AQuA: An Automated Quantification Algorithm for High-Throughput NMR-Based Metabolomics and Its Application in Human Plasma.

PubMed

Röhnisch, Hanna E; Eriksson, Jan; Müllner, Elisabeth; Agback, Peter; Sandström, Corine; Moazzami, Ali A

2018-02-06

A key limiting step for high-throughput NMR-based metabolomics is the lack of rapid and accurate tools for absolute quantification of many metabolites. We developed, implemented, and evaluated an algorithm, AQuA (Automated Quantification Algorithm), for targeted metabolite quantification from complex 1 H NMR spectra. AQuA operates based on spectral data extracted from a library consisting of one standard calibration spectrum for each metabolite. It uses one preselected NMR signal per metabolite for determining absolute concentrations and does so by effectively accounting for interferences caused by other metabolites. AQuA was implemented and evaluated using experimental NMR spectra from human plasma. The accuracy of AQuA was tested and confirmed in comparison with a manual spectral fitting approach using the ChenomX software, in which 61 out of 67 metabolites quantified in 30 human plasma spectra showed a goodness-of-fit (r 2 ) close to or exceeding 0.9 between the two approaches. In addition, three quality indicators generated by AQuA, namely, occurrence, interference, and positional deviation, were studied. These quality indicators permit evaluation of the results each time the algorithm is operated. The efficiency was tested and confirmed by implementing AQuA for quantification of 67 metabolites in a large data set comprising 1342 experimental spectra from human plasma, in which the whole computation took less than 1 s.

Strawberry: Fast and accurate genome-guided transcript reconstruction and quantification from RNA-Seq.

PubMed

Liu, Ruolin; Dickerson, Julie

2017-11-01

We propose a novel method and software tool, Strawberry, for transcript reconstruction and quantification from RNA-Seq data under the guidance of genome alignment and independent of gene annotation. Strawberry consists of two modules: assembly and quantification. The novelty of Strawberry is that the two modules use different optimization frameworks but utilize the same data graph structure, which allows a highly efficient, expandable and accurate algorithm for dealing large data. The assembly module parses aligned reads into splicing graphs, and uses network flow algorithms to select the most likely transcripts. The quantification module uses a latent class model to assign read counts from the nodes of splicing graphs to transcripts. Strawberry simultaneously estimates the transcript abundances and corrects for sequencing bias through an EM algorithm. Based on simulations, Strawberry outperforms Cufflinks and StringTie in terms of both assembly and quantification accuracies. Under the evaluation of a real data set, the estimated transcript expression by Strawberry has the highest correlation with Nanostring probe counts, an independent experiment measure for transcript expression. Strawberry is written in C++14, and is available as open source software at https://github.com/ruolin/strawberry under the MIT license.

Improved algorithm for computerized detection and quantification of pulmonary emphysema at high-resolution computed tomography (HRCT)

NASA Astrophysics Data System (ADS)

Tylen, Ulf; Friman, Ola; Borga, Magnus; Angelhed, Jan-Erik

2001-05-01

Emphysema is characterized by destruction of lung tissue with development of small or large holes within the lung. These areas will have Hounsfield values (HU) approaching -1000. It is possible to detect and quantificate such areas using simple density mask technique. The edge enhancement reconstruction algorithm, gravity and motion of the heart and vessels during scanning causes artefacts, however. The purpose of our work was to construct an algorithm that detects such image artefacts and corrects them. The first step is to apply inverse filtering to the image removing much of the effect of the edge enhancement reconstruction algorithm. The next step implies computation of the antero-posterior density gradient caused by gravity and correction for that. Motion artefacts are in a third step corrected for by use of normalized averaging, thresholding and region growing. Twenty healthy volunteers were investigated, 10 with slight emphysema and 10 without. Using simple density mask technique it was not possible to separate persons with disease from those without. Our algorithm improved separation of the two groups considerably. Our algorithm needs further refinement, but may form a basis for further development of methods for computerized diagnosis and quantification of emphysema by HRCT.

Fully automated, deep learning segmentation of oxygen-induced retinopathy images

PubMed Central

Xiao, Sa; Bucher, Felicitas; Wu, Yue; Rokem, Ariel; Lee, Cecilia S.; Marra, Kyle V.; Fallon, Regis; Diaz-Aguilar, Sophia; Aguilar, Edith; Friedlander, Martin; Lee, Aaron Y.

2017-01-01

Oxygen-induced retinopathy (OIR) is a widely used model to study ischemia-driven neovascularization (NV) in the retina and to serve in proof-of-concept studies in evaluating antiangiogenic drugs for ocular, as well as nonocular, diseases. The primary parameters that are analyzed in this mouse model include the percentage of retina with vaso-obliteration (VO) and NV areas. However, quantification of these two key variables comes with a great challenge due to the requirement of human experts to read the images. Human readers are costly, time-consuming, and subject to bias. Using recent advances in machine learning and computer vision, we trained deep learning neural networks using over a thousand segmentations to fully automate segmentation in OIR images. While determining the percentage area of VO, our algorithm achieved a similar range of correlation coefficients to that of expert inter-human correlation coefficients. In addition, our algorithm achieved a higher range of correlation coefficients compared with inter-expert correlation coefficients for quantification of the percentage area of neovascular tufts. In summary, we have created an open-source, fully automated pipeline for the quantification of key values of OIR images using deep learning neural networks. PMID:29263301

Collaborative Study of Analysis of High Resolution Infrared Atmospheric Spectra Between NASA Langley Research Center and the University of Denver

NASA Technical Reports Server (NTRS)

Goldman, Aaron

1999-01-01

The Langley-D.U. collaboration on the analysis of high resolution infrared atmospheric spectra covered a number of important studies of trace gases identification and quantification from field spectra, and spectral line parameters analysis. The collaborative work included: Quantification and monitoring of trace gases from ground-based spectra available from various locations and seasons and from balloon flights. Studies toward identification and quantification of isotopic species, mostly oxygen and Sulfur isotopes. Search for new species on the available spectra. Update of spectroscopic line parameters, by combining laboratory and atmospheric spectra with theoretical spectroscopy methods. Study of trends of atmosphere trace constituents. Algorithms developments, retrievals intercomparisons and automatization of the analysis of NDSC spectra, for both column amounts and vertical profiles.

Remote quantification of Cochlodinium polykrikoides blooms occurring in the East Sea using geostationary ocean color imager (GOCI).

PubMed

Noh, Jae Hoon; Kim, Wonkook; Son, Seung Hyun; Ahn, Jae-Hyun; Park, Young-Je

2018-03-01

Accurate and timely quantification of widespread harmful algal bloom (HAB) distribution is crucial to respond to the natural disaster, minimize the damage, and assess the environmental impact of the event. Although various remote sensing-based quantification approaches have been proposed for HAB since the advent of the ocean color satellite sensor, there have been no algorithms that were validated with in-situ quantitative measurements for the red tide occurring in the Korean seas. Furthermore, since the geostationary ocean color imager (GOCI) became available in June 2010, an algorithm that exploits its unprecedented observation frequency (every hour during the daytime) has been highly demanded to better track the changes in spatial distribution of red tide. This study developed a novel red tide quantification algorithm for GOCI that can estimate hourly chlorophyll-a (Chl a) concentration of Cochlodinium (Margalefidinium) polykrikoides, one of the major red tide species around Korean seas. The developed algorithm has been validated using in-situ Chl a measurements collected from a cruise campaign conducted in August 2013, when a massive C. polykrikoides bloom devastated Korean coasts. The proposed algorithm produced a high correlation (R 2 =0.92) with in-situ Chl a measurements with robust performance also for high Chl a concentration (300mg/m 3 ) in East Sea areas that typically have a relatively low total suspended particle concentration (<0.5mg/m 3 ). Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Algorithm for Automatic Behavior Quantification of Laboratory Mice Using High-Frame-Rate Videos

NASA Astrophysics Data System (ADS)

Nie, Yuman; Takaki, Takeshi; Ishii, Idaku; Matsuda, Hiroshi

In this paper, we propose an algorithm for automatic behavior quantification in laboratory mice to quantify several model behaviors. The algorithm can detect repetitive motions of the fore- or hind-limbs at several or dozens of hertz, which are too rapid for the naked eye, from high-frame-rate video images. Multiple repetitive motions can always be identified from periodic frame-differential image features in four segmented regions — the head, left side, right side, and tail. Even when a mouse changes its posture and orientation relative to the camera, these features can still be extracted from the shift- and orientation-invariant shape of the mouse silhouette by using the polar coordinate system and adjusting the angle coordinate according to the head and tail positions. The effectiveness of the algorithm is evaluated by analyzing long-term 240-fps videos of four laboratory mice for six typical model behaviors: moving, rearing, immobility, head grooming, left-side scratching, and right-side scratching. The time durations for the model behaviors determined by the algorithm have detection/correction ratios greater than 80% for all the model behaviors. This shows good quantification results for actual animal testing.

Two-stream Convolutional Neural Network for Methane Emissions Quantification

NASA Astrophysics Data System (ADS)

Wang, J.; Ravikumar, A. P.; McGuire, M.; Bell, C.; Tchapmi, L. P.; Brandt, A. R.

2017-12-01

Methane, a key component of natural gas, has a 25x higher global warming potential than carbon dioxide on a 100-year basis. Accurately monitoring and mitigating methane emissions require cost-effective detection and quantification technologies. Optical gas imaging, one of the most commonly used leak detection technology, adopted by Environmental Protection Agency, cannot estimate leak-sizes. In this work, we harness advances in computer science to allow for rapid and automatic leak quantification. Particularly, we utilize two-stream deep Convolutional Networks (ConvNets) to estimate leak-size by capturing complementary spatial information from still plume frames, and temporal information from plume motion between frames. We build large leak datasets for training and evaluating purposes by collecting about 20 videos (i.e. 397,400 frames) of leaks. The videos were recorded at six distances from the source, covering 10 -60 ft. Leak sources included natural gas well-heads, separators, and tanks. All frames were labeled with a true leak size, which has eight levels ranging from 0 to 140 MCFH. Preliminary analysis shows that two-stream ConvNets provides significant accuracy advantage over single steam ConvNets. Spatial stream ConvNet can achieve an accuracy of 65.2%, by extracting important features, including texture, plume area, and pattern. Temporal stream, fed by the results of optical flow analysis, results in an accuracy of 58.3%. The integration of the two-stream ConvNets gives a combined accuracy of 77.6%. For future work, we will split the training and testing datasets in distinct ways in order to test the generalization of the algorithm for different leak sources. Several analytic metrics, including confusion matrix and visualization of key features, will be used to understand accuracy rates and occurrences of false positives. The quantification algorithm can help to find and fix super-emitters, and improve the cost-effectiveness of leak detection and repair programs.

Extension of least squares spectral resolution algorithm to high-resolution lipidomics data.

PubMed

Zeng, Ying-Xu; Mjøs, Svein Are; David, Fabrice P A; Schmid, Adrien W

2016-03-31

Lipidomics, which focuses on the global study of molecular lipids in biological systems, has been driven tremendously by technical advances in mass spectrometry (MS) instrumentation, particularly high-resolution MS. This requires powerful computational tools that handle the high-throughput lipidomics data analysis. To address this issue, a novel computational tool has been developed for the analysis of high-resolution MS data, including the data pretreatment, visualization, automated identification, deconvolution and quantification of lipid species. The algorithm features the customized generation of a lipid compound library and mass spectral library, which covers the major lipid classes such as glycerolipids, glycerophospholipids and sphingolipids. Next, the algorithm performs least squares resolution of spectra and chromatograms based on the theoretical isotope distribution of molecular ions, which enables automated identification and quantification of molecular lipid species. Currently, this methodology supports analysis of both high and low resolution MS as well as liquid chromatography-MS (LC-MS) lipidomics data. The flexibility of the methodology allows it to be expanded to support more lipid classes and more data interpretation functions, making it a promising tool in lipidomic data analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

MorphoGraphX: A platform for quantifying morphogenesis in 4D.

PubMed

Barbier de Reuille, Pierre; Routier-Kierzkowska, Anne-Lise; Kierzkowski, Daniel; Bassel, George W; Schüpbach, Thierry; Tauriello, Gerardo; Bajpai, Namrata; Strauss, Sören; Weber, Alain; Kiss, Annamaria; Burian, Agata; Hofhuis, Hugo; Sapala, Aleksandra; Lipowczan, Marcin; Heimlicher, Maria B; Robinson, Sarah; Bayer, Emmanuelle M; Basler, Konrad; Koumoutsakos, Petros; Roeder, Adrienne H K; Aegerter-Wilmsen, Tinri; Nakayama, Naomi; Tsiantis, Miltos; Hay, Angela; Kwiatkowska, Dorota; Xenarios, Ioannis; Kuhlemeier, Cris; Smith, Richard S

2015-05-06

Morphogenesis emerges from complex multiscale interactions between genetic and mechanical processes. To understand these processes, the evolution of cell shape, proliferation and gene expression must be quantified. This quantification is usually performed either in full 3D, which is computationally expensive and technically challenging, or on 2D planar projections, which introduces geometrical artifacts on highly curved organs. Here we present MorphoGraphX ( www.MorphoGraphX.org), a software that bridges this gap by working directly with curved surface images extracted from 3D data. In addition to traditional 3D image analysis, we have developed algorithms to operate on curved surfaces, such as cell segmentation, lineage tracking and fluorescence signal quantification. The software's modular design makes it easy to include existing libraries, or to implement new algorithms. Cell geometries extracted with MorphoGraphX can be exported and used as templates for simulation models, providing a powerful platform to investigate the interactions between shape, genes and growth.

Automated Quantification of Pneumothorax in CT

PubMed Central

Do, Synho; Salvaggio, Kristen; Gupta, Supriya; Kalra, Mannudeep; Ali, Nabeel U.; Pien, Homer

2012-01-01

An automated, computer-aided diagnosis (CAD) algorithm for the quantification of pneumothoraces from Multidetector Computed Tomography (MDCT) images has been developed. Algorithm performance was evaluated through comparison to manual segmentation by expert radiologists. A combination of two-dimensional and three-dimensional processing techniques was incorporated to reduce required processing time by two-thirds (as compared to similar techniques). Volumetric measurements on relative pneumothorax size were obtained and the overall performance of the automated method shows an average error of just below 1%. PMID:23082091

Motion-aware stroke volume quantification in 4D PC-MRI data of the human aorta.

PubMed

Köhler, Benjamin; Preim, Uta; Grothoff, Matthias; Gutberlet, Matthias; Fischbach, Katharina; Preim, Bernhard

2016-02-01

4D PC-MRI enables the noninvasive measurement of time-resolved, three-dimensional blood flow data that allow quantification of the hemodynamics. Stroke volumes are essential to assess the cardiac function and evolution of different cardiovascular diseases. The calculation depends on the wall position and vessel orientation, which both change during the cardiac cycle due to the heart muscle contraction and the pumped blood. However, current systems for the quantitative 4D PC-MRI data analysis neglect the dynamic character and instead employ a static 3D vessel approximation. We quantify differences between stroke volumes in the aorta obtained with and without consideration of its dynamics. We describe a method that uses the approximating 3D segmentation to automatically initialize segmentation algorithms that require regions inside and outside the vessel for each temporal position. This enables the use of graph cuts to obtain 4D segmentations, extract vessel surfaces including centerlines for each temporal position and derive motion information. The stroke volume quantification is compared using measuring planes in static (3D) vessels, planes with fixed angulation inside dynamic vessels (this corresponds to the common 2D PC-MRI) and moving planes inside dynamic vessels. Seven datasets with different pathologies such as aneurysms and coarctations were evaluated in close collaboration with radiologists. Compared to the experts' manual stroke volume estimations, motion-aware quantification performs, on average, 1.57% better than calculations without motion consideration. The mean difference between stroke volumes obtained with the different methods is 7.82%. Automatically obtained 4D segmentations overlap by 85.75% with manually generated ones. Incorporating motion information in the stroke volume quantification yields slight but not statistically significant improvements. The presented method is feasible for the clinical routine, since computation times are low and essential parts run fully automatically. The 4D segmentations can be used for other algorithms as well. The simultaneous visualization and quantification may support the understanding and interpretation of cardiac blood flow.

A review of optimization and quantification techniques for chemical exchange saturation transfer (CEST) MRI toward sensitive in vivo imaging

PubMed Central

Guo, Yingkun; Zheng, Hairong; Sun, Phillip Zhe

2015-01-01

Chemical exchange saturation transfer (CEST) MRI is a versatile imaging method that probes the chemical exchange between bulk water and exchangeable protons. CEST imaging indirectly detects dilute labile protons via bulk water signal changes following selective saturation of exchangeable protons, which offers substantial sensitivity enhancement and has sparked numerous biomedical applications. Over the past decade, CEST imaging techniques have rapidly evolved due to contributions from multiple domains, including the development of CEST mathematical models, innovative contrast agent designs, sensitive data acquisition schemes, efficient field inhomogeneity correction algorithms, and quantitative CEST (qCEST) analysis. The CEST system that underlies the apparent CEST-weighted effect, however, is complex. The experimentally measurable CEST effect depends not only on parameters such as CEST agent concentration, pH and temperature, but also on relaxation rate, magnetic field strength and more importantly, experimental parameters including repetition time, RF irradiation amplitude and scheme, and image readout. Thorough understanding of the underlying CEST system using qCEST analysis may augment the diagnostic capability of conventional imaging. In this review, we provide a concise explanation of CEST acquisition methods and processing algorithms, including their advantages and limitations, for optimization and quantification of CEST MRI experiments. PMID:25641791

Cut set-based risk and reliability analysis for arbitrarily interconnected networks

DOEpatents

Wyss, Gregory D.

2000-01-01

Method for computing all-terminal reliability for arbitrarily interconnected networks such as the United States public switched telephone network. The method includes an efficient search algorithm to generate minimal cut sets for nonhierarchical networks directly from the network connectivity diagram. Efficiency of the search algorithm stems in part from its basis on only link failures. The method also includes a novel quantification scheme that likewise reduces computational effort associated with assessing network reliability based on traditional risk importance measures. Vast reductions in computational effort are realized since combinatorial expansion and subsequent Boolean reduction steps are eliminated through analysis of network segmentations using a technique of assuming node failures to occur on only one side of a break in the network, and repeating the technique for all minimal cut sets generated with the search algorithm. The method functions equally well for planar and non-planar networks.

Segmentation And Quantification Of Black Holes In Multiple Sclerosis

PubMed Central

Datta, Sushmita; Sajja, Balasrinivasa Rao; He, Renjie; Wolinsky, Jerry S.; Gupta, Rakesh K.; Narayana, Ponnada A.

2006-01-01

A technique that involves minimal operator intervention was developed and implemented for identification and quantification of black holes on T1- weighted magnetic resonance images (T1 images) in multiple sclerosis (MS). Black holes were segmented on T1 images based on grayscale morphological operations. False classification of black holes was minimized by masking the segmented images with images obtained from the orthogonalization of T2-weighted and T1 images. Enhancing lesion voxels on postcontrast images were automatically identified and eliminated from being included in the black hole volume. Fuzzy connectivity was used for the delineation of black holes. The performance of this algorithm was quantitatively evaluated on 14 MS patients. PMID:16126416

Standardless quantification by parameter optimization in electron probe microanalysis

NASA Astrophysics Data System (ADS)

Limandri, Silvina P.; Bonetto, Rita D.; Josa, Víctor Galván; Carreras, Alejo C.; Trincavelli, Jorge C.

2012-11-01

A method for standardless quantification by parameter optimization in electron probe microanalysis is presented. The method consists in minimizing the quadratic differences between an experimental spectrum and an analytical function proposed to describe it, by optimizing the parameters involved in the analytical prediction. This algorithm, implemented in the software POEMA (Parameter Optimization in Electron Probe Microanalysis), allows the determination of the elemental concentrations, along with their uncertainties. The method was tested in a set of 159 elemental constituents corresponding to 36 spectra of standards (mostly minerals) that include trace elements. The results were compared with those obtained with the commercial software GENESIS Spectrum® for standardless quantification. The quantifications performed with the method proposed here are better in the 74% of the cases studied. In addition, the performance of the method proposed is compared with the first principles standardless analysis procedure DTSA for a different data set, which excludes trace elements. The relative deviations with respect to the nominal concentrations are lower than 0.04, 0.08 and 0.35 for the 66% of the cases for POEMA, GENESIS and DTSA, respectively.

Lamb Wave Damage Quantification Using GA-Based LS-SVM.

PubMed

Sun, Fuqiang; Wang, Ning; He, Jingjing; Guan, Xuefei; Yang, Jinsong

2017-06-12

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification.

Lamb Wave Damage Quantification Using GA-Based LS-SVM

PubMed Central

Sun, Fuqiang; Wang, Ning; He, Jingjing; Guan, Xuefei; Yang, Jinsong

2017-01-01

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification. PMID:28773003

SIMPLE METHOD FOR THE REPRESENTATION, QUANTIFICATION, AND COMPARISON OF THE VOLUMES AND SHAPES OF CHEMICAL COMPOUNDS

EPA Science Inventory

A conceptually and computationally simple method for the definition, display, quantification, and comparison of the shapes of three-dimensional mathematical molecular models is presented. Molecular or solvent-accessible volume and surface area can also be calculated. Algorithms, ...

DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D.

PubMed

Shuvaev, Sergey A; Lazutkin, Alexander A; Kedrov, Alexander V; Anokhin, Konstantin V; Enikolopov, Grigori N; Koulakov, Alexei A

2017-01-01

Current 3D imaging methods, including optical projection tomography, light-sheet microscopy, block-face imaging, and serial two photon tomography enable visualization of large samples of biological tissue. Large volumes of data obtained at high resolution require development of automatic image processing techniques, such as algorithms for automatic cell detection or, more generally, point-like object detection. Current approaches to automated cell detection suffer from difficulties originating from detection of particular cell types, cell populations of different brightness, non-uniformly stained, and overlapping cells. In this study, we present a set of algorithms for robust automatic cell detection in 3D. Our algorithms are suitable for, but not limited to, whole brain regions and individual brain sections. We used watershed procedure to split regional maxima representing overlapping cells. We developed a bootstrap Gaussian fit procedure to evaluate the statistical significance of detected cells. We compared cell detection quality of our algorithm and other software using 42 samples, representing 6 staining and imaging techniques. The results provided by our algorithm matched manual expert quantification with signal-to-noise dependent confidence, including samples with cells of different brightness, non-uniformly stained, and overlapping cells for whole brain regions and individual tissue sections. Our algorithm provided the best cell detection quality among tested free and commercial software.

Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: A postmortem study

PubMed Central

Ding, Huanjun; Johnson, Travis; Lin, Muqing; Le, Huy Q.; Ducote, Justin L.; Su, Min-Ying; Molloi, Sabee

2013-01-01

Purpose: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. Methods: T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left–right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field. Results: The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left–right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left–right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction. Conclusions: The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications. PMID:24320536

Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: a postmortem study.

PubMed

Ding, Huanjun; Johnson, Travis; Lin, Muqing; Le, Huy Q; Ducote, Justin L; Su, Min-Ying; Molloi, Sabee

2013-12-01

Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left-right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field. The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left-right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left-right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction. The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications.

An Uncertainty Quantification Framework for Remote Sensing Retrievals

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Hobbs, J.

2017-12-01

Remote sensing data sets produced by NASA and other space agencies are the result of complex algorithms that infer geophysical state from observed radiances using retrieval algorithms. The processing must keep up with the downlinked data flow, and this necessitates computational compromises that affect the accuracies of retrieved estimates. The algorithms are also limited by imperfect knowledge of physics and of ancillary inputs that are required. All of this contributes to uncertainties that are generally not rigorously quantified by stepping outside the assumptions that underlie the retrieval methodology. In this talk we discuss a practical framework for uncertainty quantification that can be applied to a variety of remote sensing retrieval algorithms. Ours is a statistical approach that uses Monte Carlo simulation to approximate the sampling distribution of the retrieved estimates. We will discuss the strengths and weaknesses of this approach, and provide a case-study example from the Orbiting Carbon Observatory 2 mission.

A refined methodology for modeling volume quantification performance in CT

NASA Astrophysics Data System (ADS)

Chen, Baiyu; Wilson, Joshua; Samei, Ehsan

2014-03-01

The utility of CT lung nodule volume quantification technique depends on the precision of the quantification. To enable the evaluation of quantification precision, we previously developed a mathematical model that related precision to image resolution and noise properties in uniform backgrounds in terms of an estimability index (e'). The e' was shown to predict empirical precision across 54 imaging and reconstruction protocols, but with different correlation qualities for FBP and iterative reconstruction (IR) due to the non-linearity of IR impacted by anatomical structure. To better account for the non-linearity of IR, this study aimed to refine the noise characterization of the model in the presence of textured backgrounds. Repeated scans of an anthropomorphic lung phantom were acquired. Subtracted images were used to measure the image quantum noise, which was then used to adjust the noise component of the e' calculation measured from a uniform region. In addition to the model refinement, the validation of the model was further extended to 2 nodule sizes (5 and 10 mm) and 2 segmentation algorithms. Results showed that the magnitude of IR's quantum noise was significantly higher in structured backgrounds than in uniform backgrounds (ASiR, 30-50%; MBIR, 100-200%). With the refined model, the correlation between e' values and empirical precision no longer depended on reconstruction algorithm. In conclusion, the model with refined noise characterization relfected the nonlinearity of iterative reconstruction in structured background, and further showed successful prediction of quantification precision across a variety of nodule sizes, dose levels, slice thickness, reconstruction algorithms, and segmentation software.

Predicting termination of atrial fibrillation based on the structure and quantification of the recurrence plot.

PubMed

Sun, Rongrong; Wang, Yuanyuan

2008-11-01

Predicting the spontaneous termination of the atrial fibrillation (AF) leads to not only better understanding of mechanisms of the arrhythmia but also the improved treatment of the sustained AF. A novel method is proposed to characterize the AF based on structure and the quantification of the recurrence plot (RP) to predict the termination of the AF. The RP of the electrocardiogram (ECG) signal is firstly obtained and eleven features are extracted to characterize its three basic patterns. Then the sequential forward search (SFS) algorithm and Davies-Bouldin criterion are utilized to select the feature subset which can predict the AF termination effectively. Finally, the multilayer perceptron (MLP) neural network is applied to predict the AF termination. An AF database which includes one training set and two testing sets (A and B) of Holter ECG recordings is studied. Experiment results show that 97% of testing set A and 95% of testing set B are correctly classified. It demonstrates that this algorithm has the ability to predict the spontaneous termination of the AF effectively.

MorphoGraphX: A platform for quantifying morphogenesis in 4D

PubMed Central

Barbier de Reuille, Pierre; Routier-Kierzkowska, Anne-Lise; Kierzkowski, Daniel; Bassel, George W; Schüpbach, Thierry; Tauriello, Gerardo; Bajpai, Namrata; Strauss, Sören; Weber, Alain; Kiss, Annamaria; Burian, Agata; Hofhuis, Hugo; Sapala, Aleksandra; Lipowczan, Marcin; Heimlicher, Maria B; Robinson, Sarah; Bayer, Emmanuelle M; Basler, Konrad; Koumoutsakos, Petros; Roeder, Adrienne HK; Aegerter-Wilmsen, Tinri; Nakayama, Naomi; Tsiantis, Miltos; Hay, Angela; Kwiatkowska, Dorota; Xenarios, Ioannis; Kuhlemeier, Cris; Smith, Richard S

2015-01-01

Morphogenesis emerges from complex multiscale interactions between genetic and mechanical processes. To understand these processes, the evolution of cell shape, proliferation and gene expression must be quantified. This quantification is usually performed either in full 3D, which is computationally expensive and technically challenging, or on 2D planar projections, which introduces geometrical artifacts on highly curved organs. Here we present MorphoGraphX (www.MorphoGraphX.org), a software that bridges this gap by working directly with curved surface images extracted from 3D data. In addition to traditional 3D image analysis, we have developed algorithms to operate on curved surfaces, such as cell segmentation, lineage tracking and fluorescence signal quantification. The software's modular design makes it easy to include existing libraries, or to implement new algorithms. Cell geometries extracted with MorphoGraphX can be exported and used as templates for simulation models, providing a powerful platform to investigate the interactions between shape, genes and growth. DOI: http://dx.doi.org/10.7554/eLife.05864.001 PMID:25946108

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis-Hastings Markov Chain Monte Carlo algorithm

NASA Astrophysics Data System (ADS)

Wang, Hongrui; Wang, Cheng; Wang, Ying; Gao, Xiong; Yu, Chen

2017-06-01

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLE confidence interval and thus more precise estimation by using the related information from regional gage stations. The Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.

Image Analysis Algorithms for Immunohistochemical Assessment of Cell Death Events and Fibrosis in Tissue Sections

PubMed Central

Krajewska, Maryla; Smith, Layton H.; Rong, Juan; Huang, Xianshu; Hyer, Marc L.; Zeps, Nikolajs; Iacopetta, Barry; Linke, Steven P.; Olson, Allen H.; Reed, John C.; Krajewski, Stan

2009-01-01

Cell death is of broad physiological and pathological importance, making quantification of biochemical events associated with cell demise a high priority for experimental pathology. Fibrosis is a common consequence of tissue injury involving necrotic cell death. Using tissue specimens from experimental mouse models of traumatic brain injury, cardiac fibrosis, and cancer, as well as human tumor specimens assembled in tissue microarray (TMA) format, we undertook computer-assisted quantification of specific immunohistochemical and histological parameters that characterize processes associated with cell death. In this study, we demonstrated the utility of image analysis algorithms for color deconvolution, colocalization, and nuclear morphometry to characterize cell death events in tissue specimens: (a) subjected to immunostaining for detecting cleaved caspase-3, cleaved poly(ADP-ribose)-polymerase, cleaved lamin-A, phosphorylated histone H2AX, and Bcl-2; (b) analyzed by terminal deoxyribonucleotidyl transferase–mediated dUTP nick end labeling assay to detect DNA fragmentation; and (c) evaluated with Masson's trichrome staining. We developed novel algorithm-based scoring methods and validated them using TMAs as a high-throughput format. The proposed computer-assisted scoring methods for digital images by brightfield microscopy permit linear quantification of immunohistochemical and histochemical stainings. Examples are provided of digital image analysis performed in automated or semiautomated fashion for successful quantification of molecular events associated with cell death in tissue sections. (J Histochem Cytochem 57:649–663, 2009) PMID:19289554

Final Report: Quantification of Uncertainty in Extreme Scale Computations (QUEST)

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

Marzouk, Youssef; Conrad, Patrick; Bigoni, Daniele

QUEST (\\url{www.quest-scidac.org}) is a SciDAC Institute that is focused on uncertainty quantification (UQ) in large-scale scientific computations. Our goals are to (1) advance the state of the art in UQ mathematics, algorithms, and software; and (2) provide modeling, algorithmic, and general UQ expertise, together with software tools, to other SciDAC projects, thereby enabling and guiding a broad range of UQ activities in their respective contexts. QUEST is a collaboration among six institutions (Sandia National Laboratories, Los Alamos National Laboratory, the University of Southern California, Massachusetts Institute of Technology, the University of Texas at Austin, and Duke University) with a historymore » of joint UQ research. Our vision encompasses all aspects of UQ in leadership-class computing. This includes the well-founded setup of UQ problems; characterization of the input space given available data/information; local and global sensitivity analysis; adaptive dimensionality and order reduction; forward and inverse propagation of uncertainty; handling of application code failures, missing data, and hardware/software fault tolerance; and model inadequacy, comparison, validation, selection, and averaging. The nature of the UQ problem requires the seamless combination of data, models, and information across this landscape in a manner that provides a self-consistent quantification of requisite uncertainties in predictions from computational models. Accordingly, our UQ methods and tools span an interdisciplinary space across applied math, information theory, and statistics. The MIT QUEST effort centers on statistical inference and methods for surrogate or reduced-order modeling. MIT personnel have been responsible for the development of adaptive sampling methods, methods for approximating computationally intensive models, and software for both forward uncertainty propagation and statistical inverse problems. A key software product of the MIT QUEST effort is the MIT Uncertainty Quantification library, called MUQ (\\url{muq.mit.edu}).« less

Algorithm Diversity for Resilent Systems

DTIC Science & Technology

2016-06-27

data structures. 15. SUBJECT TERMS computer security, software diversity, program transformation 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 18...systematic method for transforming Datalog rules with general universal and existential quantification into efficient algorithms with precise complexity...worst case in the size of the ground rules. There are numerous choices during the transformation that lead to diverse algorithms and different

Toward automatic segmentation and quantification of tumor and stroma in whole-slide images of H and E stained rectal carcinomas

NASA Astrophysics Data System (ADS)

Geessink, Oscar G. F.; Baidoshvili, Alexi; Freling, Gerard; Klaase, Joost M.; Slump, Cornelis H.; van der Heijden, Ferdinand

2015-03-01

Visual estimation of tumor and stroma proportions in microscopy images yields a strong, Tumor-(lymph)Node- Metastasis (TNM) classification-independent predictor for patient survival in colorectal cancer. Therefore, it is also a potent (contra)indicator for adjuvant chemotherapy. However, quantification of tumor and stroma through visual estimation is highly subject to intra- and inter-observer variability. The aim of this study is to develop and clinically validate a method for objective quantification of tumor and stroma in standard hematoxylin and eosin (H and E) stained microscopy slides of rectal carcinomas. A tissue segmentation algorithm, based on supervised machine learning and pixel classification, was developed, trained and validated using histological slides that were prepared from surgically excised rectal carcinomas in patients who had not received neoadjuvant chemotherapy and/or radiotherapy. Whole-slide scanning was performed at 20× magnification. A total of 40 images (4 million pixels each) were extracted from 20 whole-slide images at sites showing various relative proportions of tumor and stroma. Experienced pathologists provided detailed annotations for every extracted image. The performance of the algorithm was evaluated using cross-validation by testing on 1 image at a time while using the other 39 images for training. The total classification error of the algorithm was 9.4% (SD = 3.2%). Compared to visual estimation by pathologists, the algorithm was 7.3 times (P = 0.033) more accurate in quantifying tissues, also showing 60% less variability. Automatic tissue quantification was shown to be both reliable and practicable. We ultimately intend to facilitate refined prognostic stratification of (colo)rectal cancer patients and enable better personalized treatment.

A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data.

PubMed

Engblom, Henrik; Tufvesson, Jane; Jablonowski, Robert; Carlsson, Marcus; Aletras, Anthony H; Hoffmann, Pavel; Jacquier, Alexis; Kober, Frank; Metzler, Bernhard; Erlinge, David; Atar, Dan; Arheden, Håkan; Heiberg, Einar

2016-05-04

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data. The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n = 7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n = 23 IR and n = 13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n = 124 IR and n = 49 PSIR images). Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1 ± 4%LVM (R = 0.84) in IR and -2 ± 3%LVM (R = 0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0 ± 4%LVM (R = 0.94) in IR and 0 ± 5%LVM (R = 0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2 ± 6 %LVM (R = 0.81) in IR images (n = 124) and 0 ± 5%LVM (R = 0.89) in PSIR images (n = 49). The EWA algorithm was validated experimentally and in patient data with a low bias in both IR and PSIR LGE images. Thus, the use of EM and a weighted intensity as in the EWA algorithm, may serve as a clinical standard for the quantification of myocardial infarction in LGE CMR images. CHILL-MI: NCT01379261 . NCT01374321 .

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm

DOE PAGES

Wang, Hongrui; Wang, Cheng; Wang, Ying; ...

2017-04-05

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLEmore » confidence interval and thus more precise estimation by using the related information from regional gage stations. As a result, the Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.« less

Fast dictionary generation and searching for magnetic resonance fingerprinting.

PubMed

Jun Xie; Mengye Lyu; Jian Zhang; Hui, Edward S; Wu, Ed X; Ze Wang

2017-07-01

A super-fast dictionary generation and searching (DGS) algorithm was developed for MR parameter quantification using magnetic resonance fingerprinting (MRF). MRF is a new technique for simultaneously quantifying multiple MR parameters using one temporally resolved MR scan. But it has a multiplicative computation complexity, resulting in a big burden of dictionary generating, saving, and retrieving, which can easily be intractable for any state-of-art computers. Based on retrospective analysis of the dictionary matching object function, a multi-scale ZOOM like DGS algorithm, dubbed as MRF-ZOOM, was proposed. MRF ZOOM is quasi-parameter-separable so the multiplicative computation complexity is broken into additive one. Evaluations showed that MRF ZOOM was hundreds or thousands of times faster than the original MRF parameter quantification method even without counting the dictionary generation time in. Using real data, it yielded nearly the same results as produced by the original method. MRF ZOOM provides a super-fast solution for MR parameter quantification.

A framework for optimization and quantification of uncertainty and sensitivity for developing carbon capture systems

DOE PAGES

Eslick, John C.; Ng, Brenda; Gao, Qianwen; ...

2014-12-31

Under the auspices of the U.S. Department of Energy’s Carbon Capture Simulation Initiative (CCSI), a Framework for Optimization and Quantification of Uncertainty and Sensitivity (FOQUS) has been developed. This tool enables carbon capture systems to be rapidly synthesized and rigorously optimized, in an environment that accounts for and propagates uncertainties in parameters and models. FOQUS currently enables (1) the development of surrogate algebraic models utilizing the ALAMO algorithm, which can be used for superstructure optimization to identify optimal process configurations, (2) simulation-based optimization utilizing derivative free optimization (DFO) algorithms with detailed black-box process models, and (3) rigorous uncertainty quantification throughmore » PSUADE. FOQUS utilizes another CCSI technology, the Turbine Science Gateway, to manage the thousands of simulated runs necessary for optimization and UQ. Thus, this computational framework has been demonstrated for the design and analysis of a solid sorbent based carbon capture system.« less

A Quantitative Three-Dimensional Image Analysis Tool for Maximal Acquisition of Spatial Heterogeneity Data.

PubMed

Allenby, Mark C; Misener, Ruth; Panoskaltsis, Nicki; Mantalaris, Athanasios

2017-02-01

Three-dimensional (3D) imaging techniques provide spatial insight into environmental and cellular interactions and are implemented in various fields, including tissue engineering, but have been restricted by limited quantification tools that misrepresent or underutilize the cellular phenomena captured. This study develops image postprocessing algorithms pairing complex Euclidean metrics with Monte Carlo simulations to quantitatively assess cell and microenvironment spatial distributions while utilizing, for the first time, the entire 3D image captured. Although current methods only analyze a central fraction of presented confocal microscopy images, the proposed algorithms can utilize 210% more cells to calculate 3D spatial distributions that can span a 23-fold longer distance. These algorithms seek to leverage the high sample cost of 3D tissue imaging techniques by extracting maximal quantitative data throughout the captured image.

Quantification of regional myocardial blood flow estimation with three-dimensional dynamic rubidium-82 PET and modified spillover correction model.

PubMed

Katoh, Chietsugu; Yoshinaga, Keiichiro; Klein, Ran; Kasai, Katsuhiko; Tomiyama, Yuuki; Manabe, Osamu; Naya, Masanao; Sakakibara, Mamoru; Tsutsui, Hiroyuki; deKemp, Robert A; Tamaki, Nagara

2012-08-01

Myocardial blood flow (MBF) estimation with (82)Rubidium ((82)Rb) positron emission tomography (PET) is technically difficult because of the high spillover between regions of interest, especially due to the long positron range. We sought to develop a new algorithm to reduce the spillover in image-derived blood activity curves, using non-uniform weighted least-squares fitting. Fourteen volunteers underwent imaging with both 3-dimensional (3D) (82)Rb and (15)O-water PET at rest and during pharmacological stress. Whole left ventricular (LV) (82)Rb MBF was estimated using a one-compartment model, including a myocardium-to-blood spillover correction to estimate the corresponding blood input function Ca(t)(whole). Regional K1 values were calculated using this uniform global input function, which simplifies equations and enables robust estimation of MBF. To assess the robustness of the modified algorithm, inter-operator repeatability of 3D (82)Rb MBF was compared with a previously established method. Whole LV correlation of (82)Rb MBF with (15)O-water MBF was better (P < .01) with the modified spillover correction method (r = 0.92 vs r = 0.60). The modified method also yielded significantly improved inter-operator repeatability of regional MBF quantification (r = 0.89) versus the established method (r = 0.82) (P < .01). A uniform global input function can suppress LV spillover into the image-derived blood input function, resulting in improved precision for MBF quantification with 3D (82)Rb PET.

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

PubMed Central

Lechevalier, D.; Ak, R.; Ferguson, M.; Law, K. H.; Lee, Y.-T. T.; Rachuri, S.

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain. PMID:29202125

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML).

PubMed

Park, J; Lechevalier, D; Ak, R; Ferguson, M; Law, K H; Lee, Y-T T; Rachuri, S

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.

Automated quantification of surface water inundation in wetlands using optical satellite imagery

USGS Publications Warehouse

DeVries, Ben; Huang, Chengquan; Lang, Megan W.; Jones, John W.; Huang, Wenli; Creed, Irena F.; Carroll, Mark L.

2017-01-01

We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications.

Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC-MS/MS

PubMed Central

Bilbao, Aivett; Zhang, Ying; Varesio, Emmanuel; Luban, Jeremy; Strambio-De-Castillia, Caterina; Lisacek, Frédérique; Hopfgartner, Gérard

2016-01-01

Data-independent acquisition LC-MS/MS techniques complement supervised methods for peptide quantification. However, due to the wide precursor isolation windows, these techniques are prone to interference at the fragment ion level, which in turn is detrimental for accurate quantification. The “non-outlier fragment ion” (NOFI) ranking algorithm has been developed to assign low priority to fragment ions affected by interference. By using the optimal subset of high priority fragment ions these interfered fragment ions are effectively excluded from quantification. NOFI represents each fragment ion as a vector of four dimensions related to chromatographic and MS fragmentation attributes and applies multivariate outlier detection techniques. Benchmarking conducted on a well-defined quantitative dataset (i.e. the SWATH Gold Standard), indicates that NOFI on average is able to accurately quantify 11-25% more peptides than the commonly used Top-N library intensity ranking method. The sum of the area of the Top3-5 NOFIs produces similar coefficients of variation as compared to the library intensity method but with more accurate quantification results. On a biologically relevant human dendritic cell digest dataset, NOFI properly assigns low priority ranks to 85% of annotated interferences, resulting in sensitivity values between 0.92 and 0.80 against 0.76 for the Spectronaut interference detection algorithm. PMID:26412574

A Set of Handwriting Features for Use in Automated Writer Identification.

PubMed

Miller, John J; Patterson, Robert Bradley; Gantz, Donald T; Saunders, Christopher P; Walch, Mark A; Buscaglia, JoAnn

2017-05-01

A writer's biometric identity can be characterized through the distribution of physical feature measurements ("writer's profile"); a graph-based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms ("graphemes"), which are "skeletonized" to yield the graphical topology of the handwritten segment. The graph-based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph-based system described in this article has been implemented in a highly accurate and approximately language-independent biometric recognition system of writers of cursive documents. © 2017 American Academy of Forensic Sciences.

Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry

PubMed Central

Polanski, Andrzej; Marczyk, Michal; Pietrowska, Monika; Widlak, Piotr; Polanska, Joanna

2015-01-01

Mixture - modeling of mass spectra is an approach with many potential applications including peak detection and quantification, smoothing, de-noising, feature extraction and spectral signal compression. However, existing algorithms do not allow for automated analyses of whole spectra. Therefore, despite highlighting potential advantages of mixture modeling of mass spectra of peptide/protein mixtures and some preliminary results presented in several papers, the mixture modeling approach was so far not developed to the stage enabling systematic comparisons with existing software packages for proteomic mass spectra analyses. In this paper we present an efficient algorithm for Gaussian mixture modeling of proteomic mass spectra of different types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automated partitioning of protein mass spectral signal into fragments. The obtained fragments are separately decomposed into Gaussian mixture models. The parameters of the mixture models of fragments are then aggregated to form the mixture model of the whole spectrum. We compare the elaborated algorithm to existing algorithms for peak detection and we demonstrate improvements of peak detection efficiency obtained by using Gaussian mixture modeling. We also show applications of the elaborated algorithm to real proteomic datasets of low and high resolution. PMID:26230717

Whole-Body Computed Tomography-Based Body Mass and Body Fat Quantification: A Comparison to Hydrostatic Weighing and Air Displacement Plethysmography.

PubMed

Gibby, Jacob T; Njeru, Dennis K; Cvetko, Steve T; Heiny, Eric L; Creer, Andrew R; Gibby, Wendell A

We correlate and evaluate the accuracy of accepted anthropometric methods of percent body fat (%BF) quantification, namely, hydrostatic weighing (HW) and air displacement plethysmography (ADP), to 2 automatic adipose tissue quantification methods using computed tomography (CT). Twenty volunteer subjects (14 men, 6 women) received head-to-toe CT scans. Hydrostatic weighing and ADP were obtained from 17 and 12 subjects, respectively. The CT data underwent conversion using 2 separate algorithms, namely, the Schneider method and the Beam method, to convert Hounsfield units to their respective tissue densities. The overall mass and %BF of both methods were compared with HW and ADP. When comparing ADP to CT data using the Schneider method and Beam method, correlations were r = 0.9806 and 0.9804, respectively. Paired t tests indicated there were no statistically significant biases. Additionally, observed average differences in %BF between ADP and the Schneider method and the Beam method were 0.38% and 0.77%, respectively. The %BF measured from ADP, the Schneider method, and the Beam method all had significantly higher mean differences when compared with HW (3.05%, 2.32%, and 1.94%, respectively). We have shown that total body mass correlates remarkably well with both the Schneider method and Beam method of mass quantification. Furthermore, %BF calculated with the Schneider method and Beam method CT algorithms correlates remarkably well with ADP. The application of these CT algorithms have utility in further research to accurately stratify risk factors with periorgan, visceral, and subcutaneous types of adipose tissue, and has the potential for significant clinical application.

2012 HIV Diagnostics Conference: the molecular diagnostics perspective.

PubMed

Branson, Bernard M; Pandori, Mark

2013-04-01

2012 HIV Diagnostic Conference Atlanta, GA, USA, 12-14 December 2012. This report highlights the presentations and discussions from the 2012 National HIV Diagnostic Conference held in Atlanta (GA, USA), on 12-14 December 2012. Reflecting changes in the evolving field of HIV diagnostics, the conference provided a forum for evaluating developments in molecular diagnostics and their role in HIV diagnosis. In 2010, the HIV Diagnostics Conference concluded with the proposal of a new diagnostic algorithm which included nucleic acid testing to resolve discordant screening and supplemental antibody test results. The 2012 meeting, picking up where the 2010 meeting left off, focused on scientific presentations that assessed this new algorithm and the role played by RNA testing and new developments in molecular diagnostics, including detection of total and integrated HIV-1 DNA, detection and quantification of HIV-2 RNA, and rapid formats for detection of HIV-1 RNA.

A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification

NASA Astrophysics Data System (ADS)

Wu, Keyi; Li, Jinglai

2016-09-01

In this work we consider a class of uncertainty quantification problems where the system performance or reliability is characterized by a scalar parameter y. The performance parameter y is random due to the presence of various sources of uncertainty in the system, and our goal is to estimate the probability density function (PDF) of y. We propose to use the multicanonical Monte Carlo (MMC) method, a special type of adaptive importance sampling algorithms, to compute the PDF of interest. Moreover, we develop an adaptive algorithm to construct local Gaussian process surrogates to further accelerate the MMC iterations. With numerical examples we demonstrate that the proposed method can achieve several orders of magnitudes of speedup over the standard Monte Carlo methods.

Recent developments in capabilities for analysing chlorinated paraffins in environmental matrices: A review.

PubMed

van Mourik, Louise M; Leonards, Pim E G; Gaus, Caroline; de Boer, Jacob

2015-10-01

Concerns about the high production volumes, persistency, bioaccumulation potential and toxicity of chlorinated paraffin (CP) mixtures, especially short-chain CPs (SCCPs), are rising. However, information on their levels and fate in the environment is still insufficient, impeding international classifications and regulations. This knowledge gap is mainly due to the difficulties that arise with CP analysis, in particular the chromatographic separation within CPs and between CPs and other compounds. No fully validated routine analytical method is available yet and only semi-quantitative analysis is possible, although the number of studies reporting new and improved methods have rapidly increased since 2010. Better cleanup procedures that remove interfering compounds, and new instrumental techniques, which distinguish between medium-chain CPs (MCCPs) and SCCPs, have been developed. While gas chromatography coupled to an electron capture negative ionisation mass spectrometry (GC/ECNI-MS) remains the most commonly applied technique, novel and promising use of high resolution time of flight MS (TOF-MS) has also been reported. We expect that recent developments in high resolution TOF-MS and Orbitrap technologies will further improve the detection of CPs, including long-chain CPs (LCCPs), and the group separation and quantification of CP homologues. Also, new CP quantification methods have emerged, including the use of mathematical algorithms, multiple linear regression and principal component analysis. These quantification advancements are also reflected in considerably improved interlaboratory agreements since 2010. Analysis of lower chlorinated paraffins (

Targeted Feature Detection for Data-Dependent Shotgun Proteomics

PubMed Central

2017-01-01

Label-free quantification of shotgun LC–MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification (“FFId”), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between “internal” and “external” (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the “uncertain” feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known ground-truth. The algorithm reached almost complete (>99%) quantification coverage for the full set of peptides identified at 1% FDR (PSM level). Compared with other software solutions for label-free quantification, this is an outstanding result, which was achieved at competitive quantification accuracy and reproducibility across replicates. The FDR for the feature selection was estimated at a low 1.5% on average per sample (3% for features inferred from external peptide IDs). The FFId software is open-source and freely available as part of OpenMS (www.openms.org). PMID:28673088

Targeted Feature Detection for Data-Dependent Shotgun Proteomics.

PubMed

Weisser, Hendrik; Choudhary, Jyoti S

2017-08-04

Label-free quantification of shotgun LC-MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification ("FFId"), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between "internal" and "external" (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the "uncertain" feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known ground-truth. The algorithm reached almost complete (>99%) quantification coverage for the full set of peptides identified at 1% FDR (PSM level). Compared with other software solutions for label-free quantification, this is an outstanding result, which was achieved at competitive quantification accuracy and reproducibility across replicates. The FDR for the feature selection was estimated at a low 1.5% on average per sample (3% for features inferred from external peptide IDs). The FFId software is open-source and freely available as part of OpenMS ( www.openms.org ).

Statistical characterization of multiple-reaction monitoring mass spectrometry (MRM-MS) assays for quantitative proteomics

PubMed Central

2012-01-01

Multiple reaction monitoring mass spectrometry (MRM-MS) with stable isotope dilution (SID) is increasingly becoming a widely accepted assay for the quantification of proteins and peptides. These assays have shown great promise in relatively high throughput verification of candidate biomarkers. While the use of MRM-MS assays is well established in the small molecule realm, their introduction and use in proteomics is relatively recent. As such, statistical and computational methods for the analysis of MRM-MS data from proteins and peptides are still being developed. Based on our extensive experience with analyzing a wide range of SID-MRM-MS data, we set forth a methodology for analysis that encompasses significant aspects ranging from data quality assessment, assay characterization including calibration curves, limits of detection (LOD) and quantification (LOQ), and measurement of intra- and interlaboratory precision. We draw upon publicly available seminal datasets to illustrate our methods and algorithms. PMID:23176545

Statistical characterization of multiple-reaction monitoring mass spectrometry (MRM-MS) assays for quantitative proteomics.

PubMed

Mani, D R; Abbatiello, Susan E; Carr, Steven A

2012-01-01

Multiple reaction monitoring mass spectrometry (MRM-MS) with stable isotope dilution (SID) is increasingly becoming a widely accepted assay for the quantification of proteins and peptides. These assays have shown great promise in relatively high throughput verification of candidate biomarkers. While the use of MRM-MS assays is well established in the small molecule realm, their introduction and use in proteomics is relatively recent. As such, statistical and computational methods for the analysis of MRM-MS data from proteins and peptides are still being developed. Based on our extensive experience with analyzing a wide range of SID-MRM-MS data, we set forth a methodology for analysis that encompasses significant aspects ranging from data quality assessment, assay characterization including calibration curves, limits of detection (LOD) and quantification (LOQ), and measurement of intra- and interlaboratory precision. We draw upon publicly available seminal datasets to illustrate our methods and algorithms.

ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning.

PubMed

Gandola, Emanuele; Antonioli, Manuela; Traficante, Alessio; Franceschini, Simone; Scardi, Michele; Congestri, Roberta

2016-05-01

Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments. Copyright © 2016 Elsevier B.V. All rights reserved.

Forward and inverse uncertainty quantification using multilevel Monte Carlo algorithms for an elliptic non-local equation

DOE PAGES

Jasra, Ajay; Law, Kody J. H.; Zhou, Yan

2016-01-01

Our paper considers uncertainty quantification for an elliptic nonlocal equation. In particular, it is assumed that the parameters which define the kernel in the nonlocal operator are uncertain and a priori distributed according to a probability measure. It is shown that the induced probability measure on some quantities of interest arising from functionals of the solution to the equation with random inputs is well-defined,s as is the posterior distribution on parameters given observations. As the elliptic nonlocal equation cannot be solved approximate posteriors are constructed. The multilevel Monte Carlo (MLMC) and multilevel sequential Monte Carlo (MLSMC) sampling algorithms are usedmore » for a priori and a posteriori estimation, respectively, of quantities of interest. Furthermore, these algorithms reduce the amount of work to estimate posterior expectations, for a given level of error, relative to Monte Carlo and i.i.d. sampling from the posterior at a given level of approximation of the solution of the elliptic nonlocal equation.« less

Forward and inverse uncertainty quantification using multilevel Monte Carlo algorithms for an elliptic non-local equation

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

Jasra, Ajay; Law, Kody J. H.; Zhou, Yan

Our paper considers uncertainty quantification for an elliptic nonlocal equation. In particular, it is assumed that the parameters which define the kernel in the nonlocal operator are uncertain and a priori distributed according to a probability measure. It is shown that the induced probability measure on some quantities of interest arising from functionals of the solution to the equation with random inputs is well-defined,s as is the posterior distribution on parameters given observations. As the elliptic nonlocal equation cannot be solved approximate posteriors are constructed. The multilevel Monte Carlo (MLMC) and multilevel sequential Monte Carlo (MLSMC) sampling algorithms are usedmore » for a priori and a posteriori estimation, respectively, of quantities of interest. Furthermore, these algorithms reduce the amount of work to estimate posterior expectations, for a given level of error, relative to Monte Carlo and i.i.d. sampling from the posterior at a given level of approximation of the solution of the elliptic nonlocal equation.« less

Prediction of response factors for gas chromatography with flame ionization detection: Algorithm improvement, extension to silylated compounds, and application to the quantification of metabolites

PubMed Central

de Saint Laumer, Jean‐Yves; Leocata, Sabine; Tissot, Emeline; Baroux, Lucie; Kampf, David M.; Merle, Philippe; Boschung, Alain; Seyfried, Markus

2015-01-01

We previously showed that the relative response factors of volatile compounds were predictable from either combustion enthalpies or their molecular formulae only 1. We now extend this prediction to silylated derivatives by adding an increment in the ab initio calculation of combustion enthalpies. The accuracy of the experimental relative response factors database was also improved and its population increased to 490 values. In particular, more brominated compounds were measured, and their prediction accuracy was improved by adding a correction factor in the algorithm. The correlation coefficient between predicted and measured values increased from 0.936 to 0.972, leading to a mean prediction accuracy of ± 6%. Thus, 93% of the relative response factors values were predicted with an accuracy of better than ± 10%. The capabilities of the extended algorithm are exemplified by (i) the quick and accurate quantification of hydroxylated metabolites resulting from a biodegradation test after silylation and prediction of their relative response factors, without having the reference substances available; and (ii) the rapid purity determinations of volatile compounds. This study confirms that Gas chromatography with a flame ionization detector and using predicted relative response factors is one of the few techniques that enables quantification of volatile compounds without calibrating the instrument with the pure reference substance. PMID:26179324

Toward real-time quantification of fluorescence molecular probes using target/background ratio for guiding biopsy and endoscopic therapy of esophageal neoplasia.

PubMed

Jiang, Yang; Gong, Yuanzheng; Rubenstein, Joel H; Wang, Thomas D; Seibel, Eric J

2017-04-01

Multimodal endoscopy using fluorescence molecular probes is a promising method of surveying the entire esophagus to detect cancer progression. Using the fluorescence ratio of a target compared to a surrounding background, a quantitative value is diagnostic for progression from Barrett's esophagus to high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC). However, current quantification of fluorescent images is done only after the endoscopic procedure. We developed a Chan-Vese-based algorithm to segment fluorescence targets, and subsequent morphological operations to generate background, thus calculating target/background (T/B) ratios, potentially to provide real-time guidance for biopsy and endoscopic therapy. With an initial processing speed of 2 fps and by calculating the T/B ratio for each frame, our method provides quasireal-time quantification of the molecular probe labeling to the endoscopist. Furthermore, an automatic computer-aided diagnosis algorithm can be applied to the recorded endoscopic video, and the overall T/B ratio is calculated for each patient. The receiver operating characteristic curve was employed to determine the threshold for classification of HGD/EAC using leave-one-out cross-validation. With 92% sensitivity and 75% specificity to classify HGD/EAC, our automatic algorithm shows promising results for a surveillance procedure to help manage esophageal cancer and other cancers inspected by endoscopy.

Practical quantification of necrosis in histological whole-slide images.

PubMed

Homeyer, André; Schenk, Andrea; Arlt, Janine; Dahmen, Uta; Dirsch, Olaf; Hahn, Horst K

2013-06-01

Since the histological quantification of necrosis is a common task in medical research and practice, we evaluate different image analysis methods for quantifying necrosis in whole-slide images. In a practical usage scenario, we assess the impact of different classification algorithms and feature sets on both accuracy and computation time. We show how a well-chosen combination of multiresolution features and an efficient postprocessing step enables the accurate quantification necrosis in gigapixel images in less than a minute. The results are general enough to be applied to other areas of histological image analysis as well. Copyright © 2013 Elsevier Ltd. All rights reserved.

Subnuclear foci quantification using high-throughput 3D image cytometry

NASA Astrophysics Data System (ADS)

Wadduwage, Dushan N.; Parrish, Marcus; Choi, Heejin; Engelward, Bevin P.; Matsudaira, Paul; So, Peter T. C.

2015-07-01

Ionising radiation causes various types of DNA damages including double strand breaks (DSBs). DSBs are often recognized by DNA repair protein ATM which forms gamma-H2AX foci at the site of the DSBs that can be visualized using immunohistochemistry. However most of such experiments are of low throughput in terms of imaging and image analysis techniques. Most of the studies still use manual counting or classification. Hence they are limited to counting a low number of foci per cell (5 foci per nucleus) as the quantification process is extremely labour intensive. Therefore we have developed a high throughput instrumentation and computational pipeline specialized for gamma-H2AX foci quantification. A population of cells with highly clustered foci inside nuclei were imaged, in 3D with submicron resolution, using an in-house developed high throughput image cytometer. Imaging speeds as high as 800 cells/second in 3D were achieved by using HiLo wide-field depth resolved imaging and a remote z-scanning technique. Then the number of foci per cell nucleus were quantified using a 3D extended maxima transform based algorithm. Our results suggests that while most of the other 2D imaging and manual quantification studies can count only up to about 5 foci per nucleus our method is capable of counting more than 100. Moreover we show that 3D analysis is significantly superior compared to the 2D techniques.

Combined fluorescence and reflectance spectroscopy for in vivo quantification of cancer biomarkers in low- and high-grade glioma surgery

PubMed Central

Valdés, Pablo A.; Kim, Anthony; Leblond, Frederic; Conde, Olga M.; Harris, Brent T.; Paulsen, Keith D.; Wilson, Brian C.; Roberts, David W.

2011-01-01

Biomarkers are indicators of biological processes and hold promise for the diagnosis and treatment of disease. Gliomas represent a heterogeneous group of brain tumors with marked intra- and inter-tumor variability. The extent of surgical resection is a significant factor influencing post-surgical recurrence and prognosis. Here, we used fluorescence and reflectance spectral signatures for in vivo quantification of multiple biomarkers during glioma surgery, with fluorescence contrast provided by exogenously-induced protoporphyrin IX (PpIX) following administration of 5-aminolevulinic acid. We performed light-transport modeling to quantify multiple biomarkers indicative of tumor biological processes, including the local concentration of PpIX and associated photoproducts, total hemoglobin concentration, oxygen saturation, and optical scattering parameters. We developed a diagnostic algorithm for intra-operative tissue delineation that accounts for the combined tumor-specific predictive capabilities of these quantitative biomarkers. Tumor tissue delineation achieved accuracies of up to 94% (specificity = 94%, sensitivity = 94%) across a range of glioma histologies beyond current state-of-the-art optical approaches, including state-of-the-art fluorescence image guidance. This multiple biomarker strategy opens the door to optical methods for surgical guidance that use quantification of well-established neoplastic processes. Future work would seek to validate the predictive power of this proof-of-concept study in a separate larger cohort of patients. PMID:22112112

Combined fluorescence and reflectance spectroscopy for in vivo quantification of cancer biomarkers in low- and high-grade glioma surgery

NASA Astrophysics Data System (ADS)

Valdés, Pablo A.; Kim, Anthony; Leblond, Frederic; Conde, Olga M.; Harris, Brent T.; Paulsen, Keith D.; Wilson, Brian C.; Roberts, David W.

2011-11-01

Biomarkers are indicators of biological processes and hold promise for the diagnosis and treatment of disease. Gliomas represent a heterogeneous group of brain tumors with marked intra- and inter-tumor variability. The extent of surgical resection is a significant factor influencing post-surgical recurrence and prognosis. Here, we used fluorescence and reflectance spectral signatures for in vivo quantification of multiple biomarkers during glioma surgery, with fluorescence contrast provided by exogenously-induced protoporphyrin IX (PpIX) following administration of 5-aminolevulinic acid. We performed light-transport modeling to quantify multiple biomarkers indicative of tumor biological processes, including the local concentration of PpIX and associated photoproducts, total hemoglobin concentration, oxygen saturation, and optical scattering parameters. We developed a diagnostic algorithm for intra-operative tissue delineation that accounts for the combined tumor-specific predictive capabilities of these quantitative biomarkers. Tumor tissue delineation achieved accuracies of up to 94% (specificity = 94%, sensitivity = 94%) across a range of glioma histologies beyond current state-of-the-art optical approaches, including state-of-the-art fluorescence image guidance. This multiple biomarker strategy opens the door to optical methods for surgical guidance that use quantification of well-established neoplastic processes. Future work would seek to validate the predictive power of this proof-of-concept study in a separate larger cohort of patients.

Processor architecture for airborne SAR systems

NASA Technical Reports Server (NTRS)

Glass, C. M.

1983-01-01

Digital processors for spaceborne imaging radars and application of the technology developed for airborne SAR systems are considered. Transferring algorithms and implementation techniques from airborne to spaceborne SAR processors offers obvious advantages. The following topics are discussed: (1) a quantification of the differences in processing algorithms for airborne and spaceborne SARs; and (2) an overview of three processors for airborne SAR systems.

Automated detection and segmentation of follicles in 3D ultrasound for assisted reproduction

NASA Astrophysics Data System (ADS)

Narayan, Nikhil S.; Sivanandan, Srinivasan; Kudavelly, Srinivas; Patwardhan, Kedar A.; Ramaraju, G. A.

2018-02-01

Follicle quantification refers to the computation of the number and size of follicles in 3D ultrasound volumes of the ovary. This is one of the key factors in determining hormonal dosage during female infertility treatments. In this paper, we propose an automated algorithm to detect and segment follicles in 3D ultrasound volumes of the ovary for quantification. In a first of its kind attempt, we employ noise-robust phase symmetry feature maps as likelihood function to perform mean-shift based follicle center detection. Max-flow algorithm is used for segmentation and gray weighted distance transform is employed for post-processing the results. We have obtained state-of-the-art results with a true positive detection rate of >90% on 26 3D volumes with 323 follicles.

Computational solution verification and validation applied to a thermal model of a ruggedized instrumentation package

DOE PAGES

Scott, Sarah Nicole; Templeton, Jeremy Alan; Hough, Patricia Diane; ...

2014-01-01

This study details a methodology for quantification of errors and uncertainties of a finite element heat transfer model applied to a Ruggedized Instrumentation Package (RIP). The proposed verification and validation (V&V) process includes solution verification to examine errors associated with the code's solution techniques, and model validation to assess the model's predictive capability for quantities of interest. The model was subjected to mesh resolution and numerical parameters sensitivity studies to determine reasonable parameter values and to understand how they change the overall model response and performance criteria. To facilitate quantification of the uncertainty associated with the mesh, automatic meshing andmore » mesh refining/coarsening algorithms were created and implemented on the complex geometry of the RIP. Automated software to vary model inputs was also developed to determine the solution’s sensitivity to numerical and physical parameters. The model was compared with an experiment to demonstrate its accuracy and determine the importance of both modelled and unmodelled physics in quantifying the results' uncertainty. An emphasis is placed on automating the V&V process to enable uncertainty quantification within tight development schedules.« less

Gebiss: an ImageJ plugin for the specification of ground truth and the performance evaluation of 3D segmentation algorithms

PubMed Central

2011-01-01

Background Image segmentation is a crucial step in quantitative microscopy that helps to define regions of tissues, cells or subcellular compartments. Depending on the degree of user interactions, segmentation methods can be divided into manual, automated or semi-automated approaches. 3D image stacks usually require automated methods due to their large number of optical sections. However, certain applications benefit from manual or semi-automated approaches. Scenarios include the quantification of 3D images with poor signal-to-noise ratios or the generation of so-called ground truth segmentations that are used to evaluate the accuracy of automated segmentation methods. Results We have developed Gebiss; an ImageJ plugin for the interactive segmentation, visualisation and quantification of 3D microscopic image stacks. We integrated a variety of existing plugins for threshold-based segmentation and volume visualisation. Conclusions We demonstrate the application of Gebiss to the segmentation of nuclei in live Drosophila embryos and the quantification of neurodegeneration in Drosophila larval brains. Gebiss was developed as a cross-platform ImageJ plugin and is freely available on the web at http://imaging.bii.a-star.edu.sg/projects/gebiss/. PMID:21668958

Mixture quantification using PLS in plastic scintillation measurements.

PubMed

Bagán, H; Tarancón, A; Rauret, G; García, J F

2011-06-01

This article reports the capability of plastic scintillation (PS) combined with multivariate calibration (Partial least squares; PLS) to detect and quantify alpha and beta emitters in mixtures. While several attempts have been made with this purpose in mind using liquid scintillation (LS), no attempt was done using PS that has the great advantage of not producing mixed waste after the measurements are performed. Following this objective, ternary mixtures of alpha and beta emitters ((241)Am, (137)Cs and (90)Sr/(90)Y) have been quantified. Procedure optimisation has evaluated the use of the net spectra or the sample spectra, the inclusion of different spectra obtained at different values of the Pulse Shape Analysis parameter and the application of the PLS1 or PLS2 algorithms. The conclusions show that the use of PS+PLS2 applied to the sample spectra, without the use of any pulse shape discrimination, allows quantification of the activities with relative errors less than 10% in most of the cases. This procedure not only allows quantification of mixtures but also reduces measurement time (no blanks are required) and the application of this procedure does not require detectors that include the pulse shape analysis parameter. Copyright © 2011 Elsevier Ltd. All rights reserved.

Improving single molecule force spectroscopy through automated real-time data collection and quantification of experimental conditions

PubMed Central

Scholl, Zackary N.; Marszalek, Piotr E.

2013-01-01

The benefits of single molecule force spectroscopy (SMFS) clearly outweigh the challenges which include small sample sizes, tedious data collection and introduction of human bias during the subjective data selection. These difficulties can be partially eliminated through automation of the experimental data collection process for atomic force microscopy (AFM). Automation can be accomplished using an algorithm that triages usable force-extension recordings quickly with positive and negative selection. We implemented an algorithm based on the windowed fast Fourier transform of force-extension traces that identifies peaks using force-extension regimes to correctly identify usable recordings from proteins composed of repeated domains. This algorithm excels as a real-time diagnostic because it involves <30 ms computational time, has high sensitivity and specificity, and efficiently detects weak unfolding events. We used the statistics provided by the automated procedure to clearly demonstrate the properties of molecular adhesion and how these properties change with differences in the cantilever tip and protein functional groups and protein age. PMID:24001740

Modeling and simulation of high dimensional stochastic multiscale PDE systems at the exascale

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

Zabaras, Nicolas J.

