E-mail: moonwk@snu.ac.kr [Department of Radiology, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110-744 (Korea, Republic of); Chun, In Kook, E-mail: inkook.chun@gmail.com [Department of Nuclear Medicine, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110-744 (Korea, Republic of); Eo, Jae Seon, E-mail: jaeseon76@gmail.com [Department of Nuclear Medicine, Korea University Guro Hospital, 148 Gurodongro, Guro-gu, Seoul 152-703 (Korea, Republic of); Jeyanth, Joseph Xavier, E-mail: jeyanth7@snu.ac.kr [Department of Radiology, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110-744 (Korea, Republic of); Chang, Jung Min, E-mail: imchangjm@gmail.com [Department of Radiology, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110-744 (Korea, Republic of); Cho, Nariya, E-mail: river7774@gmail.com [Department of Radiology, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110-744 (Korea, Republic of); Kang, Keon Wook, E-mail: kangkw@snu.ac.kr [Department of Nuclear Medicine, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110-744 (Korea, Republic of)
2013-10-01
We aimed to determine whether background {sup 18}F-FDG uptake in positron emission mammography (PEM) was related to mammographic density or background parenchymal enhancement in breast MRI. Methods: We studied a total of 52 patients (mean age, 50.9 years, 26 premenopausal, 26 postmenopausal) with newly diagnosed breast cancer who underwent {sup 18}F-FDG PEM (positron emission mammography), conventional mammography and breast MRI. The background mean {sup 18}F-FDG uptake value on PEM was obtained by drawing a user-defined region of interest (ROI) in a normal area of the contralateral breast. We reviewed the mammography retrospectively for overall breast density of contralateral breast according to the four-point scale (grade 1–4) of the Breast Imaging Reporting and Data System (BI-RADS) classification. The background parenchymal enhancement of breast MRI was classified as minimal, mild, moderate, or marked. All imaging findings were interpreted by two readers in consensus without knowledge of image findings of other modalities. Results: Multiple linear regression analysis revealed a significant correlation between background {sup 18}F-FDG uptake on PEM and mammographic density after adjustment for age and menopausal status (P < 0.01), but not between background {sup 18}F-FDG uptake on PEM and background parenchymal enhancement on MRI. Conclusion: Background {sup 18}F-FDG uptake on PEM significantly increases as mammographic density increases. Background parenchymal enhancement in breast MRI was not an independent predictor of the background {sup 18}F-FDG uptake on PEM unlike mammographic density.
Estimating the amount and distribution of radon flux density from the soil surface in China.
Zhuo, Weihai; Guo, Qiuju; Chen, Bo; Cheng, Guan
2008-07-01
Based on an idealized model, both the annual and the seasonal radon ((222)Rn) flux densities from the soil surface at 1099 sites in China were estimated by linking a database of soil (226)Ra content and a global ecosystems database. Digital maps of the (222)Rn flux density in China were constructed in a spatial resolution of 25 km x 25 km by interpolation among the estimated data. An area-weighted annual average (222)Rn flux density from the soil surface across China was estimated to be 29.7+/-9.4 mBq m(-2)s(-1). Both regional and seasonal variations in the (222)Rn flux densities are significant in China. Annual average flux densities in the southeastern and northwestern China are generally higher than those in other regions of China, because of high soil (226)Ra content in the southeastern area and high soil aridity in the northwestern one. The seasonal average flux density is generally higher in summer/spring than winter, since relatively higher soil temperature and lower soil water saturation in summer/spring than other seasons are common in China.
PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA
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Henrique Seixas Barros
2015-04-01
Full Text Available Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2 and Akaike information criterion (AIC and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region.
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Eléanor Brassine
Full Text Available Camera trapping studies have become increasingly popular to produce population estimates of individually recognisable mammals. Yet, monitoring techniques for rare species which occur at extremely low densities are lacking. Additionally, species which have unpredictable movements may make obtaining reliable population estimates challenging due to low detectability. Our study explores the effectiveness of intensive camera trapping for estimating cheetah (Acinonyx jubatus numbers. Using both a more traditional, systematic grid approach and pre-determined, targeted sites for camera placement, the cheetah population of the Northern Tuli Game Reserve, Botswana was sampled between December 2012 and October 2013. Placement of cameras in a regular grid pattern yielded very few (n = 9 cheetah images and these were insufficient to estimate cheetah density. However, pre-selected cheetah scent-marking posts provided 53 images of seven adult cheetahs (0.61 ± 0.18 cheetahs/100 km². While increasing the length of the camera trapping survey from 90 to 130 days increased the total number of cheetah images obtained (from 53 to 200, no new individuals were recorded and the estimated population density remained stable. Thus, our study demonstrates that targeted camera placement (irrespective of survey duration is necessary for reliably assessing cheetah densities where populations are naturally very low or dominated by transient individuals. Significantly our approach can easily be applied to other rare predator species.
Self-Consistent Density Estimation in the Presence of Errors-in-Variables
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Junhua Zhang
2014-01-01
Full Text Available This paper considers the estimation of the common probability density of independent and identically distributed variables observed with additive measurement errors. The self-consistent estimator of the density function is constructed when the error distribution is known, and a modification of the self-consistent estimation is proposed when the error distribution is unknown. The consistency properties of the proposed estimators and the upper bounds of the mean square error and mean integrated square error are investigated under some suitable conditions. Simulation studies are carried out to assess the performance of our proposed method and compare with the usual deconvolution kernel method. Two real datasets are analyzed for further illustration.
Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study
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Samir Saoudi
2008-07-01
Full Text Available The plug-in method enables optimization of the bandwidth of the kernel density estimator in order to estimate probability density functions (pdfs. Here, a faster procedure than that of the common plug-in method is proposed. The mean integrated square error (MISE depends directly upon J(f which is linked to the second-order derivative of the pdf. As we intend to introduce an analytical approximation of J(f, the pdf is estimated only once, at the end of iterations. These two kinds of algorithm are tested on different random variables having distributions known for their difficult estimation. Finally, they are applied to genetic data in order to provide a better characterisation in the mean of neutrality of Tunisian Berber populations.
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Carlos B. Kasper
2015-09-01
Full Text Available ABSTRACT Using camera traps and capture/recapture analyses we recorded the presence and abundance of cat species at Turvo State Park, in southern Brazil. Ocelot [Leopardus pardalis (Linnaeus, 1758] population density was estimated for two areas of the park, with differing management profiles. Density estimates varied from 0.14 to 0.26 indiv. km2. Another five cat species were recorded at very low frequencies, precluding more accurate analyses. We estimate 24 to 45 ocelots occur in the reserve, which is probably too small for long-term maintenance of the population, if isolated. However, if habitat integrity and connectivity between the Park and the Green Corridor of Misiones is maintained, an estimated ocelot population of 1,680 individuals should have long-term viability.
Scent Lure Effect on Camera-Trap Based Leopard Density Estimates.
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Alexander Richard Braczkowski
Full Text Available Density estimates for large carnivores derived from camera surveys often have wide confidence intervals due to low detection rates. Such estimates are of limited value to authorities, which require precise population estimates to inform conservation strategies. Using lures can potentially increase detection, improving the precision of estimates. However, by altering the spatio-temporal patterning of individuals across the camera array, lures may violate closure, a fundamental assumption of capture-recapture. Here, we test the effect of scent lures on the precision and veracity of density estimates derived from camera-trap surveys of a protected African leopard population. We undertook two surveys (a 'control' and 'treatment' survey on Phinda Game Reserve, South Africa. Survey design remained consistent except a scent lure was applied at camera-trap stations during the treatment survey. Lures did not affect the maximum movement distances (p = 0.96 or temporal activity of female (p = 0.12 or male leopards (p = 0.79, and the assumption of geographic closure was met for both surveys (p >0.05. The numbers of photographic captures were also similar for control and treatment surveys (p = 0.90. Accordingly, density estimates were comparable between surveys (although estimates derived using non-spatial methods (7.28-9.28 leopards/100km2 were considerably higher than estimates from spatially-explicit methods (3.40-3.65 leopards/100km2. The precision of estimates from the control and treatment surveys, were also comparable and this applied to both non-spatial and spatial methods of estimation. Our findings suggest that at least in the context of leopard research in productive habitats, the use of lures is not warranted.
Noninvasive assessment of breast cancer risk using time-resolved diffuse optical spectroscopy
Taroni, Paola; Pifferi, Antonio; Quarto, Giovanna; Spinelli, Lorenzo; Torricelli, Alessandro; Abbate, Francesca; Villa, Anna; Balestreri, Nicola; Menna, Simona; Cassano, Enrico; Cubeddu, Rinaldo
2010-11-01
Breast density is a recognized strong and independent risk factor for breast cancer. We propose the use of time-resolved transmittance spectroscopy to estimate breast tissue density and potentially provide even more direct information on breast cancer risk. Time-resolved optical mammography at seven wavelengths (635 to 1060 nm) is performed on 49 subjects. Average information on breast tissue of each subject is obtained on oxy- and deoxyhemoglobin, water, lipids, and collagen content, as well as scattering amplitude and power. All parameters, except for blood volume and oxygenation, correlate with mammographic breast density, even if not to the same extent. A synthetic optical index proves to be quite effective in separating different breast density categories. Finally, the estimate of collagen content as a more direct means for the assessment of breast cancer risk is discussed.
Estimation of current density distribution of PAFC by analysis of cell exhaust gas
Energy Technology Data Exchange (ETDEWEB)
Kato, S.; Seya, A. [Fuji Electric Co., Ltd., Ichihara-shi (Japan); Asano, A. [Fuji Electric Corporate, Ltd., Yokosuka-shi (Japan)
1996-12-31
To estimate distributions of Current densities, voltages, gas concentrations, etc., in phosphoric acid fuel cell (PAFC) stacks, is very important for getting fuel cells with higher quality. In this work, we leave developed a numerical simulation tool to map out the distribution in a PAFC stack. And especially to Study Current density distribution in the reaction area of the cell, we analyzed gas composition in several positions inside a gas outlet manifold of the PAFC stack. Comparing these measured data with calculated data, the current density distribution in a cell plane calculated by the simulation, was certified.
Variational estimation of the drift for stochastic differential equations from the empirical density
Batz, Philipp; Ruttor, Andreas; Opper, Manfred
2016-08-01
We present a method for the nonparametric estimation of the drift function of certain types of stochastic differential equations from the empirical density. It is based on a variational formulation of the Fokker–Planck equation. The minimization of an empirical estimate of the variational functional using kernel based regularization can be performed in closed form. We demonstrate the performance of the method on second order, Langevin-type equations and show how the method can be generalized to other noise models.
Variational estimation of the drift for stochastic differential equations from the empirical density
Batz, Philipp; Opper, Manfred
2016-01-01
We present a method for the nonparametric estimation of the drift function of certain types of stochastic differential equations from the empirical density. It is based on a variational formulation of the Fokker-Planck equation. The minimization of an empirical estimate of the variational functional using kernel based regularization can be performed in closed form. We demonstrate the performance of the method on second order, Langevin-type equations and show how the method can be generalized to other noise models.
Gupta, Cherry; Cobre, Juliana; Polpo, Adriano; Sinha, Debjayoti
2016-09-01
Existing cure-rate survival models are generally not convenient for modeling and estimating the survival quantiles of a patient with specified covariate values. This paper proposes a novel class of cure-rate model, the transform-both-sides cure-rate model (TBSCRM), that can be used to make inferences about both the cure-rate and the survival quantiles. We develop the Bayesian inference about the covariate effects on the cure-rate as well as on the survival quantiles via Markov Chain Monte Carlo (MCMC) tools. We also show that the TBSCRM-based Bayesian method outperforms existing cure-rate models based methods in our simulation studies and in application to the breast cancer survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database.
Identification of the monitoring point density needed to reliably estimate contaminant mass fluxes
Liedl, R.; Liu, S.; Fraser, M.; Barker, J.
2005-12-01
Plume monitoring frequently relies on the evaluation of point-scale measurements of concentration at observation wells which are located at control planes or `fences' perpendicular to groundwater flow. Depth-specific concentration values are used to estimate the total mass flux of individual contaminants through the fence. Results of this approach, which is based on spatial interpolation, obviously depend on the density of the measurement points. Our contribution relates the accurracy of mass flux estimation to the point density and, in particular, allows to identify a minimum point density needed to achieve a specified accurracy. In order to establish this relationship, concentration data from fences installed in the coal tar creosote plume at the Borden site are used. These fences are characterized by a rather high density of about 7 points/m2 and it is reasonable to assume that the true mass flux is obtained with this point density. This mass flux is then compared with results for less dense grids down to about 0.1points/m2. Mass flux estimates obtained for this range of point densities are analyzed by the moving window method in order to reduce purely random fluctuations. For each position of the moving window the mass flux is estimated and the coefficient of variation (CV) is calculated to quantify variablity of the results. Thus, the CV provides a relative measure of accurracy in the estimated fluxes. By applying this approach to the Borden naphthalene plume at different times, it is found that the point density changes from sufficient to insufficient due to the temporally decreasing mass flux. By comparing the results of naphthalene and phenol at the same fence and at the same time, we can see that the same grid density might be sufficient for one compound but not for another. If a rather strict CV criterion of 5% is used, a grid of 7 points/m2 is shown to allow for reliable estimates of the true mass fluxes only in the beginning of plume development when
Estimation of the global 222Rn flux density from the earth's surface
International Nuclear Information System (INIS)
Global 222Rn flux density distributions with a spatial resolution of 1deg in latitude and longitude were estimated for each month in the period of 1979-2007 using an 222Rn exhalation model based on a porous media diffusion theory. This exhalation model calculated the 222Rn flux density as a function of the 226Ra content, soil water saturation, and soil temperature. The influence of the topographical ruggedness of the ground surface on the flux density was also considered in the model. The global average of the flux density during the 29-year period was estimated to be 21.3 mBq m-2 s-1. The annual variation in the globally averaged flux density showed a slight but steady increase since 1999. The seasonal variation in the globally averaged flux density was estimated to range from a minimum of 18.8 mBq m-2 s-1 in March to a maximum of 23.6 mBq m-2 s-1 in September. The regional averages for the annual flux density varied from 8.3 mBq m-2 s-1 for the northern part of North America to 33.5 mBq m-2 s-1 for Australia. The latitudinal distribution of zonally averaged flux density was found to be in good agreement with the latitudinal decline at high latitudes in the Northern Hemisphere. The spatial and temporal variations were caused mainly by variations in the soil water content and soil 226Ra content, but to a lesser extent by the soil temperature. The topographical ruggedness was found to increase the flux density by only 2% globally but by more than 10% in mountainous regions. Atmospheric transport simulation using the estimated flux density as input data showed that the simulated atmospheric 222Rn concentration was substantially lower than that measured at Hachijo Island, implying that the flux density of the present study with topographical ruggedness is still underestimated in the East Asian region. (author)
Estimating abundance and density of Amur tigers along the Sino-Russian border.
Xiao, Wenhong; Feng, Limin; Mou, Pu; Miquelle, Dale G; Hebblewhite, Mark; Goldberg, Joshua F; Robinson, Hugh S; Zhao, Xiaodan; Zhou, Bo; Wang, Tianming; Ge, Jianping
2016-07-01
As an apex predator the Amur tiger (Panthera tigris altaica) could play a pivotal role in maintaining the integrity of forest ecosystems in Northeast Asia. Due to habitat loss and harvest over the past century, tigers rapidly declined in China and are now restricted to the Russian Far East and bordering habitat in nearby China. To facilitate restoration of the tiger in its historical range, reliable estimates of population size are essential to assess effectiveness of conservation interventions. Here we used camera trap data collected in Hunchun National Nature Reserve from April to June 2013 and 2014 to estimate tiger density and abundance using both maximum likelihood and Bayesian spatially explicit capture-recapture (SECR) methods. A minimum of 8 individuals were detected in both sample periods and the documentation of marking behavior and reproduction suggests the presence of a resident population. Using Bayesian SECR modeling within the 11 400 km(2) state space, density estimates were 0.33 and 0.40 individuals/100 km(2) in 2013 and 2014, respectively, corresponding to an estimated abundance of 38 and 45 animals for this transboundary Sino-Russian population. In a maximum likelihood framework, we estimated densities of 0.30 and 0.24 individuals/100 km(2) corresponding to abundances of 34 and 27, in 2013 and 2014, respectively. These density estimates are comparable to other published estimates for resident Amur tiger populations in the Russian Far East. This study reveals promising signs of tiger recovery in Northeast China, and demonstrates the importance of connectivity between the Russian and Chinese populations for recovering tigers in Northeast China.
Goldenshluger, Alexander
2010-01-01
We address the problem of density estimation with $\\bL_p$--loss by selection of kernel estimators. We develop a selection procedure and derive corresponiding $\\bL_p$--risk oracle inequalities. It is shown that the proposed selection rule leads to the minimax estimator that is adaptive over a scale of the anisotropic Nikol'ski classes. The main technical tools used in our derivations are uniform bounds on the $\\bL_p$--norms of empirical processes developed recently in Goldenshluger and Lepski~(2010).
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
Liu, Song; Collier, Nigel; Sugiyama, Masashi
2012-01-01
The objective of change-point detection is to discover abrupt property changes lying behind time series data. In this paper, we present a novel statistical change-point detection algorithm that is based on non-parametric divergence estimation between two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on artificial and real-world datasets including human-activity sensing, speeches, and Twitter archives, we demonstrate the usefulness of the proposed method.
Mohd Salleh, M. R.; Rahman, M. Z. Abdul; Abu Bakar, M. A.; Rasib, A. W.; Omar, H.
2016-09-01
This paper presents a framework to estimate aerodynamic roughness over specific height (zo/H) and zero plane displacement (d/H) over various landscapes in Kelantan State using airborne LiDAR data. The study begins with the filtering of airborne LiDAR, which produced ground and non-ground points. The ground points were used to generate digital terrain model (DTM) while the non-ground points were used for digital surface model (DSM) generation. Canopy height model (CHM) was generated by subtracting DTM from DSM. Individual trees in the study area were delineated by applying the Inverse Watershed segmentation method on the CHM. Forest structural parameters including tree height, height to crown base (HCB) and diameter at breast height (DBH) were estimated using existing allometric equations. The airborne LiDAR data was divided into smaller areas, which correspond to the size of the zo/H and d/H maps i.e. 50 m and 100 m. For each area individual tree were reconstructed based on the tree properties, which accounts overlapping between crowns and trunks. The individual tree models were used to estimate individual tree frontal area and the total frontal area over a specific ground surface. Finally, three roughness models were used to estimate zo/H and d/H for different wind directions, which were assumed from North/South and East/West directions. The results were shows good agreements with previous studies that based on the wind tunnel experiments.
Couto, E; Hemminki, K
2007-01-01
Using the Swedish Family-Cancer Database, the increased risk of breast cancer in women with relatives with the disease did not vary with paternal/maternal lineage. Familial breast cancer heritable component was 73% and the environmental proportion 27%. Familial aggregation of breast cancer in women below age 51 years is mainly due to heritable causes.
ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
Directory of Open Access Journals (Sweden)
Tarn Duong
2007-09-01
Full Text Available Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.
MODERATE DEVIATIONS AND LARGE DEVIATIONS FOR A TEST OF SYMMETRY BASED ON KERNEL DENSITY ESTIMATOR
Institute of Scientific and Technical Information of China (English)
He Xiaoxia; Gao Fuqing
2008-01-01
Let fn be a non-parametric kernel density estimator based on a kernel function K. And a sequence of independent and identically distributed random variables taking values in R. The goal of this article is to prove moderate deviations and large deviations for the statistic sup x∈R|fn(x) - fn(-X)|.
Gao, Nuo; Zhu, S A; He, Bin
2005-06-01
We have developed a new algorithm for magnetic resonance electrical impedance tomography (MREIT), which uses only one component of the magnetic flux density to reconstruct the electrical conductivity distribution within the body. The radial basis function (RBF) network and simplex method are used in the present approach to estimate the conductivity distribution by minimizing the errors between the 'measured' and model-predicted magnetic flux densities. Computer simulations were conducted in a realistic-geometry head model to test the feasibility of the proposed approach. Single-variable and three-variable simulations were performed to estimate the brain-skull conductivity ratio and the conductivity values of the brain, skull and scalp layers. When SNR = 15 for magnetic flux density measurements with the target skull-to-brain conductivity ratio being 1/15, the relative error (RE) between the target and estimated conductivity was 0.0737 +/- 0.0746 in the single-variable simulations. In the three-variable simulations, the RE was 0.1676 +/- 0.0317. Effects of electrode position uncertainty were also assessed by computer simulations. The present promising results suggest the feasibility of estimating important conductivity values within the head from noninvasive magnetic flux density measurements.
ASYMPTOTIC NORMALITY OF KERNEL ESTIMATES OF A DENSITY FUNCTION UNDER ASSOCIATION DEPENDENCE
Institute of Scientific and Technical Information of China (English)
林正炎
2003-01-01
Let {Xn,n> _ 1} be a strictly stationary sequence of random variables,which are either associated or negatively associated,f(·)be their common density.In this paper,the author shows a central limit theorem for a kernel estimate of f(·)under certain regular conditions.
DEFF Research Database (Denmark)
Röttger, Richard; Kalaghatgi, Prabhav; Sun, Peng;
2013-01-01
: all clustering tools need a density parameter that adjusts the number and size of the clusters. This parameter is crucial but hard to estimate without gold standard data at hand. Developing a gold standard, however, is a difficult and time consuming task. Having a reliable method for detecting...
Estimating Densities of the Pest Halotydeus destructor (Acari: Penthaleidae) in Canola.
Arthur, Aston L; Hoffmann, Ary A; Umina, Paul A
2014-12-01
Development of sampling techniques to effectively estimate invertebrate densities in the field is essential for effective implementation of pest control programs, particularly when making informed spray decisions around economic thresholds. In this article, we investigated the influence of several factors to devise a sampling strategy to estimate Halotydeus destructor Tucker densities in a canola paddock. Direct visual counts were found to be the most suitable approach for estimating mite numbers, with higher densities detected than the vacuum sampling method. Visual assessments were impacted by the operator, sampling date, and time of day. However, with the exception of operator (more experienced operator detected higher numbers of mites), no obvious trends were detected. No patterns were found between H. destructor numbers and ambient temperature, relative humidity, wind speed, cloud cover, or soil surface conditions, indicating that these factors may not be of high importance when sampling mites during autumn and winter months. We show further support for an aggregated distribution of H. destructor within paddocks, indicating that a stratified random sampling program is likely to be most appropriate. Together, these findings provide important guidelines for Australian growers around the ability to effectively and accurately estimate H. destructor densities. PMID:26470087
Compound kernel estimates for the transition probability density of a L\\'evy process in $\\rn$
Knopova, V.
2013-01-01
We construct in the small-time setting the upper and lower estimates for the transition probability density of a L\\'evy process in $\\rn$. Our approach relies on the complex analysis technique and the asymptotic analysis of the inverse Fourier transform of the characteristic function of the respective process.
Estimation of nighttime dip-equatorial E-region current density using measurements and models
Pandey, Kuldeep; Sekar, R.; Anandarao, B. G.; Gupta, S. P.; Chakrabarty, D.
2016-08-01
The existence of the possible ionospheric current during nighttime over low-equatorial latitudes is one of the unresolved issues in ionospheric physics and geomagnetism. A detailed investigation is carried out to estimate the same over Indian longitudes using in situ measurements from Thumba (8.5 ° N, 76.9 ° E), empirical plasma drift model (Fejer et al., 2008) and equatorial electrojet model developed by Anandarao (1976). This investigation reveals that the nighttime E-region current densities vary from ∼0.3 to ∼0.7 A/km2 during pre-midnight to early morning hours on geomagnetically quiet conditions. The nighttime current densities over the dip equator are estimated using three different methods (discussed in methodology section) and are found to be consistent with one another within the uncertainty limits. Altitude structures in the E-region current densities are also noticed which are shown to be associated with altitudinal structures in the electron densities. The horizontal component of the magnetic field induced by these nighttime ionospheric currents is estimated to vary between ∼2 and ∼6 nT during geomagnetically quiet periods. This investigation confirms the existence of nighttime ionospheric current and opens up a possibility of estimating base line value for geomagnetic field fluctuations as observed by ground-based magnetometer.