2016-11-08

Predictive Modeling of multiscale and Multiphysics systems requires accurate data driven characterization of the input uncertainties, and understanding of how they propagate across scales and alter the final solution. This project develops a rigorous mathematical framework and scalable uncertainty quantification algorithms to efficiently construct realistic low dimensional input models, and surrogate low complexity systems for the analysis, design, and control of physical systems represented by multiscale stochastic PDEs. The work can be applied to many areas including physical and biological processes, from climate modeling to systems biology.

Resolution and quantification accuracy enhancement of functional delay and sum beamforming for three-dimensional acoustic source identification with solid spherical arrays

NASA Astrophysics Data System (ADS)

Chu, Zhigang; Yang, Yang; Shen, Linbang

2017-05-01

Functional delay and sum (FDAS) is a novel beamforming algorithm introduced for the three-dimensional (3D) acoustic source identification with solid spherical microphone arrays. Being capable of offering significantly attenuated sidelobes with a fast speed, the algorithm promises to play an important role in interior acoustic source identification. However, it presents some intrinsic imperfections, specifically poor spatial resolution and low quantification accuracy. This paper focuses on conquering these imperfections by ridge detection (RD) and deconvolution approach for the mapping of acoustic sources (DAMAS). The suggested methods are referred to as FDAS+RD and FDAS+RD+DAMAS. Both computer simulations and experiments are utilized to validate their effects. Several interesting conclusions have emerged: (1) FDAS+RD and FDAS+RD+DAMAS both can dramatically ameliorate FDAS's spatial resolution and at the same time inherit its advantages. (2) Compared to the conventional DAMAS, FDAS+RD+DAMAS enjoys the same super spatial resolution, stronger sidelobe attenuation capability and more than two hundred times faster speed. (3) FDAS+RD+DAMAS can effectively conquer FDAS's low quantification accuracy. Whether the focus distance is equal to the distance from the source to the array center or not, it can quantify the source average pressure contribution accurately. This study will be of great significance to the accurate and quick localization and quantification of acoustic sources in cabin environments.

Automated quantification of renal interstitial fibrosis for computer-aided diagnosis: A comprehensive tissue structure segmentation method.

PubMed

Tey, Wei Keat; Kuang, Ye Chow; Ooi, Melanie Po-Leen; Khoo, Joon Joon

2018-03-01

Interstitial fibrosis in renal biopsy samples is a scarring tissue structure that may be visually quantified by pathologists as an indicator to the presence and extent of chronic kidney disease. The standard method of quantification by visual evaluation presents reproducibility issues in the diagnoses. This study proposes an automated quantification system for measuring the amount of interstitial fibrosis in renal biopsy images as a consistent basis of comparison among pathologists. The system extracts and segments the renal tissue structures based on colour information and structural assumptions of the tissue structures. The regions in the biopsy representing the interstitial fibrosis are deduced through the elimination of non-interstitial fibrosis structures from the biopsy area and quantified as a percentage of the total area of the biopsy sample. A ground truth image dataset has been manually prepared by consulting an experienced pathologist for the validation of the segmentation algorithms. The results from experiments involving experienced pathologists have demonstrated a good correlation in quantification result between the automated system and the pathologists' visual evaluation. Experiments investigating the variability in pathologists also proved the automated quantification error rate to be on par with the average intra-observer variability in pathologists' quantification. Interstitial fibrosis in renal biopsy samples is a scarring tissue structure that may be visually quantified by pathologists as an indicator to the presence and extent of chronic kidney disease. The standard method of quantification by visual evaluation presents reproducibility issues in the diagnoses due to the uncertainties in human judgement. An automated quantification system for accurately measuring the amount of interstitial fibrosis in renal biopsy images is presented as a consistent basis of comparison among pathologists. The system identifies the renal tissue structures through knowledge-based rules employing colour space transformations and structural features extraction from the images. In particular, the renal glomerulus identification is based on a multiscale textural feature analysis and a support vector machine. The regions in the biopsy representing interstitial fibrosis are deduced through the elimination of non-interstitial fibrosis structures from the biopsy area. The experiments conducted evaluate the system in terms of quantification accuracy, intra- and inter-observer variability in visual quantification by pathologists, and the effect introduced by the automated quantification system on the pathologists' diagnosis. A 40-image ground truth dataset has been manually prepared by consulting an experienced pathologist for the validation of the segmentation algorithms. The results from experiments involving experienced pathologists have demonstrated an average error of 9 percentage points in quantification result between the automated system and the pathologists' visual evaluation. Experiments investigating the variability in pathologists involving samples from 70 kidney patients also proved the automated quantification error rate to be on par with the average intra-observer variability in pathologists' quantification. The accuracy of the proposed quantification system has been validated with the ground truth dataset and compared against the pathologists' quantification results. It has been shown that the correlation between different pathologists' estimation of interstitial fibrosis area has significantly improved, demonstrating the effectiveness of the quantification system as a diagnostic aide. Copyright © 2017 Elsevier B.V. All rights reserved.

Influence of radiation dose and reconstruction algorithm in MDCT assessment of airway wall thickness: A phantom study

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

Gomez-Cardona, Daniel; Nagle, Scott K.; Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, 600 Highland Avenue, Madison, Wisconsin 53792

Purpose: Wall thickness (WT) is an airway feature of great interest for the assessment of morphological changes in the lung parenchyma. Multidetector computed tomography (MDCT) has recently been used to evaluate airway WT, but the potential risk of radiation-induced carcinogenesis—particularly in younger patients—might limit a wider use of this imaging method in clinical practice. The recent commercial implementation of the statistical model-based iterative reconstruction (MBIR) algorithm, instead of the conventional filtered back projection (FBP) algorithm, has enabled considerable radiation dose reduction in many other clinical applications of MDCT. The purpose of this work was to study the impact of radiationmore » dose and MBIR in the MDCT assessment of airway WT. Methods: An airway phantom was scanned using a clinical MDCT system (Discovery CT750 HD, GE Healthcare) at 4 kV levels and 5 mAs levels. Both FBP and a commercial implementation of MBIR (Veo{sup TM}, GE Healthcare) were used to reconstruct CT images of the airways. For each kV–mAs combination and each reconstruction algorithm, the contrast-to-noise ratio (CNR) of the airways was measured, and the WT of each airway was measured and compared with the nominal value; the relative bias and the angular standard deviation in the measured WT were calculated. For each airway and reconstruction algorithm, the overall performance of WT quantification across all of the 20 kV–mAs combinations was quantified by the sum of squares (SSQs) of the difference between the measured and nominal WT values. Finally, the particular kV–mAs combination and reconstruction algorithm that minimized radiation dose while still achieving a reference WT quantification accuracy level was chosen as the optimal acquisition and reconstruction settings. Results: The wall thicknesses of seven airways of different sizes were analyzed in the study. Compared with FBP, MBIR improved the CNR of the airways, particularly at low radiation dose levels. For FBP, the relative bias and the angular standard deviation of the measured WT increased steeply with decreasing radiation dose. Except for the smallest airway, MBIR enabled significant reduction in both the relative bias and angular standard deviation of the WT, particularly at low radiation dose levels; the SSQ was reduced by 50%–96% by using MBIR. The optimal reconstruction algorithm was found to be MBIR for the seven airways being assessed, and the combined use of MBIR and optimal kV–mAs selection resulted in a radiation dose reduction of 37%–83% compared with a reference scan protocol with a dose level of 1 mGy. Conclusions: The quantification accuracy of airway WT is strongly influenced by radiation dose and reconstruction algorithm. The MBIR algorithm potentially allows the desired WT quantification accuracy to be achieved with reduced radiation dose, which may enable a wider clinical use of MDCT for the assessment of airway WT, particularly for younger patients who may be more sensitive to exposures with ionizing radiation.« less

Towards a robust framework for catchment classification

NASA Astrophysics Data System (ADS)

Deshmukh, A.; Samal, A.; Singh, R.

2017-12-01

Classification of catchments based on various measures of similarity has emerged as an important technique to understand regional scale hydrologic behavior. Classification of catchment characteristics and/or streamflow response has been used reveal which characteristics are more likely to explain the observed variability of hydrologic response. However, numerous algorithms for supervised or unsupervised classification are available, making it hard to identify the algorithm most suitable for the dataset at hand. Consequently, existing catchment classification studies vary significantly in the classification algorithms employed with no previous attempt at understanding the degree of uncertainty in classification due to this algorithmic choice. This hinders the generalizability of interpretations related to hydrologic behavior. Our goal is to develop a protocol that can be followed while classifying hydrologic datasets. We focus on a classification framework for unsupervised classification and provide a step-by-step classification procedure. The steps include testing the clusterabiltiy of original dataset prior to classification, feature selection, validation of clustered data, and quantification of similarity of two clusterings. We test several commonly available methods within this framework to understand the level of similarity of classification results across algorithms. We apply the proposed framework on recently developed datasets for India to analyze to what extent catchment properties can explain observed catchment response. Our testing dataset includes watershed characteristics for over 200 watersheds which comprise of both natural (physio-climatic) characteristics and socio-economic characteristics. This framework allows us to understand the controls on observed hydrologic variability across India.

Developing and Implementing the Data Mining Algorithms in RAVEN

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

Sen, Ramazan Sonat; Maljovec, Daniel Patrick; Alfonsi, Andrea

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantificationmore » analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.« less

Graph cuts and neural networks for segmentation and porosity quantification in Synchrotron Radiation X-ray μCT of an igneous rock sample.

PubMed

Meneses, Anderson Alvarenga de Moura; Palheta, Dayara Bastos; Pinheiro, Christiano Jorge Gomes; Barroso, Regina Cely Rodrigues

2018-03-01

X-ray Synchrotron Radiation Micro-Computed Tomography (SR-µCT) allows a better visualization in three dimensions with a higher spatial resolution, contributing for the discovery of aspects that could not be observable through conventional radiography. The automatic segmentation of SR-µCT scans is highly valuable due to its innumerous applications in geological sciences, especially for morphology, typology, and characterization of rocks. For a great number of µCT scan slices, a manual process of segmentation would be impractical, either for the time expended and for the accuracy of results. Aiming the automatic segmentation of SR-µCT geological sample images, we applied and compared Energy Minimization via Graph Cuts (GC) algorithms and Artificial Neural Networks (ANNs), as well as the well-known K-means and Fuzzy C-Means algorithms. The Dice Similarity Coefficient (DSC), Sensitivity and Precision were the metrics used for comparison. Kruskal-Wallis and Dunn's tests were applied and the best methods were the GC algorithms and ANNs (with Levenberg-Marquardt and Bayesian Regularization). For those algorithms, an approximate Dice Similarity Coefficient of 95% was achieved. Our results confirm the possibility of usage of those algorithms for segmentation and posterior quantification of porosity of an igneous rock sample SR-µCT scan. Copyright © 2017 Elsevier Ltd. All rights reserved.

Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography.

PubMed

Kirişli, H A; Schaap, M; Metz, C T; Dharampal, A S; Meijboom, W B; Papadopoulou, S L; Dedic, A; Nieman, K; de Graaf, M A; Meijs, M F L; Cramer, M J; Broersen, A; Cetin, S; Eslami, A; Flórez-Valencia, L; Lor, K L; Matuszewski, B; Melki, I; Mohr, B; Oksüz, I; Shahzad, R; Wang, C; Kitslaar, P H; Unal, G; Katouzian, A; Örkisz, M; Chen, C M; Precioso, F; Najman, L; Masood, S; Ünay, D; van Vliet, L; Moreno, R; Goldenberg, R; Vuçini, E; Krestin, G P; Niessen, W J; van Walsum, T

2013-12-01

Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CTA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CTA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (1) (semi-)automatically detect and quantify stenosis on CTA, in comparison with quantitative coronary angiography (QCA) and CTA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CTA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CTA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/. Copyright © 2013 Elsevier B.V. All rights reserved.

How to Make Data a Blessing to Parametric Uncertainty Quantification and Reduction?

NASA Astrophysics Data System (ADS)

Ye, M.; Shi, X.; Curtis, G. P.; Kohler, M.; Wu, J.

2013-12-01

In a Bayesian point of view, probability of model parameters and predictions are conditioned on data used for parameter inference and prediction analysis. It is critical to use appropriate data for quantifying parametric uncertainty and its propagation to model predictions. However, data are always limited and imperfect. When a dataset cannot properly constrain model parameters, it may lead to inaccurate uncertainty quantification. While in this case data appears to be a curse to uncertainty quantification, a comprehensive modeling analysis may help understand the cause and characteristics of parametric uncertainty and thus turns data into a blessing. In this study, we illustrate impacts of data on uncertainty quantification and reduction using an example of surface complexation model (SCM) developed to simulate uranyl (U(VI)) adsorption. The model includes two adsorption sites, referred to as strong and weak sites. The amount of uranium adsorption on these sites determines both the mean arrival time and the long tail of the breakthrough curves. There is one reaction on the weak site but two reactions on the strong site. The unknown parameters include fractions of the total surface site density of the two sites and surface complex formation constants of the three reactions. A total of seven experiments were conducted with different geochemical conditions to estimate these parameters. The experiments with low initial concentration of U(VI) result in a large amount of parametric uncertainty. A modeling analysis shows that it is because the experiments cannot distinguish the relative adsorption affinity of the strong and weak sites on uranium adsorption. Therefore, the experiments with high initial concentration of U(VI) are needed, because in the experiments the strong site is nearly saturated and the weak site can be determined. The experiments with high initial concentration of U(VI) are a blessing to uncertainty quantification, and the experiments with low initial concentration help modelers turn a curse into a blessing. The data impacts on uncertainty quantification and reduction are quantified using probability density functions of model parameters obtained from Markov Chain Monte Carlo simulation using the DREAM algorithm. This study provides insights to model calibration, uncertainty quantification, experiment design, and data collection in groundwater reactive transport modeling and other environmental modeling.

Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations

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

Biros, George

Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. Thesemore » include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a central challenge in UQ, especially for large-scale models. We propose to develop the mathematical tools to address these challenges in the context of extreme-scale problems. 4. Parallel scalable algorithms for Bayesian optimal experimental design (OED). Bayesian inversion yields quantified uncertainties in the model parameters, which can be propagated forward through the model to yield uncertainty in outputs of interest. This opens the way for designing new experiments to reduce the uncertainties in the model parameters and model predictions. Such experimental design problems have been intractable for large-scale problems using conventional methods; we will create OED algorithms that exploit the structure of the PDE model and the parameter-to-output map to overcome these challenges. Parallel algorithms for these four problems were created, analyzed, prototyped, implemented, tuned, and scaled up for leading-edge supercomputers, including UT-Austin’s own 10 petaflops Stampede system, ANL’s Mira system, and ORNL’s Titan system. While our focus is on fundamental mathematical/computational methods and algorithms, we will assess our methods on model problems derived from several DOE mission applications, including multiscale mechanics and ice sheet dynamics.« less

Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography.

PubMed

Liu, Li; Gao, Simon S; Bailey, Steven T; Huang, David; Li, Dengwang; Jia, Yali

2015-09-01

Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area.

The detection of T-wave variation linked to arrhythmic risk: an industry perspective.

PubMed

Xue, Joel; Rowlandson, Ian

2013-01-01

Although the scientific literature contains ample descriptions of peculiar patterns of repolarization linked to arrhythmic risk, the objective quantification and classification of these patterns continues to be a challenge that impacts their widespread adoption in clinical practice. To advance the science, computerized algorithms spawned in the academic environment have been essential in order to find, extract and measure these patterns. However, outside the strict control of a core lab, these algorithms are exposed to poor quality signals and need to be effective in the presence of different forms of noise that can either obscure or mimic the T-wave variation (TWV) of interest. To provide a practical solution that can be verified and validated for the market, important tradeoffs need to be made that are based on an intimate understanding of the end-user as well as the key characteristics of either the signal or the noise that can be used by the signal processing engineer to best differentiate them. To illustrate this, two contemporary medical devices used for quantifying T-wave variation are presented, including the modified moving average (MMA) for the detection of T-wave Alternans (TWA) and the quantification of T-wave shape as inputs to the Morphology Combination Score (MCS) for the trending of drug-induced repolarization abnormalities. © 2013 Elsevier Inc. All rights reserved.

MDCT quantification is the dominant parameter in decision–making regarding chest tube drainage for stable patients with traumatic pneumothorax

PubMed Central

Cai, Wenli; Lee, June-Goo; Fikry, Karim; Yoshida, Hiroyuki; Novelline, Robert; de Moya, Marc

2013-01-01

It is commonly believed that the size of a pneumothorax is an important determinant of treatment decision, in particular regarding whether chest tube drainage (CTD) is required. However, the volumetric quantification of pneumothoraces has not routinely been performed in clinics. In this paper, we introduced an automated computer-aided volumetry (CAV) scheme for quantification of volume of pneumothoraces in chest multi-detect CT (MDCT) images. Moreover, we investigated the impact of accurate volume of pneumothoraces in the improvement of the performance in decision-making regarding CTD in the management of traumatic pneumothoraces. For this purpose, an occurrence frequency map was calculated for quantitative analysis of the importance of each clinical parameter in the decision-making regarding CTD by a computer simulation of decision-making using a genetic algorithm (GA) and a support vector machine (SVM). A total of 14 clinical parameters, including volume of pneumothorax calculated by our CAV scheme, was collected as parameters available for decision-making. The results showed that volume was the dominant parameter in decision-making regarding CTD, with an occurrence frequency value of 1.00. The results also indicated that the inclusion of volume provided the best performance that was statistically significant compared to the other tests in which volume was excluded from the clinical parameters. This study provides the scientific evidence for the application of CAV scheme in MDCT volumetric quantification of pneumothoraces in the management of clinically stable chest trauma patients with traumatic pneumothorax. PMID:22560899

A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices.

PubMed

Filho, Mercedes; Ma, Zhen; Tavares, João Manuel R S

2015-11-01

In recent years, the incidence of skin cancer cases has risen, worldwide, mainly due to the prolonged exposure to harmful ultraviolet radiation. Concurrently, the computer-assisted medical diagnosis of skin cancer has undergone major advances, through an improvement in the instrument and detection technology, and the development of algorithms to process the information. Moreover, because there has been an increased need to store medical data, for monitoring, comparative and assisted-learning purposes, algorithms for data processing and storage have also become more efficient in handling the increase of data. In addition, the potential use of common mobile devices to register high-resolution images of skin lesions has also fueled the need to create real-time processing algorithms that may provide a likelihood for the development of malignancy. This last possibility allows even non-specialists to monitor and follow-up suspected skin cancer cases. In this review, we present the major steps in the pre-processing, processing and post-processing of skin lesion images, with a particular emphasis on the quantification and classification of pigmented skin lesions. We further review and outline the future challenges for the creation of minimum-feature, automated and real-time algorithms for the detection of skin cancer from images acquired via common mobile devices.

Noninvasive diagnosis of intraamniotic infection: proteomic biomarkers in vaginal fluid.

PubMed

Hitti, Jane; Lapidus, Jodi A; Lu, Xinfang; Reddy, Ashok P; Jacob, Thomas; Dasari, Surendra; Eschenbach, David A; Gravett, Michael G; Nagalla, Srinivasa R

2010-07-01

We analyzed the vaginal fluid proteome to identify biomarkers of intraamniotic infection among women in preterm labor. Proteome analysis was performed on vaginal fluid specimens from women with preterm labor, using multidimensional liquid chromatography, tandem mass spectrometry, and label-free quantification. Enzyme immunoassays were used to quantify candidate proteins. Classification accuracy for intraamniotic infection (positive amniotic fluid bacterial culture and/or interleukin-6 >2 ng/mL) was evaluated using receiver-operator characteristic curves obtained by logistic regression. Of 170 subjects, 30 (18%) had intraamniotic infection. Vaginal fluid proteome analysis revealed 338 unique proteins. Label-free quantification identified 15 proteins differentially expressed in intraamniotic infection, including acute-phase reactants, immune modulators, high-abundance amniotic fluid proteins and extracellular matrix-signaling factors; these findings were confirmed by enzyme immunoassay. A multi-analyte algorithm showed accurate classification of intraamniotic infection. Vaginal fluid proteome analyses identified proteins capable of discriminating between patients with and without intraamniotic infection. Copyright (c) 2010 Mosby, Inc. All rights reserved.

Performance comparison of two resolution modeling PET reconstruction algorithms in terms of physical figures of merit used in quantitative imaging.

PubMed

Matheoud, R; Ferrando, O; Valzano, S; Lizio, D; Sacchetti, G; Ciarmiello, A; Foppiano, F; Brambilla, M

2015-07-01

Resolution modeling (RM) of PET systems has been introduced in iterative reconstruction algorithms for oncologic PET. The RM recovers the loss of resolution and reduces the associated partial volume effect. While these methods improved the observer performance, particularly in the detection of small and faint lesions, their impact on quantification accuracy still requires thorough investigation. The aim of this study was to characterize the performances of the RM algorithms under controlled conditions simulating a typical (18)F-FDG oncologic study, using an anthropomorphic phantom and selected physical figures of merit, used for image quantification. Measurements were performed on Biograph HiREZ (B_HiREZ) and Discovery 710 (D_710) PET/CT scanners and reconstructions were performed using the standard iterative reconstructions and the RM algorithms associated to each scanner: TrueX and SharpIR, respectively. RM determined a significant improvement in contrast recovery for small targets (≤17 mm diameter) only for the D_710 scanner. The maximum standardized uptake value (SUVmax) increased when RM was applied using both scanners. The SUVmax of small targets was on average lower with the B_HiREZ than with the D_710. Sharp IR improved the accuracy of SUVmax determination, whilst TrueX showed an overestimation of SUVmax for sphere dimensions greater than 22 mm. The goodness of fit of adaptive threshold algorithms worsened significantly when RM algorithms were employed for both scanners. Differences in general quantitative performance were observed for the PET scanners analyzed. Segmentation of PET images using adaptive threshold algorithms should not be undertaken in conjunction with RM reconstructions. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Breast density quantification with cone-beam CT: A post-mortem study

PubMed Central

Johnson, Travis; Ding, Huanjun; Le, Huy Q.; Ducote, Justin L.; Molloi, Sabee

2014-01-01

Forty post-mortem breasts were imaged with a flat-panel based cone-beam x-ray CT system at 50 kVp. The feasibility of breast density quantification has been investigated using standard histogram thresholding and an automatic segmentation method based on the fuzzy c-means algorithm (FCM). The breasts were chemically decomposed into water, lipid, and protein immediately after image acquisition was completed. The percent fibroglandular volume (%FGV) from chemical analysis was used as the gold standard for breast density comparison. Both image-based segmentation techniques showed good precision in breast density quantification with high linear coefficients between the right and left breast of each pair. When comparing with the gold standard using %FGV from chemical analysis, Pearson’s r-values were estimated to be 0.983 and 0.968 for the FCM clustering and the histogram thresholding techniques, respectively. The standard error of the estimate (SEE) was also reduced from 3.92% to 2.45% by applying the automatic clustering technique. The results of the postmortem study suggested that breast tissue can be characterized in terms of water, lipid and protein contents with high accuracy by using chemical analysis, which offers a gold standard for breast density studies comparing different techniques. In the investigated image segmentation techniques, the FCM algorithm had high precision and accuracy in breast density quantification. In comparison to conventional histogram thresholding, it was more efficient and reduced inter-observer variation. PMID:24254317

Compression of real time volumetric echocardiographic data using modified SPIHT based on the three-dimensional wavelet packet transform.

PubMed

Hang, X; Greenberg, N L; Shiota, T; Firstenberg, M S; Thomas, J D

2000-01-01

Real-time three-dimensional echocardiography has been introduced to provide improved quantification and description of cardiac function. Data compression is desired to allow efficient storage and improve data transmission. Previous work has suggested improved results utilizing wavelet transforms in the compression of medical data including 2D echocardiogram. Set partitioning in hierarchical trees (SPIHT) was extended to compress volumetric echocardiographic data by modifying the algorithm based on the three-dimensional wavelet packet transform. A compression ratio of at least 40:1 resulted in preserved image quality.

An overview of the mathematical and statistical analysis component of RICIS

NASA Technical Reports Server (NTRS)

Hallum, Cecil R.

1987-01-01

Mathematical and statistical analysis components of RICIS (Research Institute for Computing and Information Systems) can be used in the following problem areas: (1) quantification and measurement of software reliability; (2) assessment of changes in software reliability over time (reliability growth); (3) analysis of software-failure data; and (4) decision logic for whether to continue or stop testing software. Other areas of interest to NASA/JSC where mathematical and statistical analysis can be successfully employed include: math modeling of physical systems, simulation, statistical data reduction, evaluation methods, optimization, algorithm development, and mathematical methods in signal processing.

PSEA-Quant: a protein set enrichment analysis on label-free and label-based protein quantification data.

PubMed

Lavallée-Adam, Mathieu; Rauniyar, Navin; McClatchy, Daniel B; Yates, John R

2014-12-05

The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights.

PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free and Label-Based Protein Quantification Data

PubMed Central

2015-01-01

The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights. PMID:25177766

Accuracy of Rhenium-188 SPECT/CT activity quantification for applications in radionuclide therapy using clinical reconstruction methods.

PubMed

Esquinas, Pedro L; Uribe, Carlos F; Gonzalez, M; Rodríguez-Rodríguez, Cristina; Häfeli, Urs O; Celler, Anna

2017-07-20

The main applications of 188 Re in radionuclide therapies include trans-arterial liver radioembolization and palliation of painful bone-metastases. In order to optimize 188 Re therapies, the accurate determination of radiation dose delivered to tumors and organs at risk is required. Single photon emission computed tomography (SPECT) can be used to perform such dosimetry calculations. However, the accuracy of dosimetry estimates strongly depends on the accuracy of activity quantification in 188 Re images. In this study, we performed a series of phantom experiments aiming to investigate the accuracy of activity quantification for 188 Re SPECT using high-energy and medium-energy collimators. Objects of different shapes and sizes were scanned in Air, non-radioactive water (Cold-water) and water with activity (Hot-water). The ordered subset expectation maximization algorithm with clinically available corrections (CT-based attenuation, triple-energy window (TEW) scatter and resolution recovery was used). For high activities, the dead-time corrections were applied. The accuracy of activity quantification was evaluated using the ratio of the reconstructed activity in each object to this object's true activity. Each object's activity was determined with three segmentation methods: a 1% fixed threshold (for cold background), a 40% fixed threshold and a CT-based segmentation. Additionally, the activity recovered in the entire phantom, as well as the average activity concentration of the phantom background were compared to their true values. Finally, Monte-Carlo simulations of a commercial [Formula: see text]-camera were performed to investigate the accuracy of the TEW method. Good quantification accuracy (errors  <10%) was achieved for the entire phantom, the hot-background activity concentration and for objects in cold background segmented with a 1% threshold. However, the accuracy of activity quantification for objects segmented with 40% threshold or CT-based methods decreased (errors  >15%), mostly due to partial-volume effects. The Monte-Carlo simulations confirmed that TEW-scatter correction applied to 188 Re, although practical, yields only approximate estimates of the true scatter.

Preclinical Biokinetic Modelling of Tc-99m Radiophamaceuticals Obtained from Semi-Automatic Image Processing.

PubMed

Cornejo-Aragón, Luz G; Santos-Cuevas, Clara L; Ocampo-García, Blanca E; Chairez-Oria, Isaac; Diaz-Nieto, Lorenza; García-Quiroz, Janice

2017-01-01

The aim of this study was to develop a semi automatic image processing algorithm (AIPA) based on the simultaneous information provided by X-ray and radioisotopic images to determine the biokinetic models of Tc-99m radiopharmaceuticals from quantification of image radiation activity in murine models. These radioisotopic images were obtained by a CCD (charge couple device) camera coupled to an ultrathin phosphorous screen in a preclinical multimodal imaging system (Xtreme, Bruker). The AIPA consisted of different image processing methods for background, scattering and attenuation correction on the activity quantification. A set of parametric identification algorithms was used to obtain the biokinetic models that characterize the interaction between different tissues and the radiopharmaceuticals considered in the study. The set of biokinetic models corresponded to the Tc-99m biodistribution observed in different ex vivo studies. This fact confirmed the contribution of the semi-automatic image processing technique developed in this study.

AGScan: a pluggable microarray image quantification software based on the ImageJ library.

PubMed

Cathelin, R; Lopez, F; Klopp, Ch

2007-01-15

Many different programs are available to analyze microarray images. Most programs are commercial packages, some are free. In the latter group only few propose automatic grid alignment and batch mode. More often than not a program implements only one quantification algorithm. AGScan is an open source program that works on all major platforms. It is based on the ImageJ library [Rasband (1997-2006)] and offers a plug-in extension system to add new functions to manipulate images, align grid and quantify spots. It is appropriate for daily laboratory use and also as a framework for new algorithms. The program is freely distributed under X11 Licence. The install instructions can be found in the user manual. The software can be downloaded from http://mulcyber.toulouse.inra.fr/projects/agscan/. The questions and plug-ins can be sent to the contact listed below.

Full-field transient vibrometry of the human tympanic membrane by local phase correlation and high-speed holography

NASA Astrophysics Data System (ADS)

Dobrev, Ivo; Furlong, Cosme; Cheng, Jeffrey T.; Rosowski, John J.

2014-09-01

Understanding the human hearing process would be helped by quantification of the transient mechanical response of the human ear, including the human tympanic membrane (TM or eardrum). We propose a new hybrid high-speed holographic system (HHS) for acquisition and quantification of the full-field nanometer transient (i.e., >10 kHz) displacement of the human TM. We have optimized and implemented a 2+1 frame local correlation (LC) based phase sampling method in combination with a high-speed (i.e., >40 K fps) camera acquisition system. To our knowledge, there is currently no existing system that provides such capabilities for the study of the human TM. The LC sampling method has a displacement difference of <11 nm relative to measurements obtained by a four-phase step algorithm. Comparisons between our high-speed acquisition system and a laser Doppler vibrometer indicate differences of <10 μs. The high temporal (i.e., >40 kHz) and spatial (i.e., >100 k data points) resolution of our HHS enables parallel measurements of all points on the surface of the TM, which allows quantification of spatially dependent motion parameters, such as modal frequencies and acoustic delays. Such capabilities could allow inferring local material properties across the surface of the TM.

Fast sparse Raman spectral unmixing for chemical fingerprinting and quantification

NASA Astrophysics Data System (ADS)

Yaghoobi, Mehrdad; Wu, Di; Clewes, Rhea J.; Davies, Mike E.

2016-10-01

Raman spectroscopy is a well-established spectroscopic method for the detection of condensed phase chemicals. It is based on scattered light from exposure of a target material to a narrowband laser beam. The information generated enables presumptive identification from measuring correlation with library spectra. Whilst this approach is successful in identification of chemical information of samples with one component, it is more difficult to apply to spectral mixtures. The capability of handling spectral mixtures is crucial for defence and security applications as hazardous materials may be present as mixtures due to the presence of degradation, interferents or precursors. A novel method for spectral unmixing is proposed here. Most modern decomposition techniques are based on the sparse decomposition of mixture and the application of extra constraints to preserve the sum of concentrations. These methods have often been proposed for passive spectroscopy, where spectral baseline correction is not required. Most successful methods are computationally expensive, e.g. convex optimisation and Bayesian approaches. We present a novel low complexity sparsity based method to decompose the spectra using a reference library of spectra. It can be implemented on a hand-held spectrometer in near to real-time. The algorithm is based on iteratively subtracting the contribution of selected spectra and updating the contribution of each spectrum. The core algorithm is called fast non-negative orthogonal matching pursuit, which has been proposed by the authors in the context of nonnegative sparse representations. The iteration terminates when the maximum number of expected chemicals has been found or the residual spectrum has a negligible energy, i.e. in the order of the noise level. A backtracking step removes the least contributing spectrum from the list of detected chemicals and reports it as an alternative component. This feature is particularly useful in detection of chemicals with small contributions, which are normally not detected. The proposed algorithm is easily reconfigurable to include new library entries and optional preferential threat searches in the presence of predetermined threat indicators. Under Ministry of Defence funding, we have demonstrated the algorithm for fingerprinting and rough quantification of the concentration of chemical mixtures using a set of reference spectral mixtures. In our experiments, the algorithm successfully managed to detect the chemicals with concentrations below 10 percent. The running time of the algorithm is in the order of one second, using a single core of a desktop computer.

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults

PubMed Central

Verschueren, Sabine M. P.; Degens, Hans; Morse, Christopher I.; Onambélé, Gladys L.

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual’s physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry. PMID:29155839

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.

PubMed

Wullems, Jorgen A; Verschueren, Sabine M P; Degens, Hans; Morse, Christopher I; Onambélé, Gladys L

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual's physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry.

Cellular neural networks, the Navier-Stokes equation, and microarray image reconstruction.

PubMed

Zineddin, Bachar; Wang, Zidong; Liu, Xiaohui

2011-11-01

Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier-Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time.

Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR).

PubMed

Chen, Baiyu; Barnhart, Huiman; Richard, Samuel; Robins, Marthony; Colsher, James; Samei, Ehsan

2013-11-01

Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables. Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision. Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A. The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of accuracy on reconstruction algorithms, such that volumes quantified from scans of different reconstruction algorithms can be compared. The little difference found between the precision of FBP and iterative reconstructions could be a result of both iterative reconstruction's diminished noise reduction at the edge of the nodules as well as the loss of resolution at high noise levels with iterative reconstruction. The findings do not rule out potential advantage of IR that might be evident in a study that uses a larger number of nodules or repeated scans.

Frequency Response Function Based Damage Identification for Aerospace Structures

NASA Astrophysics Data System (ADS)

Oliver, Joseph Acton

Structural health monitoring technologies continue to be pursued for aerospace structures in the interests of increased safety and, when combined with health prognosis, efficiency in life-cycle management. The current dissertation develops and validates damage identification technology as a critical component for structural health monitoring of aerospace structures and, in particular, composite unmanned aerial vehicles. The primary innovation is a statistical least-squares damage identification algorithm based in concepts of parameter estimation and model update. The algorithm uses frequency response function based residual force vectors derived from distributed vibration measurements to update a structural finite element model through statistically weighted least-squares minimization producing location and quantification of the damage, estimation uncertainty, and an updated model. Advantages compared to other approaches include robust applicability to systems which are heavily damped, large, and noisy, with a relatively low number of distributed measurement points compared to the number of analytical degrees-of-freedom of an associated analytical structural model (e.g., modal finite element model). Motivation, research objectives, and a dissertation summary are discussed in Chapter 1 followed by a literature review in Chapter 2. Chapter 3 gives background theory and the damage identification algorithm derivation followed by a study of fundamental algorithm behavior on a two degree-of-freedom mass-spring system with generalized damping. Chapter 4 investigates the impact of noise then successfully proves the algorithm against competing methods using an analytical eight degree-of-freedom mass-spring system with non-proportional structural damping. Chapter 5 extends use of the algorithm to finite element models, including solutions for numerical issues, approaches for modeling damping approximately in reduced coordinates, and analytical validation using a composite sandwich plate model. Chapter 6 presents the final extension to experimental systems-including methods for initial baseline correlation and data reduction-and validates the algorithm on an experimental composite plate with impact damage. The final chapter deviates from development and validation of the primary algorithm to discuss development of an experimental scaled-wing test bed as part of a collaborative effort for developing structural health monitoring and prognosis technology. The dissertation concludes with an overview of technical conclusions and recommendations for future work.

An open tool for input function estimation and quantification of dynamic PET FDG brain scans.

PubMed

Bertrán, Martín; Martínez, Natalia; Carbajal, Guillermo; Fernández, Alicia; Gómez, Álvaro

2016-08-01

Positron emission tomography (PET) analysis of clinical studies is mostly restricted to qualitative evaluation. Quantitative analysis of PET studies is highly desirable to be able to compute an objective measurement of the process of interest in order to evaluate treatment response and/or compare patient data. But implementation of quantitative analysis generally requires the determination of the input function: the arterial blood or plasma activity which indicates how much tracer is available for uptake in the brain. The purpose of our work was to share with the community an open software tool that can assist in the estimation of this input function, and the derivation of a quantitative map from the dynamic PET study. Arterial blood sampling during the PET study is the gold standard method to get the input function, but is uncomfortable and risky for the patient so it is rarely used in routine studies. To overcome the lack of a direct input function, different alternatives have been devised and are available in the literature. These alternatives derive the input function from the PET image itself (image-derived input function) or from data gathered from previous similar studies (population-based input function). In this article, we present ongoing work that includes the development of a software tool that integrates several methods with novel strategies for the segmentation of blood pools and parameter estimation. The tool is available as an extension to the 3D Slicer software. Tests on phantoms were conducted in order to validate the implemented methods. We evaluated the segmentation algorithms over a range of acquisition conditions and vasculature size. Input function estimation algorithms were evaluated against ground truth of the phantoms, as well as on their impact over the final quantification map. End-to-end use of the tool yields quantification maps with [Formula: see text] relative error in the estimated influx versus ground truth on phantoms. The main contribution of this article is the development of an open-source, free to use tool that encapsulates several well-known methods for the estimation of the input function and the quantification of dynamic PET FDG studies. Some alternative strategies are also proposed and implemented in the tool for the segmentation of blood pools and parameter estimation. The tool was tested on phantoms with encouraging results that suggest that even bloodless estimators could provide a viable alternative to blood sampling for quantification using graphical analysis. The open tool is a promising opportunity for collaboration among investigators and further validation on real studies.

Three-dimensional Hessian matrix-based quantitative vascular imaging of rat iris with optical-resolution photoacoustic microscopy in vivo

NASA Astrophysics Data System (ADS)

Zhao, Huangxuan; Wang, Guangsong; Lin, Riqiang; Gong, Xiaojing; Song, Liang; Li, Tan; Wang, Wenjia; Zhang, Kunya; Qian, Xiuqing; Zhang, Haixia; Li, Lin; Liu, Zhicheng; Liu, Chengbo

2018-04-01

For the diagnosis and evaluation of ophthalmic diseases, imaging and quantitative characterization of vasculature in the iris are very important. The recently developed photoacoustic imaging, which is ultrasensitive in imaging endogenous hemoglobin molecules, provides a highly efficient label-free method for imaging blood vasculature in the iris. However, the development of advanced vascular quantification algorithms is still needed to enable accurate characterization of the underlying vasculature. We have developed a vascular information quantification algorithm by adopting a three-dimensional (3-D) Hessian matrix and applied for processing iris vasculature images obtained with a custom-built optical-resolution photoacoustic imaging system (OR-PAM). For the first time, we demonstrate in vivo 3-D vascular structures of a rat iris with a the label-free imaging method and also accurately extract quantitative vascular information, such as vessel diameter, vascular density, and vascular tortuosity. Our results indicate that the developed algorithm is capable of quantifying the vasculature in the 3-D photoacoustic images of the iris in-vivo, thus enhancing the diagnostic capability of the OR-PAM system for vascular-related ophthalmic diseases in vivo.

StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron microscopy images.

PubMed

De Backer, A; van den Bos, K H W; Van den Broek, W; Sijbers, J; Van Aert, S

2016-12-01

An efficient model-based estimation algorithm is introduced to quantify the atomic column positions and intensities from atomic resolution (scanning) transmission electron microscopy ((S)TEM) images. This algorithm uses the least squares estimator on image segments containing individual columns fully accounting for overlap between neighbouring columns, enabling the analysis of a large field of view. For this algorithm, the accuracy and precision with which measurements for the atomic column positions and scattering cross-sections from annular dark field (ADF) STEM images can be estimated, has been investigated. The highest attainable precision is reached even for low dose images. Furthermore, the advantages of the model-based approach taking into account overlap between neighbouring columns are highlighted. This is done for the estimation of the distance between two neighbouring columns as a function of their distance and for the estimation of the scattering cross-section which is compared to the integrated intensity from a Voronoi cell. To provide end-users this well-established quantification method, a user friendly program, StatSTEM, is developed which is freely available under a GNU public license. Copyright © 2016 Elsevier B.V. All rights reserved.

A Pulse Coupled Neural Network Segmentation Algorithm for Reflectance Confocal Images of Epithelial Tissue

PubMed Central

Malik, Bilal H.; Jabbour, Joey M.; Maitland, Kristen C.

2015-01-01

Automatic segmentation of nuclei in reflectance confocal microscopy images is critical for visualization and rapid quantification of nuclear-to-cytoplasmic ratio, a useful indicator of epithelial precancer. Reflectance confocal microscopy can provide three-dimensional imaging of epithelial tissue in vivo with sub-cellular resolution. Changes in nuclear density or nuclear-to-cytoplasmic ratio as a function of depth obtained from confocal images can be used to determine the presence or stage of epithelial cancers. However, low nuclear to background contrast, low resolution at greater imaging depths, and significant variation in reflectance signal of nuclei complicate segmentation required for quantification of nuclear-to-cytoplasmic ratio. Here, we present an automated segmentation method to segment nuclei in reflectance confocal images using a pulse coupled neural network algorithm, specifically a spiking cortical model, and an artificial neural network classifier. The segmentation algorithm was applied to an image model of nuclei with varying nuclear to background contrast. Greater than 90% of simulated nuclei were detected for contrast of 2.0 or greater. Confocal images of porcine and human oral mucosa were used to evaluate application to epithelial tissue. Segmentation accuracy was assessed using manual segmentation of nuclei as the gold standard. PMID:25816131

Multimodal Imaging and Lighting Bias Correction for Improved μPAD-based Water Quality Monitoring via Smartphones

NASA Astrophysics Data System (ADS)

McCracken, Katherine E.; Angus, Scott V.; Reynolds, Kelly A.; Yoon, Jeong-Yeol

2016-06-01

Smartphone image-based sensing of microfluidic paper analytical devices (μPADs) offers low-cost and mobile evaluation of water quality. However, consistent quantification is a challenge due to variable environmental, paper, and lighting conditions, especially across large multi-target μPADs. Compensations must be made for variations between images to achieve reproducible results without a separate lighting enclosure. We thus developed a simple method using triple-reference point normalization and a fast-Fourier transform (FFT)-based pre-processing scheme to quantify consistent reflected light intensity signals under variable lighting and channel conditions. This technique was evaluated using various light sources, lighting angles, imaging backgrounds, and imaging heights. Further testing evaluated its handle of absorbance, quenching, and relative scattering intensity measurements from assays detecting four water contaminants - Cr(VI), total chlorine, caffeine, and E. coli K12 - at similar wavelengths using the green channel of RGB images. Between assays, this algorithm reduced error from μPAD surface inconsistencies and cross-image lighting gradients. Although the algorithm could not completely remove the anomalies arising from point shadows within channels or some non-uniform background reflections, it still afforded order-of-magnitude quantification and stable assay specificity under these conditions, offering one route toward improving smartphone quantification of μPAD assays for in-field water quality monitoring.

A Novel Computer-Assisted Approach to evaluate Multicellular Tumor Spheroid Invasion Assay

PubMed Central

Cisneros Castillo, Liliana R.; Oancea, Andrei-Dumitru; Stüllein, Christian; Régnier-Vigouroux, Anne

2016-01-01

Multicellular tumor spheroids (MCTSs) embedded in a matrix are re-emerging as a powerful alternative to monolayer-based cultures. The primary information gained from a three-dimensional model is the invasiveness of treatment-exposed MCTSs through the acquisition of light microscopy images. The amount and complexity of the acquired data and the bias arisen by their manual analysis are disadvantages calling for an automated, high-throughput analysis. We present a universal algorithm we developed with the scope of being robust enough to handle images of various qualities and various invasion profiles. The novelty and strength of our algorithm lie in: the introduction of a multi-step segmentation flow, where each step is optimized for each specific MCTS area (core, halo, and periphery); the quantification through the density of the two-dimensional representation of a three-dimensional object. This latter offers a fine-granular differentiation of invasive profiles, facilitating a quantification independent of cell lines and experimental setups. Progression of density from the core towards the edges influences the resulting density map thus providing a measure no longer dependent on the sole area size of MCTS, but also on its invasiveness. In sum, we propose a new method in which the concept of quantification of MCTS invasion is completely re-thought. PMID:27731418

Quantifying Pharmaceutical Film Coating with Optical Coherence Tomography and Terahertz Pulsed Imaging: An Evaluation.

PubMed

Lin, Hungyen; Dong, Yue; Shen, Yaochun; Zeitler, J Axel

2015-10-01

Spectral domain optical coherence tomography (OCT) has recently attracted a lot of interest in the pharmaceutical industry as a fast and non-destructive modality for quantification of thin film coatings that cannot easily be resolved with other techniques. Because of the relative infancy of this technique, much of the research to date has focused on developing the in-line measurement technique for assessing film coating thickness. To better assess OCT for pharmaceutical coating quantification, this paper evaluates tablets with a range of film coating thickness measured using OCT and terahertz pulsed imaging (TPI) in an off-line setting. In order to facilitate automated coating quantification for film coating thickness in the range of 30-200 μm, an algorithm that uses wavelet denoising and a tailored peak finding method is proposed to analyse each of the acquired A-scan. Results obtained from running the algorithm reveal an increasing disparity between the TPI and OCT measured intra-tablet variability when film coating thickness exceeds 100 μm. The finding further confirms that OCT is a suitable modality for characterising pharmaceutical dosage forms with thin film coatings, whereas TPI is well suited for thick coatings. © 2015 The Authors. Journal of Pharmaceutical Sciences published by Wiley Periodicals, Inc. and the American Pharmacists Association.

MDCT quantification is the dominant parameter in decision-making regarding chest tube drainage for stable patients with traumatic pneumothorax.

PubMed

Cai, Wenli; Lee, June-Goo; Fikry, Karim; Yoshida, Hiroyuki; Novelline, Robert; de Moya, Marc

2012-07-01

It is commonly believed that the size of a pneumothorax is an important determinant of treatment decision, in particular regarding whether chest tube drainage (CTD) is required. However, the volumetric quantification of pneumothoraces has not routinely been performed in clinics. In this paper, we introduced an automated computer-aided volumetry (CAV) scheme for quantification of volume of pneumothoraces in chest multi-detect CT (MDCT) images. Moreover, we investigated the impact of accurate volume of pneumothoraces in the improvement of the performance in decision-making regarding CTD in the management of traumatic pneumothoraces. For this purpose, an occurrence frequency map was calculated for quantitative analysis of the importance of each clinical parameter in the decision-making regarding CTD by a computer simulation of decision-making using a genetic algorithm (GA) and a support vector machine (SVM). A total of 14 clinical parameters, including volume of pneumothorax calculated by our CAV scheme, was collected as parameters available for decision-making. The results showed that volume was the dominant parameter in decision-making regarding CTD, with an occurrence frequency value of 1.00. The results also indicated that the inclusion of volume provided the best performance that was statistically significant compared to the other tests in which volume was excluded from the clinical parameters. This study provides the scientific evidence for the application of CAV scheme in MDCT volumetric quantification of pneumothoraces in the management of clinically stable chest trauma patients with traumatic pneumothorax. Copyright © 2012 Elsevier Ltd. All rights reserved.

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

PubMed

Zhang, Jia-Hua; Li, Xin; Yao, Feng-Mei; Li, Xian-Hua

2009-08-01

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics

PubMed Central

Röst, Hannes L.; Liu, Yansheng; D’Agostino, Giuseppe; Zanella, Matteo; Navarro, Pedro; Rosenberger, George; Collins, Ben C.; Gillet, Ludovic; Testa, Giuseppe; Malmström, Lars; Aebersold, Ruedi

2016-01-01

Large scale, quantitative proteomic studies have become essential for the analysis of clinical cohorts, large perturbation experiments and systems biology studies. While next-generation mass spectrometric techniques such as SWATH-MS have substantially increased throughput and reproducibility, ensuring consistent quantification of thousands of peptide analytes across multiple LC-MS/MS runs remains a challenging and laborious manual process. To produce highly consistent and quantitatively accurate proteomics data matrices in an automated fashion, we have developed the TRIC software which utilizes fragment ion data to perform cross-run alignment, consistent peak-picking and quantification for high throughput targeted proteomics. TRIC uses a graph-based alignment strategy based on non-linear retention time correction to integrate peak elution information from all LC-MS/MS runs acquired in a study. When compared to state-of-the-art SWATH-MS data analysis, the algorithm was able to reduce the identification error by more than 3-fold at constant recall, while correcting for highly non-linear chromatographic effects. On a pulsed-SILAC experiment performed on human induced pluripotent stem (iPS) cells, TRIC was able to automatically align and quantify thousands of light and heavy isotopic peak groups and substantially increased the quantitative completeness and biological information in the data, providing insights into protein dynamics of iPS cells. Overall, this study demonstrates the importance of consistent quantification in highly challenging experimental setups, and proposes an algorithm to automate this task, constituting the last missing piece in a pipeline for automated analysis of massively parallel targeted proteomics datasets. PMID:27479329

Identification of Reliable Reference Genes for Quantification of MicroRNAs in Serum Samples of Sulfur Mustard-Exposed Veterans.

PubMed

Gharbi, Sedigheh; Shamsara, Mehdi; Khateri, Shahriar; Soroush, Mohammad Reza; Ghorbanmehr, Nassim; Tavallaei, Mahmood; Nourani, Mohammad Reza; Mowla, Seyed Javad

2015-01-01

In spite of accumulating information about pathological aspects of sulfur mustard (SM), the precise mechanism responsible for its effects is not well understood. Circulating microRNAs (miRNAs) are promising biomarkers for disease diagnosis and prognosis. Accurate normalization using appropriate reference genes, is a critical step in miRNA expression studies. In this study, we aimed to identify appropriate reference gene for microRNA quantification in serum samples of SM victims. In this case and control experimental study, using quantitative real-time polymerase chain reaction (qRT-PCR), we evaluated the suitability of a panel of small RNAs including SNORD38B, SNORD49A, U6, 5S rRNA, miR-423-3p, miR-191, miR-16 and miR-103 in sera of 28 SM-exposed veterans of Iran-Iraq war (1980-1988) and 15 matched control volunteers. Different statistical algorithms including geNorm, Normfinder, best-keeper and comparative delta-quantification cycle (Cq) method were employed to find the least variable reference gene. miR-423-3p was identified as the most stably expressed reference gene, and miR- 103 and miR-16 ranked after that. We demonstrate that non-miRNA reference genes have the least stabil- ity in serum samples and that some house-keeping miRNAs may be used as more reliable reference genes for miRNAs in serum. In addition, using the geometric mean of two reference genes could increase the reliability of the normalizers.

Simulation of Seasonal Snow Microwave TB Using Coupled Multi-Layered Snow Evolution and Microwave Emission Models

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Royer, Alain; Picard, Ghislain; Langlois, Alex; Fily, Michel

2014-01-01

The accurate quantification of SWE has important societal benefits, including improving domestic and agricultural water planning, flood forecasting and electric power generation. However, passive-microwave SWE algorithms suffer from variations in TB due to snow metamorphism, difficult to distinguish from those due to SWE variations. Coupled snow evolution-emission models are able to predict snow metamorphism, allowing us to account for emissivity changes. They can also be used to identify weaknesses in the snow evolution model. Moreover, thoroughly evaluating coupled models is a contribution toward the assimilation of TB, which leads to a significant increase in the accuracy of SWE estimates.

Metabolic flux estimation using particle swarm optimization with penalty function.

PubMed

Long, Hai-Xia; Xu, Wen-Bo; Sun, Jun

2009-01-01

Metabolic flux estimation through 13C trace experiment is crucial for quantifying the intracellular metabolic fluxes. In fact, it corresponds to a constrained optimization problem that minimizes a weighted distance between measured and simulated results. In this paper, we propose particle swarm optimization (PSO) with penalty function to solve 13C-based metabolic flux estimation problem. The stoichiometric constraints are transformed to an unconstrained one, by penalizing the constraints and building a single objective function, which in turn is minimized using PSO algorithm for flux quantification. The proposed algorithm is applied to estimate the central metabolic fluxes of Corynebacterium glutamicum. From simulation results, it is shown that the proposed algorithm has superior performance and fast convergence ability when compared to other existing algorithms.

An efficient framework for optimization and parameter sensitivity analysis in arterial growth and remodeling computations

PubMed Central

Sankaran, Sethuraman; Humphrey, Jay D.; Marsden, Alison L.

2013-01-01

Computational models for vascular growth and remodeling (G&R) are used to predict the long-term response of vessels to changes in pressure, flow, and other mechanical loading conditions. Accurate predictions of these responses are essential for understanding numerous disease processes. Such models require reliable inputs of numerous parameters, including material properties and growth rates, which are often experimentally derived, and inherently uncertain. While earlier methods have used a brute force approach, systematic uncertainty quantification in G&R models promises to provide much better information. In this work, we introduce an efficient framework for uncertainty quantification and optimal parameter selection, and illustrate it via several examples. First, an adaptive sparse grid stochastic collocation scheme is implemented in an established G&R solver to quantify parameter sensitivities, and near-linear scaling with the number of parameters is demonstrated. This non-intrusive and parallelizable algorithm is compared with standard sampling algorithms such as Monte-Carlo. Second, we determine optimal arterial wall material properties by applying robust optimization. We couple the G&R simulator with an adaptive sparse grid collocation approach and a derivative-free optimization algorithm. We show that an artery can achieve optimal homeostatic conditions over a range of alterations in pressure and flow; robustness of the solution is enforced by including uncertainty in loading conditions in the objective function. We then show that homeostatic intramural and wall shear stress is maintained for a wide range of material properties, though the time it takes to achieve this state varies. We also show that the intramural stress is robust and lies within 5% of its mean value for realistic variability of the material parameters. We observe that prestretch of elastin and collagen are most critical to maintaining homeostasis, while values of the material properties are most critical in determining response time. Finally, we outline several challenges to the G&R community for future work. We suggest that these tools provide the first systematic and efficient framework to quantify uncertainties and optimally identify G&R model parameters. PMID:23626380

A Pragmatic Smoothing Method for Improving the Quality of the Results in Atomic Spectroscopy

NASA Astrophysics Data System (ADS)

Bennun, Leonardo

2017-07-01

A new smoothing method for the improvement on the identification and quantification of spectral functions based on the previous knowledge of the signals that are expected to be quantified, is presented. These signals are used as weighted coefficients in the smoothing algorithm. This smoothing method was conceived to be applied in atomic and nuclear spectroscopies preferably to these techniques where net counts are proportional to acquisition time, such as particle induced X-ray emission (PIXE) and other X-ray fluorescence spectroscopic methods, etc. This algorithm, when properly applied, does not distort the form nor the intensity of the signal, so it is well suited for all kind of spectroscopic techniques. This method is extremely effective at reducing high-frequency noise in the signal much more efficient than a single rectangular smooth of the same width. As all of smoothing techniques, the proposed method improves the precision of the results, but in this case we found also a systematic improvement on the accuracy of the results. We still have to evaluate the improvement on the quality of the results when this method is applied over real experimental results. We expect better characterization of the net area quantification of the peaks, and smaller Detection and Quantification Limits. We have applied this method to signals that obey Poisson statistics, but with the same ideas and criteria, it could be applied to time series. In a general case, when this algorithm is applied over experimental results, also it would be required that the sought characteristic functions, required for this weighted smoothing method, should be obtained from a system with strong stability. If the sought signals are not perfectly clean, this method should be carefully applied

Three-dimensional quantification of orthodontic root resorption with time-lapsed imaging of micro-computed tomography in a rodent model.

PubMed

Yang, Chongshi; Zhang, Yuanyuan; Zhang, Yan; Fan, Yubo; Deng, Feng

2015-01-01

Despite various X-ray approaches have been widely used to monitor root resorption after orthodontic treatment, a non-invasive and accurate method is highly desirable for long-term follow up. The aim of this study was to build a non-invasive method to quantify longitudinal orthodontic root resorption with time-lapsed images of micro-computed tomography (micro-CT) in a rodent model. Twenty male Sprague Dawley (SD) rats (aged 6-8 weeks, weighing 180-220 g) were used in this study. A 25 g orthodontic force generated by nickel-titanium coil spring was applied to the right maxillary first molar for each rat, while contralateral first molar was severed as a control. Micro-CT scan was performed at day 0 (before orthodontic load) and days 3, 7, 14, and 28 after orthodontic load. Resorption of mesial root of maxillary first molars at bilateral sides was calculated from micro-CT images with registration algorithm via reconstruction, superimposition and partition operations. Obvious resorption of mesial root of maxillary first molar can be detected at day 14 and day 28 at orthodontic side. Most of the resorption occurred in the apical region at distal side and cervical region at mesiolingual side. Desirable development of molar root of rats was identified from day 0 to day 28 at control side. The development of root concentrated on apical region. This non-invasive 3D quantification method with registration algorithm can be used in longitudinal study of root resorption. Obvious root resorption in rat molar can be observed three-dimensionally at day 14 and day 28 after orthodontic load. This indicates that registration algorithm combined with time-lapsed images provides clinic potential application in detection and quantification of root contour.

Survey of Quantification and Distance Functions Used for Internet-based Weak-link Sociological Phenomena

DTIC Science & Technology

2016-03-01

well as the Yahoo search engine and a classic SearchKing HIST algorithm. The co-PI immersed herself in the sociology literature for the relevant...Google matrix, PageRank as well as the Yahoo search engine and a classic SearchKing HIST algorithm. The co-PI immersed herself in the sociology...The PI studied all mathematical literature he can find related to the Google search engine, Google matrix, PageRank as well as the Yahoo search

Interval-based reconstruction for uncertainty quantification in PET

NASA Astrophysics Data System (ADS)

Kucharczak, Florentin; Loquin, Kevin; Buvat, Irène; Strauss, Olivier; Mariano-Goulart, Denis

2018-02-01

A new directed interval-based tomographic reconstruction algorithm, called non-additive interval based expectation maximization (NIBEM) is presented. It uses non-additive modeling of the forward operator that provides intervals instead of single-valued projections. The detailed approach is an extension of the maximum likelihood—expectation maximization algorithm based on intervals. The main motivation for this extension is that the resulting intervals have appealing properties for estimating the statistical uncertainty associated with the reconstructed activity values. After reviewing previously published theoretical concepts related to interval-based projectors, this paper describes the NIBEM algorithm and gives examples that highlight the properties and advantages of this interval valued reconstruction.

Validation of a DIXON-based fat quantification technique for the measurement of visceral fat using a CT-based reference standard.

PubMed

Heckman, Katherine M; Otemuyiwa, Bamidele; Chenevert, Thomas L; Malyarenko, Dariya; Derstine, Brian A; Wang, Stewart C; Davenport, Matthew S

2018-06-27

The purpose of the study is to determine whether a novel semi-automated DIXON-based fat quantification algorithm can reliably quantify visceral fat using a CT-based reference standard. This was an IRB-approved retrospective cohort study of 27 subjects who underwent abdominopelvic CT within 7 days of proton density fat fraction (PDFF) mapping on a 1.5T MRI. Cross-sectional visceral fat area per slice (cm 2 ) was measured in blinded fashion in each modality at intervertebral disc levels from T12 to L4. CT estimates were obtained using a previously published semi-automated computational image processing system that sums pixels with attenuation - 205 to - 51 HU. MR estimates were obtained using two novel semi-automated DIXON-based fat quantification algorithms that measure visceral fat area by spatially regularizing non-uniform fat-only signal intensity or de-speckling PDFF 2D images and summing pixels with PDFF ≥ 50%. Pearson's correlations and Bland-Altman analyses were performed. Visceral fat area per slice ranged from 9.2 to 429.8 cm 2 for MR and from 1.6 to 405.5 cm 2 for CT. There was a strong correlation between CT and MR methods in measured visceral fat area across all studied vertebral body levels (r = 0.97; n = 101 observations); the least (r = 0.93) correlation was at T12. Bland-Altman analysis revealed a bias of 31.7 cm 2 (95% CI [- 27.1]-90.4 cm 2 ), indicating modestly higher visceral fat assessed by MR. MR- and CT-based visceral fat quantification are highly correlated and have good cross-modality reliability, indicating that visceral fat quantification by either method can yield a stable and reliable biomarker.

Full-field transient vibrometry of the human tympanic membrane by local phase correlation and high-speed holography

PubMed Central

Dobrev, Ivo; Furlong, Cosme; Cheng, Jeffrey T.; Rosowski, John J.

2014-01-01

Abstract. Understanding the human hearing process would be helped by quantification of the transient mechanical response of the human ear, including the human tympanic membrane (TM or eardrum). We propose a new hybrid high-speed holographic system (HHS) for acquisition and quantification of the full-field nanometer transient (i.e., >10  kHz) displacement of the human TM. We have optimized and implemented a 2+1 frame local correlation (LC) based phase sampling method in combination with a high-speed (i.e., >40  K fps) camera acquisition system. To our knowledge, there is currently no existing system that provides such capabilities for the study of the human TM. The LC sampling method has a displacement difference of <11  nm relative to measurements obtained by a four-phase step algorithm. Comparisons between our high-speed acquisition system and a laser Doppler vibrometer indicate differences of <10  μs. The high temporal (i.e., >40  kHz) and spatial (i.e., >100  k data points) resolution of our HHS enables parallel measurements of all points on the surface of the TM, which allows quantification of spatially dependent motion parameters, such as modal frequencies and acoustic delays. Such capabilities could allow inferring local material properties across the surface of the TM. PMID:25191832

Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

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

Huan, Xun; Safta, Cosmin; Sargsyan, Khachik

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis ismore » conducted to identify influential uncertain input parameters, which can help reduce the system’s stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. In conclusion, these methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.« less

Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

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

Huan, Xun; Safta, Cosmin; Sargsyan, Khachik

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis ismore » conducted to identify influential uncertain input parameters, which can help reduce the system’s stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. Finally, these methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.« less

Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

NASA Astrophysics Data System (ADS)

Huan, Xun; Safta, Cosmin; Sargsyan, Khachik; Geraci, Gianluca; Eldred, Michael S.; Vane, Zachary P.; Lacaze, Guilhem; Oefelein, Joseph C.; Najm, Habib N.

2018-03-01

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the systems stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. These methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.

Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

DOE PAGES

Huan, Xun; Safta, Cosmin; Sargsyan, Khachik; ...

2018-02-09

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis ismore » conducted to identify influential uncertain input parameters, which can help reduce the system’s stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. In conclusion, these methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.« less

Tasked-based quantification of measurement utility for ex vivo multi-spectral Mueller polarimetry of the uterine cervix

NASA Astrophysics Data System (ADS)

Kupinski, Meredith; Rehbinder, Jean; Haddad, Huda; Deby, Stanislas; Vizet, Jérémy; Teig, Benjamin; Nazac, André; Pierangelo, Angelo; Moreau, François; Novikova, Tatiana

2017-07-01

Significant contrast in visible wavelength Mueller matrix images for healthy and pre-cancerous regions of excised cervical tissue is shown. A novel classification algorithm is used to compute a test statistic from a small patient population.

TAPAS: tools to assist the targeted protein quantification of human alternative splice variants.

PubMed

Yang, Jae-Seong; Sabidó, Eduard; Serrano, Luis; Kiel, Christina

2014-10-15

In proteomes of higher eukaryotes, many alternative splice variants can only be detected by their shared peptides. This makes it highly challenging to use peptide-centric mass spectrometry to distinguish and to quantify protein isoforms resulting from alternative splicing events. We have developed two complementary algorithms based on linear mathematical models to efficiently compute a minimal set of shared and unique peptides needed to quantify a set of isoforms and splice variants. Further, we developed a statistical method to estimate the splice variant abundances based on stable isotope labeled peptide quantities. The algorithms and databases are integrated in a web-based tool, and we have experimentally tested the limits of our quantification method using spiked proteins and cell extracts. The TAPAS server is available at URL http://davinci.crg.es/tapas/. luis.serrano@crg.eu or christina.kiel@crg.eu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Application of Photoshop and Scion Image analysis to quantification of signals in histochemistry, immunocytochemistry and hybridocytochemistry.

PubMed

Tolivia, Jorge; Navarro, Ana; del Valle, Eva; Perez, Cristina; Ordoñez, Cristina; Martínez, Eva

2006-02-01

To describe a simple method to achieve the differential selection and subsequent quantification of the strength signal using only one section. Several methods for performing quantitative histochemistry, immunocytochemistry or hybridocytochemistry, without use of specific commercial image analysis systems, rely on pixel-counting algorithms, which do not provide information on the amount of chromogen present in the section. Other techniques use complex algorithms to calculate the cumulative signal strength using two consecutive sections. To separate the chromogen signal we used the "Color range" option of the Adobe Photoshop program, which provides a specific file for a particular chromogen selection that could be applied on similar sections. The measurement of the chromogen signal strength of the specific staining is achieved with the Scion Image software program. The method described in this paper can also be applied to simultaneous detection of different signals on the same section or different parameters (area of particles, number of particles, etc.) when the "Analyze particles" tool of the Scion program is used.

Automated quantification of myocardial infarction from MR images by accounting for partial volume effects: animal, phantom, and human study.

PubMed

Heiberg, Einar; Ugander, Martin; Engblom, Henrik; Götberg, Matthias; Olivecrona, Göran K; Erlinge, David; Arheden, Håkan

2008-02-01

Ethics committees approved human and animal study components; informed written consent was provided (prospective human study [20 men; mean age, 62 years]) or waived (retrospective human study [16 men, four women; mean age, 59 years]). The purpose of this study was to prospectively evaluate a clinically applicable method, accounting for the partial volume effect, to automatically quantify myocardial infarction from delayed contrast material-enhanced magnetic resonance images. Pixels were weighted according to signal intensity to calculate infarct fraction for each pixel. Mean bias +/- variability (or standard deviation), expressed as percentage left ventricular myocardium (%LVM), were -0.3 +/- 1.3 (animals), -1.2 +/- 1.7 (phantoms), and 0.3 +/- 2.7 (patients), respectively. Algorithm had lower variability than dichotomous approach (2.7 vs 7.7 %LVM, P < .01) and did not differ from interobserver variability for bias (P = .31) or variability (P = .38). The weighted approach provides automatic quantification of myocardial infarction with higher accuracy and lower variability than a dichotomous algorithm. (c) RSNA, 2007.

Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models

NASA Astrophysics Data System (ADS)

Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.

2015-03-01

We present Π4U, an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.

Automatic and Reproducible Positioning of Phase-Contrast MRI for the Quantification of Global Cerebral Blood Flow

PubMed Central

Liu, Peiying; Lu, Hanzhang; Filbey, Francesca M.; Pinkham, Amy E.; McAdams, Carrie J.; Adinoff, Bryon; Daliparthi, Vamsi; Cao, Yan

2014-01-01

Phase-Contrast MRI (PC-MRI) is a noninvasive technique to measure blood flow. In particular, global but highly quantitative cerebral blood flow (CBF) measurement using PC-MRI complements several other CBF mapping methods such as arterial spin labeling and dynamic susceptibility contrast MRI by providing a calibration factor. The ability to estimate blood supply in physiological units also lays a foundation for assessment of brain metabolic rate. However, a major obstacle before wider applications of this method is that the slice positioning of the scan, ideally placed perpendicular to the feeding arteries, requires considerable expertise and can present a burden to the operator. In the present work, we proposed that the majority of PC-MRI scans can be positioned using an automatic algorithm, leaving only a small fraction of arteries requiring manual positioning. We implemented and evaluated an algorithm for this purpose based on feature extraction of a survey angiogram, which is of minimal operator dependence. In a comparative test-retest study with 7 subjects, the blood flow measurement using this algorithm showed an inter-session coefficient of variation (CoV) of . The Bland-Altman method showed that the automatic method differs from the manual method by between and , for of the CBF measurements. This is comparable to the variance in CBF measurement using manually-positioned PC MRI alone. In a further application of this algorithm to 157 consecutive subjects from typical clinical cohorts, the algorithm provided successful positioning in 89.7% of the arteries. In 79.6% of the subjects, all four arteries could be planned using the algorithm. Chi-square tests of independence showed that the success rate was not dependent on the age or gender, but the patients showed a trend of lower success rate (p = 0.14) compared to healthy controls. In conclusion, this automatic positioning algorithm could improve the application of PC-MRI in CBF quantification. PMID:24787742

IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT.