Topological Pressure and Coding Sequence Density Estimation in the Human Genome
Koslicki, David
2011-01-01
Inspired by concepts from ergodic theory, we give new insight into coding sequence (CDS) density estimation for the human genome. Our approach is based on the introduction and study of topological pressure: a numerical quantity assigned to any finite sequence based on an appropriate notion of `weighted information content'. For human DNA sequences, each codon is assigned a suitable weight, and using a window size of approximately 60,000bp, we obtain a very strong positive correlation between CDS density and topological pressure. The weights are selected by an optimization procedure, and can be interpreted as quantitative data on the relative importance of different codons for the density estimation of coding sequences. This gives new insight into codon usage bias which is an important subject where long standing questions remain open. Inspired again by ergodic theory, we use the weightings on the codons to define a probability measure on finite sequences. We demonstrate that this measure is effective in disti...
Robel, G.L.; Fisher, W.L.
1999-01-01
Production of and consumption by hatchery-reared tingerling (age-0) smallmouth bass Micropterus dolomieu at various simulated stocking densities were estimated with a bioenergetics model. Fish growth rates and pond water temperatures during the 1996 growing season at two hatcheries in Oklahoma were used in the model. Fish growth and simulated consumption and production differed greatly between the two hatcheries, probably because of differences in pond fertilization and mortality rates. Our results suggest that appropriate stocking density depends largely on prey availability as affected by pond fertilization and on fingerling mortality rates. The bioenergetics model provided a useful tool for estimating production at various stocking density rates. However, verification of physiological parameters for age-0 fish of hatchery-reared species is needed.
Estimation of high-resolution dust column density maps: Empirical model fits
Juvela, M
2013-01-01
Sub-millimetre dust emission is an important tracer of density N of dense interstellar clouds. One has to combine surface brightness information at different spatial resolutions, and specific methods are needed to derive N at a resolution higher than the lowest resolution of the observations. Some methods have been discussed in the literature, including a method (in the following, method B) that constructs the N estimate in stages, where the smallest spatial scales being derived only use the shortest wavelength maps. We propose simple model fitting as a flexible way to estimate high-resolution column density maps. Our goal is to evaluate the accuracy of this procedure and to determine whether it is a viable alternative for making these maps. The new method consists of model maps of column density (or intensity at a reference wavelength) and colour temperature. The model is fitted using Markov chain Monte Carlo (MCMC) methods, comparing model predictions with observations at their native resolution. We analyse...
Codis, Sandrine; Bernardeau, Francis; Pichon, Christophe
2016-08-01
In order to quantify the error budget in the measured probability distribution functions of cell densities, the two-point statistics of cosmic densities in concentric spheres is investigated. Bias functions are introduced as the ratio of their two-point correlation function to the two-point correlation of the underlying dark matter distribution. They describe how cell densities are spatially correlated. They are computed here via the so-called large deviation principle in the quasi-linear regime. Their large-separation limit is presented and successfully compared to simulations for density and density slopes: this regime is shown to be rapidly reached allowing to get sub-percent precision for a wide range of densities and variances. The corresponding asymptotic limit provides an estimate of the cosmic variance of standard concentric cell statistics applied to finite surveys. More generally, no assumption on the separation is required for some specific moments of the two-point statistics, for instance when predicting the generating function of cumulants containing any powers of concentric densities in one location and one power of density at some arbitrary distance from the rest. This exact `one external leg' cumulant generating function is used in particular to probe the rate of convergence of the large-separation approximation.
Local linear density estimation for filtered survival data, with bias correction
DEFF Research Database (Denmark)
Nielsen, Jens Perch; Tanggaard, Carsten; Jones, M.C.
2009-01-01
it comes to exposure robustness, and a simple alternative weighting is to be preferred. Indeed, this weighting has, effectively, to be well chosen in a 'pilot' estimator of the survival function as well as in the main estimator itself. We also investigate multiplicative and additive bias-correction methods......A class of local linear kernel density estimators based on weighted least-squares kernel estimation is considered within the framework of Aalen's multiplicative intensity model. This model includes the filtered data model that, in turn, allows for truncation and/or censoring in addition...... to accommodating unusual patterns of exposure as well as occurrence. It is shown that the local linear estimators corresponding to all different weightings have the same pointwise asymptotic properties. However, the weighting previously used in the literature in the i.i.d. case is seen to be far from optimal when...
Local Linear Density Estimation for Filtered Survival Data with Bias Correction
DEFF Research Database (Denmark)
Tanggaard, Carsten; Nielsen, Jens Perch; Jones, M.C.
it comes to exposure robustness, and a simple alternative weighting is to be preferred. Indeed, this weighting has, effectively, to be well chosen in a ‘pilot' estimator of the survival function as well as in the main estimator itself. We also investigate multiplicative and additive bias correction methods......A class of local linear kernel density estimators based on weighted least squares kernel estimation is considered within the framework of Aalen's multiplicative intensity model. This model includes the filtered data model that, in turn, allows for truncation and/or censoring in addition...... to accommodating unusual patterns of exposure as well as occurrence. It is shown that the local linear estimators corresponding to all different weightings have the same pointwise asymptotic properties. However, the weighting previously used in the literature in the i.i.d. case is seen to be far from optimal when...
Local Linear Density Estimation for Filtered Survival Data, with Bias Correction
DEFF Research Database (Denmark)
Nielsen, Jens Perch; Tanggaard, Carsten; Jones, M.C.
it comes to exposure robustness, and a simple alternative weighting is to be preferred. Indeed, this weighting has, effectively, to be well chosen in a `pilot' estimator of the survival function as well as in the main estimator itself. We also investigate multiplicative and additive bias correction methods......A class of local linear kernel density estimators based on weighted least squares kernel estimation is considered within the framework of Aalen's multiplicative intensity model. This model includes the filtered data model that, in turn, allows for truncation and/or censoring in addition...... to accommodating unusual patterns of exposure as well as occurrence. It is shown that the local linear estimators corresponding to all different weightings have the same pointwise asymptotic properties. However, the weighting previously used in the literature in the i.i.d. case is seen to be far from optimal when...
Application of Kernel Density Estimation in Lamb Wave-Based Damage Detection
Directory of Open Access Journals (Sweden)
Long Yu
2012-01-01
Full Text Available The present work concerns the estimation of the probability density function (p.d.f. of measured data in the Lamb wave-based damage detection. Although there was a number of research work which focused on the consensus algorithm of combining all the results of individual sensors, the p.d.f. of measured data, which was the fundamental part of the probability-based method, was still given by experience in existing work. Based on the analysis about the noise-induced errors in measured data, it was learned that the type of distribution was related with the level of noise. In the case of weak noise, the p.d.f. of measured data could be considered as the normal distribution. The empirical methods could give satisfied estimating results. However, in the case of strong noise, the p.d.f. was complex and did not belong to any type of common distribution function. Nonparametric methods, therefore, were needed. As the most popular nonparametric method, kernel density estimation was introduced. In order to demonstrate the performance of the kernel density estimation methods, a numerical model was built to generate the signals of Lamb waves. Three levels of white Gaussian noise were intentionally added into the simulated signals. The estimation results showed that the nonparametric methods outperformed the empirical methods in terms of accuracy.
Institute of Scientific and Technical Information of China (English)
Farah Yasmeen[1; Sidra Zaheer[2
2014-01-01
Background： Breast cancer is the most common female cancer in Pakistan. The incidence of breast cancer in Pakistan is about 2.5 times higher than that in the neighboring countries India and Iran. In Karachi, the most populated city of Pakistan, the age-standardized rate of breast cancer was 69.1 per 100,000 women during 1998-2002, which is the highest recorded rate in Asia. The carcinoma of breast in Pakistan is an enormous public health concern. In this study, we examined the recent trends of breast cancer incidence rates among the women in Karachi. Methods： We obtained the secondary data of breast cancer incidence from various hospitals. They included Jinnah Hospital, KIRAN （Karachi Institute of Radiotherapy and Nuclear Medicine）, and Civil hospital, where the data were available for the years 2004-2011. A total of 5331 new cases of female breast cancer were registered during this period. We analyzed the data in 5-year age groups 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75＋. Nonparametric smoothing were used to obtained age-specific incidence curves, and then the curves are decomposed using principal components analysis to fit FTS （functional time series） model. We then used exponential smoothing statspace models to estimate the forecasts of incidence curve and construct prediction intervals. Results： The breast cancer incidence rates in Karachi increased with age for all available years. The rates increased monotonically and are relatively sharp with the age from 15 years to 50 years and then they show variability after the age of 50 years. 10-year forecasts for the female breast cancer incidence rates in Karachi show that the future rates are expected to remain stable for the age-groups 15-50 years, but they will increase for the females of 50-years and over. Hence in future, the newly diagnosed breast cancer cases in the older women in Karachi are expected to increase. Conclusion： Prediction of age
Directory of Open Access Journals (Sweden)
Sabouri
2007-05-01
Full Text Available Background and Objective: The main purpose of this study was determination of the frequency and distri-bution of dense mammograms among the diverse age groups of women who underwent mammography effect and other clinical characteristics such as parity, age at first conception, use of hormone replacement therapy (HRT, and family history of breast carci-noma on breast density. Materials and Methods: We considered 1000 women between 25 and 79 years old consecutively undergo-ing mammography in Mashhad Qaem hospital from April 2003 to April 2004 were grouped in 5-year age cohorts. Breast density, breast size and presence of benign calcifications were analyzed. Clinical data such as age, parity, age at first conception, use of hormone replacement therapy (HRT, and family his-tory of breast carcinoma were recorded. Results: Radiological parenchymal density on mam-mograms was found to correlate with age (decreased progressively in patient cohorts 25-29 years old through 75-79 years old, P<0.0001. Increased paren-chymal density was more common in women who underwent HRT (P<0.0001, had smaller breast (P<0.0001 and had less than two pregnancies (P<0.0001. Prevalence of benign calcifications in-creased with age. Conclusion: This study confirms the effect of age and HRT on radiological breast density suggesting a pos-sible reduction of sensitivity and efficacy of mam-mography for detecting malignancy in women who undergo HRT. Such a hypothesis still needs validation by means of a prospective analysis of the correlation between HRT and the frequency of interval cancer occurrence (the best indicator of sensitivity. Never-theless a significant percentage (38% of women who were 25-39 years old had predominantly fatty breast tissue that should not impede selective mammo-graghic screening or diagnostic efforts in this age group.
Wavelet-based density estimation for noise reduction in plasma simulations using particles
van yen, Romain Nguyen; Schneider, Kai; Farge, Marie; Chen, Guangye
2009-01-01
For given computational resources, the accuracy of plasma simulations using particles is mainly held back by the noise due to limited statistical sampling in the reconstruction of the particle distribution function. A method based on wavelet analysis is proposed and tested to reduce this noise. The method, known as wavelet based density estimation (WBDE), was previously introduced in the statistical literature to estimate probability densities given a finite number of independent measurements. Its novel application to plasma simulations can be viewed as a natural extension of the finite size particles (FSP) approach, with the advantage of estimating more accurately distribution functions that have localized sharp features. The proposed method preserves the moments of the particle distribution function to a good level of accuracy, has no constraints on the dimensionality of the system, does not require an a priori selection of a global smoothing scale, and its able to adapt locally to the smoothness of the den...
Density estimation of small-mammal populations using a trapping web and distance sampling methods
Anderson, David R.; Burnham, Kenneth P.; White, Gary C.; Otis, David L.
1983-01-01
Distance sampling methodology is adapted to enable animal density (number per unit of area) to be estimated from capture-recapture and removal data. A trapping web design provides the link between capture data and distance sampling theory. The estimator of density is D = Mt+1f(0), where Mt+1 is the number of individuals captured and f(0) is computed from the Mt+1 distances from the web center to the traps in which those individuals were first captured. It is possible to check qualitatively the critical assumption on which the web design and the estimator are based. This is a conceptual paper outlining a new methodology, not a definitive investigation of the best specific way to implement this method. Several alternative sampling and analysis methods are possible within the general framework of distance sampling theory; a few alternatives are discussed and an example is given.
Kim, Sunmi; Lee, Jangwoo; Park, Jeongim; Kim, Hai-Joong; Cho, Geumjoon; Kim, Gun-Ha; Eun, So-Hee; Lee, Jeong Jae; Choi, Gyuyeon; Suh, Eunsook; Choi, Sooran; Kim, Sungjoo; Kim, Young Don; Kim, Sung Koo; Kim, Su Young; Kim, Seunghyo; Eom, Soyong; Moon, Hyo-Bang; Kim, Sungkyoon; Choi, Kyungho
2015-03-01
Phthalates have been associated with endocrine disruption and developmental effects in many experimental and epidemiological studies. Developing infants are among the most susceptible populations to endocrine disruption. However, limited information is available on phthalate exposure and its associated risks among breast-fed newborn infants. In the present study, breast milk samples were collected from 62 lactating mothers at 1 month post-partum from four cities of Korea in 2012 and were evaluated for six phthalate metabolites (mono-isobutyl phthalate (MiBP), mono-n-butyl phthalate (MnBP), mono(2-ethyl-hexyl) phthalate (MEHP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP) and monoethyl phthalate (MEP)). MEP was detected in all breast milk samples, with a median concentration of 0.37 μg/L, and MiBP, MnBP and MEHP were detected in 79-89% of samples, with median concentrations of 1.10, 1.70, and 2.08 μg/L, respectively. However, MEHHP and MEOHP, the oxidized forms of di-ethyl-hexyl phthalate (DEHP), were detected in only one sample. For exposure assessment, the levels of phthalate diesters were estimated based on the parent:metabolite ratios in the breast milk that are reported elsewhere. For risk assessment, the endocrine-related toxicity of the monoester was assumed to be the same as that of its diester form. Median daily intake estimates of phthalates, including both monoester and diester forms, through breast milk consumption ranged between 0.91 and 6.52 μg/kg body weight (bw) for DEHP and between 0.38 and 1.43 μg/kg bw for di-n-butyl phthalate (DnBP). Based on the estimated daily intake, up to 8% of infants exceeded the reference dose of anti-androgenicity (RfD AA) for DEHP, and 6% of infants exceeded the tolerable daily intake (TDI) for DnBP. Breast milk MiBP and MnBP concentrations showed significant positive associations with maternal consumption of whipped cream or purified water. Considering vulnerability of
DEFF Research Database (Denmark)
Hansen, S; Sørensen, F B; Vach, W;
2004-01-01
Evaluation of angiogenesis by intratumoral vessel profiles can be performed by different methods. The aim of this study was to investigate the prognostic value of estimates obtained by the intratumoral microvessel density (MVD) method and then to compare with corresponding estimates obtained...
Density of Jatropha curcas Seed Oil and its Methyl Esters: Measurement and Estimations
Veny, Harumi; Baroutian, Saeid; Aroua, Mohamed Kheireddine; Hasan, Masitah; Raman, Abdul Aziz; Sulaiman, Nik Meriam Nik
2009-04-01
Density data as a function of temperature have been measured for Jatropha curcas seed oil, as well as biodiesel jatropha methyl esters at temperatures from above their melting points to 90 ° C. The data obtained were used to validate the method proposed by Spencer and Danner using a modified Rackett equation. The experimental and estimated density values using the modified Rackett equation gave almost identical values with average absolute percent deviations less than 0.03% for the jatropha oil and 0.04% for the jatropha methyl esters. The Janarthanan empirical equation was also employed to predict jatropha biodiesel densities. This equation performed equally well with average absolute percent deviations within 0.05%. Two simple linear equations for densities of jatropha oil and its methyl esters are also proposed in this study.
International Nuclear Information System (INIS)
The UK and European protocols for mammographic dosimetry use conversion factors that relate incident air kerma to the mean glandular dose (MGD) within the breast. The conversion factors currently used were obtained by computer simulation of a model breast with a composition of 50% adipose and 50% glandular tissues by weight (50% glandularity). Relative conversion factors have been calculated which allow the extension of the protocols to breasts of varying glandularity and for a wider range of mammographic x-ray spectra. The data have also been extended to breasts of a compressed thickness of 11 cm. To facilitate the calculation of MGD in patient surveys, typical breast glandularities are tabulated for women in the age ranges 40-49 and 50-64 years, and for breasts in the thickness range 2-11 cm. In addition, tables of equivalent thickness of polymethyl methacrylate have been provided to allow the simulation for dosimetric purposes of typical breasts of various thicknesses. (author)
International Nuclear Information System (INIS)
Brain metastases (BM) are the most common form of intracranial cancer. The incidence of BM seems to have increased over the past decade. Recursive partitioning analysis (RPA) of data from three Radiation Therapy Oncology Group (RTOG) trials (1200 patients) has allowed three prognostic groups to be identified. More recently a simplified stratification system that uses the evaluation of three main prognostics factors for radiosurgery in BM was developed. To analyze the overall survival rate (OS), prognostic factors affecting outcomes and to estimate the potential improvement in OS for patients with BM from breast cancer, stratified by RPA class and brain metastases score (BS-BM). From January 1996 to December 2004, 174 medical records of patients with diagnosis of BM from breast cancer, who received WBRT were analyzed. The surgery followed by WBRT was used in 15.5% of patients and 84.5% of others patients were submitted at WBRT alone; 108 patients (62.1%) received the fractionation schedule of 30 Gy in 10 fractions. Solitary BM was present in 37.9 % of patients. The prognostic factors evaluated for OS were: age, Karnofsky Performance Status (KPS), number of lesions, localization of lesions, neurosurgery, chemotherapy, absence extracranial disease, RPA class, BS-BM and radiation doses and fractionation. The OS in 1, 2 and 3 years was 33.4 %, 16.7%, and 8.8 %, respectively. The RPA class analysis showed strong relation with OS (p < 0.0001). The median survival time by RPA class in months was: class I 11.7, class II 6.2 and class III 3.0. The significant prognostic factors associated with better OS were: higher KPS (p < 0.0001), neurosurgery (P < 0.0001), single metastases (p = 0.003), BS-BM (p < 0.0001), control primary tumor (p = 0.002) and absence of extracranial metastases (p = 0.001). In multivariate analysis, the factors associated positively with OS were: neurosurgery (p < 0.0001), absence of extracranial metastases (p <0.0001) and RPA class I (p < 0.0001). Our
A method to estimate the neutral atmospheric density near the ionospheric main peak of Mars
Zou, Hong; Ye, Yu Guang; Wang, Jin Song; Nielsen, Erling; Cui, Jun; Wang, Xiao Dong
2016-04-01
A method to estimate the neutral atmospheric density near the ionospheric main peak of Mars is introduced in this study. The neutral densities at 130 km can be derived from the ionospheric and atmospheric measurements of the Radio Science experiment on board Mars Global Surveyor (MGS). The derived neutral densities cover a large longitude range in northern high latitudes from summer to late autumn during 3 Martian years, which fills the gap of the previous observations for the upper atmosphere of Mars. The simulations of the Laboratoire de Météorologie Dynamique Mars global circulation model can be corrected with a simple linear equation to fit the neutral densities derived from the first MGS/RS (Radio Science) data sets (EDS1). The corrected simulations with the same correction parameters as for EDS1 match the derived neutral densities from two other MGS/RS data sets (EDS2 and EDS3) very well. The derived neutral density from EDS3 shows a dust storm effect, which is in accord with the Mars Express (MEX) Spectroscopy for Investigation of Characteristics of the Atmosphere of Mars measurement. The neutral density derived from the MGS/RS measurements can be used to validate the Martian atmospheric models. The method presented in this study can be applied to other radio occultation measurements, such as the result of the Radio Science experiment on board MEX.
Estimation of energy density of Li-S batteries with liquid and solid electrolytes
Li, Chunmei; Zhang, Heng; Otaegui, Laida; Singh, Gurpreet; Armand, Michel; Rodriguez-Martinez, Lide M.
2016-09-01
With the exponential growth of technology in mobile devices and the rapid expansion of electric vehicles into the market, it appears that the energy density of the state-of-the-art Li-ion batteries (LIBs) cannot satisfy the practical requirements. Sulfur has been one of the best cathode material choices due to its high charge storage (1675 mAh g-1), natural abundance and easy accessibility. In this paper, calculations are performed for different cell design parameters such as the active material loading, the amount/thickness of electrolyte, the sulfur utilization, etc. to predict the energy density of Li-S cells based on liquid, polymeric and ceramic electrolytes. It demonstrates that Li-S battery is most likely to be competitive in gravimetric energy density, but not volumetric energy density, with current technology, when comparing with LIBs. Furthermore, the cells with polymer and thin ceramic electrolytes show promising potential in terms of high gravimetric energy density, especially the cells with the polymer electrolyte. This estimation study of Li-S energy density can be used as a good guidance for controlling the key design parameters in order to get desirable energy density at cell-level.
Joint estimation of crown of thorns (Acanthaster planci) densities on the Great Barrier Reef.
MacNeil, M Aaron; Mellin, Camille; Pratchett, Morgan S; Hoey, Jessica; Anthony, Kenneth R N; Cheal, Alistair J; Miller, Ian; Sweatman, Hugh; Cowan, Zara L; Taylor, Sascha; Moon, Steven; Fonnesbeck, Chris J
2016-01-01
Crown-of-thorns starfish (CoTS; Acanthaster spp.) are an outbreaking pest among many Indo-Pacific coral reefs that cause substantial ecological and economic damage. Despite ongoing CoTS research, there remain critical gaps in observing CoTS populations and accurately estimating their numbers, greatly limiting understanding of the causes and sources of CoTS outbreaks. Here we address two of these gaps by (1) estimating the detectability of adult CoTS on typical underwater visual count (UVC) surveys using covariates and (2) inter-calibrating multiple data sources to estimate CoTS densities within the Cairns sector of the Great Barrier Reef (GBR). We find that, on average, CoTS detectability is high at 0.82 [0.77, 0.87] (median highest posterior density (HPD) and [95% uncertainty intervals]), with CoTS disc width having the greatest influence on detection. Integrating this information with coincident surveys from alternative sampling programs, we estimate CoTS densities in the Cairns sector of the GBR averaged 44 [41, 48] adults per hectare in 2014.
Joint estimation of crown of thorns (Acanthaster planci) densities on the Great Barrier Reef
Mellin, Camille; Pratchett, Morgan S.; Hoey, Jessica; Anthony, Kenneth R.N.; Cheal, Alistair J.; Miller, Ian; Sweatman, Hugh; Cowan, Zara L.; Taylor, Sascha; Moon, Steven; Fonnesbeck, Chris J.