PubMed

Arntzen, Magnus Ø; Koehler, Christian J; Barsnes, Harald; Berven, Frode S; Treumann, Achim; Thiede, Bernd

2011-02-04

Isobaric peptide labeling plays an important role in relative quantitative comparisons of proteomes. Isobaric labeling techniques utilize MS/MS spectra for relative quantification, which can be either based on the relative intensities of reporter ions in the low mass region (iTRAQ and TMT) or on the relative intensities of quantification signatures throughout the spectrum due to isobaric peptide termini labeling (IPTL). Due to the increased quantitative information found in MS/MS fragment spectra generated by the recently developed IPTL approach, new software was required to extract the quantitative information. IsobariQ was specifically developed for this purpose; however, support for the reporter ion techniques iTRAQ and TMT is also included. In addition, to address recently emphasized issues about heterogeneity of variance in proteomics data sets, IsobariQ employs the statistical software package R and variance stabilizing normalization (VSN) algorithms available therein. Finally, the functionality of IsobariQ is validated with data sets of experiments using 6-plex TMT and IPTL. Notably, protein substrates resulting from cleavage by proteases can be identified as shown for caspase targets in apoptosis.

Intellicount: High-Throughput Quantification of Fluorescent Synaptic Protein Puncta by Machine Learning

PubMed Central

Fantuzzo, J. A.; Mirabella, V. R.; Zahn, J. D.

2017-01-01

Abstract Synapse formation analyses can be performed by imaging and quantifying fluorescent signals of synaptic markers. Traditionally, these analyses are done using simple or multiple thresholding and segmentation approaches or by labor-intensive manual analysis by a human observer. Here, we describe Intellicount, a high-throughput, fully-automated synapse quantification program which applies a novel machine learning (ML)-based image processing algorithm to systematically improve region of interest (ROI) identification over simple thresholding techniques. Through processing large datasets from both human and mouse neurons, we demonstrate that this approach allows image processing to proceed independently of carefully set thresholds, thus reducing the need for human intervention. As a result, this method can efficiently and accurately process large image datasets with minimal interaction by the experimenter, making it less prone to bias and less liable to human error. Furthermore, Intellicount is integrated into an intuitive graphical user interface (GUI) that provides a set of valuable features, including automated and multifunctional figure generation, routine statistical analyses, and the ability to run full datasets through nested folders, greatly expediting the data analysis process. PMID:29218324

A 3D Scan Model and Thermal Image Data Fusion Algorithms for 3D Thermography in Medicine

PubMed Central

Klima, Ondrej

2017-01-01

Objectives At present, medical thermal imaging is still considered a mere qualitative tool enabling us to distinguish between but lacking the ability to quantify the physiological and nonphysiological states of the body. Such a capability would, however, facilitate solving the problem of medical quantification, whose presence currently manifests itself within the entire healthcare system. Methods A generally applicable method to enhance captured 3D spatial data carrying temperature-related information is presented; in this context, all equations required for other data fusions are derived. The method can be utilized for high-density point clouds or detailed meshes at a high resolution but is conveniently usable in large objects with sparse points. Results The benefits of the approach are experimentally demonstrated on 3D thermal scans of injured subjects. We obtained diagnostic information inaccessible via traditional methods. Conclusion Using a 3D model and thermal image data fusion allows the quantification of inflammation, facilitating more precise injury and illness diagnostics or monitoring. The technique offers a wide application potential in medicine and multiple technological domains, including electrical and mechanical engineering. PMID:29250306

The Speech multi features fusion perceptual hash algorithm based on tensor decomposition

NASA Astrophysics Data System (ADS)

Huang, Y. B.; Fan, M. H.; Zhang, Q. Y.

2018-03-01

With constant progress in modern speech communication technologies, the speech data is prone to be attacked by the noise or maliciously tampered. In order to make the speech perception hash algorithm has strong robustness and high efficiency, this paper put forward a speech perception hash algorithm based on the tensor decomposition and multi features is proposed. This algorithm analyses the speech perception feature acquires each speech component wavelet packet decomposition. LPCC, LSP and ISP feature of each speech component are extracted to constitute the speech feature tensor. Speech authentication is done by generating the hash values through feature matrix quantification which use mid-value. Experimental results showing that the proposed algorithm is robust for content to maintain operations compared with similar algorithms. It is able to resist the attack of the common background noise. Also, the algorithm is highly efficiency in terms of arithmetic, and is able to meet the real-time requirements of speech communication and complete the speech authentication quickly.

Quantitative Analysis of Mouse Retinal Layers Using Automated Segmentation of Spectral Domain Optical Coherence Tomography Images

PubMed Central

Dysli, Chantal; Enzmann, Volker; Sznitman, Raphael; Zinkernagel, Martin S.

2015-01-01

Purpose Quantification of retinal layers using automated segmentation of optical coherence tomography (OCT) images allows for longitudinal studies of retinal and neurological disorders in mice. The purpose of this study was to compare the performance of automated retinal layer segmentation algorithms with data from manual segmentation in mice using the Spectralis OCT. Methods Spectral domain OCT images from 55 mice from three different mouse strains were analyzed in total. The OCT scans from 22 C57Bl/6, 22 BALBc, and 11 C3A.Cg-Pde6b+Prph2Rd2/J mice were automatically segmented using three commercially available automated retinal segmentation algorithms and compared to manual segmentation. Results Fully automated segmentation performed well in mice and showed coefficients of variation (CV) of below 5% for the total retinal volume. However, all three automated segmentation algorithms yielded much thicker total retinal thickness values compared to manual segmentation data (P < 0.0001) due to segmentation errors in the basement membrane. Conclusions Whereas the automated retinal segmentation algorithms performed well for the inner layers, the retinal pigmentation epithelium (RPE) was delineated within the sclera, leading to consistently thicker measurements of the photoreceptor layer and the total retina. Translational Relevance The introduction of spectral domain OCT allows for accurate imaging of the mouse retina. Exact quantification of retinal layer thicknesses in mice is important to study layers of interest under various pathological conditions. PMID:26336634

Open-path FTIR data reduction algorithm with atmospheric absorption corrections: the NONLIN code

NASA Astrophysics Data System (ADS)

Phillips, William; Russwurm, George M.

1999-02-01

This paper describes the progress made to date in developing, testing, and refining a data reduction computer code, NONLIN, that alleviates many of the difficulties experienced in the analysis of open path FTIR data. Among the problems that currently effect FTIR open path data quality are: the inability to obtain a true I degree or background, spectral interferences of atmospheric gases such as water vapor and carbon dioxide, and matching the spectral resolution and shift of the reference spectra to a particular field instrument. This algorithm is based on a non-linear fitting scheme and is therefore not constrained by many of the assumptions required for the application of linear methods such as classical least squares (CLS). As a result, a more realistic mathematical model of the spectral absorption measurement process can be employed in the curve fitting process. Applications of the algorithm have proven successful in circumventing open path data reduction problems. However, recent studies, by one of the authors, of the temperature and pressure effects on atmospheric absorption indicate there exist temperature and water partial pressure effects that should be incorporated into the NONLIN algorithm for accurate quantification of gas concentrations. This paper investigates the sources of these phenomena. As a result of this study a partial pressure correction has been employed in NONLIN computer code. Two typical field spectra are examined to determine what effect the partial pressure correction has on gas quantification.

Reference genes for real-time PCR quantification of messenger RNAs and microRNAs in mouse model of obesity.

PubMed

Matoušková, Petra; Bártíková, Hana; Boušová, Iva; Hanušová, Veronika; Szotáková, Barbora; Skálová, Lenka

2014-01-01

Obesity and metabolic syndrome is increasing health problem worldwide. Among other ways, nutritional intervention using phytochemicals is important method for treatment and prevention of this disease. Recent studies have shown that certain phytochemicals could alter the expression of specific genes and microRNAs (miRNAs) that play a fundamental role in the pathogenesis of obesity. For study of the obesity and its treatment, monosodium glutamate (MSG)-injected mice with developed central obesity, insulin resistance and liver lipid accumulation are frequently used animal models. To understand the mechanism of phytochemicals action in obese animals, the study of selected genes expression together with miRNA quantification is extremely important. For this purpose, real-time quantitative PCR is a sensitive and reproducible method, but it depends on proper normalization entirely. The aim of present study was to identify the appropriate reference genes for mRNA and miRNA quantification in MSG mice treated with green tea catechins, potential anti-obesity phytochemicals. Two sets of reference genes were tested: first set contained seven commonly used genes for normalization of messenger RNA, the second set of candidate reference genes included ten small RNAs for normalization of miRNA. The expression stability of these reference genes were tested upon treatment of mice with catechins using geNorm, NormFinder and BestKeeper algorithms. Selected normalizers for mRNA quantification were tested and validated on expression of quinone oxidoreductase, biotransformation enzyme known to be modified by catechins. The effect of selected normalizers for miRNA quantification was tested on two obesity- and diabetes- related miRNAs, miR-221 and miR-29b, respectively. Finally, the combinations of B2M/18S/HPRT1 and miR-16/sno234 were validated as optimal reference genes for mRNA and miRNA quantification in liver and 18S/RPlP0/HPRT1 and sno234/miR-186 in small intestine of MSG mice. These reference genes will be used for mRNA and miRNA normalization in further study of green tea catechins action in obese mice.

Quantifying Pharmaceutical Film Coating with Optical Coherence Tomography and Terahertz Pulsed Imaging: An Evaluation

PubMed Central

Lin, Hungyen; Dong, Yue; Shen, Yaochun; Zeitler, J Axel

2015-01-01

Spectral domain optical coherence tomography (OCT) has recently attracted a lot of interest in the pharmaceutical industry as a fast and non-destructive modality for quantification of thin film coatings that cannot easily be resolved with other techniques. Because of the relative infancy of this technique, much of the research to date has focused on developing the in-line measurement technique for assessing film coating thickness. To better assess OCT for pharmaceutical coating quantification, this paper evaluates tablets with a range of film coating thickness measured using OCT and terahertz pulsed imaging (TPI) in an off-line setting. In order to facilitate automated coating quantification for film coating thickness in the range of 30–200 μm, an algorithm that uses wavelet denoising and a tailored peak finding method is proposed to analyse each of the acquired A-scan. Results obtained from running the algorithm reveal an increasing disparity between the TPI and OCT measured intra-tablet variability when film coating thickness exceeds 100 μm. The finding further confirms that OCT is a suitable modality for characterising pharmaceutical dosage forms with thin film coatings, whereas TPI is well suited for thick coatings. © 2015 The Authors. Journal of Pharmaceutical Sciences published by Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:3377–3385, 2015 PMID:26284354

Quantifying Pharmaceutical Film Coating with Optical Coherence Tomography and Terahertz Pulsed Imaging: An Evaluation.

PubMed

Lin, Hungyen; Dong, Yue; Shen, Yaochun; Axel Zeitler, J

2015-10-01

Spectral domain optical coherence tomography (OCT) has recently attracted a lot of interest in the pharmaceutical industry as a fast and non-destructive modality for quantification of thin film coatings that cannot easily be resolved with other techniques. Because of the relative infancy of this technique, much of the research to date has focused on developing the in-line measurement technique for assessing film coating thickness. To better assess OCT for pharmaceutical coating quantification, this paper evaluates tablets with a range of film coating thickness measured using OCT and terahertz pulsed imaging (TPI) in an off-line setting. In order to facilitate automated coating quantification for film coating thickness in the range of 30-200μm, an algorithm that uses wavelet denoising and a tailored peak finding method is proposed to analyse each of the acquired A-scan. Results obtained from running the algorithm reveal an increasing disparity between the TPI and OCT measured intra-tablet variability when film coating thickness exceeds 100μm. The finding further confirms that OCT is a suitable modality for characterising pharmaceutical dosage forms with thin film coatings, whereas TPI is well suited for thick coatings. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:3377-3385, 2015. Copyright © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

Method for accurate quantitation of background tissue optical properties in the presence of emission from a strong fluorescence marker

NASA Astrophysics Data System (ADS)

Bravo, Jaime; Davis, Scott C.; Roberts, David W.; Paulsen, Keith D.; Kanick, Stephen C.

2015-03-01

Quantification of targeted fluorescence markers during neurosurgery has the potential to improve and standardize surgical distinction between normal and cancerous tissues. However, quantitative analysis of marker fluorescence is complicated by tissue background absorption and scattering properties. Correction algorithms that transform raw fluorescence intensity into quantitative units, independent of absorption and scattering, require a paired measurement of localized white light reflectance to provide estimates of the optical properties. This study focuses on the unique problem of developing a spectral analysis algorithm to extract tissue absorption and scattering properties from white light spectra that contain contributions from both elastically scattered photons and fluorescence emission from a strong fluorophore (i.e. fluorescein). A fiber-optic reflectance device was used to perform measurements in a small set of optical phantoms, constructed with Intralipid (1% lipid), whole blood (1% volume fraction) and fluorescein (0.16-10 μg/mL). Results show that the novel spectral analysis algorithm yields accurate estimates of tissue parameters independent of fluorescein concentration, with relative errors of blood volume fraction, blood oxygenation fraction (BOF), and the reduced scattering coefficient (at 521 nm) of <7%, <1%, and <22%, respectively. These data represent a first step towards quantification of fluorescein in tissue in vivo.

Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images.

PubMed

Spagnolo, Daniel M; Al-Kofahi, Yousef; Zhu, Peihong; Lezon, Timothy R; Gough, Albert; Stern, Andrew M; Lee, Adrian V; Ginty, Fiona; Sarachan, Brion; Taylor, D Lansing; Chennubhotla, S Chakra

2017-11-01

We introduce THRIVE (Tumor Heterogeneity Research Interactive Visualization Environment), an open-source tool developed to assist cancer researchers in interactive hypothesis testing. The focus of this tool is to quantify spatial intratumoral heterogeneity (ITH), and the interactions between different cell phenotypes and noncellular constituents. Specifically, we foresee applications in phenotyping cells within tumor microenvironments, recognizing tumor boundaries, identifying degrees of immune infiltration and epithelial/stromal separation, and identification of heterotypic signaling networks underlying microdomains. The THRIVE platform provides an integrated workflow for analyzing whole-slide immunofluorescence images and tissue microarrays, including algorithms for segmentation, quantification, and heterogeneity analysis. THRIVE promotes flexible deployment, a maintainable code base using open-source libraries, and an extensible framework for customizing algorithms with ease. THRIVE was designed with highly multiplexed immunofluorescence images in mind, and, by providing a platform to efficiently analyze high-dimensional immunofluorescence signals, we hope to advance these data toward mainstream adoption in cancer research. Cancer Res; 77(21); e71-74. ©2017 AACR . ©2017 American Association for Cancer Research.

Image processing for identification and quantification of filamentous bacteria in in situ acquired images.

PubMed

Dias, Philipe A; Dunkel, Thiemo; Fajado, Diego A S; Gallegos, Erika de León; Denecke, Martin; Wiedemann, Philipp; Schneider, Fabio K; Suhr, Hajo

2016-06-11

In the activated sludge process, problems of filamentous bulking and foaming can occur due to overgrowth of certain filamentous bacteria. Nowadays, these microorganisms are typically monitored by means of light microscopy, commonly combined with staining techniques. As drawbacks, these methods are susceptible to human errors, subjectivity and limited by the use of discontinuous microscopy. The in situ microscope appears as a suitable tool for continuous monitoring of filamentous bacteria, providing real-time examination, automated analysis and eliminating sampling, preparation and transport of samples. In this context, a proper image processing algorithm is proposed for automated recognition and measurement of filamentous objects. This work introduces a method for real-time evaluation of images without any staining, phase-contrast or dilution techniques, differently from studies present in the literature. Moreover, we introduce an algorithm which estimates the total extended filament length based on geodesic distance calculation. For a period of twelve months, samples from an industrial activated sludge plant were weekly collected and imaged without any prior conditioning, replicating real environment conditions. Trends of filament growth rate-the most important parameter for decision making-are correctly identified. For reference images whose filaments were marked by specialists, the algorithm correctly recognized 72 % of the filaments pixels, with a false positive rate of at most 14 %. An average execution time of 0.7 s per image was achieved. Experiments have shown that the designed algorithm provided a suitable quantification of filaments when compared with human perception and standard methods. The algorithm's average execution time proved its suitability for being optimally mapped into a computational architecture to provide real-time monitoring.

Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice

NASA Astrophysics Data System (ADS)

Li, Weixuan; Lin, Guang; Li, Bing

2016-09-01

Many uncertainty quantification (UQ) approaches suffer from the curse of dimensionality, that is, their computational costs become intractable for problems involving a large number of uncertainty parameters. In these situations, the classic Monte Carlo often remains the preferred method of choice because its convergence rate O (n - 1 / 2), where n is the required number of model simulations, does not depend on the dimension of the problem. However, many high-dimensional UQ problems are intrinsically low-dimensional, because the variation of the quantity of interest (QoI) is often caused by only a few latent parameters varying within a low-dimensional subspace, known as the sufficient dimension reduction (SDR) subspace in the statistics literature. Motivated by this observation, we propose two inverse regression-based UQ algorithms (IRUQ) for high-dimensional problems. Both algorithms use inverse regression to convert the original high-dimensional problem to a low-dimensional one, which is then efficiently solved by building a response surface for the reduced model, for example via the polynomial chaos expansion. The first algorithm, which is for the situations where an exact SDR subspace exists, is proved to converge at rate O (n-1), hence much faster than MC. The second algorithm, which doesn't require an exact SDR, employs the reduced model as a control variate to reduce the error of the MC estimate. The accuracy gain could still be significant, depending on how well the reduced model approximates the original high-dimensional one. IRUQ also provides several additional practical advantages: it is non-intrusive; it does not require computing the high-dimensional gradient of the QoI; and it reports an error bar so the user knows how reliable the result is.

Fast, shape-directed, landmark-based deep gray matter segmentation for quantification of iron deposition

NASA Astrophysics Data System (ADS)

Ekin, Ahmet; Jasinschi, Radu; van der Grond, Jeroen; van Buchem, Mark A.; van Muiswinkel, Arianne

2006-03-01

This paper introduces image processing methods to automatically detect the 3D volume-of-interest (VOI) and 2D region-of-interest (ROI) for deep gray matter organs (thalamus, globus pallidus, putamen, and caudate nucleus) of patients with suspected iron deposition from MR dual echo images. Prior to the VOI and ROI detection, cerebrospinal fluid (CSF) region is segmented by a clustering algorithm. For the segmentation, we automatically determine the cluster centers with the mean shift algorithm that can quickly identify the modes of a distribution. After the identification of the modes, we employ the K-Harmonic means clustering algorithm to segment the volumetric MR data into CSF and non-CSF. Having the CSF mask and observing that the frontal lobe of the lateral ventricle has more consistent shape accross age and pathological abnormalities, we propose a shape-directed landmark detection algorithm to detect the VOI in a speedy manner. The proposed landmark detection algorithm utilizes a novel shape model of the front lobe of the lateral ventricle for the slices where thalamus, globus pallidus, putamen, and caudate nucleus are expected to appear. After this step, for each slice in the VOI, we use horizontal and vertical projections of the CSF map to detect the approximate locations of the relevant organs to define the ROI. We demonstrate the robustness of the proposed VOI and ROI localization algorithms to pathologies, including severe amounts of iron accumulation as well as white matter lesions, and anatomical variations. The proposed algorithms achieved very high detection accuracy, 100% in the VOI detection , over a large set of a challenging MR dataset.

Scalable Methods for Uncertainty Quantification, Data Assimilation and Target Accuracy Assessment for Multi-Physics Advanced Simulation of Light Water Reactors

NASA Astrophysics Data System (ADS)

Khuwaileh, Bassam

High fidelity simulation of nuclear reactors entails large scale applications characterized with high dimensionality and tremendous complexity where various physics models are integrated in the form of coupled models (e.g. neutronic with thermal-hydraulic feedback). Each of the coupled modules represents a high fidelity formulation of the first principles governing the physics of interest. Therefore, new developments in high fidelity multi-physics simulation and the corresponding sensitivity/uncertainty quantification analysis are paramount to the development and competitiveness of reactors achieved through enhanced understanding of the design and safety margins. Accordingly, this dissertation introduces efficient and scalable algorithms for performing efficient Uncertainty Quantification (UQ), Data Assimilation (DA) and Target Accuracy Assessment (TAA) for large scale, multi-physics reactor design and safety problems. This dissertation builds upon previous efforts for adaptive core simulation and reduced order modeling algorithms and extends these efforts towards coupled multi-physics models with feedback. The core idea is to recast the reactor physics analysis in terms of reduced order models. This can be achieved via identifying the important/influential degrees of freedom (DoF) via the subspace analysis, such that the required analysis can be recast by considering the important DoF only. In this dissertation, efficient algorithms for lower dimensional subspace construction have been developed for single physics and multi-physics applications with feedback. Then the reduced subspace is used to solve realistic, large scale forward (UQ) and inverse problems (DA and TAA). Once the elite set of DoF is determined, the uncertainty/sensitivity/target accuracy assessment and data assimilation analysis can be performed accurately and efficiently for large scale, high dimensional multi-physics nuclear engineering applications. Hence, in this work a Karhunen-Loeve (KL) based algorithm previously developed to quantify the uncertainty for single physics models is extended for large scale multi-physics coupled problems with feedback effect. Moreover, a non-linear surrogate based UQ approach is developed, used and compared to performance of the KL approach and brute force Monte Carlo (MC) approach. On the other hand, an efficient Data Assimilation (DA) algorithm is developed to assess information about model's parameters: nuclear data cross-sections and thermal-hydraulics parameters. Two improvements are introduced in order to perform DA on the high dimensional problems. First, a goal-oriented surrogate model can be used to replace the original models in the depletion sequence (MPACT -- COBRA-TF - ORIGEN). Second, approximating the complex and high dimensional solution space with a lower dimensional subspace makes the sampling process necessary for DA possible for high dimensional problems. Moreover, safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. Accordingly, an inverse problem can be defined and solved to assess the contributions from sources of uncertainty; and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this dissertation a subspace-based gradient-free and nonlinear algorithm for inverse uncertainty quantification namely the Target Accuracy Assessment (TAA) has been developed and tested. The ideas proposed in this dissertation were first validated using lattice physics applications simulated using SCALE6.1 package (Pressurized Water Reactor (PWR) and Boiling Water Reactor (BWR) lattice models). Ultimately, the algorithms proposed her were applied to perform UQ and DA for assembly level (CASL progression problem number 6) and core wide problems representing Watts Bar Nuclear 1 (WBN1) for cycle 1 of depletion (CASL Progression Problem Number 9) modeled via simulated using VERA-CS which consists of several multi-physics coupled models. The analysis and algorithms developed in this dissertation were encoded and implemented in a newly developed tool kit algorithms for Reduced Order Modeling based Uncertainty/Sensitivity Estimator (ROMUSE).

Quantification and Segmentation of Brain Tissues from MR Images: A Probabilistic Neural Network Approach

PubMed Central

Wang, Yue; Adalý, Tülay; Kung, Sun-Yuan; Szabo, Zsolt

2007-01-01

This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation of brain tissues from magnetic resonance images. It is shown that this problem can be solved by distribution learning and relaxation labeling, resulting in an efficient method that may be particularly useful in quantifying and segmenting abnormal brain tissues where the number of tissue types is unknown and the distributions of tissue types heavily overlap. The new technique uses suitable statistical models for both the pixel and context images and formulates the problem in terms of model-histogram fitting and global consistency labeling. The quantification is achieved by probabilistic self-organizing mixtures and the segmentation by a probabilistic constraint relaxation network. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms the conventional classification based approaches. PMID:18172510

Multi-tissue partial volume quantification in multi-contrast MRI using an optimised spectral unmixing approach.

PubMed

Collewet, Guylaine; Moussaoui, Saïd; Deligny, Cécile; Lucas, Tiphaine; Idier, Jérôme

2018-06-01

Multi-tissue partial volume estimation in MRI images is investigated with a viewpoint related to spectral unmixing as used in hyperspectral imaging. The main contribution of this paper is twofold. It firstly proposes a theoretical analysis of the statistical optimality conditions of the proportion estimation problem, which in the context of multi-contrast MRI data acquisition allows to appropriately set the imaging sequence parameters. Secondly, an efficient proportion quantification algorithm based on the minimisation of a penalised least-square criterion incorporating a regularity constraint on the spatial distribution of the proportions is proposed. Furthermore, the resulting developments are discussed using empirical simulations. The practical usefulness of the spectral unmixing approach for partial volume quantification in MRI is illustrated through an application to food analysis on the proving of a Danish pastry. Copyright © 2018 Elsevier Inc. All rights reserved.

An Imager Gaussian Process Machine Learning Methodology for Cloud Thermodynamic Phase classification

NASA Astrophysics Data System (ADS)

Marchant, B.; Platnick, S. E.; Meyer, K.

2017-12-01

The determination of cloud thermodynamic phase from MODIS and VIIRS instruments is an important first step in cloud optical retrievals, since ice and liquid clouds have different optical properties. To continue improving the cloud thermodynamic phase classification algorithm, a machine-learning approach, based on Gaussian processes, has been developed. The new proposed methodology provides cloud phase uncertainty quantification and improves the algorithm portability between MODIS and VIIRS. We will present new results, through comparisons between MODIS and CALIOP v4, and for VIIRS as well.

Normal Databases for the Relative Quantification of Myocardial Perfusion

PubMed Central

Rubeaux, Mathieu; Xu, Yuan; Germano, Guido; Berman, Daniel S.; Slomka, Piotr J.

2016-01-01

Purpose of review Myocardial perfusion imaging (MPI) with SPECT is performed clinically worldwide to detect and monitor coronary artery disease (CAD). MPI allows an objective quantification of myocardial perfusion at stress and rest. This established technique relies on normal databases to compare patient scans against reference normal limits. In this review, we aim to introduce the process of MPI quantification with normal databases and describe the associated perfusion quantitative measures that are used. Recent findings New equipment and new software reconstruction algorithms have been introduced which require the development of new normal limits. The appearance and regional count variations of normal MPI scan may differ between these new scanners and standard Anger cameras. Therefore, these new systems may require the determination of new normal limits to achieve optimal accuracy in relative myocardial perfusion quantification. Accurate diagnostic and prognostic results rivaling those obtained by expert readers can be obtained by this widely used technique. Summary Throughout this review, we emphasize the importance of the different normal databases and the need for specific databases relative to distinct imaging procedures. use of appropriate normal limits allows optimal quantification of MPI by taking into account subtle image differences due to the hardware and software used, and the population studied. PMID:28138354

Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems.

PubMed

Sapsis, Themistoklis P; Majda, Andrew J

2013-08-20

A framework for low-order predictive statistical modeling and uncertainty quantification in turbulent dynamical systems is developed here. These reduced-order, modified quasilinear Gaussian (ROMQG) algorithms apply to turbulent dynamical systems in which there is significant linear instability or linear nonnormal dynamics in the unperturbed system and energy-conserving nonlinear interactions that transfer energy from the unstable modes to the stable modes where dissipation occurs, resulting in a statistical steady state; such turbulent dynamical systems are ubiquitous in geophysical and engineering turbulence. The ROMQG method involves constructing a low-order, nonlinear, dynamical system for the mean and covariance statistics in the reduced subspace that has the unperturbed statistics as a stable fixed point and optimally incorporates the indirect effect of non-Gaussian third-order statistics for the unperturbed system in a systematic calibration stage. This calibration procedure is achieved through information involving only the mean and covariance statistics for the unperturbed equilibrium. The performance of the ROMQG algorithm is assessed on two stringent test cases: the 40-mode Lorenz 96 model mimicking midlatitude atmospheric turbulence and two-layer baroclinic models for high-latitude ocean turbulence with over 125,000 degrees of freedom. In the Lorenz 96 model, the ROMQG algorithm with just a single mode captures the transient response to random or deterministic forcing. For the baroclinic ocean turbulence models, the inexpensive ROMQG algorithm with 252 modes, less than 0.2% of the total, captures the nonlinear response of the energy, the heat flux, and even the one-dimensional energy and heat flux spectra.

Novel pure component contribution, mean centering of ratio spectra and factor based algorithms for simultaneous resolution and quantification of overlapped spectral signals: An application to recently co-formulated tablets of chlorzoxazone, aceclofenac and paracetamol

NASA Astrophysics Data System (ADS)

Toubar, Safaa S.; Hegazy, Maha A.; Elshahed, Mona S.; Helmy, Marwa I.

2016-06-01

In this work, resolution and quantitation of spectral signals are achieved by several univariate and multivariate techniques. The novel pure component contribution algorithm (PCCA) along with mean centering of ratio spectra (MCR) and the factor based partial least squares (PLS) algorithms were developed for simultaneous determination of chlorzoxazone (CXZ), aceclofenac (ACF) and paracetamol (PAR) in their pure form and recently co-formulated tablets. The PCCA method allows the determination of each drug at its λmax. While, the mean centered values at 230, 302 and 253 nm, were used for quantification of CXZ, ACF and PAR, respectively, by MCR method. Partial least-squares (PLS) algorithm was applied as a multivariate calibration method. The three methods were successfully applied for determination of CXZ, ACF and PAR in pure form and tablets. Good linear relationships were obtained in the ranges of 2-50, 2-40 and 2-30 μg mL- 1 for CXZ, ACF and PAR, in order, by both PCCA and MCR, while the PLS model was built for the three compounds each in the range of 2-10 μg mL- 1. The results obtained from the proposed methods were statistically compared with a reported one. PCCA and MCR methods were validated according to ICH guidelines, while PLS method was validated by both cross validation and an independent data set. They are found suitable for the determination of the studied drugs in bulk powder and tablets.

Criteria for the use of regression analysis for remote sensing of sediment and pollutants

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R. (Principal Investigator)

1982-01-01

Data analysis procedures for quantification of water quality parameters that are already identified and are known to exist within the water body are considered. The liner multiple-regression technique was examined as a procedure for defining and calibrating data analysis algorithms for such instruments as spectrometers and multispectral scanners.

Quantification of transplant-derived circulating cell-free DNA in absence of a donor genotype

PubMed Central

Kharbanda, Sandhya; Koh, Winston; Martin, Lance R.; Khush, Kiran K.; Valantine, Hannah; Pritchard, Jonathan K.; De Vlaminck, Iwijn

2017-01-01

Quantification of cell-free DNA (cfDNA) in circulating blood derived from a transplanted organ is a powerful approach to monitoring post-transplant injury. Genome transplant dynamics (GTD) quantifies donor-derived cfDNA (dd-cfDNA) by taking advantage of single-nucleotide polymorphisms (SNPs) distributed across the genome to discriminate donor and recipient DNA molecules. In its current implementation, GTD requires genotyping of both the transplant recipient and donor. However, in practice, donor genotype information is often unavailable. Here, we address this issue by developing an algorithm that estimates dd-cfDNA levels in the absence of a donor genotype. Our algorithm predicts heart and lung allograft rejection with an accuracy that is similar to conventional GTD. We furthermore refined the algorithm to handle closely related recipients and donors, a scenario that is common in bone marrow and kidney transplantation. We show that it is possible to estimate dd-cfDNA in bone marrow transplant patients that are unrelated or that are siblings of the donors, using a hidden Markov model (HMM) of identity-by-descent (IBD) states along the genome. Last, we demonstrate that comparing dd-cfDNA to the proportion of donor DNA in white blood cells can differentiate between relapse and the onset of graft-versus-host disease (GVHD). These methods alleviate some of the barriers to the implementation of GTD, which will further widen its clinical application. PMID:28771616

Validating and improving long-term aerosol data records from SeaWiFS

NASA Astrophysics Data System (ADS)

Bettenhausen, C.; Hsu, N. C.; Sayer, A. M.; Huang, J.; Gautam, R.

2011-12-01

Natural and anthropogenic aerosols influence the radiative balance of the Earth through direct and indirect interactions with incoming solar radiation. However, the quantification of these interactions and their ultimate effect on the Earth's climate still have large uncertainties. This is partly due to the limitations of current satellite data records which include short satellite lifetimes, retrieval algorithm uncertainty, or insufficient calibration accuracy. We have taken the first steps in overcoming this hurdle with the production and public release of an aerosol data record using the radiances from the Sea-viewing Wide Field-of-View Sensor (SeaWiFS). SeaWiFS was launched in late 1997 and provided exceptionally well-calibrated top-of-atmosphere radiance data until December 2010, more than 13 years. We have partnered this data with an expanded Deep Blue aerosol retrieval algorithm. In accordance with Deep Blue's original focus, the latest algorithm retrieves aerosol properties not only over bright desert surfaces, but also over oceans and vegetated surfaces. With this combination of a long time series and global algorithm, we can finally identify the changing patterns of regional aerosol loading and provide insight into long-term variability and trends of aerosols on regional and global scales. In this work, we provide an introduction to SeaWiFS, the current algorithms, and our aerosol data records. We have validated the data over land and ocean with ground measurements from the Aerosol Robotic Network (AERONET) and compared them with other satellites such as MODIS and MISR. Looking ahead to the next data release, we will also provide details on the implemented and planned algorithm improvements, and subsequent validation results.

Validating and Improving Long-Term Aerosol Data Records from SeaWiFS

NASA Technical Reports Server (NTRS)

Bettenhausen, Corey; Hsu, N. Christina; Sayer, Andrew; Huang, Jinhfeng; Gautam, Ritesh

2011-01-01

Natural and anthropogenic aerosols influence the radiative balance of the Earth through direct and indirect interactions with incoming solar radiation. However, the quantification of these interactions and their ultimate effect on the Earth's climate still have large uncertainties. This is partly due to the limitations of current satellite data records which include short satellite lifetimes, retrieval algorithm uncertainty, or insufficient calibration accuracy. We have taken the first steps in overcoming this hurdle with the production and public release of an aerosol data record using the radiances from the Sea-viewing Wide Field-of-View Sensor (Sea WiFS). Sea WiFS was launched in late 1997 and provided exceptionally well-calibrated top-of-atmosphere radiance data until December 2010, more than 13 years. We have partnered this data with an expanded Deep Blue aerosol retrieval algorithm. In accordance with Deep Blue's original focus, the latest algorithm retrieves aerosol properties not only over bright desert surfaces, but also over oceans and vegetated surfaces. With this combination of a long time series and global algorithm, we can finally identify the changing patterns of regional aerosol loading and provide insight into longterm variability and trends of aerosols on regional and global scales. In this work, we provide an introduction to Sea WiFS, the current algorithms, and our aerosol data records. We have validated the data over land and ocean with ground measurements from the Aerosol Robotic Network (AERONET) and compared them with other satellites such as MODIS and MISR. Looking ahead to the next data release, we will also provide details on the implemented and planned algorithm improvements, and subsequent validation results.

Quantification of Uncertainty in Extreme Scale Computations (QUEST)

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

Ghanem, Roger

QUEST was a SciDAC Institute comprising Sandia National Laboratories, Los Alamos National Laboratory, the University of Southern California, the Massachusetts Institute of Technology, the University of Texas at Austin, and Duke University. The mission of QUEST is to: (1) develop a broad class of uncertainty quantification (UQ) methods/tools, and (2) provide UQ expertise and software to other SciDAC projects, thereby enabling/guiding their UQ activities. The USC effort centered on the development of reduced models and efficient algorithms for implementing various components of the UQ pipeline. USC personnel were responsible for the development of adaptive bases, adaptive quadrature, and reduced modelsmore » to be used in estimation and inference.« less

Polarization-sensitive optical coherence tomography-based imaging, parameterization, and quantification of human cartilage degeneration

NASA Astrophysics Data System (ADS)

Brill, Nicolai; Wirtz, Mathias; Merhof, Dorit; Tingart, Markus; Jahr, Holger; Truhn, Daniel; Schmitt, Robert; Nebelung, Sven

2016-07-01

Polarization-sensitive optical coherence tomography (PS-OCT) is a light-based, high-resolution, real-time, noninvasive, and nondestructive imaging modality yielding quasimicroscopic cross-sectional images of cartilage. As yet, comprehensive parameterization and quantification of birefringence and tissue properties have not been performed on human cartilage. PS-OCT and algorithm-based image analysis were used to objectively grade human cartilage degeneration in terms of surface irregularity, tissue homogeneity, signal attenuation, as well as birefringence coefficient and band width, height, depth, and number. Degeneration-dependent changes were noted for the former three parameters exclusively, thereby questioning the diagnostic value of PS-OCT in the assessment of human cartilage degeneration.

User Guidelines and Best Practices for CASL VUQ Analysis Using Dakota

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

Adams, Brian M.; Coleman, Kayla; Hooper, Russell W.

2016-10-04

In general, Dakota is the Consortium for Advanced Simulation of Light Water Reactors (CASL) delivery vehicle for verification, validation, and uncertainty quantification (VUQ) algorithms. It permits ready application of the VUQ methods described above to simulation codes by CASL researchers, code developers, and application engineers. More specifically, the CASL VUQ Strategy [33] prescribes the use of Predictive Capability Maturity Model (PCMM) assessments [37]. PCMM is an expert elicitation tool designed to characterize and communicate completeness of the approaches used for computational model definition, verification, validation, and uncertainty quantification associated with an intended application. Exercising a computational model with the methodsmore » in Dakota will yield, in part, evidence for a predictive capability maturity model (PCMM) assessment. Table 1.1 summarizes some key predictive maturity related activities (see details in [33]), with examples of how Dakota fits in. This manual offers CASL partners a guide to conducting Dakota-based VUQ studies for CASL problems. It motivates various classes of Dakota methods and includes examples of their use on representative application problems. On reading, a CASL analyst should understand why and how to apply Dakota to a simulation problem.« less

The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.

PubMed

Tyanova, Stefka; Temu, Tikira; Cox, Juergen

2016-12-01

MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org.

Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods

NASA Astrophysics Data System (ADS)

Ma, Xiaoke; Wang, Bingbo; Yu, Liang

2018-01-01

Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues-the quantitative function for community as well as algorithms to discover communities. Despite significant research on either of them, few attempt has been made to establish the connection between the two issues. To attack this problem, a generalized quantification function is proposed for community in weighted networks, which provides a framework that unifies several well-known measures. Then, we prove that the trace optimization of the proposed measure is equivalent with the objective functions of algorithms such as nonnegative matrix factorization, kernel K-means as well as spectral clustering. It serves as the theoretical foundation for designing algorithms for community detection. On the second issue, a semi-supervised spectral clustering algorithm is developed by exploring the equivalence relation via combining the nonnegative matrix factorization and spectral clustering. Different from the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the spectral algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method improves the accuracy of the traditional spectral algorithms in community detection.

Computational nuclear quantum many-body problem: The UNEDF project

NASA Astrophysics Data System (ADS)

Bogner, S.; Bulgac, A.; Carlson, J.; Engel, J.; Fann, G.; Furnstahl, R. J.; Gandolfi, S.; Hagen, G.; Horoi, M.; Johnson, C.; Kortelainen, M.; Lusk, E.; Maris, P.; Nam, H.; Navratil, P.; Nazarewicz, W.; Ng, E.; Nobre, G. P. A.; Ormand, E.; Papenbrock, T.; Pei, J.; Pieper, S. C.; Quaglioni, S.; Roche, K. J.; Sarich, J.; Schunck, N.; Sosonkina, M.; Terasaki, J.; Thompson, I.; Vary, J. P.; Wild, S. M.

2013-10-01

The UNEDF project was a large-scale collaborative effort that applied high-performance computing to the nuclear quantum many-body problem. The primary focus of the project was on constructing, validating, and applying an optimized nuclear energy density functional, which entailed a wide range of pioneering developments in microscopic nuclear structure and reactions, algorithms, high-performance computing, and uncertainty quantification. UNEDF demonstrated that close associations among nuclear physicists, mathematicians, and computer scientists can lead to novel physics outcomes built on algorithmic innovations and computational developments. This review showcases a wide range of UNEDF science results to illustrate this interplay.

Phylogenetic Quantification of Intra-tumour Heterogeneity

PubMed Central

Schwarz, Roland F.; Trinh, Anne; Sipos, Botond; Brenton, James D.; Goldman, Nick; Markowetz, Florian

2014-01-01

Intra-tumour genetic heterogeneity is the result of ongoing evolutionary change within each cancer. The expansion of genetically distinct sub-clonal populations may explain the emergence of drug resistance, and if so, would have prognostic and predictive utility. However, methods for objectively quantifying tumour heterogeneity have been missing and are particularly difficult to establish in cancers where predominant copy number variation prevents accurate phylogenetic reconstruction owing to horizontal dependencies caused by long and cascading genomic rearrangements. To address these challenges, we present MEDICC, a method for phylogenetic reconstruction and heterogeneity quantification based on a Minimum Event Distance for Intra-tumour Copy-number Comparisons. Using a transducer-based pairwise comparison function, we determine optimal phasing of major and minor alleles, as well as evolutionary distances between samples, and are able to reconstruct ancestral genomes. Rigorous simulations and an extensive clinical study show the power of our method, which outperforms state-of-the-art competitors in reconstruction accuracy, and additionally allows unbiased numerical quantification of tumour heterogeneity. Accurate quantification and evolutionary inference are essential to understand the functional consequences of tumour heterogeneity. The MEDICC algorithms are independent of the experimental techniques used and are applicable to both next-generation sequencing and array CGH data. PMID:24743184

Kerr Reservoir LANDSAT experiment analysis for March 1981

NASA Technical Reports Server (NTRS)

Lecroy, S. R. (Principal Investigator)

1982-01-01

LANDSAT radiance data were used in an experiment conducted on the waters of Kerr Reservoir to determine if reliable algorithms could be developed that relate water quality parameters to remotely sensed data. A mix of different types of algorithms using the LANDSAT bands was generated to provide a thorough understanding of the relationships among the data involved. Except for secchi depth, the study demonstrated that for the ranges measured, the algorithms that satisfactorily represented the data encompass a mix of linear and nonlinear forms using only one LANDSAT band. Ratioing techniques did not improve the results since the initial design of the experiment minimized the errors against which this procedure is effective. Good correlations were found for total suspended solids, iron, turbidity, and secchi depth. Marginal correlations were discovered for nitrate and tannin + lignin. Quantification maps of Kerr Reservoir are presented for many of the water quality parameters using the developed algorithms.

Ultra-wideband electronics, design methods, algorithms, and systems for dielectric spectroscopy of isolated B16 tumor cells in liquid medium

NASA Astrophysics Data System (ADS)

Maxwell, Erick N.

Quantifying and characterizing isolated tumor cells (ITCs) is of interest in surgical pathology and cytology for its potential to provide data for cancer staging, classification, and treatment. Although the independent prognostic significance of circulating ITCs has not been proven, their presence is gaining clinical relevance as an indicator. However, researchers have not established an optimal method for detecting ITCs. Consequently, this Ph.D. dissertation is concerned with the development and evaluation of dielectric spectroscopy as a low-cost method for cell characterization and quantification. In support of this goal, ultra-wideband (UWB), microwave pulse generator circuits, coaxial transmission line fixtures, permittivity extraction algorithms, and dielectric spectroscopy measurement systems were developed for evaluating the capacity to quantify B16-F10 tumor cells in suspension. First, this research addressed challenges in developing tunable UWB circuits for pulse generation. In time-domain dielectric spectroscopy, a tunable UWB pulse generator facilitates exploration of microscopic dielectric mechanisms, which contribute to dispersion characteristics. Conventional approaches to tunable pulse generator design have resulted in complex circuit topologies and unsymmetrical waveform morphologies. In this research, a new design approach for low-complexity, tunable, sub-nanosecond and UWB pulse generator was developed. This approach was applied to the development of a novel generator that produces symmetrical waveforms (patent pending 60/597,746). Next, this research addressed problems with transmission-reflection (T/R) measurement of cell suspensions. In T/R measurement, coaxial transmission line fixtures have historically required an elaborate sample holder for containing liquids, resulting in high cost and complexity. Furthermore, the algorithms used to extract T/R dielectric properties have suffered from myriad problems including local minima and halfwavelength resonance. In this dissertation, a simple coaxial transmission line fixture for holding liquids by dispensing with the air-core assumption inherent in previous designs was developed (patent pending 60/916,042). In addition, a genetic algorithm was applied towards extracting dielectric properties from measurement data to circumvent problems of local minima and half wavelength resonance. Finally, in this research the capacity for using dielectric properties to quantify isolated B16-F10 tumor cells in McCoy's liquid medium was investigated. In so doing, the utility of the Maxwell-Wagner mixture formula for cell quantification was demonstrated by measuring distinct dielectric properties for differing volumes of cell suspensions using frequency- and time-domain dielectric spectroscopy.

Accounting for the Confound of Meninges in Segmenting Entorhinal and Perirhinal Cortices in T1-Weighted MRI.

PubMed

Xie, Long; Wisse, Laura E M; Das, Sandhitsu R; Wang, Hongzhi; Wolk, David A; Manjón, Jose V; Yushkevich, Paul A

2016-10-01

Quantification of medial temporal lobe (MTL) cortices, including entorhinal cortex (ERC) and perirhinal cortex (PRC), from in vivo MRI is desirable for studying the human memory system as well as in early diagnosis and monitoring of Alzheimer's disease. However, ERC and PRC are commonly over-segmented in T1-weighted (T1w) MRI because of the adjacent meninges that have similar intensity to gray matter in T1 contrast. This introduces errors in the quantification and could potentially confound imaging studies of ERC/PRC. In this paper, we propose to segment MTL cortices along with the adjacent meninges in T1w MRI using an established multi-atlas segmentation framework together with super-resolution technique. Experimental results comparing the proposed pipeline with existing pipelines support the notion that a large portion of meninges is segmented as gray matter by existing algorithms but not by our algorithm. Cross-validation experiments demonstrate promising segmentation accuracy. Further, agreement between the volume and thickness measures from the proposed pipeline and those from the manual segmentations increase dramatically as a result of accounting for the confound of meninges. Evaluated in the context of group discrimination between patients with amnestic mild cognitive impairment and normal controls, the proposed pipeline generates more biologically plausible results and improves the statistical power in discriminating groups in absolute terms comparing to other techniques using T1w MRI. Although the performance of the proposed pipeline is inferior to that using T2-weighted MRI, which is optimized to image MTL sub-structures, the proposed pipeline could still provide important utilities in analyzing many existing large datasets that only have T1w MRI available.

A new fringeline-tracking approach for color Doppler ultrasound imaging phase unwrapping

NASA Astrophysics Data System (ADS)

Saad, Ashraf A.; Shapiro, Linda G.

2008-03-01

Color Doppler ultrasound imaging is a powerful non-invasive diagnostic tool for many clinical applications that involve examining the anatomy and hemodynamics of human blood vessels. These clinical applications include cardio-vascular diseases, obstetrics, and abdominal diseases. Since its commercial introduction in the early eighties, color Doppler ultrasound imaging has been used mainly as a qualitative tool with very little attempts to quantify its images. Many imaging artifacts hinder the quantification of the color Doppler images, the most important of which is the aliasing artifact that distorts the blood flow velocities measured by the color Doppler technique. In this work we will address the color Doppler aliasing problem and present a recovery methodology for the true flow velocities from the aliased ones. The problem is formulated as a 2D phase-unwrapping problem, which is a well-defined problem with solid theoretical foundations for other imaging domains, including synthetic aperture radar and magnetic resonance imaging. This paper documents the need for a phase unwrapping algorithm for use in color Doppler ultrasound image analysis. It describes a new phase-unwrapping algorithm that relies on the recently developed cutline detection approaches. The algorithm is novel in its use of heuristic information provided by the ultrasound imaging modality to guide the phase unwrapping process. Experiments have been performed on both in-vitro flow-phantom data and in-vivo human blood flow data. Both data types were acquired under a controlled acquisition protocol developed to minimize the distortion of the color Doppler data and hence to simplify the phase-unwrapping task. In addition to the qualitative assessment of the results, a quantitative assessment approach was developed to measure the success of the results. The results of our new algorithm have been compared on ultrasound data to those from other well-known algorithms, and it outperforms all of them.

SU-F-J-73: Simple Approach for Quantification of Metal Artifact Reduction Capabalities of Dual-Energy CT

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

Lamichhane, N; Padgett, K; Li, X

Purpose: To present a simple method for quantification of dual-energy CT metal artifact reduction capabilities Methods: A phantom was constructed from solid water and a steel cylinder. Solid water is commonly used for radiotherapy QA, while steel cylinders are readily available in hardware stores. The phantom was scanned on Siemens Somatom 64-slice dual-energy CT system. Three CTs were acquired at energies of 80kV (low), 120kV (nominal), and 140kV (high). The low and high energy acquisitions were used to generate dual-energy (DE) monoenergetic image sets, which also utilized metal artifact reduction algorithm (Maris). Several monoenergetic DE image sets, ranging from 70keVmore » to 190keV were generated. The size of the metal artifact was measured by two different approaches. The first approach measured the distance from the center of the steel cylinder to a location with nominal (undisturbed by metal) HU value for the 120kV, DE 70keV, and DE 190keV image sets. In the second approach, the distance from the center of the cylinder to the edge of the air pocket for the above mentioned three image sets was measured. Results: The DE 190keV synthetic image set demonstrated the largest reduction of the metal artifacts. The size of the artifact was more than three times the actual size of the milled hole in the solid water in the DE 190keV, as compared to more than 7.5 times larger as estimated from the 120kV uncorrected image Conclusion: A simple phantom for quantification of dual-energy CT metal artifact reduction capabilities was presented. This inexpensive phantom can be easily built from components available in every radiation oncology department. It allows quick assessment and quantification of the properties of different metal artifact reduction algorithms, available on modern dual-energy CT scanners.« less

Interference-free spectrofluorometric quantification of aristolochic acid I and aristololactam I in five Chinese herbal medicines using chemical derivatization enhancement and second-order calibration methods

NASA Astrophysics Data System (ADS)

Hu, Yong; Wu, Hai-Long; Yin, Xiao-Li; Gu, Hui-Wen; Xiao, Rong; Wang, Li; Fang, Huan; Yu, Ru-Qin

2017-03-01

A rapid interference-free spectrofluorometric method combined with the excitation-emission matrix fluorescence and the second-order calibration methods based on the alternating penalty trilinear decomposition (APTLD) and the self-weighted alternating trilinear decomposition (SWATLD) algorithms, was proposed for the simultaneous determination of nephrotoxic aristolochic acid I (AA-I) and aristololactam I (AL-I) in five Chinese herbal medicines. The method was based on a chemical derivatization that converts the non-fluorescent AA-I to high-fluorescent AL-I, achieving a high sensitive and simultaneous quantification of the analytes. The variables of the derivatization reaction that conducted by using zinc powder in acetose methanol aqueous solution, were studied and optimized for best quantification results of AA-I and AL-I. The satisfactory results of AA-I and AL-I for the spiked recovery assay were achieved with average recoveries in the range of 100.4-103.8% and RMSEPs < 0.78 ng mL- 1, which validate the accuracy and reliability of the proposed method. The contents of AA-I and AL-I in five herbal medicines obtained from the proposed method were also in good accordance with those of the validated LC-MS/MS method. In light of high sensitive fluorescence detection, the limits of detection (LODs) of AA-I and AL-I for the proposed method compare favorably with that of the LC-MS/MS method, with the LODs < 0.35 and 0.29 ng mL- 1, respectively. The proposed strategy based on the APTLD and SWATLD algorithms by virtue of the "second-order advantage", can be considered as an attractive and green alternative for the quantification of AA-I and AL-I in complex herbal medicine matrices without any prior separations and clear-up processes.

Temporal Processing of Dynamic Positron Emission Tomography via Principal Component Analysis in the Sinogram Domain

NASA Astrophysics Data System (ADS)

Chen, Zhe; Parker, B. J.; Feng, D. D.; Fulton, R.

2004-10-01

In this paper, we compare various temporal analysis schemes applied to dynamic PET for improved quantification, image quality and temporal compression purposes. We compare an optimal sampling schedule (OSS) design, principal component analysis (PCA) applied in the image domain, and principal component analysis applied in the sinogram domain; for region-of-interest quantification, sinogram-domain PCA is combined with the Huesman algorithm to quantify from the sinograms directly without requiring reconstruction of all PCA channels. Using a simulated phantom FDG brain study and three clinical studies, we evaluate the fidelity of the compressed data for estimation of local cerebral metabolic rate of glucose by a four-compartment model. Our results show that using a noise-normalized PCA in the sinogram domain gives similar compression ratio and quantitative accuracy to OSS, but with substantially better precision. These results indicate that sinogram-domain PCA for dynamic PET can be a useful preprocessing stage for PET compression and quantification applications.

Diagnosis of glaucoma and detection of glaucoma progression using spectral domain optical coherence tomography.

PubMed

Grewal, Dilraj S; Tanna, Angelo P

2013-03-01

With the rapid adoption of spectral domain optical coherence tomography (SDOCT) in clinical practice and the recent advances in software technology, there is a need for a review of the literature on glaucoma detection and progression analysis algorithms designed for the commercially available instruments. Peripapillary retinal nerve fiber layer (RNFL) thickness and macular thickness, including segmental macular thickness calculation algorithms, have been demonstrated to be repeatable and reproducible, and have a high degree of diagnostic sensitivity and specificity in discriminating between healthy and glaucomatous eyes across the glaucoma continuum. Newer software capabilities such as glaucoma progression detection algorithms provide an objective analysis of longitudinally obtained structural data that enhances our ability to detect glaucomatous progression. RNFL measurements obtained with SDOCT appear more sensitive than time domain OCT (TDOCT) for glaucoma progression detection; however, agreement with the assessments of visual field progression is poor. Over the last few years, several studies have been performed to assess the diagnostic performance of SDOCT structural imaging and its validity in assessing glaucoma progression. Most evidence suggests that SDOCT performs similarly to TDOCT for glaucoma diagnosis; however, SDOCT may be superior for the detection of early stage disease. With respect to progression detection, SDOCT represents an important technological advance because of its improved resolution and repeatability. Advancements in RNFL thickness quantification, segmental macular thickness calculation and progression detection algorithms, when used correctly, may help to improve our ability to diagnose and manage glaucoma.

Bayesian calibration of coarse-grained forces: Efficiently addressing transferability

NASA Astrophysics Data System (ADS)

Patrone, Paul N.; Rosch, Thomas W.; Phelan, Frederick R.

2016-04-01

Generating and calibrating forces that are transferable across a range of state-points remains a challenging task in coarse-grained (CG) molecular dynamics. In this work, we present a coarse-graining workflow, inspired by ideas from uncertainty quantification and numerical analysis, to address this problem. The key idea behind our approach is to introduce a Bayesian correction algorithm that uses functional derivatives of CG simulations to rapidly and inexpensively recalibrate initial estimates f0 of forces anchored by standard methods such as force-matching. Taking density-temperature relationships as a running example, we demonstrate that this algorithm, in concert with various interpolation schemes, can be used to efficiently compute physically reasonable force curves on a fine grid of state-points. Importantly, we show that our workflow is robust to several choices available to the modeler, including the interpolation schemes and tools used to construct f0. In a related vein, we also demonstrate that our approach can speed up coarse-graining by reducing the number of atomistic simulations needed as inputs to standard methods for generating CG forces.

A Generic Authentication LoA Derivation Model

NASA Astrophysics Data System (ADS)

Yao, Li; Zhang, Ning

One way of achieving a more fine-grained access control is to link an authentication level of assurance (LoA) derived from a requester’s authentication instance to the authorisation decision made to the requester. To realise this vision, there is a need for designing a LoA derivation model that supports the use and quantification of multiple LoA-effecting attributes, and analyse their composite effect on a given authentication instance. This paper reports the design of such a model, namely a generic LoA derivation model (GEA- LoADM). GEA-LoADM takes into account of multiple authentication attributes along with their relationships, abstracts the composite effect by the multiple attributes into a generic value, authentication LoA, and provides algorithms for the run-time derivation of LoA. The algorithms are tailored to reflect the relationships among the attributes involved in an authentication instance. The model has a number of valuable properties, including flexibility and extensibility; it can be applied to different application contexts and support easy addition of new attributes and removal of obsolete ones.

TU-AB-BRA-12: Impact of Image Registration Algorithms On the Prediction of Pathological Response with Radiomic Textures

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

Yip, S; Coroller, T; Niu, N

2015-06-15

Purpose: Tumor regions-of-interest (ROI) can be propagated from the pre-onto the post-treatment PET/CT images using image registration of their CT counterparts, providing an automatic way to compute texture features on longitudinal scans. This exploratory study assessed the impact of image registration algorithms on textures to predict pathological response. Methods: Forty-six esophageal cancer patients (1 tumor/patient) underwent PET/CT scans before and after chemoradiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumor ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. One co-occurrence, two run-length and size zone matrix texturesmore » were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs and texture quantification resulting from different algorithms were compared using overlap volume (OV) and coefficient of variation (CoV), respectively. Results: Tumor volumes were better captured by ROIs propagated by deformable rather than the rigid registration. The OV between rigidly and deformably propagated ROIs were 69%. The deformably propagated ROIs were found to be similar (OV∼80%) except for fast-demons (OV∼60%). Rigidly propagated ROIs with run-length matrix textures failed to significantly differentiate between responders and non-responders (AUC=0.65, p=0.07), while the differentiation was significant with other textures (AUC=0.69–0.72, p<0.03). Among the deformable algorithms, fast-demons was the least predictive (AUC=0.68–0.71, p<0.04). ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC=0.71–0.78, p<0.01) despite substantial variation in texture quantification (CoV>70%). Conclusion: Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, rigid and fast-demons deformable algorithms are not recommended due to their inferior performance compared to other algorithms. The project was supported in part by a Kaye Scholar Award.« less

YM500v2: a small RNA sequencing (smRNA-seq) database for human cancer miRNome research

PubMed Central

Cheng, Wei-Chung; Chung, I-Fang; Tsai, Cheng-Fong; Huang, Tse-Shun; Chen, Chen-Yang; Wang, Shao-Chuan; Chang, Ting-Yu; Sun, Hsing-Jen; Chao, Jeffrey Yung-Chuan; Cheng, Cheng-Chung; Wu, Cheng-Wen; Wang, Hsei-Wei

2015-01-01

We previously presented YM500, which is an integrated database for miRNA quantification, isomiR identification, arm switching discovery and novel miRNA prediction from 468 human smRNA-seq datasets. Here in this updated YM500v2 database (http://ngs.ym.edu.tw/ym500/), we focus on the cancer miRNome to make the database more disease-orientated. New miRNA-related algorithms developed after YM500 were included in YM500v2, and, more significantly, more than 8000 cancer-related smRNA-seq datasets (including those of primary tumors, paired normal tissues, PBMC, recurrent tumors, and metastatic tumors) were incorporated into YM500v2. Novel miRNAs (miRNAs not included in the miRBase R21) were not only predicted by three independent algorithms but also cleaned by a new in silico filtration strategy and validated by wetlab data such as Cross-Linked ImmunoPrecipitation sequencing (CLIP-seq) to reduce the false-positive rate. A new function ‘Meta-analysis’ is additionally provided for allowing users to identify real-time differentially expressed miRNAs and arm-switching events according to customer-defined sample groups and dozens of clinical criteria tidying up by proficient clinicians. Cancer miRNAs identified hold the potential for both basic research and biotech applications. PMID:25398902

Assessment of cardiac fibrosis: a morphometric method comparison for collagen quantification.

PubMed

Schipke, Julia; Brandenberger, Christina; Rajces, Alexandra; Manninger, Martin; Alogna, Alessio; Post, Heiner; Mühlfeld, Christian

2017-04-01

Fibrotic remodeling of the heart is a frequent condition linked to various diseases and cardiac dysfunction. Collagen quantification is an important objective in cardiac fibrosis research; however, a variety of different histological methods are currently used that may differ in accuracy. Here, frequently applied collagen quantification techniques were compared. A porcine model of early stage heart failure with preserved ejection fraction was used as an example. Semiautomated threshold analyses were imprecise, mainly due to inclusion of noncollagen structures or failure to detect certain collagen deposits. In contrast, collagen assessment by automated image analysis and light microscopy (LM)-stereology was more sensitive. Depending on the quantification method, the amount of estimated collagen varied and influenced intergroup comparisons. PicroSirius Red, Masson's trichrome, and Azan staining protocols yielded similar results, whereas the measured collagen area increased with increasing section thickness. Whereas none of the LM-based methods showed significant differences between the groups, electron microscopy (EM)-stereology revealed a significant collagen increase between cardiomyocytes in the experimental group, but not at other localizations. In conclusion, in contrast to the staining protocol, section thickness and the quantification method being used directly influence the estimated collagen content and thus, possibly, intergroup comparisons. EM in combination with stereology is a precise and sensitive method for collagen quantification if certain prerequisites are considered. For subtle fibrotic alterations, consideration of collagen localization may be necessary. Among LM methods, LM-stereology and automated image analysis are appropriate to quantify fibrotic changes, the latter depending on careful control of algorithm and comparable section staining. NEW & NOTEWORTHY Direct comparison of frequently applied histological fibrosis assessment techniques revealed a distinct relation of measured collagen and utilized quantification method as well as section thickness. Besides electron microscopy-stereology, which was precise and sensitive, light microscopy-stereology and automated image analysis proved to be appropriate for collagen quantification. Moreover, consideration of collagen localization might be important in revealing minor fibrotic changes. Copyright © 2017 the American Physiological Society.

Uncertainty Quantification For Physical and Numerical Diffusion Models In Inertial Confinement Fusion Simulations

NASA Astrophysics Data System (ADS)

Rana, Verinder S.

This thesis concerns simulations of Inertial Confinement Fusion. Inertial confinement is carried out in a large scale facility at National Ignition Facility. The experiments have failed to reproduce design calculations, and so uncertainty quantification of calculations is an important asset. Uncertainties can be classified as aleatoric or epistemic. This thesis is concerned with aleatoric uncertainty quantification. Among the many uncertain aspects that affect the simulations, we have narrowed our study of possible uncertainties. The first source of uncertainty we present is the amount of pre-heating of the fuel done by hot electrons. The second source of uncertainty we consider is the effect of the algorithmic and physical transport diffusion and their effect on the hot spot thermodynamics. Physical transport mechanisms play an important role for the entire duration of the ICF capsule, so modeling them correctly becomes extremely vital. In addition, codes that simulate material mixing introduce numerical (algorithmically) generated transport across the material interfaces. This adds another layer of uncertainty in the solution through the artificially added diffusion. The third source of uncertainty we consider is physical model uncertainty. The fourth source of uncertainty we focus on a single localized surface perturbation (a divot) which creates a perturbation to the solution that can potentially enter the hot spot to diminish the thermonuclear environment. Jets of ablator material are hypothesized to enter the hot spot and cool the core, contributing to the observed lower reactions than predicted levels. A plasma transport package, Transport for Inertial Confinement Fusion (TICF) has been implemented into the Radiation Hydrodynamics code FLASH, from the University of Chicago. TICF has thermal, viscous and mass diffusion models that span the entire ICF implosion regime. We introduced a Quantum Molecular Dynamics calibrated thermal conduction model due to Hu for thermal transport. The numerical approximation uncertainties are introduced by the choice of a hydrodynamic solver for a particular flow. Solvers tend to be diffusive at material interfaces and the Front Tracking (FT) algorithm, which is an already available software code in the form of an API, helps to ameliorate such effects. The FT algorithm has also been implemented in FLASH and we use this to study the effect that divots can have on the hot spot properties.

An efficient assisted history matching and uncertainty quantification workflow using Gaussian processes proxy models and variogram based sensitivity analysis: GP-VARS

NASA Astrophysics Data System (ADS)

Rana, Sachin; Ertekin, Turgay; King, Gregory R.

2018-05-01

Reservoir history matching is frequently viewed as an optimization problem which involves minimizing misfit between simulated and observed data. Many gradient and evolutionary strategy based optimization algorithms have been proposed to solve this problem which typically require a large number of numerical simulations to find feasible solutions. Therefore, a new methodology referred to as GP-VARS is proposed in this study which uses forward and inverse Gaussian processes (GP) based proxy models combined with a novel application of variogram analysis of response surface (VARS) based sensitivity analysis to efficiently solve high dimensional history matching problems. Empirical Bayes approach is proposed to optimally train GP proxy models for any given data. The history matching solutions are found via Bayesian optimization (BO) on forward GP models and via predictions of inverse GP model in an iterative manner. An uncertainty quantification method using MCMC sampling in conjunction with GP model is also presented to obtain a probabilistic estimate of reservoir properties and estimated ultimate recovery (EUR). An application of the proposed GP-VARS methodology on PUNQ-S3 reservoir is presented in which it is shown that GP-VARS provides history match solutions in approximately four times less numerical simulations as compared to the differential evolution (DE) algorithm. Furthermore, a comparison of uncertainty quantification results obtained by GP-VARS, EnKF and other previously published methods shows that the P50 estimate of oil EUR obtained by GP-VARS is in close agreement to the true values for the PUNQ-S3 reservoir.

User Guidelines and Best Practices for CASL VUQ Analysis Using Dakota

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

Adams, Brian M.; Coleman, Kayla; Gilkey, Lindsay N.

Sandia’s Dakota software (available at http://dakota.sandia.gov) supports science and engineering transformation through advanced exploration of simulations. Specifically it manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to enhance understanding of risk, improve products, and assess simulation credibility. In its simplest mode, Dakota can automate typical parameter variation studies through a generic interface to a physics-based computational model. This can lend efficiency and rigor to manual parameter perturbation studies already being conducted by analysts. However, Dakota also delivers advanced parametric analysis techniques enabling design exploration, optimization, model calibration, riskmore » analysis, and quantification of margins and uncertainty with such models. It directly supports verification and validation activities. Dakota algorithms enrich complex science and engineering models, enabling an analyst to answer crucial questions of - Sensitivity: Which are the most important input factors or parameters entering the simulation, and how do they influence key outputs?; Uncertainty: What is the uncertainty or variability in simulation output, given uncertainties in input parameters? How safe, reliable, robust, or variable is my system? (Quantification of margins and uncertainty, QMU); Optimization: What parameter values yield the best performing design or operating condition, given constraints? Calibration: What models and/or parameters best match experimental data? In general, Dakota is the Consortium for Advanced Simulation of Light Water Reactors (CASL) delivery vehicle for verification, validation, and uncertainty quantification (VUQ) algorithms. It permits ready application of the VUQ methods described above to simulation codes by CASL researchers, code developers, and application engineers.« less

A General Uncertainty Quantification Methodology for Cloud Microphysical Property Retrievals

NASA Astrophysics Data System (ADS)

Tang, Q.; Xie, S.; Chen, X.; Zhao, C.

2014-12-01

The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program provides long-term (~20 years) ground-based cloud remote sensing observations. However, there are large uncertainties in the retrieval products of cloud microphysical properties based on the active and/or passive remote-sensing measurements. To address this uncertainty issue, a DOE Atmospheric System Research scientific focus study, Quantification of Uncertainties in Cloud Retrievals (QUICR), has been formed. In addition to an overview of recent progress of QUICR, we will demonstrate the capacity of an observation-based general uncertainty quantification (UQ) methodology via the ARM Climate Research Facility baseline cloud microphysical properties (MICROBASE) product. This UQ method utilizes the Karhunen-Loéve expansion (KLE) and Central Limit Theorems (CLT) to quantify the retrieval uncertainties from observations and algorithm parameters. The input perturbations are imposed on major modes to take into account the cross correlations between input data, which greatly reduces the dimension of random variables (up to a factor of 50) and quantifies vertically resolved full probability distribution functions of retrieved quantities. Moreover, this KLE/CLT approach has the capability of attributing the uncertainties in the retrieval output to individual uncertainty source and thus sheds light on improving the retrieval algorithm and observations. We will present the results of a case study for the ice water content at the Southern Great Plains during an intensive observing period on March 9, 2000. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

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

Naseri, M; Rajabi, H; Wang, J

Purpose: Respiration causes lesion smearing, image blurring and quality degradation, affecting lesion contrast and the ability to define correct lesion size. The spatial resolution of current multi pinhole SPECT (MPHS) scanners is sub-millimeter. Therefore, the effect of motion is more noticeable in comparison to conventional SPECT scanner. Gated imaging aims to reduce motion artifacts. A major issue in gating is the lack of statistics and individual reconstructed frames are noisy. The increased noise in each frame, deteriorates the quantitative accuracy of the MPHS Images. The objective of this work, is to enhance the image quality in 4D-MPHS imaging, by 4Dmore » image reconstruction. Methods: The new algorithm requires deformation vector fields (DVFs) that are calculated by non-rigid Demons registration. The algorithm is based on the motion-incorporated version of ordered subset expectation maximization (OSEM) algorithm. This iterative algorithm is capable to make full use of all projections to reconstruct each individual frame. To evaluate the performance of the proposed algorithm a simulation study was conducted. A fast ray tracing method was used to generate MPHS projections of a 4D digital mouse phantom with a small tumor in liver in eight different respiratory phases. To evaluate the 4D-OSEM algorithm potential, tumor to liver activity ratio was compared with other image reconstruction methods including 3D-MPHS and post reconstruction registered with Demons-derived DVFs. Results: Image quality of 4D-MPHS is greatly improved by the 4D-OSEM algorithm. When all projections are used to reconstruct a 3D-MPHS, motion blurring artifacts are present, leading to overestimation of the tumor size and 24% tumor contrast underestimation. This error reduced to 16% and 10% for post reconstruction registration methods and 4D-OSEM respectively. Conclusion: 4D-OSEM method can be used for motion correction in 4D-MPHS. The statistics and quantification are improved since all projection data are combined together to update the image.« less

Albany v. 3.0

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

Salinger, Andrew; Phipps, Eric; Ostien, Jakob

2016-01-13

The Albany code is a general-purpose finite element code for solving partial differential equations (PDEs). Albany is a research code that demonstrates how a PDE code can be built by interfacing many of the open-source software libraries that are released under Sandia's Trilinos project. Part of the mission of Albany is to be a testbed for new Trilinos libraries, to refine their methods, usability, and interfaces. Albany includes hooks to optimization and uncertainty quantification algorithms, including those in Trilinos as well as those in the Dakota toolkit. Because of this, Albany is a desirable starting point for new code developmentmore » efforts that wish to make heavy use of Trilinos. Albany is both a framework and the host for specific finite element applications. These applications have project names, and can be controlled by configuration option when the code is compiled, but are all developed and released as part of the single Albany code base, These include LCM, QCAD, FELIX, Aeras, and ATO applications.« less

Scaling-up of CO2 fluxes to assess carbon sequestration in rangelands of Central Asia

Treesearch

Bruce K. Wylie; Tagir G. Gilmanov; Douglas A. Johnson; Nicanor Z. Saliendra; Larry L. Tieszen; Ruth Anne F. Doyle; Emilio A. Laca

2006-01-01

Flux towers provide temporal quantification of local carbon dynamics at specific sites. The number and distribution of flux towers, however, are generally inadequate to quantify carbon fluxes across a landscape or ecoregion. Thus, scaling up of flux tower measurements through use of algorithms developed from remote sensing and GIS data is needed for spatial...

PeakCaller: an automated graphical interface for the quantification of intracellular calcium obtained by high-content screening.

PubMed

Artimovich, Elena; Jackson, Russell K; Kilander, Michaela B C; Lin, Yu-Chih; Nestor, Michael W

2017-10-16

Intracellular calcium is an important ion involved in the regulation and modulation of many neuronal functions. From regulating cell cycle and proliferation to initiating signaling cascades and regulating presynaptic neurotransmitter release, the concentration and timing of calcium activity governs the function and fate of neurons. Changes in calcium transients can be used in high-throughput screening applications as a basic measure of neuronal maturity, especially in developing or immature neuronal cultures derived from stem cells. Using human induced pluripotent stem cell derived neurons and dissociated mouse cortical neurons combined with the calcium indicator Fluo-4, we demonstrate that PeakCaller reduces type I and type II error in automated peak calling when compared to the oft-used PeakFinder algorithm under both basal and pharmacologically induced conditions. Here we describe PeakCaller, a novel MATLAB script and graphical user interface for the quantification of intracellular calcium transients in neuronal cultures. PeakCaller allows the user to set peak parameters and smoothing algorithms to best fit their data set. This new analysis script will allow for automation of calcium measurements and is a powerful software tool for researchers interested in high-throughput measurements of intracellular calcium.

Reconstruction of explicit structural properties at the nanoscale via spectroscopic microscopy

NASA Astrophysics Data System (ADS)

Cherkezyan, Lusik; Zhang, Di; Subramanian, Hariharan; Taflove, Allen; Backman, Vadim

2016-02-01

The spectrum registered by a reflected-light bright-field spectroscopic microscope (SM) can quantify the microscopically indiscernible, deeply subdiffractional length scales within samples such as biological cells and tissues. Nevertheless, quantification of biological specimens via any optical measures most often reveals ambiguous information about the specific structural properties within the studied samples. Thus, optical quantification remains nonintuitive to users from the diverse fields of technique application. In this work, we demonstrate that the SM signal can be analyzed to reconstruct explicit physical measures of internal structure within label-free, weakly scattering samples: characteristic length scale and the amplitude of spatial refractive-index (RI) fluctuations. We present and validate the reconstruction algorithm via finite-difference time-domain solutions of Maxwell's equations on an example of exponential spatial correlation of RI. We apply the validated algorithm to experimentally measure structural properties within isolated cells from two genetic variants of HT29 colon cancer cell line as well as within a prostate tissue biopsy section. The presented methodology can lead to the development of novel biophotonics techniques that create two-dimensional maps of explicit structural properties within biomaterials: the characteristic size of macromolecular complexes and the variance of local mass density.

Quantification of intensity variations in functional MR images using rotated principal components

NASA Astrophysics Data System (ADS)

Backfrieder, W.; Baumgartner, R.; Sámal, M.; Moser, E.; Bergmann, H.

1996-08-01

In functional MRI (fMRI), the changes in cerebral haemodynamics related to stimulated neural brain activity are measured using standard clinical MR equipment. Small intensity variations in fMRI data have to be detected and distinguished from non-neural effects by careful image analysis. Based on multivariate statistics we describe an algorithm involving oblique rotation of the most significant principal components for an estimation of the temporal and spatial distribution of the stimulated neural activity over the whole image matrix. This algorithm takes advantage of strong local signal variations. A mathematical phantom was designed to generate simulated data for the evaluation of the method. In simulation experiments, the potential of the method to quantify small intensity changes, especially when processing data sets containing multiple sources of signal variations, was demonstrated. In vivo fMRI data collected in both visual and motor stimulation experiments were analysed, showing a proper location of the activated cortical regions within well known neural centres and an accurate extraction of the activation time profile. The suggested method yields accurate absolute quantification of in vivo brain activity without the need of extensive prior knowledge and user interaction.

MR-Consistent Simultaneous Reconstruction of Attenuation and Activity for Non-TOF PET/MR

NASA Astrophysics Data System (ADS)

Heußer, Thorsten; Rank, Christopher M.; Freitag, Martin T.; Dimitrakopoulou-Strauss, Antonia; Schlemmer, Heinz-Peter; Beyer, Thomas; Kachelrieß, Marc

2016-10-01

Attenuation correction (AC) is required for accurate quantification of the reconstructed activity distribution in positron emission tomography (PET). For simultaneous PET/magnetic resonance (MR), however, AC is challenging, since the MR images do not provide direct information on the attenuating properties of the underlying tissue. Standard MR-based AC does not account for the presence of bone and thus leads to an underestimation of the activity distribution. To improve quantification for non-time-of-flight PET/MR, we propose an algorithm which simultaneously reconstructs activity and attenuation distribution from the PET emission data using available MR images as anatomical prior information. The MR information is used to derive voxel-dependent expectations on the attenuation coefficients. The expectations are modeled using Gaussian-like probability functions. An iterative reconstruction scheme incorporating the prior information on the attenuation coefficients is used to update attenuation and activity distribution in an alternating manner. We tested and evaluated the proposed algorithm for simulated 3D PET data of the head and the pelvis region. Activity deviations were below 5% in soft tissue and lesions compared to the ground truth whereas standard MR-based AC resulted in activity underestimation values of up to 12%.

Combined process automation for large-scale EEG analysis.

PubMed

Sfondouris, John L; Quebedeaux, Tabitha M; Holdgraf, Chris; Musto, Alberto E

2012-01-01

Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

Axial-Stereo 3-D Optical Metrology for Inner Profile of Pipes Using a Scanning Laser Endoscope

NASA Astrophysics Data System (ADS)

Gong, Yuanzheng; Johnston, Richard S.; Melville, C. David; Seibel, Eric J.

2015-07-01

As the rapid progress in the development of optoelectronic components and computational power, 3-D optical metrology becomes more and more popular in manufacturing and quality control due to its flexibility and high speed. However, most of the optical metrology methods are limited to external surfaces. This article proposed a new approach to measure tiny internal 3-D surfaces with a scanning fiber endoscope and axial-stereo vision algorithm. A dense, accurate point cloud of internally machined threads was generated to compare with its corresponding X-ray 3-D data as ground truth, and the quantification was analyzed by Iterative Closest Points algorithm.

Advances and Challenges In Uncertainty Quantification with Application to Climate Prediction, ICF design and Science Stockpile Stewardship

NASA Astrophysics Data System (ADS)

Klein, R.; Woodward, C. S.; Johannesson, G.; Domyancic, D.; Covey, C. C.; Lucas, D. D.

2012-12-01

Uncertainty Quantification (UQ) is a critical field within 21st century simulation science that resides at the very center of the web of emerging predictive capabilities. The science of UQ holds the promise of giving much greater meaning to the results of complex large-scale simulations, allowing for quantifying and bounding uncertainties. This powerful capability will yield new insights into scientific predictions (e.g. Climate) of great impact on both national and international arenas, allow informed decisions on the design of critical experiments (e.g. ICF capsule design, MFE, NE) in many scientific fields, and assign confidence bounds to scientifically predictable outcomes (e.g. nuclear weapons design). In this talk I will discuss a major new strategic initiative (SI) we have developed at Lawrence Livermore National Laboratory to advance the science of Uncertainty Quantification at LLNL focusing in particular on (a) the research and development of new algorithms and methodologies of UQ as applied to multi-physics multi-scale codes, (b) incorporation of these advancements into a global UQ Pipeline (i.e. a computational superstructure) that will simplify user access to sophisticated tools for UQ studies as well as act as a self-guided, self-adapting UQ engine for UQ studies on extreme computing platforms and (c) use laboratory applications as a test bed for new algorithms and methodologies. The initial SI focus has been on applications for the quantification of uncertainty associated with Climate prediction, but the validated UQ methodologies we have developed are now being fed back into Science Based Stockpile Stewardship (SSS) and ICF UQ efforts. To make advancements in several of these UQ grand challenges, I will focus in talk on the following three research areas in our Strategic Initiative: Error Estimation in multi-physics and multi-scale codes ; Tackling the "Curse of High Dimensionality"; and development of an advanced UQ Computational Pipeline to enable complete UQ workflow and analysis for ensemble runs at the extreme scale (e.g. exascale) with self-guiding adaptation in the UQ Pipeline engine. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was funded by the Uncertainty Quantification Strategic Initiative Laboratory Directed Research and Development Project at LLNL under project tracking code 10-SI-013 (UCRL LLNL-ABS-569112).

Nondestructive Detection and Quantification of Blueberry Bruising using Near-infrared (NIR) Hyperspectral Reflectance Imaging

NASA Astrophysics Data System (ADS)

Jiang, Yu; Li, Changying; Takeda, Fumiomi

2016-10-01

Currently, blueberry bruising is evaluated by either human visual/tactile inspection or firmness measurement instruments. These methods are destructive, time-consuming, and subjective. The goal of this paper was to develop a non-destructive approach for blueberry bruising detection and quantification. Experiments were conducted on 300 samples of southern highbush blueberry (Camellia, Rebel, and Star) and on 1500 samples of northern highbush blueberry (Bluecrop, Jersey, and Liberty) for hyperspectral imaging analysis, firmness measurement, and human evaluation. An algorithm was developed to automatically calculate a bruise ratio index (ratio of bruised to whole fruit area) for bruise quantification. The spectra of bruised and healthy tissues were statistically separated and the separation was independent of cultivars. Support vector machine (SVM) classification of the spectra from the regions of interest (ROIs) achieved over 94%, 92%, and 96% accuracy on the training set, independent testing set, and combined set, respectively. The statistical results showed that the bruise ratio index was equivalent to the measured firmness but better than the predicted firmness in regard to effectiveness of bruise quantification, and the bruise ratio index had a strong correlation with human assessment (R2 = 0.78 - 0.83). Therefore, the proposed approach and the bruise ratio index are effective to non-destructively detect and quantify blueberry bruising.

Nondestructive Detection and Quantification of Blueberry Bruising using Near-infrared (NIR) Hyperspectral Reflectance Imaging.

PubMed

Jiang, Yu; Li, Changying; Takeda, Fumiomi

2016-10-21

Currently, blueberry bruising is evaluated by either human visual/tactile inspection or firmness measurement instruments. These methods are destructive, time-consuming, and subjective. The goal of this paper was to develop a non-destructive approach for blueberry bruising detection and quantification. Experiments were conducted on 300 samples of southern highbush blueberry (Camellia, Rebel, and Star) and on 1500 samples of northern highbush blueberry (Bluecrop, Jersey, and Liberty) for hyperspectral imaging analysis, firmness measurement, and human evaluation. An algorithm was developed to automatically calculate a bruise ratio index (ratio of bruised to whole fruit area) for bruise quantification. The spectra of bruised and healthy tissues were statistically separated and the separation was independent of cultivars. Support vector machine (SVM) classification of the spectra from the regions of interest (ROIs) achieved over 94%, 92%, and 96% accuracy on the training set, independent testing set, and combined set, respectively. The statistical results showed that the bruise ratio index was equivalent to the measured firmness but better than the predicted firmness in regard to effectiveness of bruise quantification, and the bruise ratio index had a strong correlation with human assessment (R2 = 0.78 - 0.83). Therefore, the proposed approach and the bruise ratio index are effective to non-destructively detect and quantify blueberry bruising.

Adaptive polynomial chaos techniques for uncertainty quantification of a gas cooled fast reactor transient

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

Perko, Z.; Gilli, L.; Lathouwers, D.

2013-07-01

Uncertainty quantification plays an increasingly important role in the nuclear community, especially with the rise of Best Estimate Plus Uncertainty methodologies. Sensitivity analysis, surrogate models, Monte Carlo sampling and several other techniques can be used to propagate input uncertainties. In recent years however polynomial chaos expansion has become a popular alternative providing high accuracy at affordable computational cost. This paper presents such polynomial chaos (PC) methods using adaptive sparse grids and adaptive basis set construction, together with an application to a Gas Cooled Fast Reactor transient. Comparison is made between a new sparse grid algorithm and the traditionally used techniquemore » proposed by Gerstner. An adaptive basis construction method is also introduced and is proved to be advantageous both from an accuracy and a computational point of view. As a demonstration the uncertainty quantification of a 50% loss of flow transient in the GFR2400 Gas Cooled Fast Reactor design was performed using the CATHARE code system. The results are compared to direct Monte Carlo sampling and show the superior convergence and high accuracy of the polynomial chaos expansion. Since PC techniques are easy to implement, they can offer an attractive alternative to traditional techniques for the uncertainty quantification of large scale problems. (authors)« less

Nonbinary quantification technique accounting for myocardial infarct heterogeneity: Feasibility of applying percent infarct mapping in patients.

PubMed

Mastrodicasa, Domenico; Elgavish, Gabriel A; Schoepf, U Joseph; Suranyi, Pal; van Assen, Marly; Albrecht, Moritz H; De Cecco, Carlo N; van der Geest, Rob J; Hardy, Rayphael; Mantini, Cesare; Griffith, L Parkwood; Ruzsics, Balazs; Varga-Szemes, Akos

2018-02-15

Binary threshold-based quantification techniques ignore myocardial infarct (MI) heterogeneity, yielding substantial misquantification of MI. To assess the technical feasibility of MI quantification using percent infarct mapping (PIM), a prototype nonbinary algorithm, in patients with suspected MI. Prospective cohort POPULATION: Patients (n = 171) with suspected MI referred for cardiac MRI. Inversion recovery balanced steady-state free-precession for late gadolinium enhancement (LGE) and modified Look-Locker inversion recovery (MOLLI) T 1 -mapping on a 1.5T system. Infarct volume (IV) and infarct fraction (IF) were quantified by two observers based on manual delineation, binary approaches (2-5 standard deviations [SD] and full-width at half-maximum [FWHM] thresholds) in LGE images, and by applying the PIM algorithm in T 1 and LGE images (PIM T1 ; PIM LGE ). IV and IF were analyzed using repeated measures analysis of variance (ANOVA). Agreement between the approaches was determined with Bland-Altman analysis. Interobserver agreement was assessed by intraclass correlation coefficient (ICC) analysis. MI was observed in 89 (54.9%) patients, and 185 (38%) short-axis slices. IF with 2, 3, 4, 5SDs and FWHM techniques were 15.7 ± 6.6, 13.4 ± 5.6, 11.6 ± 5.0, 10.8 ± 5.2, and 10.0 ± 5.2%, respectively. The 5SD and FWHM techniques had the best agreement with manual IF (9.9 ± 4.8%) determination (bias 1.0 and 0.2%; P = 0.1426 and P = 0.8094, respectively). The 2SD and 3SD algorithms significantly overestimated manual IF (9.9 ± 4.8%; both P < 0.0001). PIM LGE measured significantly lower IF (7.8 ± 3.7%) compared to manual values (P < 0.0001). PIM LGE , however, showed the best agreement with the PIM T1 reference (7.6 ± 3.6%, P = 0.3156). Interobserver agreement was rated good to excellent for IV (ICCs between 0.727-0.820) and fair to good for IF (0.589-0.736). The application of the PIM LGE technique for MI quantification in patients is feasible. PIM LGE , with its ability to account for voxelwise MI content, provides significantly smaller IF than any thresholding technique and shows excellent agreement with the T 1 -based reference. 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Algorithm for computing descriptive statistics for very large data sets and the exa-scale era

NASA Astrophysics Data System (ADS)

Beekman, Izaak

2017-11-01

An algorithm for Single-point, Parallel, Online, Converging Statistics (SPOCS) is presented. It is suited for in situ analysis that traditionally would be relegated to post-processing, and can be used to monitor the statistical convergence and estimate the error/residual in the quantity-useful for uncertainty quantification too. Today, data may be generated at an overwhelming rate by numerical simulations and proliferating sensing apparatuses in experiments and engineering applications. Monitoring descriptive statistics in real time lets costly computations and experiments be gracefully aborted if an error has occurred, and monitoring the level of statistical convergence allows them to be run for the shortest amount of time required to obtain good results. This algorithm extends work by Pébay (Sandia Report SAND2008-6212). Pébay's algorithms are recast into a converging delta formulation, with provably favorable properties. The mean, variance, covariances and arbitrary higher order statistical moments are computed in one pass. The algorithm is tested using Sillero, Jiménez, & Moser's (2013, 2014) publicly available UPM high Reynolds number turbulent boundary layer data set, demonstrating numerical robustness, efficiency and other favorable properties.

Fitting-free algorithm for efficient quantification of collagen fiber alignment in SHG imaging applications.

PubMed

Hall, Gunnsteinn; Liang, Wenxuan; Li, Xingde

2017-10-01

Collagen fiber alignment derived from second harmonic generation (SHG) microscopy images can be important for disease diagnostics. Image processing algorithms are needed to robustly quantify the alignment in images with high sensitivity and reliability. Fourier transform (FT) magnitude, 2D power spectrum, and image autocorrelation have previously been used to extract fiber information from images by assuming a certain mathematical model (e.g. Gaussian distribution of the fiber-related parameters) and fitting. The fitting process is slow and fails to converge when the data is not Gaussian. Herein we present an efficient constant-time deterministic algorithm which characterizes the symmetricity of the FT magnitude image in terms of a single parameter, named the fiber alignment anisotropy R ranging from 0 (randomized fibers) to 1 (perfect alignment). This represents an important improvement of the technology and may bring us one step closer to utilizing the technology for various applications in real time. In addition, we present a digital image phantom-based framework for characterizing and validating the algorithm, as well as assessing the robustness of the algorithm against different perturbations.

Passive Fourier-transform infrared spectroscopy of chemical plumes: an algorithm for quantitative interpretation and real-time background removal

NASA Astrophysics Data System (ADS)

Polak, Mark L.; Hall, Jeffrey L.; Herr, Kenneth C.

1995-08-01

We present a ratioing algorithm for quantitative analysis of the passive Fourier-transform infrared spectrum of a chemical plume. We show that the transmission of a near-field plume is given by tau plume = (Lobsd - Lbb-plume)/(Lbkgd - Lbb-plume), where tau _{plume is the frequency-dependent transmission of the plume, L obsd is the spectral radiance of the scene that contains the plume, Lbkgd is the spectral radiance of the same scene without the plume, and Lbb-plume is the spectral radiance of a blackbody at the plume temperature. The algorithm simultaneously achieves background removal, elimination of the spectrometer internal signature, and quantification of the plume spectral transmission. It has applications to both real-time processing for plume visualization and quantitative measurements of plume column densities. The plume temperature (Lbb-plume ), which is not always precisely known, can have a profound effect on the quantitative interpretation of the algorithm and is discussed in detail. Finally, we provide an illustrative example of the use of the algorithm on a trichloroethylene and acetone plume.}

Comparison of algorithms to quantify muscle fatigue in upper limb muscles based on sEMG signals.

PubMed

Kahl, Lorenz; Hofmann, Ulrich G

2016-11-01

This work compared the performance of six different fatigue detection algorithms quantifying muscle fatigue based on electromyographic signals. Surface electromyography (sEMG) was obtained by an experiment from upper arm contractions at three different load levels from twelve volunteers. Fatigue detection algorithms mean frequency (MNF), spectral moments ratio (SMR), the wavelet method WIRM1551, sample entropy (SampEn), fuzzy approximate entropy (fApEn) and recurrence quantification analysis (RQA%DET) were calculated. The resulting fatigue signals were compared considering the disturbances incorporated in fatiguing situations as well as according to the possibility to differentiate the load levels based on the fatigue signals. Furthermore we investigated the influence of the electrode locations on the fatigue detection quality and whether an optimized channel set is reasonable. The results of the MNF, SMR, WIRM1551 and fApEn algorithms fell close together. Due to the small amount of subjects in this study significant differences could not be found. In terms of disturbances the SMR algorithm showed a slight tendency to out-perform the others. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

VESGEN Software for Mapping and Quantification of Vascular Regulators

NASA Technical Reports Server (NTRS)

Parsons-Wingerter, Patricia A.; Vickerman, Mary B.; Keith, Patricia A.

2012-01-01

VESsel GENeration (VESGEN) Analysis is an automated software that maps and quantifies effects of vascular regulators on vascular morphology by analyzing important vessel parameters. Quantification parameters include vessel diameter, length, branch points, density, and fractal dimension. For vascular trees, measurements are reported as dependent functions of vessel branching generation. VESGEN maps and quantifies vascular morphological events according to fractal-based vascular branching generation. It also relies on careful imaging of branching and networked vascular form. It was developed as a plug-in for ImageJ (National Institutes of Health, USA). VESGEN uses image-processing concepts of 8-neighbor pixel connectivity, skeleton, and distance map to analyze 2D, black-and-white (binary) images of vascular trees, networks, and tree-network composites. VESGEN maps typically 5 to 12 (or more) generations of vascular branching, starting from a single parent vessel. These generations are tracked and measured for critical vascular parameters that include vessel diameter, length, density and number, and tortuosity per branching generation. The effects of vascular therapeutics and regulators on vascular morphology and branching tested in human clinical or laboratory animal experimental studies are quantified by comparing vascular parameters with control groups. VESGEN provides a user interface to both guide and allow control over the users vascular analysis process. An option is provided to select a morphological tissue type of vascular trees, network or tree-network composites, which determines the general collections of algorithms, intermediate images, and output images and measurements that will be produced.

STEM VQ Method, Using Scanning Transmission Electron Microscopy (STEM) for Accurate Virus Quantification

DTIC Science & Technology

2017-02-02

Corresponding Author Abstract Accurate virus quantification is sought, but a perfect method still eludes the scientific community. Electron...unlimited. UNCLASSIFIED 2 provides morphology data and counts all viral particles, including partial or noninfectious particles; however, EM methods ...consistent, reproducible virus quantification method called Scanning Transmission Electron Microscopy – Virus Quantification (STEM-VQ) which simplifies

Semi-Automated Digital Image Analysis of Pick’s Disease and TDP-43 Proteinopathy

PubMed Central

Irwin, David J.; Byrne, Matthew D.; McMillan, Corey T.; Cooper, Felicia; Arnold, Steven E.; Lee, Edward B.; Van Deerlin, Vivianna M.; Xie, Sharon X.; Lee, Virginia M.-Y.; Grossman, Murray; Trojanowski, John Q.

2015-01-01

Digital image analysis of histology sections provides reliable, high-throughput methods for neuropathological studies but data is scant in frontotemporal lobar degeneration (FTLD), which has an added challenge of study due to morphologically diverse pathologies. Here, we describe a novel method of semi-automated digital image analysis in FTLD subtypes including: Pick’s disease (PiD, n=11) with tau-positive intracellular inclusions and neuropil threads, and TDP-43 pathology type C (FTLD-TDPC, n=10), defined by TDP-43-positive aggregates predominantly in large dystrophic neurites. To do this, we examined three FTLD-associated cortical regions: mid-frontal gyrus (MFG), superior temporal gyrus (STG) and anterior cingulate gyrus (ACG) by immunohistochemistry. We used a color deconvolution process to isolate signal from the chromogen and applied both object detection and intensity thresholding algorithms to quantify pathological burden. We found object-detection algorithms had good agreement with gold-standard manual quantification of tau- and TDP-43-positive inclusions. Our sampling method was reliable across three separate investigators and we obtained similar results in a pilot analysis using open-source software. Regional comparisons using these algorithms finds differences in regional anatomic disease burden between PiD and FTLD-TDP not detected using traditional ordinal scale data, suggesting digital image analysis is a powerful tool for clinicopathological studies in morphologically diverse FTLD syndromes. PMID:26538548

Semi-Automated Digital Image Analysis of Pick's Disease and TDP-43 Proteinopathy.

PubMed

Irwin, David J; Byrne, Matthew D; McMillan, Corey T; Cooper, Felicia; Arnold, Steven E; Lee, Edward B; Van Deerlin, Vivianna M; Xie, Sharon X; Lee, Virginia M-Y; Grossman, Murray; Trojanowski, John Q

2016-01-01

Digital image analysis of histology sections provides reliable, high-throughput methods for neuropathological studies but data is scant in frontotemporal lobar degeneration (FTLD), which has an added challenge of study due to morphologically diverse pathologies. Here, we describe a novel method of semi-automated digital image analysis in FTLD subtypes including: Pick's disease (PiD, n=11) with tau-positive intracellular inclusions and neuropil threads, and TDP-43 pathology type C (FTLD-TDPC, n=10), defined by TDP-43-positive aggregates predominantly in large dystrophic neurites. To do this, we examined three FTLD-associated cortical regions: mid-frontal gyrus (MFG), superior temporal gyrus (STG) and anterior cingulate gyrus (ACG) by immunohistochemistry. We used a color deconvolution process to isolate signal from the chromogen and applied both object detection and intensity thresholding algorithms to quantify pathological burden. We found object-detection algorithms had good agreement with gold-standard manual quantification of tau- and TDP-43-positive inclusions. Our sampling method was reliable across three separate investigators and we obtained similar results in a pilot analysis using open-source software. Regional comparisons using these algorithms finds differences in regional anatomic disease burden between PiD and FTLD-TDP not detected using traditional ordinal scale data, suggesting digital image analysis is a powerful tool for clinicopathological studies in morphologically diverse FTLD syndromes. © The Author(s) 2015.

MARS spectral molecular imaging of lamb tissue: data collection and image analysis

NASA Astrophysics Data System (ADS)

Aamir, R.; Chernoglazov, A.; Bateman, C. J.; Butler, A. P. H.; Butler, P. H.; Anderson, N. G.; Bell, S. T.; Panta, R. K.; Healy, J. L.; Mohr, J. L.; Rajendran, K.; Walsh, M. F.; de Ruiter, N.; Gieseg, S. P.; Woodfield, T.; Renaud, P. F.; Brooke, L.; Abdul-Majid, S.; Clyne, M.; Glendenning, R.; Bones, P. J.; Billinghurst, M.; Bartneck, C.; Mandalika, H.; Grasset, R.; Schleich, N.; Scott, N.; Nik, S. J.; Opie, A.; Janmale, T.; Tang, D. N.; Kim, D.; Doesburg, R. M.; Zainon, R.; Ronaldson, J. P.; Cook, N. J.; Smithies, D. J.; Hodge, K.

2014-02-01

Spectral molecular imaging is a new imaging technique able to discriminate and quantify different components of tissue simultaneously at high spatial and high energy resolution. Our MARS scanner is an x-ray based small animal CT system designed to be used in the diagnostic energy range (20-140 keV). In this paper, we demonstrate the use of the MARS scanner, equipped with the Medipix3RX spectroscopic photon-processing detector, to discriminate fat, calcium, and water in tissue. We present data collected from a sample of lamb meat including bone as an illustrative example of human tissue imaging. The data is analyzed using our 3D Algebraic Reconstruction Algorithm (MARS-ART) and by material decomposition based on a constrained linear least squares algorithm. The results presented here clearly show the quantification of lipid-like, water-like and bone-like components of tissue. However, it is also clear to us that better algorithms could extract more information of clinical interest from our data. Because we are one of the first to present data from multi-energy photon-processing small animal CT systems, we make the raw, partial and fully processed data available with the intention that others can analyze it using their familiar routines. The raw, partially processed and fully processed data of lamb tissue along with the phantom calibration data can be found at http://hdl.handle.net/10092/8531.

A novel algorithm for solving the true coincident counting issues in Monte Carlo simulations for radiation spectroscopy.

PubMed

Guan, Fada; Johns, Jesse M; Vasudevan, Latha; Zhang, Guoqing; Tang, Xiaobin; Poston, John W; Braby, Leslie A

2015-06-01

Coincident counts can be observed in experimental radiation spectroscopy. Accurate quantification of the radiation source requires the detection efficiency of the spectrometer, which is often experimentally determined. However, Monte Carlo analysis can be used to supplement experimental approaches to determine the detection efficiency a priori. The traditional Monte Carlo method overestimates the detection efficiency as a result of omitting coincident counts caused mainly by multiple cascade source particles. In this study, a novel "multi-primary coincident counting" algorithm was developed using the Geant4 Monte Carlo simulation toolkit. A high-purity Germanium detector for ⁶⁰Co gamma-ray spectroscopy problems was accurately modeled to validate the developed algorithm. The simulated pulse height spectrum agreed well qualitatively with the measured spectrum obtained using the high-purity Germanium detector. The developed algorithm can be extended to other applications, with a particular emphasis on challenging radiation fields, such as counting multiple types of coincident radiations released from nuclear fission or used nuclear fuel.

Assessment of metal ion concentration in water with structured feature selection.

PubMed

Naula, Pekka; Airola, Antti; Pihlasalo, Sari; Montoya Perez, Ileana; Salakoski, Tapio; Pahikkala, Tapio

2017-10-01

We propose a cost-effective system for the determination of metal ion concentration in water, addressing a central issue in water resources management. The system combines novel luminometric label array technology with a machine learning algorithm that selects a minimal number of array reagents (modulators) and liquid sample dilutions, such that enable accurate quantification. The algorithm is able to identify the optimal modulators and sample dilutions leading to cost reductions since less manual labour and resources are needed. Inferring the ion detector involves a unique type of a structured feature selection problem, which we formalize in this paper. We propose a novel Cartesian greedy forward feature selection algorithm for solving the problem. The novel algorithm was evaluated in the concentration assessment of five metal ions and the performance was compared to two known feature selection approaches. The results demonstrate that the proposed system can assist in lowering the costs with minimal loss in accuracy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN.

PubMed

Hao, Jie; Liebeke, Manuel; Astle, William; De Iorio, Maria; Bundy, Jacob G; Ebbels, Timothy M D

2014-01-01

Data processing for 1D NMR spectra is a key bottleneck for metabolomic and other complex-mixture studies, particularly where quantitative data on individual metabolites are required. We present a protocol for automated metabolite deconvolution and quantification from complex NMR spectra by using the Bayesian automated metabolite analyzer for NMR (BATMAN) R package. BATMAN models resonances on the basis of a user-controllable set of templates, each of which specifies the chemical shifts, J-couplings and relative peak intensities for a single metabolite. Peaks are allowed to shift position slightly between spectra, and peak widths are allowed to vary by user-specified amounts. NMR signals not captured by the templates are modeled non-parametrically by using wavelets. The protocol covers setting up user template libraries, optimizing algorithmic input parameters, improving prior information on peak positions, quality control and evaluation of outputs. The outputs include relative concentration estimates for named metabolites together with associated Bayesian uncertainty estimates, as well as the fit of the remainder of the spectrum using wavelets. Graphical diagnostics allow the user to examine the quality of the fit for multiple spectra simultaneously. This approach offers a workflow to analyze large numbers of spectra and is expected to be useful in a wide range of metabolomics studies.

Multi-fidelity uncertainty quantification in large-scale predictive simulations of turbulent flow

NASA Astrophysics Data System (ADS)

Geraci, Gianluca; Jofre-Cruanyes, Lluis; Iaccarino, Gianluca

2017-11-01

The performance characterization of complex engineering systems often relies on accurate, but computationally intensive numerical simulations. It is also well recognized that in order to obtain a reliable numerical prediction the propagation of uncertainties needs to be included. Therefore, Uncertainty Quantification (UQ) plays a fundamental role in building confidence in predictive science. Despite the great improvement in recent years, even the more advanced UQ algorithms are still limited to fairly simplified applications and only moderate parameter dimensionality. Moreover, in the case of extremely large dimensionality, sampling methods, i.e. Monte Carlo (MC) based approaches, appear to be the only viable alternative. In this talk we describe and compare a family of approaches which aim to accelerate the convergence of standard MC simulations. These methods are based on hierarchies of generalized numerical resolutions (multi-level) or model fidelities (multi-fidelity), and attempt to leverage the correlation between Low- and High-Fidelity (HF) models to obtain a more accurate statistical estimator without introducing additional HF realizations. The performance of these methods are assessed on an irradiated particle laden turbulent flow (PSAAP II solar energy receiver). This investigation was funded by the United States Department of Energy's (DoE) National Nuclear Security Administration (NNSA) under the Predicitive Science Academic Alliance Program (PSAAP) II at Stanford University.

Efficient uncertainty quantification in fully-integrated surface and subsurface hydrologic simulations

NASA Astrophysics Data System (ADS)

Miller, K. L.; Berg, S. J.; Davison, J. H.; Sudicky, E. A.; Forsyth, P. A.

2018-01-01

Although high performance computers and advanced numerical methods have made the application of fully-integrated surface and subsurface flow and transport models such as HydroGeoSphere common place, run times for large complex basin models can still be on the order of days to weeks, thus, limiting the usefulness of traditional workhorse algorithms for uncertainty quantification (UQ) such as Latin Hypercube simulation (LHS) or Monte Carlo simulation (MCS), which generally require thousands of simulations to achieve an acceptable level of accuracy. In this paper we investigate non-intrusive polynomial chaos for uncertainty quantification, which in contrast to random sampling methods (e.g., LHS and MCS), represents a model response of interest as a weighted sum of polynomials over the random inputs. Once a chaos expansion has been constructed, approximating the mean, covariance, probability density function, cumulative distribution function, and other common statistics as well as local and global sensitivity measures is straightforward and computationally inexpensive, thus making PCE an attractive UQ method for hydrologic models with long run times. Our polynomial chaos implementation was validated through comparison with analytical solutions as well as solutions obtained via LHS for simple numerical problems. It was then used to quantify parametric uncertainty in a series of numerical problems with increasing complexity, including a two-dimensional fully-saturated, steady flow and transient transport problem with six uncertain parameters and one quantity of interest; a one-dimensional variably-saturated column test involving transient flow and transport, four uncertain parameters, and two quantities of interest at 101 spatial locations and five different times each (1010 total); and a three-dimensional fully-integrated surface and subsurface flow and transport problem for a small test catchment involving seven uncertain parameters and three quantities of interest at 241 different times each. Numerical experiments show that polynomial chaos is an effective and robust method for quantifying uncertainty in fully-integrated hydrologic simulations, which provides a rich set of features and is computationally efficient. Our approach has the potential for significant speedup over existing sampling based methods when the number of uncertain model parameters is modest ( ≤ 20). To our knowledge, this is the first implementation of the algorithm in a comprehensive, fully-integrated, physically-based three-dimensional hydrosystem model.

Feasibility and performance of an adaptive contrast-oriented FDG PET/CT quantification technique for global disease assessment of malignant pleural mesothelioma and a brief review of the literature.

PubMed

Marin-Oyaga, Victor A; Salavati, Ali; Houshmand, Sina; Pasha, Ahmed Khurshid; Gharavi, Mohammad; Saboury, Babak; Basu, Sandip; Torigian, Drew A; Alavi, Abass

2015-01-01

Treatment of malignant pleural mesothelioma (MPM) remains very challenging. Assessment of response to treatment is necessary for modifying treatment and using new drugs. Global disease assessment (GDA) by implementing image processing methods to extract more information out of positron emission tomography (PET) images may provide reliable information. In this study we show the feasibility of this method of semi-quantification in patients with mesothelioma, and compare it with the conventional methods. We also present a review of the literature about this topic. Nineteen subjects with histologically proven MPM who had undergone fluoride-18-fluorodeoxyglucose PET/computed tomography ((18)F-FDG PET/CT) before and after treatment were included in this study. An adaptive contrast-oriented thresholding algorithm was used for the image analysis and semi-quantification. Metabolic tumor volume (MTV), maximum and mean standardized uptake volume (SUVmax, SUVmean) and total lesion glycolysis (TLG) were calculated for each region of interest. The global tumor glycolysis (GTG) was obtained by summing up all TLG. Treatment response was assessed by the European Organisation for Research and Treatment of Cancer (EORTC) criteria and the changes of GTG. Agreement between global disease assessment and conventional method was also determined. In patients with progressive disease based on EORTC criteria, GTG showed an increase of 150.7 but in patients with stable or partial response, GTG showed a decrease of 433.1. The SUVmax of patients before treatment was 5.95 (SD: 2.93) and after the treatment it increased to 6.38 (SD: 3.19). Overall concordance of conventional method with GDA method was 57%. Concordance of progression of disease based on conventional method was 44%, stable disease was 85% and partial response was 33%. Discordance was 55%, 14% and 66%. Adaptive contrast-oriented thresholding algorithm is a promising method to quantify the whole tumor glycolysis in patients with mesothelioma. We are able to assess the total metabolic lesion volume, lesion glycolysis, SUVmax, tumor SUVmean and GTG for this particular tumor. Also we were able to demonstrate the potential use of this technique in the monitoring of treatment response. More studies comparing this technique with conventional and other global disease assessment methods are needed in order to clarify its role in the assessment of treatment response and prognosis of these patients.

Blade Sections in Streamwise Oscillations into Reverse Flow

DTIC Science & Technology

2015-05-07

NC 27709-2211 Reverse Flow, Oscillating Airfoils , Oscillating Freesteam REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR...plate or bluff body rather than an airfoil . Reverse flow operation requires investigation and quantification to accurately capture these Submitted for... airfoil integrated quantities (lift, drag, moment) in reverse flow and developed new algorithms for comprehensive codes, reducing errors from 30 %–50

High content analysis of phagocytic activity and cell morphology with PuntoMorph.

PubMed

Al-Ali, Hassan; Gao, Han; Dalby-Hansen, Camilla; Peters, Vanessa Ann; Shi, Yan; Brambilla, Roberta

2017-11-01

Phagocytosis is essential for maintenance of normal homeostasis and healthy tissue. As such, it is a therapeutic target for a wide range of clinical applications. The development of phenotypic screens targeting phagocytosis has lagged behind, however, due to the difficulties associated with image-based quantification of phagocytic activity. We present a robust algorithm and cell-based assay system for high content analysis of phagocytic activity. The method utilizes fluorescently labeled beads as a phagocytic substrate with defined physical properties. The algorithm employs statistical modeling to determine the mean fluorescence of individual beads within each image, and uses the information to conduct an accurate count of phagocytosed beads. In addition, the algorithm conducts detailed and sophisticated analysis of cellular morphology, making it a standalone tool for high content screening. We tested our assay system using microglial cultures. Our results recapitulated previous findings on the effects of microglial stimulation on cell morphology and phagocytic activity. Moreover, our cell-level analysis revealed that the two phenotypes associated with microglial activation, specifically cell body hypertrophy and increased phagocytic activity, are not highly correlated. This novel finding suggests the two phenotypes may be under the control of distinct signaling pathways. We demonstrate that our assay system outperforms preexisting methods for quantifying phagocytic activity in multiple dimensions including speed, accuracy, and resolution. We provide a framework to facilitate the development of high content assays suitable for drug screening. For convenience, we implemented our algorithm in a standalone software package, PuntoMorph. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantification of early cutaneous manifestations of chronic venous insufficiency by automated analysis of photographic images: Feasibility and technical considerations.

PubMed

Becker, François; Fourgeau, Patrice; Carpentier, Patrick H; Ouchène, Amina

2018-06-01

We postulate that blue telangiectasia and brownish pigmentation at ankle level, early markers of chronic venous insufficiency, can be quantified for longitudinal studies of chronic venous disease in Caucasian people. Objectives and methods To describe a photographic technique specially developed for this purpose. The pictures were acquired using a dedicated photo stand to position the foot in a reproducible way, with a normalized lighting and acquisition protocol. The image analysis was performed with a tool developed using algorithms optimized to detect and quantify blue telangiectasia and brownish pigmentation and their relative surface in the region of interest. To test the short-term reproducibility of the measures. Results The quantification of the blue telangiectasia and of the brownish pigmentation using an automated digital photo analysis is feasible. The short-term reproducibility is good for blue telangiectasia quantification. It is a less accurate for the brownish pigmentation. Conclusion The blue telangiectasia of the corona phlebectatica and the ankle flare can be assessed using a clinimetric approach based on the automated digital photo analysis.

Performance of different reflectance and diffuse optical imaging tomographic approaches in fluorescence molecular imaging of small animals

NASA Astrophysics Data System (ADS)

Dinten, Jean-Marc; Petié, Philippe; da Silva, Anabela; Boutet, Jérôme; Koenig, Anne; Hervé, Lionel; Berger, Michel; Laidevant, Aurélie; Rizo, Philippe

2006-03-01

Optical imaging of fluorescent probes is an essential tool for investigation of molecular events in small animals for drug developments. In order to get localization and quantification information of fluorescent labels, CEA-LETI has developed efficient approaches in classical reflectance imaging as well as in diffuse optical tomographic imaging with continuous and temporal signals. This paper presents an overview of the different approaches investigated and their performances. High quality fluorescence reflectance imaging is obtained thanks to the development of an original "multiple wavelengths" system. The uniformity of the excitation light surface area is better than 15%. Combined with the use of adapted fluorescent probes, this system enables an accurate detection of pathological tissues, such as nodules, beneath the animal's observed area. Performances for the detection of ovarian nodules on a nude mouse are shown. In order to investigate deeper inside animals and get 3D localization, diffuse optical tomography systems are being developed for both slab and cylindrical geometries. For these two geometries, our reconstruction algorithms are based on analytical expression of light diffusion. Thanks to an accurate introduction of light/matter interaction process in the algorithms, high quality reconstructions of tumors in mice have been obtained. Reconstruction of lung tumors on mice are presented. By the use of temporal diffuse optical imaging, localization and quantification performances can be improved at the price of a more sophisticated acquisition system and more elaborate information processing methods. Such a system based on a pulsed laser diode and a time correlated single photon counting system has been set up. Performances of this system for localization and quantification of fluorescent probes are presented.

Implementation and evaluation of a new workflow for registration and segmentation of pulmonary MRI data for regional lung perfusion assessment.

PubMed

Böttger, T; Grunewald, K; Schöbinger, M; Fink, C; Risse, F; Kauczor, H U; Meinzer, H P; Wolf, Ivo

2007-03-07

Recently it has been shown that regional lung perfusion can be assessed using time-resolved contrast-enhanced magnetic resonance (MR) imaging. Quantification of the perfusion images has been attempted, based on definition of small regions of interest (ROIs). Use of complete lung segmentations instead of ROIs could possibly increase quantification accuracy. Due to the low signal-to-noise ratio, automatic segmentation algorithms cannot be applied. On the other hand, manual segmentation of the lung tissue is very time consuming and can become inaccurate, as the borders of the lung to adjacent tissues are not always clearly visible. We propose a new workflow for semi-automatic segmentation of the lung from additionally acquired morphological HASTE MR images. First the lung is delineated semi-automatically in the HASTE image. Next the HASTE image is automatically registered with the perfusion images. Finally, the transformation resulting from the registration is used to align the lung segmentation from the morphological dataset with the perfusion images. We evaluated rigid, affine and locally elastic transformations, suitable optimizers and different implementations of mutual information (MI) metrics to determine the best possible registration algorithm. We located the shortcomings of the registration procedure and under which conditions automatic registration will succeed or fail. Segmentation results were evaluated using overlap and distance measures. Integration of the new workflow reduces the time needed for post-processing of the data, simplifies the perfusion quantification and reduces interobserver variability in the segmentation process. In addition, the matched morphological data set can be used to identify morphologic changes as the source for the perfusion abnormalities.

A New Strategy for Fast MRI-Based Quantification of the Myelin Water Fraction: Application to Brain Imaging in Infants

PubMed Central

Kulikova, Sofya; Hertz-Pannier, Lucie; Dehaene-Lambertz, Ghislaine

2016-01-01

The volume fraction of water related to myelin (fmy) is a promising MRI index for in vivo assessment of brain myelination, that can be derived from multi-component analysis of T1 and T2 relaxometry signals. However, existing quantification methods require rather long acquisition and/or post-processing times, making implementation difficult both in research studies on healthy unsedated children and in clinical examinations. The goal of this work was to propose a novel strategy for fmy quantification within acceptable acquisition and post-processing times. Our approach is based on a 3-compartment model (myelin-related water, intra/extra-cellular water and unrestricted water), and uses calibrated values of inherent relaxation times (T1c and T2c) for each compartment c. Calibration was first performed on adult relaxometry datasets (N = 3) acquired with large numbers of inversion times (TI) and echo times (TE), using an original combination of a region contraction approach and a non-negative least-square (NNLS) algorithm. This strategy was compared with voxel-wise fitting, and showed robust estimation of T1c and T2c. The accuracy of fmy calculations depending on multiple factors was investigated using simulated data. In the testing stage, our strategy enabled fast fmy mapping, based on relaxometry datasets acquired with reduced TI and TE numbers (acquisition <6 min), and analyzed with NNLS algorithm (post-processing <5min). In adults (N = 13, mean age 22.4±1.6 years), fmy maps showed variability across white matter regions, in agreement with previous studies. In healthy infants (N = 18, aged 3 to 34 weeks), asynchronous changes in fmy values were demonstrated across bundles, confirming the well-known progression of myelination. PMID:27736872

Quantitative Rapid Assessment of Leukoaraiosis in CT : Comparison to Gold Standard MRI.

PubMed

Hanning, Uta; Sporns, Peter Bernhard; Schmidt, Rene; Niederstadt, Thomas; Minnerup, Jens; Bier, Georg; Knecht, Stefan; Kemmling, André

2017-10-20

The severity of white matter lesions (WML) is a risk factor of hemorrhage and predictor of clinical outcome after ischemic stroke; however, in contrast to magnetic resonance imaging (MRI) reliable quantification for this surrogate marker is limited for computed tomography (CT), the leading stroke imaging technique. We aimed to present and evaluate a CT-based automated rater-independent method for quantification of microangiopathic white matter changes. Patients with suspected minor stroke (National Institutes of Health Stroke scale, NIHSS < 4) were screened for the analysis of non-contrast computerized tomography (NCCT) at admission and compared to follow-up MRI. The MRI-based WML volume and visual Fazekas scores were assessed as the gold standard reference. We employed a recently published probabilistic brain segmentation algorithm for CT images to determine the tissue-specific density of WM space. All voxel-wise densities were quantified in WM space and weighted according to partial probabilistic WM content. The resulting mean weighted density of WM space in NCCT, the surrogate of WML, was correlated with reference to MRI-based WML parameters. The process of CT-based tissue-specific segmentation was reliable in 79 cases with varying severity of microangiopathy. Voxel-wise weighted density within WM spaces showed a noticeable correlation (r = -0.65) with MRI-based WML volume. Particularly in patients with moderate or severe lesion load according to the visual Fazekas score the algorithm provided reliable prediction of MRI-based WML volume. Automated observer-independent quantification of voxel-wise WM density in CT significantly correlates with microangiopathic WM disease in gold standard MRI. This rapid surrogate of white matter lesion load in CT may support objective WML assessment and therapeutic decision-making during acute stroke triage.

Objective automated quantification of fluorescence signal in histological sections of rat lens.

PubMed

Talebizadeh, Nooshin; Hagström, Nanna Zhou; Yu, Zhaohua; Kronschläger, Martin; Söderberg, Per; Wählby, Carolina

2017-08-01

Visual quantification and classification of fluorescent signals is the gold standard in microscopy. The purpose of this study was to develop an automated method to delineate cells and to quantify expression of fluorescent signal of biomarkers in each nucleus and cytoplasm of lens epithelial cells in a histological section. A region of interest representing the lens epithelium was manually demarcated in each input image. Thereafter, individual cell nuclei within the region of interest were automatically delineated based on watershed segmentation and thresholding with an algorithm developed in Matlab™. Fluorescence signal was quantified within nuclei, cytoplasms and juxtaposed backgrounds. The classification of cells as labelled or not labelled was based on comparison of the fluorescence signal within cells with local background. The classification rule was thereafter optimized as compared with visual classification of a limited dataset. The performance of the automated classification was evaluated by asking 11 independent blinded observers to classify all cells (n = 395) in one lens image. Time consumed by the automatic algorithm and visual classification of cells was recorded. On an average, 77% of the cells were correctly classified as compared with the majority vote of the visual observers. The average agreement among visual observers was 83%. However, variation among visual observers was high, and agreement between two visual observers was as low as 71% in the worst case. Automated classification was on average 10 times faster than visual scoring. The presented method enables objective and fast detection of lens epithelial cells and quantification of expression of fluorescent signal with an accuracy comparable with the variability among visual observers. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

The influence of different signal-to-background ratios on spatial resolution and F18-FDG-PET quantification using point spread function and time-of-flight reconstruction.

PubMed

Rogasch, Julian Mm; Hofheinz, Frank; Lougovski, Alexandr; Furth, Christian; Ruf, Juri; Großer, Oliver S; Mohnike, Konrad; Hass, Peter; Walke, Mathias; Amthauer, Holger; Steffen, Ingo G

2014-12-01

F18-fluorodeoxyglucose positron-emission tomography (FDG-PET) reconstruction algorithms can have substantial influence on quantitative image data used, e.g., for therapy planning or monitoring in oncology. We analyzed radial activity concentration profiles of differently reconstructed FDG-PET images to determine the influence of varying signal-to-background ratios (SBRs) on the respective spatial resolution, activity concentration distribution, and quantification (standardized uptake value [SUV], metabolic tumor volume [MTV]). Measurements were performed on a Siemens Biograph mCT 64 using a cylindrical phantom containing four spheres (diameter, 30 to 70 mm) filled with F18-FDG applying three SBRs (SBR1, 16:1; SBR2, 6:1; SBR3, 2:1). Images were reconstructed employing six algorithms (filtered backprojection [FBP], FBP + time-of-flight analysis [FBP + TOF], 3D-ordered subset expectation maximization [3D-OSEM], 3D-OSEM + TOF, point spread function [PSF], PSF + TOF). Spatial resolution was determined by fitting the convolution of the object geometry with a Gaussian point spread function to radial activity concentration profiles. MTV delineation was performed using fixed thresholds and semiautomatic background-adapted thresholding (ROVER, ABX, Radeberg, Germany). The pairwise Wilcoxon test revealed significantly higher spatial resolutions for PSF + TOF (up to 4.0 mm) compared to PSF, FBP, FBP + TOF, 3D-OSEM, and 3D-OSEM + TOF at all SBRs (each P < 0.05) with the highest differences for SBR1 decreasing to the lowest for SBR3. Edge elevations in radial activity profiles (Gibbs artifacts) were highest for PSF and PSF + TOF declining with decreasing SBR (PSF + TOF largest sphere; SBR1, 6.3%; SBR3, 2.7%). These artifacts induce substantial SUVmax overestimation compared to the reference SUV for PSF algorithms at SBR1 and SBR2 leading to substantial MTV underestimation in threshold-based segmentation. In contrast, both PSF algorithms provided the lowest deviation of SUVmean from reference SUV at SBR1 and SBR2. At high contrast, the PSF algorithms provided the highest spatial resolution and lowest SUVmean deviation from the reference SUV. In contrast, both algorithms showed the highest deviations in SUVmax and threshold-based MTV definition. At low contrast, all investigated reconstruction algorithms performed approximately equally. The use of PSF algorithms for quantitative PET data, e.g., for target volume definition or in serial PET studies, should be performed with caution - especially if comparing SUV of lesions with high and low contrasts.

YM500v2: a small RNA sequencing (smRNA-seq) database for human cancer miRNome research.

PubMed

Cheng, Wei-Chung; Chung, I-Fang; Tsai, Cheng-Fong; Huang, Tse-Shun; Chen, Chen-Yang; Wang, Shao-Chuan; Chang, Ting-Yu; Sun, Hsing-Jen; Chao, Jeffrey Yung-Chuan; Cheng, Cheng-Chung; Wu, Cheng-Wen; Wang, Hsei-Wei

2015-01-01

We previously presented YM500, which is an integrated database for miRNA quantification, isomiR identification, arm switching discovery and novel miRNA prediction from 468 human smRNA-seq datasets. Here in this updated YM500v2 database (http://ngs.ym.edu.tw/ym500/), we focus on the cancer miRNome to make the database more disease-orientated. New miRNA-related algorithms developed after YM500 were included in YM500v2, and, more significantly, more than 8000 cancer-related smRNA-seq datasets (including those of primary tumors, paired normal tissues, PBMC, recurrent tumors, and metastatic tumors) were incorporated into YM500v2. Novel miRNAs (miRNAs not included in the miRBase R21) were not only predicted by three independent algorithms but also cleaned by a new in silico filtration strategy and validated by wetlab data such as Cross-Linked ImmunoPrecipitation sequencing (CLIP-seq) to reduce the false-positive rate. A new function 'Meta-analysis' is additionally provided for allowing users to identify real-time differentially expressed miRNAs and arm-switching events according to customer-defined sample groups and dozens of clinical criteria tidying up by proficient clinicians. Cancer miRNAs identified hold the potential for both basic research and biotech applications. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Quantitative Imaging with a Mobile Phone Microscope

PubMed Central

Skandarajah, Arunan; Reber, Clay D.; Switz, Neil A.; Fletcher, Daniel A.

2014-01-01

Use of optical imaging for medical and scientific applications requires accurate quantification of features such as object size, color, and brightness. High pixel density cameras available on modern mobile phones have made photography simple and convenient for consumer applications; however, the camera hardware and software that enables this simplicity can present a barrier to accurate quantification of image data. This issue is exacerbated by automated settings, proprietary image processing algorithms, rapid phone evolution, and the diversity of manufacturers. If mobile phone cameras are to live up to their potential to increase access to healthcare in low-resource settings, limitations of mobile phone–based imaging must be fully understood and addressed with procedures that minimize their effects on image quantification. Here we focus on microscopic optical imaging using a custom mobile phone microscope that is compatible with phones from multiple manufacturers. We demonstrate that quantitative microscopy with micron-scale spatial resolution can be carried out with multiple phones and that image linearity, distortion, and color can be corrected as needed. Using all versions of the iPhone and a selection of Android phones released between 2007 and 2012, we show that phones with greater than 5 MP are capable of nearly diffraction-limited resolution over a broad range of magnifications, including those relevant for single cell imaging. We find that automatic focus, exposure, and color gain standard on mobile phones can degrade image resolution and reduce accuracy of color capture if uncorrected, and we devise procedures to avoid these barriers to quantitative imaging. By accommodating the differences between mobile phone cameras and the scientific cameras, mobile phone microscopes can be reliably used to increase access to quantitative imaging for a variety of medical and scientific applications. PMID:24824072

A Portable Electronic Nose For Toxic Vapor Detection, Identification, and Quantification

NASA Technical Reports Server (NTRS)

Linnell, B. R.; Young, R. C.; Griffin, T. P.; Meneghelli, B. J.; Peterson, B. V.; Brooks, K. B.

2005-01-01

A new prototype instrument based on electronic nose (e-nose) technology has demonstrated the ability to identify and quantify many vapors of interest to the Space Program at their minimum required concentrations for both single vapors and two-component vapor mixtures, and may easily be adapted to detect many other toxic vapors. To do this, it was necessary to develop algorithms to classify unknown vapors, recognize when a vapor is not any of the vapors of interest, and estimate the concentrations of the contaminants. This paper describes the design of the portable e-nose instrument, test equipment setup, test protocols, pattern recognition algorithms, concentration estimation methods, and laboratory test results.

Robust tracking and quantification of C. elegans body shape and locomotion through coiling, entanglement, and omega bends

PubMed Central

Roussel, Nicolas; Sprenger, Jeff; Tappan, Susan J; Glaser, Jack R

2014-01-01

The behavior of the well-characterized nematode, Caenorhabditis elegans (C. elegans), is often used to study the neurologic control of sensory and motor systems in models of health and neurodegenerative disease. To advance the quantification of behaviors to match the progress made in the breakthroughs of genetics, RNA, proteins, and neuronal circuitry, analysis must be able to extract subtle changes in worm locomotion across a population. The analysis of worm crawling motion is complex due to self-overlap, coiling, and entanglement. Using current techniques, the scope of the analysis is typically restricted to worms to their non-occluded, uncoiled state which is incomplete and fundamentally biased. Using a model describing the worm shape and crawling motion, we designed a deformable shape estimation algorithm that is robust to coiling and entanglement. This model-based shape estimation algorithm has been incorporated into a framework where multiple worms can be automatically detected and tracked simultaneously throughout the entire video sequence, thereby increasing throughput as well as data validity. The newly developed algorithms were validated against 10 manually labeled datasets obtained from video sequences comprised of various image resolutions and video frame rates. The data presented demonstrate that tracking methods incorporated in WormLab enable stable and accurate detection of these worms through coiling and entanglement. Such challenging tracking scenarios are common occurrences during normal worm locomotion. The ability for the described approach to provide stable and accurate detection of C. elegans is critical to achieve unbiased locomotory analysis of worm motion. PMID:26435884

Unsupervised quantification of abdominal fat from CT images using Greedy Snakes

NASA Astrophysics Data System (ADS)

Agarwal, Chirag; Dallal, Ahmed H.; Arbabshirani, Mohammad R.; Patel, Aalpen; Moore, Gregory

2017-02-01

Adipose tissue has been associated with adverse consequences of obesity. Total adipose tissue (TAT) is divided into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). Intra-abdominal fat (VAT), located inside the abdominal cavity, is a major factor for the classic obesity related pathologies. Since direct measurement of visceral and subcutaneous fat is not trivial, substitute metrics like waist circumference (WC) and body mass index (BMI) are used in clinical settings to quantify obesity. Abdominal fat can be assessed effectively using CT or MRI, but manual fat segmentation is rather subjective and time-consuming. Hence, an automatic and accurate quantification tool for abdominal fat is needed. The goal of this study is to extract TAT, VAT and SAT fat from abdominal CT in a fully automated unsupervised fashion using energy minimization techniques. We applied a four step framework consisting of 1) initial body contour estimation, 2) approximation of the body contour, 3) estimation of inner abdominal contour using Greedy Snakes algorithm, and 4) voting, to segment the subcutaneous and visceral fat. We validated our algorithm on 952 clinical abdominal CT images (from 476 patients with a very wide BMI range) collected from various radiology departments of Geisinger Health System. To our knowledge, this is the first study of its kind on such a large and diverse clinical dataset. Our algorithm obtained a 3.4% error for VAT segmentation compared to manual segmentation. These personalized and accurate measurements of fat can complement traditional population health driven obesity metrics such as BMI and WC.

Evaluation of an Automatic Registration-Based Algorithm for Direct Measurement of Volume Change in Tumors

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

Sarkar, Saradwata; Johnson, Timothy D.; Ma, Bing

2012-07-01

Purpose: Assuming that early tumor volume change is a biomarker for response to therapy, accurate quantification of early volume changes could aid in adapting an individual patient's therapy and lead to shorter clinical trials. We investigated an image registration-based approach for tumor volume change quantification that may more reliably detect smaller changes that occur in shorter intervals than can be detected by existing algorithms. Methods and Materials: Variance and bias of the registration-based approach were evaluated using retrospective, in vivo, very-short-interval diffusion magnetic resonance imaging scans where true zero tumor volume change is unequivocally known and synthetic data, respectively. Themore » interval scans were nonlinearly registered using two similarity measures: mutual information (MI) and normalized cross-correlation (NCC). Results: The 95% confidence interval of the percentage volume change error was (-8.93% to 10.49%) for MI-based and (-7.69%, 8.83%) for NCC-based registrations. Linear mixed-effects models demonstrated that error in measuring volume change increased with increase in tumor volume and decreased with the increase in the tumor's normalized mutual information, even when NCC was the similarity measure being optimized during registration. The 95% confidence interval of the relative volume change error for the synthetic examinations with known changes over {+-}80% of reference tumor volume was (-3.02% to 3.86%). Statistically significant bias was not demonstrated. Conclusion: A low-noise, low-bias tumor volume change measurement algorithm using nonlinear registration is described. Errors in change measurement were a function of tumor volume and the normalized mutual information content of the tumor.« less

Algorithm for the Measure of Vitreous Hyper-Reflective Foci (VHRF) in Optical Coherence Tomography: VHRF Correlates with Diabetic Retinopathy Disease Progression

PubMed Central

Korot, Edward; Comer, Grant; Steffens, Timothy; Antonetti, David A.

2015-01-01

Importance Developing a non-invasive measure of diabetic retinopathy disease progression may provide clinicians information for patient specific intervention. Objective To develop an algorithm to measure vitreous hyper-reflective foci (VHRF) from standard 3-dimensional OCT images in an unbiased manner. This algorithm was applied to OCT scans from controls, patients with diabetes, and with diabetic macular edema to determine whether VHRF score is associated with macular edema and may serve as a non-invasive measure of inflammation. Design, Setting and Participants We retrospectively analyzed the OCT scans from 97 patients that were seen at the University of Michigan. Patients with diabetes without signs of retinopathy and patients with DME were compared to controls. Main Outcomes and Measures An algorithm was developed in order to enhance the vitreous imaging from OCT to allow automated quantification of VHRF and calculation of a VHRF score. This score was compared between controls and patients with diabetic retinopathy. Results VHRF scores were increased in patients with DME by 2.95 fold, (mean (SD): 5.60 (8.65)) compared to control patients (mean (SD): 1.90 (3.42)) 95% CI [0.75, 7.45] (p=0.012) and by 6.83 fold compared to patients with diabetes without retinopathy (mean (SD): 0.82 (1.26)) 95% CI [1.46, 8.82], (p=0.005). Conclusions and Relevance VHRF scores may be obtained from OCT images that include the vitreous and may provide a rapid and non-invasive clinical correlate for ocular inflammation. Limitations include study size, specifically regarding comorbidities affecting VHRF score. Higher VHRF scores in patients with DME as compared to controls and diabetic patients without retinopathy warrant further population based and longitudinal studies to help determine the value of VHRF score in selecting therapeutic intervention. PMID:26502148

Tree-based approach for exploring marine spatial patterns with raster datasets.

PubMed

Liao, Xiaohan; Xue, Cunjin; Su, Fenzhen

2017-01-01

From multiple raster datasets to spatial association patterns, the data-mining technique is divided into three subtasks, i.e., raster dataset pretreatment, mining algorithm design, and spatial pattern exploration from the mining results. Comparison with the former two subtasks reveals that the latter remains unresolved. Confronted with the interrelated marine environmental parameters, we propose a Tree-based Approach for eXploring Marine Spatial Patterns with multiple raster datasets called TAXMarSP, which includes two models. One is the Tree-based Cascading Organization Model (TCOM), and the other is the Spatial Neighborhood-based CAlculation Model (SNCAM). TCOM designs the "Spatial node→Pattern node" from top to bottom layers to store the table-formatted frequent patterns. Together with TCOM, SNCAM considers the spatial neighborhood contributions to calculate the pattern-matching degree between the specified marine parameters and the table-formatted frequent patterns and then explores the marine spatial patterns. Using the prevalent quantification Apriori algorithm and a real remote sensing dataset from January 1998 to December 2014, a successful application of TAXMarSP to marine spatial patterns in the Pacific Ocean is described, and the obtained marine spatial patterns present not only the well-known but also new patterns to Earth scientists.

The role of image registration in brain mapping

PubMed Central

Toga, A.W.; Thompson, P.M.

2008-01-01

Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain. PMID:19890483

Exact and Approximate Probabilistic Symbolic Execution

NASA Technical Reports Server (NTRS)

Luckow, Kasper; Pasareanu, Corina S.; Dwyer, Matthew B.; Filieri, Antonio; Visser, Willem

2014-01-01

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes.

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2002-01-01

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

From prompt gamma distribution to dose: a novel approach combining an evolutionary algorithm and filtering based on Gaussian-powerlaw convolutions.

PubMed

Schumann, A; Priegnitz, M; Schoene, S; Enghardt, W; Rohling, H; Fiedler, F

2016-10-07

Range verification and dose monitoring in proton therapy is considered as highly desirable. Different methods have been developed worldwide, like particle therapy positron emission tomography (PT-PET) and prompt gamma imaging (PGI). In general, these methods allow for a verification of the proton range. However, quantification of the dose from these measurements remains challenging. For the first time, we present an approach for estimating the dose from prompt γ-ray emission profiles. It combines a filtering procedure based on Gaussian-powerlaw convolution with an evolutionary algorithm. By means of convolving depth dose profiles with an appropriate filter kernel, prompt γ-ray depth profiles are obtained. In order to reverse this step, the evolutionary algorithm is applied. The feasibility of this approach is demonstrated for a spread-out Bragg-peak in a water target.

Quantification of HIV-1 DNA using real-time recombinase polymerase amplification.

PubMed

Crannell, Zachary Austin; Rohrman, Brittany; Richards-Kortum, Rebecca

2014-06-17

Although recombinase polymerase amplification (RPA) has many advantages for the detection of pathogenic nucleic acids in point-of-care applications, RPA has not yet been implemented to quantify sample concentration using a standard curve. Here, we describe a real-time RPA assay with an internal positive control and an algorithm that analyzes real-time fluorescence data to quantify HIV-1 DNA. We show that DNA concentration and the onset of detectable amplification are correlated by an exponential standard curve. In a set of experiments in which the standard curve and algorithm were used to analyze and quantify additional DNA samples, the algorithm predicted an average concentration within 1 order of magnitude of the correct concentration for all HIV-1 DNA concentrations tested. These results suggest that quantitative RPA (qRPA) may serve as a powerful tool for quantifying nucleic acids and may be adapted for use in single-sample point-of-care diagnostic systems.

Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance.

PubMed

Sjögren, Jane; Ubachs, Joey F A; Engblom, Henrik; Carlsson, Marcus; Arheden, Håkan; Heiberg, Einar

2012-01-31

T2-weighted cardiovascular magnetic resonance (CMR) has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR), after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD), full width half maximum intensity (FWHM) or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. MaR was 32.9 ± 10.9% of left ventricular mass (LVM) when assessed by the reference observer and 31.0 ± 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 ± 6.4% of LVM, R = 0.81 (p < 0.001) for Segment MaR, -2.3 ± 4.9%, R = 0.91 (p < 0.001) for inter observer variability of manual delineation, -7.7 ± 11.4%, R = 0.38 (p = 0.008) for 2SD, -21.0 ± 9.9%, R = 0.41 (p = 0.004) for FWHM, and 5.3 ± 9.6%, R = 0.47 (p < 0.001) for Otsu. There is a good agreement between automatic Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a promising, objective method for standardized MaR quantification in T2-weighted CMR.

Multiple-approaches to the identification and quantification of cytochromes P450 in human liver tissue by mass spectrometry.