2016-01-01
Crown-of-thorns starfish (CoTS; Acanthaster spp.) are an outbreaking pest among many Indo-Pacific coral reefs that cause substantial ecological and economic damage. Despite ongoing CoTS research, there remain critical gaps in observing CoTS populations and accurately estimating their numbers, greatly limiting understanding of the causes and sources of CoTS outbreaks. Here we address two of these gaps by (1) estimating the detectability of adult CoTS on typical underwater visual count (UVC) surveys using covariates and (2) inter-calibrating multiple data sources to estimate CoTS densities within the Cairns sector of the Great Barrier Reef (GBR). We find that, on average, CoTS detectability is high at 0.82 [0.77, 0.87] (median highest posterior density (HPD) and [95% uncertainty intervals]), with CoTS disc width having the greatest influence on detection. Integrating this information with coincident surveys from alternative sampling programs, we estimate CoTS densities in the Cairns sector of the GBR averaged 44 [41, 48] adults per hectare in 2014. PMID:27635314
Density estimation in a wolverine population using spatial capture-recapture models
Royle, J. Andrew; Magoun, Audrey J.; Gardner, Beth; Valkenbury, Patrick; Lowell, Richard E.; McKelvey, Kevin
2011-01-01
Classical closed-population capture-recapture models do not accommodate the spatial information inherent in encounter history data obtained from camera-trapping studies. As a result, individual heterogeneity in encounter probability is induced, and it is not possible to estimate density objectively because trap arrays do not have a well-defined sample area. We applied newly-developed, capture-recapture models that accommodate the spatial attribute inherent in capture-recapture data to a population of wolverines (Gulo gulo) in Southeast Alaska in 2008. We used camera-trapping data collected from 37 cameras in a 2,140-km2 area of forested and open habitats largely enclosed by ocean and glacial icefields. We detected 21 unique individuals 115 times. Wolverines exhibited a strong positive trap response, with an increased tendency to revisit previously visited traps. Under the trap-response model, we estimated wolverine density at 9.7 individuals/1,000-km2(95% Bayesian CI: 5.9-15.0). Our model provides a formal statistical framework for estimating density from wolverine camera-trapping studies that accounts for a behavioral response due to baited traps. Further, our model-based estimator does not have strict requirements about the spatial configuration of traps or length of trapping sessions, providing considerable operational flexibility in the development of field studies.
Energy Technology Data Exchange (ETDEWEB)
Burke, TImothy P. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Kiedrowski, Brian C. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Martin, William R. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Brown, Forrest B. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-11-19
Kernel Density Estimators (KDEs) are a non-parametric density estimation technique that has recently been applied to Monte Carlo radiation transport simulations. Kernel density estimators are an alternative to histogram tallies for obtaining global solutions in Monte Carlo tallies. With KDEs, a single event, either a collision or particle track, can contribute to the score at multiple tally points with the uncertainty at those points being independent of the desired resolution of the solution. Thus, KDEs show potential for obtaining estimates of a global solution with reduced variance when compared to a histogram. Previously, KDEs have been applied to neutronics for one-group reactor physics problems and fixed source shielding applications. However, little work was done to obtain reaction rates using KDEs. This paper introduces a new form of the MFP KDE that is capable of handling general geometries. Furthermore, extending the MFP KDE to 2-D problems in continuous energy introduces inaccuracies to the solution. An ad-hoc solution to these inaccuracies is introduced that produces errors smaller than 4% at material interfaces.
Estimation of the density of the clay-organic complex in soil
Czyż, Ewa A.; Dexter, Anthony R.
2016-01-01
Soil bulk density was investigated as a function of soil contents of clay and organic matter in arable agricultural soils at a range of locations. The contents of clay and organic matter were used in an algorithmic procedure to calculate the amounts of clay-organic complex in the soils. Values of soil bulk density as a function of soil organic matter content were used to estimate the amount of pore space occupied by unit amount of complex. These estimations show that the effective density of the clay-organic matter complex is very low with a mean value of 0.17 ± 0.04 g ml-1 in arable soils. This value is much smaller than the soil bulk density and smaller than any of the other components of the soil considered separately (with the exception of the gas content). This low value suggests that the clay-soil complex has an extremely porous and open structure. When the complex is considered as a separate phase in soil, it can account for the observed reduction of bulk density with increasing content of organic matter.
Efficient Estimation of Dynamic Density Functions with Applications in Streaming Data
Qahtan, Abdulhakim
2016-05-11
Recent advances in computing technology allow for collecting vast amount of data that arrive continuously in the form of streams. Mining data streams is challenged by the speed and volume of the arriving data. Furthermore, the underlying distribution of the data changes over the time in unpredicted scenarios. To reduce the computational cost, data streams are often studied in forms of condensed representation, e.g., Probability Density Function (PDF). This thesis aims at developing an online density estimator that builds a model called KDE-Track for characterizing the dynamic density of the data streams. KDE-Track estimates the PDF of the stream at a set of resampling points and uses interpolation to estimate the density at any given point. To reduce the interpolation error and computational complexity, we introduce adaptive resampling where more/less resampling points are used in high/low curved regions of the PDF. The PDF values at the resampling points are updated online to provide up-to-date model of the data stream. Comparing with other existing online density estimators, KDE-Track is often more accurate (as reflected by smaller error values) and more computationally efficient (as reflected by shorter running time). The anytime available PDF estimated by KDE-Track can be applied for visualizing the dynamic density of data streams, outlier detection and change detection in data streams. In this thesis work, the first application is to visualize the taxi traffic volume in New York city. Utilizing KDE-Track allows for visualizing and monitoring the traffic flow on real time without extra overhead and provides insight analysis of the pick up demand that can be utilized by service providers to improve service availability. The second application is to detect outliers in data streams from sensor networks based on the estimated PDF. The method detects outliers accurately and outperforms baseline methods designed for detecting and cleaning outliers in sensor data. The
Institute of Scientific and Technical Information of China (English)
XUE Yun-feng; WANG Yu-jia; YANG Jie
2009-01-01
A new algorithm for linear instantaneous independent component analysis is proposed based on max-imizing the log-likelihood contrast function which can be changed into a gradient equation. An iterative method is introduced to solve this equation efficiently. The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method. Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.
Recursive Density Estimation of NA Samples%样本的递归密度估计
Institute of Scientific and Technical Information of China (English)
张冬霞; 梁汉营
2008-01-01
Let{Xn,n≥1} be a strictly stationary sequence of negatively associated random variables with the marginal probability density function f (x). In this paper, we discuss the point asymptotic normality for recursive kernel density estimator of f (x).%设{{Xn, n≥1}是一个严平稳的负相协的随机变量序列,其概率密谋函数为f(x).本文讨论了f(x)的递归核估计量的联合渐近正态性.
Institute of Scientific and Technical Information of China (English)
LI Ning; SHI Tielin
2007-01-01
Blind source Separation and estimation of the number of sources usually demand that the number of sensors should be greater than or equal to that of the sources, which, however, is very difficult to satisfy for the complex Systems. A new estimating method based on power spectral density (PSD) is presented. When the relation between the number of sensors and that of sources is unknown, the PSD matrix is first obtained by the ratio of PSD of the observation signals, and then the bound of the number of correlated sources with common frequencies can be estimated by comparing every column vector of PSD matrix. The effectiveness of the proposed method is verified by theoretical analysis and experiments, and the influence of noise on the estimation of number of source is simulated.
Nearest neighbor density ratio estimation for large-scale applications in astronomy
Kremer, J.; Gieseke, F.; Steenstrup Pedersen, K.; Igel, C.
2015-09-01
In astronomical applications of machine learning, the distribution of objects used for building a model is often different from the distribution of the objects the model is later applied to. This is known as sample selection bias, which is a major challenge for statistical inference as one can no longer assume that the labeled training data are representative. To address this issue, one can re-weight the labeled training patterns to match the distribution of unlabeled data that are available already in the training phase. There are many examples in practice where this strategy yielded good results, but estimating the weights reliably from a finite sample is challenging. We consider an efficient nearest neighbor density ratio estimator that can exploit large samples to increase the accuracy of the weight estimates. To solve the problem of choosing the right neighborhood size, we propose to use cross-validation on a model selection criterion that is unbiased under covariate shift. The resulting algorithm is our method of choice for density ratio estimation when the feature space dimensionality is small and sample sizes are large. The approach is simple and, because of the model selection, robust. We empirically find that it is on a par with established kernel-based methods on relatively small regression benchmark datasets. However, when applied to large-scale photometric redshift estimation, our approach outperforms the state-of-the-art.
mBEEF-vdW: Robust fitting of error estimation density functionals
DEFF Research Database (Denmark)
Lundgård, Keld Troen; Wellendorff, Jess; Voss, Johannes;
2016-01-01
. The functional is fitted within the Bayesian error estimation functional (BEEF) framework [J. Wellendorff et al., Phys. Rev. B 85, 235149 (2012); J. Wellendorff et al., J. Chem. Phys. 140, 144107 (2014)]. We improve the previously used fitting procedures by introducing a robust MM-estimator based loss function...... the potential-energy curve of graphene on the nickel(111) surface, where mBEEF-vdW matches the experimental binding length. mBEEF-vdW is currently available in GPAW and other density functional theory codes through Libxc, version 3.0.0....
Kernel Density Estimation for Interdeparture Time of GI/G/1 Queues
Directory of Open Access Journals (Sweden)
Hsing Luh
2005-01-01
Full Text Available The departure process of a single queue has been studied since the 1960s. Due to its inherent complexity, closed form solutions for the distribution of the departure process are nearly intractable. In this study, kernel type estimators of the density of interdeparture time in a GI/G/1 queue are studied. Uniform strong consistency of the estimators in a GI/G/1 queue and their rates of convergence are obtained. The stochastic processes are shown to satisfy the strong mixing condition with random instants of sampling. With the analysis presented, we provide a novel analytic tool for studying the departure process in a general queueing model.
A Note on the Estimation of Semiparametric Two-Sample Density Ratio Models
Institute of Scientific and Technical Information of China (English)
Gang YU; Wei GAO; Ningzhong SHI
2012-01-01
In this paper,a semiparametric two-sample density ratio model is considered and the empirical likelihood method is applied to obtain the parameters estimation.A commonly occurring problem in computing is that the empirical likelihood function may be a concaveconvex function.Here a simple Lagrange saddle point algorithm is presented for computing the saddle point of the empirical likelihood function when the Lagrange multiplier has no explicit solution.So we can obtain the maximum empirical likelihood estimation (MELE) of parameters.Monte Carlo simulations are presented to illustrate the Lagrange saddle point algorithm.
Pedotransfer functions for Irish soils - estimation of bulk density (ρb) per horizon type
Reidy, B.; Simo, I.; Sills, P.; Creamer, R. E.
2016-01-01
Soil bulk density is a key property in defining soil characteristics. It describes the packing structure of the soil and is also essential for the measurement of soil carbon stock and nutrient assessment. In many older surveys this property was neglected and in many modern surveys this property is omitted due to cost both in laboratory and labour and in cases where the core method cannot be applied. To overcome these oversights pedotransfer functions are applied using other known soil properties to estimate bulk density. Pedotransfer functions have been derived from large international data sets across many studies, with their own inherent biases, many ignoring horizonation and depth variances. Initially pedotransfer functions from the literature were used to predict different horizon type bulk densities using local known bulk density data sets. Then the best performing of the pedotransfer functions were selected to recalibrate and then were validated again using the known data. The predicted co-efficient of determination was 0.5 or greater in 12 of the 17 horizon types studied. These new equations allowed gap filling where bulk density data were missing in part or whole soil profiles. This then allowed the development of an indicative soil bulk density map for Ireland at 0-30 and 30-50 cm horizon depths. In general the horizons with the largest known data sets had the best predictions, using the recalibrated and validated pedotransfer functions.
Haben, Stephen
2016-01-01
We present a model for generating probabilistic forecasts by combining kernel density estimation (KDE) and quantile regression techniques, as part of the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014. The KDE method is initially implemented with a time-decay parameter. We later improve this method by conditioning on the temperature or the period of the week variables to provide more accurate forecasts. Secondly, we develop a simple but effective quantile regression forecast. The novel aspects of our methodology are two-fold. First, we introduce symmetry into the time-decay parameter of the kernel density estimation based forecast. Secondly we combine three probabilistic forecasts with different weights for different periods of the month.
Kernel density estimation-based solution of the nuclear Schrödinger equation
Unke, Oliver Thorsten; Meuwly, Markus
2015-10-01
Solving the time-dependent Schrödinger equation for nuclear motion remains a challenge. Despite novel approaches based on Bohmian mechanics, the long-time stability and generalization to multiple dimensions remains an open question. In the present work a method based on an ensemble of classical particles instead of a wave function is employed to evolve the system. Quantum effects are introduced through forces derived from the quantum potential Q and the necessary derivatives are obtained from a density estimate using kernel density estimation. Application of the procedure to typical 1- and 2-dimensional problems yields good agreement with numerically exact solutions and favourable scaling with the number of particles is found.
Directory of Open Access Journals (Sweden)
José Fajardo
2012-12-01
Full Text Available This paper uses the Liu et al. (2007 approach to estimate the optionimplied Risk-Neutral Densities (RND, real-world density (RWD, and relative risk aversion from the Brazilian Real/US Dollar exchange rate distribution. Our empirical application uses a sample of exchange-traded Brazilian Real currency options from 1999 to 2011. Our estimated value of the relative risk aversion is around 2.7, which is in line with other articles for the Brazilian Economy. Our out-of-sample results showed that the RND has some ability to forecast the Brazilian Real exchange rate, but when we incorporate the risk aversion, the out-of-sample performance improves substantially.
Breast Tissue Composition and Susceptibility to Breast Cancer
Boyd, Norman F.; Lisa J Martin; Bronskill, Michael; Martin J. Yaffe; Duric, Neb; Minkin, Salomon
2010-01-01
Breast density, as assessed by mammography, reflects breast tissue composition. Breast epithelium and stroma attenuate x-rays more than fat and thus appear light on mammograms while fat appears dark. In this review, we provide an overview of selected areas of current knowledge about the relationship between breast density and susceptibility to breast cancer. We review the evidence that breast density is a risk factor for breast cancer, the histological and other risk factors that are associat...
Use of Camera-Trapping to Estimate Puma Density and Influencing Factors in Central Brazil
Negroes, Nuno; Sarmento, Pedro; Cruz, Joana; Eira, Catarina; Revilla, Eloy; Fonseca, Carlos; Torres, Natália M.; Furtado, Mariana M.; Sollmann, Rahel
2010-01-01
We used remotely triggered cameras to collect data on Puma (Puma concolor) abundance and occupancy in an area of tropical forest in Brazil where the species’ status is poorly known. To evaluate factors influencing puma occupancy we used data from 5 sampling campaigns in 3 consecutive years (2005 to 2007) and 2 seasons (wet and dry), at a state park and a private forest reserve. We estimated puma numbers and density for the 2007 sampling data by developing a standardized individua...
Garde, Ainara; Karlen, Walter; Ansermino, J Mark; Dumont, Guy A.
2014-01-01
The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory...
An elegant method to estimate helium-like ion densities from visible and VUV plasma spectroscopy
Rachlew, Elisabeth
1993-01-01
A method is presented to estimate the densities of He-like carbon and oxygen ions from the observation of the Li-like 3p + 3s transitions inthe visible and the 3p + 2s transitions in the vacuum ulbaviolet The method is based on a zerodimensional model for plasmas with short particle confinement times. General coefficients for this study are derived and evaluated numerically. The method is applied to Extrap-T1 reversed field pinch data
Use of spatial capture-recapture modeling and DNA data to estimate densities of elusive animals
Kery, Marc; Gardner, Beth; Stoeckle, Tabea; Weber, Darius; Royle, J. Andrew
2011-01-01
Assessment of abundance, survival, recruitment rates, and density (i.e., population assessment) is especially challenging for elusive species most in need of protection (e.g., rare carnivores). Individual identification methods, such as DNA sampling, provide ways of studying such species efficiently and noninvasively. Additionally, statistical methods that correct for undetected animals and account for locations where animals are captured are available to efficiently estimate density and other demographic parameters. We collected hair samples of European wildcat (Felis silvestris) from cheek-rub lure sticks, extracted DNA from the samples, and identified each animals' genotype. To estimate the density of wildcats, we used Bayesian inference in a spatial capture-recapture model. We used WinBUGS to fit a model that accounted for differences in detection probability among individuals and seasons and between two lure arrays. We detected 21 individual wildcats (including possible hybrids) 47 times. Wildcat density was estimated at 0.29/km2 (SE 0.06), and 95% of the activity of wildcats was estimated to occur within 1.83 km from their home-range center. Lures located systematically were associated with a greater number of detections than lures placed in a cell on the basis of expert opinion. Detection probability of individual cats was greatest in late March. Our model is a generalized linear mixed model; hence, it can be easily extended, for instance, to incorporate trap- and individual-level covariates. We believe that the combined use of noninvasive sampling techniques and spatial capture-recapture models will improve population assessments, especially for rare and elusive animals.
Dillon, A.; Kelly, M J
2007-01-01
We used remote cameras to obtain information on an elusive species and to examine the effects of different camera trapping methodologies on abundance estimates. We determined activity pattern, trail use, trap success, and density of ocelot Leopardus pardalis in seven camera-trap surveys across two habitat types in western Belize: tropical broad-leaf rainforest and tropical pine forest. Ocelots in the rainforest were active mostly at night, in particular immediately after sunset, and they trav...
Gonzalez, Raelvim
2010-01-01
Ultrasonic Consolidation of stainless steel structures is being investigated for potential applications. This study investigates the suitability of Stainless Steel 316L annealed (SS316L annealed) as a building material for Ultrasonic Consolidation (UC), including research on Linear Welding Density (LWD) estimation on micrographs of samples. Experiment results are presented that include the effect of UC process parameters on SS316L annealed UC, optimum levels of these parameters, and bond qual...
Kernel Density Feature Points Estimator for Content-Based Image Retrieval
Zuva, Tranos; Ojo, Sunday O; Ngwira, Seleman M
2012-01-01
Research is taking place to find effective algorithms for content-based image representation and description. There is a substantial amount of algorithms available that use visual features (color, shape, texture). Shape feature has attracted much attention from researchers that there are many shape representation and description algorithms in literature. These shape image representation and description algorithms are usually not application independent or robust, making them undesirable for generic shape description. This paper presents an object shape representation using Kernel Density Feature Points Estimator (KDFPE). In this method, the density of feature points within defined rings around the centroid of the image is obtained. The KDFPE is then applied to the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of image representation shows improved retrieval rate when compared to Density Histogram Feature Points (DHFP) method. Analytic analysis is done to justify our m...
Estimation of Plasma Density by Surface Plasmons for Surface-Wave Plasmas
Institute of Scientific and Technical Information of China (English)
CHEN Zhao-Quan; LIU Ming-Hai; LAN Chao-Hui; CHEN Wei; LUO Zhi-Qing; HU Xi-Wei
2008-01-01
@@ An estimation method of plasma density based on surface plasmons theory for surface-wave plasmas is proposed. The number of standing-wave is obtained directly from the discharge image, and the propagation constant is calculated with the trim size of the apparatus in this method, then plasma density can be determined with the value of 9.1 × 1017 m-3. Plasma density is measured using a Langmuir probe, the value is 8.1 × 1017 m-3 which is very close to the predicted value of surface plasmons theory. Numerical simulation is used to check the number of standing-wave by the finite-difference time-domain (FDTD) method also. All results are compatible both of theoretical analysis and experimental measurement.
Energy Technology Data Exchange (ETDEWEB)
Balsa Terzic, Gabriele Bassi
2011-07-01
In this paper we discuss representations of charge particle densities in particle-in-cell (PIC) simulations, analyze the sources and profiles of the intrinsic numerical noise, and present efficient methods for their removal. We devise two alternative estimation methods for charged particle distribution which represent significant improvement over the Monte Carlo cosine expansion used in the 2d code of Bassi, designed to simulate coherent synchrotron radiation (CSR) in charged particle beams. The improvement is achieved by employing an alternative beam density estimation to the Monte Carlo cosine expansion. The representation is first binned onto a finite grid, after which two grid-based methods are employed to approximate particle distributions: (i) truncated fast cosine transform (TFCT); and (ii) thresholded wavelet transform (TWT). We demonstrate that these alternative methods represent a staggering upgrade over the original Monte Carlo cosine expansion in terms of efficiency, while the TWT approximation also provides an appreciable improvement in accuracy. The improvement in accuracy comes from a judicious removal of the numerical noise enabled by the wavelet formulation. The TWT method is then integrated into Bassi's CSR code, and benchmarked against the original version. We show that the new density estimation method provides a superior performance in terms of efficiency and spatial resolution, thus enabling high-fidelity simulations of CSR effects, including microbunching instability.
Carroll, Raymond J.
2011-03-01
In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment or dosage, the conditional mean of the response, Y , is often anticipated to be a monotone function of X. Indeed, if this regression mean is not monotone (in the appropriate direction) then the medical or commercial value of the treatment is likely to be significantly curtailed, at least for values of X that lie beyond the point at which monotonicity fails. In the case of a density, common shape constraints include log-concavity and unimodality. If we can correctly guess the shape of a curve, then nonparametric estimators can be improved by taking this information into account. Addressing such problems requires a method for testing the hypothesis that the curve of interest satisfies a shape constraint, and, if the conclusion of the test is positive, a technique for estimating the curve subject to the constraint. Nonparametric methodology for solving these problems already exists, but only in cases where the covariates are observed precisely. However in many problems, data can only be observed with measurement errors, and the methods employed in the error-free case typically do not carry over to this error context. In this paper we develop a novel approach to hypothesis testing and function estimation under shape constraints, which is valid in the context of measurement errors. Our method is based on tilting an estimator of the density or the regression mean until it satisfies the shape constraint, and we take as our test statistic the distance through which it is tilted. Bootstrap methods are used to calibrate the test. The constrained curve estimators that we develop are also based on tilting, and in that context our work has points of contact with methodology in the error-free case.
Lawrence, G.; Wallis, M.; Allgood, P.; Nagtegaal, I.D.; Warwick, J.; Cafferty, F.H.; Houssami, N.; Kearins, O.; Tappenden, N.; O'Sullivan, E.; Duffy, S.W.
2009-01-01
BACKGROUND: Evidence of the impact of breast screening is limited by biases inherent in non-randomised studies and often by lack of complete population data. We address this by estimating the effect of screen detection on cause-specific fatality in breast cancer, corrected for all potential biases,
Sui, Ning; He, Ping
2014-01-01
In this work, we investigate the statistical computation of the Boltzmann entropy of statistical samples. For this purpose, we use both histogram and kernel function to estimate the probability density function of statistical samples. We find that, due to coarse-graining, the entropy is a monotonic increasing function of the bin width for histogram or bandwidth for kernel estimation, which seems to be difficult to select an optimal bin width/bandwidth for computing the entropy. Fortunately, we notice that there exists a minimum of the first derivative of entropy for both histogram and kernel estimation, and this minimum point of the first derivative asymptotically points to the optimal bin width or bandwidth. We have verified these findings by large amounts of numerical experiments. Hence, we suggest that the minimum of the first derivative of entropy be used as a selector for the optimal bin width or bandwidth of density estimation. Moreover, the optimal bandwidth selected by the minimum of the first derivat...
Verdoolaege, G.; Von Hellermann, M. G.; Jaspers, R.; Ichir, M. M.; Van Oost, G.
2006-11-01
The validation of diagnostic date from a nuclear fusion experiment is an important issue. The concept of an Integrated Data Analysis (IDA) allows the consistent estimation of plasma parameters from heterogeneous data sets. Here, the determination of the ion effective charge (Zeff) is considered. Several diagnostic methods exist for the determination of Zeff, but the results are in general not in agreement. In this work, the problem of Zeff estimation on the TEXTOR tokamak is approached from the perspective of IDA, in the framework of Bayesian probability theory. The ultimate goal is the estimation of a full Zeff profile that is consistent both with measured bremsstrahlung emissivities, as well as individual impurity spectral line intensities obtained from Charge Exchange Recombination Spectroscopy (CXRS). We present an overview of the various uncertainties that enter the calculation of a Zeff profile from bremsstrahlung date on the one hand, and line intensity data on the other hand. We discuss a simple linear and nonlinear Bayesian model permitting the estimation of a central value for Zeff and the electron density ne on TEXTOR from bremsstrahlung emissivity measurements in the visible, and carbon densities derived from CXRS. Both the central Zeff and ne are sampled using an MCMC algorithm. An outlook is given towards possible model improvements.
International Nuclear Information System (INIS)
Quantitation of cell density in tissues has proven problematic over the years. The manual microscopic methodology, where an investigator visually samples multiple areas within slides of tissue sections, has long remained the basic ‘standard’ for many studies and for routine histopathologic reporting. Nevertheless, novel techniques that may provide a more standardized approach to quantitation of cells in tissue sections have been made possible by computerized video image analysis methods over recent years. The present study describes a novel, computer-assisted video image analysis method of quantitating immunostained cells within tissue sections, providing continuous graphical data. This technique enables the measurement of both distribution and density of cells within tissue sections. Specifically, the study considered immunoperoxidase-stained tumor infiltrating lymphocytes within breast tumor specimens, using the number of immunostained pixels within tissue sections to determine cellular density and number. Comparison was made between standard manual graded quantitation methods and video image analysis, using the same tissue sections. The study demonstrates that video image techniques and computer analysis can provide continuous data on cell density and number in immunostained tissue sections, which compares favorably with standard visual quantitation methods, and may offer an alternative
Sadeh, Iftach; Lahav, Ofer
2015-01-01
We present ANNz2, a new implementation of the public software for photometric redshift (photo-z) estimation of Collister and Lahav (2004). Large photometric galaxy surveys are important for cosmological studies, and in particular for characterizing the nature of dark energy. The success of such surveys greatly depends on the ability to measure photo-zs, based on limited spectral data. ANNz2 utilizes multiple machine learning methods, such as artificial neural networks, boosted decision/regression trees and k-nearest neighbours. The objective of the algorithm is to dynamically optimize the performance of the photo-z estimation, and to properly derive the associated uncertainties. In addition to single-value solutions, the new code also generates full probability density functions (PDFs) in two different ways. In addition, estimators are incorporated to mitigate possible problems of spectroscopic training samples which are not representative or are incomplete. ANNz2 is also adapted to provide optimized solution...
Estimates of the Electron Density Profile on LTX Using FMCW Reflectometry and mm-Wave Interferometry
Peebles, W. A.; Kubota, S.; Nguyen, X. V.; Holoman, T.; Kaita, R.; Kozub, T.; Labrie, D.; Schmitt, J. C.; Majeski, R.
2014-10-01
An FMCW (frequency-modulated continuous-wave) reflectometer has been installed on the Lithium Tokamak Experiment (LTX) for electron density profile and fluctuation measurements. This diagnostic consists of two channels using bistatic antennas with a combined frequency coverage of 13.5 -33 GHz, which corresponds to electron density measurements in the range of 0 . 2 - 1 . 3 ×1013 cm-3 (in O-mode). Initial measurements will utilize O-mode polarization, which will require modeling of the plasma edge. Reflections from the center stack (delayometry above the peak cutoff frequency), as well as line density measurements from a 296 GHz interferometer (single-chord, radial midplane), will provide constraints for the profile reconstruction/estimate. Typical chord-averaged line densities on LTX range from 2 -6 ×1012 cm-3, which correspond to peak densities of 0 . 6 - 1 . 8 ×1013 cm-3 assuming a parabolic shape. If available, EFIT/LRDFIT results will provide additional constraints, as well as the possibility of utilizing data from measurements with X-mode or dual-mode (simultaneous O- and X-mode) polarization. Supported by U.S. DoE Grants DE-FG02-99ER54527 and DE-AC02-09CH11466.
Dynamics and density estimation of hydroxyl radicals in a pulsed corona discharge
International Nuclear Information System (INIS)
Hydroxyl radicals generated by a pulsed corona discharge are measured by laser-induced fluorescence (LIF) with a tunable KrF excimer laser. The discharge with 35 kV voltage and 100 ns pulse current occurs between needle and plate electrodes in H2O/O2/N2 mixture at atmospheric pressure. The density and decay profile of OH radicals are studied. OH radicals decay with time after the discharge with a time constant of about 30-60 μs. The OH density is estimated to be about 7x1014 cm-3 in H2O(2.4%)/N2 mixture 10 μs after the discharge. The OH density is approximately proportional to the energy dissipated in the discharge. The O2 content influences the OH production. When the O2 content is varied in H2O(2.4%)/O2/N2 mixture, the OH density is maximum at an O2 content of 2%. The spatial distribution of OH density shows that OH radicals are produced in the streamers under positive discharge. (author)
mBEEF-vdW: Robust fitting of error estimation density functionals
Lundgaard, Keld T.; Wellendorff, Jess; Voss, Johannes; Jacobsen, Karsten W.; Bligaard, Thomas
2016-06-01
We propose a general-purpose semilocal/nonlocal exchange-correlation functional approximation, named mBEEF-vdW. The exchange is a meta generalized gradient approximation, and the correlation is a semilocal and nonlocal mixture, with the Rutgers-Chalmers approximation for van der Waals (vdW) forces. The functional is fitted within the Bayesian error estimation functional (BEEF) framework [J. Wellendorff et al., Phys. Rev. B 85, 235149 (2012), 10.1103/PhysRevB.85.235149; J. Wellendorff et al., J. Chem. Phys. 140, 144107 (2014), 10.1063/1.4870397]. We improve the previously used fitting procedures by introducing a robust MM-estimator based loss function, reducing the sensitivity to outliers in the datasets. To more reliably determine the optimal model complexity, we furthermore introduce a generalization of the bootstrap 0.632 estimator with hierarchical bootstrap sampling and geometric mean estimator over the training datasets. Using this estimator, we show that the robust loss function leads to a 10 % improvement in the estimated prediction error over the previously used least-squares loss function. The mBEEF-vdW functional is benchmarked against popular density functional approximations over a wide range of datasets relevant for heterogeneous catalysis, including datasets that were not used for its training. Overall, we find that mBEEF-vdW has a higher general accuracy than competing popular functionals, and it is one of the best performing functionals on chemisorption systems, surface energies, lattice constants, and dispersion. We also show the potential-energy curve of graphene on the nickel(111) surface, where mBEEF-vdW matches the experimental binding length. mBEEF-vdW is currently available in gpaw and other density functional theory codes through Libxc, version 3.0.0.
Energy Technology Data Exchange (ETDEWEB)
Donovan, E. M.; James, H.; Bonora, M.; Yarnold, J. R.; Evans, P. M. [Joint Department of Physics, Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Sutton SM2 5PT (United Kingdom); Physics Department, Ipswich Hospital NHS Foundation Trust, Ipswich IP4 5PD (United Kingdom); Department of Academic Radiotherapy, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton SM2 5PT, United Kingdom and School of Radiotherapy, University of Milan, Milan 20122 (Italy); Department of Academic Radiotherapy, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton SM2 5PT (United Kingdom); Centre for Vision Speech and Signal Processing, University of Surrey, Guildford GU2 7XH (United Kingdom)
2012-10-15
contralateral breast doses and LAR were comparable to WBRT, despite their added complexity. The smaller irradiated volume of the ABPI plan contributed to a halving of LAR for contralateral breast compared with the other plan types. Daily image guided radiotherapy (IGRT) for a left breast protocol using kilovoltage CBCT contributed <10% to LAR for the majority of organs, and did not exceed 22% of total organ dose. Conclusions: Phantom measurements and calculations of LAR from the BEIR VII models predict that complex breast radiotherapy techniques do not increase the theoretical risk of second cancer incidence for organs distant from the treated breast, or the contralateral breast where appropriate plan constraints are applied. Complex SIB treatments are predicted to increase the risk of second cancer incidence in the lungs compared to standard whole breast radiotherapy; this is outweighed by the threefold reduction in 5 yr local recurrence risk for patients of high risk of recurrence, and young age, from the use of radiotherapy. APBI may have a favorable impact on risk of second cancer in the contralateral breast and lung for older patients at low risk of recurrence. Intensive use of IGRTincreased the estimated values of LAR but these are dominated by the effect of the dose from the radiotherapy, and any increase in LAR from IGRT is much lower than the models' uncertainties.
Directory of Open Access Journals (Sweden)
Tara Chestnut
Full Text Available Biodiversity losses are occurring worldwide due to a combination of stressors. For example, by one estimate, 40% of amphibian species are vulnerable to extinction, and disease is one threat to amphibian populations. The emerging infectious disease chytridiomycosis, caused by the aquatic fungus Batrachochytrium dendrobatidis (Bd, is a contributor to amphibian declines worldwide. Bd research has focused on the dynamics of the pathogen in its amphibian hosts, with little emphasis on investigating the dynamics of free-living Bd. Therefore, we investigated patterns of Bd occupancy and density in amphibian habitats using occupancy models, powerful tools for estimating site occupancy and detection probability. Occupancy models have been used to investigate diseases where the focus was on pathogen occurrence in the host. We applied occupancy models to investigate free-living Bd in North American surface waters to determine Bd seasonality, relationships between Bd site occupancy and habitat attributes, and probability of detection from water samples as a function of the number of samples, sample volume, and water quality. We also report on the temporal patterns of Bd density from a 4-year case study of a Bd-positive wetland. We provide evidence that Bd occurs in the environment year-round. Bd exhibited temporal and spatial heterogeneity in density, but did not exhibit seasonality in occupancy. Bd was detected in all months, typically at less than 100 zoospores L(-1. The highest density observed was ∼3 million zoospores L(-1. We detected Bd in 47% of sites sampled, but estimated that Bd occupied 61% of sites, highlighting the importance of accounting for imperfect detection. When Bd was present, there was a 95% chance of detecting it with four samples of 600 ml of water or five samples of 60 mL. Our findings provide important baseline information to advance the study of Bd disease ecology, and advance our understanding of amphibian exposure to free
Chestnut, Tara E.; Anderson, Chauncey; Popa, Radu; Blaustein, Andrew R.; Voytek, Mary; Olson, Deanna H.; Kirshtein, Julie
2014-01-01
Biodiversity losses are occurring worldwide due to a combination of stressors. For example, by one estimate, 40% of amphibian species are vulnerable to extinction, and disease is one threat to amphibian populations. The emerging infectious disease chytridiomycosis, caused by the aquatic fungus Batrachochytrium dendrobatidis (Bd), is a contributor to amphibian declines worldwide. Bd research has focused on the dynamics of the pathogen in its amphibian hosts, with little emphasis on investigating the dynamics of free-living Bd. Therefore, we investigated patterns of Bd occupancy and density in amphibian habitats using occupancy models, powerful tools for estimating site occupancy and detection probability. Occupancy models have been used to investigate diseases where the focus was on pathogen occurrence in the host. We applied occupancy models to investigate free-living Bd in North American surface waters to determine Bd seasonality, relationships between Bd site occupancy and habitat attributes, and probability of detection from water samples as a function of the number of samples, sample volume, and water quality. We also report on the temporal patterns of Bd density from a 4-year case study of a Bd-positive wetland. We provide evidence that Bd occurs in the environment year-round. Bd exhibited temporal and spatial heterogeneity in density, but did not exhibit seasonality in occupancy. Bd was detected in all months, typically at less than 100 zoospores L−1. The highest density observed was ∼3 million zoospores L−1. We detected Bd in 47% of sites sampled, but estimated that Bd occupied 61% of sites, highlighting the importance of accounting for imperfect detection. When Bd was present, there was a 95% chance of detecting it with four samples of 600 ml of water or five samples of 60 mL. Our findings provide important baseline information to advance the study of Bd disease ecology, and advance our understanding of amphibian exposure to free-living Bd in aquatic
International Nuclear Information System (INIS)
The aim of the work is to assess the probability of the breast cancer occurrence on the basis of analysis of the clinical and mammographical factors in women with unpalpable breast tumor. In the period from the 1st February 1995 to the 31st August 2000, 163 surgical procedures for the removal of any lesions in the breasts were conducted, after being previously marked by localized needle, in women who earlier underwent mammography exam.Following data was taken into consideration: patients age, type of the breast structure; side of the breast, where the lesion was localized in the mammography exam; localized lesions depending on the quadrant; shape of the lesion; size of the lesion in millimeters; presence and the type of microcalcifications. 1. The only one population factor, which can be distinguished as characteristic for the women suffering from the breast cancer impalpable in clinical testing, is the age of the patient, because the breast cancer in these women more frequently occurs after 53 years of age. 2. On the basis of our own material the following radiological symptoms characteristic for the breast cancer in mammography exam were stated: the breast cancer is more frequently found in the upper external quadrant; all lesions, which in mammography exam were identified as multifocal and radiologically suspected in histopathology exam turned out to be the cancer; pleomorphic microcalcifications are characteristic for the malignant lesions; external outline and the shape of the lesion are the features, which allow to differentiate malignant and benign lesions. 3. The analysis of the material indicates that the greatest probability of the breast cancer occurrence is in case of the four risk factors occurrence simultaneously, and the smallest in case of only one risk factor occurrence. (author)
Crowe, D.E.; Longshore, K.M.
2010-01-01
We estimated relative abundance and density of Western Burrowing Owls (Athene cunicularia hypugaea) at two sites in the Mojave Desert (200304). We made modifications to previously established Burrowing Owl survey techniques for use in desert shrublands and evaluated several factors that might influence the detection of owls. We tested the effectiveness of the call-broadcast technique for surveying this species, the efficiency of this technique at early and late breeding stages, and the effectiveness of various numbers of vocalization intervals during broadcasting sessions. Only 1 (3) of 31 initial (new) owl responses was detected during passive-listening sessions. We found that surveying early in the nesting season was more likely to produce new owl detections compared to surveying later in the nesting season. New owls detected during each of the three vocalization intervals (each consisting of 30 sec of vocalizations followed by 30 sec of silence) of our broadcasting session were similar (37, 40, and 23; n 30). We used a combination of detection trials (sighting probability) and double-observer method to estimate the components of detection probability, i.e., availability and perception. Availability for all sites and years, as determined by detection trials, ranged from 46.158.2. Relative abundance, measured as frequency of occurrence and defined as the proportion of surveys with at least one owl, ranged from 19.232.0 for both sites and years. Density at our eastern Mojave Desert site was estimated at 0.09 ?? 0.01 (SE) owl territories/km2 and 0.16 ?? 0.02 (SE) owl territories/km2 during 2003 and 2004, respectively. In our southern Mojave Desert site, density estimates were 0.09 ?? 0.02 (SE) owl territories/km2 and 0.08 ?? 0.02 (SE) owl territories/km 2 during 2004 and 2005, respectively. ?? 2010 The Raptor Research Foundation, Inc.
Directory of Open Access Journals (Sweden)
Yongjun Ahn
Full Text Available The charging infrastructure location problem is becoming more significant due to the extensive adoption of electric vehicles. Efficient charging station planning can solve deeply rooted problems, such as driving-range anxiety and the stagnation of new electric vehicle consumers. In the initial stage of introducing electric vehicles, the allocation of charging stations is difficult to determine due to the uncertainty of candidate sites and unidentified charging demands, which are determined by diverse variables. This paper introduces the Estimating the Required Density of EV Charging (ERDEC stations model, which is an analytical approach to estimating the optimal density of charging stations for certain urban areas, which are subsequently aggregated to city level planning. The optimal charging station's density is derived to minimize the total cost. A numerical study is conducted to obtain the correlations among the various parameters in the proposed model, such as regional parameters, technological parameters and coefficient factors. To investigate the effect of technological advances, the corresponding changes in the optimal density and total cost are also examined by various combinations of technological parameters. Daejeon city in South Korea is selected for the case study to examine the applicability of the model to real-world problems. With real taxi trajectory data, the optimal density map of charging stations is generated. These results can provide the optimal number of chargers for driving without driving-range anxiety. In the initial planning phase of installing charging infrastructure, the proposed model can be applied to a relatively extensive area to encourage the usage of electric vehicles, especially areas that lack information, such as exact candidate sites for charging stations and other data related with electric vehicles. The methods and results of this paper can serve as a planning guideline to facilitate the extensive
Ahn, Yongjun; Yeo, Hwasoo
2015-01-01
The charging infrastructure location problem is becoming more significant due to the extensive adoption of electric vehicles. Efficient charging station planning can solve deeply rooted problems, such as driving-range anxiety and the stagnation of new electric vehicle consumers. In the initial stage of introducing electric vehicles, the allocation of charging stations is difficult to determine due to the uncertainty of candidate sites and unidentified charging demands, which are determined by diverse variables. This paper introduces the Estimating the Required Density of EV Charging (ERDEC) stations model, which is an analytical approach to estimating the optimal density of charging stations for certain urban areas, which are subsequently aggregated to city level planning. The optimal charging station's density is derived to minimize the total cost. A numerical study is conducted to obtain the correlations among the various parameters in the proposed model, such as regional parameters, technological parameters and coefficient factors. To investigate the effect of technological advances, the corresponding changes in the optimal density and total cost are also examined by various combinations of technological parameters. Daejeon city in South Korea is selected for the case study to examine the applicability of the model to real-world problems. With real taxi trajectory data, the optimal density map of charging stations is generated. These results can provide the optimal number of chargers for driving without driving-range anxiety. In the initial planning phase of installing charging infrastructure, the proposed model can be applied to a relatively extensive area to encourage the usage of electric vehicles, especially areas that lack information, such as exact candidate sites for charging stations and other data related with electric vehicles. The methods and results of this paper can serve as a planning guideline to facilitate the extensive adoption of electric
On the method of logarithmic cumulants for parametric probability density function estimation.
Krylov, Vladimir A; Moser, Gabriele; Serpico, Sebastiano B; Zerubia, Josiane
2013-10-01
Parameter estimation of probability density functions is one of the major steps in the area of statistical image and signal processing. In this paper we explore several properties and limitations of the recently proposed method of logarithmic cumulants (MoLC) parameter estimation approach which is an alternative to the classical maximum likelihood (ML) and method of moments (MoM) approaches. We derive the general sufficient condition for a strong consistency of the MoLC estimates which represents an important asymptotic property of any statistical estimator. This result enables the demonstration of the strong consistency of MoLC estimates for a selection of widely used distribution families originating from (but not restricted to) synthetic aperture radar image processing. We then derive the analytical conditions of applicability of MoLC to samples for the distribution families in our selection. Finally, we conduct various synthetic and real data experiments to assess the comparative properties, applicability and small sample performance of MoLC notably for the generalized gamma and K families of distributions. Supervised image classification experiments are considered for medical ultrasound and remote-sensing SAR imagery. The obtained results suggest that MoLC is a feasible and computationally fast yet not universally applicable alternative to MoM. MoLC becomes especially useful when the direct ML approach turns out to be unfeasible.
Soil Organic Carbon Density in Hebei Province, China:Estimates and Uncertainty
Institute of Scientific and Technical Information of China (English)
ZHAO Yong-Cun; SHI Xue-Zheng; YU Dong-Sheng; T. F. PAGELLA; SUN Wei-Xia; XU Xiang-Hua
2005-01-01
In order to improve the precision of soil organic carbon (SOC) estimates, the sources of uncertainty in soil organic carbon density (SOCD) estimates and SOC stocks were examined using 363 soil profiles in Hebei Province, China, with three methods: the soil profile statistics (SPS), GIS-based soil type (GST), and kriging interpolation (KI). The GST method, utilizing both pedological professional knowledge and GIS technology, was considered the most accurate method of the three estimations, with SOCD estimates for SPS 10% lower and KI 10% higher. The SOCD range for GST was 84% wider than KI as KI smoothing effect narrowed the SOCD range. Nevertheless, the coefficient of variation for SOCD with KI (41.7%) was less than GST and SPS. Comparing SOCD's lower estimates for SPS versus GST, the major sources of uncertainty were the conflicting area of proportional relations. Meanwhile, the fewer number of soil profiles and the necessity of using the smoothing effect with KI were its sources of uncertainty. Moreover, for local detailed variations of SOCD, GST was more advantageous in reflecting the distribution pattern than KI.
Large-sample study of the kernel density estimators under multiplicative censoring
Asgharian, Masoud; Fakoor, Vahid; 10.1214/11-AOS954
2012-01-01
The multiplicative censoring model introduced in Vardi [Biometrika 76 (1989) 751--761] is an incomplete data problem whereby two independent samples from the lifetime distribution $G$, $\\mathcal{X}_m=(X_1,...,X_m)$ and $\\mathcal{Z}_n=(Z_1,...,Z_n)$, are observed subject to a form of coarsening. Specifically, sample $\\mathcal{X}_m$ is fully observed while $\\mathcal{Y}_n=(Y_1,...,Y_n)$ is observed instead of $\\mathcal{Z}_n$, where $Y_i=U_iZ_i$ and $(U_1,...,U_n)$ is an independent sample from the standard uniform distribution. Vardi [Biometrika 76 (1989) 751--761] showed that this model unifies several important statistical problems, such as the deconvolution of an exponential random variable, estimation under a decreasing density constraint and an estimation problem in renewal processes. In this paper, we establish the large-sample properties of kernel density estimators under the multiplicative censoring model. We first construct a strong approximation for the process $\\sqrt{k}(\\hat{G}-G)$, where $\\hat{G}$ is...
Estimation of graphite density and mechanical strength variation of VHTR during air-ingress accident
International Nuclear Information System (INIS)
An air-ingress accident in a Very High Temperature Gas-Cooled Reactor (VHTR) is anticipated to cause severe changes to graphite density and mechanical strength by an oxidation process that has many side effects. However, quantitative estimations have not yet been performed. This study focuses on predicting the changes in graphite density and mechanical strength via thermal hydraulic system analysis code. In order to analyze the change in graphite density, a simple graphite burn-off model was developed. The model is based on the similarities between a parallel electrical circuit and graphite oxidation. It was used to determine overall changes in the graphite's geometry and density. The model was validated by comparing its results to experimental data that was obtained for several temperatures. In the experiment, cylindrically shaped graphite specimens were oxidized in an electrical furnace and the variations of its mass were measured against time. The experiment's range covered temperatures between 6000 C and 9000 C. Experimental data validated the model's accuracy. Finally, the developed model along with other comprehensive graphite oxidation models was integrated into the VHTR system analysis code, GAMMA. GT-MHR 600 MWt reactor was selected as a reference reactor. Based on the calculation, the main oxidation process was observed 5.5 days after the accident when followed by natural convection. The core maximum temperature reached 16000 C, but never exceeded the maximum temperature criteria, 18000 C. However, the oxidation process did significantly decrease the density of bottom reflector, making it vulnerable to mechanical stress. The stress on the bottom reflector is greatly increased because it sustains the reactor core. The calculation proceeded until 11 days after the accident, resulting in an observed 4.5% decrease in density and a 25% reduction of mechanical strength
A maximum volume density estimator generalised over a proper motion limited sample
Lam, M C; Hambly, N C
2015-01-01
The traditional Schmidt density estimator has been proven to be unbiased and effective in a magnitude limited sample. Previously, efforts have been made to generalise it for populations with non-uniform density and proper motion limited cases. This work shows that the then good assumptions for a proper motion limited sample are no longer sufficient to cope with modern data. Populations with larger differences in the kinematics as compared to the Local Standard of Rest are most severely affected. We show that this systematic bias can be removed by treating the discovery fraction inseparable from the generalised maximum volume integrand. The treatment can be applied to any proper motion limited sample with good knowledge of the kinematics. This work demonstrates the method through application to a mock catalogue of a white dwarf-only solar neighbourhood for various scenarios and compared against the traditional treatment using a survey with Pan-STARRS-like characteristics.