PubMed

Seibert, Cathrin; Davidson, Brian R; Fuller, Barry J; Patterson, Laurence H; Griffiths, William J; Wang, Yuqin

2009-04-01

Here we report the identification and approximate quantification of cytochrome P450 (CYP) proteins in human liver microsomes as determined by nano-LC-MS/MS with application of the exponentially modified protein abundance index (emPAI) algorithm during database searching. Protocols based on 1D-gel protein separation and 2D-LC peptide separation gave comparable results. In total, 18 CYP isoforms were unambiguously identified based on unique peptide matches. Further, we have determined the absolute quantity of two CYP enzymes (2E1 and 1A2) in human liver microsomes using stable-isotope dilution mass spectrometry, where microsomal proteins were separated by 1D-gel electrophoresis, digested with trypsin in the presence of either a CYP2E1- or 1A2-specific stable-isotope labeled tryptic peptide and analyzed by LC-MS/MS. Using multiple reaction monitoring (MRM) for the isotope-labeled tryptic peptides and their natural unlabeled analogues quantification could be performed over the range of 0.1-1.5 pmol on column. Liver microsomes from four individuals were analyzed for CYP2E1 giving values of 88-200 pmol/mg microsomal protein. The CYP1A2 content of microsomes from a further three individuals ranged from 165 to 263 pmol/mg microsomal protein. Although, in this proof-of-concept study for CYP quantification, the two CYP isoforms were quantified from different samples, there are no practical reasons to prevent multiplexing the method to allow the quantification of multiple CYP isoforms in a single sample.

Multiple-approaches to the identification and quantification of cytochromes P450 in human liver tissue by mass spectrometry

PubMed Central

Seibert, Cathrin; Davidson, Brian R.; Fuller, Barry J.; Patterson, Laurence H.; Griffiths, William J.; Wang, Yuqin

2009-01-01

Here we report the identification and approximate quantification of cytochrome P450 (CYP) proteins in human liver microsomes as determined by nano-LC-MS/MS with application of the exponentially modified protein abundance index (emPAI) algorithm during database searching. Protocols based on 1D-gel protein separation and 2D-LC peptide separation gave comparable results. In total 18 CYP isoforms were unambiguously identified based on unique peptide matches. Further, we have determined the absolute quantity of two CYP enzymes (2E1 and 1A2) in human liver microsomes using stable-isotope dilution mass spectrometry, where microsomal proteins were separated by 1D-gel electrophoresis, digested with trypsin in the presence of either a CYP2E1- or 1A2-specific stable-isotope labelled tryptic peptide and analysed by LC-MS/MS. Using multiple reaction monitoring (MRM) for the isotope-labelled tryptic peptides and their natural unlabelled analogues quantification could be performed over the range of 0.1 – 1.5 pmol on column. Liver microsomes from four individuals were analysed for CYP2E1 giving values of 88 - 200 pmol/mg microsomal protein. The CYP1A2 content of microsomes from a further three individuals ranged from 165 – 263 pmol/mg microsomal protein. Although, in this proof-of-concept study for CYP quantification, the two CYP-isoforms were quantified from different samples, there are no practical reasons to prevent multiplexing the method to allow the quantification of multiple CYP-isoforms in a single sample. PMID:19714871

Multisensor Arrays for Greater Reliability and Accuracy

NASA Technical Reports Server (NTRS)

Immer, Christopher; Eckhoff, Anthony; Lane, John; Perotti, Jose; Randazzo, John; Blalock, Norman; Ree, Jeff

2004-01-01

Arrays of multiple, nominally identical sensors with sensor-output-processing electronic hardware and software are being developed in order to obtain accuracy, reliability, and lifetime greater than those of single sensors. The conceptual basis of this development lies in the statistical behavior of multiple sensors and a multisensor-array (MSA) algorithm that exploits that behavior. In addition, advances in microelectromechanical systems (MEMS) and integrated circuits are exploited. A typical sensor unit according to this concept includes multiple MEMS sensors and sensor-readout circuitry fabricated together on a single chip and packaged compactly with a microprocessor that performs several functions, including execution of the MSA algorithm. In the MSA algorithm, the readings from all the sensors in an array at a given instant of time are compared and the reliability of each sensor is quantified. This comparison of readings and quantification of reliabilities involves the calculation of the ratio between every sensor reading and every other sensor reading, plus calculation of the sum of all such ratios. Then one output reading for the given instant of time is computed as a weighted average of the readings of all the sensors. In this computation, the weight for each sensor is the aforementioned value used to quantify its reliability. In an optional variant of the MSA algorithm that can be implemented easily, a running sum of the reliability value for each sensor at previous time steps as well as at the present time step is used as the weight of the sensor in calculating the weighted average at the present time step. In this variant, the weight of a sensor that continually fails gradually decreases, so that eventually, its influence over the output reading becomes minimal: In effect, the sensor system "learns" which sensors to trust and which not to trust. The MSA algorithm incorporates a criterion for deciding whether there remain enough sensor readings that approximate each other sufficiently closely to constitute a majority for the purpose of quantifying reliability. This criterion is, simply, that if there do not exist at least three sensors having weights greater than a prescribed minimum acceptable value, then the array as a whole is deemed to have failed.

Enhanced Trajectory Based Similarity Prediction with Uncertainty Quantification

DTIC Science & Technology

2014-10-02

challenge by obtaining the highest score by using a data-driven prognostics method to predict the RUL of a turbofan engine (Saxena & Goebel, PHM08...process for multi-regime health assessment. To illustrate multi-regime partitioning, the “ Turbofan Engine Degradation simulation” data set from...hence the name k- means. Figure 3 shows the results of the k-means clustering algorithm on the “ Turbofan Engine Degradation simulation” data set. As

Large signal-to-noise ratio quantification in MLE for ARARMAX models

NASA Astrophysics Data System (ADS)

Zou, Yiqun; Tang, Xiafei

2014-06-01

It has been shown that closed-loop linear system identification by indirect method can be generally transferred to open-loop ARARMAX (AutoRegressive AutoRegressive Moving Average with eXogenous input) estimation. For such models, the gradient-related optimisation with large enough signal-to-noise ratio (SNR) can avoid the potential local convergence in maximum likelihood estimation. To ease the application of this condition, the threshold SNR needs to be quantified. In this paper, we build the amplitude coefficient which is an equivalence to the SNR and prove the finiteness of the threshold amplitude coefficient within the stability region. The quantification of threshold is achieved by the minimisation of an elaborately designed multi-variable cost function which unifies all the restrictions on the amplitude coefficient. The corresponding algorithm based on two sets of physically realisable system input-output data details the minimisation and also points out how to use the gradient-related method to estimate ARARMAX parameters when local minimum is present as the SNR is small. Then, the algorithm is tested on a theoretical AutoRegressive Moving Average with eXogenous input model for the derivation of the threshold and a gas turbine engine real system for model identification, respectively. Finally, the graphical validation of threshold on a two-dimensional plot is discussed.

Understanding of the Geomorphological Elements in Discrimination of Typical Mediterranean Land Cover Types

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2017-12-01

Quantification of geomorphometric features is the keystone concern of the current study. The quantification was based on the statistical approach in term of multivariate analysis of local topographic features. The implemented algorithm utilizes the Digital Elevation Model (DEM) to categorize and extract the geomorphometric features embedded in the topographic dataset. The morphological settings were exercised on the central pixel of 3x3 per-defined convolution kernel to evaluate the surrounding pixels under the right directional pour point model (D8) of the azimuth viewpoints. Realization of unsupervised classification algorithm in term of Iterative Self-Organizing Data Analysis Technique (ISODATA) was carried out on ASTER GDEM within the boundary of the designated study area to distinguish 10 morphometric classes. The morphometric classes expressed spatial distribution variation in the study area. The adopted methodology is successful to appreciate the spatial distribution of the geomorphometric features under investigation. The conducted results verified the superimposition of the delineated geomorphometric elements over a given remote sensing imagery to be further analyzed. Robust relationship between different Land Cover types and the geomorphological elements was established in the context of the study area. The domination and the relative association of different Land Cover types in corresponding to its geomorphological elements were demonstrated.

Nondestructive Detection and Quantification of Blueberry Bruising using Near-infrared (NIR) Hyperspectral Reflectance Imaging

PubMed Central

Jiang, Yu; Li, Changying; Takeda, Fumiomi

2016-01-01

Currently, blueberry bruising is evaluated by either human visual/tactile inspection or firmness measurement instruments. These methods are destructive, time-consuming, and subjective. The goal of this paper was to develop a non-destructive approach for blueberry bruising detection and quantification. Experiments were conducted on 300 samples of southern highbush blueberry (Camellia, Rebel, and Star) and on 1500 samples of northern highbush blueberry (Bluecrop, Jersey, and Liberty) for hyperspectral imaging analysis, firmness measurement, and human evaluation. An algorithm was developed to automatically calculate a bruise ratio index (ratio of bruised to whole fruit area) for bruise quantification. The spectra of bruised and healthy tissues were statistically separated and the separation was independent of cultivars. Support vector machine (SVM) classification of the spectra from the regions of interest (ROIs) achieved over 94%, 92%, and 96% accuracy on the training set, independent testing set, and combined set, respectively. The statistical results showed that the bruise ratio index was equivalent to the measured firmness but better than the predicted firmness in regard to effectiveness of bruise quantification, and the bruise ratio index had a strong correlation with human assessment (R2 = 0.78 − 0.83). Therefore, the proposed approach and the bruise ratio index are effective to non-destructively detect and quantify blueberry bruising. PMID:27767050

Digital Protocol for Chemical Analysis at Ultralow Concentrations by Surface-Enhanced Raman Scattering.

PubMed

de Albuquerque, Carlos Diego L; Sobral-Filho, Regivaldo G; Poppi, Ronei J; Brolo, Alexandre G

2018-01-16

Single molecule surface-enhanced Raman spectroscopy (SM-SERS) has the potential to revolutionize quantitative analysis at ultralow concentrations (less than 1 nM). However, there are no established protocols to generalize the application of this technique in analytical chemistry. Here, a protocol for quantification at ultralow concentrations using SM-SERS is proposed. The approach aims to take advantage of the stochastic nature of the single-molecule regime to achieved lower limits of quantification (LOQ). Two emerging contaminants commonly found in aquatic environments, enrofloxacin (ENRO) and ciprofloxacin (CIPRO), were chosen as nonresonant molecular probes. The methodology involves a multivariate resolution curve fitting known as non-negative matrix factorization with alternating least-squares algorithm (NMF-ALS) to solve spectral overlaps. The key element of the quantification is to realize that, under SM-SERS conditions, the Raman intensity generated by a molecule adsorbed on a "hotspot" can be digitalized. Therefore, the number of SERS event counts (rather than SERS intensities) was shown to be proportional to the solution concentration. This allowed the determination of both ENRO and CIPRO with high accuracy and precision even at ultralow concentrations regime. The LOQ for both ENRO and CIPRO were achieved at 2.8 pM. The digital SERS protocol, suggested here, is a roadmap for the implementation of SM-SERS as a routine tool for quantification at ultralow concentrations.

Automatic pelvis segmentation from x-ray images of a mouse model

NASA Astrophysics Data System (ADS)

Al Okashi, Omar M.; Du, Hongbo; Al-Assam, Hisham

2017-05-01

The automatic detection and quantification of skeletal structures has a variety of different applications for biological research. Accurate segmentation of the pelvis from X-ray images of mice in a high-throughput project such as the Mouse Genomes Project not only saves time and cost but also helps achieving an unbiased quantitative analysis within the phenotyping pipeline. This paper proposes an automatic solution for pelvis segmentation based on structural and orientation properties of the pelvis in X-ray images. The solution consists of three stages including pre-processing image to extract pelvis area, initial pelvis mask preparation and final pelvis segmentation. Experimental results on a set of 100 X-ray images showed consistent performance of the algorithm. The automated solution overcomes the weaknesses of a manual annotation procedure where intra- and inter-observer variations cannot be avoided.

Investigation of Natural Gas Fugitive Leak Detection Using an Unmanned Aerial Vehicle

NASA Astrophysics Data System (ADS)

Yang, S.; Talbot, R. W.; Frish, M. B.; Golston, L.; Aubut, N. F.; Zondlo, M. A.

2017-12-01

The U.S is now the world's largest natural gas producer, of which methane (CH4) is the main component. About 2% of the CH4 is lost through fugitive leaks. This research is under the DOE Methane Observation Networks with Innovative Technology to Obtain Reductions (MONITOR) program of ARPA-E. Our sentry measurement system is composed of four state-of-the-art technologies centered around the RMLDTM (Remote Methane Leak Detector). An open path RMLDTM measures column-integrated CH4 concentration that incorporates fluctuations in the vertical CH4 distribution. Based on Backscatter Tunable Diode Laser Absorption Spectroscopy and Small Unmanned Aerial Vehicles, the sentry system can autonomously, consistently and cost-effectively monitor and quantify CH4 leakage from sites associated with natural gas production. This system provides an advanced capability in detecting leaks at hard-to-access sites (e.g., wellheads) compared to traditional manual methods. Automated leak detecting and reporting algorithms combined with wireless data link implement real-time leak information reporting. Early data were gathered to set up and test the prototype system, and to optimize the leak localization and calculation strategies. The flight pattern is based on a raster scan which can generate interpolated CH4 concentration maps. The localization and quantification algorithms can be derived from the plume images combined with wind vectors. Currently, the accuracy of localization algorithm can reach 2 m and the calculation algorithm has a factor of 2 accuracy. This study places particular emphasis on flux quantification. The data collected at Colorado and Houston test fields were processed, and the correlation between flux and other parameters analyzed. Higher wind speeds and lower wind variation are preferred to optimize flux estimation. Eventually, this system will supply an enhanced detection capability to significantly reduce fugitive CH4 emissions in the natural gas industry.

Automated Quantification of Volumetric Optic Disc Swelling in Papilledema Using Spectral-Domain Optical Coherence Tomography

PubMed Central

Wang, Jui-Kai; Kardon, Randy H.; Kupersmith, Mark J.; Garvin, Mona K.

2012-01-01

Purpose. To develop an automated method for the quantification of volumetric optic disc swelling in papilledema subjects using spectral-domain optical coherence tomography (SD-OCT) and to determine the extent that such volumetric measurements correlate with Frisén scale grades (from fundus photographs) and two-dimensional (2-D) peripapillary retinal nerve fiber layer (RNFL) and total retinal (TR) thickness measurements from SD-OCT. Methods. A custom image-analysis algorithm was developed to obtain peripapillary circular RNFL thickness, TR thickness, and TR volume measurements from SD-OCT volumes of subjects with papilledema. In addition, peripapillary RNFL thickness measures from the commercially available Zeiss SD-OCT machine were obtained. Expert Frisén scale grades were independently obtained from corresponding fundus photographs. Results. In 71 SD-OCT scans, the mean (± standard deviation) resulting TR volumes for Frisén scale 0 to scale 4 were 11.36 ± 0.56, 12.53 ± 1.21, 14.42 ± 2.11, 17.48 ± 2.63, and 21.81 ± 3.16 mm3, respectively. The Spearman's rank correlation coefficient was 0.737. Using 55 eyes with valid Zeiss RNFL measurements, Pearson's correlation coefficient (r) between the TR volume and the custom algorithm's TR thickness, the custom algorithm's RNFL thickness, and Zeiss' RNFL thickness was 0.980, 0.929, and 0.946, respectively. Between Zeiss' RNFL and the custom algorithm's RNFL, and the study's TR thickness, r was 0.901 and 0.961, respectively. Conclusions. Volumetric measurements of the degree of disc swelling in subjects with papilledema can be obtained from SD-OCT volumes, with the mean volume appearing to be roughly linearly related to the Frisén scale grade. Using such an approach can provide a more continuous, objective, and robust means for assessing the degree of disc swelling compared with presently available approaches. PMID:22599584

Visualizing Ecosystem Energy Flow in Complex Food Web Networks: A Comparison of Three Alaskan Large Marine Ecosystems

NASA Astrophysics Data System (ADS)

Kearney, K.; Aydin, K.

2016-02-01

Oceanic food webs are often depicted as network graphs, with the major organisms or functional groups displayed as nodes and the fluxes of between them as the edges. However, the large number of nodes and edges and high connectance of many management-oriented food webs coupled with graph layout algorithms poorly-suited to certain desired characteristics of food web visualizations often lead to hopelessly tangled diagrams that convey little information other than, "It's complex." Here, I combine several new graph visualization techniques- including a new node layout alorithm based on a trophic similarity (quantification of shared predator and prey) and trophic level, divided edge bundling for edge routing, and intelligent automated placement of labels- to create a much clearer visualization of the important fluxes through a food web. The technique will be used to highlight the differences in energy flow within three Alaskan Large Marine Ecosystems (the Bering Sea, Gulf of Alaska, and Aleutian Islands) that include very similar functional groups but unique energy pathways.

Optimal design and uncertainty quantification in blood flow simulations for congenital heart disease

NASA Astrophysics Data System (ADS)

Marsden, Alison

2009-11-01

Recent work has demonstrated substantial progress in capabilities for patient-specific cardiovascular flow simulations. Recent advances include increasingly complex geometries, physiological flow conditions, and fluid structure interaction. However inputs to these simulations, including medical image data, catheter-derived pressures and material properties, can have significant uncertainties associated with them. For simulations to predict clinically useful and reliable output information, it is necessary to quantify the effects of input uncertainties on outputs of interest. In addition, blood flow simulation tools can now be efficiently coupled to shape optimization algorithms for surgery design applications, and these tools should incorporate uncertainty information. We present a unified framework to systematically and efficient account for uncertainties in simulations using adaptive stochastic collocation. In addition, we present a framework for derivative-free optimization of cardiovascular geometries, and layer these tools to perform optimization under uncertainty. These methods are demonstrated using simulations and surgery optimization to improve hemodynamics in pediatric cardiology applications.

Quantitative Evaluation of 2 Scatter-Correction Techniques for 18F-FDG Brain PET/MRI in Regard to MR-Based Attenuation Correction.

PubMed

Teuho, Jarmo; Saunavaara, Virva; Tolvanen, Tuula; Tuokkola, Terhi; Karlsson, Antti; Tuisku, Jouni; Teräs, Mika

2017-10-01

In PET, corrections for photon scatter and attenuation are essential for visual and quantitative consistency. MR attenuation correction (MRAC) is generally conducted by image segmentation and assignment of discrete attenuation coefficients, which offer limited accuracy compared with CT attenuation correction. Potential inaccuracies in MRAC may affect scatter correction, because the attenuation image (μ-map) is used in single scatter simulation (SSS) to calculate the scatter estimate. We assessed the impact of MRAC to scatter correction using 2 scatter-correction techniques and 3 μ-maps for MRAC. Methods: The tail-fitted SSS (TF-SSS) and a Monte Carlo-based single scatter simulation (MC-SSS) algorithm implementations on the Philips Ingenuity TF PET/MR were used with 1 CT-based and 2 MR-based μ-maps. Data from 7 subjects were used in the clinical evaluation, and a phantom study using an anatomic brain phantom was conducted. Scatter-correction sinograms were evaluated for each scatter correction method and μ-map. Absolute image quantification was investigated with the phantom data. Quantitative assessment of PET images was performed by volume-of-interest and ratio image analysis. Results: MRAC did not result in large differences in scatter algorithm performance, especially with TF-SSS. Scatter sinograms and scatter fractions did not reveal large differences regardless of the μ-map used. TF-SSS showed slightly higher absolute quantification. The differences in volume-of-interest analysis between TF-SSS and MC-SSS were 3% at maximum in the phantom and 4% in the patient study. Both algorithms showed excellent correlation with each other with no visual differences between PET images. MC-SSS showed a slight dependency on the μ-map used, with a difference of 2% on average and 4% at maximum when a μ-map without bone was used. Conclusion: The effect of different MR-based μ-maps on the performance of scatter correction was minimal in non-time-of-flight 18 F-FDG PET/MR brain imaging. The SSS algorithm was not affected significantly by MRAC. The performance of the MC-SSS algorithm is comparable but not superior to TF-SSS, warranting further investigations of algorithm optimization and performance with different radiotracers and time-of-flight imaging. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Fast HPLC-DAD quantification of nine polyphenols in honey by using second-order calibration method based on trilinear decomposition algorithm.

PubMed

Zhang, Xiao-Hua; Wu, Hai-Long; Wang, Jian-Yao; Tu, De-Zhu; Kang, Chao; Zhao, Juan; Chen, Yao; Miu, Xiao-Xia; Yu, Ru-Qin

2013-05-01

This paper describes the use of second-order calibration for development of HPLC-DAD method to quantify nine polyphenols in five kinds of honey samples. The sample treatment procedure was simplified effectively relative to the traditional ways. Baselines drift was also overcome by means of regarding the drift as additional factor(s) as well as the analytes of interest in the mathematical model. The contents of polyphenols obtained by the alternating trilinear decomposition (ATLD) method have been successfully used to distinguish different types of honey. This method shows good linearity (r>0.99), rapidity (t<7.60 min) and accuracy, which may be extremely promising as an excellent routine strategy for identification and quantification of polyphenols in the complex matrices. Copyright © 2012 Elsevier Ltd. All rights reserved.

Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

NASA Astrophysics Data System (ADS)

Wang, Z.

2015-12-01

For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

Solving the stability-accuracy-diversity dilemma of recommender systems

NASA Astrophysics Data System (ADS)

Hou, Lei; Liu, Kecheng; Liu, Jianguo; Zhang, Runtong

2017-02-01

Recommender systems are of great significance in predicting the potential interesting items based on the target user's historical selections. However, the recommendation list for a specific user has been found changing vastly when the system changes, due to the unstable quantification of item similarities, which is defined as the recommendation stability problem. To improve the similarity stability and recommendation stability is crucial for the user experience enhancement and the better understanding of user interests. While the stability as well as accuracy of recommendation could be guaranteed by recommending only popular items, studies have been addressing the necessity of diversity which requires the system to recommend unpopular items. By ranking the similarities in terms of stability and considering only the most stable ones, we present a top- n-stability method based on the Heat Conduction algorithm (denoted as TNS-HC henceforth) for solving the stability-accuracy-diversity dilemma. Experiments on four benchmark data sets indicate that the TNS-HC algorithm could significantly improve the recommendation stability and accuracy simultaneously and still retain the high-diversity nature of the Heat Conduction algorithm. Furthermore, we compare the performance of the TNS-HC algorithm with a number of benchmark recommendation algorithms. The result suggests that the TNS-HC algorithm is more efficient in solving the stability-accuracy-diversity triple dilemma of recommender systems.

43 CFR 11.71 - Quantification phase-service reduction quantification.

Code of Federal Regulations, 2011 CFR

2011-10-01

...-discharge-or-release condition. (c) Contents of the quantification. The following factors should be included...; and (6) Factors identified in the specific guidance in paragraphs (h), (i), (j), (k), and (l) of this section dealing with the different kinds of natural resources. (d) Selection of resources, services, and...

43 CFR 11.71 - Quantification phase-service reduction quantification.

Code of Federal Regulations, 2010 CFR

2010-10-01

...-discharge-or-release condition. (c) Contents of the quantification. The following factors should be included...; and (6) Factors identified in the specific guidance in paragraphs (h), (i), (j), (k), and (l) of this section dealing with the different kinds of natural resources. (d) Selection of resources, services, and...

Sensitivity and specificity of automated detection of early repolarization in standard 12-lead electrocardiography.

PubMed

Kenttä, Tuomas; Porthan, Kimmo; Tikkanen, Jani T; Väänänen, Heikki; Oikarinen, Lasse; Viitasalo, Matti; Karanko, Hannu; Laaksonen, Maarit; Huikuri, Heikki V

2015-07-01

Early repolarization (ER) is defined as an elevation of the QRS-ST junction in at least two inferior or lateral leads of the standard 12-lead electrocardiogram (ECG). Our purpose was to create an algorithm for the automated detection and classification of ER. A total of 6,047 electrocardiograms were manually graded for ER by two experienced readers. The automated detection of ER was based on quantification of the characteristic slurring or notching in ER-positive leads. The ER detection algorithm was tested and its results were compared with manual grading, which served as the reference. Readers graded 183 ECGs (3.0%) as ER positive, of which the algorithm detected 176 recordings, resulting in sensitivity of 96.2%. Of the 5,864 ER-negative recordings, the algorithm classified 5,281 as negative, resulting in 90.1% specificity. Positive and negative predictive values for the algorithm were 23.2% and 99.9%, respectively, and its accuracy was 90.2%. Inferior ER was correctly detected in 84.6% and lateral ER in 98.6% of the cases. As the automatic algorithm has high sensitivity, it could be used as a prescreening tool for ER; only the electrocardiograms graded positive by the algorithm would be reviewed manually. This would reduce the need for manual labor by 90%. © 2014 Wiley Periodicals, Inc.

The use of zeolites to generate PET phantoms for the validation of quantification strategies in oncology.

PubMed

Zito, Felicia; De Bernardi, Elisabetta; Soffientini, Chiara; Canzi, Cristina; Casati, Rosangela; Gerundini, Paolo; Baselli, Giuseppe

2012-09-01

In recent years, segmentation algorithms and activity quantification methods have been proposed for oncological (18)F-fluorodeoxyglucose (FDG) PET. A full assessment of these algorithms, necessary for a clinical transfer, requires a validation on data sets provided with a reliable ground truth as to the imaged activity distribution, which must be as realistic as possible. The aim of this work is to propose a strategy to simulate lesions of uniform uptake and irregular shape in an anthropomorphic phantom, with the possibility to easily obtain a ground truth as to lesion activity and borders. Lesions were simulated with samples of clinoptilolite, a family of natural zeolites of irregular shape, able to absorb aqueous solutions of (18)F-FDG, available in a wide size range, and nontoxic. Zeolites were soaked in solutions of (18)F-FDG for increasing times up to 120 min and their absorptive properties were characterized as function of soaking duration, solution concentration, and zeolite dry weight. Saturated zeolites were wrapped in Parafilm, positioned inside an Alderson thorax-abdomen phantom and imaged with a PET-CT scanner. The ground truth for the activity distribution of each zeolite was obtained by segmenting high-resolution finely aligned CT images, on the basis of independently obtained volume measurements. The fine alignment between CT and PET was validated by comparing the CT-derived ground truth to a set of zeolites' PET threshold segmentations in terms of Dice index and volume error. The soaking time necessary to achieve saturation increases with zeolite dry weight, with a maximum of about 90 min for the largest sample. At saturation, a linear dependence of the uptake normalized to the solution concentration on zeolite dry weight (R(2) = 0.988), as well as a uniform distribution of the activity over the entire zeolite volume from PET imaging were demonstrated. These findings indicate that the (18)F-FDG solution is able to saturate the zeolite pores and that the concentration does not influence the distribution uniformity of both solution and solute, at least at the trace concentrations used for zeolite activation. An additional proof of uniformity of zeolite saturation was obtained observing a correspondence between uptake and adsorbed volume of solution, corresponding to about 27.8% of zeolite volume. As to the ground truth for zeolites positioned inside the phantom, the segmentation of finely aligned CT images provided reliable borders, as demonstrated by a mean absolute volume error of 2.8% with respect to the PET threshold segmentation corresponding to the maximum Dice. The proposed methodology allowed obtaining an experimental phantom data set that can be used as a feasible tool to test and validate quantification and segmentation algorithms for PET in oncology. The phantom is currently under consideration for being included in a benchmark designed by AAPM TG211, which will be available to the community to evaluate PET automatic segmentation methods.

CLMSVault: A Software Suite for Protein Cross-Linking Mass-Spectrometry Data Analysis and Visualization.

PubMed

Courcelles, Mathieu; Coulombe-Huntington, Jasmin; Cossette, Émilie; Gingras, Anne-Claude; Thibault, Pierre; Tyers, Mike

2017-07-07

Protein cross-linking mass spectrometry (CL-MS) enables the sensitive detection of protein interactions and the inference of protein complex topology. The detection of chemical cross-links between protein residues can identify intra- and interprotein contact sites or provide physical constraints for molecular modeling of protein structure. Recent innovations in cross-linker design, sample preparation, mass spectrometry, and software tools have significantly improved CL-MS approaches. Although a number of algorithms now exist for the identification of cross-linked peptides from mass spectral data, a dearth of user-friendly analysis tools represent a practical bottleneck to the broad adoption of the approach. To facilitate the analysis of CL-MS data, we developed CLMSVault, a software suite designed to leverage existing CL-MS algorithms and provide intuitive and flexible tools for cross-platform data interpretation. CLMSVault stores and combines complementary information obtained from different cross-linkers and search algorithms. CLMSVault provides filtering, comparison, and visualization tools to support CL-MS analyses and includes a workflow for label-free quantification of cross-linked peptides. An embedded 3D viewer enables the visualization of quantitative data and the mapping of cross-linked sites onto PDB structural models. We demonstrate the application of CLMSVault for the analysis of a noncovalent Cdc34-ubiquitin protein complex cross-linked under different conditions. CLMSVault is open-source software (available at https://gitlab.com/courcelm/clmsvault.git ), and a live demo is available at http://democlmsvault.tyerslab.com/ .

Assessing the Effectiveness of Landsat 8 Chlorophyll-a Retrieval Algorithms for Regional Freshwater Management

NASA Astrophysics Data System (ADS)

Boucher, J. M.; Weathers, K. C.; Norouzi, H.; Prakash, S.; Saberi, S. J.

2016-12-01

Predicting algal blooms has become a priority for municipalities, businesses, and citizens. Remote sensing (RS) offers solutions to the spatial and temporal challenges facing existing lake monitoring programs that rely primarily on high-investment in situ measurements. Techniques to remotely measure chlorophyll-a (chl-a) as a proxy for algal biomass have been limited to large water bodies in particular seasons and chl-a ranges. This study explores the relationship between in-lake measured chl-a data in Maine and New Hampshire and chl-a retrieval algorithms. Landsat 8 images were obtained and required atmospheric and radiometric corrections. Six indices including the NDVI and KIVU algorithms were tested to validate their applicability on a regional scale on ten scenes from 2013-2015 covering 169 lakes. In addition, more robust novel models were also explored. For late-summer scenes, existing algorithms accounted for nearly 90% of the variation in in-situ measurements, however, we found a significant effect of time of year on each index. A sensitivity analysis revealed that rainfall in the region as well as a longer time difference between in situ measurements and the satellite image increased noise in the models. The quantification of these confounding influences points to potential solutions such as incorporating remotely sensed water temperature into models as a proxy of seasonal effects. Novel models built to fit particular scenes reduced this variability, but they required more satellite band inputs that do not yet have a clear ecological relevance. These results suggest that RS could be an effective and accessible tool for monitoring programs at the regional scale. Although they are subject to some of the limitations of traditional monitoring imposed by the weather, high-resolution satellites like Landsat 8 provide a promising opportunity for protecting freshwater resources.

Reference gene selection for quantitative real-time PCR in Solanum lycopersicum L. inoculated with the mycorrhizal fungus Rhizophagus irregularis.

PubMed

Fuentes, Alejandra; Ortiz, Javier; Saavedra, Nicolás; Salazar, Luis A; Meneses, Claudio; Arriagada, Cesar

2016-04-01

The gene expression stability of candidate reference genes in the roots and leaves of Solanum lycopersicum inoculated with arbuscular mycorrhizal fungi was investigated. Eight candidate reference genes including elongation factor 1 α (EF1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK), protein phosphatase 2A (PP2Acs), ribosomal protein L2 (RPL2), β-tubulin (TUB), ubiquitin (UBI) and actin (ACT) were selected, and their expression stability was assessed to determine the most stable internal reference for quantitative PCR normalization in S. lycopersicum inoculated with the arbuscular mycorrhizal fungus Rhizophagus irregularis. The stability of each gene was analysed in leaves and roots together and separated using the geNorm and NormFinder algorithms. Differences were detected between leaves and roots, varying among the best-ranked genes depending on the algorithm used and the tissue analysed. PGK, TUB and EF1 genes showed higher stability in roots, while EF1 and UBI had higher stability in leaves. Statistical algorithms indicated that the GAPDH gene was the least stable under the experimental conditions assayed. Then, we analysed the expression levels of the LePT4 gene, a phosphate transporter whose expression is induced by fungal colonization in host plant roots. No differences were observed when the most stable genes were used as reference genes. However, when GAPDH was used as the reference gene, we observed an overestimation of LePT4 expression. In summary, our results revealed that candidate reference genes present variable stability in S. lycopersicum arbuscular mycorrhizal symbiosis depending on the algorithm and tissue analysed. Thus, reference gene selection is an important issue for obtaining reliable results in gene expression quantification. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Automated quantification of proliferation with automated hot-spot selection in phosphohistone H3/MART1 dual-stained stage I/II melanoma.

PubMed

Nielsen, Patricia Switten; Riber-Hansen, Rikke; Schmidt, Henrik; Steiniche, Torben

2016-04-09

Staging of melanoma includes quantification of a proliferation index, i.e., presumed melanocytic mitoses of H&E stains are counted manually in hot spots. Yet, its reproducibility and prognostic impact increases by immunohistochemical dual staining for phosphohistone H3 (PHH3) and MART1, which also may enable fully automated quantification by image analysis. To ensure manageable workloads and repeatable measurements in modern pathology, the study aimed to present an automated quantification of proliferation with automated hot-spot selection in PHH3/MART1-stained melanomas. Formalin-fixed, paraffin-embedded tissue from 153 consecutive stage I/II melanoma patients was immunohistochemically dual-stained for PHH3 and MART1. Whole slide images were captured, and the number of PHH3/MART1-positive cells was manually and automatically counted in the global tumor area and in a manually and automatically selected hot spot, i.e., a fixed 1-mm(2) square. Bland-Altman plots and hypothesis tests compared manual and automated procedures, and the Cox proportional hazards model established their prognostic impact. The mean difference between manual and automated global counts was 2.9 cells/mm(2) (P = 0.0071) and 0.23 cells per hot spot (P = 0.96) for automated counts in manually and automatically selected hot spots. In 77 % of cases, manual and automated hot spots overlapped. Fully manual hot-spot counts yielded the highest prognostic performance with an adjusted hazard ratio of 5.5 (95 % CI, 1.3-24, P = 0.024) as opposed to 1.3 (95 % CI, 0.61-2.9, P = 0.47) for automated counts with automated hot spots. The automated index and automated hot-spot selection were highly correlated to their manual counterpart, but altogether their prognostic impact was noticeably reduced. Because correct recognition of only one PHH3/MART1-positive cell seems important, extremely high sensitivity and specificity of the algorithm is required for prognostic purposes. Thus, automated analysis may still aid and improve the pathologists' detection of mitoses in melanoma and possibly other malignancies.

ATAQS: A computational software tool for high throughput transition optimization and validation for selected reaction monitoring mass spectrometry

PubMed Central

2011-01-01

Background Since its inception, proteomics has essentially operated in a discovery mode with the goal of identifying and quantifying the maximal number of proteins in a sample. Increasingly, proteomic measurements are also supporting hypothesis-driven studies, in which a predetermined set of proteins is consistently detected and quantified in multiple samples. Selected reaction monitoring (SRM) is a targeted mass spectrometric technique that supports the detection and quantification of specific proteins in complex samples at high sensitivity and reproducibility. Here, we describe ATAQS, an integrated software platform that supports all stages of targeted, SRM-based proteomics experiments including target selection, transition optimization and post acquisition data analysis. This software will significantly facilitate the use of targeted proteomic techniques and contribute to the generation of highly sensitive, reproducible and complete datasets that are particularly critical for the discovery and validation of targets in hypothesis-driven studies in systems biology. Result We introduce a new open source software pipeline, ATAQS (Automated and Targeted Analysis with Quantitative SRM), which consists of a number of modules that collectively support the SRM assay development workflow for targeted proteomic experiments (project management and generation of protein, peptide and transitions and the validation of peptide detection by SRM). ATAQS provides a flexible pipeline for end-users by allowing the workflow to start or end at any point of the pipeline, and for computational biologists, by enabling the easy extension of java algorithm classes for their own algorithm plug-in or connection via an external web site. This integrated system supports all steps in a SRM-based experiment and provides a user-friendly GUI that can be run by any operating system that allows the installation of the Mozilla Firefox web browser. Conclusions Targeted proteomics via SRM is a powerful new technique that enables the reproducible and accurate identification and quantification of sets of proteins of interest. ATAQS is the first open-source software that supports all steps of the targeted proteomics workflow. ATAQS also provides software API (Application Program Interface) documentation that enables the addition of new algorithms to each of the workflow steps. The software, installation guide and sample dataset can be found in http://tools.proteomecenter.org/ATAQS/ATAQS.html PMID:21414234

Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline.

PubMed

Gao, Shan; van 't Klooster, Ronald; Brandts, Anne; Roes, Stijntje D; Alizadeh Dehnavi, Reza; de Roos, Albert; Westenberg, Jos J M; van der Geest, Rob J

2017-01-01

To develop and evaluate a method that can fully automatically identify the vessel wall boundaries and quantify the wall thickness for both common carotid artery (CCA) and descending aorta (DAO) from axial magnetic resonance (MR) images. 3T MRI data acquired with T 1 -weighted gradient-echo black-blood imaging sequence from carotid (39 subjects) and aorta (39 subjects) were used to develop and test the algorithm. The vessel wall segmentation was achieved by respectively fitting a 3D cylindrical B-spline surface to the boundaries of lumen and outer wall. The tube-fitting was based on the edge detection performed on the signal intensity (SI) profile along the surface normal. To achieve a fully automated process, Hough Transform (HT) was developed to estimate the lumen centerline and radii for the target vessel. Using the outputs of HT, a tube model for lumen segmentation was initialized and deformed to fit the image data. Finally, lumen segmentation was dilated to initiate the adaptation procedure of outer wall tube. The algorithm was validated by determining: 1) its performance against manual tracing; 2) its interscan reproducibility in quantifying vessel wall thickness (VWT); 3) its capability of detecting VWT difference in hypertensive patients compared with healthy controls. Statistical analysis including Bland-Altman analysis, t-test, and sample size calculation were performed for the purpose of algorithm evaluation. The mean distance between the manual and automatically detected lumen/outer wall contours was 0.00 ± 0.23/0.09 ± 0.21 mm for CCA and 0.12 ± 0.24/0.14 ± 0.35 mm for DAO. No significant difference was observed between the interscan VWT assessment using automated segmentation for both CCA (P = 0.19) and DAO (P = 0.94). Both manual and automated segmentation detected significantly higher carotid (P = 0.016 and P = 0.005) and aortic (P < 0.001 and P = 0.021) wall thickness in the hypertensive patients. A reliable and reproducible pipeline for fully automatic vessel wall quantification was developed and validated on healthy volunteers as well as patients with increased vessel wall thickness. This method holds promise for helping in efficient image interpretation for large-scale cohort studies. 4 J. Magn. Reson. Imaging 2017;45:215-228. © 2016 International Society for Magnetic Resonance in Medicine.

Periodic modulation-based stochastic resonance algorithm applied to quantitative analysis for weak liquid chromatography-mass spectrometry signal of granisetron in plasma

NASA Astrophysics Data System (ADS)

Xiang, Suyun; Wang, Wei; Xiang, Bingren; Deng, Haishan; Xie, Shaofei

2007-05-01

The periodic modulation-based stochastic resonance algorithm (PSRA) was used to amplify and detect the weak liquid chromatography-mass spectrometry (LC-MS) signal of granisetron in plasma. In the algorithm, the stochastic resonance (SR) was achieved by introducing an external periodic force to the nonlinear system. The optimization of parameters was carried out in two steps to give attention to both the signal-to-noise ratio (S/N) and the peak shape of output signal. By applying PSRA with the optimized parameters, the signal-to-noise ratio of LC-MS peak was enhanced significantly and distorted peak shape that often appeared in the traditional stochastic resonance algorithm was corrected by the added periodic force. Using the signals enhanced by PSRA, this method extended the limit of detection (LOD) and limit of quantification (LOQ) of granisetron in plasma from 0.05 and 0.2 ng/mL, respectively, to 0.01 and 0.02 ng/mL, and exhibited good linearity, accuracy and precision, which ensure accurate determination of the target analyte.

Chemical-Specific Representation of Air-Soil Exchange and Soil Penetration in Regional Multimedia Models

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

McKone, T.E.; Bennett, D.H.

2002-08-01

In multimedia mass-balance models, the soil compartment is an important sink as well as a conduit for transfers to vegetation and shallow groundwater. Here a novel approach for constructing soil transport algorithms for multimedia fate models is developed and evaluated. The resulting algorithms account for diffusion in gas and liquid components; advection in gas, liquid, or solid phases; and multiple transformation processes. They also provide an explicit quantification of the characteristic soil penetration depth. We construct a compartment model using three and four soil layers to replicate with high reliability the flux and mass distribution obtained from the exact analyticalmore » solution describing the transient dispersion, advection, and transformation of chemicals in soil with fixed properties and boundary conditions. Unlike the analytical solution, which requires fixed boundary conditions, the soil compartment algorithms can be dynamically linked to other compartments (air, vegetation, ground water, surface water) in multimedia fate models. We demonstrate and evaluate the performance of the algorithms in a model with applications to benzene, benzo(a)pyrene, MTBE, TCDD, and tritium.« less

Comparison of the algorithms classifying the ABC and GCB subtypes in diffuse large B-cell lymphoma.

PubMed

Boltežar, Lučka; Prevodnik, Veronika Kloboves; Perme, Maja Pohar; Gašljević, Gorana; Novaković, Barbara Jezeršek

2018-05-01

Different immunohistochemical algorithms for the classification of the activated B-cell (ABC) and germinal center B-cell (GCB) subtypes of diffuse large B-cell lymphoma (DLBCL) are applied in different laboratories. In the present study, 127 patients with DLCBL were investigated, all treated with rituximab and cyclophosphamide, hydroxydaunorubicin, oncovin and prednisone (CHOP) or CHOP-like regimens between April 2004 and December 2010. Multi-tumor tissue microarrays were prepared and were tested according to 4 algorithms: Hans; modified Hans; Choi; and modified Choi. For 39 patients, the flow cytometric quantification of CD19 and CD20 antigen expression was performed and the level of expression presented as molecules of equivalent soluble fluorochrome units. The Choi algorithm was demonstrated to be prognostic for OS and classified patients into the GCB subgroup with an HR of 0.91. No difference in the expression of the CD19 antigen between the ABC and GCB groups was observed, but the ABC subtype exhibited a decreased expression of the CD20 antigen compared with the GCB subtype.

Automatic layer segmentation of H&E microscopic images of mice skin

NASA Astrophysics Data System (ADS)

Hussein, Saif; Selway, Joanne; Jassim, Sabah; Al-Assam, Hisham

2016-05-01

Mammalian skin is a complex organ composed of a variety of cells and tissue types. The automatic detection and quantification of changes in skin structures has a wide range of applications for biological research. To accurately segment and quantify nuclei, sebaceous gland, hair follicles, and other skin structures, there is a need for a reliable segmentation of different skin layers. This paper presents an efficient segmentation algorithm to segment the three main layers of mice skin, namely epidermis, dermis, and subcutaneous layers. It also segments the epidermis layer into two sub layers, basal and cornified layers. The proposed algorithm uses adaptive colour deconvolution technique on H&E stain images to separate different tissue structures, inter-modes and Otsu thresholding techniques were effectively combined to segment the layers. It then uses a set of morphological and logical operations on each layer to removing unwanted objects. A dataset of 7000 H&E microscopic images of mutant and wild type mice were used to evaluate the effectiveness of the algorithm. Experimental results examined by domain experts have confirmed the viability of the proposed algorithms.

ROMUSE 2.0 User Manual

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

Khuwaileh, Bassam; Turinsky, Paul; Williams, Brian J.

2016-10-04

ROMUSE (Reduced Order Modeling Based Uncertainty/Sensitivity Estimator) is an effort within the Consortium for Advanced Simulation of Light water reactors (CASL) to provide an analysis tool to be used in conjunction with reactor core simulators, especially the Virtual Environment for Reactor Applications (VERA). ROMUSE is written in C++ and is currently capable of performing various types of parameters perturbations, uncertainty quantification, surrogate models construction and subspace analysis. Version 2.0 has the capability to interface with DAKOTA which gives ROMUSE access to the various algorithms implemented within DAKOTA. ROMUSE is mainly designed to interface with VERA and the Comprehensive Modeling andmore » Simulation Suite for Nuclear Safety Analysis and Design (SCALE) [1,2,3], however, ROMUSE can interface with any general model (e.g. python and matlab) with Input/Output (I/O) format that follows the Hierarchical Data Format 5 (HDF5). In this brief user manual, the use of ROMUSE will be overviewed and example problems will be presented and briefly discussed. The algorithms provided here range from algorithms inspired by those discussed in Ref.[4] to nuclear-specific algorithms discussed in Ref. [3].« less

Ultra-Scalable Algorithms for Large-Scale Uncertainty Quantification in Inverse Wave Propagation

DTIC Science & Technology

2016-03-04

53] N. Petra , J. Martin , G. Stadler, and O. Ghattas, A computational framework for infinite-dimensional Bayesian inverse problems: Part II...positions: Alen Alexanderian (NC State), Tan Bui-Thanh (UT-Austin), Carsten Burstedde (University of Bonn), Noemi Petra (UC Merced), Georg Stalder (NYU), Hari...Baltimore, MD, Nov. 2002. SC2002 Best Technical Paper Award. [3] A. Alexanderian, N. Petra , G. Stadler, and O. Ghattas, A-optimal design of exper

Streaming fragment assignment for real-time analysis of sequencing experiments

PubMed Central

Roberts, Adam; Pachter, Lior

2013-01-01

We present eXpress, a software package for highly efficient probabilistic assignment of ambiguously mapping sequenced fragments. eXpress uses a streaming algorithm with linear run time and constant memory use. It can determine abundances of sequenced molecules in real time, and can be applied to ChIP-seq, metagenomics and other large-scale sequencing data. We demonstrate its use on RNA-seq data, showing greater efficiency than other quantification methods. PMID:23160280

EEG-based "serious" games and monitoring tools for pain management.

PubMed

Sourina, Olga; Wang, Qiang; Nguyen, Minh Khoa

2011-01-01

EEG-based "serious games" for medical applications attracted recently more attention from the research community and industry as wireless EEG reading devices became easily available on the market. EEG-based technology has been applied in anesthesiology, psychology, etc. In this paper, we proposed and developed EEG-based "serious" games and doctor's monitoring tools that could be used for pain management. As EEG signal is considered to have a fractal nature, we proposed and develop a novel spatio-temporal fractal based algorithm for brain state quantification. The algorithm is implemented with blobby visualization tools for patient monitoring and in EEG-based "serious" games. Such games could be used by patient even at home convenience for pain management as an alternative to traditional drug treatment.

Selection of reference genes for gene expression studies in virus-infected monocots using quantitative real-time PCR.

PubMed

Zhang, Kun; Niu, Shaofang; Di, Dianping; Shi, Lindan; Liu, Deshui; Cao, Xiuling; Miao, Hongqin; Wang, Xianbing; Han, Chenggui; Yu, Jialin; Li, Dawei; Zhang, Yongliang

2013-10-10

Both genome-wide transcriptomic surveys of the mRNA expression profiles and virus-induced gene silencing-based molecular studies of target gene during virus-plant interaction involve the precise estimation of the transcript abundance. Quantitative real-time PCR (qPCR) is the most widely adopted technique for mRNA quantification. In order to obtain reliable quantification of transcripts, identification of the best reference genes forms the basis of the preliminary work. Nevertheless, the stability of internal controls in virus-infected monocots needs to be fully explored. In this work, the suitability of ten housekeeping genes (ACT, EF1α, FBOX, GAPDH, GTPB, PP2A, SAND, TUBβ, UBC18 and UK) for potential use as reference genes in qPCR were investigated in five different monocot plants (Brachypodium, barley, sorghum, wheat and maize) under infection with different viruses including Barley stripe mosaic virus (BSMV), Brome mosaic virus (BMV), Rice black-streaked dwarf virus (RBSDV) and Sugarcane mosaic virus (SCMV). By using three different algorithms, the most appropriate reference genes or their combinations were identified for different experimental sets and their effectiveness for the normalisation of expression studies were further validated by quantitative analysis of a well-studied PR-1 gene. These results facilitate the selection of desirable reference genes for more accurate gene expression studies in virus-infected monocots. Copyright © 2013 Elsevier B.V. All rights reserved.

A quasi-static continuum model describing interactions between plasmons and non-absorbing biomolecules

NASA Astrophysics Data System (ADS)

Salary, Mohammad Mahdi; Mosallaei, Hossein

2015-06-01

Interactions between the plasmons of noble metal nanoparticles and non-absorbing biomolecules forms the basis of the plasmonic sensors, which have received much attention. Studying these interactions can help to exploit the full potentials of plasmonic sensors in quantification and analysis of biomolecules. Here, a quasi-static continuum model is adopted for this purpose. We present a boundary-element method for computing the optical response of plasmonic particles to the molecular binding events by solving the Poisson equation. The model represents biomolecules with their molecular surfaces, thus accurately accounting for the influence of exact binding conformations as well as structural differences between different proteins on the response of plasmonic nanoparticles. The linear systems arising in the method are solved iteratively with Krylov generalized minimum residual algorithm, and the acceleration is achieved by applying precorrected-Fast Fourier Transformation technique. We apply the developed method to investigate interactions of biotinylated gold nanoparticles (nanosphere and nanorod) with four different types of biotin-binding proteins. The interactions are studied at both ensemble and single-molecule level. Computational results demonstrate the ability of presented model for analyzing realistic nanoparticle-biomolecule configurations. The method can provide comprehensive study for wide variety of applications, including protein structures, monitoring structural and conformational transitions, and quantification of protein concentrations. In addition, it is suitable for design and optimization of the nano-plasmonic sensors.

Automated and Adaptable Quantification of Cellular Alignment from Microscopic Images for Tissue Engineering Applications

PubMed Central

Xu, Feng; Beyazoglu, Turker; Hefner, Evan; Gurkan, Umut Atakan

2011-01-01

Cellular alignment plays a critical role in functional, physical, and biological characteristics of many tissue types, such as muscle, tendon, nerve, and cornea. Current efforts toward regeneration of these tissues include replicating the cellular microenvironment by developing biomaterials that facilitate cellular alignment. To assess the functional effectiveness of the engineered microenvironments, one essential criterion is quantification of cellular alignment. Therefore, there is a need for rapid, accurate, and adaptable methodologies to quantify cellular alignment for tissue engineering applications. To address this need, we developed an automated method, binarization-based extraction of alignment score (BEAS), to determine cell orientation distribution in a wide variety of microscopic images. This method combines a sequenced application of median and band-pass filters, locally adaptive thresholding approaches and image processing techniques. Cellular alignment score is obtained by applying a robust scoring algorithm to the orientation distribution. We validated the BEAS method by comparing the results with the existing approaches reported in literature (i.e., manual, radial fast Fourier transform-radial sum, and gradient based approaches). Validation results indicated that the BEAS method resulted in statistically comparable alignment scores with the manual method (coefficient of determination R2=0.92). Therefore, the BEAS method introduced in this study could enable accurate, convenient, and adaptable evaluation of engineered tissue constructs and biomaterials in terms of cellular alignment and organization. PMID:21370940

Digital pathology and image analysis for robust high-throughput quantitative assessment of Alzheimer disease neuropathologic changes.

PubMed

Neltner, Janna Hackett; Abner, Erin Lynn; Schmitt, Frederick A; Denison, Stephanie Kay; Anderson, Sonya; Patel, Ela; Nelson, Peter T

2012-12-01

Quantitative neuropathologic methods provide information that is important for both research and clinical applications. The technologic advancement of digital pathology and image analysis offers new solutions to enable valid quantification of pathologic severity that is reproducible between raters regardless of experience. Using an Aperio ScanScope XT and its accompanying image analysis software, we designed algorithms for quantitation of amyloid and tau pathologies on 65 β-amyloid (6F/3D antibody) and 48 phospho-tau (PHF-1)-immunostained sections of human temporal neocortex. Quantitative digital pathologic data were compared with manual pathology counts. There were excellent correlations between manually counted and digitally analyzed neuropathologic parameters (R² = 0.56-0.72). Data were highly reproducible among 3 participants with varying degrees of expertise in neuropathology (intraclass correlation coefficient values, >0.910). Digital quantification also provided additional parameters, including average plaque area, which shows statistically significant differences when samples are stratified according to apolipoprotein E allele status (average plaque area, 380.9 μm² in apolipoprotein E [Latin Small Letter Open E]4 carriers vs 274.4 μm² for noncarriers; p < 0.001). Thus, digital pathology offers a rigorous and reproducible method for quantifying Alzheimer disease neuropathologic changes and may provide additional insights into morphologic characteristics that were previously more challenging to assess because of technical limitations.

Technical advances in proteomics: new developments in data-independent acquisition.

PubMed

Hu, Alex; Noble, William S; Wolf-Yadlin, Alejandro

2016-01-01

The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low‑abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.

Uncertainty quantification and experimental design based on unsupervised machine learning identification of contaminant sources and groundwater types using hydrogeochemical data

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.

2017-12-01

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical species. Numerous geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. As a result, these types of model analyses are typically extremely challenging. Here, we demonstrate a new contaminant source identification approach that performs decomposition of the observation mixtures based on Nonnegative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. We also demonstrate how NMFk can be extended to perform uncertainty quantification and experimental design related to real-world site characterization. The NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios). The NMFk algorithm has been extensively tested on synthetic datasets; NMFk analyses have been actively performed on real-world data collected at the Los Alamos National Laboratory (LANL) groundwater sites related to Chromium and RDX contamination.

Stable isotope labelling methods in mass spectrometry-based quantitative proteomics.

PubMed

Chahrour, Osama; Cobice, Diego; Malone, John

2015-09-10

Mass-spectrometry based proteomics has evolved as a promising technology over the last decade and is undergoing a dramatic development in a number of different areas, such as; mass spectrometric instrumentation, peptide identification algorithms and bioinformatic computational data analysis. The improved methodology allows quantitative measurement of relative or absolute protein amounts, which is essential for gaining insights into their functions and dynamics in biological systems. Several different strategies involving stable isotopes label (ICAT, ICPL, IDBEST, iTRAQ, TMT, IPTL, SILAC), label-free statistical assessment approaches (MRM, SWATH) and absolute quantification methods (AQUA) are possible, each having specific strengths and weaknesses. Inductively coupled plasma mass spectrometry (ICP-MS), which is still widely recognised as elemental detector, has recently emerged as a complementary technique to the previous methods. The new application area for ICP-MS is targeting the fast growing field of proteomics related research, allowing absolute protein quantification using suitable elemental based tags. This document describes the different stable isotope labelling methods which incorporate metabolic labelling in live cells, ICP-MS based detection and post-harvest chemical label tagging for protein quantification, in addition to summarising their pros and cons. Copyright © 2015 Elsevier B.V. All rights reserved.

Samplers for evaluation and quantification of ultra-low volume space sprays

USDA-ARS?s Scientific Manuscript database

A field study was conducted to investigate the suitability of sampling devices for quantification of spray deposition from ULV space sprays. Five different samplers were included in an experiment conducted in an open grassy field. Samplers included horizontally stretched stationary cotton ribbon at ...

Cell Motility Dynamics: A Novel Segmentation Algorithm to Quantify Multi-Cellular Bright Field Microscopy Images

PubMed Central

Zaritsky, Assaf; Natan, Sari; Horev, Judith; Hecht, Inbal; Wolf, Lior; Ben-Jacob, Eshel; Tsarfaty, Ilan

2011-01-01

Confocal microscopy analysis of fluorescence and morphology is becoming the standard tool in cell biology and molecular imaging. Accurate quantification algorithms are required to enhance the understanding of different biological phenomena. We present a novel approach based on image-segmentation of multi-cellular regions in bright field images demonstrating enhanced quantitative analyses and better understanding of cell motility. We present MultiCellSeg, a segmentation algorithm to separate between multi-cellular and background regions for bright field images, which is based on classification of local patches within an image: a cascade of Support Vector Machines (SVMs) is applied using basic image features. Post processing includes additional classification and graph-cut segmentation to reclassify erroneous regions and refine the segmentation. This approach leads to a parameter-free and robust algorithm. Comparison to an alternative algorithm on wound healing assay images demonstrates its superiority. The proposed approach was used to evaluate common cell migration models such as wound healing and scatter assay. It was applied to quantify the acceleration effect of Hepatocyte growth factor/scatter factor (HGF/SF) on healing rate in a time lapse confocal microscopy wound healing assay and demonstrated that the healing rate is linear in both treated and untreated cells, and that HGF/SF accelerates the healing rate by approximately two-fold. A novel fully automated, accurate, zero-parameters method to classify and score scatter-assay images was developed and demonstrated that multi-cellular texture is an excellent descriptor to measure HGF/SF-induced cell scattering. We show that exploitation of textural information from differential interference contrast (DIC) images on the multi-cellular level can prove beneficial for the analyses of wound healing and scatter assays. The proposed approach is generic and can be used alone or alongside traditional fluorescence single-cell processing to perform objective, accurate quantitative analyses for various biological applications. PMID:22096600

Cell motility dynamics: a novel segmentation algorithm to quantify multi-cellular bright field microscopy images.

PubMed

Zaritsky, Assaf; Natan, Sari; Horev, Judith; Hecht, Inbal; Wolf, Lior; Ben-Jacob, Eshel; Tsarfaty, Ilan

2011-01-01

Confocal microscopy analysis of fluorescence and morphology is becoming the standard tool in cell biology and molecular imaging. Accurate quantification algorithms are required to enhance the understanding of different biological phenomena. We present a novel approach based on image-segmentation of multi-cellular regions in bright field images demonstrating enhanced quantitative analyses and better understanding of cell motility. We present MultiCellSeg, a segmentation algorithm to separate between multi-cellular and background regions for bright field images, which is based on classification of local patches within an image: a cascade of Support Vector Machines (SVMs) is applied using basic image features. Post processing includes additional classification and graph-cut segmentation to reclassify erroneous regions and refine the segmentation. This approach leads to a parameter-free and robust algorithm. Comparison to an alternative algorithm on wound healing assay images demonstrates its superiority. The proposed approach was used to evaluate common cell migration models such as wound healing and scatter assay. It was applied to quantify the acceleration effect of Hepatocyte growth factor/scatter factor (HGF/SF) on healing rate in a time lapse confocal microscopy wound healing assay and demonstrated that the healing rate is linear in both treated and untreated cells, and that HGF/SF accelerates the healing rate by approximately two-fold. A novel fully automated, accurate, zero-parameters method to classify and score scatter-assay images was developed and demonstrated that multi-cellular texture is an excellent descriptor to measure HGF/SF-induced cell scattering. We show that exploitation of textural information from differential interference contrast (DIC) images on the multi-cellular level can prove beneficial for the analyses of wound healing and scatter assays. The proposed approach is generic and can be used alone or alongside traditional fluorescence single-cell processing to perform objective, accurate quantitative analyses for various biological applications.

Quantification of chromatin condensation level by image processing.

PubMed

Irianto, Jerome; Lee, David A; Knight, Martin M

2014-03-01

The level of chromatin condensation is related to the silencing/activation of chromosomal territories and therefore impacts on gene expression. Chromatin condensation changes during cell cycle, progression and differentiation, and is influenced by various physicochemical and epigenetic factors. This study describes a validated experimental technique to quantify chromatin condensation. A novel image processing procedure is developed using Sobel edge detection to quantify the level of chromatin condensation from nuclei images taken by confocal microscopy. The algorithm was developed in MATLAB and used to quantify different levels of chromatin condensation in chondrocyte nuclei achieved through alteration in osmotic pressure. The resulting chromatin condensation parameter (CCP) is in good agreement with independent multi-observer qualitative visual assessment. This image processing technique thereby provides a validated unbiased parameter for rapid and highly reproducible quantification of the level of chromatin condensation. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Information transduction capacity reduces the uncertainties in annotation-free isoform discovery and quantification

PubMed Central

Deng, Yue; Bao, Feng; Yang, Yang; Ji, Xiangyang; Du, Mulong; Zhang, Zhengdong

2017-01-01

Abstract The automated transcript discovery and quantification of high-throughput RNA sequencing (RNA-seq) data are important tasks of next-generation sequencing (NGS) research. However, these tasks are challenging due to the uncertainties that arise in the inference of complete splicing isoform variants from partially observed short reads. Here, we address this problem by explicitly reducing the inherent uncertainties in a biological system caused by missing information. In our approach, the RNA-seq procedure for transforming transcripts into short reads is considered an information transmission process. Consequently, the data uncertainties are substantially reduced by exploiting the information transduction capacity of information theory. The experimental results obtained from the analyses of simulated datasets and RNA-seq datasets from cell lines and tissues demonstrate the advantages of our method over state-of-the-art competitors. Our algorithm is an open-source implementation of MaxInfo. PMID:28911101

Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI.

PubMed

Mehranian, Abolfazl; Arabi, Hossein; Zaidi, Habib

2016-04-15

In quantitative PET/MR imaging, attenuation correction (AC) of PET data is markedly challenged by the need of deriving accurate attenuation maps from MR images. A number of strategies have been developed for MRI-guided attenuation correction with different degrees of success. In this work, we compare the quantitative performance of three generic AC methods, including standard 3-class MR segmentation-based, advanced atlas-registration-based and emission-based approaches in the context of brain time-of-flight (TOF) PET/MRI. Fourteen patients referred for diagnostic MRI and (18)F-FDG PET/CT brain scans were included in this comparative study. For each study, PET images were reconstructed using four different attenuation maps derived from CT-based AC (CTAC) serving as reference, standard 3-class MR-segmentation, atlas-registration and emission-based AC methods. To generate 3-class attenuation maps, T1-weighted MRI images were segmented into background air, fat and soft-tissue classes followed by assignment of constant linear attenuation coefficients of 0, 0.0864 and 0.0975 cm(-1) to each class, respectively. A robust atlas-registration based AC method was developed for pseudo-CT generation using local weighted fusion of atlases based on their morphological similarity to target MR images. Our recently proposed MRI-guided maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm was employed to estimate the attenuation map from TOF emission data. The performance of the different AC algorithms in terms of prediction of bones and quantification of PET tracer uptake was objectively evaluated with respect to reference CTAC maps and CTAC-PET images. Qualitative evaluation showed that the MLAA-AC method could sparsely estimate bones and accurately differentiate them from air cavities. It was found that the atlas-AC method can accurately predict bones with variable errors in defining air cavities. Quantitative assessment of bone extraction accuracy based on Dice similarity coefficient (DSC) showed that MLAA-AC and atlas-AC resulted in DSC mean values of 0.79 and 0.92, respectively, in all patients. The MLAA-AC and atlas-AC methods predicted mean linear attenuation coefficients of 0.107 and 0.134 cm(-1), respectively, for the skull compared to reference CTAC mean value of 0.138cm(-1). The evaluation of the relative change in tracer uptake within 32 distinct regions of the brain with respect to CTAC PET images showed that the 3-class MRAC, MLAA-AC and atlas-AC methods resulted in quantification errors of -16.2 ± 3.6%, -13.3 ± 3.3% and 1.0 ± 3.4%, respectively. Linear regression and Bland-Altman concordance plots showed that both 3-class MRAC and MLAA-AC methods result in a significant systematic bias in PET tracer uptake, while the atlas-AC method results in a negligible bias. The standard 3-class MRAC method significantly underestimated cerebral PET tracer uptake. While current state-of-the-art MLAA-AC methods look promising, they were unable to noticeably reduce quantification errors in the context of brain imaging. Conversely, the proposed atlas-AC method provided the most accurate attenuation maps, and thus the lowest quantification bias. Copyright © 2016 Elsevier Inc. All rights reserved.

Increasing the feasibility of minimally invasive procedures in type A aortic dissections: a framework for segmentation and quantification.

PubMed

Morariu, Cosmin Adrian; Terheiden, Tobias; Dohle, Daniel Sebastian; Tsagakis, Konstantinos; Pauli, Josef

2016-02-01

Our goal is to provide precise measurements of the aortic dimensions in case of dissection pathologies. Quantification of surface lengths and aortic radii/diameters together with the visualization of the dissection membrane represents crucial prerequisites for enabling minimally invasive treatment of type A dissections, which always also imply the ascending aorta. We seek a measure invariant to luminance and contrast for aortic outer wall segmentation. Therefore, we propose a 2D graph-based approach using phase congruency combined with additional features. Phase congruency is extended to 3D by designing a novel conic directional filter and adding a lowpass component to the 3D Log-Gabor filterbank for extracting the fine dissection membrane, which separates the true lumen from the false one within the aorta. The result of the outer wall segmentation is compared with manually annotated axial slices belonging to 11 CTA datasets. Quantitative assessment of our novel 2D/3D membrane extraction algorithms has been obtained for 10 datasets and reveals subvoxel accuracy in all cases. Aortic inner and outer surface lengths, determined within 2 cadaveric CT datasets, are validated against manual measurements performed by a vascular surgeon on excised aortas of the body donors. This contribution proposes a complete pipeline for segmentation and quantification of aortic dissections. Validation against ground truth of the 3D contour lengths quantification represents a significant step toward custom-designed stent-grafts.

Variance decomposition in stochastic simulators.

PubMed

Le Maître, O P; Knio, O M; Moraes, A

2015-06-28

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Lung lobe modeling and segmentation with individualized surface meshes

NASA Astrophysics Data System (ADS)

Blaffert, Thomas; Barschdorf, Hans; von Berg, Jens; Dries, Sebastian; Franz, Astrid; Klinder, Tobias; Lorenz, Cristian; Renisch, Steffen; Wiemker, Rafael

2008-03-01

An automated segmentation of lung lobes in thoracic CT images is of interest for various diagnostic purposes like the quantification of emphysema or the localization of tumors within the lung. Although the separating lung fissures are visible in modern multi-slice CT-scanners, their contrast in the CT-image often does not separate the lobes completely. This makes it impossible to build a reliable segmentation algorithm without additional information. Our approach uses general anatomical knowledge represented in a geometrical mesh model to construct a robust lobe segmentation, which even gives reasonable estimates of lobe volumes if fissures are not visible at all. The paper describes the generation of the lung model mesh including lobes by an average volume model, its adaptation to individual patient data using a special fissure feature image, and a performance evaluation over a test data set showing an average segmentation accuracy of 1 to 3 mm.

Automated three-dimensional quantification of myocardial perfusion and brain SPECT.

PubMed

Slomka, P J; Radau, P; Hurwitz, G A; Dey, D

2001-01-01

To allow automated and objective reading of nuclear medicine tomography, we have developed a set of tools for clinical analysis of myocardial perfusion tomography (PERFIT) and Brain SPECT/PET (BRASS). We exploit algorithms for image registration and use three-dimensional (3D) "normal models" for individual patient comparisons to composite datasets on a "voxel-by-voxel basis" in order to automatically determine the statistically significant abnormalities. A multistage, 3D iterative inter-subject registration of patient images to normal templates is applied, including automated masking of the external activity before final fit. In separate projects, the software has been applied to the analysis of myocardial perfusion SPECT, as well as brain SPECT and PET data. Automatic reading was consistent with visual analysis; it can be applied to the whole spectrum of clinical images, and aid physicians in the daily interpretation of tomographic nuclear medicine images.

Quantitative Assessment of Parkinsonian Tremor Based on an Inertial Measurement Unit

PubMed Central

Dai, Houde; Zhang, Pengyue; Lueth, Tim C.

2015-01-01

Quantitative assessment of parkinsonian tremor based on inertial sensors can provide reliable feedback on the effect of medication. In this regard, the features of parkinsonian tremor and its unique properties such as motor fluctuations and dyskinesia are taken into account. Least-square-estimation models are used to assess the severities of rest, postural, and action tremors. In addition, a time-frequency signal analysis algorithm for tremor state detection was also included in the tremor assessment method. This inertial sensor-based method was verified through comparison with an electromagnetic motion tracking system. Seven Parkinson’s disease (PD) patients were tested using this tremor assessment system. The measured tremor amplitudes correlated well with the judgments of a neurologist (r = 0.98). The systematic analysis of sensor-based tremor quantification and the corresponding experiments could be of great help in monitoring the severity of parkinsonian tremor. PMID:26426020

Variance decomposition in stochastic simulators

NASA Astrophysics Data System (ADS)

Le Maître, O. P.; Knio, O. M.; Moraes, A.

2015-06-01

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Automatic Fibrosis Quantification By Using a k-NN Classificator

DTIC Science & Technology

2001-10-25

Fluthrope, “Stages in fiber breakdown in duchenne muscular dystrophy ,” J. Neurol. Sci., vol. 24, pp. 179– 186, 1975. [6] F. Cornelio and I. Dones, “ Muscle ...an automatic algorithm to measure fibrosis in muscle sections of mdx mice, a mutant species used as a model of the Duchenne dystrophy . The al- gorithm...fiber degeneration and necro- sis in muscular dystrophy and other muscle diseases: cytochem- ical and immunocytochemical data,” Ann. Neurol., vol. 16

Progress in defect quantification in multi-layered structures using ultrasonic inspection

NASA Astrophysics Data System (ADS)

Dierken, Josiah; Aldrin, John C.; Holec, Robert; LaCivita, Michael; Shearer, Joshua; Lindgren, Eric

2013-01-01

This study investigates the ability to resolve flaws in aluminum panel stackups representative of aircraft structural components. Using immersion ultrasound techniques, the specimens were examined for known fatigue cracks and electric discharge machined (EDM) notches at various fastener sites. Initial assessments suggested a possible trend between measured ultrasound parameters of flaw intensity and size, and known physical defect length. To improve analytical reliability and efficiency, development of automated data analysis (ADA) algorithms has been initiated.

Cutaneous T cell lymphoma: Current practices in blood assessment and the utility of T-cell receptor Vβ chain restriction

PubMed Central

Gibson, Juliet F; Huang, Jing; Liu, Kristina J; Carlson, Kacie R; Foss, Francine; Choi, Jaehyuk; Edelson, Richard; Hussong, Jerry W.; Mohl, Ramsey; Hill, Sally; Girardi, Sally

2016-01-01

Background Accurate quantification of malignant cells in the peripheral blood of patients with cutaneous T cell lymphoma (CTCL) is important for early detection, prognosis, and monitoring disease burden. Objective Determine the spectrum of current clinical practices; critically evaluate elements of current ISCL B1 and B2 staging criteria; and assess the potential role of TCR-Vβ analysis by flow cytometry. Methods We assessed current clinical practices by survey, and performed a retrospective analysis of 161 patients evaluated at Yale (2011-2014) to compare the sensitivity, specificity, PPV, and NPV of parameters for ISCL B2 staging. Results There was heterogeneity in clinical practices among institutions. ISCL B1 criteria did not capture five Yale cohort patients with immunophenotypic abnormalities who later progressed. TCR-Vβ testing was more specific than PCR and aided diagnosis in detecting clonality, but was of limited benefit in quantification of tumor burden. Limitations Because of limited follow-up involving a single center, further investigation will be necessary to conclude whether our proposed diagnostic algorithm is of general clinical benefit. Conclusion We propose further study of “modified B1 criteria”: CD4/CD8 ratio ≥5, %CD4+/CD26- ≥ 20%, %CD4+/CD7- ≥ 20%, with evidence of clonality. TCR-Vβ testing should be considered in future diagnostic and staging algorithms. PMID:26874819

Heat-Treatment-Responsive Proteins in Different Developmental Stages of Tomato Pollen Detected by Targeted Mass Accuracy Precursor Alignment (tMAPA).

PubMed

Chaturvedi, Palak; Doerfler, Hannes; Jegadeesan, Sridharan; Ghatak, Arindam; Pressman, Etan; Castillejo, Maria Angeles; Wienkoop, Stefanie; Egelhofer, Volker; Firon, Nurit; Weckwerth, Wolfram

2015-11-06

Recently, we have developed a quantitative shotgun proteomics strategy called mass accuracy precursor alignment (MAPA). The MAPA algorithm uses high mass accuracy to bin mass-to-charge (m/z) ratios of precursor ions from LC-MS analyses, determines their intensities, and extracts a quantitative sample versus m/z ratio data alignment matrix from a multitude of samples. Here, we introduce a novel feature of this algorithm that allows the extraction and alignment of proteotypic peptide precursor ions or any other target peptide from complex shotgun proteomics data for accurate quantification of unique proteins. This strategy circumvents the problem of confusing the quantification of proteins due to indistinguishable protein isoforms by a typical shotgun proteomics approach. We applied this strategy to a comparison of control and heat-treated tomato pollen grains at two developmental stages, post-meiotic and mature. Pollen is a temperature-sensitive tissue involved in the reproductive cycle of plants and plays a major role in fruit setting and yield. By LC-MS-based shotgun proteomics, we identified more than 2000 proteins in total for all different tissues. By applying the targeted MAPA data-processing strategy, 51 unique proteins were identified as heat-treatment-responsive protein candidates. The potential function of the identified candidates in a specific developmental stage is discussed.

Impact of non-specific normal databases on perfusion quantification of low-dose myocardial SPECT studies.

PubMed

Scabbio, Camilla; Zoccarato, Orazio; Malaspina, Simona; Lucignani, Giovanni; Del Sole, Angelo; Lecchi, Michela

2017-10-17

To evaluate the impact of non-specific normal databases on the percent summed rest score (SR%) and stress score (SS%) from simulated low-dose SPECT studies by shortening the acquisition time/projection. Forty normal-weight and 40 overweight/obese patients underwent myocardial studies with a conventional gamma-camera (BrightView, Philips) using three different acquisition times/projection: 30, 15, and 8 s (100%-counts, 50%-counts, and 25%-counts scan, respectively) and reconstructed using the iterative algorithm with resolution recovery (IRR) Astonish TM (Philips). Three sets of normal databases were used: (1) full-counts IRR; (2) half-counts IRR; and (3) full-counts traditional reconstruction algorithm database (TRAD). The impact of these databases and the acquired count statistics on the SR% and SS% was assessed by ANOVA analysis and Tukey test (P < 0.05). Significantly higher SR% and SS% values (> 40%) were found for the full-counts TRAD databases respect to the IRR databases. For overweight/obese patients, significantly higher SS% values for 25%-counts scans (+19%) are confirmed compared to those of 50%-counts scan, independently of using the half-counts or the full-counts IRR databases. Astonish TM requires the adoption of the own specific normal databases in order to prevent very high overestimation of both stress and rest perfusion scores. Conversely, the count statistics of the normal databases seems not to influence the quantification scores.

Where do students struggle in the field? Computer-aided evaluation of mapping errors from an undergraduate Field Geology summer course

NASA Astrophysics Data System (ADS)

Lang, K. A.; Petrie, G.

2014-12-01

Extended field-based summer courses provide an invaluable field experience for undergraduate majors in the geosciences. These courses often utilize the construction of geological maps and structural cross sections as the primary pedagogical tool to teach basic map orientation, rock identification and structural interpretation. However, advances in the usability and ubiquity of Geographic Information Systems in these courses presents new opportunities to evaluate student work. In particular, computer-based quantification of systematic mapping errors elucidates the factors influencing student success in the field. We present a case example from a mapping exercise conducted in a summer Field Geology course at a popular field location near Dillon, Montana. We use a computer algorithm to automatically compare the placement and attribution of unit contacts with spatial variables including topographic slope, aspect, bedding attitude, ground cover and distance from starting location. We compliment analyses with anecdotal and survey data that suggest both physical factors (e.g. steep topographic slope) as well as structural nuance (e.g. low angle bedding) may dominate student frustration, particularly in courses with a high student to instructor ratio. We propose mechanisms to improve student experience by allowing students to practice skills with orientation games and broadening student background with tangential lessons (e.g. on colluvial transport processes). As well, we suggest low-cost ways to decrease the student to instructor ratio by supporting returning undergraduates from previous years or staging mapping over smaller areas. Future applications of this analysis might include a rapid and objective system for evaluation of student maps (including point-data, such as attitude measurements) and quantification of temporal trends in student work as class sizes, pedagogical approaches or environmental variables change. Long-term goals include understanding and characterizing stochasticity in geological mapping beyond the undergraduate classroom, and better quantifying uncertainty in published map products.

Study to assess the importance of errors introduced by applying NOAA 6 and NOAA 7 AVHRR data as an estimator of vegetative vigor: Feasibility study of data normalization

NASA Technical Reports Server (NTRS)

Duggin, M. J. (Principal Investigator); Piwinski, D.

1982-01-01

The use of NOAA AVHRR data to map and monitor vegetation types and conditions in near real-time can be enhanced by using a portion of each GAC image that is larger than the central 25% now considered. Enlargement of the cloud free image data set can permit development of a series of algorithms for correcting imagery for ground reflectance and for atmospheric scattering anisotropy within certain accuracy limits. Empirical correction algorithms used to normalize digital radiance or VIN data must contain factors for growth stage and for instrument spectral response. While it is not possible to correct for random fluctuations in target radiance, it is possible to estimate the necessary radiance difference between targets in order to provide target discrimination and quantification within predetermined limits of accuracy. A major difficulty lies in the lack of documentation of preprocessing algorithms used on AVHRR digital data.

Automated Video-Based Analysis of Contractility and Calcium Flux in Human-Induced Pluripotent Stem Cell-Derived Cardiomyocytes Cultured over Different Spatial Scales.

PubMed

Huebsch, Nathaniel; Loskill, Peter; Mandegar, Mohammad A; Marks, Natalie C; Sheehan, Alice S; Ma, Zhen; Mathur, Anurag; Nguyen, Trieu N; Yoo, Jennie C; Judge, Luke M; Spencer, C Ian; Chukka, Anand C; Russell, Caitlin R; So, Po-Lin; Conklin, Bruce R; Healy, Kevin E

2015-05-01

Contractile motion is the simplest metric of cardiomyocyte health in vitro, but unbiased quantification is challenging. We describe a rapid automated method, requiring only standard video microscopy, to analyze the contractility of human-induced pluripotent stem cell-derived cardiomyocytes (iPS-CM). New algorithms for generating and filtering motion vectors combined with a newly developed isogenic iPSC line harboring genetically encoded calcium indicator, GCaMP6f, allow simultaneous user-independent measurement and analysis of the coupling between calcium flux and contractility. The relative performance of these algorithms, in terms of improving signal to noise, was tested. Applying these algorithms allowed analysis of contractility in iPS-CM cultured over multiple spatial scales from single cells to three-dimensional constructs. This open source software was validated with analysis of isoproterenol response in these cells, and can be applied in future studies comparing the drug responsiveness of iPS-CM cultured in different microenvironments in the context of tissue engineering.

Wind turbine blade shear web disbond detection using rotor blade operational sensing and data analysis.

PubMed

Myrent, Noah; Adams, Douglas E; Griffith, D Todd

2015-02-28

A wind turbine blade's structural dynamic response is simulated and analysed with the goal of characterizing the presence and severity of a shear web disbond. Computer models of a 5 MW offshore utility-scale wind turbine were created to develop effective algorithms for detecting such damage. Through data analysis and with the use of blade measurements, a shear web disbond was quantified according to its length. An aerodynamic sensitivity study was conducted to ensure robustness of the detection algorithms. In all analyses, the blade's flap-wise acceleration and root-pitching moment were the clearest indicators of the presence and severity of a shear web disbond. A combination of blade and non-blade measurements was formulated into a final algorithm for the detection and quantification of the disbond. The probability of detection was 100% for the optimized wind speed ranges in laminar, 30% horizontal shear and 60% horizontal shear conditions. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Accurate quantification of local changes for carotid arteries in 3D ultrasound images using convex optimization-based deformable registration

NASA Astrophysics Data System (ADS)

Cheng, Jieyu; Qiu, Wu; Yuan, Jing; Fenster, Aaron; Chiu, Bernard

2016-03-01

Registration of longitudinally acquired 3D ultrasound (US) images plays an important role in monitoring and quantifying progression/regression of carotid atherosclerosis. We introduce an image-based non-rigid registration algorithm to align the baseline 3D carotid US with longitudinal images acquired over several follow-up time points. This algorithm minimizes the sum of absolute intensity differences (SAD) under a variational optical-flow perspective within a multi-scale optimization framework to capture local and global deformations. Outer wall and lumen were segmented manually on each image, and the performance of the registration algorithm was quantified by Dice similarity coefficient (DSC) and mean absolute distance (MAD) of the outer wall and lumen surfaces after registration. In this study, images for 5 subjects were registered initially by rigid registration, followed by the proposed algorithm. Mean DSC generated by the proposed algorithm was 79:3+/-3:8% for lumen and 85:9+/-4:0% for outer wall, compared to 73:9+/-3:4% and 84:7+/-3:2% generated by rigid registration. Mean MAD of 0:46+/-0:08mm and 0:52+/-0:13mm were generated for lumen and outer wall respectively by the proposed algorithm, compared to 0:55+/-0:08mm and 0:54+/-0:11mm generated by rigid registration. The mean registration time of our method per image pair was 143+/-23s.

Multispectra CWT-based algorithm (MCWT) in mass spectra for peak extraction.

PubMed

Hsueh, Huey-Miin; Kuo, Hsun-Chih; Tsai, Chen-An

2008-01-01

An important objective in mass spectrometry (MS) is to identify a set of biomarkers that can be used to potentially distinguish patients between distinct treatments (or conditions) from tens or hundreds of spectra. A common two-step approach involving peak extraction and quantification is employed to identify the features of scientific interest. The selected features are then used for further investigation to understand underlying biological mechanism of individual protein or for development of genomic biomarkers to early diagnosis. However, the use of inadequate or ineffective peak detection and peak alignment algorithms in peak extraction step may lead to a high rate of false positives. Also, it is crucial to reduce the false positive rate in detecting biomarkers from ten or hundreds of spectra. Here a new procedure is introduced for feature extraction in mass spectrometry data that extends the continuous wavelet transform-based (CWT-based) algorithm to multiple spectra. The proposed multispectra CWT-based algorithm (MCWT) not only can perform peak detection for multiple spectra but also carry out peak alignment at the same time. The author' MCWT algorithm constructs a reference, which integrates information of multiple raw spectra, for feature extraction. The algorithm is applied to a SELDI-TOF mass spectra data set provided by CAMDA 2006 with known polypeptide m/z positions. This new approach is easy to implement and it outperforms the existing peak extraction method from the Bioconductor PROcess package.

Quantification of Training and Competition Loads in Endurance Sports: Methods and Applications.

PubMed

Mujika, Iñigo

2017-04-01

Training quantification is basic to evaluate an endurance athlete's responses to training loads, ensure adequate stress/recovery balance, and determine the relationship between training and performance. Quantifying both external and internal workload is important, because external workload does not measure the biological stress imposed by the exercise sessions. Generally used quantification methods include retrospective questionnaires, diaries, direct observation, and physiological monitoring, often based on the measurement of oxygen uptake, heart rate, and blood lactate concentration. Other methods in use in endurance sports include speed measurement and the measurement of power output, made possible by recent technological advances such as power meters in cycling and triathlon. Among subjective methods of quantification, rating of perceived exertion stands out because of its wide use. Concurrent assessments of the various quantification methods allow researchers and practitioners to evaluate stress/recovery balance, adjust individual training programs, and determine the relationships between external load, internal load, and athletes' performance. This brief review summarizes the most relevant external- and internal-workload-quantification methods in endurance sports and provides practical examples of their implementation to adjust the training programs of elite athletes in accordance with their individualized stress/recovery balance.

Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques.

PubMed

Hofmann, Matthias; Pichler, Bernd; Schölkopf, Bernhard; Beyer, Thomas

2009-03-01

Positron emission tomography (PET) is a fully quantitative technology for imaging metabolic pathways and dynamic processes in vivo. Attenuation correction of raw PET data is a prerequisite for quantification and is typically based on separate transmission measurements. In PET/CT attenuation correction, however, is performed routinely based on the available CT transmission data. Recently, combined PET/magnetic resonance (MR) has been proposed as a viable alternative to PET/CT. Current concepts of PET/MRI do not include CT-like transmission sources and, therefore, alternative methods of PET attenuation correction must be found. This article reviews existing approaches to MR-based attenuation correction (MR-AC). Most groups have proposed MR-AC algorithms for brain PET studies and more recently also for torso PET/MR imaging. Most MR-AC strategies require the use of complementary MR and transmission images, or morphology templates generated from transmission images. We review and discuss these algorithms and point out challenges for using MR-AC in clinical routine. MR-AC is work-in-progress with potentially promising results from a template-based approach applicable to both brain and torso imaging. While efforts are ongoing in making clinically viable MR-AC fully automatic, further studies are required to realize the potential benefits of MR-based motion compensation and partial volume correction of the PET data.

Total anthocyanin content determination in intact açaí (Euterpe oleracea Mart.) and palmitero-juçara (Euterpe edulis Mart.) fruit using near infrared spectroscopy (NIR) and multivariate calibration.

PubMed

Inácio, Maria Raquel Cavalcanti; de Lima, Kássio Michell Gomes; Lopes, Valquiria Garcia; Pessoa, José Dalton Cruz; de Almeida Teixeira, Gustavo Henrique

2013-02-15

The aim of this study was to evaluate near-infrared reflectance spectroscopy (NIR), and multivariate calibration potential as a rapid method to determinate anthocyanin content in intact fruit (açaí and palmitero-juçara). Several multivariate calibration techniques, including partial least squares (PLS), interval partial least squares, genetic algorithm, successive projections algorithm, and net analyte signal were compared and validated by establishing figures of merit. Suitable results were obtained with the PLS model (four latent variables and 5-point smoothing) with a detection limit of 6.2 g kg(-1), limit of quantification of 20.7 g kg(-1), accuracy estimated as root mean square error of prediction of 4.8 g kg(-1), mean selectivity of 0.79 g kg(-1), sensitivity of 5.04×10(-3) g kg(-1), precision of 27.8 g kg(-1), and signal-to-noise ratio of 1.04×10(-3) g kg(-1). These results suggest NIR spectroscopy and multivariate calibration can be effectively used to determine anthocyanin content in intact açaí and palmitero-juçara fruit. Copyright © 2012 Elsevier Ltd. All rights reserved.

NeuroCa: integrated framework for systematic analysis of spatiotemporal neuronal activity patterns from large-scale optical recording data

PubMed Central

Jang, Min Jee; Nam, Yoonkey

2015-01-01

Abstract. Optical recording facilitates monitoring the activity of a large neural network at the cellular scale, but the analysis and interpretation of the collected data remain challenging. Here, we present a MATLAB-based toolbox, named NeuroCa, for the automated processing and quantitative analysis of large-scale calcium imaging data. Our tool includes several computational algorithms to extract the calcium spike trains of individual neurons from the calcium imaging data in an automatic fashion. Two algorithms were developed to decompose the imaging data into the activity of individual cells and subsequently detect calcium spikes from each neuronal signal. Applying our method to dense networks in dissociated cultures, we were able to obtain the calcium spike trains of ∼1000 neurons in a few minutes. Further analyses using these data permitted the quantification of neuronal responses to chemical stimuli as well as functional mapping of spatiotemporal patterns in neuronal firing within the spontaneous, synchronous activity of a large network. These results demonstrate that our method not only automates time-consuming, labor-intensive tasks in the analysis of neural data obtained using optical recording techniques but also provides a systematic way to visualize and quantify the collective dynamics of a network in terms of its cellular elements. PMID:26229973

CT coronary angiography: impact of adapted statistical iterative reconstruction (ASIR) on coronary stenosis and plaque composition analysis.

PubMed

Fuchs, Tobias A; Fiechter, Michael; Gebhard, Cathérine; Stehli, Julia; Ghadri, Jelena R; Kazakauskaite, Egle; Herzog, Bernhard A; Husmann, Lars; Gaemperli, Oliver; Kaufmann, Philipp A

2013-03-01

To assess the impact of adaptive statistical iterative reconstruction (ASIR) on coronary plaque volume and composition analysis as well as on stenosis quantification in high definition coronary computed tomography angiography (CCTA). We included 50 plaques in 29 consecutive patients who were referred for the assessment of known or suspected coronary artery disease (CAD) with contrast-enhanced CCTA on a 64-slice high definition CT scanner (Discovery HD 750, GE Healthcare). CCTA scans were reconstructed with standard filtered back projection (FBP) with no ASIR (0 %) or with increasing contributions of ASIR, i.e. 20, 40, 60, 80 and 100 % (no FBP). Plaque analysis (volume, components and stenosis degree) was performed using a previously validated automated software. Mean values for minimal diameter and minimal area as well as degree of stenosis did not change significantly using different ASIR reconstructions. There was virtually no impact of reconstruction algorithms on mean plaque volume or plaque composition (e.g. soft, intermediate and calcified component). However, with increasing ASIR contribution, the percentage of plaque volume component between 401 and 500 HU decreased significantly (p < 0.05). Modern image reconstruction algorithms such as ASIR, which has been developed for noise reduction in latest high resolution CCTA scans, can be used reliably without interfering with the plaque analysis and stenosis severity assessment.

Quantitative fluorescence angiography for neurosurgical interventions.

PubMed

Weichelt, Claudia; Duscha, Philipp; Steinmeier, Ralf; Meyer, Tobias; Kuß, Julia; Cimalla, Peter; Kirsch, Matthias; Sobottka, Stephan B; Koch, Edmund; Schackert, Gabriele; Morgenstern, Ute

2013-06-01

Present methods for quantitative measurement of cerebral perfusion during neurosurgical operations require additional technology for measurement, data acquisition, and processing. This study used conventional fluorescence video angiography--as an established method to visualize blood flow in brain vessels--enhanced by a quantifying perfusion software tool. For these purposes, the fluorescence dye indocyanine green is given intravenously, and after activation by a near-infrared light source the fluorescence signal is recorded. Video data are analyzed by software algorithms to allow quantification of the blood flow. Additionally, perfusion is measured intraoperatively by a reference system. Furthermore, comparing reference measurements using a flow phantom were performed to verify the quantitative blood flow results of the software and to validate the software algorithm. Analysis of intraoperative video data provides characteristic biological parameters. These parameters were implemented in the special flow phantom for experimental validation of the developed software algorithms. Furthermore, various factors that influence the determination of perfusion parameters were analyzed by means of mathematical simulation. Comparing patient measurement, phantom experiment, and computer simulation under certain conditions (variable frame rate, vessel diameter, etc.), the results of the software algorithms are within the range of parameter accuracy of the reference methods. Therefore, the software algorithm for calculating cortical perfusion parameters from video data presents a helpful intraoperative tool without complex additional measurement technology.

Improved quantification for local regions of interest in preclinical PET imaging

NASA Astrophysics Data System (ADS)

Cal-González, J.; Moore, S. C.; Park, M.-A.; Herraiz, J. L.; Vaquero, J. J.; Desco, M.; Udias, J. M.

2015-09-01

In Positron Emission Tomography, there are several causes of quantitative inaccuracy, such as partial volume or spillover effects. The impact of these effects is greater when using radionuclides that have a large positron range, e.g. 68Ga and 124I, which have been increasingly used in the clinic. We have implemented and evaluated a local projection algorithm (LPA), originally evaluated for SPECT, to compensate for both partial-volume and spillover effects in PET. This method is based on the use of a high-resolution CT or MR image, co-registered with a PET image, which permits a high-resolution segmentation of a few tissues within a volume of interest (VOI) centered on a region within which tissue-activity values need to be estimated. The additional boundary information is used to obtain improved activity estimates for each tissue within the VOI, by solving a simple inversion problem. We implemented this algorithm for the preclinical Argus PET/CT scanner and assessed its performance using the radionuclides 18F, 68Ga and 124I. We also evaluated and compared the results obtained when it was applied during the iterative reconstruction, as well as after the reconstruction as a postprocessing procedure. In addition, we studied how LPA can help to reduce the ‘spillover contamination’, which causes inaccurate quantification of lesions in the immediate neighborhood of large, ‘hot’ sources. Quantification was significantly improved by using LPA, which provided more accurate ratios of lesion-to-background activity concentration for hot and cold regions. For 18F, the contrast was improved from 3.0 to 4.0 in hot lesions (when the true ratio was 4.0) and from 0.16 to 0.06 in cold lesions (true ratio  =  0.0), when using the LPA postprocessing. Furthermore, activity values estimated within the VOI using LPA during reconstruction were slightly more accurate than those obtained by post-processing, while also visually improving the image contrast and uniformity within the VOI.

Improved quantification for local regions of interest in preclinical PET imaging

PubMed Central

Cal-González, J.; Moore, S. C.; Park, M.-A.; Herraiz, J. L.; Vaquero, J. J.; Desco, M.; Udias, J. M.

2015-01-01

In Positron Emission Tomography, there are several causes of quantitative inaccuracy, such as partial volume or spillover effects. The impact of these effects is greater when using radionuclides that have a large positron range, e.g., 68Ga and 124I, which have been increasingly used in the clinic. We have implemented and evaluated a local projection algorithm (LPA), originally evaluated for SPECT, to compensate for both partial-volume and spillover effects in PET. This method is based on the use of a high-resolution CT or MR image, co-registered with a PET image, which permits a high-resolution segmentation of a few tissues within a volume of interest (VOI) centered on a region within which tissue-activity values need to be estimated. The additional boundary information is used to obtain improved activity estimates for each tissue within the VOI, by solving a simple inversion problem. We implemented this algorithm for the preclinical Argus PET/CT scanner and assessed its performance using the radionuclides 18F, 68Ga and 124I. We also evaluated and compared the results obtained when it was applied during the iterative reconstruction, as well as after the reconstruction as a postprocessing procedure. In addition, we studied how LPA can help to reduce the “spillover contamination”, which causes inaccurate quantification of lesions in the immediate neighborhood of large, “hot” sources. Quantification was significantly improved by using LPA, which provided more accurate ratios of lesion-to-background activity concentration for hot and cold regions. For 18F, the contrast was improved from 3.0 to 4.0 in hot lesions (when the true ratio was 4.0) and from 0.16 to 0.06 in cold lesions (true ratio = 0.0), when using the LPA postprocessing. Furthermore, activity values estimated within the VOI using LPA during reconstruction were slightly more accurate than those obtained by post-processing, while also visually improving the image contrast and uniformity within the VOI. PMID:26334312

An Open-Source Standard T-Wave Alternans Detector for Benchmarking.

PubMed

Khaustov, A; Nemati, S; Clifford, Gd

2008-09-14

We describe an open source algorithm suite for T-Wave Alternans (TWA) detection and quantification. The software consists of Matlab implementations of the widely used Spectral Method and Modified Moving Average with libraries to read both WFDB and ASCII data under windows and Linux. The software suite can run in both batch mode and with a provided graphical user interface to aid waveform exploration. Our software suite was calibrated using an open source TWA model, described in a partner paper [1] by Clifford and Sameni. For the PhysioNet/CinC Challenge 2008 we obtained a score of 0.881 for the Spectral Method and 0.400 for the MMA method. However, our objective was not to provide the best TWA detector, but rather a basis for detailed discussion of algorithms.

Phase 1 of the near term hybrid passenger vehicle development program, appendix A. Mission analysis and performance specification studies. Volume 2: Appendices

NASA Technical Reports Server (NTRS)

Traversi, M.; Barbarek, L. A. C.

1979-01-01

A handy reference for JPL minimum requirements and guidelines is presented as well as information on the use of the fundamental information source represented by the Nationwide Personal Transportation Survey. Data on U.S. demographic statistics and highway speeds are included along with methodology for normal parameters evaluation, synthesis of daily distance distributions, and projection of car ownership distributions. The synthesis of tentative mission quantification results, of intermediate mission quantification results, and of mission quantification parameters are considered and 1985 in place fleet fuel economy data are included.

A tyre slip-based integrated chassis control of front/rear traction distribution and four-wheel independent brake from moderate driving to limit handling

NASA Astrophysics Data System (ADS)

Joa, Eunhyek; Park, Kwanwoo; Koh, Youngil; Yi, Kyongsu; Kim, Kilsoo

2018-04-01

This paper presents a tyre slip-based integrated chassis control of front/rear traction distribution and four-wheel braking for enhanced performance from moderate driving to limit handling. The proposed algorithm adopted hierarchical structure: supervisor - desired motion tracking controller - optimisation-based control allocation. In the supervisor, by considering transient cornering characteristics, desired vehicle motion is calculated. In the desired motion tracking controller, in order to track desired vehicle motion, virtual control input is determined in the manner of sliding mode control. In the control allocation, virtual control input is allocated to minimise cost function. The cost function consists of two major parts. First part is a slip-based tyre friction utilisation quantification, which does not need a tyre force estimation. Second part is an allocation guideline, which guides optimally allocated inputs to predefined solution. The proposed algorithm has been investigated via simulation from moderate driving to limit handling scenario. Compared to Base and direct yaw moment control system, the proposed algorithm can effectively reduce tyre dissipation energy in the moderate driving situation. Moreover, the proposed algorithm enhances limit handling performance compared to Base and direct yaw moment control system. In addition to comparison with Base and direct yaw moment control, comparison the proposed algorithm with the control algorithm based on the known tyre force information has been conducted. The results show that the performance of the proposed algorithm is similar with that of the control algorithm with the known tyre force information.

Regional uncertainty of GOSAT XCO2 retrievals in China: quantification and attribution

NASA Astrophysics Data System (ADS)

Bie, Nian; Lei, Liping; Zeng, ZhaoCheng; Cai, Bofeng; Yang, Shaoyuan; He, Zhonghua; Wu, Changjiang; Nassar, Ray

2018-03-01

The regional uncertainty of the column-averaged dry air mole fraction of CO2 (XCO2) retrieved using different algorithms from the Greenhouse gases Observing SATellite (GOSAT) and its attribution are still not well understood. This paper investigates the regional performance of XCO2 within a latitude band of 37-42° N segmented into 8 cells in a grid of 5° from west to east (80-120° E) in China, where typical land surface types and geographic conditions exist. The former includes desert, grassland and built-up areas mixed with cropland; and the latter includes anthropogenic emissions that change from small to large from west to east, including those from the megacity of Beijing. For these specific cells, we evaluate the regional uncertainty of GOSAT XCO2 retrievals by quantifying and attributing the consistency of XCO2 retrievals from four algorithms (ACOS, NIES, OCFP and SRFP) by intercomparison. These retrievals are then specifically compared with simulated XCO2 from the high-resolution nested model in East Asia of the Goddard Earth Observing System 3-D chemical transport model (GEOS-Chem). We also introduce the anthropogenic CO2 emissions data generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China to GEOS-Chem simulations of XCO2 over the Chinese mainland. The results indicate that (1) regionally, the four algorithms demonstrate smaller absolute biases of 0.7-1.1 ppm in eastern cells, which are covered by built-up areas mixed with cropland with intensive anthropogenic emissions, than those in the western desert cells (1.0-1.6 ppm) with a high-brightness surface from the pairwise comparison results of XCO2 retrievals. (2) Compared with XCO2 simulated by GEOS-Chem (GEOS-XCO2), the XCO2 values from ACOS and SRFP have better agreement, while values from OCFP are the least consistent with GEOS-XCO2. (3) Viewing attributions of XCO2 in the spatio-temporal pattern, ACOS and SRFP demonstrate similar patterns, while OCFP is largely different from the others. In conclusion, the discrepancy in the four algorithms is the smallest in eastern cells in the study area, where the megacity of Beijing is located and where there are strong anthropogenic CO2 emissions, which implies that XCO2 from satellite observations could be reliably applied in the assessment of atmospheric CO2 enhancements induced by anthropogenic CO2 emissions. The large inconsistency among the four algorithms presented in western deserts which displays a high albedo and dust aerosols, moreover, demonstrates that further improvement is still necessary in such regions, even though many algorithms have endeavored to minimize the effects of aerosols scattering and surface albedo.

Identification and Validation of Reference Genes and Their Impact on Normalized Gene Expression Studies across Cultivated and Wild Cicer Species

PubMed Central

Reddy, Palakolanu Sudhakar; Sri Cindhuri, Katamreddy; Sivaji Ganesh, Adusumalli; Sharma, Kiran Kumar

2016-01-01

Quantitative Real-Time PCR (qPCR) is a preferred and reliable method for accurate quantification of gene expression to understand precise gene functions. A total of 25 candidate reference genes including traditional and new generation reference genes were selected and evaluated in a diverse set of chickpea samples. The samples used in this study included nine chickpea genotypes (Cicer spp.) comprising of cultivated and wild species, six abiotic stress treatments (drought, salinity, high vapor pressure deficit, abscisic acid, cold and heat shock), and five diverse tissues (leaf, root, flower, seedlings and seed). The geNorm, NormFinder and RefFinder algorithms used to identify stably expressed genes in four sample sets revealed stable expression of UCP and G6PD genes across genotypes, while TIP41 and CAC were highly stable under abiotic stress conditions. While PP2A and ABCT genes were ranked as best for different tissues, ABCT, UCP and CAC were most stable across all samples. This study demonstrated the usefulness of new generation reference genes for more accurate qPCR based gene expression quantification in cultivated as well as wild chickpea species. Validation of the best reference genes was carried out by studying their impact on normalization of aquaporin genes PIP1;4 and TIP3;1, in three contrasting chickpea genotypes under high vapor pressure deficit (VPD) treatment. The chickpea TIP3;1 gene got significantly up regulated under high VPD conditions with higher relative expression in the drought susceptible genotype, confirming the suitability of the selected reference genes for expression analysis. This is the first comprehensive study on the stability of the new generation reference genes for qPCR studies in chickpea across species, different tissues and abiotic stresses. PMID:26863232

Identification and Validation of Reference Genes and Their Impact on Normalized Gene Expression Studies across Cultivated and Wild Cicer Species.

PubMed

Reddy, Dumbala Srinivas; Bhatnagar-Mathur, Pooja; Reddy, Palakolanu Sudhakar; Sri Cindhuri, Katamreddy; Sivaji Ganesh, Adusumalli; Sharma, Kiran Kumar

2016-01-01

Quantitative Real-Time PCR (qPCR) is a preferred and reliable method for accurate quantification of gene expression to understand precise gene functions. A total of 25 candidate reference genes including traditional and new generation reference genes were selected and evaluated in a diverse set of chickpea samples. The samples used in this study included nine chickpea genotypes (Cicer spp.) comprising of cultivated and wild species, six abiotic stress treatments (drought, salinity, high vapor pressure deficit, abscisic acid, cold and heat shock), and five diverse tissues (leaf, root, flower, seedlings and seed). The geNorm, NormFinder and RefFinder algorithms used to identify stably expressed genes in four sample sets revealed stable expression of UCP and G6PD genes across genotypes, while TIP41 and CAC were highly stable under abiotic stress conditions. While PP2A and ABCT genes were ranked as best for different tissues, ABCT, UCP and CAC were most stable across all samples. This study demonstrated the usefulness of new generation reference genes for more accurate qPCR based gene expression quantification in cultivated as well as wild chickpea species. Validation of the best reference genes was carried out by studying their impact on normalization of aquaporin genes PIP1;4 and TIP3;1, in three contrasting chickpea genotypes under high vapor pressure deficit (VPD) treatment. The chickpea TIP3;1 gene got significantly up regulated under high VPD conditions with higher relative expression in the drought susceptible genotype, confirming the suitability of the selected reference genes for expression analysis. This is the first comprehensive study on the stability of the new generation reference genes for qPCR studies in chickpea across species, different tissues and abiotic stresses.

Single DNA imaging and length quantification through a mobile phone microscope

NASA Astrophysics Data System (ADS)

Wei, Qingshan; Luo, Wei; Chiang, Samuel; Kappel, Tara; Mejia, Crystal; Tseng, Derek; Chan, Raymond Yan L.; Yan, Eddie; Qi, Hangfei; Shabbir, Faizan; Ozkan, Haydar; Feng, Steve; Ozcan, Aydogan

2016-03-01

The development of sensitive optical microscopy methods for the detection of single DNA molecules has become an active research area which cultivates various promising applications including point-of-care (POC) genetic testing and diagnostics. Direct visualization of individual DNA molecules usually relies on sophisticated optical microscopes that are mostly available in well-equipped laboratories. For POC DNA testing/detection, there is an increasing need for the development of new single DNA imaging and sensing methods that are field-portable, cost-effective, and accessible for diagnostic applications in resource-limited or field-settings. For this aim, we developed a mobile-phone integrated fluorescence microscopy platform that allows imaging and sizing of single DNA molecules that are stretched on a chip. This handheld device contains an opto-mechanical attachment integrated onto a smartphone camera module, which creates a high signal-to-noise ratio dark-field imaging condition by using an oblique illumination/excitation configuration. Using this device, we demonstrated imaging of individual linearly stretched λ DNA molecules (48 kilobase-pair, kbp) over 2 mm2 field-of-view. We further developed a robust computational algorithm and a smartphone app that allowed the users to quickly quantify the length of each DNA fragment imaged using this mobile interface. The cellphone based device was tested by five different DNA samples (5, 10, 20, 40, and 48 kbp), and a sizing accuracy of <1 kbp was demonstrated for DNA strands longer than 10 kbp. This mobile DNA imaging and sizing platform can be very useful for various diagnostic applications including the detection of disease-specific genes and quantification of copy-number-variations at POC settings.

Transthoracic 3D echocardiographic left heart chamber quantification in patients with bicuspid aortic valve disease.

PubMed

van den Hoven, Allard T; Mc-Ghie, Jackie S; Chelu, Raluca G; Duijnhouwer, Anthonie L; Baggen, Vivan J M; Coenen, Adriaan; Vletter, Wim B; Dijkshoorn, Marcel L; van den Bosch, Annemien E; Roos-Hesselink, Jolien W

2017-12-01

Integration of volumetric heart chamber quantification by 3D echocardiography into clinical practice has been hampered by several factors which a new fully automated algorithm (Left Heart Model, (LHM)) may help overcome. This study therefore aims to evaluate the feasibility and accuracy of the LHM software in quantifying left atrial and left ventricular volumes and left ventricular ejection fraction in a cohort of patients with a bicuspid aortic valve. Patients with a bicuspid aortic valve were prospectively included. All patients underwent 2D and 3D transthoracic echocardiography and computed tomography. Left atrial and ventricular volumes were obtained using the automated program, which did not require manual contour detection. For comparison manual and semi-automated measurements were performed using conventional 2D and 3D datasets. 53 patients were included, in four of those patients no 3D dataset could be acquired. Additionally, 12 patients were excluded based on poor imaging quality. Left ventricular end-diastolic and end-systolic volumes and ejection fraction calculated by the LHM correlated well with manual 2D and 3D measurements (Pearson's r between 0.43 and 0.97, p < 0.05). Left atrial volume (LAV) also correlated significantly although LHM did estimate larger LAV compared to both 2DE and 3DE (Pearson's r between 0.61 and 0.81, p < 0.01). The fully automated software works well in a real-world setting and helps to overcome some of the major hurdles in integrating 3D analysis into daily practice, as it is user-independent and highly reproducible in a group of patients with a clearly defined and well-studied valvular abnormality.

Assessing uncertainty and sensitivity of model parameterizations and parameters in WRF affecting simulated surface fluxes and land-atmosphere coupling over the Amazon region

NASA Astrophysics Data System (ADS)

Qian, Y.; Wang, C.; Huang, M.; Berg, L. K.; Duan, Q.; Feng, Z.; Shrivastava, M. B.; Shin, H. H.; Hong, S. Y.

2016-12-01

This study aims to quantify the relative importance and uncertainties of different physical processes and parameters in affecting simulated surface fluxes and land-atmosphere coupling strength over the Amazon region. We used two-legged coupling metrics, which include both terrestrial (soil moisture to surface fluxes) and atmospheric (surface fluxes to atmospheric state or precipitation) legs, to diagnose the land-atmosphere interaction and coupling strength. Observations made using the Department of Energy's Atmospheric Radiation Measurement (ARM) Mobile Facility during the GoAmazon field campaign together with satellite and reanalysis data are used to evaluate model performance. To quantify the uncertainty in physical parameterizations, we performed a 120 member ensemble of simulations with the WRF model using a stratified experimental design including 6 cloud microphysics, 3 convection, 6 PBL and surface layer, and 3 land surface schemes. A multiple-way analysis of variance approach is used to quantitatively analyze the inter- and intra-group (scheme) means and variances. To quantify parameter sensitivity, we conducted an additional 256 WRF simulations in which an efficient sampling algorithm is used to explore the multiple-dimensional parameter space. Three uncertainty quantification approaches are applied for sensitivity analysis (SA) of multiple variables of interest to 20 selected parameters in YSU PBL and MM5 surface layer schemes. Results show consistent parameter sensitivity across different SA methods. We found that 5 out of 20 parameters contribute more than 90% total variance, and first-order effects dominate comparing to the interaction effects. Results of this uncertainty quantification study serve as guidance for better understanding the roles of different physical processes in land-atmosphere interactions, quantifying model uncertainties from various sources such as physical processes, parameters and structural errors, and providing insights for improving the model physics parameterizations.

Probabilistic methods for sensitivity analysis and calibration in the NASA challenge problem

DOE PAGES

Safta, Cosmin; Sargsyan, Khachik; Najm, Habib N.; ...

2015-01-01

In this study, a series of algorithms are proposed to address the problems in the NASA Langley Research Center Multidisciplinary Uncertainty Quantification Challenge. A Bayesian approach is employed to characterize and calibrate the epistemic parameters based on the available data, whereas a variance-based global sensitivity analysis is used to rank the epistemic and aleatory model parameters. A nested sampling of the aleatory–epistemic space is proposed to propagate uncertainties from model parameters to output quantities of interest.

Probabilistic methods for sensitivity analysis and calibration in the NASA challenge problem

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

Safta, Cosmin; Sargsyan, Khachik; Najm, Habib N.

In this study, a series of algorithms are proposed to address the problems in the NASA Langley Research Center Multidisciplinary Uncertainty Quantification Challenge. A Bayesian approach is employed to characterize and calibrate the epistemic parameters based on the available data, whereas a variance-based global sensitivity analysis is used to rank the epistemic and aleatory model parameters. A nested sampling of the aleatory–epistemic space is proposed to propagate uncertainties from model parameters to output quantities of interest.

Root Gravitropism: Quantification, Challenges, and Solutions.

PubMed

Muller, Lukas; Bennett, Malcolm J; French, Andy; Wells, Darren M; Swarup, Ranjan

2018-01-01

Better understanding of root traits such as root angle and root gravitropism will be crucial for development of crops with improved resource use efficiency. This chapter describes a high-throughput, automated image analysis method to trace Arabidopsis (Arabidopsis thaliana) seedling roots grown on agar plates. The method combines a "particle-filtering algorithm with a graph-based method" to trace the center line of a root and can be adopted for the analysis of several root parameters such as length, curvature, and stimulus from original root traces.

An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications

NASA Technical Reports Server (NTRS)

Schuet, Stefan R.; Lombaerts, Thomas Jan; Acosta, Diana; Wheeler, Kevin; Kaneshige, John

2014-01-01

A nonlinear aircraft model is presented and used to develop an overall unified robust and adaptive approach to passive trim and maneuverability envelope estimation with uncertainty quantification. The concept of time scale separation makes this method suitable for the online characterization of altered safe maneuvering limitations after impairment. The results can be used to provide pilot feedback and/or be combined with flight planning, trajectory generation, and guidance algorithms to help maintain safe aircraft operations in both nominal and off-nominal scenarios.

Automated podosome identification and characterization in fluorescence microscopy images.

PubMed

Meddens, Marjolein B M; Rieger, Bernd; Figdor, Carl G; Cambi, Alessandra; van den Dries, Koen

2013-02-01

Podosomes are cellular adhesion structures involved in matrix degradation and invasion that comprise an actin core and a ring of cytoskeletal adaptor proteins. They are most often identified by staining with phalloidin, which binds F-actin and therefore visualizes the core. However, not only podosomes, but also many other cytoskeletal structures contain actin, which makes podosome segmentation by automated image processing difficult. Here, we have developed a quantitative image analysis algorithm that is optimized to identify podosome cores within a typical sample stained with phalloidin. By sequential local and global thresholding, our analysis identifies up to 76% of podosome cores excluding other F-actin-based structures. Based on the overlap in podosome identifications and quantification of podosome numbers, our algorithm performs equally well compared to three experts. Using our algorithm we show effects of actin polymerization and myosin II inhibition on the actin intensity in both podosome core and associated actin network. Furthermore, by expanding the core segmentations, we reveal a previously unappreciated differential distribution of cytoskeletal adaptor proteins within the podosome ring. These applications illustrate that our algorithm is a valuable tool for rapid and accurate large-scale analysis of podosomes to increase our understanding of these characteristic adhesion structures.

Multimodal molecular 3D imaging for the tumoral volumetric distribution assessment of folate-based biosensors.

PubMed

Ramírez-Nava, Gerardo J; Santos-Cuevas, Clara L; Chairez, Isaac; Aranda-Lara, Liliana

2017-12-01

The aim of this study was to characterize the in vivo volumetric distribution of three folate-based biosensors by different imaging modalities (X-ray, fluorescence, Cerenkov luminescence, and radioisotopic imaging) through the development of a tridimensional image reconstruction algorithm. The preclinical and multimodal Xtreme imaging system, with a Multimodal Animal Rotation System (MARS), was used to acquire bidimensional images, which were processed to obtain the tridimensional reconstruction. Images of mice at different times (biosensor distribution) were simultaneously obtained from the four imaging modalities. The filtered back projection and inverse Radon transformation were used as main image-processing techniques. The algorithm developed in Matlab was able to calculate the volumetric profiles of 99m Tc-Folate-Bombesin (radioisotopic image), 177 Lu-Folate-Bombesin (Cerenkov image), and FolateRSense™ 680 (fluorescence image) in tumors and kidneys of mice, and no significant differences were detected in the volumetric quantifications among measurement techniques. The imaging tridimensional reconstruction algorithm can be easily extrapolated to different 2D acquisition-type images. This characteristic flexibility of the algorithm developed in this study is a remarkable advantage in comparison to similar reconstruction methods.

Optimization of monitoring networks based on uncertainty quantification of model predictions of contaminant transport

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Harp, D.

2010-12-01

The process of decision making to protect groundwater resources requires a detailed estimation of uncertainties in model predictions. Various uncertainties associated with modeling a natural system, such as: (1) measurement and computational errors; (2) uncertainties in the conceptual model and model-parameter estimates; (3) simplifications in model setup and numerical representation of governing processes, contribute to the uncertainties in the model predictions. Due to this combination of factors, the sources of predictive uncertainties are generally difficult to quantify individually. Decision support related to optimal design of monitoring networks requires (1) detailed analyses of existing uncertainties related to model predictions of groundwater flow and contaminant transport, (2) optimization of the proposed monitoring network locations in terms of their efficiency to detect contaminants and provide early warning. We apply existing and newly-proposed methods to quantify predictive uncertainties and to optimize well locations. An important aspect of the analysis is the application of newly-developed optimization technique based on coupling of Particle Swarm and Levenberg-Marquardt optimization methods which proved to be robust and computationally efficient. These techniques and algorithms are bundled in a software package called MADS. MADS (Model Analyses for Decision Support) is an object-oriented code that is capable of performing various types of model analyses and supporting model-based decision making. The code can be executed under different computational modes, which include (1) sensitivity analyses (global and local), (2) Monte Carlo analysis, (3) model calibration, (4) parameter estimation, (5) uncertainty quantification, and (6) model selection. The code can be externally coupled with any existing model simulator through integrated modules that read/write input and output files using a set of template and instruction files (consistent with the PEST I/O protocol). MADS can also be internally coupled with a series of built-in analytical simulators. MADS provides functionality to work directly with existing control files developed for the code PEST (Doherty 2009). To perform the computational modes mentioned above, the code utilizes (1) advanced Latin-Hypercube sampling techniques (including Improved Distributed Sampling), (2) various gradient-based Levenberg-Marquardt optimization methods, (3) advanced global optimization methods (including Particle Swarm Optimization), and (4) a selection of alternative objective functions. The code has been successfully applied to perform various model analyses related to environmental management of real contamination sites. Examples include source identification problems, quantification of uncertainty, model calibration, and optimization of monitoring networks. The methodology and software codes are demonstrated using synthetic and real case studies where monitoring networks are optimized taking into account the uncertainty in model predictions of contaminant transport.

Novel image analysis approach for quantifying expression of nuclear proteins assessed by immunohistochemistry: application to measurement of oestrogen and progesterone receptor levels in breast cancer.

PubMed

Rexhepaj, Elton; Brennan, Donal J; Holloway, Peter; Kay, Elaine W; McCann, Amanda H; Landberg, Goran; Duffy, Michael J; Jirstrom, Karin; Gallagher, William M

2008-01-01

Manual interpretation of immunohistochemistry (IHC) is a subjective, time-consuming and variable process, with an inherent intra-observer and inter-observer variability. Automated image analysis approaches offer the possibility of developing rapid, uniform indicators of IHC staining. In the present article we describe the development of a novel approach for automatically quantifying oestrogen receptor (ER) and progesterone receptor (PR) protein expression assessed by IHC in primary breast cancer. Two cohorts of breast cancer patients (n = 743) were used in the study. Digital images of breast cancer tissue microarrays were captured using the Aperio ScanScope XT slide scanner (Aperio Technologies, Vista, CA, USA). Image analysis algorithms were developed using MatLab 7 (MathWorks, Apple Hill Drive, MA, USA). A fully automated nuclear algorithm was developed to discriminate tumour from normal tissue and to quantify ER and PR expression in both cohorts. Random forest clustering was employed to identify optimum thresholds for survival analysis. The accuracy of the nuclear algorithm was initially confirmed by a histopathologist, who validated the output in 18 representative images. In these 18 samples, an excellent correlation was evident between the results obtained by manual and automated analysis (Spearman's rho = 0.9, P < 0.001). Optimum thresholds for survival analysis were identified using random forest clustering. This revealed 7% positive tumour cells as the optimum threshold for the ER and 5% positive tumour cells for the PR. Moreover, a 7% cutoff level for the ER predicted a better response to tamoxifen than the currently used 10% threshold. Finally, linear regression was employed to demonstrate a more homogeneous pattern of expression for the ER (R = 0.860) than for the PR (R = 0.681). In summary, we present data on the automated quantification of the ER and the PR in 743 primary breast tumours using a novel unsupervised image analysis algorithm. This novel approach provides a useful tool for the quantification of biomarkers on tissue specimens, as well as for objective identification of appropriate cutoff thresholds for biomarker positivity. It also offers the potential to identify proteins with a homogeneous pattern of expression.

Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data.

PubMed

Kumar, Dushyant; Hariharan, Hari; Faizy, Tobias D; Borchert, Patrick; Siemonsen, Susanne; Fiehler, Jens; Reddy, Ravinder; Sedlacik, Jan

2018-05-12

We present a computationally feasible and iterative multi-voxel spatially regularized algorithm for myelin water fraction (MWF) reconstruction. This method utilizes 3D spatial correlations present in anatomical/pathological tissues and underlying B1 + -inhomogeneity or flip angle inhomogeneity to enhance the noise robustness of the reconstruction while intrinsically accounting for stimulated echo contributions using T2-distribution data alone. Simulated data and in vivo data acquired using 3D non-selective multi-echo spin echo (3DNS-MESE) were used to compare the reconstruction quality of the proposed approach against those of the popular algorithm (the method by Prasloski et al.) and our previously proposed 2D multi-slice spatial regularization spatial regularization approach. We also investigated whether the inter-sequence correlations and agreements improved as a result of the proposed approach. MWF-quantifications from two sequences, 3DNS-MESE vs 3DNS-gradient and spin echo (3DNS-GRASE), were compared for both reconstruction approaches to assess correlations and agreements between inter-sequence MWF-value pairs. MWF values from whole-brain data of six volunteers and two multiple sclerosis patients are being reported as well. In comparison with competing approaches such as Prasloski's method or our previously proposed 2D multi-slice spatial regularization method, the proposed method showed better agreements with simulated truths using regression analyses and Bland-Altman analyses. For 3DNS-MESE data, MWF-maps reconstructed using the proposed algorithm provided better depictions of white matter structures in subcortical areas adjoining gray matter which agreed more closely with corresponding contrasts on T2-weighted images than MWF-maps reconstructed with the method by Prasloski et al. We also achieved a higher level of correlations and agreements between inter-sequence (3DNS-MESE vs 3DNS-GRASE) MWF-value pairs. The proposed algorithm provides more noise-robust fits to T2-decay data and improves MWF-quantifications in white matter structures especially in the sub-cortical white matter and major white matter tract regions. Copyright © 2018 Elsevier Inc. All rights reserved.

Reproducibility study of whole-brain 1H spectroscopic imaging with automated quantification.

PubMed

Gu, Meng; Kim, Dong-Hyun; Mayer, Dirk; Sullivan, Edith V; Pfefferbaum, Adolf; Spielman, Daniel M

2008-09-01

A reproducibility study of proton MR spectroscopic imaging ((1)H-MRSI) of the human brain was conducted to evaluate the reliability of an automated 3D in vivo spectroscopic imaging acquisition and associated quantification algorithm. A PRESS-based pulse sequence was implemented using dualband spectral-spatial RF pulses designed to fully excite the singlet resonances of choline (Cho), creatine (Cre), and N-acetyl aspartate (NAA) while simultaneously suppressing water and lipids; 1% of the water signal was left to be used as a reference signal for robust data processing, and additional lipid suppression was obtained using adiabatic inversion recovery. Spiral k-space trajectories were used for fast spectral and spatial encoding yielding high-quality spectra from 1 cc voxels throughout the brain with a 13-min acquisition time. Data were acquired with an 8-channel phased-array coil and optimal signal-to-noise ratio (SNR) for the combined signals was achieved using a weighting based on the residual water signal. Automated quantification of the spectrum of each voxel was performed using LCModel. The complete study consisted of eight healthy adult subjects to assess intersubject variations and two subjects scanned six times each to assess intrasubject variations. The results demonstrate that reproducible whole-brain (1)H-MRSI data can be robustly obtained with the proposed methods.

A Comparison of Ultrasound Tomography Methods in Circular Geometry

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

Leach, R R; Azevedo, S G; Berryman, J G

2002-01-24

Extremely high quality data was acquired using an experimental ultrasound scanner developed at Lawrence Livermore National Laboratory using a 2D ring geometry with up to 720 transmitter/receiver transducer positions. This unique geometry allows reflection and transmission modes and transmission imaging and quantification of a 3D volume using 2D slice data. Standard image reconstruction methods were applied to the data including straight-ray filtered back projection, reflection tomography, and diffraction tomography. Newer approaches were also tested such as full wave, full wave adjoint method, bent-ray filtered back projection, and full-aperture tomography. A variety of data sets were collected including a formalin-fixed humanmore » breast tissue sample, a commercial ultrasound complex breast phantom, and cylindrical objects with and without inclusions. The resulting reconstruction quality of the images ranges from poor to excellent. The method and results of this study are described including like-data reconstructions produced by different algorithms with side-by-side image comparisons. Comparisons to medical B-scan and x-ray CT scan images are also shown. Reconstruction methods with respect to image quality using resolution, noise, and quantitative accuracy, and computational efficiency metrics will also be discussed.« less

Antibody-free PRISM-SRM for multiplexed protein quantification: Is this the new competition for immunoassays in bioanalysis?

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

Shi, Tujin; Qian, Weijun

2013-02-01

Highly sensitive technologies for multiplexed quantification of a large number of candidate proteins will play an increasingly important role in clinical biomarker discovery, systems biology, and general biomedical research. Herein we introduce the new PRISM-SRM technology, which represents a highly sensitive multiplexed quantification technology capable of simultaneous quantification of many low-abundance proteins without the need of affinity reagents. The versatility of antibody-free PRISM-SRM for quantifying various types of targets including protein isoforms, protein modifications, metabolites, and others, thus offering new competition with immunoassays.

Simultaneous quantification of protein phosphorylation sites using liquid chromatography-tandem mass spectrometry-based targeted proteomics: a linear algebra approach for isobaric phosphopeptides.

PubMed

Xu, Feifei; Yang, Ting; Sheng, Yuan; Zhong, Ting; Yang, Mi; Chen, Yun

2014-12-05

As one of the most studied post-translational modifications (PTM), protein phosphorylation plays an essential role in almost all cellular processes. Current methods are able to predict and determine thousands of phosphorylation sites, whereas stoichiometric quantification of these sites is still challenging. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based targeted proteomics is emerging as a promising technique for site-specific quantification of protein phosphorylation using proteolytic peptides as surrogates of proteins. However, several issues may limit its application, one of which relates to the phosphopeptides with different phosphorylation sites and the same mass (i.e., isobaric phosphopeptides). While employment of site-specific product ions allows for these isobaric phosphopeptides to be distinguished and quantified, site-specific product ions are often absent or weak in tandem mass spectra. In this study, linear algebra algorithms were employed as an add-on to targeted proteomics to retrieve information on individual phosphopeptides from their common spectra. To achieve this simultaneous quantification, a LC-MS/MS-based targeted proteomics assay was first developed and validated for each phosphopeptide. Given the slope and intercept of calibration curves of phosphopeptides in each transition, linear algebraic equations were developed. Using a series of mock mixtures prepared with varying concentrations of each phosphopeptide, the reliability of the approach to quantify isobaric phosphopeptides containing multiple phosphorylation sites (≥ 2) was discussed. Finally, we applied this approach to determine the phosphorylation stoichiometry of heat shock protein 27 (HSP27) at Ser78 and Ser82 in breast cancer cells and tissue samples.

A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation.

PubMed

Välikangas, Tommi; Suomi, Tomi; Elo, Laura L

2017-05-31

Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them.In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets. © The Author 2017. Published by Oxford University Press.

Mm-Wave Spectroscopic Sensors, Catalogs, and Uncatalogued Lines

NASA Astrophysics Data System (ADS)

Medvedev, Ivan; Neese, Christopher F.; De Lucia, Frank C.

2014-06-01

Analytical chemical sensing based on high resolution rotational molecular spectra has been recognized as a viable technique for decades. We recently demonstrated a compact implementation of such a sensor. Future generations of these sensors will rely on automated algorithms for quantification of chemical dilutions based on their spectral libraries, as well as identification of spectral features not present in spectral catalogs. Here we present an algorithm aimed at detection of unidentified lines in complex molecular species based on spectroscopic libraries developed in our previous projects. We will discuss the approaches suitable for data mining in feature-rich rotational molecular spectra. Neese, C.F., I.R. Medvedev, G.M. Plummer, A.J. Frank, C.D. Ball, and F.C. De Lucia, "A Compact Submillimeter/Terahertz Gas Sensor with Efficient Gas Collection, Preconcentration, and ppt Sensitivity." Sensors Journal, IEEE, 2012. 12(8): p. 2565-2574

Quantification of whispering gallery mode spectrum variability in application to sensing nanobiophotonics

NASA Astrophysics Data System (ADS)

Saetchnikov, Anton; Skakun, Victor; Saetchnikov, Vladimir; Tcherniavskaia, Elina; Ostendorf, Andreas

2017-10-01

An approach for the automated whispering gallery mode (WGM) signal decomposition and its parameter estimation is discussed. The algorithm is based on the peak picking and can be applied for the preprocessing of the raw signal acquired from the multiplied WGM-based biosensing chips. Quantitative estimations representing physically meaningful parameters of the external disturbing factors on the WGM spectral shape are the output values. Derived parameters can be directly applied to the further deep qualitative and quantitative interpretations of the sensed disturbing factors. The algorithm is tested on both simulated and experimental data taken from the bovine serum albumin biosensing task. The proposed solution is expected to be a useful contribution to the preprocessing phase of the complete data analysis engine and is expected to push the WGM technology toward the real-live sensing nanobiophotonics.

FANSe2: a robust and cost-efficient alignment tool for quantitative next-generation sequencing applications.

PubMed

Xiao, Chuan-Le; Mai, Zhi-Biao; Lian, Xin-Lei; Zhong, Jia-Yong; Jin, Jing-Jie; He, Qing-Yu; Zhang, Gong

2014-01-01

Correct and bias-free interpretation of the deep sequencing data is inevitably dependent on the complete mapping of all mappable reads to the reference sequence, especially for quantitative RNA-seq applications. Seed-based algorithms are generally slow but robust, while Burrows-Wheeler Transform (BWT) based algorithms are fast but less robust. To have both advantages, we developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the accuracy. Its sensitivity and accuracy are higher than the BWT-based algorithms in the tests using both prokaryotic and eukaryotic sequencing datasets. The gene identification results of FANSe2 is experimentally validated, while the previous algorithms have false positives and false negatives. FANSe2 showed remarkably better consistency to the microarray than most other algorithms in terms of gene expression quantifications. We implemented a scalable and almost maintenance-free parallelization method that can utilize the computational power of multiple office computers, a novel feature not present in any other mainstream algorithm. With three normal office computers, we demonstrated that FANSe2 mapped an RNA-seq dataset generated from an entire Illunima HiSeq 2000 flowcell (8 lanes, 608 M reads) to masked human genome within 4.1 hours with higher sensitivity than Bowtie/Bowtie2. FANSe2 thus provides robust accuracy, full indel sensitivity, fast speed, versatile compatibility and economical computational utilization, making it a useful and practical tool for deep sequencing applications. FANSe2 is freely available at http://bioinformatics.jnu.edu.cn/software/fanse2/.

Wavelet-based algorithm to the evaluation of contrasted hepatocellular carcinoma in CT-images after transarterial chemoembolization.

PubMed

Alvarez, Matheus; de Pina, Diana Rodrigues; Romeiro, Fernando Gomes; Duarte, Sérgio Barbosa; Miranda, José Ricardo de Arruda

2014-07-26

Hepatocellular carcinoma is a primary tumor of the liver and involves different treatment modalities according to the tumor stage. After local therapies, the tumor evaluation is based on the mRECIST criteria, which involves the measurement of the maximum diameter of the viable lesion. This paper describes a computed methodology to measure through the contrasted area of the lesions the maximum diameter of the tumor by a computational algorithm. 63 computed tomography (CT) slices from 23 patients were assessed. Non-contrasted liver and HCC typical nodules were evaluated, and a virtual phantom was developed for this purpose. Optimization of the algorithm detection and quantification was made using the virtual phantom. After that, we compared the algorithm findings of maximum diameter of the target lesions against radiologist measures. Computed results of the maximum diameter are in good agreement with the results obtained by radiologist evaluation, indicating that the algorithm was able to detect properly the tumor limits. A comparison of the estimated maximum diameter by radiologist versus the algorithm revealed differences on the order of 0.25 cm for large-sized tumors (diameter > 5 cm), whereas agreement lesser than 1.0 cm was found for small-sized tumors. Differences between algorithm and radiologist measures were accurate for small-sized tumors with a trend to a small decrease for tumors greater than 5 cm. Therefore, traditional methods for measuring lesion diameter should be complemented non-subjective measurement methods, which would allow a more correct evaluation of the contrast-enhanced areas of HCC according to the mRECIST criteria.

Fluorescent quantification of terazosin hydrochloride content in human plasma and tablets using second-order calibration based on both parallel factor analysis and alternating penalty trilinear decomposition.

PubMed

Zou, Hong-Yan; Wu, Hai-Long; OuYang, Li-Qun; Zhang, Yan; Nie, Jin-Fang; Fu, Hai-Yan; Yu, Ru-Qin

2009-09-14

Two second-order calibration methods based on the parallel factor analysis (PARAFAC) and the alternating penalty trilinear decomposition (APTLD) method, have been utilized for the direct determination of terazosin hydrochloride (THD) in human plasma samples, coupled with the excitation-emission matrix fluorescence spectroscopy. Meanwhile, the two algorithms combing with the standard addition procedures have been applied for the determination of terazosin hydrochloride in tablets and the results were validated by the high-performance liquid chromatography with fluorescence detection. These second-order calibrations all adequately exploited the second-order advantages. For human plasma samples, the average recoveries by the PARAFAC and APTLD algorithms with the factor number of 2 (N=2) were 100.4+/-2.7% and 99.2+/-2.4%, respectively. The accuracy of two algorithms was also evaluated through elliptical joint confidence region (EJCR) tests and t-test. It was found that both algorithms could give accurate results, and only the performance of APTLD was slightly better than that of PARAFAC. Figures of merit, such as sensitivity (SEN), selectivity (SEL) and limit of detection (LOD) were also calculated to compare the performances of the two strategies. For tablets, the average concentrations of THD in tablet were 63.5 and 63.2 ng mL(-1) by using the PARAFAC and APTLD algorithms, respectively. The accuracy was evaluated by t-test and both algorithms could give accurate results, too.

Emphysema quantification and lung volumetry in chest X-ray equivalent ultralow dose CT - Intra-individual comparison with standard dose CT.

PubMed

Messerli, Michael; Ottilinger, Thorsten; Warschkow, René; Leschka, Sebastian; Alkadhi, Hatem; Wildermuth, Simon; Bauer, Ralf W

2017-06-01

To determine whether ultralow dose chest CT with tin filtration can be used for emphysema quantification and lung volumetry and to assess differences in emphysema measurements and lung volume between standard dose and ultralow dose CT scans using advanced modeled iterative reconstruction (ADMIRE). 84 consecutive patients from a prospective, IRB-approved single-center study were included and underwent clinically indicated standard dose chest CT (1.7±0.6mSv) and additional single-energy ultralow dose CT (0.14±0.01mSv) at 100kV and fixed tube current at 70mAs with tin filtration in the same session. Forty of the 84 patients (48%) had no emphysema, 44 (52%) had emphysema. One radiologist performed fully automated software-based pulmonary emphysema quantification and lung volumetry of standard and ultralow dose CT with different levels of ADMIRE. Friedman test and Wilcoxon rank sum test were used for multiple comparison of emphysema and lung volume. Lung volumes were compared using the concordance correlation coefficient. The median low-attenuation areas (LAA) using filtered back projection (FBP) in standard dose was 4.4% and decreased to 2.6%, 2.1% and 1.8% using ADMIRE 3, 4, and 5, respectively. The median values of LAA in ultralow dose CT were 5.7%, 4.1% and 2.4% for ADMIRE 3, 4, and 5, respectively. There was no statistically significant difference between LAA in standard dose CT using FBP and ultralow dose using ADMIRE 4 (p=0.358) as well as in standard dose CT using ADMIRE 3 and ultralow dose using ADMIRE 5 (p=0.966). In comparison with standard dose FBP the concordance correlation coefficients of lung volumetry were 1.000, 0.999, and 0.999 for ADMIRE 3, 4, and 5 in standard dose, and 0.972 for ADMIRE 3, 4 and 5 in ultralow dose CT. Ultralow dose CT at chest X-ray equivalent dose levels allows for lung volumetry as well as detection and quantification of emphysema. However, longitudinal emphysema analyses should be performed with the same scan protocol and reconstruction algorithms for reproducibility. Copyright © 2017 Elsevier B.V. All rights reserved.

An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues

PubMed Central

Ribeiro, Angela; Ranz, Juan; Burgos-Artizzu, Xavier P.; Pajares, Gonzalo; Sanchez del Arco, Maria J.; Navarrete, Luis

2011-01-01

Determination of the soil coverage by crop residues after ploughing is a fundamental element of Conservation Agriculture. This paper presents the application of genetic algorithms employed during the fine tuning of the segmentation process of a digital image with the aim of automatically quantifying the residue coverage. In other words, the objective is to achieve a segmentation that would permit the discrimination of the texture of the residue so that the output of the segmentation process is a binary image in which residue zones are isolated from the rest. The RGB images used come from a sample of images in which sections of terrain were photographed with a conventional camera positioned in zenith orientation atop a tripod. The images were taken outdoors under uncontrolled lighting conditions. Up to 92% similarity was achieved between the images obtained by the segmentation process proposed in this paper and the templates made by an elaborate manual tracing process. In addition to the proposed segmentation procedure and the fine tuning procedure that was developed, a global quantification of the soil coverage by residues for the sampled area was achieved that differed by only 0.85% from the quantification obtained using template images. Moreover, the proposed method does not depend on the type of residue present in the image. The study was conducted at the experimental farm “El Encín” in Alcalá de Henares (Madrid, Spain). PMID:22163966

Methane Leak Detection and Emissions Quantification with UAVs

NASA Astrophysics Data System (ADS)

Barchyn, T.; Fox, T. A.; Hugenholtz, C.

2016-12-01

Robust leak detection and emissions quantification algorithms are required to accurately monitor greenhouse gas emissions. Unmanned aerial vehicles (UAVs, `drones') could both reduce the cost and increase the accuracy of monitoring programs. However, aspects of the platform create unique challenges. UAVs typically collect large volumes of data that are close to source (due to limited range) and often lower quality (due to weight restrictions on sensors). Here we discuss algorithm development for (i) finding sources of unknown position (`leak detection') and (ii) quantifying emissions from a source of known position. We use data from a simulated leak and field study in Alberta, Canada. First, we detail a method for localizing a leak of unknown spatial location using iterative fits against a forward Gaussian plume model. We explore sources of uncertainty, both inherent to the method and operational. Results suggest this method is primarily constrained by accurate wind direction data, distance downwind from source, and the non-Gaussian shape of close range plumes. Second, we examine sources of uncertainty in quantifying emissions with the mass balance method. Results suggest precision is constrained by flux plane interpolation errors and time offsets between spatially adjacent measurements. Drones can provide data closer to the ground than piloted aircraft, but large portions of the plume are still unquantified. Together, we find that despite larger volumes of data, working with close range plumes as measured with UAVs is inherently difficult. We describe future efforts to mitigate these challenges and work towards more robust benchmarking for application in industrial and regulatory settings.