"Prospecting Asteroids: Indirect technique to estimate overall density and inner composition"
Such, Pamela
2016-07-01
Spectroscopic studies of asteroids make possible to obtain some information on their composition from the surface but say little about the innermost material, porosity and density of the object. In addition, spectroscopic observations are affected by the effects of "space weathering" produced by the bombardment of charged particles for certain materials that change their chemical structure, albedo and other physical properties, partly altering their chances of identification. Data such as the mass, size and density of the asteroids are essential at the time to propose space missions in order to determine the best candidates for space exploration and is of great importance to determine a priori any of them remotely from Earth. From many years ago its determined masses of largest asteroids studying the gravitational effects they have on smaller asteroids when they approach them (see Davis and Bender, 1977; Schubart and Matson, 1979; School et al 1987; Hoffman, 1989b, among others), but estimates of the masses of the smallest objects is limited to the effects that occur in extreme close encounters to other asteroids of similar size. This paper presents the results of a search for approaches of pair of asteroids that approximate distances less than 0.0004 UA (50,000 km) of each other in order to study their masses through the astrometric method and to estimate in a future their densities and internal composition. References Davis, D. R., and D. F. Bender. 1977. Asteroid mass determinations: search for futher encounter opportunities. Bull. Am. Astron. Soc. 9, 502-503. Hoffman, M. 1989b. Asteroid mass determination: Present situation and perspectives. In asteroids II (R. P. Binzel, T. Gehreis, and M. S. Matthews, Eds.), pp 228-239. Univ. Arizona Press, Tucson. School, H. L. D. Schmadel and S. Roser 1987. The mass of the asteroid (10) Hygiea derived from observations of (829) Academia. Astron. Astrophys. 179, 311-316. Schubart, J. And D. L. Matson 1979. Masses and
Inamdar, Padmanabh; Mehta, Garima
2011-01-01
Despite advances in management of breast cancer, etiology is still elusive. Diet, obesity and other life style factors have been implicated in its etiology. We assessed the role of obesity and HDL-C levels in patients with rural background in etiology of breast cancer. To know the relation between obesity and incidence of breast cancer in local population. Also to know serum HDL-C level in breast cancer and its correlation with breast cancer. A nested pilot study of 50 breast cancer patients ...
Tenreiro, Carlos
1998-01-01
In this paper we consider the weighted average square error An([pi]) = (1/n) [Sigma]nj=1{fn(Xj) -; f(Xj)}2[pi](Xj), where f is the common density function of the independent and identically distributed random vectors X1,..., Xn, fn is the kernel estimator based on these vectors and [pi] is a weight function. Using U-statistics techniques and the results of Gouriéroux and Tenreiro (Preprint 9617, Departamento de Matemática, Universidade de Coimbra, 1996), we establish a central limit theorem f...
Central limit theorem for integrated square error of kernel estimators of spherical density
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Let X1,…,Xn be iid observations of a random variable X with pr obab ility density function f(x) on the q-dimensional unit sphere Ωq I n Rq+1 ,q≥1. Let fn(x)=n-1 c(h)∑ni=1 K［(1-x′Xi)/ h2］be a kernel estimator of f(x). In this paper we establish a central limit theorem for integrated square error of fn under some mild conditions.
Automated voxelization of 3D atom probe data through kernel density estimation.
Srinivasan, Srikant; Kaluskar, Kaustubh; Dumpala, Santoshrupa; Broderick, Scott; Rajan, Krishna
2015-12-01
Identifying nanoscale chemical features from atom probe tomography (APT) data routinely involves adjustment of voxel size as an input parameter, through visual supervision, making the final outcome user dependent, reliant on heuristic knowledge and potentially prone to error. This work utilizes Kernel density estimators to select an optimal voxel size in an unsupervised manner to perform feature selection, in particular targeting resolution of interfacial features and chemistries. The capability of this approach is demonstrated through analysis of the γ / γ' interface in a Ni-Al-Cr superalloy. PMID:25825028
Smooth estimation of a monotone hazard and a monotone density under random censoring
Lopuhaä, Hendrik P.; Musta, Eni
2015-01-01
We consider kernel smoothed Grenander-type estimators for a monotone hazard rate and a monotone density in the presence of randomly right censored data. We show that they converge at rate $n^{2/5}$ and that the limit distribution at a fixed point is Gaussian with explicitly given mean and variance. It is well-known that standard kernel smoothing leads to inconsistency problems at the boundary points. It turns out that, also by using a boundary correction, we can only establish uniform consist...
Bayesian semiparametric power spectral density estimation in gravitational wave data analysis
Edwards, Matthew C; Christensen, Nelson
2015-01-01
The standard noise model in gravitational wave (GW) data analysis assumes detector noise is stationary and Gaussian distributed, with a known power spectral density (PSD) that is usually estimated using clean off-source data. Real GW data often depart from these assumptions, and misspecified parametric models of the PSD could result in misleading inferences. We propose a Bayesian semiparametric approach to improve this. We use a nonparametric Bernstein polynomial prior on the PSD, with weights attained via a Dirichlet process distribution, and update this using the Whittle likelihood. Posterior samples are obtained using a Metropolis-within-Gibbs sampler. We simultaneously estimate the reconstruction parameters of a rotating core collapse supernova GW burst that has been embedded in simulated Advanced LIGO noise. We also discuss an approach to deal with non-stationary data by breaking longer data streams into smaller and locally stationary components.
Edwards, Matthew C.; Meyer, Renate; Christensen, Nelson
2015-09-01
The standard noise model in gravitational wave (GW) data analysis assumes detector noise is stationary and Gaussian distributed, with a known power spectral density (PSD) that is usually estimated using clean off-source data. Real GW data often depart from these assumptions, and misspecified parametric models of the PSD could result in misleading inferences. We propose a Bayesian semiparametric approach to improve this. We use a nonparametric Bernstein polynomial prior on the PSD, with weights attained via a Dirichlet process distribution, and update this using the Whittle likelihood. Posterior samples are obtained using a blocked Metropolis-within-Gibbs sampler. We simultaneously estimate the reconstruction parameters of a rotating core collapse supernova GW burst that has been embedded in simulated Advanced LIGO noise. We also discuss an approach to deal with nonstationary data by breaking longer data streams into smaller and locally stationary components.
Daniell method for power spectral density estimation in atomic force microscopy.
Labuda, Aleksander
2016-03-01
An alternative method for power spectral density (PSD) estimation--the Daniell method--is revisited and compared to the most prevalent method used in the field of atomic force microscopy for quantifying cantilever thermal motion--the Bartlett method. Both methods are shown to underestimate the Q factor of a simple harmonic oscillator (SHO) by a predictable, and therefore correctable, amount in the absence of spurious deterministic noise sources. However, the Bartlett method is much more prone to spectral leakage which can obscure the thermal spectrum in the presence of deterministic noise. By the significant reduction in spectral leakage, the Daniell method leads to a more accurate representation of the true PSD and enables clear identification and rejection of deterministic noise peaks. This benefit is especially valuable for the development of automated PSD fitting algorithms for robust and accurate estimation of SHO parameters from a thermal spectrum. PMID:27036781
Directory of Open Access Journals (Sweden)
Kaihan Fakhar
Full Text Available OBJECTIVE: We aimed in this investigation to study deep brain stimulation (DBS battery drain with special attention directed toward patient symptoms prior to and following battery replacement. BACKGROUND: Previously our group developed web-based calculators and smart phone applications to estimate DBS battery life (http://mdc.mbi.ufl.edu/surgery/dbs-battery-estimator. METHODS: A cohort of 320 patients undergoing DBS battery replacement from 2002-2012 were included in an IRB approved study. Statistical analysis was performed using SPSS 20.0 (IBM, Armonk, NY. RESULTS: The mean charge density for treatment of Parkinson's disease was 7.2 µC/cm(2/phase (SD = 3.82, for dystonia was 17.5 µC/cm(2/phase (SD = 8.53, for essential tremor was 8.3 µC/cm(2/phase (SD = 4.85, and for OCD was 18.0 µC/cm(2/phase (SD = 4.35. There was a significant relationship between charge density and battery life (r = -.59, p<.001, as well as total power and battery life (r = -.64, p<.001. The UF estimator (r = .67, p<.001 and the Medtronic helpline (r = .74, p<.001 predictions of battery life were significantly positively associated with actual battery life. Battery status indicators on Soletra and Kinetra were poor predictors of battery life. In 38 cases, the symptoms improved following a battery change, suggesting that the neurostimulator was likely responsible for symptom worsening. For these cases, both the UF estimator and the Medtronic helpline were significantly correlated with battery life (r = .65 and r = .70, respectively, both p<.001. CONCLUSIONS: Battery estimations, charge density, total power and clinical symptoms were important factors. The observation of clinical worsening that was rescued following neurostimulator replacement reinforces the notion that changes in clinical symptoms can be associated with battery drain.
Constrained Kalman Filtering Via Density Function Truncation for Turbofan Engine Health Estimation
Simon, Dan; Simon, Donald L.
2006-01-01
Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter truncates the PDF (probability density function) of the Kalman filter estimate at the known constraints and then computes the constrained filter estimate as the mean of the truncated PDF. The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is demonstrated via simulation results obtained from a turbofan engine model. The turbofan engine model contains 3 state variables, 11 measurements, and 10 component health parameters. It is also shown that the truncated Kalman filter may be a more accurate way of incorporating inequality constraints than other constrained filters (e.g., the projection approach to constrained filtering).
Directory of Open Access Journals (Sweden)
Nikica Šprem
2012-01-01
Full Text Available Chamois (Rupicapra rupicapra L. with the Biokovo Nature Park is one of the largest and most stable populations of chamois in Croatia. Reintroduced in year 1964, and present estimated population size is estimated at 350 to 400 individuals. Estimate of population size is very important, since the animals inhabit and are often distributed across forest habitats, it is sometimes difficult and/or very expensive. Therefore, our objective was use of sensor cameras to estimate population density and population structure of chamois on the Biokovo mountain. The study was conducted in summer 2011th year, with three sensor cameras in three different localities. The cameras have recorded a total of 1003 JPEG images. During monitoring, we recorded 164 different individuals of chamois, of which there are 119 female and 45 male specimens. Daily dynamics of arriving was the highest in two periods, morning between 06:00 and 08:00, and in the evening between 18:00 and 20:00. The results of sensor cameras can be considered valid, and are extremely technical aid for the better management and protection of animal species, because we have data on the structure of the population.
Gonzalez, Ruben; Huang, Biao; Lau, Eric
2015-09-01
Principal component analysis has been widely used in the process industries for the purpose of monitoring abnormal behaviour. The process of reducing dimension is obtained through PCA, while T-tests are used to test for abnormality. Some of the main contributions to the success of PCA is its ability to not only detect problems, but to also give some indication as to where these problems are located. However, PCA and the T-test make use of Gaussian assumptions which may not be suitable in process fault detection. A previous modification of this method is the use of independent component analysis (ICA) for dimension reduction combined with kernel density estimation for detecting abnormality; like PCA, this method points out location of the problems based on linear data-driven methods, but without the Gaussian assumptions. Both ICA and PCA, however, suffer from challenges in interpreting results, which can make it difficult to quickly act once a fault has been detected online. This paper proposes the use of Bayesian networks for dimension reduction which allows the use of process knowledge enabling more intelligent dimension reduction and easier interpretation of results. The dimension reduction technique is combined with multivariate kernel density estimation, making this technique effective for non-linear relationships with non-Gaussian variables. The performance of PCA, ICA and Bayesian networks are compared on data from an industrial scale plant. PMID:25930233
Density and Biomass Estimates by Removal for an Amazonian Crocodilian, Paleosuchus palpebrosus
2016-01-01
Direct counts of crocodilians are rarely feasible and it is difficult to meet the assumptions of mark-recapture methods for most species in most habitats. Catch-out experiments are also usually not logistically or morally justifiable because it would be necessary to destroy the habitat in order to be confident that most individuals had been captured. We took advantage of the draining and filling of a large area of flooded forest during the building of the Santo Antônio dam on the Madeira River to obtain accurate estimates of the density and biomass of Paleosuchus palpebrosus. The density, 28.4 non-hatchling individuals per km2, is one of the highest reported for any crocodilian, except for species that are temporarily concentrated in small areas during dry-season drought. The biomass estimate of 63.15 kg*km-2 is higher than that for most or even all mammalian carnivores in tropical forest. P. palpebrosus may be one of the World´s most abundant crocodilians. PMID:27224473
The Wegner Estimate and the Integrated Density of States for some Random Operators
Indian Academy of Sciences (India)
J M Combes; P D Hislop; Frédéric Klopp; Shu Nakamura
2002-02-01
The integrated density of states (IDS) for random operators is an important function describing many physical characteristics of a random system. Properties of the IDS are derived from the Wegner estimate that describes the influence of finite-volume perturbations on a background system. In this paper, we present a simple proof of the Wegner estimate applicable to a wide variety of random perturbations of deterministic background operators. The proof yields the correct volume dependence of the upper bound. This implies the local Hölder continuity of the integrated density of states at energies in the unperturbed spectral gap. The proof depends on the -theory of the spectral shift function (SSF), for ≥ 1, applicable to pairs of self-adjoint operators whose difference is in the trace ideal $\\mathcal{I}_p$, for 0 < ≤ 1. We present this and other results on the SSF due to other authors. Under an additional condition of the single-site potential, local Hölder continuity is proved at all energies. Finally, we present extensions of this work to random potentials with nonsign definite single-site potentials.
Power spectral density of velocity fluctuations estimated from phase Doppler data
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Jicha Miroslav
2012-04-01
Full Text Available Laser Doppler Anemometry (LDA and its modifications such as PhaseDoppler Particle Anemometry (P/DPA is point-wise method for optical nonintrusive measurement of particle velocity with high data rate. Conversion of the LDA velocity data from temporal to frequency domain – calculation of power spectral density (PSD of velocity fluctuations, is a non trivial task due to nonequidistant data sampling in time. We briefly discuss possibilities for the PSD estimation and specify limitations caused by seeding density and other factors of the flow and LDA setup. Arbitrary results of LDA measurements are compared with corresponding Hot Wire Anemometry (HWA data in the frequency domain. Slot correlation (SC method implemented in software program Kern by Nobach (2006 is used for the PSD estimation. Influence of several input parameters on resulting PSDs is described. Optimum setup of the software for our data of particle-laden air flow in realistic human airway model is documented. Typical character of the flow is described using PSD plots of velocity fluctuations with comments on specific properties of the flow. Some recommendations for improvements of future experiments to acquire better PSD results are given.
International Nuclear Information System (INIS)
Elderly patients become more important in oncology. In this group, personalized treatment approaches taking into account survival prognoses and comorbidities play a major role. Predictive instruments are necessary to estimate the survival of elderly cancer patients. The importance of separate instruments for different tumor entities has been recognized. In this study, an instrument was generated to estimate the survival of elderly patients developing metastatic spinal cord compression (MSCC) from breast cancer. In 218 elderly patients (age ≥65 years) irradiated for MSCC from breast cancer, nine factors were evaluated for survival: fractionation regimen, age, time from breast cancer diagnosis to RT of MSCC, visceral metastases, other bone metastases, time developing motor deficits, pre-radiotherapy ambulatory status, number of involved vertebrae, and Eastern Cooperative Oncology Group (ECOG) performance score. Factors significantly associated with survival in the Cox regression analysis were included in the prognostic instrument. Scores for each factor were calculated by dividing the 6-months survival rates by 10. The sums of these scores represented the patients’ scores. On multivariate analyses, visceral metastases (p < 0.001), time developing motor deficits (p < 0.001), ambulatory status (p < 0.001), number of involved vertebrae (p = 0.032), and ECOG performance score (p < 0.001) were significant and included in the prognostic instrument. Based on the patients’ scores, three groups were designed: 18–27 points, 28–39 points and 40–42 points. Six-months survival rates were 4, 62 and 100 %, respectively (p < 0.001). This new instrument contributes to personalized treatment in elderly patients with MSCC from breast cancer by predicting an individual patient’s survival prognosis
Hong, Byung-Woo
2013-11-01
We propose a mathematical framework for simultaneously delineating the boundary of object and estimating its temporal motion in the application of lesion detection in a dynamic contrast-enhanced (DCE) breast MRI sequence where both the appearance and the shape of region of interest is assumed to change in time. A unified energy functional for a joint segmentation and registration is proposed based on the assumption that the statistical properties of dynamic intensity curves within a region of interest are homogeneous. Our algorithm is designed to provide the morphological properties of the enhanced region and its dynamic intensity profiles, called kinetic signatures, in the analysis of DCE imagery since these features are considered as significant cues in understanding images. The proposed energy comprises a combination of a segmentation energy and a registration energy. The segmentation energy is developed based on a convex formulation being insensitive to the initialization. The registration energy is designed to compensate motion artifacts that are usually involved in the temporal imaging procedure. The major objective of this work is to provide a mathematical framework for a joint segmentation and registration on a dynamic sequence of images, and we demonstrate the mutual benefit of the estimation of temporal deformations for the registration step and the localization of regions of interest for the segmentation step. The effectiveness of the developed algorithm has been demonstrated on a number of clinical DCE breast MRI data in the application of breast lesion detection and the results show its potential to improve the accuracy and the efficiency in the diagnosis of breast cancer.
Directory of Open Access Journals (Sweden)
Lingling Jiang
2016-01-01
Full Text Available A multiband and a single-band semianalytical model were developed to predict algae cell density distribution. The models were based on cell density (N dependent parameterizations of the spectral backscattering coefficients, bb(λ, obtained from in situ measurements. There was a strong relationship between bb(λ and N, with a minimum regression coefficient of 0.97 at 488 nm and a maximum value of 0.98 at other bands. The cell density calculated by the multiband inversion model was similar to the field measurements of the coastal waters (the average relative error was only 8.9%, but it could not accurately discern the red tide from mixed pixels, and this led to overestimation of the area affected by the red tide. While the single-band inversion model is less precise than the former model in the high chlorophyll water, it could eliminate the impact of the suspended sediments and make more accurate estimates of the red tide area. We concluded that the two models both have advantages and disadvantages; these methods lay the foundation for developing a remote sensing forecasting system for red tides.
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Shanshan Yang
Full Text Available Detection of dysphonia is useful for monitoring the progression of phonatory impairment for patients with Parkinson's disease (PD, and also helps assess the disease severity. This paper describes the statistical pattern analysis methods to study different vocal measurements of sustained phonations. The feature dimension reduction procedure was implemented by using the sequential forward selection (SFS and kernel principal component analysis (KPCA methods. Four selected vocal measures were projected by the KPCA onto the bivariate feature space, in which the class-conditional feature densities can be approximated with the nonparametric kernel density estimation technique. In the vocal pattern classification experiments, Fisher's linear discriminant analysis (FLDA was applied to perform the linear classification of voice records for healthy control subjects and PD patients, and the maximum a posteriori (MAP decision rule and support vector machine (SVM with radial basis function kernels were employed for the nonlinear classification tasks. Based on the KPCA-mapped feature densities, the MAP classifier successfully distinguished 91.8% voice records, with a sensitivity rate of 0.986, a specificity rate of 0.708, and an area value of 0.94 under the receiver operating characteristic (ROC curve. The diagnostic performance provided by the MAP classifier was superior to those of the FLDA and SVM classifiers. In addition, the classification results indicated that gender is insensitive to dysphonia detection, and the sustained phonations of PD patients with minimal functional disability are more difficult to be correctly identified.
Estimation of Engine Intake Air Mass Flow using a generic Speed-Density method
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Vojtíšek Michal
2014-10-01
Full Text Available Measurement of real driving emissions (RDE from internal combustion engines under real-world operation using portable, onboard monitoring systems (PEMS is becoming an increasingly important tool aiding the assessment of the effects of new fuels and technologies on environment and human health. The knowledge of exhaust flow is one of the prerequisites for successful RDE measurement with PEMS. One of the simplest approaches for estimating the exhaust flow from virtually any engine is its computation from the intake air flow, which is calculated from measured engine rpm and intake manifold charge pressure and temperature using a generic speed-density algorithm, applicable to most contemporary four-cycle engines. In this work, a generic speed-density algorithm was compared against several reference methods on representative European production engines - a gasoline port-injected automobile engine, two turbocharged diesel automobile engines, and a heavy-duty turbocharged diesel engine. The overall results suggest that the uncertainty of the generic speed-density method is on the order of 10% throughout most of the engine operating range, but increasing to tens of percent where high-volume exhaust gas recirculation is used. For non-EGR engines, such uncertainty is acceptable for many simpler and screening measurements, and may be, where desired, reduced by engine-specific calibration.
Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong
2016-05-30
Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data. PMID:27410085
2011-01-01
Background Mammographic density (MD) and malignant-appearing microcalcifications (MAMCs) represent the earliest mammographic findings of non-palpable breast carcinomas. Matrix proteoglycans versican and decorin are frequently over-expressed in various malignancies and are differently involved in the progression of cancer. In the present study, we have evaluated the expression of versican and decorin in non-palpable breast carcinomas and their association with high risk mammographic findings and tumor characteristics. Methods Three hundred and ten patients with non-palpable suspicious breast lesions, detected during screening mammography, were studied. Histological examination was carried out and the expression of decorin, versican, estrogen receptor α (ERα), progesterone receptor (PR) and c-erbB2 (HER-2/neu) was assessed by immunohistochemistry. Results Histological examination showed 83 out of 310 (26.8%) carcinomas of various subtypes. Immunohistochemistry was carried out in 62/83 carcinomas. Decorin was accumulated in breast tissues with MD and MAMCs independently of the presence of malignancy. In contrast, versican was significantly increased only in carcinomas with MAMCs (median ± SE: 42.0 ± 9.1) and MD (22.5 ± 10.1) as compared to normal breast tissue with MAMCs (14.0 ± 5.8), MD (11.0 ± 4.4) and normal breast tissue without mammographic findings (10.0 ± 2.0). Elevated levels of versican were correlated with higher tumor grade and invasiveness in carcinomas with MD and MAMCs, whereas increased amounts of decorin were associated with in situ carcinomas in MAMCs. Stromal deposition of both proteoglycans was related to higher expression of ERα and PR in tumor cells only in MAMCs. Conclusions The specific accumulation of versican in breast tissue with high MD and MAMCs only in the presence of malignant transformation and its association with the aggressiveness of the tumor suggests its possible use as molecular marker in non-palpable breast carcinomas
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Kalofonos Haralabos P
2011-07-01
Full Text Available Abstract Background Mammographic density (MD and malignant-appearing microcalcifications (MAMCs represent the earliest mammographic findings of non-palpable breast carcinomas. Matrix proteoglycans versican and decorin are frequently over-expressed in various malignancies and are differently involved in the progression of cancer. In the present study, we have evaluated the expression of versican and decorin in non-palpable breast carcinomas and their association with high risk mammographic findings and tumor characteristics. Methods Three hundred and ten patients with non-palpable suspicious breast lesions, detected during screening mammography, were studied. Histological examination was carried out and the expression of decorin, versican, estrogen receptor α (ERα, progesterone receptor (PR and c-erbB2 (HER-2/neu was assessed by immunohistochemistry. Results Histological examination showed 83 out of 310 (26.8% carcinomas of various subtypes. Immunohistochemistry was carried out in 62/83 carcinomas. Decorin was accumulated in breast tissues with MD and MAMCs independently of the presence of malignancy. In contrast, versican was significantly increased only in carcinomas with MAMCs (median ± SE: 42.0 ± 9.1 and MD (22.5 ± 10.1 as compared to normal breast tissue with MAMCs (14.0 ± 5.8, MD (11.0 ± 4.4 and normal breast tissue without mammographic findings (10.0 ± 2.0. Elevated levels of versican were correlated with higher tumor grade and invasiveness in carcinomas with MD and MAMCs, whereas increased amounts of decorin were associated with in situ carcinomas in MAMCs. Stromal deposition of both proteoglycans was related to higher expression of ERα and PR in tumor cells only in MAMCs. Conclusions The specific accumulation of versican in breast tissue with high MD and MAMCs only in the presence of malignant transformation and its association with the aggressiveness of the tumor suggests its possible use as molecular marker in non
International Nuclear Information System (INIS)
Mammographic density (MD) and malignant-appearing microcalcifications (MAMCs) represent the earliest mammographic findings of non-palpable breast carcinomas. Matrix proteoglycans versican and decorin are frequently over-expressed in various malignancies and are differently involved in the progression of cancer. In the present study, we have evaluated the expression of versican and decorin in non-palpable breast carcinomas and their association with high risk mammographic findings and tumor characteristics. Three hundred and ten patients with non-palpable suspicious breast lesions, detected during screening mammography, were studied. Histological examination was carried out and the expression of decorin, versican, estrogen receptor α (ERα), progesterone receptor (PR) and c-erbB2 (HER-2/neu) was assessed by immunohistochemistry. Histological examination showed 83 out of 310 (26.8%) carcinomas of various subtypes. Immunohistochemistry was carried out in 62/83 carcinomas. Decorin was accumulated in breast tissues with MD and MAMCs independently of the presence of malignancy. In contrast, versican was significantly increased only in carcinomas with MAMCs (median ± SE: 42.0 ± 9.1) and MD (22.5 ± 10.1) as compared to normal breast tissue with MAMCs (14.0 ± 5.8), MD (11.0 ± 4.4) and normal breast tissue without mammographic findings (10.0 ± 2.0). Elevated levels of versican were correlated with higher tumor grade and invasiveness in carcinomas with MD and MAMCs, whereas increased amounts of decorin were associated with in situ carcinomas in MAMCs. Stromal deposition of both proteoglycans was related to higher expression of ERα and PR in tumor cells only in MAMCs. The specific accumulation of versican in breast tissue with high MD and MAMCs only in the presence of malignant transformation and its association with the aggressiveness of the tumor suggests its possible use as molecular marker in non-palpable breast carcinomas
Höing, Andrea; Quinten, Marcel C; Indrawati, Yohana Maria; Cheyne, Susan M; Waltert, Matthias
2013-02-01
Estimating population densities of key species is crucial for many conservation programs. Density estimates provide baseline data and enable monitoring of population size. Several different survey methods are available, and the choice of method depends on the species and study aims. Few studies have compared the accuracy and efficiency of different survey methods for large mammals, particularly for primates. Here we compare estimates of density and abundance of Kloss' gibbons (Hylobates klossii) using two of the most common survey methods: line transect distance sampling and triangulation. Line transect surveys (survey effort: 155.5 km) produced a total of 101 auditory and visual encounters and a density estimate of 5.5 gibbon clusters (groups or subgroups of primate social units)/km(2). Triangulation conducted from 12 listening posts during the same period revealed a similar density estimate of 5.0 clusters/km(2). Coefficients of variation of cluster density estimates were slightly higher from triangulation (0.24) than from line transects (0.17), resulting in a lack of precision in detecting changes in cluster densities of triangulation and triangulation method also may be appropriate.
Bayes and empirical Bayes estimators of abundance and density from spatial capture-recapture data.
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Robert M Dorazio
Full Text Available In capture-recapture and mark-resight surveys, movements of individuals both within and between sampling periods can alter the susceptibility of individuals to detection over the region of sampling. In these circumstances spatially explicit capture-recapture (SECR models, which incorporate the observed locations of individuals, allow population density and abundance to be estimated while accounting for differences in detectability of individuals. In this paper I propose two Bayesian SECR models, one for the analysis of recaptures observed in trapping arrays and another for the analysis of recaptures observed in area searches. In formulating these models I used distinct submodels to specify the distribution of individual home-range centers and the observable recaptures associated with these individuals. This separation of ecological and observational processes allowed me to derive a formal connection between Bayes and empirical Bayes estimators of population abundance that has not been established previously. I showed that this connection applies to every Poisson point-process model of SECR data and provides theoretical support for a previously proposed estimator of abundance based on recaptures in trapping arrays. To illustrate results of both classical and Bayesian methods of analysis, I compared Bayes and empirical Bayes esimates of abundance and density using recaptures from simulated and real populations of animals. Real populations included two iconic datasets: recaptures of tigers detected in camera-trap surveys and recaptures of lizards detected in area-search surveys. In the datasets I analyzed, classical and Bayesian methods provided similar - and often identical - inferences, which is not surprising given the sample sizes and the noninformative priors used in the analyses.
A Bayesian Hierarchical Model for Estimation of Abundance and Spatial Density of Aedes aegypti.
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Daniel A M Villela
Full Text Available Strategies to minimize dengue transmission commonly rely on vector control, which aims to maintain Ae. aegypti density below a theoretical threshold. Mosquito abundance is traditionally estimated from mark-release-recapture (MRR experiments, which lack proper analysis regarding accurate vector spatial distribution and population density. Recently proposed strategies to control vector-borne diseases involve replacing the susceptible wild population by genetically modified individuals' refractory to the infection by the pathogen. Accurate measurements of mosquito abundance in time and space are required to optimize the success of such interventions. In this paper, we present a hierarchical probabilistic model for the estimation of population abundance and spatial distribution from typical mosquito MRR experiments, with direct application to the planning of these new control strategies. We perform a Bayesian analysis using the model and data from two MRR experiments performed in a neighborhood of Rio de Janeiro, Brazil, during both low- and high-dengue transmission seasons. The hierarchical model indicates that mosquito spatial distribution is clustered during the winter (0.99 mosquitoes/premise 95% CI: 0.80-1.23 and more homogeneous during the high abundance period (5.2 mosquitoes/premise 95% CI: 4.3-5.9. The hierarchical model also performed better than the commonly used Fisher-Ford's method, when using simulated data. The proposed model provides a formal treatment of the sources of uncertainty associated with the estimation of mosquito abundance imposed by the sampling design. Our approach is useful in strategies such as population suppression or the displacement of wild vector populations by refractory Wolbachia-infected mosquitoes, since the invasion dynamics have been shown to follow threshold conditions dictated by mosquito abundance. The presence of spatially distributed abundance hotspots is also formally addressed under this modeling
Bayes and empirical Bayes estimators of abundance and density from spatial capture-recapture data
Dorazio, Robert M.
2013-01-01
In capture-recapture and mark-resight surveys, movements of individuals both within and between sampling periods can alter the susceptibility of individuals to detection over the region of sampling. In these circumstances spatially explicit capture-recapture (SECR) models, which incorporate the observed locations of individuals, allow population density and abundance to be estimated while accounting for differences in detectability of individuals. In this paper I propose two Bayesian SECR models, one for the analysis of recaptures observed in trapping arrays and another for the analysis of recaptures observed in area searches. In formulating these models I used distinct submodels to specify the distribution of individual home-range centers and the observable recaptures associated with these individuals. This separation of ecological and observational processes allowed me to derive a formal connection between Bayes and empirical Bayes estimators of population abundance that has not been established previously. I showed that this connection applies to every Poisson point-process model of SECR data and provides theoretical support for a previously proposed estimator of abundance based on recaptures in trapping arrays. To illustrate results of both classical and Bayesian methods of analysis, I compared Bayes and empirical Bayes esimates of abundance and density using recaptures from simulated and real populations of animals. Real populations included two iconic datasets: recaptures of tigers detected in camera-trap surveys and recaptures of lizards detected in area-search surveys. In the datasets I analyzed, classical and Bayesian methods provided similar – and often identical – inferences, which is not surprising given the sample sizes and the noninformative priors used in the analyses.
How does spatial study design influence density estimates from spatial capture-recapture models?
Directory of Open Access Journals (Sweden)
Rahel Sollmann
Full Text Available When estimating population density from data collected on non-invasive detector arrays, recently developed spatial capture-recapture (SCR models present an advance over non-spatial models by accounting for individual movement. While these models should be more robust to changes in trapping designs, they have not been well tested. Here we investigate how the spatial arrangement and size of the trapping array influence parameter estimates for SCR models. We analysed black bear data collected with 123 hair snares with an SCR model accounting for differences in detection and movement between sexes and across the trapping occasions. To see how the size of the trap array and trap dispersion influence parameter estimates, we repeated analysis for data from subsets of traps: 50% chosen at random, 50% in the centre of the array and 20% in the South of the array. Additionally, we simulated and analysed data under a suite of trap designs and home range sizes. In the black bear study, we found that results were similar across trap arrays, except when only 20% of the array was used. Black bear density was approximately 10 individuals per 100 km(2. Our simulation study showed that SCR models performed well as long as the extent of the trap array was similar to or larger than the extent of individual movement during the study period, and movement was at least half the distance between traps. SCR models performed well across a range of spatial trap setups and animal movements. Contrary to non-spatial capture-recapture models, they do not require the trapping grid to cover an area several times the average home range of the studied species. This renders SCR models more appropriate for the study of wide-ranging mammals and more flexible to design studies targeting multiple species.
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Noritaka Shimizu
2016-02-01
Full Text Available We introduce a novel method to obtain level densities in large-scale shell-model calculations. Our method is a stochastic estimation of eigenvalue count based on a shifted Krylov-subspace method, which enables us to obtain level densities of huge Hamiltonian matrices. This framework leads to a successful description of both low-lying spectroscopy and the experimentally observed equilibration of Jπ=2+ and 2− states in 58Ni in a unified manner.
Chacón, José E
2012-01-01
In a recent paper, Chac\\'on, Duong and Wand (2011) provided an asymptotic analysis for kernel estimation of multivariate density derivatives of arbitrary order. However, that paper did not address in detail the most important topic for any kernel estimator in practice, that is, the choice of the bandwidth. In the multivariate context there are different levels of sophistication on the bandwidth matrix to be used in the estimator. The simplest parameterization of such a bandwidth, consisting of a positive scalar multiple of the identity matrix, is easier to analyze from a mathematical point of view, but its lack of flexibility can lead to a substantial loss in terms of efficiency, which worsens as the order of the derivative increases, as compared to the most general parameterization, using a symmetric positive definite matrix. Here we present three new methods which allow for an automatic (data-dependent) selection of the bandwidth matrix within the most general class of matrices. We study their theoretical a...
Giambini, Hugo; Dragomir-Daescu, Dan; Huddleston, Paul M; Camp, Jon J; An, Kai-Nan; Nassr, Ahmad
2015-11-01
Osteoporosis is characterized by bony material loss and decreased bone strength leading to a significant increase in fracture risk. Patient-specific quantitative computed tomography (QCT) finite element (FE) models may be used to predict fracture under physiological loading. Material properties for the FE models used to predict fracture are obtained by converting grayscale values from the CT into volumetric bone mineral density (vBMD) using calibration phantoms. If there are any variations arising from the CT acquisition protocol, vBMD estimation and material property assignment could be affected, thus, affecting fracture risk prediction. We hypothesized that material property assignments may be dependent on scanning and postprocessing settings including voltage, current, and reconstruction kernel, thus potentially having an effect in fracture risk prediction. A rabbit femur and a standard calibration phantom were imaged by QCT using different protocols. Cortical and cancellous regions were segmented, their average Hounsfield unit (HU) values obtained and converted to vBMD. Estimated vBMD for the cortical and cancellous regions were affected by voltage and kernel but not by current. Our study demonstrated that there exists a significant variation in the estimated vBMD values obtained with different scanning acquisitions. In addition, the large noise differences observed utilizing different scanning parameters could have an important negative effect on small subregions containing fewer voxels. PMID:26355694
Phase-space structures I: A comparison of 6D density estimators
Maciejewski, M; Alard, C; Bouchet, F; Pichon, C
2008-01-01
This paper reviews and analyses methods used to identify neighbours in 6D space and estimate the corresponding phase-space density. It compares SPH methods to 6D Delaunay tessellation on statical and dynamical realisation of single halo profiles, paying attention to the unknown scaling, S_G, used to relate the spatial dimensions to the velocity dimensions. The methods with local adaptive metric provide the best phase-space estimators. They make use of a Shannon entropy criterion combined with a binary tree partitioning and with SPH interpolation using 10-40 neighbours. Local scaling implemented by such methods, which enforces local isotropy of the distribution function, can vary by about one order of magnitude in different regions within the system. It presents a bimodal distribution, in which one component is dominated by the main part of the halo and the other one is dominated by the substructures. While potentially better than SPH techniques, since it yields an optimal estimate of the local softening volum...
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Wolfe, Adam R. [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Atkinson, Rachel L. [Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Reddy, Jay P. [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Debeb, Bisrat G.; Larson, Richard; Li, Li [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Masuda, Hiroko; Brewer, Takae [Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Atkinson, Bradley J. [Department of Clinical Pharmacy Services, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Brewster, Abeena [Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Ueno, Naoto T. [Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Woodward, Wendy A., E-mail: wwoodward@mdanderson.org [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)
2015-04-01
Purpose: We previously demonstrated that cholesterol-lowering agents regulate radiation sensitivity of inflammatory breast cancer (IBC) cell lines in vitro and are associated with less radiation resistance among IBC patients who undergo postmastectomy radiation. We hypothesized that decreasing IBC cellular cholesterol induced by treatment with lipoproteins would increase radiation sensitivity. Here, we examined the impact of specific transporters of cholesterol (ie lipoproteins) on the responses of IBC cells to self-renewal and to radiation in vitro and on clinical outcomes in IBC patients. Methods and Materials: Two patient-derived IBC cell lines, SUM 149 and KPL4, were incubated with low-density lipoproteins (LDL), very-low-density lipoproteins (VLDL), or high-density lipoproteins (HDL) for 24 hours prior to irradiation (0-6 Gy) and mammosphere formation assay. Cholesterol panels were examined in a cohort of patients with primary IBC diagnosed between 1995 and 2011 at MD Anderson Cancer Center. Lipoprotein levels were then correlated to patient outcome, using the log rank statistical model, and examined in multivariate analysis using Cox regression. Results: VLDL increased and HDL decreased mammosphere formation compared to untreated SUM 149 and KPL4 cells. Survival curves showed enhancement of survival in both of the IBC cell lines when pretreated with VLDL and, conversely, radiation sensitization in all cell lines when pretreated with HDL. In IBC patients, higher VLDL values (>30 mg/dL) predicted a lower 5-year overall survival rate than normal values (hazard ratio [HR] = 1.9 [95% confidence interval [CI]: 1.05-3.45], P=.035). Lower-than-normal patient HDL values (<60 mg/dL) predicted a lower 5-year overall survival rate than values higher than 60 mg/dL (HR = 3.21 [95% CI: 1.25-8.27], P=.015). Conclusions: This study discovered a relationship among the plasma levels of lipoproteins, overall patient response, and radiation resistance in IBC patients
International Nuclear Information System (INIS)
Purpose: We previously demonstrated that cholesterol-lowering agents regulate radiation sensitivity of inflammatory breast cancer (IBC) cell lines in vitro and are associated with less radiation resistance among IBC patients who undergo postmastectomy radiation. We hypothesized that decreasing IBC cellular cholesterol induced by treatment with lipoproteins would increase radiation sensitivity. Here, we examined the impact of specific transporters of cholesterol (ie lipoproteins) on the responses of IBC cells to self-renewal and to radiation in vitro and on clinical outcomes in IBC patients. Methods and Materials: Two patient-derived IBC cell lines, SUM 149 and KPL4, were incubated with low-density lipoproteins (LDL), very-low-density lipoproteins (VLDL), or high-density lipoproteins (HDL) for 24 hours prior to irradiation (0-6 Gy) and mammosphere formation assay. Cholesterol panels were examined in a cohort of patients with primary IBC diagnosed between 1995 and 2011 at MD Anderson Cancer Center. Lipoprotein levels were then correlated to patient outcome, using the log rank statistical model, and examined in multivariate analysis using Cox regression. Results: VLDL increased and HDL decreased mammosphere formation compared to untreated SUM 149 and KPL4 cells. Survival curves showed enhancement of survival in both of the IBC cell lines when pretreated with VLDL and, conversely, radiation sensitization in all cell lines when pretreated with HDL. In IBC patients, higher VLDL values (>30 mg/dL) predicted a lower 5-year overall survival rate than normal values (hazard ratio [HR] = 1.9 [95% confidence interval [CI]: 1.05-3.45], P=.035). Lower-than-normal patient HDL values (<60 mg/dL) predicted a lower 5-year overall survival rate than values higher than 60 mg/dL (HR = 3.21 [95% CI: 1.25-8.27], P=.015). Conclusions: This study discovered a relationship among the plasma levels of lipoproteins, overall patient response, and radiation resistance in IBC patients
Garde, Ainara; Karlen, Walter; Ansermino, J Mark; Dumont, Guy A
2014-01-01
The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool. PMID:24466088
Directory of Open Access Journals (Sweden)
Ainara Garde
Full Text Available The photoplethysmogram (PPG obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD as a novel way to estimate respiratory rate (RR and heart rate (HR from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min and cardiac (30 to 180 beats/min frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS error. We investigated two window sizes (60 and 120 s on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles obtained for RR was 0.95 (0.27, 6.20 breaths/min and for HR was 0.76 (0.34, 1.45 beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.
Berzins, Juris; Sneiders, Uldis; Plegere, Daina; Freivalds, Talivaldis; Grigalinovica, Romalda
2000-04-01
We performed a multi parameter image analysis assessment of breast cancer cell population nuclear grade (NG), which is regarded as one of the main prognostic factors for treatment efficacy and survival of the patients and compared it with light microscopic estimation of NG. Cytological imprint slides from 20 ductal carcinomas were stained according to Leischmann-AzureII-eosine method, and NG was estimated by light microscopic observation according to Black in Fisher's modification. Simultaneously, using specially elaborated software, in each patient 100 cancer cells were analyzed for nuclear perimeter, diameter, area, nucleolar area, and average intensity of staining. The chromatin structure was assessed using mean diameter of chromatin grains and relatively chromatic are within the nucleus. Light microscopic estimation revealed 4/15 grade 2 and 7/15 grade 3 tumors out of 15 filtrating ductal carcinomas, with 4/15 classified as intermediate between grade 2-3. Multifactoral linear correlation coefficient r equals 0.39, p < 0.001 for ductal cancer, higher NG comes with increasing nucleolar area, nuclear roundness factor, nuclear are, and chromatin area within the cell nucleus. Image analysis may yield precise information on NG as a prognostic factor in breast cancer patients.
Optimal estimation of free energies and stationary densities from multiple biased simulations
Wu, Hao
2013-01-01
When studying high-dimensional dynamical systems such as macromolecules, quantum systems and polymers, a prime concern is the identification of the most probable states and their stationary probabilities or free energies. Often, these systems have metastable regions or phases, prohibiting to estimate the stationary probabilities by direct simulation. Efficient sampling methods such as umbrella sampling, metadynamics and conformational flooding have developed that perform a number of simulations where the system's potential is biased such as to accelerate the rare barrier crossing events. A joint free energy profile or stationary density can then be obtained from these biased simulations with weighted histogram analysis method (WHAM). This approach (a) requires a few essential order parameters to be defined in which the histogram is set up, and (b) assumes that each simulation is in global equilibrium. Both assumptions make the investigation of high-dimensional systems with previously unknown energy landscape ...
A new method for estimating the density of coal for resource and reserve calculation
Energy Technology Data Exchange (ETDEWEB)
Quinn, G.W. [BHP Minerals International Exploration Inc., Kuala Lumpur (Malaysia)
2000-01-01
It is generally agreed that it is currently impossible to measure accurately in situ coal moisture by direct sampling because once the sample is taken it is no longer representative of the in situ condition. This paper presents an alternative proposition that what is really relevant is the weight of coal after mining, on the surface, either as crushed run of mine (ROM) product or beneficiated product, not the weight of coal in the ground. This is the form in which the coal is first weighed and the form in which it is sold and utilised. Thus it is only essential to estimate as accurately as possible the volume of coal in the ground from thickness information. It is the relative density (RD) of the broken coal on the surface that is important, not that of the solid coal in situ. It is shown however, that on a dry basis the two are essentially the same. This could be a useful and realistic way to determine suitable relative densities for coal resource and reserve calculations. 2 refs., 3 figs., 1 tab.
Evaluation of a brushing machine for estimating density of spider mites on grape leaves.
Macmillan, Craig D; Costello, Michael J
2015-12-01
Direct visual inspection and enumeration for estimating field population density of economically important arthropods, such as spider mites, provide more information than alternative methods, such as binomial sampling, but is laborious and time consuming. A brushing machine can reduce sampling time and perhaps improve accuracy. Although brushing technology has been investigated and recommended as a useful tool for researchers and integrated pest management practitioners, little work to demonstrate the validity of this technique has been performed since the 1950's. We investigated the brushing machine manufactured by Leedom Enterprises (Mi-Wuk Village, CA, USA) for studies on spider mites. We evaluated (1) the mite recovery efficiency relative to the number of passes of a leaf through the brushes, (2) mite counts as generated by the machine compared to visual counts under a microscope, (3) the lateral distribution of mites on the collection plate and (4) the accuracy and precision of a 10% sub-sample using a double-transect counting grid. We found that about 95% of mites on a leaf were recovered after five passes, and 99% after nine passes, and mite counts from brushing were consistently higher than those from visual inspection. Lateral distribution of mites was not uniform, being highest in concentration at the center and lowest at the periphery. The 10% double-transect pattern did not result in a significant correlation with the total plate count at low mite density, but accuracy and precision improved at medium and high density. We suggest that a more accurate and precise sample may be achieved using a modified pattern which concentrates on the center plus some of the adjacent area. PMID:26459377
Evaluation of a brushing machine for estimating density of spider mites on grape leaves.
Macmillan, Craig D; Costello, Michael J
2015-12-01
Direct visual inspection and enumeration for estimating field population density of economically important arthropods, such as spider mites, provide more information than alternative methods, such as binomial sampling, but is laborious and time consuming. A brushing machine can reduce sampling time and perhaps improve accuracy. Although brushing technology has been investigated and recommended as a useful tool for researchers and integrated pest management practitioners, little work to demonstrate the validity of this technique has been performed since the 1950's. We investigated the brushing machine manufactured by Leedom Enterprises (Mi-Wuk Village, CA, USA) for studies on spider mites. We evaluated (1) the mite recovery efficiency relative to the number of passes of a leaf through the brushes, (2) mite counts as generated by the machine compared to visual counts under a microscope, (3) the lateral distribution of mites on the collection plate and (4) the accuracy and precision of a 10% sub-sample using a double-transect counting grid. We found that about 95% of mites on a leaf were recovered after five passes, and 99% after nine passes, and mite counts from brushing were consistently higher than those from visual inspection. Lateral distribution of mites was not uniform, being highest in concentration at the center and lowest at the periphery. The 10% double-transect pattern did not result in a significant correlation with the total plate count at low mite density, but accuracy and precision improved at medium and high density. We suggest that a more accurate and precise sample may be achieved using a modified pattern which concentrates on the center plus some of the adjacent area.
Directory of Open Access Journals (Sweden)
Yang Zu
2015-07-01
Full Text Available This paper studies the asymptotic normality for the kernel deconvolution estimator when the noise distribution is logarithmic chi-square; both identical and independently distributed observations and strong mixing observations are considered. The dependent case of the result is applied to obtain the pointwise asymptotic distribution of the deconvolution volatility density estimator in discrete-time stochastic volatility models.
Bankert, D.; Groen, in 't K.C.G.; Wieren, van S.E.
2003-01-01
In dune areas in the Netherlands, the standard method for estimating rabbit (Oryctolagus cuniculus) populations is by counting the number of rabbits that can be seen in the headlights of a car along standard transects. This paper evaluates whether estimated rabbit densities derived from this method
NEAR INFRARED SPECTROSCOPY FOR ESTIMATING SUGARCANE BAGASSE CONTENT IN MEDIUM DENSITY FIBERBOARD
Directory of Open Access Journals (Sweden)
Ugo Leandro Belini
2011-04-01
Full Text Available Medium density fiberboard (MDF is an engineered wood product formed by breaking down selected lignin-cellulosic material residuals into fibers, combining it with wax and a resin binder, and then forming panels by applying high temperature and pressure. Because the raw material in the industrial process is ever-changing, the panel industry requires methods for monitoring the composition of their products. The aim of this study was to estimate the ratio of sugarcane (SC bagasse to Eucalyptus wood in MDF panels using near infrared (NIR spectroscopy. Principal component analysis (PCA and partial least square (PLS regressions were performed. MDF panels having different bagasse contents were easily distinguished from each other by the PCA of their NIR spectra with clearly different patterns of response. The PLS-R models for SC content of these MDF samples presented a strong coefficient of determination (0.96 between the NIR-predicted and Lab-determined values and a low standard error of prediction (~1.5% in the cross-validations. A key role of resins (adhesives, cellulose, and lignin for such PLS-R calibrations was shown. PLS-DA model correctly classified ninety-four percent of MDF samples by cross-validations and ninety-eight percent of the panels by independent test set. These NIR-based models can be useful to quickly estimate sugarcane bagasse vs. Eucalyptus wood content ratio in unknown MDF samples and to verify the quality of these engineered wood products in an online process.
Spatial Variation in Tree Density and Estimated Aboveground Carbon Stocks in Southern Africa
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Lulseged Tamene
2016-03-01
Full Text Available Variability in woody plant species, vegetation assemblages and anthropogenic activities derails the efforts to have common approaches for estimating biomass and carbon stocks in Africa. In order to suggest management options, it is important to understand the vegetation dynamics and the major drivers governing the observed conditions. This study uses data from 29 sentinel landscapes (4640 plots across the southern Africa. We used T-Square distance method to sample trees. Allometric models were used to estimate aboveground tree biomass from which aboveground biomass carbon stock (AGBCS was derived for each site. Results show average tree density of 502 trees·ha−1 with semi-arid areas having the highest (682 trees·ha−1 and arid regions the lowest (393 trees·ha−1. The overall AGBCS was 56.4 Mg·ha−1. However, significant site to site variability existed across the region. Over 60 fold differences were noted between the lowest AGBCS (2.2 Mg·ha−1 in the Musungwa plains of Zambia and the highest (138.1 Mg·ha−1 in the scrublands of Kenilworth in Zimbabwe. Semi-arid and humid sites had higher carbon stocks than sites in sub-humid and arid regions. Anthropogenic activities also influenced the observed carbon stocks. Repeated measurements would reveal future trends in tree cover and carbon stocks across different systems.
Institute of Scientific and Technical Information of China (English)
YAN Hao; WANG Hu; WANG Yong-hui; ZHANG Yu-mei
2013-01-01
Background The classification of Alzheimer's disease (AD) from magnetic resonance imaging (MRI) has been challenged by lack of effective and reliable biomarkers due to inter-subject variability.This article presents a classification method for AD based on kernel density estimation (KDE) of local features.Methods First,a large number of local features were extracted from stable image blobs to represent various anatomical patterns for potential effective biomarkers.Based on distinctive descriptors and locations,the local features were robustly clustered to identify correspondences of the same underlying patterns.Then,the KDE was used to estimate distribution parameters of the correspondences by weighting contributions according to their distances.Thus,biomarkers could be reliably quantified by reducing the effects of further away correspondences which were more likely noises from inter-subject variability.Finally,the Bayes classifier was applied on the distribution parameters for the classification of AD.Results Experiments were performed on different divisions of a publicly available database to investigate the accuracy and the effects of age and AD severity.Our method achieved an equal error classification rate of 0.85 for subject aged 60-80 years exhibiting mild AD and outperformed a recent local feature-based work regardless of both effects.Conclusions We proposed a volumetric brain MRI classification method for neurodegenerative disease based on statistics of local features using KDE.The method may be potentially useful for the computer-aided diagnosis in clinical settings.
Probability density function and estimation for error of digitized map coordinates in GIS
Institute of Scientific and Technical Information of China (English)
童小华; 刘大杰
2004-01-01
Traditionally, it is widely accepted that measurement error usually obeys the normal distribution. However, in this paper a new idea is proposed that the error in digitized data which is a major derived data source in GIS does not obey the normal distribution but the p-norm distribution with a determinate parameter. Assuming that the error is random and has the same statistical properties, the probability density function of the normal distribution,Laplace distribution and p-norm distribution are derived based on the arithmetic mean axiom, median axiom and pmedian axiom, which means that the normal distribution is only one of these distributions but not the least one.Based on this idea, distribution fitness tests such as Skewness and Kurtosis coefficient test, Pearson chi-square x2 test and Kolmogorov test for digitized data are conducted. The results show that the error in map digitization obeys the p-norm distribution whose parameter is close to 1.60. A least p-norm estimation and the least square estimation of digitized data are further analyzed, showing that the least p-norm adiustment is better than the least square adjustment for digitized data processing in GIS.
mBEEF: An accurate semi-local Bayesian error estimation density functional
Wellendorff, Jess; Lundgaard, Keld T.; Jacobsen, Karsten W.; Bligaard, Thomas
2014-04-01
We present a general-purpose meta-generalized gradient approximation (MGGA) exchange-correlation functional generated within the Bayesian error estimation functional framework [J. Wellendorff, K. T. Lundgaard, A. Møgelhøj, V. Petzold, D. D. Landis, J. K. Nørskov, T. Bligaard, and K. W. Jacobsen, Phys. Rev. B 85, 235149 (2012)]. The functional is designed to give reasonably accurate density functional theory (DFT) predictions of a broad range of properties in materials physics and chemistry, while exhibiting a high degree of transferability. Particularly, it improves upon solid cohesive energies and lattice constants over the BEEF-vdW functional without compromising high performance on adsorption and reaction energies. We thus expect it to be particularly well-suited for studies in surface science and catalysis. An ensemble of functionals for error estimation in DFT is an intrinsic feature of exchange-correlation models designed this way, and we show how the Bayesian ensemble may provide a systematic analysis of the reliability of DFT based simulations.
Zeng, L.; Doyle, E. J.; Rhodes, T. L.; Wang, G.; Sung, C.; Peebles, W. A.; Bobrek, M.
2016-11-01
A new model-based technique for fast estimation of the pedestal electron density gradient has been developed. The technique uses ordinary mode polarization profile reflectometer time delay data and does not require direct profile inversion. Because of its simple data processing, the technique can be readily implemented via a Field-Programmable Gate Array, so as to provide a real-time density gradient estimate, suitable for use in plasma control systems such as envisioned for ITER, and possibly for DIII-D and Experimental Advanced Superconducting Tokamak. The method is based on a simple edge plasma model with a linear pedestal density gradient and low scrape-off-layer density. By measuring reflectometer time delays for three adjacent frequencies, the pedestal density gradient can be estimated analytically via the new approach. Using existing DIII-D profile reflectometer data, the estimated density gradients obtained from the new technique are found to be in good agreement with the actual density gradients for a number of dynamic DIII-D plasma conditions.
Directory of Open Access Journals (Sweden)
Ehud Lebel
2014-01-01
Full Text Available Changes of bone during pregnancy and during lactation evaluated by bone mineral density (BMD may have implications for risk of osteoporosis and fractures. We studied BMD in women of differing ages, parity, and lactation histories immediately postpartum for BMD, T-scores, and Z-scores. Institutional Review Board approval was received. All women while still in hospital postpartum were asked to participate. BMD was performed by dual-energy X-ray absorptiometry (DXA machine at femoral neck (FN and lumbar spine (LS by a single technician. Of 132 participants, 73 (55.3% were ≤30 years; 27 (20.5% were primiparous; 36 (27.3% were grand multiparous; 35 (26.5% never breast fed. Mean FN T-scores and Z-scores were higher than respective mean LS scores, but all means were within the normal limits. Mean LS T-scores and Z-scores were highest in the grand multiparas. There were only 2 (1.5% outliers with low Z-scores. We conclude that, in a large cohort of Israeli women with BMD parameters assessed by DXA within two days postpartum, mean T-scores and Z-scores at both the LS and FN were within normal limits regardless of age (20–46 years, parity (1–13 viable births, and history of either no or prolonged months of lactation (up to 11.25 years.
Priyatikanto, Rhorom
2014-01-01
Stellar membership determination of an open cluster is an important process to do before further analysis. Basically, there are two classes of membership determination method: parametric and non-parametric. In this study, an alternative of non-parametric method based on Binned Kernel Density Estimation that accounts measurements errors (simply called BKDE-e) is proposed. This method is applied upon proper motions data to determine cluster's membership kinematically and estimate the average proper motions of the cluster. Monte Carlo simulations show that the average proper motions determination using this proposed method is statistically more accurate than ordinary Kernel Density Estimator (KDE). By including measurement errors in the calculation, the mode location from the resulting density estimate is less sensitive to non-physical or stochastic fluctuation as compared to ordinary KDE that excludes measurement errors. For the typical mean measurement error of 7 mas/yr, BKDE-e suppresses the potential of misc...
Kaye, Jason; Yang, Chao
2014-01-01
Kohn-Sham density functional theory is one of the most widely used electronic structure theories. The recently developed adaptive local basis functions form an accurate and systematically improvable basis set for solving Kohn-Sham density functional theory using discontinuous Galerkin methods, requiring a small number of basis functions per atom. In this paper we develop residual-based a posteriori error estimates for the adaptive local basis approach, which can be used to guide non-uniform basis refinement for highly inhomogeneous systems such as surfaces and large molecules. The adaptive local basis functions are non-polynomial basis functions, and standard a posteriori error estimates for $hp$-refinement using polynomial basis functions do not directly apply. We generalize the error estimates for $hp$-refinement to non-polynomial basis functions. We demonstrate the practical use of the a posteriori error estimator in performing three-dimensional Kohn-Sham density functional theory calculations for quasi-2D...
基于估计点的双窗宽核密度估计算法%Dual-bandwidth kernel density estimation algorithm based on estimate points
Institute of Scientific and Technical Information of China (English)
邓飙; 于传强; 李天石; 苏文斌; 盘仰珂
2011-01-01
针对核密度估计中窗宽确定困难的问题,提出了基于估计点的滑动双窗宽核密度估计算法.通过采用固定窗宽的密度估计函数代替假设的正态分布密度函数以及增加求解二次导数的窗宽的方法,对估计域中的每一估计点求取其最优窗宽值,实现了窗宽根据样本的分布情况,在不同的估计点自动调整窗宽的取值.文中给出了算法的具体推导以及实现步骤,给出了多组对比实验.结果表明双窗宽算法在估计结果的精度、平滑度等方面比现有固定窗宽的算法有明显提高.%Aiming at the problem that the bandwidth of kemel density estimation is difficult to choose, this paper proposes a slide double bandwidth kernel density estimation algorithm based on estimate points. We adopt the kernel density estimation function obtained with fixed bandwidth to replace the hypothetic normal distribution density function and increase the bandwidth for solving the second derivative to obtain the optimal bandwidth at every point in the estimate interval. The method can automatically adjust the value of the bandwidth at different estimate points according to the sample distribution In the paper, the detailed deduction process, realization steps are given.Groups of comparison experiments were carried out. Results show that the new algorithm improves the precision and gliding property of kernel density estimation obviously compared with fixed bandwidth algorithm.
Rosso, Aldana; Lång, Kristina; Petersson, Ingemar F; Zackrisson, Sophia
2015-10-01
In this study, we investigate which factors affect the false positive fraction (FPF) for digital breast tomosynthesis (DBT) compared to digital mammography (DM) in a screening population by using classification and regression trees (C&RT) and binary marginal generalized linear models. The data was obtained from the Malmö Breast Tomosynthesis Screening Trial, which aimed to compare the performance of DBT to DM in breast cancer screening. By using data from the first half of the study population (7500 women), a tree with the recall probability for different groups was calculated. The effect of age and breast density on the FPF was estimated using a binary marginal generalized linear model. Our results show that breast density and breast cancer were the main factors influencing recall. The FPF is mainly affected by breast density and increases with breast density for DBT and DM. In conclusion, the results obtained with C&RT are easy to interpret and similar to those obtained using binary marginal generalized linear models. The FPF is approximately 40% higher for DBT compared to DM for all breast density categories.
International Nuclear Information System (INIS)
We designed this study to quantify the effects of radiotherapy (RT) on bone density as a local response in spinal bone metastases of women with breast cancer and, secondly, to establish bone density as an accurate and reproducible marker for assessment of local response to RT in spinal bone metastases. We retrospectively assessed 135 osteolytic spinal metastases in 115 women with metastatic breast cancer treated at our department between January 2000 and January 2012. Primary endpoint was to compare bone density in the bone metastases before, 3 months after and 6 months after RT. Bone density was measured in Hounsfield units (HU) in computed tomography scans. We calculated mean values in HU and the standard deviation (SD) as a measurement of bone density before, 3 months and 6 months after RT. T-test was used for statistical analysis of difference in bone density as well as for univariate analysis of prognostic factors for difference in bone density 3 and 6 months after RT. Mean bone density was 194.8 HU ± SD 123.0 at baseline. Bone density increased significantly by a mean of 145.8 HU ± SD 139.4 after 3 months (p = .0001) and by 250.3 HU ± SD 147.1 after 6 months (p < .0001). Women receiving bisphosphonates showed a tendency towards higher increase in bone density in the metastases after 3 months (152.6 HU ± SD 141.9 vs. 76.0 HU ± SD 86.1; p = .069) and pathological fractures before RT were associated with a significantly higher increase in bone density after 3 months (202.3 HU ± SD 161.9 vs. 130.3 HU ± SD 129.2; p = .013). Concomitant chemotherapy (ChT) or endocrine therapy (ET), hormone receptor status, performance score, applied overall RT dose and prescription of a surgical corset did not correlate with a difference in bone density after RT. Bone density measurement in HU is a practicable and reproducible method for assessment of local RT response in osteolytic metastases in breast cancer. Our analysis demonstrated an excellent local response within
Optical Density Analysis of X-Rays Utilizing Calibration Tooling to Estimate Thickness of Parts
Grau, David
2012-01-01
This process is designed to estimate the thickness change of a material through data analysis of a digitized version of an x-ray (or a digital x-ray) containing the material (with the thickness in question) and various tooling. Using this process, it is possible to estimate a material's thickness change in a region of the material or part that is thinner than the rest of the reference thickness. However, that same principle process can be used to determine the thickness change of material using a thinner region to determine thickening, or it can be used to develop contour plots of an entire part. Proper tooling must be used. An x-ray film with an S-shaped characteristic curve or a digital x-ray device with a product resulting in like characteristics is necessary. If a film exists with linear characteristics, this type of film would be ideal; however, at the time of this reporting, no such film has been known. Machined components (with known fractional thicknesses) of a like material (similar density) to that of the material to be measured are necessary. The machined components should have machined through-holes. For ease of use and better accuracy, the throughholes should be a size larger than 0.125 in. (.3 mm). Standard components for this use are known as penetrameters or image quality indicators. Also needed is standard x-ray equipment, if film is used in place of digital equipment, or x-ray digitization equipment with proven conversion properties. Typical x-ray digitization equipment is commonly used in the medical industry, and creates digital images of x-rays in DICOM format. It is recommended to scan the image in a 16-bit format. However, 12-bit and 8-bit resolutions are acceptable. Finally, x-ray analysis software that allows accurate digital image density calculations, such as Image-J freeware, is needed. The actual procedure requires the test article to be placed on the raw x-ray, ensuring the region of interest is aligned for perpendicular x-ray exposure
SU-D-204-01: Dual-Energy Calibration for Breast Density Measurement Using Spectral Mammography
Energy Technology Data Exchange (ETDEWEB)
Ding, H; Cho, H; Kumar, N; Sennung, D; Molloi, S [Department of Radiological Sciences, University of California, Irvine, CA (United States)
2015-06-15
Purpose: To investigate the feasibility of minimizing the systematic errors in dual-energy breast density quantification induced by the use of tissue-equivalent plastic phantoms as the calibration basis materials. Methods: Dual-energy calibration using tissue-equivalent plastic phantoms was performed on a spectral mammography system based on scanning multi-slit Si strip photon-counting detectors. The plastic phantom calibration used plastic water and adipose-equivalent phantoms as the basis materials, which have different x-ray attenuation properties compared to water and lipid in actual breast tissue. Two methods were used to convert the dual-energy decomposition measurements in plastic phantom thicknesses into true water and lipid basis. The first method was based entirely on the theoretical x-ray attenuation coefficients of the investigated materials in the mammographic energy range. The conversion matrix was determined from least-squares fitting of the target material using the reported attenuation coefficients of water and lipid. The second method was developed based on experimental calibrations, which measured the low-and high-energy signals of pure water and lipid of known thicknesses. A non-linear rational function was used to correlate the decomposed thicknesses to the known values, so that the conversion coefficients can be determined. Both methods were validated using independent measurements of water and lipid mixture phantoms. The correlation of the dual-energy decomposition measurements and the known values were studied with linear regression analysis. Results: There was an excellent linear correlation between the converted water thicknesses and the known values. The slopes of the linear fits were determined to be 0.63 and 1.03 for the simulation and experimental results, respectively. The non-linear fitting in the experimental approach reduced the root-mean-square (RMS) errors from approximately 3.4 mm to 1.5 mm. Conclusion: The results suggested
Directory of Open Access Journals (Sweden)
Marianne Sandin
2015-09-01
Full Text Available Protein translocation between organelles in the cell is an important process that regulates many cellular functions. However, organelles can rarely be isolated to purity so several methods have been developed to analyse the fractions obtained by density gradient centrifugation. We present an analysis of the distribution of proteins amongst organelles in the human breast cell line, MDA-MB-231 using two approaches: an isotopic labelling and a label-free approach.
George, Stephanie M; McTiernan, Anne; Villaseñor, Adriana; Alfano, Catherine M.; Irwin, Melinda L.; Neuhouser, Marian L.; Baumgartner, Richard N.; Baumgartner, Kathy B.; Bernstein, Leslie; Smith, Ashley W.; Ballard-Barbash, Rachel
2013-01-01
Abstract Background Bone mineral density (BMD) and lean mass (LM) may both decrease in breast cancer survivors, thereby increasing risk of falls and fractures. Research is needed to determine whether lean mass (LM) and fat mass (FM) independently relate to BMD in this patient group. Methods The Health, Eating, Activity, and Lifestyle Study participants included 599 women, ages 29–87 years, diagnosed...
Prop, J; Quinn, JL
2003-01-01
In this paper we aim to explain the distribution of red-breasted geese Branta ruficollis over different nesting habitats. To be safe from land predators red-breasted goose colonies were restricted to i) islands on rivers, ii) cliffs with peregrine falcons Falco peregrinus, and iii) the close proximi
Wang, Ying; Wu, Fengchang; Giesy, John P; Feng, Chenglian; Liu, Yuedan; Qin, Ning; Zhao, Yujie
2015-09-01
Due to use of different parametric models for establishing species sensitivity distributions (SSDs), comparison of water quality criteria (WQC) for metals of the same group or period in the periodic table is uncertain and results can be biased. To address this inadequacy, a new probabilistic model, based on non-parametric kernel density estimation was developed and optimal bandwidths and testing methods are proposed. Zinc (Zn), cadmium (Cd), and mercury (Hg) of group IIB of the periodic table are widespread in aquatic environments, mostly at small concentrations, but can exert detrimental effects on aquatic life and human health. With these metals as target compounds, the non-parametric kernel density estimation method and several conventional parametric density estimation methods were used to derive acute WQC of metals for protection of aquatic species in China that were compared and contrasted with WQC for other jurisdictions. HC5 values for protection of different types of species were derived for three metals by use of non-parametric kernel density estimation. The newly developed probabilistic model was superior to conventional parametric density estimations for constructing SSDs and for deriving WQC for these metals. HC5 values for the three metals were inversely proportional to atomic number, which means that the heavier atoms were more potent toxicants. The proposed method provides a novel alternative approach for developing SSDs that could have wide application prospects in deriving WQC and use in assessment of risks to ecosystems. PMID:25953609
Brown, S; Gaston, G
1995-01-01
One of the most important databases needed for estimating emissions of carbon dioxide resulting from changes in the cover, use, and management of tropical forests is the total quantity of biomass per unit area, referred to as biomass density. Forest inventories have been shown to be valuable sources of data for estimating biomass density, but inventories for the tropics are few in number and their quality is poor. This lack of reliable data has been overcome by use of a promising approach that produces geographically referenced estimates by modeling in a geographic information system (GIS). This approach has been used to produce geographically referenced, spatial distributions of potential and actual (circa 1980) aboveground biomass density of all forests types in tropical Africa. Potential and actual biomass density estimates ranged from 33 to 412 Mg ha(-1) (10(6)g ha(-1)) and 20 to 299 Mg ha(-1), respectively, for very dry lowland to moist lowland forests and from 78 to 197 Mg ha(-1) and 37 to 105 Mg ha(-1), respectively, for montane-seasonal to montane-moist forests. Of the 37 countries included in this study, more than half (51%) contained forests that had less than 60% of their potential biomass. Actual biomass density for forest vegetation was lowest in Botswana, Niger, Somalia, and Zimbabwe (about 10 to 15 Mg ha(-1)). Highest estimates for actual biomass density were found in Congo, Equatorial Guinea, Gabon, and Liberia (305 to 344 Mg ha(-1)). Results from this research effort can contribute to reducing uncertainty in the inventory of country-level emission by providing consistent estimates of biomass density at subnational scales that can be used with other similarly scaled databases on change in land cover and use.
Directory of Open Access Journals (Sweden)
Niels Halama
Full Text Available BACKGROUND: Determining the correct number of positive immune cells in immunohistological sections of colorectal cancer and other tumor entities is emerging as an important clinical predictor and therapy selector for an individual patient. This task is usually obstructed by cell conglomerates of various sizes. We here show that at least in colorectal cancer the inclusion of immune cell conglomerates is indispensable for estimating reliable patient cell counts. Integrating virtual microscopy and image processing principally allows the high-throughput evaluation of complete tissue slides. METHODOLOGY/PRINCIPAL FINDINGS: For such large-scale systems we demonstrate a robust quantitative image processing algorithm for the reproducible quantification of cell conglomerates on CD3 positive T cells in colorectal cancer. While isolated cells (28 to 80 microm(2 are counted directly, the number of cells contained in a conglomerate is estimated by dividing the area of the conglomerate in thin tissues sections (< or =6 microm by the median area covered by an isolated T cell which we determined as 58 microm(2. We applied our algorithm to large numbers of CD3 positive T cell conglomerates and compared the results to cell counts obtained manually by two independent observers. While especially for high cell counts, the manual counting showed a deviation of up to 400 cells/mm(2 (41% variation, algorithm-determined T cell numbers generally lay in between the manually observed cell numbers but with perfect reproducibility. CONCLUSION: In summary, we recommend our approach as an objective and robust strategy for quantifying immune cell densities in immunohistological sections which can be directly implemented into automated full slide image processing systems.
Near-native protein loop sampling using nonparametric density estimation accommodating sparcity.
Directory of Open Access Journals (Sweden)
Hyun Joo
2011-10-01
Full Text Available Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM. Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD 7.0 Å, this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/.
Directory of Open Access Journals (Sweden)
Yu Xu
2016-06-01
Full Text Available Estimates of abundance or density are essential for wildlife management and conservation. There are few effective density estimates for the Buff-throated Partridge Tetraophasis szechenyii, a rare and elusive high-mountain Galliform species endemic to western China. In this study, we used the temporary emigration N-mixture model to estimate density of this species, with data acquired from playback point count surveys around a sacred area based on indigenous Tibetan culture of protection of wildlife, in Yajiang County, Sichuan, China, during April-June 2009. Within 84 125-m radius points, we recorded 53 partridge groups during three repeats. The best model indicated that detection probability was described by covariates of vegetation cover type, week of visit, time of day, and weather with weak effects, and a partridge group was present during a sampling period with a constant probability. The abundance component was accounted for by vegetation association. Abundance was substantially higher in rhododendron shrubs, fir-larch forests, mixed spruce-larch-birch forests, and especially oak thickets than in pine forests. The model predicted a density of 5.14 groups/km², which is similar to an estimate of 4.7 - 5.3 groups/km² quantified via an intensive spot-mapping effort. The post-hoc estimate of individual density was 14.44 individuals/km², based on the estimated mean group size of 2.81. We suggest that the method we employed is applicable to estimate densities of Buff-throated Partridges in large areas. Given importance of a mosaic habitat for this species, local logging should be regulated. Despite no effect of the conservation area (sacred on the abundance of Buff-throated Partridges, we suggest regulations linking the sacred mountain conservation area with the official conservation system because of strong local participation facilitated by sacred mountains in land conservation.
Effect of sampling density and design on estimation of streambed attributes
Kennedy, Casey D.; Genereux, David P.; Mitasova, Helena; Corbett, D. Reide; Leahy, Scott
2008-06-01
effect of "diminishing returns" was evident for sampling densities greater than ∼24 points per reach (roughly 0.05-0.06 points per m2 of streambed). Relative to sampling density, sampling design had little effect on values of p. Average error in streambed attributes was generally small (⩽10%) and less than the 95% confidence limits about the reach-average values of the attributes. The ability to estimate unknown point values by interpolation among other point values was poor using both 12- and 36-point subsets, though results suggest the 24 additional known point values (in going from 12 to 36) were helpful in one case in which there was some degree of autocorrelation between the additional known values and the values to be predicted in the interpolation. Visual inspection of 130 contour maps showed that those based on 36-point values were far more realistic in appearance than those based on 12-point values (where the standard for "realistic" appearance was the contour maps based on the full datasets of 54-point values).
Institute of Scientific and Technical Information of China (English)
张凌; 常加峰; 张炜; 李颖颖; 钱金平; 徐国盛; 丁斯晔; 高伟; 吴振伟; 陈颖杰; 黄娟; 刘晓菊; 臧庆
2011-01-01
In this work, population coefficients of hydrogen＇s n = 3 excited state from the hydrogen collisional-radiative （CR） model, from the data file of DEGAS 2, are used to calculate the photon emissivity coefficients （PECs） of hydrogen Balmer-α （n = 3 →n = 2） （Hα）. The results are compared with the PECs from Atomic Data and Analysis Structure （ADAS） database, and a good agreement is found. A magnetic surface-averaged neutral density profile of typical double-null （DN） plasma in EAST is obtained by using FRANTIC, the 1.5-D fluid transport code. It is found that the sum of integral Dα and Hα emission intensity calculated via the neutral density agrees with the measured results obtained by using the absolutely calibrated multi-channel poloidal photodiode array systems viewing the lower divertor at the last closed flux surface （LCFS）. It is revealed that the typical magnetic surface-averaged neutral density at LCFS is about 3.5×10^16 m^-3 .
Prado, Karl L; Kirsner, Steven M; Erice, Rolly C
2003-01-01
This paper describes an innovative method for correctly estimating the effective field size of tangential-breast fields. The method uses an "equivalent triangle" to verify intact breast tangential field monitor-unit settings calculated by a 3D planning system to within 2%. The effects on verification calculations of loss of full scatter due to beam oblique incidence, proximity to field boundaries, and reduced scattering volumes are handled properly. The methodology is validated by comparing calculations performed by the 3D planning system with the respective verification estimates. The accuracy of this technique is established for dose calculations both with and without heterogeneity corrections.
Novelty detection by multivariate kernel density estimation and growing neural gas algorithm
Fink, Olga; Zio, Enrico; Weidmann, Ulrich
2015-01-01
One of the underlying assumptions when using data-based methods for pattern recognition in diagnostics or prognostics is that the selected data sample used to train and test the algorithm is representative of the entire dataset and covers all combinations of parameters and conditions, and resulting system states. However in practice, operating and environmental conditions may change, unexpected and previously unanticipated events may occur and corresponding new anomalous patterns develop. Therefore for practical applications, techniques are required to detect novelties in patterns and give confidence to the user on the validity of the performed diagnosis and predictions. In this paper, the application of two types of novelty detection approaches is compared: a statistical approach based on multivariate kernel density estimation and an approach based on a type of unsupervised artificial neural network, called the growing neural gas (GNG). The comparison is performed on a case study in the field of railway turnout systems. Both approaches demonstrate their suitability for detecting novel patterns. Furthermore, GNG proves to be more flexible, especially with respect to dimensionality of the input data and suitability for online learning.
Semivariogram models for estimating fig fly population density throughout the year
Directory of Open Access Journals (Sweden)
Mauricio Paulo Batistella Pasini
2014-07-01
Full Text Available The objective of this work was to select semivariogram models to estimate the population density of fig fly (Zaprionus indianus; Diptera: Drosophilidae throughout the year, using ordinary kriging. Nineteen monitoring sites were demarcated in an area of 8,200 m2, cropped with six fruit tree species: persimmon, citrus, fig, guava, apple, and peach. During a 24 month period, 106 weekly evaluations were done in these sites. The average number of adult fig flies captured weekly per trap, during each month, was subjected to the circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, hole effect, K-Bessel, J-Bessel, and stable semivariogram models, using ordinary kriging interpolation. The models with the best fit were selected by cross-validation. Each data set (months has a particular spatial dependence structure, which makes it necessary to define specific models of semivariograms in order to enhance the adjustment to the experimental semivariogram. Therefore, it was not possible to determine a standard semivariogram model; instead, six theoretical models were selected: circular, Gaussian, hole effect, K-Bessel, J-Bessel, and stable.
Generalized Consistency for Kernel Density Estimation%密度核估计的广义相合性
Institute of Scientific and Technical Information of China (English)
王敏; 李开灿
2015-01-01
研究独立样本下密度核估计的相合性. 在 Peason-χ2距离和Kullback-Leibler距离意义下,提出密度核估计广义相合性的概念,并获得密度核估计的各种广义相合性.%In this paper, we discuss the consistency of the density kernel estimation under the independent sample. We give the definitions of generalized consistency for kernel density estimation and obtain several kinds of generalized consistency of kernel density estimation under thePeason-χ2distance and the Kullback-Leibler distance.
Chen, W.; Shao, Z.; Tiong, L. K.
2015-11-01
Drought caused the most widespread damage in China, making up over 50 % of the total affected area nationwide in recent decades. In the paper, a Standardized Precipitation Index-based (SPI-based) drought risk study is conducted using historical rainfall data of 19 weather stations in Shandong province, China. Kernel density based method is adopted to carry out the risk analysis. Comparison between the bivariate Gaussian kernel density estimation (GKDE) and diffusion kernel density estimation (DKDE) are carried out to analyze the effect of drought intensity and drought duration. The results show that DKDE is relatively more accurate without boundary-leakage. Combined with the GIS technique, the drought risk is presented which reveals the spatial and temporal variation of agricultural droughts for corn in Shandong. The estimation provides a different way to study the occurrence frequency and severity of drought risk from multiple perspectives.
Directory of Open Access Journals (Sweden)
W. Chen
2015-11-01
Full Text Available Drought caused the most widespread damage in China, making up over 50 % of the total affected area nationwide in recent decades. In the paper, a Standardized Precipitation Index-based (SPI-based drought risk study is conducted using historical rainfall data of 19 weather stations in Shandong province, China. Kernel density based method is adopted to carry out the risk analysis. Comparison between the bivariate Gaussian kernel density estimation (GKDE and diffusion kernel density estimation (DKDE are carried out to analyze the effect of drought intensity and drought duration. The results show that DKDE is relatively more accurate without boundary-leakage. Combined with the GIS technique, the drought risk is presented which reveals the spatial and temporal variation of agricultural droughts for corn in Shandong. The estimation provides a different way to study the occurrence frequency and severity of drought risk from multiple perspectives.
Ghammraoui, Bahaa; Badal, Andreu; Popescu, Lucretiu M.
2016-04-01
Coherent scatter computed tomography (CSCT) is a reconstructive x-ray imaging technique that yields the spatially resolved coherent-scatter cross section of the investigated object revealing structural information of tissue under investigation. In the original CSCT proposals the reconstruction of images from coherently scattered x-rays is done at each scattering angle separately using analytic reconstruction. In this work we develop a maximum likelihood estimation of scatter components algorithm (ML-ESCA) that iteratively reconstructs images using a few material component basis functions from coherent scatter projection data. The proposed algorithm combines the measured scatter data at different angles into one reconstruction equation with only a few component images. Also, it accounts for data acquisition statistics and physics, modeling effects such as polychromatic energy spectrum and detector response function. We test the algorithm with simulated projection data obtained with a pencil beam setup using a new version of MC-GPU code, a Graphical Processing Unit version of PENELOPE Monte Carlo particle transport simulation code, that incorporates an improved model of x-ray coherent scattering using experimentally measured molecular interference functions. The results obtained for breast imaging phantoms using adipose and glandular tissue cross sections show that the new algorithm can separate imaging data into basic adipose and water components at radiation doses comparable with Breast Computed Tomography. Simulation results also show the potential for imaging microcalcifications. Overall, the component images obtained with ML-ESCA algorithm have a less noisy appearance than the images obtained with the conventional filtered back projection algorithm for each individual scattering angle. An optimization study for x-ray energy range selection for breast CSCT is also presented.
Chen, W.; Shao, Z; L. K. Tiong
2015-01-01
Drought caused the most widespread damage in China, making up over 50 % of the total affected area nationwide in recent decades. In the paper, a Standardized Precipitation Index-based (SPI-based) drought risk study is conducted using historical rainfall data of 19 weather stations in Shandong province, China. Kernel density based method is adopted to carry out the risk analysis. Comparison between the bivariate Gaussian kernel density estimation (GKDE) and d...
International Nuclear Information System (INIS)
Low-density lipoprotein (LDL) receptors are overexpressed in most neoplastic cell lines and provide a mechanism for the internalization and concentration of drug-laden nanoemulsions that bind to these receptors. The aim of the present study was to determine whether the administration of standard chemotherapeutic schemes can alter the expression of LDL and LDL receptor-related protein 1 (LRP-1) receptors in breast carcinoma. Fragments of tumoral and normal breast tissue from 16 consecutive volunteer women with breast cancer in stage II or III were obtained from biopsies before the beginning of neoadjuvant chemotherapy and after chemotherapy, from fragments excised during mastectomy. Tissues were analyzed by immunohistochemistry for both receptors. Because complete response to treatment was achieved in 4 patients, only the tumors from 12 were analyzed. Before chemotherapy, there was overexpression of LDL receptor in the tumoral tissue compared to normal breast tissue in 8 of these patients. LRP-1 receptor overexpression was observed in tumors of 4 patients. After chemotherapy, expression of both receptors decreased in the tumors of 6 patients, increased in 4 and was unchanged in 2. Nonetheless, even when chemotherapy reduced receptors expression, the expression was still above normal. The fact that chemotherapy does not impair LDL receptors expression supports the use of drug carrier systems that target neoplastic cells by the LDL receptor endocytic pathway in patients on conventional chemotherapy
Energy Technology Data Exchange (ETDEWEB)
Pires, L.A. [Laboratório de Metabolismo de Lípides, Instituto do Coração, Faculdade de Medicina, Hospital das Clínicas, Universidade de São Paulo, São Paulo, SP (Brazil); Departamento de Ginecologia, Faculdade de Medicina, Hospital das Clínicas, Universidade de São Paulo, São Paulo, SP (Brazil); Hegg, R. [Departamento de Ginecologia, Faculdade de Medicina, Hospital das Clínicas, Universidade de São Paulo, São Paulo, SP (Brazil); Freitas, F.R.; Tavares, E.R.; Almeida, C.P. [Laboratório de Metabolismo de Lípides, Instituto do Coração, Faculdade de Medicina, Hospital das Clínicas, Universidade de São Paulo, São Paulo, SP (Brazil); Baracat, E.C. [Departamento de Ginecologia, Faculdade de Medicina, Hospital das Clínicas, Universidade de São Paulo, São Paulo, SP (Brazil); Maranhão, R.C. [Laboratório de Metabolismo de Lípides, Instituto do Coração, Faculdade de Medicina, Hospital das Clínicas, Universidade de São Paulo, São Paulo, SP (Brazil); Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, SP (Brazil)
2012-05-04
Low-density lipoprotein (LDL) receptors are overexpressed in most neoplastic cell lines and provide a mechanism for the internalization and concentration of drug-laden nanoemulsions that bind to these receptors. The aim of the present study was to determine whether the administration of standard chemotherapeutic schemes can alter the expression of LDL and LDL receptor-related protein 1 (LRP-1) receptors in breast carcinoma. Fragments of tumoral and normal breast tissue from 16 consecutive volunteer women with breast cancer in stage II or III were obtained from biopsies before the beginning of neoadjuvant chemotherapy and after chemotherapy, from fragments excised during mastectomy. Tissues were analyzed by immunohistochemistry for both receptors. Because complete response to treatment was achieved in 4 patients, only the tumors from 12 were analyzed. Before chemotherapy, there was overexpression of LDL receptor in the tumoral tissue compared to normal breast tissue in 8 of these patients. LRP-1 receptor overexpression was observed in tumors of 4 patients. After chemotherapy, expression of both receptors decreased in the tumors of 6 patients, increased in 4 and was unchanged in 2. Nonetheless, even when chemotherapy reduced receptors expression, the expression was still above normal. The fact that chemotherapy does not impair LDL receptors expression supports the use of drug carrier systems that target neoplastic cells by the LDL receptor endocytic pathway in patients on conventional chemotherapy.
Trenkel, Verena M.; Lorance, Pascal
2011-01-01
Kaup's arrowtooth eel Synaphobranchus kaupii is a small-bodied fish and an abundant inhabitant of the European continental slope. To estimate its local density video information using the remotely operated vehicle (ROV) Victor 6000 were collected at three locations in the Bay of Biscay slope. Two methods for estimating local densities were tested: strip transect counts and bait experiments. For bait experiments three behaviour types were observed in about equal proportions for individuals arriving near the seafloor: moving up the current towards the ROV, moving across the current and drifting with the current. Visible attraction towards the bait was the highest for individuals swimming against the current (80%) and about equally low for the other two types (around 30%); it did not depend on current speed nor temperature. Three main innovations were introduced for estimating population densities from bait experiments: (i) inclusion of an additional behaviour category—that of passively drifting individuals, (ii) inclusion of reaction behaviour for actively swimming individuals and (iii) a hierarchical Bayesian estimation framework. The results indicated that about half of individuals were foraging actively of which less than one third reacted on encountering the bait plume and the other half were drifting with the current. Taking account of drifting individuals and the reaction probability made density estimates from bait experiments and strip transects more similar.
On the expected value and variance for an estimator of the spatio-temporal product density function
DEFF Research Database (Denmark)
Rodríguez-Corté, Francisco J.; Ghorbani, Mohammad; Mateu, Jorge;
Second-order characteristics are used to analyse the spatio-temporal structure of the underlying point process, and thus these methods provide a natural starting point for the analysis of spatio-temporal point process data. We restrict our attention to the spatio-temporal product density function......, and develop a non-parametric edge-corrected kernel estimate of the product density under the second-order intensity-reweighted stationary hypothesis. The expectation and variance of the estimator are obtained, and closed form expressions derived under the Poisson case. A detailed simulation study is presented...... to compare our close expression for the variance with estimated ones for Poisson cases. The simulation experiments show that the theoretical form for the variance gives acceptable values, which can be used in practice. Finally, we apply the resulting estimator to data on the spatio-temporal distribution...
Oyang, Yen-Jen; Hwang, Shien-Ching; Ou, Yu-Yen; Chen, Chien-Yu; Chen, Zhi-Wei
2005-01-01
This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where n is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is
Freer, Phoebe E; Slanetz, Priscilla J; Haas, Jennifer S; Tung, Nadine M; Hughes, Kevin S; Armstrong, Katrina; Semine, A Alan; Troyan, Susan L; Birdwell, Robyn L
2015-09-01
Stemming from breast density notification legislation in Massachusetts effective 2015, we sought to develop a collaborative evidence-based approach to density notification that could be used by practitioners across the state. Our goal was to develop an evidence-based consensus management algorithm to help patients and health care providers follow best practices to implement a coordinated, evidence-based, cost-effective, sustainable practice and to standardize care in recommendations for supplemental screening. We formed the Massachusetts Breast Risk Education and Assessment Task Force (MA-BREAST) a multi-institutional, multi-disciplinary panel of expert radiologists, surgeons, primary care physicians, and oncologists to develop a collaborative approach to density notification legislation. Using evidence-based data from the Institute for Clinical and Economic Review, the Cochrane review, National Comprehensive Cancer Network guidelines, American Cancer Society recommendations, and American College of Radiology appropriateness criteria, the group collaboratively developed an evidence-based best-practices algorithm. The expert consensus algorithm uses breast density as one element in the risk stratification to determine the need for supplemental screening. Women with dense breasts and otherwise low risk (20% lifetime) should consider supplemental screening MRI in addition to routine mammography regardless of breast density. We report the development of the multi-disciplinary collaborative approach to density notification. We propose a risk stratification algorithm to assess personal level of risk to determine the need for supplemental screening for an individual woman. PMID:26290416
Institute of Scientific and Technical Information of China (English)
陈清平; 戴永隆
2003-01-01
Based on left truncated and right censored dependent data, the estimatorsof higher derivatives of density function and hazard rate function are given by kernelsmoothing method. When observed data exhibit α-mixing dependence, local propertiesincluding strong consistency and law of iterated logarithm are presented. Moreover, whenthe mode estimator is defined as the random variable that maximizes the kernel densityestimator, the asymptotic normality of the mode estimator is established.
Institute of Scientific and Technical Information of China (English)
于代友; 刘秀梅; 陈雯; 冯娜娜; 于洁; 吴月清; 刘红娟
2015-01-01
Objective It is to analyze the relevance between the structure of female breast density and physiology, repro-ductive characteristics and breast cancer.Methods 5006 cases of local women with complete data whom had accepted digital breast X-ray angiography were selected.ACR BI-RADS was used as standard to evaluate mammary gland density type.Be-sides, breast cancer was diagnosed on the basis of pathological diagnosis.In the study, all data was divided according to phys-iology and reproductive characteristics, and the composing and correlation between breast cancer and breast density was ana-lyzed.Results The average age was 44.22 ±8.09 and median age was 43.Constitution of the mammary gland density in 5 006:lard type 256(5.11%), small type 726(14.51%), quantity type 3719(74.29%), and density type 305(6.09%).There was statistically significant difference between breast density and age, lactation history, history of abortion, menopause age, sterilization etc(P0.05).Pathology confirmed breast cancer 184 cases, constitution of the mammary gland density:lard type 26(14.13%), small type 66(35.87%), quantity type 91(49.46%)and density type 1(0.54%). Significant statistically difference was found between mammary gland density and age, menopause age, whether take strogen etc(P0.05).Conclusion The important factors that affect female breast density showing as follows:age, lactation history, history of abortion, menopause age, whether done sterilization, nev-ertheless, age, menopause women and low density of breast could increase the risk .%目的：分析女性乳腺密度的构成与生理、生育因素及乳腺癌之间相关性。方法选取接受全数字乳腺X射线摄影检查且临床资料完整的当地女性5006例，以ACR BI－RADS标准，依据乳腺X射线摄影结果评价乳腺腺体密度类型，乳腺癌以病理诊断为标准。按不同年龄、生育特征因素分组，分析其与乳腺密度构成及乳腺癌之间相关性。结果5006
International Nuclear Information System (INIS)
To evaluate the accordance of size measurements of malignant breast lesions 65 women with 76 malignant lesions were preoperatively examined with triple diagnosis (mammography was performed in three views with additional views if necessary) and dynamic MR imaging using a subtraction technique with a 3D T1-weighted sequence. Maximum lesion size at histopathology was used as gold standard and compared with maximum lesion size at MRI and mammography. All measurements were made independently for each method. Histopathology verified 48 invasive, 5 in situ, and 23 mixed lesions. No significant difference was found for the pure invasive lesions (p=0.366). In the mixed lesions a slightly better result for MRI was indicated (p=0.116), although there was a great spread. Only five pure in situ lesions were assessed, too few to draw any statistical conclusions (p>0.5). An overall difference indicated a slight superiority of MRI (p=0.097). The MR imaging and mammography are both good at measuring the size of detected invasive breast malignancies. The total sizes of mixed lesions are frequently underestimated by both MRI and mammography, although the invasive parts were equally well described and measured with both methods. (orig.)
Directory of Open Access Journals (Sweden)
Urrutia Jackie D.
2016-01-01
Full Text Available Breast cancer is one of the leading causes of death in the Philippines. One out of four who are diagnosed with breast cancer die within the first five years, and no less than 40 percent die within 10 years and it has continous rise as time passes by. Therefore, it is very important to know the factors that can help for the survival rate of the patients. The purpose of this study is to identify the best possible treatment or combination of treatments. The researchers considered four independent variables namely: Completed Surgery, Completed Chemotherapy, Completed Hormonotherapy and Completed Radiotherapy. The researchers limit this study for only 160 patients with stage 2 and 135 with stage 3 for a total of 295 patients considering the data gathered from three hospitals from Metro Manila. The names of the hospitals were not declared due to confidentiality of data. In identifying the best treatment or combination of treatments, odds, probabilities and odds ratios of patients, Logistic Regression Analysis was used.
International Nuclear Information System (INIS)
Background: The Icelandic breast cancer screening program, initiated November 1987 in Reykjavik and covering the whole country from December 1989, comprises biennial invitation to mammography for women aged 40-69 years old. Purpose: To estimate the impact of mammography service screening in Iceland on deaths from breast cancer. Material and Methods: Cases were deaths from breast cancer from 1990 onwards in women aged 40 and over at diagnosis, during the period November 1987 to December 31, 2002. Age- and screening-area-matched, population-based controls were women who had also been invited to screening but were alive at the time their case died. Results: Using conditional logistic regression on the data from 226 cases and 902 controls, the odds ratio for the risk of death from breast cancer in those attending at least one screen compared to those never screened was 0.59 (95% CI 0.41-0.84). After adjustment for healthy-volunteer bias and screening-opportunity bias, the odds ratio was 0.65 (95% CI 0.39-1.09). Conclusion: These results indicate a 35-40% reduction in breast cancer deaths by attending the Icelandic breast cancer screening program. These results are consistent with the overall evidence from other observational evaluations of mammography-based programs
Institute of Scientific and Technical Information of China (English)
ZHANG Baoqiang; CHEN Guoping; GUO Qintao
2012-01-01
An improved method using kernel density estimation (KDE) and confidence level is presented for model validation with small samples.Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements.However,model validation provides more confidence for decision makers when improving prediction accuracy at the same time.The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation.As a numerical example,the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen.The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data.The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model.Finally,the target structure prediction is performed using validated model,which are consistent with the results obtained by other researchers.The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.
Tosic Golo, Natasa; Wal, van der Siebrigje; Kuper, F.G.; Mouthaan, A.J.
2002-01-01
The objective of this letter is to give an estimation of the impact of an electrostatic discharge (ESD) stress on the density of states (DOS) within the energy gap of hydrogenated amorphous silicon (a-Si:H) thin-film transistors. ESD stresses were applied by means of a transmission line model tester
JOURNAL NRMRL-RTP-P- 437 Baugh, W., Klinger, L., Guenther, A., and Geron*, C.D. Measurement of Oak Tree Density with Landsat TM Data for Estimating Biogenic Isoprene Emissions in Tennessee, USA. International Journal of Remote Sensing (Taylor and Francis) 22 (14):2793-2810 (2001)...
Digital Repository Service at National Institute of Oceanography (India)
Madhupratap, M.; Achuthankutty, C.T.; Nair, S.R.S.
, by the American Society of Limnology and Oceanography, Inc. Separation and concentration of living dinoflagellate resting cysts from marine sediments via density-gradient centrifugation Abstract-A method for separating and con- centrating resting cysts... of dinoflagellates from marine sediments via centrifugation in a non- toxic, isosmotic density gradient has been de- veloped and tested. The density-gradient medi- um is an aqueous suspension of colloidal silica (Nalco 1060) made isosmotic with seawater...