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

NASA Astrophysics Data System (ADS)

Scheidt, Céline; Fernandes, Anjali M.; Paola, Chris; Caers, Jef

2016-10-01

We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple-point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training images should be provided to reproduce natural autogenic variability. The first method relies on distance-based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the "eigenpatterns" of the natural system. The eigenpattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.

Sci-Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques - 11: Quantification of chest wall motion during deep inspiration breast hold treatments using cine EPID images and a physics based algorithm

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

Alpuche Aviles, Jorge E.; VanBeek, Timothy

Purpose: This work presents an algorithm used to quantify intra-fraction motion for patients treated using deep inspiration breath hold (DIBH). The algorithm quantifies the position of the chest wall in breast tangent fields using electronic portal images. Methods: The algorithm assumes that image profiles, taken along a direction perpendicular to the medial border of the field, follow a monotonically and smooth decreasing function. This assumption is invalid in the presence of lung and can be used to calculate chest wall position. The algorithm was validated by determining the position of the chest wall for varying field edge positions in portalmore » images of a thoracic phantom. The algorithm was used to quantify intra-fraction motion in cine images for 7 patients treated with DIBH. Results: Phantom results show that changes in the distance between chest wall and field edge were accurate within 0.1 mm on average. For a fixed field edge, the algorithm calculates the position of the chest wall with a 0.2 mm standard deviation. Intra-fraction motion for DIBH patients was within 1 mm 91.4% of the time and within 1.5 mm 97.9% of the time. The maximum intra-fraction motion was 3.0 mm. Conclusions: A physics based algorithm was developed and can be used to quantify the position of chest wall irradiated in tangent portal images with an accuracy of 0.1 mm and precision of 0.6 mm. Intra-fraction motion for patients treated with DIBH at our clinic is less than 3 mm.« less

Automated Quantification and Integrative Analysis of 2D and 3D Mitochondrial Shape and Network Properties

PubMed Central

Nikolaisen, Julie; Nilsson, Linn I. H.; Pettersen, Ina K. N.; Willems, Peter H. G. M.; Lorens, James B.; Koopman, Werner J. H.; Tronstad, Karl J.

2014-01-01

Mitochondrial morphology and function are coupled in healthy cells, during pathological conditions and (adaptation to) endogenous and exogenous stress. In this sense mitochondrial shape can range from small globular compartments to complex filamentous networks, even within the same cell. Understanding how mitochondrial morphological changes (i.e. “mitochondrial dynamics”) are linked to cellular (patho) physiology is currently the subject of intense study and requires detailed quantitative information. During the last decade, various computational approaches have been developed for automated 2-dimensional (2D) analysis of mitochondrial morphology and number in microscopy images. Although these strategies are well suited for analysis of adhering cells with a flat morphology they are not applicable for thicker cells, which require a three-dimensional (3D) image acquisition and analysis procedure. Here we developed and validated an automated image analysis algorithm allowing simultaneous 3D quantification of mitochondrial morphology and network properties in human endothelial cells (HUVECs). Cells expressing a mitochondria-targeted green fluorescence protein (mitoGFP) were visualized by 3D confocal microscopy and mitochondrial morphology was quantified using both the established 2D method and the new 3D strategy. We demonstrate that both analyses can be used to characterize and discriminate between various mitochondrial morphologies and network properties. However, the results from 2D and 3D analysis were not equivalent when filamentous mitochondria in normal HUVECs were compared with circular/spherical mitochondria in metabolically stressed HUVECs treated with rotenone (ROT). 2D quantification suggested that metabolic stress induced mitochondrial fragmentation and loss of biomass. In contrast, 3D analysis revealed that the mitochondrial network structure was dissolved without affecting the amount and size of the organelles. Thus, our results demonstrate that 3D imaging and quantification are crucial for proper understanding of mitochondrial shape and topology in non-flat cells. In summary, we here present an integrative method for unbiased 3D quantification of mitochondrial shape and network properties in mammalian cells. PMID:24988307

The need for improved brain lesion segmentation techniques for children with cerebral palsy: A review.

PubMed

Pagnozzi, Alex M; Gal, Yaniv; Boyd, Roslyn N; Fiori, Simona; Fripp, Jurgen; Rose, Stephen; Dowson, Nicholas

2015-12-01

Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. Accurate diagnosis and prognosis, in terms of motor type and severity, is difficult to obtain due to the heterogeneous appearance of brain injury and large anatomical distortions commonly observed in children with CP. There is a need to optimise treatment strategies for individual patients in order to lead to lifelong improvements in function and capabilities. Magnetic resonance imaging (MRI) is critical to non-invasively visualizing brain lesions, and is currently used to assist the diagnosis and qualitative classification in CP patients. Although such qualitative approaches under-utilise available data, the quantification of MRIs is not automated and therefore not widely performed in clinical assessment. Automated brain lesion segmentation techniques are necessary to provide valid and reproducible quantifications of injury. Such techniques have been used to study other neurological disorders, however the technical challenges unique to CP mean that existing algorithms require modification to be sufficiently reliable, and therefore have not been widely applied to MRIs of children with CP. In this paper, we present a review of a subset of available brain injury segmentation approaches that could be applied to CP, including the detection of cortical malformations, white and grey matter lesions and ventricular enlargement. Following a discussion of strengths and weaknesses, we suggest areas of future research in applying segmentation techniques to the MRI of children with CP. Specifically, we identify atlas-based priors to be ineffective in regions of substantial malformations, instead propose relying on adaptive, spatially consistent algorithms, with fast initialisation mechanisms to provide additional robustness to injury. We also identify several cortical shape parameters that could be used to identify cortical injury, and shape modelling approaches to identify anatomical injury. The benefits of automatic segmentation in CP is important as it has the potential to elucidate the underlying relationship between image derived features and patient outcome, enabling better tailoring of therapy to individual patients. Copyright © 2015 Elsevier Ltd. All rights reserved.

A novel mesh processing based technique for 3D plant analysis

PubMed Central

2012-01-01

Background In recent years, imaging based, automated, non-invasive, and non-destructive high-throughput plant phenotyping platforms have become popular tools for plant biology, underpinning the field of plant phenomics. Such platforms acquire and record large amounts of raw data that must be accurately and robustly calibrated, reconstructed, and analysed, requiring the development of sophisticated image understanding and quantification algorithms. The raw data can be processed in different ways, and the past few years have seen the emergence of two main approaches: 2D image processing and 3D mesh processing algorithms. Direct image quantification methods (usually 2D) dominate the current literature due to comparative simplicity. However, 3D mesh analysis provides the tremendous potential to accurately estimate specific morphological features cross-sectionally and monitor them over-time. Result In this paper, we present a novel 3D mesh based technique developed for temporal high-throughput plant phenomics and perform initial tests for the analysis of Gossypium hirsutum vegetative growth. Based on plant meshes previously reconstructed from multi-view images, the methodology involves several stages, including morphological mesh segmentation, phenotypic parameters estimation, and plant organs tracking over time. The initial study focuses on presenting and validating the accuracy of the methodology on dicotyledons such as cotton but we believe the approach will be more broadly applicable. This study involved applying our technique to a set of six Gossypium hirsutum (cotton) plants studied over four time-points. Manual measurements, performed for each plant at every time-point, were used to assess the accuracy of our pipeline and quantify the error on the morphological parameters estimated. Conclusion By directly comparing our automated mesh based quantitative data with manual measurements of individual stem height, leaf width and leaf length, we obtained the mean absolute errors of 9.34%, 5.75%, 8.78%, and correlation coefficients 0.88, 0.96, and 0.95 respectively. The temporal matching of leaves was accurate in 95% of the cases and the average execution time required to analyse a plant over four time-points was 4.9 minutes. The mesh processing based methodology is thus considered suitable for quantitative 4D monitoring of plant phenotypic features. PMID:22553969

Diagnostic yield of 90-kVp low-tube-voltage carotid and intracerebral CT-angiography: effects on radiation dose, image quality and diagnostic performance for the detection of carotid stenosis.

PubMed

Leithner, Doris; Wichmann, Julian L; Mahmoudi, Scherwin; Martin, Simon S; Albrecht, Moritz H; Vogl, Thomas J; Scholtz, Jan-Erik

2018-06-01

To investigate the impact of low-tube-voltage 90-kVp acquisition combined with advanced modeled iterative reconstruction algorithm (Admire) on radiation exposure, image quality, artifacts, and assessment of stenosis in carotid and intracranial CT angiography (CTA). Dual-energy CTA studies of 43 patients performed on a third-generation 192-slice dual-source CT were retrospectively evaluated. Intraindividual comparison of 90-kVp and linearly blended 120-kVp equivalent image series (M_0.6, 60% 90-kVp, 40% Sn-150-kVp) was performed. Contrast-to-noise and signal-to-noise ratios of common carotid artery, internal carotid artery, middle cerebral artery, and basilar artery were calculated. Qualitative image analysis included evaluation of artifacts and suitability for angiographical assessment at shoulder level, carotid bifurcation, siphon, and intracranial by three independent radiologists. Detection and quantification of carotid stenosis were performed. Radiation dose was expressed as dose-length product (DLP). Contrast-to-noise values of all arteries were significantly increased in 90-kVp compared to M_0.6 (p < 0.001). Suitability for angiographical evaluation was rated excellent with low artifacts for all levels in both image series. Both 90-kVp and M_0.6 showed excellent accordance for detection and grading of carotid stenosis with almost perfect interobserver agreement (carotid stenoses in 32 of 129 segments; intraclass correlation coefficient, 0.94). dose-length product was reduced by 40.3% in 90-kVp (110.6 ± 32.1 vs 185.4 ± 47.5 mGy·cm, p < 0.001). 90-kVp carotid and intracranial CTA with Admire provides increased quantitative and similarly good qualitative image quality, while reducing radiation exposure substantially compared to M_0.6. Diagnostic performance for arterial stenosis detection and quantification remained excellent. Advances in knowledge: 90-kVp carotid and intracranial CTA with an advanced iterative reconstruction algorithm results in excellent image quality and reduction of radiation exposure without limiting diagnostic performance.

MEMS-based sensing and algorithm development for fall detection and gait analysis

NASA Astrophysics Data System (ADS)

Gupta, Piyush; Ramirez, Gabriel; Lie, Donald Y. C.; Dallas, Tim; Banister, Ron E.; Dentino, Andrew

2010-02-01

Falls by the elderly are highly detrimental to health, frequently resulting in injury, high medical costs, and even death. Using a MEMS-based sensing system, algorithms are being developed for detecting falls and monitoring the gait of elderly and disabled persons. In this study, wireless sensors utilize Zigbee protocols were incorporated into planar shoe insoles and a waist mounted device. The insole contains four sensors to measure pressure applied by the foot. A MEMS based tri-axial accelerometer is embedded in the insert and a second one is utilized by the waist mounted device. The primary fall detection algorithm is derived from the waist accelerometer. The differential acceleration is calculated from samples received in 1.5s time intervals. This differential acceleration provides the quantification via an energy index. From this index one may ascertain different gait and identify fall events. Once a pre-determined index threshold is exceeded, the algorithm will classify an event as a fall or a stumble. The secondary algorithm is derived from frequency analysis techniques. The analysis consists of wavelet transforms conducted on the waist accelerometer data. The insole pressure data is then used to underline discrepancies in the transforms, providing more accurate data for classifying gait and/or detecting falls. The range of the transform amplitude in the fourth iteration of a Daubechies-6 transform was found sufficient to detect and classify fall events.

Automatic detection of cardiac cycle and measurement of the mitral annulus diameter in 4D TEE images

NASA Astrophysics Data System (ADS)

Graser, Bastian; Hien, Maximilian; Rauch, Helmut; Meinzer, Hans-Peter; Heimann, Tobias

2012-02-01

Mitral regurgitation is a wide spread problem. For successful surgical treatment quantification of the mitral annulus, especially its diameter, is essential. Time resolved 3D transesophageal echocardiography (TEE) is suitable for this task. Yet, manual measurement in four dimensions is extremely time consuming, which confirms the need for automatic quantification methods. The method we propose is capable of automatically detecting the cardiac cycle (systole or diastole) for each time step and measuring the mitral annulus diameter. This is done using total variation noise filtering, the graph cut segmentation algorithm and morphological operators. An evaluation took place using expert measurements on 4D TEE data of 13 patients. The cardiac cycle was detected correctly on 78% of all images and the mitral annulus diameter was measured with an average error of 3.08 mm. Its full automatic processing makes the method easy to use in the clinical workflow and it provides the surgeon with helpful information.

Quantification of temperature effect on impedance monitoring via PZT interface for prestressed tendon anchorage

NASA Astrophysics Data System (ADS)

Huynh, Thanh-Canh; Kim, Jeong-Tae

2017-12-01

In this study, the quantification of temperature effect on impedance monitoring via a PZT interface for prestressed tendon-anchorage is presented. Firstly, a PZT interface-based impedance monitoring technique is selected to monitor impedance signatures by predetermining sensitive frequency bands. An analytical model is designed to represent coupled dynamic responses of the PZT interface-tendon anchorage system. Secondly, experiments on a lab-scaled tendon anchorage are described. Impedance signatures are measured via the PZT interface for a series of temperature and prestress-force changes. Thirdly, temperature effects on measured impedance responses of the tendon anchorage are estimated by quantifying relative changes in impedance features (such as RMSD and CCD indices) induced by temperature variation and prestress-force change. Finally, finite element analyses are conducted to investigate the mechanism of temperature variation and prestress-loss effects on the impedance responses of prestressed tendon anchorage. Temperature effects on impedance monitoring are filtered by effective frequency shift-based algorithm for distinguishing prestress-loss effects on impedance signatures.

High-speed (20 kHz) digital in-line holography for transient particle tracking and sizing in multiphase flows

DOE PAGES

Guildenbecher, Daniel R.; Cooper, Marcia A.; Sojka, Paul E.

2016-04-05

High-speed (20 kHz) digital in-line holography (DIH) is applied for 3D quantification of the size and velocity of fragments formed from the impact of a single water drop onto a thin film of water and burning aluminum particles from the combustion of a solid rocket propellant. To address the depth-of-focus problem in DIH, a regression-based multiframe tracking algorithm is employed, and out-of-plane experimental displacement accuracy is shown to be improved by an order-of-magnitude. Comparison of the results with previous DIH measurements using low-speed recording shows improved positional accuracy with the added advantage of detailed resolution of transient dynamics from singlemore » experimental realizations. Furthermore, the method is shown to be particularly advantageous for quantification of particle mass flow rates. For the investigated particle fields, the mass flows rates, which have been automatically measured from single experimental realizations, are found to be within 8% of the expected values.« less

Liquid chromatography with diode array detection and multivariate curve resolution for the selective and sensitive quantification of estrogens in natural waters.

PubMed

Pérez, Rocío L; Escandar, Graciela M

2014-07-04

Following the green analytical chemistry principles, an efficient strategy involving second-order data provided by liquid chromatography (LC) with diode array detection (DAD) was applied for the simultaneous determination of estriol, 17β-estradiol, 17α-ethinylestradiol and estrone in natural water samples. After a simple pre-concentration step, LC-DAD matrix data were rapidly obtained (in less than 5 min) with a chromatographic system operating isocratically. Applying a second-order calibration algorithm based on multivariate curve resolution with alternating least-squares (MCR-ALS), successful resolution was achieved in the presence of sample constituents that strongly coelute with the analytes. The flexibility of this multivariate model allowed the quantification of the four estrogens in tap, mineral, underground and river water samples. Limits of detection in the range between 3 and 13 ng L(-1), and relative prediction errors from 2 to 11% were achieved. Copyright © 2014 Elsevier B.V. All rights reserved.

Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis.

PubMed

Peng, Dan; Bi, Yanlan; Ren, Xiaona; Yang, Guolong; Sun, Shangde; Wang, Xuede

2015-12-01

This study was performed to develop a hierarchical approach for detection and quantification of adulteration of sesame oil with vegetable oils using gas chromatography (GC). At first, a model was constructed to discriminate the difference between authentic sesame oils and adulterated sesame oils using support vector machine (SVM) algorithm. Then, another SVM-based model is developed to identify the type of adulterant in the mixed oil. At last, prediction models for sesame oil were built for each kind of oil using partial least square method. To validate this approach, 746 samples were prepared by mixing authentic sesame oils with five types of vegetable oil. The prediction results show that the detection limit for authentication is as low as 5% in mixing ratio and the root-mean-square errors for prediction range from 1.19% to 4.29%, meaning that this approach is a valuable tool to detect and quantify the adulteration of sesame oil. Copyright © 2015 Elsevier Ltd. All rights reserved.

In vivo analysis of the time and spatial activation pattern of microglia in the retina following laser-induced choroidal neovascularization.

PubMed

Crespo-Garcia, Sergio; Reichhart, Nadine; Hernandez-Matas, Carlos; Zabulis, Xenophon; Kociok, Norbert; Brockmann, Claudia; Joussen, Antonia M; Strauss, Olaf

2015-10-01

Microglia play a major role in retinal neovascularization and degeneration and are thus potential targets for therapeutic intervention. In vivo assessment of microglia behavior in disease models can provide important information to understand patho-mechanisms and develop therapeutic strategies. Although scanning laser ophthalmoscope (SLO) permits the monitoring of microglia in transgenic mice with microglia-specific GFP expression, there are fundamental limitations in reliable identification and quantification of activated cells. Therefore, we aimed to improve the SLO-based analysis of microglia using enhanced image processing with subsequent testing in laser-induced neovascularization (CNV). CNV was induced by argon laser in MacGreen mice. Microglia was visualized in vivo by SLO in the fundus auto-fluorescence (FAF) mode and verified ex vivo using retinal preparations. Three image processing algorithms based on different analysis of sequences of images were tested. The amount of recorded frames was limiting the effectiveness of the different algorithms. Best results from short recordings were obtained with a pixel averaging algorithm, further used to quantify spatial and temporal distribution of activated microglia in CNV. Morphologically, different microglia populations were detected in the inner and outer retinal layers. In CNV, the peak of microglia activation occurred in the inner layer at day 4 after laser, lacking an acute reaction. Besides, the spatial distribution of the activation changed by the time over the inner retina. No significant time and spatial changes were observed in the outer layer. An increase in laser power did not increase number of activated microglia. The SLO, in conjunction with enhanced image processing, is suitable for in vivo quantification of microglia activation. This surprisingly revealed that laser damage at the outer retina led to more reactive microglia in the inner retina, shedding light upon a new perspective to approach the immune response in the retina in vivo. Copyright © 2015 Elsevier Ltd. All rights reserved.

dAcquisition setting optimization and quantitative imaging for 124I studies with the Inveon microPET-CT system.

PubMed

Anizan, Nadège; Carlier, Thomas; Hindorf, Cecilia; Barbet, Jacques; Bardiès, Manuel

2012-02-13

Noninvasive multimodality imaging is essential for preclinical evaluation of the biodistribution and pharmacokinetics of radionuclide therapy and for monitoring tumor response. Imaging with nonstandard positron-emission tomography [PET] isotopes such as 124I is promising in that context but requires accurate activity quantification. The decay scheme of 124I implies an optimization of both acquisition settings and correction processing. The PET scanner investigated in this study was the Inveon PET/CT system dedicated to small animal imaging. The noise equivalent count rate [NECR], the scatter fraction [SF], and the gamma-prompt fraction [GF] were used to determine the best acquisition parameters for mouse- and rat-sized phantoms filled with 124I. An image-quality phantom as specified by the National Electrical Manufacturers Association NU 4-2008 protocol was acquired and reconstructed with two-dimensional filtered back projection, 2D ordered-subset expectation maximization [2DOSEM], and 3DOSEM with maximum a posteriori [3DOSEM/MAP] algorithms, with and without attenuation correction, scatter correction, and gamma-prompt correction (weighted uniform distribution subtraction). Optimal energy windows were established for the rat phantom (390 to 550 keV) and the mouse phantom (400 to 590 keV) by combining the NECR, SF, and GF results. The coincidence time window had no significant impact regarding the NECR curve variation. Activity concentration of 124I measured in the uniform region of an image-quality phantom was underestimated by 9.9% for the 3DOSEM/MAP algorithm with attenuation and scatter corrections, and by 23% with the gamma-prompt correction. Attenuation, scatter, and gamma-prompt corrections decreased the residual signal in the cold insert. The optimal energy windows were chosen with the NECR, SF, and GF evaluation. Nevertheless, an image quality and an activity quantification assessment were required to establish the most suitable reconstruction algorithm and corrections for 124I small animal imaging.

Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images.

PubMed

Tian, Jing; Marziliano, Pina; Baskaran, Mani; Tun, Tin Aung; Aung, Tin

2013-03-01

Enhanced Depth Imaging (EDI) optical coherence tomography (OCT) provides high-definition cross-sectional images of the choroid in vivo, and hence is used in many clinical studies. However, the quantification of the choroid depends on the manual labelings of two boundaries, Bruch's membrane and the choroidal-scleral interface. This labeling process is tedious and subjective of inter-observer differences, hence, automatic segmentation of the choroid layer is highly desirable. In this paper, we present a fast and accurate algorithm that could segment the choroid automatically. Bruch's membrane is detected by searching the pixel with the biggest gradient value above the retinal pigment epithelium (RPE) and the choroidal-scleral interface is delineated by finding the shortest path of the graph formed by valley pixels using Dijkstra's algorithm. The experiments comparing automatic segmentation results with the manual labelings are conducted on 45 EDI-OCT images and the average of Dice's Coefficient is 90.5%, which shows good consistency of the algorithm with the manual labelings. The processing time for each image is about 1.25 seconds.

Nonlinear Least-Squares Based Method for Identifying and Quantifying Single and Mixed Contaminants in Air with an Electronic Nose

PubMed Central

Zhou, Hanying; Homer, Margie L.; Shevade, Abhijit V.; Ryan, Margaret A.

2006-01-01

The Jet Propulsion Laboratory has recently developed and built an electronic nose (ENose) using a polymer-carbon composite sensing array. This ENose is designed to be used for air quality monitoring in an enclosed space, and is designed to detect, identify and quantify common contaminants at concentrations in the parts-per-million range. Its capabilities were demonstrated in an experiment aboard the National Aeronautics and Space Administration's Space Shuttle Flight STS-95. This paper describes a modified nonlinear least-squares based algorithm developed to analyze data taken by the ENose, and its performance for the identification and quantification of single gases and binary mixtures of twelve target analytes in clean air. Results from laboratory-controlled events demonstrate the effectiveness of the algorithm to identify and quantify a gas event if concentration exceeds the ENose detection threshold. Results from the flight test demonstrate that the algorithm correctly identifies and quantifies all registered events (planned or unplanned, as singles or mixtures) with no false positives and no inconsistencies with the logged events and the independent analysis of air samples.

Fast multiclonal clusterization of V(D)J recombinations from high-throughput sequencing.

PubMed

Giraud, Mathieu; Salson, Mikaël; Duez, Marc; Villenet, Céline; Quief, Sabine; Caillault, Aurélie; Grardel, Nathalie; Roumier, Christophe; Preudhomme, Claude; Figeac, Martin

2014-05-28

V(D)J recombinations in lymphocytes are essential for immunological diversity. They are also useful markers of pathologies. In leukemia, they are used to quantify the minimal residual disease during patient follow-up. However, the full breadth of lymphocyte diversity is not fully understood. We propose new algorithms that process high-throughput sequencing (HTS) data to extract unnamed V(D)J junctions and gather them into clones for quantification. This analysis is based on a seed heuristic and is fast and scalable because in the first phase, no alignment is performed with germline database sequences. The algorithms were applied to TR γ HTS data from a patient with acute lymphoblastic leukemia, and also on data simulating hypermutations. Our methods identified the main clone, as well as additional clones that were not identified with standard protocols. The proposed algorithms provide new insight into the analysis of high-throughput sequencing data for leukemia, and also to the quantitative assessment of any immunological profile. The methods described here are implemented in a C++ open-source program called Vidjil.

Respiratory motion compensation algorithm of ultrasound hepatic perfusion data acquired in free-breathing

NASA Astrophysics Data System (ADS)

Wu, Kaizhi; Zhang, Xuming; Chen, Guangxie; Weng, Fei; Ding, Mingyue

2013-10-01

Images acquired in free breathing using contrast enhanced ultrasound exhibit a periodic motion that needs to be compensated for if a further accurate quantification of the hepatic perfusion analysis is to be executed. In this work, we present an algorithm to compensate the respiratory motion by effectively combining the PCA (Principal Component Analysis) method and block matching method. The respiratory kinetics of the ultrasound hepatic perfusion image sequences was firstly extracted using the PCA method. Then, the optimal phase of the obtained respiratory kinetics was detected after normalizing the motion amplitude and determining the image subsequences of the original image sequences. The image subsequences were registered by the block matching method using cross-correlation as the similarity. Finally, the motion-compensated contrast images can be acquired by using the position mapping and the algorithm was evaluated by comparing the TICs extracted from the original image sequences and compensated image subsequences. Quantitative comparisons demonstrated that the average fitting error estimated of ROIs (region of interest) was reduced from 10.9278 +/- 6.2756 to 5.1644 +/- 3.3431 after compensating.

High-accuracy peak picking of proteomics data using wavelet techniques.

PubMed

Lange, Eva; Gröpl, Clemens; Reinert, Knut; Kohlbacher, Oliver; Hildebrandt, Andreas

2006-01-01

A new peak picking algorithm for the analysis of mass spectrometric (MS) data is presented. It is independent of the underlying machine or ionization method, and is able to resolve highly convoluted and asymmetric signals. The method uses the multiscale nature of spectrometric data by first detecting the mass peaks in the wavelet-transformed signal before a given asymmetric peak function is fitted to the raw data. In an optional third stage, the resulting fit can be further improved using techniques from nonlinear optimization. In contrast to currently established techniques (e.g. SNAP, Apex) our algorithm is able to separate overlapping peaks of multiply charged peptides in ESI-MS data of low resolution. Its improved accuracy with respect to peak positions makes it a valuable preprocessing method for MS-based identification and quantification experiments. The method has been validated on a number of different annotated test cases, where it compares favorably in both runtime and accuracy with currently established techniques. An implementation of the algorithm is freely available in our open source framework OpenMS.

Automated Video-Based Analysis of Contractility and Calcium Flux in Human-Induced Pluripotent Stem Cell-Derived Cardiomyocytes Cultured over Different Spatial Scales

PubMed Central

Huebsch, Nathaniel; Loskill, Peter; Mandegar, Mohammad A.; Marks, Natalie C.; Sheehan, Alice S.; Ma, Zhen; Mathur, Anurag; Nguyen, Trieu N.; Yoo, Jennie C.; Judge, Luke M.; Spencer, C. Ian; Chukka, Anand C.; Russell, Caitlin R.; So, Po-Lin

2015-01-01

Contractile motion is the simplest metric of cardiomyocyte health in vitro, but unbiased quantification is challenging. We describe a rapid automated method, requiring only standard video microscopy, to analyze the contractility of human-induced pluripotent stem cell-derived cardiomyocytes (iPS-CM). New algorithms for generating and filtering motion vectors combined with a newly developed isogenic iPSC line harboring genetically encoded calcium indicator, GCaMP6f, allow simultaneous user-independent measurement and analysis of the coupling between calcium flux and contractility. The relative performance of these algorithms, in terms of improving signal to noise, was tested. Applying these algorithms allowed analysis of contractility in iPS-CM cultured over multiple spatial scales from single cells to three-dimensional constructs. This open source software was validated with analysis of isoproterenol response in these cells, and can be applied in future studies comparing the drug responsiveness of iPS-CM cultured in different microenvironments in the context of tissue engineering. PMID:25333967

Dose-response algorithms for water-borne Pseudomonas aeruginosa folliculitis.

PubMed

Roser, D J; Van Den Akker, B; Boase, S; Haas, C N; Ashbolt, N J; Rice, S A

2015-05-01

We developed two dose-response algorithms for P. aeruginosa pool folliculitis using bacterial and lesion density estimates, associated with undetectable, significant, and almost certain folliculitis. Literature data were fitted to Furumoto & Mickey's equations, developed for plant epidermis-invading pathogens: N l = A ln(1 + BC) (log-linear model); P inf = 1-e(-r c C) (exponential model), where A and B are 2.51644 × 107 lesions/m2 and 2.28011 × 10-11 c.f.u./ml P. aeruginosa, respectively; C = pathogen density (c.f.u./ml), N l = folliculitis lesions/m2, P inf = probability of infection, and r C = 4·3 × 10-7 c.f.u./ml P. aeruginosa. Outbreak data indicates these algorithms apply to exposure durations of 41 ± 25 min. Typical water quality benchmarks (≈10-2 c.f.u./ml) appear conservative but still useful as the literature indicated repeated detection likely implies unstable control barriers and bacterial bloom potential. In future, culture-based outbreak testing should be supplemented with quantitative polymerase chain reaction and organic carbon assays, and quantification of folliculitis aetiology to better understand P. aeruginosa risks.

Spectral X-ray Radiography for Safeguards at Nuclear Fuel Fabrication Facilities: A Feasibility Study

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

Gilbert, Andrew J.; McDonald, Benjamin S.; Smith, Leon E.

The methods currently used by the International Atomic Energy Agency to account for nuclear materials at fuel fabrication facilities are time consuming and require in-field chemistry and operation by experts. Spectral X-ray radiography, along with advanced inverse algorithms, is an alternative inspection that could be completed noninvasively, without any in-field chemistry, with inspections of tens of seconds. The proposed inspection system and algorithms are presented here. The inverse algorithm uses total variation regularization and adaptive regularization parameter selection with the unbiased predictive risk estimator. Performance of the system is quantified with simulated X-ray inspection data and sensitivity of the outputmore » is tested against various inspection system instabilities. Material quantification from a fully-characterized inspection system is shown to be very accurate, with biases on nuclear material estimations of < 0.02%. It is shown that the results are sensitive to variations in the fuel powder sample density and detector pixel gain, which increase biases to 1%. Options to mitigate these inaccuracies are discussed.« less

A three-dimensional model-based partial volume correction strategy for gated cardiac mouse PET imaging

NASA Astrophysics Data System (ADS)

Dumouchel, Tyler; Thorn, Stephanie; Kordos, Myra; DaSilva, Jean; Beanlands, Rob S. B.; deKemp, Robert A.

2012-07-01

Quantification in cardiac mouse positron emission tomography (PET) imaging is limited by the imaging spatial resolution. Spillover of left ventricle (LV) myocardial activity into adjacent organs results in partial volume (PV) losses leading to underestimation of myocardial activity. A PV correction method was developed to restore accuracy of the activity distribution for FDG mouse imaging. The PV correction model was based on convolving an LV image estimate with a 3D point spread function. The LV model was described regionally by a five-parameter profile including myocardial, background and blood activities which were separated into three compartments by the endocardial radius and myocardium wall thickness. The PV correction was tested with digital simulations and a physical 3D mouse LV phantom. In vivo cardiac FDG mouse PET imaging was also performed. Following imaging, the mice were sacrificed and the tracer biodistribution in the LV and liver tissue was measured using a gamma-counter. The PV correction algorithm improved recovery from 50% to within 5% of the truth for the simulated and measured phantom data and image uniformity by 5-13%. The PV correction algorithm improved the mean myocardial LV recovery from 0.56 (0.54) to 1.13 (1.10) without (with) scatter and attenuation corrections. The mean image uniformity was improved from 26% (26%) to 17% (16%) without (with) scatter and attenuation corrections applied. Scatter and attenuation corrections were not observed to significantly impact PV-corrected myocardial recovery or image uniformity. Image-based PV correction algorithm can increase the accuracy of PET image activity and improve the uniformity of the activity distribution in normal mice. The algorithm may be applied using different tracers, in transgenic models that affect myocardial uptake, or in different species provided there is sufficient image quality and similar contrast between the myocardium and surrounding structures.

Low-cost Assessment for Early Vigor and Canopy Cover Estimation in Durum Wheat Using RGB Images.

NASA Astrophysics Data System (ADS)

Fernandez-Gallego, J. A.; Kefauver, S. C.; Aparicio Gutiérrez, N.; Nieto-Taladriz, M. T.; Araus, J. L.

2017-12-01

Early vigor and canopy cover is an important agronomical component for determining grain yield in wheat. Estimates of the canopy cover area at early stages of the crop cycle may contribute to efficiency of crop management practices and breeding programs. Canopy-image segmentation is complicated in field conditions by numerous factors, including soil, shadows and unexpected objects, such as rocks, weeds, plant remains, or even part of the photographer's boots (many times it appears in the scene); and the algorithms must be robust to accommodate these conditions. Field trials were carried out in two sites (Aranjuez and Valladolid, Spain) during the 2016/2017 crop season. A set of 24 varieties of durum wheat in two growing conditions (rainfed and support irrigation) per site were used to create the image database. This work uses zenithal RGB images taken from above the crop in natural light conditions. The images were taken with Canon IXUS 320HS camera in Aranjuez, holding the camera by hand, and with a Nikon D300 camera in Valladolid, using a monopod. The algorithm for early vigor and canopy cover area estimation uses three main steps: (i) Image decorrelation (ii) Colour space transformation and (iii) Canopy cover segmentation using an automatic threshold based on the image histogram. The first step was chosen to enhance the visual interpretation and separate the pixel colors into the scene; the colour space transformation contributes to further separate the colours. Finally an automatic threshold using a minimum method allows for correct segmentation and quantification of the canopy pixels. The percent of area covered by the canopy was calculated using a simple algorithm for counting pixels in the final binary segmented image. The comparative results demonstrate the algorithm's effectiveness through significant correlations between early vigor and canopy cover estimation compared to NDVI (Normalized difference vegetation index) and grain yield.

Novel Fluorescein Angiography-Based Computer-Aided Algorithm for Assessment of Retinal Vessel Permeability

PubMed Central

Chassidim, Yoash; Parmet, Yisrael; Tomkins, Oren; Knyazer, Boris; Friedman, Alon; Levy, Jaime

2013-01-01

Purpose To present a novel method for quantitative assessment of retinal vessel permeability using a fluorescein angiography-based computer algorithm. Methods Twenty-one subjects (13 with diabetic retinopathy, 8 healthy volunteers) underwent fluorescein angiography (FA). Image pre-processing included removal of non-retinal and noisy images and registration to achieve spatial and temporal pixel-based analysis. Permeability was assessed for each pixel by computing intensity kinetics normalized to arterial values. A linear curve was fitted and the slope value was assigned, color-coded and displayed. The initial FA studies and the computed permeability maps were interpreted in a masked and randomized manner by three experienced ophthalmologists for statistical validation of diagnosis accuracy and efficacy. Results Permeability maps were successfully generated for all subjects. For healthy volunteers permeability values showed a normal distribution with a comparable range between subjects. Based on the mean cumulative histogram for the healthy population a threshold (99.5%) for pathological permeability was determined. Clear differences were found between patients and healthy subjects in the number and spatial distribution of pixels with pathological vascular leakage. The computed maps improved the discrimination between patients and healthy subjects, achieved sensitivity and specificity of 0.974 and 0.833 respectively, and significantly improved the consensus among raters for the localization of pathological regions. Conclusion The new algorithm allows quantification of retinal vessel permeability and provides objective, more sensitive and accurate evaluation than the present subjective clinical diagnosis. Future studies with a larger patients’ cohort and different retinal pathologies are awaited to further validate this new approach and its role in diagnosis and treatment follow-up. Successful evaluation of vasculature permeability may be used for the early diagnosis of brain microvascular pathology and potentially predict associated neurological sequelae. Finally, the algorithm could be implemented for intraoperative evaluation of micovascular integrity in other organs or during animal experiments. PMID:23626701

Automated identification of retinal vessels using a multiscale directional contrast quantification (MDCQ) strategy

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

Zhen, Yi; Zhang, Xinyuan; Wang, Ningli, E-mail: wningli@vip.163.com, E-mail: puj@upmc.edu

2014-09-15

Purpose: A novel algorithm is presented to automatically identify the retinal vessels depicted in color fundus photographs. Methods: The proposed algorithm quantifies the contrast of each pixel in retinal images at multiple scales and fuses the resulting consequent contrast images in a progressive manner by leveraging their spatial difference and continuity. The multiscale strategy is to deal with the variety of retinal vessels in width, intensity, resolution, and orientation; and the progressive fusion is to combine consequent images and meanwhile avoid a sudden fusion of image noise and/or artifacts in space. To quantitatively assess the performance of the algorithm, wemore » tested it on three publicly available databases, namely, DRIVE, STARE, and HRF. The agreement between the computer results and the manual delineation in these databases were quantified by computing their overlapping in both area and length (centerline). The measures include sensitivity, specificity, and accuracy. Results: For the DRIVE database, the sensitivities in identifying vessels in area and length were around 90% and 70%, respectively, the accuracy in pixel classification was around 99%, and the precisions in terms of both area and length were around 94%. For the STARE database, the sensitivities in identifying vessels were around 90% in area and 70% in length, and the accuracy in pixel classification was around 97%. For the HRF database, the sensitivities in identifying vessels were around 92% in area and 83% in length for the healthy subgroup, around 92% in area and 75% in length for the glaucomatous subgroup, around 91% in area and 73% in length for the diabetic retinopathy subgroup. For all three subgroups, the accuracy was around 98%. Conclusions: The experimental results demonstrate that the developed algorithm is capable of identifying retinal vessels depicted in color fundus photographs in a relatively reliable manner.« less

Design and development of an ethnically-diverse imaging informatics-based eFolder system for multiple sclerosis patients.

PubMed

Ma, Kevin C; Fernandez, James R; Amezcua, Lilyana; Lerner, Alex; Shiroishi, Mark S; Liu, Brent J

2015-12-01

MRI has been used to identify multiple sclerosis (MS) lesions in brain and spinal cord visually. Integrating patient information into an electronic patient record system has become key for modern patient care in medicine in recent years. Clinically, it is also necessary to track patients' progress in longitudinal studies, in order to provide comprehensive understanding of disease progression and response to treatment. As the amount of required data increases, there exists a need for an efficient systematic solution to store and analyze MS patient data, disease profiles, and disease tracking for both clinical and research purposes. An imaging informatics based system, called MS eFolder, has been developed as an integrated patient record system for data storage and analysis of MS patients. The eFolder system, with a DICOM-based database, includes a module for lesion contouring by radiologists, a MS lesion quantification tool to quantify MS lesion volume in 3D, brain parenchyma fraction analysis, and provide quantitative analysis and tracking of volume changes in longitudinal studies. Patient data, including MR images, have been collected retrospectively at University of Southern California Medical Center (USC) and Los Angeles County Hospital (LAC). The MS eFolder utilizes web-based components, such as browser-based graphical user interface (GUI) and web-based database. The eFolder database stores patient clinical data (demographics, MS disease history, family history, etc.), MR imaging-related data found in DICOM headers, and lesion quantification results. Lesion quantification results are derived from radiologists' contours on brain MRI studies and quantified into 3-dimensional volumes and locations. Quantified results of white matter lesions are integrated into a structured report based on DICOM-SR protocol and templates. The user interface displays patient clinical information, original MR images, and viewing structured reports of quantified results. The GUI also includes a data mining tool to handle unique search queries for MS. System workflow and dataflow steps has been designed based on the IHE post-processing workflow profile, including workflow process tracking, MS lesion contouring and quantification of MR images at a post-processing workstation, and storage of quantitative results as DICOM-SR in DICOM-based storage system. The web-based GUI is designed to display zero-footprint DICOM web-accessible data objects (WADO) and the SR objects. The MS eFolder system has been designed and developed as an integrated data storage and mining solution in both clinical and research environments, while providing unique features, such as quantitative lesion analysis and disease tracking over a longitudinal study. A comprehensive image and clinical data integrated database provided by MS eFolder provides a platform for treatment assessment, outcomes analysis and decision-support. The proposed system serves as a platform for future quantitative analysis derived automatically from CAD algorithms that can also be integrated within the system for individual disease tracking and future MS-related research. Ultimately the eFolder provides a decision-support infrastructure that can eventually be used as add-on value to the overall electronic medical record. Copyright © 2015 Elsevier Ltd. All rights reserved.

Design and development of an ethnically-diverse imaging informatics-based eFolder system for multiple sclerosis patients

PubMed Central

Ma, Kevin C.; Fernandez, James R.; Amezcua, Lilyana; Lerner, Alex; Shiroishi, Mark S.; Liu, Brent J.

2016-01-01

Purpose MRI has been used to identify multiple sclerosis (MS) lesions in brain and spinal cord visually. Integrating patient information into an electronic patient record system has become key for modern patient care in medicine in recent years. Clinically, it is also necessary to track patients' progress in longitudinal studies, in order to provide comprehensive understanding of disease progression and response to treatment. As the amount of required data increases, there exists a need for an efficient systematic solution to store and analyze MS patient data, disease profiles, and disease tracking for both clinical and research purposes. Method An imaging informatics based system, called MS eFolder, has been developed as an integrated patient record system for data storage and analysis of MS patients. The eFolder system, with a DICOM-based database, includes a module for lesion contouring by radiologists, a MS lesion quantification tool to quantify MS lesion volume in 3D, brain parenchyma fraction analysis, and provide quantitative analysis and tracking of volume changes in longitudinal studies. Patient data, including MR images, have been collected retrospectively at University of Southern California Medical Center (USC) and Los Angeles County Hospital (LAC). The MS eFolder utilizes web-based components, such as browser-based graphical user interface (GUI) and web-based database. The eFolder database stores patient clinical data (demographics, MS disease history, family history, etc.), MR imaging-related data found in DICOM headers, and lesion quantification results. Lesion quantification results are derived from radiologists' contours on brain MRI studies and quantified into 3-dimensional volumes and locations. Quantified results of white matter lesions are integrated into a structured report based on DICOM-SR protocol and templates. The user interface displays patient clinical information, original MR images, and viewing structured reports of quantified results. The GUI also includes a data mining tool to handle unique search queries for MS. System workflow and dataflow steps has been designed based on the IHE post-processing workflow profile, including workflow process tracking, MS lesion contouring and quantification of MR images at a post-processing workstation, and storage of quantitative results as DICOM-SR in DICOM-based storage system. The web-based GUI is designed to display zero-footprint DICOM web-accessible data objects (WADO) and the SR objects. Summary The MS eFolder system has been designed and developed as an integrated data storage and mining solution in both clinical and research environments, while providing unique features, such as quantitative lesion analysis and disease tracking over a longitudinal study. A comprehensive image and clinical data integrated database provided by MS eFolder provides a platform for treatment assessment, outcomes analysis and decision-support. The proposed system serves as a platform for future quantitative analysis derived automatically from CAD algorithms that can also be integrated within the system for individual disease tracking and future MS-related research. Ultimately the eFolder provides a decision-support infrastructure that can eventually be used as add-on value to the overall electronic medical record. PMID:26564667

The mass-action law based algorithms for quantitative econo-green bio-research.

PubMed

Chou, Ting-Chao

2011-05-01

The relationship between dose and effect is not random, but rather governed by the unified theory based on the median-effect equation (MEE) of the mass-action law. Rearrangement of MEE yields the mathematical form of the Michaelis-Menten, Hill, Henderson-Hasselbalch and Scatchard equations of biochemistry and biophysics, and the median-effect plot allows linearization of all dose-effect curves regardless of potency and shape. The "median" is the universal common-link and reference-point for the 1st-order to higher-order dynamics, and from single-entities to multiple-entities and thus, it allows the all for one and one for all unity theory to "integrate" simple and complex systems. Its applications include the construction of a dose-effect curve with a theoretical minimum of only two data points if they are accurately determined; quantification of synergism or antagonism at all dose and effect levels; the low-dose risk assessment for carcinogens, toxic substances or radiation; and the determination of competitiveness and exclusivity for receptor binding. Since the MEE algorithm allows the reduced requirement of the number of data points for small size experimentation, and yields quantitative bioinformatics, it points to the deterministic, efficient, low-cost biomedical research and drug discovery, and ethical planning for clinical trials. It is concluded that the contemporary biomedical sciences would greatly benefit from the mass-action law based "Green Revolution".

Systematic Comparison of Label-Free, Metabolic Labeling, and Isobaric Chemical Labeling for Quantitative Proteomics on LTQ Orbitrap Velos

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

Li, Zhou; Adams, Rachel M; Chourey, Karuna

2012-01-01

A variety of quantitative proteomics methods have been developed, including label-free, metabolic labeling, and isobaric chemical labeling using iTRAQ or TMT. Here, these methods were compared in terms of the depth of proteome coverage, quantification accuracy, precision, and reproducibility using a high-performance hybrid mass spectrometer, LTQ Orbitrap Velos. Our results show that (1) the spectral counting method provides the deepest proteome coverage for identification, but its quantification performance is worse than labeling-based approaches, especially the quantification reproducibility; (2) metabolic labeling and isobaric chemical labeling are capable of accurate, precise, and reproducible quantification and provide deep proteome coverage for quantification. Isobaricmore » chemical labeling surpasses metabolic labeling in terms of quantification precision and reproducibility; (3) iTRAQ and TMT perform similarly in all aspects compared in the current study using a CID-HCD dual scan configuration. Based on the unique advantages of each method, we provide guidance for selection of the appropriate method for a quantitative proteomics study.« less

Plant Growth Biophysics: the Basis for Growth Asymmetry Induced by Gravity

NASA Technical Reports Server (NTRS)

Cosgrove, D.

1985-01-01

The identification and quantification of the physical properties altered by gravity when plant stems grow upward was studied. Growth of the stem in vertical and horizontal positions was recorded by time lapse photography. A computer program that uses a cubic spline fitting algorithm was used to calculate the growth rate and curvature of the stem as a function of time. Plant stems were tested to ascertain whether cell osmotic pressure was altered by gravity. A technique for measuring the yielding properties of the cell wall was developed.

Proceedings of the 2013 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering - M and C 2013

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

NONE

2013-07-01

The Mathematics and Computation Division of the American Nuclear (ANS) and the Idaho Section of the ANS hosted the 2013 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M and C 2013). This proceedings contains over 250 full papers with topics ranging from reactor physics; radiation transport; materials science; nuclear fuels; core performance and optimization; reactor systems and safety; fluid dynamics; medical applications; analytical and numerical methods; algorithms for advanced architectures; and validation verification, and uncertainty quantification.

A Bayesian approach to modeling diffraction profiles and application to ferroelectric materials

DOE PAGES

Iamsasri, Thanakorn; Guerrier, Jonathon; Esteves, Giovanni; ...

2017-02-01

A new statistical approach for modeling diffraction profiles is introduced, using Bayesian inference and a Markov chain Monte Carlo (MCMC) algorithm. This method is demonstrated by modeling the degenerate reflections during application of an electric field to two different ferroelectric materials: thin-film lead zirconate titanate (PZT) of composition PbZr 0.3Ti 0.7O 3and a bulk commercial PZT polycrystalline ferroelectric. Here, the new method offers a unique uncertainty quantification of the model parameters that can be readily propagated into new calculated parameters.

Respiratory sinus arrhythmia in Chagas disease.

PubMed

Neves, Victor Ribeiro; Peltola, Mirja; Huikuri, Heikki; Rocha, Manoel Otávio da Costa; Ribeiro, Antonio Luiz

2014-10-01

We applied the respiratory sinus arrhythmia (RSA) quantification algorithm to 24-hour ECG recordings of Chagas disease (ChD) patients with (G1, n=148) and without left ventricular dysfunction (LVD) (G2, n=33), and in control subjects (G0, n=28). Both ChD groups displayed a reduced RSA index; G1=299 (144-812); G2=335 (162-667), p=0.011, which was correlated with vagal indexes of heart rate variability analysis. RSA index is a marker of vagal modulation in ChD patients. Copyright © 2014 Elsevier B.V. All rights reserved.

Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning.

PubMed

Koydemir, Hatice Ceylan; Gorocs, Zoltan; Tseng, Derek; Cortazar, Bingen; Feng, Steve; Chan, Raymond Yan Lok; Burbano, Jordi; McLeod, Euan; Ozcan, Aydogan

2015-03-07

Rapid and sensitive detection of waterborne pathogens in drinkable and recreational water sources is crucial for treating and preventing the spread of water related diseases, especially in resource-limited settings. Here we present a field-portable and cost-effective platform for detection and quantification of Giardia lamblia cysts, one of the most common waterborne parasites, which has a thick cell wall that makes it resistant to most water disinfection techniques including chlorination. The platform consists of a smartphone coupled with an opto-mechanical attachment weighing ~205 g, which utilizes a hand-held fluorescence microscope design aligned with the camera unit of the smartphone to image custom-designed disposable water sample cassettes. Each sample cassette is composed of absorbent pads and mechanical filter membranes; a membrane with 8 μm pore size is used as a porous spacing layer to prevent the backflow of particles to the upper membrane, while the top membrane with 5 μm pore size is used to capture the individual Giardia cysts that are fluorescently labeled. A fluorescence image of the filter surface (field-of-view: ~0.8 cm(2)) is captured and wirelessly transmitted via the mobile-phone to our servers for rapid processing using a machine learning algorithm that is trained on statistical features of Giardia cysts to automatically detect and count the cysts captured on the membrane. The results are then transmitted back to the mobile-phone in less than 2 minutes and are displayed through a smart application running on the phone. This mobile platform, along with our custom-developed sample preparation protocol, enables analysis of large volumes of water (e.g., 10-20 mL) for automated detection and enumeration of Giardia cysts in ~1 hour, including all the steps of sample preparation and analysis. We evaluated the performance of this approach using flow-cytometer-enumerated Giardia-contaminated water samples, demonstrating an average cyst capture efficiency of ~79% on our filter membrane along with a machine learning based cyst counting sensitivity of ~84%, yielding a limit-of-detection of ~12 cysts per 10 mL. Providing rapid detection and quantification of microorganisms, this field-portable imaging and sensing platform running on a mobile-phone could be useful for water quality monitoring in field and resource-limited settings.

Effects of light refraction on the accuracy of camera calibration and reconstruction in underwater motion analysis.

PubMed

Kwon, Young-Hoo; Casebolt, Jeffrey B

2006-01-01

One of the most serious obstacles to accurate quantification of the underwater motion of a swimmer's body is image deformation caused by refraction. Refraction occurs at the water-air interface plane (glass) owing to the density difference. Camera calibration-reconstruction algorithms commonly used in aquatic research do not have the capability to correct this refraction-induced nonlinear image deformation and produce large reconstruction errors. The aim of this paper is to provide a through review of: the nature of the refraction-induced image deformation and its behaviour in underwater object-space plane reconstruction; the intrinsic shortcomings of the Direct Linear Transformation (DLT) method in underwater motion analysis; experimental conditions that interact with refraction; and alternative algorithms and strategies that can be used to improve the calibration-reconstruction accuracy. Although it is impossible to remove the refraction error completely in conventional camera calibration-reconstruction methods, it is possible to improve the accuracy to some extent by manipulating experimental conditions or calibration frame characteristics. Alternative algorithms, such as the localized DLT and the double-plane method are also available for error reduction. The ultimate solution for the refraction problem is to develop underwater camera calibration and reconstruction algorithms that have the capability to correct refraction.

Effects of light refraction on the accuracy of camera calibration and reconstruction in underwater motion analysis.

PubMed

Kwon, Young-Hoo; Casebolt, Jeffrey B

2006-07-01

One of the most serious obstacles to accurate quantification of the underwater motion of a swimmer's body is image deformation caused by refraction. Refraction occurs at the water-air interface plane (glass) owing to the density difference. Camera calibration-reconstruction algorithms commonly used in aquatic research do not have the capability to correct this refraction-induced nonlinear image deformation and produce large reconstruction errors. The aim of this paper is to provide a thorough review of: the nature of the refraction-induced image deformation and its behaviour in underwater object-space plane reconstruction; the intrinsic shortcomings of the Direct Linear Transformation (DLT) method in underwater motion analysis; experimental conditions that interact with refraction; and alternative algorithms and strategies that can be used to improve the calibration-reconstruction accuracy. Although it is impossible to remove the refraction error completely in conventional camera calibration-reconstruction methods, it is possible to improve the accuracy to some extent by manipulating experimental conditions or calibration frame characteristics. Alternative algorithms, such as the localized DLT and the double-plane method are also available for error reduction. The ultimate solution for the refraction problem is to develop underwater camera calibration and reconstruction algorithms that have the capability to correct refraction.

Limited-memory adaptive snapshot selection for proper orthogonal decomposition

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

Oxberry, Geoffrey M.; Kostova-Vassilevska, Tanya; Arrighi, Bill

2015-04-02

Reduced order models are useful for accelerating simulations in many-query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, offline training of reduced order models can have prohibitively expensive memory and floating-point operation costs in high-performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits offline training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time-stepping ordinary differential equation solvers. The error estimator used in this work is related to theory boundingmore » the approximation error in time of proper orthogonal decomposition-based reduced order models, and memory usage is minimized by computing the singular value decomposition using a single-pass incremental algorithm. Results for a viscous Burgers’ test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full order model is recovered to within discretization error. The resulting method can be used on supercomputers to generate proper orthogonal decomposition-based reduced order models, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space.« less

Low-resolution simulations of vesicle suspensions in 2D

NASA Astrophysics Data System (ADS)

Kabacaoğlu, Gökberk; Quaife, Bryan; Biros, George

2018-03-01

Vesicle suspensions appear in many biological and industrial applications. These suspensions are characterized by rich and complex dynamics of vesicles due to their interaction with the bulk fluid, and their large deformations and nonlinear elastic properties. Many existing state-of-the-art numerical schemes can resolve such complex vesicle flows. However, even when using provably optimal algorithms, these simulations can be computationally expensive, especially for suspensions with a large number of vesicles. These high computational costs can limit the use of simulations for parameter exploration, optimization, or uncertainty quantification. One way to reduce the cost is to use low-resolution discretizations in space and time. However, it is well-known that simply reducing the resolution results in vesicle collisions, numerical instabilities, and often in erroneous results. In this paper, we investigate the effect of a number of algorithmic empirical fixes (which are commonly used by many groups) in an attempt to make low-resolution simulations more stable and more predictive. Based on our empirical studies for a number of flow configurations, we propose a scheme that attempts to integrate these fixes in a systematic way. This low-resolution scheme is an extension of our previous work [51,53]. Our low-resolution correction algorithms (LRCA) include anti-aliasing and membrane reparametrization for avoiding spurious oscillations in vesicles' membranes, adaptive time stepping and a repulsion force for handling vesicle collisions and, correction of vesicles' area and arc-length for maintaining physical vesicle shapes. We perform a systematic error analysis by comparing the low-resolution simulations of dilute and dense suspensions with their high-fidelity, fully resolved, counterparts. We observe that the LRCA enables both efficient and statistically accurate low-resolution simulations of vesicle suspensions, while it can be 10× to 100× faster.

Quantifying circular RNA expression from RNA-seq data using model-based framework.

PubMed

Li, Musheng; Xie, Xueying; Zhou, Jing; Sheng, Mengying; Yin, Xiaofeng; Ko, Eun-A; Zhou, Tong; Gu, Wanjun

2017-07-15

Circular RNAs (circRNAs) are a class of non-coding RNAs that are widely expressed in various cell lines and tissues of many organisms. Although the exact function of many circRNAs is largely unknown, the cell type-and tissue-specific circRNA expression has implicated their crucial functions in many biological processes. Hence, the quantification of circRNA expression from high-throughput RNA-seq data is becoming important to ascertain. Although many model-based methods have been developed to quantify linear RNA expression from RNA-seq data, these methods are not applicable to circRNA quantification. Here, we proposed a novel strategy that transforms circular transcripts to pseudo-linear transcripts and estimates the expression values of both circular and linear transcripts using an existing model-based algorithm, Sailfish. The new strategy can accurately estimate transcript expression of both linear and circular transcripts from RNA-seq data. Several factors, such as gene length, amount of expression and the ratio of circular to linear transcripts, had impacts on quantification performance of circular transcripts. In comparison to count-based tools, the new computational framework had superior performance in estimating the amount of circRNA expression from both simulated and real ribosomal RNA-depleted (rRNA-depleted) RNA-seq datasets. On the other hand, the consideration of circular transcripts in expression quantification from rRNA-depleted RNA-seq data showed substantial increased accuracy of linear transcript expression. Our proposed strategy was implemented in a program named Sailfish-cir. Sailfish-cir is freely available at https://github.com/zerodel/Sailfish-cir . tongz@medicine.nevada.edu or wanjun.gu@gmail.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Accurate joint space quantification in knee osteoarthritis: a digital x-ray tomosynthesis phantom study

NASA Astrophysics Data System (ADS)

Sewell, Tanzania S.; Piacsek, Kelly L.; Heckel, Beth A.; Sabol, John M.

2011-03-01

The current imaging standard for diagnosis and monitoring of knee osteoarthritis (OA) is projection radiography. However radiographs may be insensitive to markers of early disease such as osteophytes and joint space narrowing (JSN). Relative to standard radiography, digital X-ray tomosynthesis (DTS) may provide improved visualization of the markers of knee OA without the interference of superimposed anatomy. DTS utilizes a series of low-dose projection images over an arc of +/-20 degrees to reconstruct tomographic images parallel to the detector. We propose that DTS can increase accuracy and precision in JSN quantification. The geometric accuracy of DTS was characterized by quantifying joint space width (JSW) as a function of knee flexion and position using physical and anthropomorphic phantoms. Using a commercially available digital X-ray system, projection and DTS images were acquired for a Lucite rod phantom with known gaps at various source-object-distances, and angles of flexion. Gap width, representative of JSW, was measured using a validated algorithm. Over an object-to-detector-distance range of 5-21cm, a 3.0mm gap width was reproducibly measured in the DTS images, independent of magnification. A simulated 0.50mm (+/-0.13) JSN was quantified accurately (95% CI 0.44-0.56mm) in the DTS images. Angling the rods to represent knee flexion, the minimum gap could be precisely determined from the DTS images and was independent of flexion angle. JSN quantification using DTS was insensitive to distance from patient barrier and flexion angle. Potential exists for the optimization of DTS for accurate radiographic quantification of knee OA independent of patient positioning.

A preliminary study for fully automated quantification of psoriasis severity using image mapping

NASA Astrophysics Data System (ADS)

Mukai, Kazuhiro; Iyatomi, Hitoshi

2014-03-01

Psoriasis is a common chronic skin disease and it detracts patients' QoL seriously. Since there is no known permanent cure so far, controlling appropriate disease condition is necessary and therefore quantification of its severity is important. In clinical, psoriasis area and severity index (PASI) is commonly used for abovementioned purpose, however it is often subjective and troublesome. A fully automatic computer-assisted area and severity index (CASI) was proposed to make an objective quantification of skin disease. It investigates the size and density of erythema based on digital image analysis, however it does not consider various inadequate effects caused by different geometrical conditions under clinical follow-up (i.e. variability in direction and distance between camera and patient). In this study, we proposed an image alignment method for clinical images and investigated to quantify the severity of psoriasis under clinical follow-up combined with the idea of CASI. The proposed method finds geometrical same points in patient's body (ROI) between images with Scale Invariant Feature Transform (SIFT) and performs the Affine transform to map the pixel value to the other. In this study, clinical images from 7 patients with psoriasis lesions on their trunk under clinical follow-up were used. In each series, our image alignment algorithm align images to the geometry of their first image. Our proposed method aligned images appropriately on visual assessment and confirmed that psoriasis areas were properly extracted using the approach of CASI. Although we cannot evaluate PASI and CASI directly due to their different definition of ROI, we confirmed that there is a large correlation between those scores with our image quantification method.

Current position of high-resolution MS for drug quantification in clinical & forensic toxicology.

PubMed

Meyer, Markus R; Helfer, Andreas G; Maurer, Hans H

2014-08-01

This paper reviews high-resolution MS approaches published from January 2011 until March 2014 for the quantification of drugs (of abuse) and/or their metabolites in biosamples using LC-MS with time-of-flight or Orbitrap™ mass analyzers. Corresponding approaches are discussed including sample preparation and mass spectral settings. The advantages and limitations of high-resolution MS for drug quantification, as well as the demand for a certain resolution or a specific mass accuracy are also explored.

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions.

PubMed

Saha, Tanumoy; Rathmann, Isabel; Galic, Milos

2017-07-11

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex signaling mechanisms underlying filopodial initiation, elongation and subsequent stabilization or retraction, it is crucial to determine the spatio-temporal protein activity in these dynamic structures. To analyze protein function in filopodia, we recently developed a semi-automated tracking algorithm that adapts to filopodial shape-changes, thus allowing parallel analysis of protrusion dynamics and relative protein concentration along the whole filopodial length. Here, we present a detailed step-by-step protocol for optimized cell handling, image acquisition and software analysis. We further provide instructions for the use of optional features during image analysis and data representation, as well as troubleshooting guidelines for all critical steps along the way. Finally, we also include a comparison of the described image analysis software with other programs available for filopodia quantification. Together, the presented protocol provides a framework for accurate analysis of protein dynamics in filopodial protrusions using image analysis software.

Variance decomposition in stochastic simulators

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

Le Maître, O. P., E-mail: olm@limsi.fr; Knio, O. M., E-mail: knio@duke.edu; Moraes, A., E-mail: alvaro.moraesgutierrez@kaust.edu.sa

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance.more » Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.« less

Novel SPECT Technologies and Approaches in Cardiac Imaging

PubMed Central

Slomka, Piotr; Hung, Guang-Uei; Germano, Guido; Berman, Daniel S.

2017-01-01

Recent novel approaches in myocardial perfusion single photon emission CT (SPECT) have been facilitated by new dedicated high-efficiency hardware with solid-state detectors and optimized collimators. New protocols include very low-dose (1 mSv) stress-only, two-position imaging to mitigate attenuation artifacts, and simultaneous dual-isotope imaging. Attenuation correction can be performed by specialized low-dose systems or by previously obtained CT coronary calcium scans. Hybrid protocols using CT angiography have been proposed. Image quality improvements have been demonstrated by novel reconstructions and motion correction. Fast SPECT acquisition facilitates dynamic flow and early function measurements. Image processing algorithms have become automated with virtually unsupervised extraction of quantitative imaging variables. This automation facilitates integration with clinical variables derived by machine learning to predict patient outcome or diagnosis. In this review, we describe new imaging protocols made possible by the new hardware developments. We also discuss several novel software approaches for the quantification and interpretation of myocardial perfusion SPECT scans. PMID:29034066

In vivo quantitative bioluminescence tomography using heterogeneous and homogeneous mouse models.

PubMed

Liu, Junting; Wang, Yabin; Qu, Xiaochao; Li, Xiangsi; Ma, Xiaopeng; Han, Runqiang; Hu, Zhenhua; Chen, Xueli; Sun, Dongdong; Zhang, Rongqing; Chen, Duofang; Chen, Dan; Chen, Xiaoyuan; Liang, Jimin; Cao, Feng; Tian, Jie

2010-06-07

Bioluminescence tomography (BLT) is a new optical molecular imaging modality, which can monitor both physiological and pathological processes by using bioluminescent light-emitting probes in small living animal. Especially, this technology possesses great potential in drug development, early detection, and therapy monitoring in preclinical settings. In the present study, we developed a dual modality BLT prototype system with Micro-computed tomography (MicroCT) registration approach, and improved the quantitative reconstruction algorithm based on adaptive hp finite element method (hp-FEM). Detailed comparisons of source reconstruction between the heterogeneous and homogeneous mouse models were performed. The models include mice with implanted luminescence source and tumor-bearing mice with firefly luciferase report gene. Our data suggest that the reconstruction based on heterogeneous mouse model is more accurate in localization and quantification than the homogeneous mouse model with appropriate optical parameters and that BLT allows super-early tumor detection in vivo based on tomographic reconstruction of heterogeneous mouse model signal.

Management of advanced NK/T-cell lymphoma.

PubMed

Tse, Eric; Kwong, Yok-Lam

2014-09-01

NK/T-cell lymphomas are aggressive malignancies, and the outlook is poor when conventional anthracycline-containing regimens designed for B-cell lymphomas are used. With the advent of L-asparaginase-containing regimens, treatment outcome has significantly improved. L-asparaginase-containing regimens are now considered the standard in the management of NK/T-cell lymphomas. In advanced diseases, however, outcome remains unsatisfactory, with durable remission achieved in only about 50% of cases. Stratification of patients with advanced NK/T-cell lymphomas is needed, so that poor-risk patients can be given additional therapy to improve outcome. Conventional presentation parameters are untested and appear inadequate for prognostication when L-asparaginase-containing regimens are used. Recent evidence suggests that dynamic factors during treatment and interim assessment, including Epstein-Barr virus (EBV) DNA quantification and positron emission tomography computed tomography findings, are more useful in patient stratification. The role of high-dose chemotherapy and haematopoietic stem cell transplantation requires evaluation in an overall risk-adapted treatment algorithm.

Living cell dry mass measurement using quantitative phase imaging with quadriwave lateral shearing interferometry: an accuracy and sensitivity discussion.

PubMed

Aknoun, Sherazade; Savatier, Julien; Bon, Pierre; Galland, Frédéric; Abdeladim, Lamiae; Wattellier, Benoit; Monneret, Serge

2015-01-01

Single-cell dry mass measurement is used in biology to follow cell cycle, to address effects of drugs, or to investigate cell metabolism. Quantitative phase imaging technique with quadriwave lateral shearing interferometry (QWLSI) allows measuring cell dry mass. The technique is very simple to set up, as it is integrated in a camera-like instrument. It simply plugs onto a standard microscope and uses a white light illumination source. Its working principle is first explained, from image acquisition to automated segmentation algorithm and dry mass quantification. Metrology of the whole process, including its sensitivity, repeatability, reliability, sources of error, over different kinds of samples and under different experimental conditions, is developed. We show that there is no influence of magnification or spatial light coherence on dry mass measurement; effect of defocus is more critical but can be calibrated. As a consequence, QWLSI is a well-suited technique for fast, simple, and reliable cell dry mass study, especially for live cells.

Uncertainty quantification-based robust aerodynamic optimization of laminar flow nacelle

NASA Astrophysics Data System (ADS)

Xiong, Neng; Tao, Yang; Liu, Zhiyong; Lin, Jun

2018-05-01

The aerodynamic performance of laminar flow nacelle is highly sensitive to uncertain working conditions, especially the surface roughness. An efficient robust aerodynamic optimization method on the basis of non-deterministic computational fluid dynamic (CFD) simulation and Efficient Global Optimization (EGO)algorithm was employed. A non-intrusive polynomial chaos method is used in conjunction with an existing well-verified CFD module to quantify the uncertainty propagation in the flow field. This paper investigates the roughness modeling behavior with the γ-Ret shear stress transport model including modeling flow transition and surface roughness effects. The roughness effects are modeled to simulate sand grain roughness. A Class-Shape Transformation-based parametrical description of the nacelle contour as part of an automatic design evaluation process is presented. A Design-of-Experiments (DoE) was performed and surrogate model by Kriging method was built. The new design nacelle process demonstrates that significant improvements of both mean and variance of the efficiency are achieved and the proposed method can be applied to laminar flow nacelle design successfully.

Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance

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

Ahn, Tae-Hyuk; Chai, Juanjuan; Pan, Chongle

Motivation: Metagenomic sequencing of clinical samples provides a promising technique for direct pathogen detection and characterization in biosurveillance. Taxonomic analysis at the strain level can be used to resolve serotypes of a pathogen in biosurveillance. Sigma was developed for strain-level identification and quantification of pathogens using their reference genomes based on metagenomic analysis. Results: Sigma provides not only accurate strain-level inferences, but also three unique capabilities: (i) Sigma quantifies the statistical uncertainty of its inferences, which includes hypothesis testing of identified genomes and confidence interval estimation of their relative abundances; (ii) Sigma enables strain variant calling by assigning metagenomic readsmore » to their most likely reference genomes; and (iii) Sigma supports parallel computing for fast analysis of large datasets. In conclusion, the algorithm performance was evaluated using simulated mock communities and fecal samples with spike-in pathogen strains. Availability and Implementation: Sigma was implemented in C++ with source codes and binaries freely available at http://sigma.omicsbio.org.« less

Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance

DOE PAGES

Ahn, Tae-Hyuk; Chai, Juanjuan; Pan, Chongle

2014-09-29

Motivation: Metagenomic sequencing of clinical samples provides a promising technique for direct pathogen detection and characterization in biosurveillance. Taxonomic analysis at the strain level can be used to resolve serotypes of a pathogen in biosurveillance. Sigma was developed for strain-level identification and quantification of pathogens using their reference genomes based on metagenomic analysis. Results: Sigma provides not only accurate strain-level inferences, but also three unique capabilities: (i) Sigma quantifies the statistical uncertainty of its inferences, which includes hypothesis testing of identified genomes and confidence interval estimation of their relative abundances; (ii) Sigma enables strain variant calling by assigning metagenomic readsmore » to their most likely reference genomes; and (iii) Sigma supports parallel computing for fast analysis of large datasets. In conclusion, the algorithm performance was evaluated using simulated mock communities and fecal samples with spike-in pathogen strains. Availability and Implementation: Sigma was implemented in C++ with source codes and binaries freely available at http://sigma.omicsbio.org.« less

Impact of local electrostatic field rearrangement on field ionization

NASA Astrophysics Data System (ADS)

Katnagallu, Shyam; Dagan, Michal; Parviainen, Stefan; Nematollahi, Ali; Grabowski, Blazej; Bagot, Paul A. J.; Rolland, Nicolas; Neugebauer, Jörg; Raabe, Dierk; Vurpillot, François; Moody, Michael P.; Gault, Baptiste

2018-03-01

Field ion microscopy allows for direct imaging of surfaces with true atomic resolution. The high charge density distribution on the surface generates an intense electric field that can induce ionization of gas atoms. We investigate the dynamic nature of the charge and the consequent electrostatic field redistribution following the departure of atoms initially constituting the surface in the form of an ion, a process known as field evaporation. We report on a new algorithm for image processing and tracking of individual atoms on the specimen surface enabling quantitative assessment of shifts in the imaged atomic positions. By combining experimental investigations with molecular dynamics simulations, which include the full electric charge, we confirm that change is directly associated with the rearrangement of the electrostatic field that modifies the imaging gas ionization zone. We derive important considerations for future developments of data reconstruction in 3D field ion microscopy, in particular for precise quantification of lattice strains and characterization of crystalline defects at the atomic scale.

Development of defined microbial population standards using fluorescence activated cell sorting for the absolute quantification of S. aureus using real-time PCR.

PubMed

Martinon, Alice; Cronin, Ultan P; Wilkinson, Martin G

2012-01-01

In this article, four types of standards were assessed in a SYBR Green-based real-time PCR procedure for the quantification of Staphylococcus aureus (S. aureus) in DNA samples. The standards were purified S. aureus genomic DNA (type A), circular plasmid DNA containing a thermonuclease (nuc) gene fragment (type B), DNA extracted from defined populations of S. aureus cells generated by Fluorescence Activated Cell Sorting (FACS) technology with (type C) or without purification of DNA by boiling (type D). The optimal efficiency of 2.016 was obtained on Roche LightCycler(®) 4.1. software for type C standards, whereas the lowest efficiency (1.682) corresponded to type D standards. Type C standards appeared to be more suitable for quantitative real-time PCR because of the use of defined populations for construction of standard curves. Overall, Fieller Confidence Interval algorithm may be improved for replicates having a low standard deviation in Cycle Threshold values such as found for type B and C standards. Stabilities of diluted PCR standards stored at -20°C were compared after 0, 7, 14 and 30 days and were lower for type A or C standards compared with type B standards. However, FACS generated standards may be useful for bacterial quantification in real-time PCR assays once optimal storage and temperature conditions are defined.

Guided Wave Delamination Detection and Quantification With Wavefield Data Analysis

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Campbell Leckey, Cara A.; Seebo, Jeffrey P.; Yu, Lingyu

2014-01-01

Unexpected damage can occur in aerospace composites due to impact events or material stress during off-nominal loading events. In particular, laminated composites are susceptible to delamination damage due to weak transverse tensile and inter-laminar shear strengths. Developments of reliable and quantitative techniques to detect delamination damage in laminated composites are imperative for safe and functional optimally-designed next-generation composite structures. In this paper, we investigate guided wave interactions with delamination damage and develop quantification algorithms by using wavefield data analysis. The trapped guided waves in the delamination region are observed from the wavefield data and further quantitatively interpreted by using different wavenumber analysis methods. The frequency-wavenumber representation of the wavefield shows that new wavenumbers are present and correlate to trapped waves in the damage region. These new wavenumbers are used to detect and quantify the delamination damage through the wavenumber analysis, which can show how the wavenumber changes as a function of wave propagation distance. The location and spatial duration of the new wavenumbers can be identified, providing a useful means not only for detecting the presence of delamination damage but also allowing for estimation of the delamination size. Our method has been applied to detect and quantify real delamination damage with complex geometry (grown using a quasi-static indentation technique). The detection and quantification results show the location, size, and shape of the delamination damage.

Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS

PubMed Central

Röst, Hannes L; Ludwig, Christina; Buil, Alfonso; Bensimon, Ariel; Soste, Martin; Spector, Tim D; Dermitzakis, Emmanouil T; Collins, Ben C; Malmström, Lars; Aebersold, Ruedi

2017-01-01

The consistent detection and quantification of protein post-translational modifications (PTMs) across sample cohorts is an essential prerequisite for the functional analysis of biological processes. Data-independent acquisition (DIA), a bottom-up mass spectrometry based proteomic strategy, exemplified by SWATH-MS, provides complete precursor and fragment ion information of a sample and thus, in principle, the information to identify peptidoforms, the modified variants of a peptide. However, due to the convoluted structure of DIA data sets the confident and systematic identification and quantification of peptidoforms has remained challenging. Here we present IPF (Inference of PeptidoForms), a fully automated algorithm that uses spectral libraries to query, validate and quantify peptidoforms in DIA data sets. The method was developed on data acquired by SWATH-MS and benchmarked using a synthetic phosphopeptide reference data set and phosphopeptide-enriched samples. The data indicate that IPF reduced false site-localization by more than 7-fold in comparison to previous approaches, while recovering 85.4% of the true signals. IPF was applied to detect and quantify peptidoforms carrying ten different types of PTMs in DIA data acquired from more than 200 samples of undepleted blood plasma of a human twin cohort. The data approportioned, for the first time, the contribution of heritable, environmental and longitudinal effects on the observed quantitative variability of specific modifications in blood plasma of a human population. PMID:28604659

In-depth evaluation of software tools for data-independent acquisition based label-free quantification.

PubMed

Kuharev, Jörg; Navarro, Pedro; Distler, Ute; Jahn, Olaf; Tenzer, Stefan

2015-09-01

Label-free quantification (LFQ) based on data-independent acquisition workflows currently experiences increasing popularity. Several software tools have been recently published or are commercially available. The present study focuses on the evaluation of three different software packages (Progenesis, synapter, and ISOQuant) supporting ion mobility enhanced data-independent acquisition data. In order to benchmark the LFQ performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, Escherichia coli). This model dataset simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and downregulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on quantification results. Analysis of technical reproducibility revealed median coefficients of variation of reported protein abundances below 5% for MS(E) data for Progenesis and ISOQuant. Regarding accuracy of LFQ, evaluation with synapter and ISOQuant yielded superior results compared to Progenesis. In addition, we discuss reporting formats and user friendliness of the software packages. The data generated in this study have been deposited to the ProteomeXchange Consortium with identifier PXD001240 (http://proteomecentral.proteomexchange.org/dataset/PXD001240). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[Evaluation study on a new method of dietary assessment with instant photography applied in urban pregnant women in Nanjing city].

PubMed

Jiang, Tingting; Dai, Yongmei; Miao, Miao; Zhang, Yue; Song, Chenglin; Wang, Zhixu

2015-07-01

To evaluate the usefulness and efficiency of a novel dietary method among urban pregnant women. Sixty one pregnant women were recruited from the ward and provided with a meal accurately weighed before cooking. The meal was photographed from three different angles before and after eating. The subjects were also interviewed for 24 h dietary recall by the investigators. Food weighting, image quantification and 24 h dietary recall were conducted by investigators from three different groups, and the messages were isolated from each other. Food consumption was analyzed on bases of classification and total summation. Nutrient intake from the meal was calculated for each subject. The data obtained from the dietary recall and the image quantification were compared with the actual values. Correlation and regression analyses were carried out on values between weight method and image quantification as well as dietary recall. Total twenty three kinds of food including rice, vegetables, fish, meats and soy bean curd were included in the experimental meal for the study. Compared with data from 24 h dietary recall (r = 0.413, P < 0.05), food weight estimated by image quantification (r = 0.778, P < 0.05, n = 308) were more correlated with weighed data, and show more concentrated linear distribution. Absolute difference distribution between image quantification and weight method of all food was 77.23 ± 56.02 (P < 0.05, n = 61), which was much small than the difference (172.77 ± 115.18) between 24 h recall and weight method. Values of almost all nutrients, including energy, protein, fat, carbohydrate, vitamin A, vitamin C, calcium, iron and zine calculated based on food weight from image quantification were more close to those of weighed data compared with 24 h dietary recall (P < 0.01). The results found by the Bland Altman analysis showed that the majority of the measurements for nutrient intake, were scattered along the mean difference line and close to the equality line (difference = 0). The plots show fairly good agreement between estimated and actual food consumption. It indicate that the differences (including the outliers) were random and did not exhibit any systematic bias, being consistent over different levels of mean food amount. On the other hand, the questionnaire showed that fifty six pregnant women considered the image quantification was less time-consuming and burdened than 24 h recall. Fifty eight of them would like to use image quantification to know their dietary status. The novel method which called instant photography (image quantification) for dietary assessment is more effective than conventional 24 h dietary recall and it also can obtain food intake values close to weighed data.

A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images.

PubMed

Katouzian, Amin; Angelini, Elsa D; Carlier, Stéphane G; Suri, Jasjit S; Navab, Nassir; Laine, Andrew F

2012-09-01

Over the past two decades, intravascular ultrasound (IVUS) image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in catheterization procedures and in research studies. IVUS provides cross-sectional grayscale images of the arterial wall and the extent of atherosclerotic plaques with high spatial resolution in real time. In this paper, we review recently developed image processing methods for the detection of media-adventitia and luminal borders in IVUS images acquired with different transducers operating at frequencies ranging from 20 to 45 MHz. We discuss methodological challenges, lack of diversity in reported datasets, and weaknesses of quantification metrics that make IVUS segmentation still an open problem despite all efforts. In conclusion, we call for a common reference database, validation metrics, and ground-truth definition with which new and existing algorithms could be benchmarked.

Signal and noise modeling in confocal laser scanning fluorescence microscopy.

PubMed

Herberich, Gerlind; Windoffer, Reinhard; Leube, Rudolf E; Aach, Til

2012-01-01

Fluorescence confocal laser scanning microscopy (CLSM) has revolutionized imaging of subcellular structures in biomedical research by enabling the acquisition of 3D time-series of fluorescently-tagged proteins in living cells, hence forming the basis for an automated quantification of their morphological and dynamic characteristics. Due to the inherently weak fluorescence, CLSM images exhibit a low SNR. We present a novel model for the transfer of signal and noise in CLSM that is both theoretically sound as well as corroborated by a rigorous analysis of the pixel intensity statistics via measurement of the 3D noise power spectra, signal-dependence and distribution. Our model provides a better fit to the data than previously proposed models. Further, it forms the basis for (i) the simulation of the CLSM imaging process indispensable for the quantitative evaluation of CLSM image analysis algorithms, (ii) the application of Poisson denoising algorithms and (iii) the reconstruction of the fluorescence signal.

[Investigation on remote measurement of air pollution by a method of infrared passive scanning imaging].

PubMed

Jiao, Yang; Xu, Liang; Gao, Min-Guang; Feng, Ming-Chun; Jin, Ling; Tong, Jing-Jing; Li, Sheng

2012-07-01

Passive remote sensing by Fourier-transform infrared (FTIR) spectrometry allows detection of air pollution. However, for the localization of a leak and a complete assessment of the situation in the case of the release of a hazardous cloud, information about the position and the distribution of a cloud is essential. Therefore, an imaging passive remote sensing system comprising an interferometer, a data acquisition and processing software, scan system, a video system, and a personal computer has been developed. The remote sensing of SF6 was done. The column densities of all directions in which a target compound has been identified may be retrieved by a nonlinear least squares fitting algorithm and algorithm of radiation transfer, and a false color image is displayed. The results were visualized by a video image, overlaid by false color concentration distribution image. The system has a high selectivity, and allows visualization and quantification of pollutant clouds.

Automated segmentation of ventricles from serial brain MRI for the quantification of volumetric changes associated with communicating hydrocephalus in patients with brain tumor

NASA Astrophysics Data System (ADS)

Pura, John A.; Hamilton, Allison M.; Vargish, Geoffrey A.; Butman, John A.; Linguraru, Marius George

2011-03-01

Accurate ventricle volume estimates could improve the understanding and diagnosis of postoperative communicating hydrocephalus. For this category of patients, associated changes in ventricle volume can be difficult to identify, particularly over short time intervals. We present an automated segmentation algorithm that evaluates ventricle size from serial brain MRI examination. The technique combines serial T1- weighted images to increase SNR and segments the means image to generate a ventricle template. After pre-processing, the segmentation is initiated by a fuzzy c-means clustering algorithm to find the seeds used in a combination of fast marching methods and geodesic active contours. Finally, the ventricle template is propagated onto the serial data via non-linear registration. Serial volume estimates were obtained in an automated robust and accurate manner from difficult data.

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part 1; Method and Uncertainties

NASA Technical Reports Server (NTRS)

Olson, William S.; Kummerow, Christian D.; Yang, Song; Petty, Grant W.; Tao, Wei-Kuo; Bell, Thomas L.; Braun, Scott A.; Wang, Yansen; Lang, Stephen E.; Johnson, Daniel E.

2004-01-01

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Recent application of quantification II in Japanese medical research.

PubMed Central

Suzuki, T; Kudo, A

1979-01-01

Hayashi's Quantification II is a method of multivariate discrimination analysis to manipulate attribute data as predictor variables. It is very useful in the medical research field for estimation, diagnosis, prognosis, evaluation of epidemiological factors, and other problems based on multiplicity of attribute data. In Japan, this method is so well known that most of the computer program packages include the Hayashi Quantification, but it seems to be yet unfamiliar with the method for researchers outside Japan. In view of this situation, we introduced 19 selected articles of recent applications of the Quantification II in Japanese medical research. In reviewing these papers, special mention is made to clarify how the researchers were satisfied with findings provided by the method. At the same time, some recommendations are made about terminology and program packages. Also a brief discussion of the background of the quantification methods is given with special reference to the Behaviormetric Society of Japan. PMID:540587

Relative quantification of biomarkers using mixed-isotope labeling coupled with MS

PubMed Central

Chapman, Heidi M; Schutt, Katherine L; Dieter, Emily M; Lamos, Shane M

2013-01-01

The identification and quantification of important biomarkers is a critical first step in the elucidation of biological systems. Biomarkers take many forms as cellular responses to stimuli and can be manifested during transcription, translation, and/or metabolic processing. Increasingly, researchers have relied upon mixed-isotope labeling (MIL) coupled with MS to perform relative quantification of biomarkers between two or more biological samples. MIL effectively tags biomarkers of interest for ease of identification and quantification within the mass spectrometer by using isotopic labels that introduce a heavy and light form of the tag. In addition to MIL coupled with MS, a number of other approaches have been used to quantify biomarkers including protein gel staining, enzymatic labeling, metabolic labeling, and several label-free approaches that generate quantitative data from the MS signal response. This review focuses on MIL techniques coupled with MS for the quantification of protein and small-molecule biomarkers. PMID:23157360

The NASA Soil Moisture Active Passive (SMAP) Mission - Science and Data Product Development Status

NASA Technical Reports Server (NTRS)

Nloku, E.; Entekhabi, D.; O'Neill, P.

2012-01-01

The Soil Moisture Active Passive (SMAP) mission, planned for launch in late 2014, has the objective of frequent, global mapping of near-surface soil moisture and its freeze-thaw state. The SMAP measurement system utilizes an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna. The instruments will operate on a spacecraft in a 685 km polar orbit with 6am/6pm nodal crossings, viewing the surface at a constant 40-degree incidence angle with a 1000-km swath width, providing 3-day global coverage. Data from the instruments will yield global maps of soil moisture and freeze/thaw state at 10 km and 3 km resolutions, respectively, every two to three days. The 10-km soil moisture product will be generated using a combined radar and radiometer retrieval algorithm. SMAP will also provide a radiometer-only soil moisture product at 40-km spatial resolution and a radar-only soil moisture product at 3-km resolution. The relative accuracies of these products will vary regionally and will depend on surface characteristics such as vegetation water content, vegetation type, surface roughness, and landscape heterogeneity. The SMAP soil moisture and freeze/thaw measurements will enable significantly improved estimates of the fluxes of water, energy and carbon between the land and atmosphere. Soil moisture and freeze/thaw controls of these fluxes are key factors in the performance of models used for weather and climate predictions and for quantifYing the global carbon balance. Soil moisture measurements are also of importance in modeling and predicting extreme events such as floods and droughts. The algorithms and data products for SMAP are being developed in the SMAP Science Data System (SDS) Testbed. In the Testbed algorithms are developed and evaluated using simulated SMAP observations as well as observational data from current airborne and spaceborne L-band sensors including data from the SMOS and Aquarius missions. We report here on the development status of the SMAP data products. The Testbed simulations are designed to capture various sources of errors in the products including environment effects, instrument effects (nonideal aspects of the measurement system), and retrieval algorithm errors. The SMAP project has developed a Calibration and Validation (Cal/Val) Plan that is designed to support algorithm development (pre-launch) and data product validation (post-launch). A key component of the Cal/Val Plan is the identification, characterization, and instrumentation of sites that can be used to calibrate and validate the sensor data (Level l) and derived geophysical products (Level 2 and higher).

Cutaneous T-cell lymphoma (CTCL): Current practices in blood assessment and the utility of T-cell receptor (TCR)-Vβ chain restriction.

PubMed

Gibson, Juliet F; Huang, Jing; Liu, Kristina J; Carlson, Kacie R; Foss, Francine; Choi, Jaehyuk; Edelson, Richard; Hussong, Jerry W; Mohl, Ramsey; Hill, Sally; Girardi, Michael

2016-05-01

Accurate quantification of malignant cells in the peripheral blood of patients with cutaneous T-cell lymphoma is important for early detection, prognosis, and monitoring disease burden. We sought to determine the spectrum of current clinical practices; critically evaluate elements of current International Society for Cutaneous Lymphomas (ISCL) B1 and B2 staging criteria; and assess the potential role of T-cell receptor-Vβ analysis by flow cytometry. We assessed current clinical practices by survey, and performed a retrospective analysis of 161 patients evaluated at Yale (2011-2014) to compare the sensitivity, specificity, positive predictive value, and negative predictive value of parameters for ISCL B2 staging. There was heterogeneity in clinical practices among institutions. ISCL B1 criteria did not capture 5 Yale cohort cases with immunophenotypic abnormalities that later progressed. T-cell receptor-Vβ testing was more specific than polymerase chain reaction and aided diagnosis in detecting clonality, but was of limited benefit in quantification of tumor burden. Because of limited follow-up involving a single center, further investigation will be necessary to conclude whether our proposed diagnostic algorithm is of general clinical benefit. We propose further study of modified B1 criteria: CD4/CD8 ratio 5 or greater, %CD4(+) CD26(-) 20% or greater, or %CD4(+) CD7(-) 20% or greater, with evidence of clonality. T-cell receptor-Vβ testing should be considered in future diagnostic and staging algorithms. Copyright © 2015 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach

PubMed Central

van Diest, Mike; Stegenga, Jan; Wörtche, Heinrich J.; Roerdink, Jos B. T. M; Verkerke, Gijsbertus J.; Lamoth, Claudine J. C.

2015-01-01

Background Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user’s balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain to be developed. The aim of the present study was to identify movement patterns, and variability therein, of young and older adults playing a custom-made weight-shifting (ice-skating) exergame. Methods Twenty older adults and twenty young adults played a weight-shifting exergame under five conditions of varying complexity, while multi-segmental whole-body movement data were captured using Kinect. Movement coordination patterns expressed during gameplay were identified using Self Organizing Maps (SOM), an artificial neural network, and variability in these patterns was quantified by computing Total Trajectory Variability (TTvar). Additionally a k Nearest Neighbor (kNN) classifier was trained to discriminate between young and older adults based on the SOM features. Results Results showed that TTvar was significantly higher in older adults than in young adults, when playing the exergame under complex task conditions. The kNN classifier showed a classification accuracy of 65.8%. Conclusions Older adults display more variable sway behavior than young adults, when playing the exergame under complex task conditions. The SOM features characterizing movement patterns expressed during exergaming allow for discriminating between young and older adults with limited accuracy. Our findings contribute to the development of algorithms for quantification of balance ability during home-based exergaming for balance training. PMID:26230655

Assessing flow paths in a karst aquifer based on multiple dye tracing tests using stochastic simulation and the MODFLOW-CFP code

NASA Astrophysics Data System (ADS)

Assari, Amin; Mohammadi, Zargham

2017-09-01

Karst systems show high spatial variability of hydraulic parameters over small distances and this makes their modeling a difficult task with several uncertainties. Interconnections of fractures have a major role on the transport of groundwater, but many of the stochastic methods in use do not have the capability to reproduce these complex structures. A methodology is presented for the quantification of tortuosity using the single normal equation simulation (SNESIM) algorithm and a groundwater flow model. A training image was produced based on the statistical parameters of fractures and then used in the simulation process. The SNESIM algorithm was used to generate 75 realizations of the four classes of fractures in a karst aquifer in Iran. The results from six dye tracing tests were used to assign hydraulic conductivity values to each class of fractures. In the next step, the MODFLOW-CFP and MODPATH codes were consecutively implemented to compute the groundwater flow paths. The 9,000 flow paths obtained from the MODPATH code were further analyzed to calculate the tortuosity factor. Finally, the hydraulic conductivity values calculated from the dye tracing experiments were refined using the actual flow paths of groundwater. The key outcomes of this research are: (1) a methodology for the quantification of tortuosity; (2) hydraulic conductivities, that are incorrectly estimated (biased low) with empirical equations that assume Darcian (laminar) flow with parallel rather than tortuous streamlines; and (3) an understanding of the scale-dependence and non-normal distributions of tortuosity.

Study of Environmental Data Complexity using Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach.

PubMed

van Diest, Mike; Stegenga, Jan; Wörtche, Heinrich J; Roerdink, Jos B T M; Verkerke, Gijsbertus J; Lamoth, Claudine J C

2015-01-01

Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user's balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain to be developed. The aim of the present study was to identify movement patterns, and variability therein, of young and older adults playing a custom-made weight-shifting (ice-skating) exergame. Twenty older adults and twenty young adults played a weight-shifting exergame under five conditions of varying complexity, while multi-segmental whole-body movement data were captured using Kinect. Movement coordination patterns expressed during gameplay were identified using Self Organizing Maps (SOM), an artificial neural network, and variability in these patterns was quantified by computing Total Trajectory Variability (TTvar). Additionally a k Nearest Neighbor (kNN) classifier was trained to discriminate between young and older adults based on the SOM features. Results showed that TTvar was significantly higher in older adults than in young adults, when playing the exergame under complex task conditions. The kNN classifier showed a classification accuracy of 65.8%. Older adults display more variable sway behavior than young adults, when playing the exergame under complex task conditions. The SOM features characterizing movement patterns expressed during exergaming allow for discriminating between young and older adults with limited accuracy. Our findings contribute to the development of algorithms for quantification of balance ability during home-based exergaming for balance training.

Hampton Roads climate impact quantification initiative : baseline assessment of the transportation assets & overview of economic analyses useful in quantifying impacts

DOT National Transportation Integrated Search

2016-09-13

The Hampton Roads Climate Impact Quantification Initiative (HRCIQI) is a multi-part study sponsored by the U.S. Department of Transportation (DOT) Climate Change Center with the goals that include developing a cost tool that provides methods for volu...

An Image Analysis Algorithm for Malaria Parasite Stage Classification and Viability Quantification

PubMed Central

Moon, Seunghyun; Lee, Sukjun; Kim, Heechang; Freitas-Junior, Lucio H.; Kang, Myungjoo; Ayong, Lawrence; Hansen, Michael A. E.

2013-01-01

With more than 40% of the world’s population at risk, 200–300 million infections each year, and an estimated 1.2 million deaths annually, malaria remains one of the most important public health problems of mankind today. With the propensity of malaria parasites to rapidly develop resistance to newly developed therapies, and the recent failures of artemisinin-based drugs in Southeast Asia, there is an urgent need for new antimalarial compounds with novel mechanisms of action to be developed against multidrug resistant malaria. We present here a novel image analysis algorithm for the quantitative detection and classification of Plasmodium lifecycle stages in culture as well as discriminating between viable and dead parasites in drug-treated samples. This new algorithm reliably estimates the number of red blood cells (isolated or clustered) per fluorescence image field, and accurately identifies parasitized erythrocytes on the basis of high intensity DAPI-stained parasite nuclei spots and Mitotracker-stained mitochondrial in viable parasites. We validated the performance of the algorithm by manual counting of the infected and non-infected red blood cells in multiple image fields, and the quantitative analyses of the different parasite stages (early rings, rings, trophozoites, schizonts) at various time-point post-merozoite invasion, in tightly synchronized cultures. Additionally, the developed algorithm provided parasitological effective concentration 50 (EC50) values for both chloroquine and artemisinin, that were similar to known growth inhibitory EC50 values for these compounds as determined using conventional SYBR Green I and lactate dehydrogenase-based assays. PMID:23626733

Creating an anthropomorphic digital MR phantom—an extensible tool for comparing and evaluating quantitative imaging algorithms

NASA Astrophysics Data System (ADS)

Bosca, Ryan J.; Jackson, Edward F.

2016-01-01

Assessing and mitigating the various sources of bias and variance associated with image quantification algorithms is essential to the use of such algorithms in clinical research and practice. Assessment is usually accomplished with grid-based digital reference objects (DRO) or, more recently, digital anthropomorphic phantoms based on normal human anatomy. Publicly available digital anthropomorphic phantoms can provide a basis for generating realistic model-based DROs that incorporate the heterogeneity commonly found in pathology. Using a publicly available vascular input function (VIF) and digital anthropomorphic phantom of a normal human brain, a methodology was developed to generate a DRO based on the general kinetic model (GKM) that represented realistic and heterogeneously enhancing pathology. GKM parameters were estimated from a deidentified clinical dynamic contrast-enhanced (DCE) MRI exam. This clinical imaging volume was co-registered with a discrete tissue model, and model parameters estimated from clinical images were used to synthesize a DCE-MRI exam that consisted of normal brain tissues and a heterogeneously enhancing brain tumor. An example application of spatial smoothing was used to illustrate potential applications in assessing quantitative imaging algorithms. A voxel-wise Bland-Altman analysis demonstrated negligible differences between the parameters estimated with and without spatial smoothing (using a small radius Gaussian kernel). In this work, we reported an extensible methodology for generating model-based anthropomorphic DROs containing normal and pathological tissue that can be used to assess quantitative imaging algorithms.

Evaluating low pass filters on SPECT reconstructed cardiac orientation estimation

NASA Astrophysics Data System (ADS)

Dwivedi, Shekhar

2009-02-01

Low pass filters can affect the quality of clinical SPECT images by smoothing. Appropriate filter and parameter selection leads to optimum smoothing that leads to a better quantification followed by correct diagnosis and accurate interpretation by the physician. This study aims at evaluating the low pass filters on SPECT reconstruction algorithms. Criteria for evaluating the filters are estimating the SPECT reconstructed cardiac azimuth and elevation angle. Low pass filters studied are butterworth, gaussian, hamming, hanning and parzen. Experiments are conducted using three reconstruction algorithms, FBP (filtered back projection), MLEM (maximum likelihood expectation maximization) and OSEM (ordered subsets expectation maximization), on four gated cardiac patient projections (two patients with stress and rest projections). Each filter is applied with varying cutoff and order for each reconstruction algorithm (only butterworth used for MLEM and OSEM). The azimuth and elevation angles are calculated from the reconstructed volume and the variation observed in the angles with varying filter parameters is reported. Our results demonstrate that behavior of hamming, hanning and parzen filter (used with FBP) with varying cutoff is similar for all the datasets. Butterworth filter (cutoff > 0.4) behaves in a similar fashion for all the datasets using all the algorithms whereas with OSEM for a cutoff < 0.4, it fails to generate cardiac orientation due to oversmoothing, and gives an unstable response with FBP and MLEM. This study on evaluating effect of low pass filter cutoff and order on cardiac orientation using three different reconstruction algorithms provides an interesting insight into optimal selection of filter parameters.

A Bayes network approach to uncertainty quantification in hierarchically developed computational models

DOE PAGES

Urbina, Angel; Mahadevan, Sankaran; Paez, Thomas L.

2012-03-01

Here, performance assessment of complex systems is ideally accomplished through system-level testing, but because they are expensive, such tests are seldom performed. On the other hand, for economic reasons, data from tests on individual components that are parts of complex systems are more readily available. The lack of system-level data leads to a need to build computational models of systems and use them for performance prediction in lieu of experiments. Because their complexity, models are sometimes built in a hierarchical manner, starting with simple components, progressing to collections of components, and finally, to the full system. Quantification of uncertainty inmore » the predicted response of a system model is required in order to establish confidence in the representation of actual system behavior. This paper proposes a framework for the complex, but very practical problem of quantification of uncertainty in system-level model predictions. It is based on Bayes networks and uses the available data at multiple levels of complexity (i.e., components, subsystem, etc.). Because epistemic sources of uncertainty were shown to be secondary, in this application, aleatoric only uncertainty is included in the present uncertainty quantification. An example showing application of the techniques to uncertainty quantification of measures of response of a real, complex aerospace system is included.« less

PET Quantification of the Norepinephrine Transporter in Human Brain with (S,S)-18F-FMeNER-D2.

PubMed

Moriguchi, Sho; Kimura, Yasuyuki; Ichise, Masanori; Arakawa, Ryosuke; Takano, Harumasa; Seki, Chie; Ikoma, Yoko; Takahata, Keisuke; Nagashima, Tomohisa; Yamada, Makiko; Mimura, Masaru; Suhara, Tetsuya

2017-07-01

Norepinephrine transporter (NET) in the brain plays important roles in human cognition and the pathophysiology of psychiatric disorders. Two radioligands, ( S , S )- 11 C-MRB and ( S , S )- 18 F-FMeNER-D 2 , have been used for imaging NETs in the thalamus and midbrain (including locus coeruleus) using PET in humans. However, NET density in the equally important cerebral cortex has not been well quantified because of unfavorable kinetics with ( S , S )- 11 C-MRB and defluorination with ( S , S )- 18 F-FMeNER-D 2 , which can complicate NET quantification in the cerebral cortex adjacent to the skull containing defluorinated 18 F radioactivity. In this study, we have established analysis methods of quantification of NET density in the brain including the cerebral cortex using ( S , S )- 18 F-FMeNER-D 2 PET. Methods: We analyzed our previous ( S , S )- 18 F-FMeNER-D 2 PET data of 10 healthy volunteers dynamically acquired for 240 min with arterial blood sampling. The effects of defluorination on the NET quantification in the superficial cerebral cortex was evaluated by establishing a time stability of NET density estimations with an arterial input 2-tissue-compartment model, which guided the less-invasive reference tissue model and area under the time-activity curve methods to accurately quantify NET density in all brain regions including the cerebral cortex. Results: Defluorination of ( S , S )- 18 F-FMeNER-D 2 became prominent toward the latter half of the 240-min scan. Total distribution volumes in the superficial cerebral cortex increased with the scan duration beyond 120 min. We verified that 90-min dynamic scans provided a sufficient amount of data for quantification of NET density unaffected by defluorination. Reference tissue model binding potential values from the 90-min scan data and area under the time-activity curve ratios of 70- to 90-min data allowed for the accurate quantification of NET density in the cerebral cortex. Conclusion: We have established methods of quantification of NET densities in the brain including the cerebral cortex unaffected by defluorination using ( S , S )- 18 F-FMeNER-D 2 These results suggest that we can accurately quantify NET density with a 90-min ( S , S )- 18 F-FMeNER-D 2 scan in broad brain areas. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

New techniques for positron emission tomography in the study of human neurological disorders

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

Kuhl, D.E.

1992-07-01

The general goals of the physics and kinetic modeling projects are to: (1) improve the quantitative information extractable from PET images, and (2) develop, implement and optimize tracer kinetic models for new PET neurotransmitter/receptor ligands aided by computer simulations. Work towards improving PET quantification has included projects evaluating: (1) iterative reconstruction algorithms using supplemental boundary information, (2) automated registration of dynamic PET emission and transmission data using sinogram edge detection, and (3) automated registration of multiple subjects to a common coordinate system, including the use of non-linear warping methods. Simulation routines have been developed providing more accurate representation of datamore » generated from neurotransmitter/receptor studies. Routines consider data generated from complex compartmental models, high or low specific activity administrations, non-specific binding, pre- or post-injection of cold or competing ligands, temporal resolution of the data, and radiolabeled metabolites. Computer simulations and human PET studies have been performed to optimize kinetic models for four new neurotransmitter/receptor ligands, [{sup 11}C]TRB (muscarinic), [{sup 11}C]flumazenil (benzodiazepine), [{sup 18}F]GBR12909, (dopamine), and [{sup 11}C]NMPB (muscarinic).« less

New techniques for positron emission tomography in the study of human neurological disorders

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

Kuhl, D.E.

1992-01-01

The general goals of the physics and kinetic modeling projects are to: (1) improve the quantitative information extractable from PET images, and (2) develop, implement and optimize tracer kinetic models for new PET neurotransmitter/receptor ligands aided by computer simulations. Work towards improving PET quantification has included projects evaluating: (1) iterative reconstruction algorithms using supplemental boundary information, (2) automated registration of dynamic PET emission and transmission data using sinogram edge detection, and (3) automated registration of multiple subjects to a common coordinate system, including the use of non-linear warping methods. Simulation routines have been developed providing more accurate representation of datamore » generated from neurotransmitter/receptor studies. Routines consider data generated from complex compartmental models, high or low specific activity administrations, non-specific binding, pre- or post-injection of cold or competing ligands, temporal resolution of the data, and radiolabeled metabolites. Computer simulations and human PET studies have been performed to optimize kinetic models for four new neurotransmitter/receptor ligands, ({sup 11}C)TRB (muscarinic), ({sup 11}C)flumazenil (benzodiazepine), ({sup 18}F)GBR12909, (dopamine), and ({sup 11}C)NMPB (muscarinic).« less

Selection of reference genes for expression analysis in the entomophthoralean fungus Pandora neoaphidis.

PubMed

Chen, Chun; Xie, Tingna; Ye, Sudan; Jensen, Annette Bruun; Eilenberg, Jørgen

2016-01-01

The selection of suitable reference genes is crucial for accurate quantification of gene expression and can add to our understanding of host-pathogen interactions. To identify suitable reference genes in Pandora neoaphidis, an obligate aphid pathogenic fungus, the expression of three traditional candidate genes including 18S rRNA(18S), 28S rRNA(28S) and elongation factor 1 alpha-like protein (EF1), were measured by quantitative polymerase chain reaction at different developmental stages (conidia, conidia with germ tubes, short hyphae and elongated hyphae), and under different nutritional conditions. We calculated the expression stability of candidate reference genes using four algorithms including geNorm, NormFinder, BestKeeper and Delta Ct. The analysis results revealed that the comprehensive ranking of candidate reference genes from the most stable to the least stable was 18S (1.189), 28S (1.414) and EF1 (3). The 18S was, therefore, the most suitable reference gene for real-time RT-PCR analysis of gene expression under all conditions. These results will support further studies on gene expression in P. neoaphidis. Copyright © 2015 Sociedade Brasileira de Microbiologia. Published by Elsevier Editora Ltda. All rights reserved.

Rapid quantification and sex determination of forensic evidence materials.

PubMed

Andréasson, Hanna; Allen, Marie

2003-11-01

DNA quantification of forensic evidence is very valuable for an optimal use of the available biological material. Moreover, sex determination is of great importance as additional information in criminal investigations as well as in identification of missing persons, no suspect cases, and ancient DNA studies. While routine forensic DNA analysis based on short tandem repeat markers includes a marker for sex determination, analysis of samples containing scarce amounts of DNA is often based on mitochondrial DNA, and sex determination is not performed. In order to allow quantification and simultaneous sex determination on minute amounts of DNA, an assay based on real-time PCR analysis of a marker within the human amelogenin gene has been developed. The sex determination is based on melting curve analysis, while an externally standardized kinetic analysis allows quantification of the nuclear DNA copy number in the sample. This real-time DNA quantification assay has proven to be highly sensitive, enabling quantification of single DNA copies. Although certain limitations were apparent, the system is a rapid, cost-effective, and flexible assay for analysis of forensic casework samples.

A novel method for quantification of beam's-eye-view tumor tracking performance.

PubMed

Hu, Yue-Houng; Myronakis, Marios; Rottmann, Joerg; Wang, Adam; Morf, Daniel; Shedlock, Daniel; Baturin, Paul; Star-Lack, Josh; Berbeco, Ross

2017-11-01

In-treatment imaging using an electronic portal imaging device (EPID) can be used to confirm patient and tumor positioning. Real-time tumor tracking performance using current digital megavolt (MV) imagers is hindered by poor image quality. Novel EPID designs may help to improve quantum noise response, while also preserving the high spatial resolution of the current clinical detector. Recently investigated EPID design improvements include but are not limited to multi-layer imager (MLI) architecture, thick crystalline and amorphous scintillators, and phosphor pixilation and focusing. The goal of the present study was to provide a method of quantitating improvement in tracking performance as well as to reveal the physical underpinnings of detector design that impact tracking quality. The study employs a generalizable ideal observer methodology for the quantification of tumor tracking performance. The analysis is applied to study both the effect of increasing scintillator thickness on a standard, single-layer imager (SLI) design as well as the effect of MLI architecture on tracking performance. The present study uses the ideal observer signal-to-noise ratio (d') as a surrogate for tracking performance. We employ functions which model clinically relevant tasks and generalized frequency-domain imaging metrics to connect image quality with tumor tracking. A detection task for relevant Cartesian shapes (i.e., spheres and cylinders) was used to quantitate trackability of cases employing fiducial markers. Automated lung tumor tracking algorithms often leverage the differences in benign and malignant lung tissue textures. These types of algorithms (e.g., soft-tissue localization - STiL) were simulated by designing a discrimination task, which quantifies the differentiation of tissue textures, measured experimentally and fit as a power-law in trend (with exponent β) using a cohort of MV images of patient lungs. The modeled MTF and NPS were used to investigate the effect of scintillator thickness and MLI architecture on tumor tracking performance. Quantification of MV images of lung tissue as an inverse power-law with respect to frequency yields exponent values of β = 3.11 and 3.29 for benign and malignant tissues, respectively. Tracking performance with and without fiducials was found to be generally limited by quantum noise, a factor dominated by quantum detective efficiency (QDE). For generic SLI construction, increasing the scintillator thickness (gadolinium oxysulfide - GOS) from a standard 290 μm to 1720 μm reduces noise to about 10%. However, 81% of this reduction is appreciated between 290 and 1000 μm. In comparing MLI and SLI detectors of equivalent individual GOS layer thickness, the improvement in noise is equal to the number of layers in the detector (i.e., 4) with almost no difference in MTF. Further, improvement in tracking performance was slightly less than the square-root of the reduction in noise, approximately 84-90%. In comparing an MLI detector with an SLI with a GOS scintillator of equivalent total thickness, improvement in object detectability is approximately 34-39%. We have presented a novel method for quantification of tumor tracking quality and have applied this model to evaluate the performance of SLI and MLI EPID designs. We showed that improved tracking quality is primarily limited by improvements in NPS. When compared to very thick scintillator SLI, employing MLI architecture exhibits the same gains in QDE, but by mitigating the effect of optical Swank noise, results in more dramatic improvements in tracking performance. © 2017 American Association of Physicists in Medicine.

The use of self-quantification systems for personal health information: big data management activities and prospects.

PubMed

Almalki, Manal; Gray, Kathleen; Sanchez, Fernando Martin

2015-01-01

Self-quantification is seen as an emerging paradigm for health care self-management. Self-quantification systems (SQS) can be used for tracking, monitoring, and quantifying health aspects including mental, emotional, physical, and social aspects in order to gain self-knowledge. However, there has been a lack of a systematic approach for conceptualising and mapping the essential activities that are undertaken by individuals who are using SQS in order to improve health outcomes. In this paper, we propose a new model of personal health information self-quantification systems (PHI-SQS). PHI-SQS model describes two types of activities that individuals go through during their journey of health self-managed practice, which are 'self-quantification' and 'self-activation'. In this paper, we aimed to examine thoroughly the first type of activity in PHI-SQS which is 'self-quantification'. Our objectives were to review the data management processes currently supported in a representative set of self-quantification tools and ancillary applications, and provide a systematic approach for conceptualising and mapping these processes with the individuals' activities. We reviewed and compared eleven self-quantification tools and applications (Zeo Sleep Manager, Fitbit, Actipressure, MoodPanda, iBGStar, Sensaris Senspod, 23andMe, uBiome, Digifit, BodyTrack, and Wikilife), that collect three key health data types (Environmental exposure, Physiological patterns, Genetic traits). We investigated the interaction taking place at different data flow stages between the individual user and the self-quantification technology used. We found that these eleven self-quantification tools and applications represent two major tool types (primary and secondary self-quantification systems). In each type, the individuals experience different processes and activities which are substantially influenced by the technologies' data management capabilities. Self-quantification in personal health maintenance appears promising and exciting. However, more studies are needed to support its use in this field. The proposed model will in the future lead to developing a measure for assessing the effectiveness of interventions to support using SQS for health self-management (e.g., assessing the complexity of self-quantification activities, and activation of the individuals).

The use of self-quantification systems for personal health information: big data management activities and prospects

PubMed Central

2015-01-01

Background Self-quantification is seen as an emerging paradigm for health care self-management. Self-quantification systems (SQS) can be used for tracking, monitoring, and quantifying health aspects including mental, emotional, physical, and social aspects in order to gain self-knowledge. However, there has been a lack of a systematic approach for conceptualising and mapping the essential activities that are undertaken by individuals who are using SQS in order to improve health outcomes. In this paper, we propose a new model of personal health information self-quantification systems (PHI-SQS). PHI-SQS model describes two types of activities that individuals go through during their journey of health self-managed practice, which are 'self-quantification' and 'self-activation'. Objectives In this paper, we aimed to examine thoroughly the first type of activity in PHI-SQS which is 'self-quantification'. Our objectives were to review the data management processes currently supported in a representative set of self-quantification tools and ancillary applications, and provide a systematic approach for conceptualising and mapping these processes with the individuals' activities. Method We reviewed and compared eleven self-quantification tools and applications (Zeo Sleep Manager, Fitbit, Actipressure, MoodPanda, iBGStar, Sensaris Senspod, 23andMe, uBiome, Digifit, BodyTrack, and Wikilife), that collect three key health data types (Environmental exposure, Physiological patterns, Genetic traits). We investigated the interaction taking place at different data flow stages between the individual user and the self-quantification technology used. Findings We found that these eleven self-quantification tools and applications represent two major tool types (primary and secondary self-quantification systems). In each type, the individuals experience different processes and activities which are substantially influenced by the technologies' data management capabilities. Conclusions Self-quantification in personal health maintenance appears promising and exciting. However, more studies are needed to support its use in this field. The proposed model will in the future lead to developing a measure for assessing the effectiveness of interventions to support using SQS for health self-management (e.g., assessing the complexity of self-quantification activities, and activation of the individuals). PMID:26019809

Quantification of protein carbonylation.

PubMed

Wehr, Nancy B; Levine, Rodney L

2013-01-01

Protein carbonylation is the most commonly used measure of oxidative modification of proteins. It is most often measured spectrophotometrically or immunochemically by derivatizing proteins with the classical carbonyl reagent 2,4 dinitrophenylhydrazine (DNPH). We present protocols for the derivatization and quantification of protein carbonylation with these two methods, including a newly described dot blot with greatly increased sensitivity.

Stochastic Analysis and Design of Heterogeneous Microstructural Materials System

NASA Astrophysics Data System (ADS)

Xu, Hongyi

Advanced materials system refers to new materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to superior properties over the conventional materials. To accelerate the development of new advanced materials system, the objective of this dissertation is to develop a computational design framework and the associated techniques for design automation of microstructure materials systems, with an emphasis on addressing the uncertainties associated with the heterogeneity of microstructural materials. Five key research tasks are identified: design representation, design evaluation, design synthesis, material informatics and uncertainty quantification. Design representation of microstructure includes statistical characterization and stochastic reconstruction. This dissertation develops a new descriptor-based methodology, which characterizes 2D microstructures using descriptors of composition, dispersion and geometry. Statistics of 3D descriptors are predicted based on 2D information to enable 2D-to-3D reconstruction. An efficient sequential reconstruction algorithm is developed to reconstruct statistically equivalent random 3D digital microstructures. In design evaluation, a stochastic decomposition and reassembly strategy is developed to deal with the high computational costs and uncertainties induced by material heterogeneity. The properties of Representative Volume Elements (RVE) are predicted by stochastically reassembling SVE elements with stochastic properties into a coarse representation of the RVE. In design synthesis, a new descriptor-based design framework is developed, which integrates computational methods of microstructure characterization and reconstruction, sensitivity analysis, Design of Experiments (DOE), metamodeling and optimization the enable parametric optimization of the microstructure for achieving the desired material properties. Material informatics is studied to efficiently reduce the dimension of microstructure design space. This dissertation develops a machine learning-based methodology to identify the key microstructure descriptors that highly impact properties of interest. In uncertainty quantification, a comparative study on data-driven random process models is conducted to provide guidance for choosing the most accurate model in statistical uncertainty quantification. Two new goodness-of-fit metrics are developed to provide quantitative measurements of random process models' accuracy. The benefits of the proposed methods are demonstrated by the example of designing the microstructure of polymer nanocomposites. This dissertation provides material-generic, intelligent modeling/design methodologies and techniques to accelerate the process of analyzing and designing new microstructural materials system.

Investigating the limits of PET/CT imaging at very low true count rates and high random fractions in ion-beam therapy monitoring.

PubMed

Kurz, Christopher; Bauer, Julia; Conti, Maurizio; Guérin, Laura; Eriksson, Lars; Parodi, Katia

2015-07-01

External beam radiotherapy with protons and heavier ions enables a tighter conformation of the applied dose to arbitrarily shaped tumor volumes with respect to photons, but is more sensitive to uncertainties in the radiotherapeutic treatment chain. Consequently, an independent verification of the applied treatment is highly desirable. For this purpose, the irradiation-induced β(+)-emitter distribution within the patient is detected shortly after irradiation by a commercial full-ring positron emission tomography/x-ray computed tomography (PET/CT) scanner installed next to the treatment rooms at the Heidelberg Ion-Beam Therapy Center (HIT). A major challenge to this approach is posed by the small number of detected coincidences. This contribution aims at characterizing the performance of the used PET/CT device and identifying the best-performing reconstruction algorithm under the particular statistical conditions of PET-based treatment monitoring. Moreover, this study addresses the impact of radiation background from the intrinsically radioactive lutetium-oxyorthosilicate (LSO)-based detectors at low counts. The authors have acquired 30 subsequent PET scans of a cylindrical phantom emulating a patientlike activity pattern and spanning the entire patient counting regime in terms of true coincidences and random fractions (RFs). Accuracy and precision of activity quantification, image noise, and geometrical fidelity of the scanner have been investigated for various reconstruction algorithms and settings in order to identify a practical, well-suited reconstruction scheme for PET-based treatment verification. Truncated listmode data have been utilized for separating the effects of small true count numbers and high RFs on the reconstructed images. A corresponding simulation study enabled extending the results to an even wider range of counting statistics and to additionally investigate the impact of scatter coincidences. Eventually, the recommended reconstruction scheme has been applied to exemplary postirradiation patient data-sets. Among the investigated reconstruction options, the overall best results in terms of image noise, activity quantification, and accurate geometrical recovery were achieved using the ordered subset expectation maximization reconstruction algorithm with time-of-flight (TOF) and point-spread function (PSF) information. For this algorithm, reasonably accurate (better than 5%) and precise (uncertainty of the mean activity below 10%) imaging can be provided down to 80,000 true coincidences at 96% RF. Image noise and geometrical fidelity are generally improved for fewer iterations. The main limitation for PET-based treatment monitoring has been identified in the small number of true coincidences, rather than the high intrinsic random background. Application of the optimized reconstruction scheme to patient data-sets results in a 25% - 50% reduced image noise at a comparable activity quantification accuracy and an improved geometrical performance with respect to the formerly used reconstruction scheme at HIT, adopted from nuclear medicine applications. Under the poor statistical conditions in PET-based treatment monitoring, improved results can be achieved by considering PSF and TOF information during image reconstruction and by applying less iterations than in conventional nuclear medicine imaging. Geometrical fidelity and image noise are mainly limited by the low number of true coincidences, not the high LSO-related random background. The retrieved results might also impact other emerging PET applications at low counting statistics.

Structural health monitoring approach for detecting ice accretion on bridge cable using the Haar Wavelet Transform

NASA Astrophysics Data System (ADS)

Andre, Julia; Kiremidjian, Anne; Liao, Yizheng; Georgakis, Christos; Rajagopal, Ram

2016-04-01

Ice accretion on cables of bridge structures poses serious risk to the structure as well as to vehicular traffic when the ice falls onto the road. Detection of ice formation, quantification of the amount of ice accumulated, and prediction of icefalls will increase the safety and serviceability of the structure. In this paper, an ice accretion detection algorithm is presented based on the Continuous Wavelet Transform (CWT). In the proposed algorithm, the acceleration signals obtained from bridge cables are transformed using wavelet method. The damage sensitive features (DSFs) are defined as a function of the wavelet energy at specific wavelet scales. It is found that as ice accretes on the cables, the mass of cable increases, thus changing the wavelet energies. Hence, the DSFs can be used to track the change of cables mass. To validate the proposed algorithm, we use the data collected from a laboratory experiment conducted at the Technical University of Denmark (DTU). In this experiment, a cable was placed in a wind tunnel as ice volume grew progressively. Several accelerometers were installed at various locations along the testing cable to collect vibration signals.

Automatic segmentation of amyloid plaques in MR images using unsupervised SVM

PubMed Central

Iordanescu, Gheorghe; Venkatasubramanian, Palamadai N.; Wyrwicz, Alice M.

2011-01-01

Deposition of the β-amyloid peptide (Aβ) is an important pathological hallmark of Alzheimer’s disease (AD). However, reliable quantification of amyloid plaques in both human and animal brains remains a challenge. We present here a novel automatic plaque segmentation algorithm based on the intrinsic MR signal characteristics of plaques. This algorithm identifies plaque candidates in MR data by using watershed transform, which extracts regions with low intensities completely surrounded by higher intensity neighbors. These candidates are classified as plaque or non-plaque by an unsupervised learning method using features derived from the MR data intensity. The algorithm performance is validated by comparison with histology. We also demonstrate the algorithm’s ability to detect age-related changes in plaque load ex vivo in 5×FAD APP transgenic mice. To our knowledge, this work represents the first quantitative method for characterizing amyloid plaques in MRI data. The proposed method can be used to describe the spatio-temporal progression of amyloid deposition, which is necessary for understanding the evolution of plaque pathology in mouse models of AD and to evaluate the efficacy of emergent amyloid-targeting therapies in preclinical trials. PMID:22189675

ICPD-a new peak detection algorithm for LC/MS.

PubMed

Zhang, Jianqiu; Haskins, William

2010-12-01

The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. However, LC/MS systems are subject to considerable noise and sample variability, whose statistical characteristics are still elusive, making computational identification of low abundance proteins extremely challenging. As a result, the inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery. In this paper, we propose a new peak detection method called Information Combining Peak Detection (ICPD ) for high resolution LC/MS. In LC/MS, peptides elute during a certain time period and as a result, peptide isotope patterns are registered in multiple MS scans. The key feature of the new algorithm is that the observed isotope patterns registered in multiple scans are combined together for estimating the likelihood of the peptide existence. An isotope pattern matching score based on the likelihood probability is provided and utilized for peak detection. The performance of the new algorithm is evaluated based on protein standards with 48 known proteins. The evaluation shows better peak detection accuracy for low abundance proteins than other LC/MS peak detection methods.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

PubMed

Atkinson, Jonathan A; Lobet, Guillaume; Noll, Manuel; Meyer, Patrick E; Griffiths, Marcus; Wells, Darren M

2017-10-01

Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping. © The Authors 2017. Published by Oxford University Press.

Machine Learning Algorithms for prediction of regions of high Reynolds Averaged Navier Stokes Uncertainty

NASA Astrophysics Data System (ADS)

Mishra, Aashwin; Iaccarino, Gianluca

2017-11-01

In spite of their deficiencies, RANS models represent the workhorse for industrial investigations into turbulent flows. In this context, it is essential to provide diagnostic measures to assess the quality of RANS predictions. To this end, the primary step is to identify feature importances amongst massive sets of potentially descriptive and discriminative flow features. This aids the physical interpretability of the resultant discrepancy model and its extensibility to similar problems. Recent investigations have utilized approaches such as Random Forests, Support Vector Machines and the Least Absolute Shrinkage and Selection Operator for feature selection. With examples, we exhibit how such methods may not be suitable for turbulent flow datasets. The underlying rationale, such as the correlation bias and the required conditions for the success of penalized algorithms, are discussed with illustrative examples. Finally, we provide alternate approaches using convex combinations of regularized regression approaches and randomized sub-sampling in combination with feature selection algorithms, to infer model structure from data. This research was supported by the Defense Advanced Research Projects Agency under the Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) project (technical monitor: Dr Fariba Fahroo).

Informed-Proteomics: open-source software package for top-down proteomics

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

Park, Jungkap; Piehowski, Paul D.; Wilkins, Christopher

Top-down proteomics involves the analysis of intact proteins. This approach is very attractive as it allows for analyzing proteins in their endogenous form without proteolysis, preserving valuable information about post-translation modifications, isoforms, proteolytic processing or their combinations collectively called proteoforms. Moreover, the quality of the top-down LC-MS/MS datasets is rapidly increasing due to advances in the liquid chromatography and mass spectrometry instrumentation and sample processing protocols. However, the top-down mass spectra are substantially more complex compare to the more conventional bottom-up data. To take full advantage of the increasing quality of the top-down LC-MS/MS datasets there is an urgent needmore » to develop algorithms and software tools for confident proteoform identification and quantification. In this study we present a new open source software suite for top-down proteomics analysis consisting of an LC-MS feature finding algorithm, a database search algorithm, and an interactive results viewer. The presented tool along with several other popular tools were evaluated using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis.« less

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies

PubMed Central

Atkinson, Jonathan A.; Lobet, Guillaume; Noll, Manuel; Meyer, Patrick E.; Griffiths, Marcus

2017-01-01

Abstract Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping. PMID:29020748

Single-Input and Multiple-Output Surface Acoustic Wave Sensing for Damage Quantification in Piezoelectric Sensors.

PubMed

Pamwani, Lavish; Habib, Anowarul; Melandsø, Frank; Ahluwalia, Balpreet Singh; Shelke, Amit

2018-06-22

The main aim of the paper is damage detection at the microscale in the anisotropic piezoelectric sensors using surface acoustic waves (SAWs). A novel technique based on the single input and multiple output of Rayleigh waves is proposed to detect the microscale cracks/flaws in the sensor. A convex-shaped interdigital transducer is fabricated for excitation of divergent SAWs in the sensor. An angularly shaped interdigital transducer (IDT) is fabricated at 0 degrees and ±20 degrees for sensing the convex shape evolution of SAWs. A precalibrated damage was introduced in the piezoelectric sensor material using a micro-indenter in the direction perpendicular to the pointing direction of the SAW. Damage detection algorithms based on empirical mode decomposition (EMD) and principal component analysis (PCA) are implemented to quantify the evolution of damage in piezoelectric sensor material. The evolution of the damage was quantified using a proposed condition indicator (CI) based on normalized Euclidean norm of the change in principal angles, corresponding to pristine and damaged states. The CI indicator provides a robust and accurate metric for detection and quantification of damage.

Final Technical Report: Quantification of Uncertainty in Extreme Scale Computations (QUEST)

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

Knio, Omar M.

QUEST is a SciDAC Institute comprising Sandia National Laboratories, Los Alamos National Laboratory, University of Southern California, Massachusetts Institute of Technology, University of Texas at Austin, and Duke University. The mission of QUEST is to: (1) develop a broad class of uncertainty quantification (UQ) methods/tools, and (2) provide UQ expertise and software to other SciDAC projects, thereby enabling/guiding their UQ activities. The Duke effort focused on the development of algorithms and utility software for non-intrusive sparse UQ representations, and on participation in the organization of annual workshops and tutorials to disseminate UQ tools to the community, and to gather inputmore » in order to adapt approaches to the needs of SciDAC customers. In particular, fundamental developments were made in (a) multiscale stochastic preconditioners, (b) gradient-based approaches to inverse problems, (c) adaptive pseudo-spectral approximations, (d) stochastic limit cycles, and (e) sensitivity analysis tools for noisy systems. In addition, large-scale demonstrations were performed, namely in the context of ocean general circulation models.« less

Evaluation of RNA extraction methods and identification of putative reference genes for real-time quantitative polymerase chain reaction expression studies on olive (Olea europaea L.) fruits.

PubMed

Nonis, Alberto; Vezzaro, Alice; Ruperti, Benedetto

2012-07-11

Genome wide transcriptomic surveys together with targeted molecular studies are uncovering an ever increasing number of differentially expressed genes in relation to agriculturally relevant processes in olive (Olea europaea L). These data need to be supported by quantitative approaches enabling the precise estimation of transcript abundance. qPCR being the most widely adopted technique for mRNA quantification, preliminary work needs to be done to set up robust methods for extraction of fully functional RNA and for the identification of the best reference genes to obtain reliable quantification of transcripts. In this work, we have assessed different methods for their suitability for RNA extraction from olive fruits and leaves and we have evaluated thirteen potential candidate reference genes on 21 RNA samples belonging to fruit developmental/ripening series and to leaves subjected to wounding. By using two different algorithms, GAPDH2 and PP2A1 were identified as the best reference genes for olive fruit development and ripening, and their effectiveness for normalization of expression of two ripening marker genes was demonstrated.

Inventory Uncertainty Quantification using TENDL Covariance Data in Fispact-II

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

Eastwood, J.W.; Morgan, J.G.; Sublet, J.-Ch., E-mail: jean-christophe.sublet@ccfe.ac.uk

2015-01-15

The new inventory code Fispact-II provides predictions of inventory, radiological quantities and their uncertainties using nuclear data covariance information. Central to the method is a novel fast pathways search algorithm using directed graphs. The pathways output provides (1) an aid to identifying important reactions, (2) fast estimates of uncertainties, (3) reduced models that retain important nuclides and reactions for use in the code's Monte Carlo sensitivity analysis module. Described are the methods that are being implemented for improving uncertainty predictions, quantification and propagation using the covariance data that the recent nuclear data libraries contain. In the TENDL library, above themore » upper energy of the resolved resonance range, a Monte Carlo method in which the covariance data come from uncertainties of the nuclear model calculations is used. The nuclear data files are read directly by FISPACT-II without any further intermediate processing. Variance and covariance data are processed and used by FISPACT-II to compute uncertainties in collapsed cross sections, and these are in turn used to predict uncertainties in inventories and all derived radiological data.« less

A Generalized Perturbation Theory Solver In Rattlesnake Based On PETSc With Application To TREAT Steady State Uncertainty Quantification

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

Schunert, Sebastian; Wang, Congjian; Wang, Yaqi

Rattlesnake and MAMMOTH are the designated TREAT analysis tools currently being developed at the Idaho National Laboratory. Concurrent with development of the multi-physics, multi-scale capabilities, sensitivity analysis and uncertainty quantification (SA/UQ) capabilities are required for predicitive modeling of the TREAT reactor. For steady-state SA/UQ, that is essential for setting initial conditions for the transients, generalized perturbation theory (GPT) will be used. This work describes the implementation of a PETSc based solver for the generalized adjoint equations that constitute a inhomogeneous, rank deficient problem. The standard approach is to use an outer iteration strategy with repeated removal of the fundamental modemore » contamination. The described GPT algorithm directly solves the GPT equations without the need of an outer iteration procedure by using Krylov subspaces that are orthogonal to the operator’s nullspace. Three test problems are solved and provide sufficient verification for the Rattlesnake’s GPT capability. We conclude with a preliminary example evaluating the impact of the Boron distribution in the TREAT reactor using perturbation theory.« less

Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models.

PubMed

Vijayakumar, Vishal; Case, Michelle; Shirinpour, Sina; He, Bin

2017-12-01

Effective pain assessment and management strategies are needed to better manage pain. In addition to self-report, an objective pain assessment system can provide a more complete picture of the neurophysiological basis for pain. In this study, a robust and accurate machine learning approach is developed to quantify tonic thermal pain across healthy subjects into a maximum of ten distinct classes. A random forest model was trained to predict pain scores using time-frequency wavelet representations of independent components obtained from electroencephalography (EEG) data, and the relative importance of each frequency band to pain quantification is assessed. The mean classification accuracy for predicting pain on an independent test subject for a range of 1-10 is 89.45%, highest among existing state of the art quantification algorithms for EEG. The gamma band is the most important to both intersubject and intrasubject classification accuracy. The robustness and generalizability of the classifier are demonstrated. Our results demonstrate the potential of this tool to be used clinically to help us to improve chronic pain treatment and establish spectral biomarkers for future pain-related studies using EEG.

Assessing senescence in Drosophila using video tracking.

PubMed

Ardekani, Reza; Tavaré, Simon; Tower, John

2013-01-01

Senescence is associated with changes in gene expression, including the upregulation of stress response- and innate immune response-related genes. In addition, aging animals exhibit characteristic changes in movement behaviors including decreased gait speed and a deterioration in sleep/wake rhythms. Here, we describe methods for tracking Drosophila melanogaster movements in 3D with simultaneous quantification of fluorescent transgenic reporters. This approach allows for the assessment of correlations between behavior, aging, and gene expression as well as for the quantification of biomarkers of aging.

Evaluation of developmental metrics for utilization in a pediatric advanced automatic crash notification algorithm.

PubMed

Doud, Andrea N; Weaver, Ashley A; Talton, Jennifer W; Barnard, Ryan T; Petty, John; Stitzel, Joel D

2016-01-01

Appropriate treatment at designated trauma centers (TCs) improves outcomes among injured children after motor vehicle crashes (MVCs). Advanced Automatic Crash Notification (AACN) has shown promise in improving triage to appropriate TCs. Pediatric-specific AACN algorithms have not yet been created. To create such an algorithm, it will be necessary to include some metric of development (age, height, or weight) as a covariate in the injury risk algorithm. This study sought to determine which marker of development should serve as a covariate in such an algorithm and to quantify injury risk at different levels of this metric. A retrospective review of occupants age < 19 years within the MVC data set NASS-CDS 2000-2011 was performed. R(2) values of logistic regression models using age, height, or weight to predict 18 key injury types were compared to determine which metric should be used as a covariate in a pediatric AACN algorithm. Clinical judgment, literature review, and chi-square analysis were used to create groupings of the chosen metric that would discriminate injury patterns. Adjusted odds of particular injury types at the different levels of this metric were calculated from logistic regression while controlling for gender, vehicle velocity change (delta V), belted status (optimal, suboptimal, or unrestrained), and crash mode (rollover, rear, frontal, near-side, or far-side). NASS-CDS analysis produced 11,541 occupants age < 19 years with nonmissing data. Age, height, and weight were correlated with one another and with injury patterns. Age demonstrated the best predictive power in injury patterns and was categorized into bins of 0-4 years, 5-9 years, 10-14 years, and 15-18 years. Age was a significant predictor of all 18 injury types evaluated even when controlling for all other confounders and when controlling for age- and gender-specific body mass index (BMI) classifications. Adjusted odds of key injury types with respect to these age categorizations revealed that younger children were at increased odds of sustaining Abbreviated Injury Scale (AIS) 2+ and 3+ head injuries and AIS 3+ spinal injuries, whereas older children were at increased odds of sustaining thoracic fractures, AIS 3+ abdominal injuries, and AIS 2+ upper and lower extremity injuries. The injury patterns observed across developmental metrics in this study mirror those previously described among children with blunt trauma. This study identifies age as the metric best suited for use in a pediatric AACN algorithm and utilizes 12 years of data to provide quantifiable risks of particular injuries at different levels of this metric. This risk quantification will have important predictive purposes in a pediatric-specific AACN algorithm.

The properties of SIRT, TVM, and DART for 3D imaging of tubular domains in nanocomposite thin-films and sections.

PubMed

Chen, Delei; Goris, Bart; Bleichrodt, Folkert; Mezerji, Hamed Heidari; Bals, Sara; Batenburg, Kees Joost; de With, Gijsbertus; Friedrich, Heiner

2014-12-01

In electron tomography, the fidelity of the 3D reconstruction strongly depends on the employed reconstruction algorithm. In this paper, the properties of SIRT, TVM and DART reconstructions are studied with respect to having only a limited number of electrons available for imaging and applying different angular sampling schemes. A well-defined realistic model is generated, which consists of tubular domains within a matrix having slab-geometry. Subsequently, the electron tomography workflow is simulated from calculated tilt-series over experimental effects to reconstruction. In comparison with the model, the fidelity of each reconstruction method is evaluated qualitatively and quantitatively based on global and local edge profiles and resolvable distance between particles. Results show that the performance of all reconstruction methods declines with the total electron dose. Overall, SIRT algorithm is the most stable method and insensitive to changes in angular sampling. TVM algorithm yields significantly sharper edges in the reconstruction, but the edge positions are strongly influenced by the tilt scheme and the tubular objects become thinned. The DART algorithm markedly suppresses the elongation artifacts along the beam direction and moreover segments the reconstruction which can be considered a significant advantage for quantification. Finally, no advantage of TVM and DART to deal better with fewer projections was observed. Copyright © 2014 Elsevier B.V. All rights reserved.

Study on the influence of X-ray tube spectral distribution on the analysis of bulk samples and thin films: Fundamental parameters method and theoretical coefficient algorithms

NASA Astrophysics Data System (ADS)

Sitko, Rafał

2008-11-01

Knowledge of X-ray tube spectral distribution is necessary in theoretical methods of matrix correction, i.e. in both fundamental parameter (FP) methods and theoretical influence coefficient algorithms. Thus, the influence of X-ray tube distribution on the accuracy of the analysis of thin films and bulk samples is presented. The calculations are performed using experimental X-ray tube spectra taken from the literature and theoretical X-ray tube spectra evaluated by three different algorithms proposed by Pella et al. (X-Ray Spectrom. 14 (1985) 125-135), Ebel (X-Ray Spectrom. 28 (1999) 255-266), and Finkelshtein and Pavlova (X-Ray Spectrom. 28 (1999) 27-32). In this study, Fe-Cr-Ni system is selected as an example and the calculations are performed for X-ray tubes commonly applied in X-ray fluorescence analysis (XRF), i.e., Cr, Mo, Rh and W. The influence of X-ray tube spectra on FP analysis is evaluated when quantification is performed using various types of calibration samples. FP analysis of bulk samples is performed using pure-element bulk standards and multielement bulk standards similar to the analyzed material, whereas for FP analysis of thin films, the bulk and thin pure-element standards are used. For the evaluation of the influence of X-ray tube spectra on XRF analysis performed by theoretical influence coefficient methods, two algorithms for bulk samples are selected, i.e. Claisse-Quintin (Can. Spectrosc. 12 (1967) 129-134) and COLA algorithms (G.R. Lachance, Paper Presented at the International Conference on Industrial Inorganic Elemental Analysis, Metz, France, June 3, 1981) and two algorithms (constant and linear coefficients) for thin films recently proposed by Sitko (X-Ray Spectrom. 37 (2008) 265-272).

An effective medium inversion algorithm for gas hydrate quantification and its application to laboratory and borehole measurements of gas hydrate-bearing sediments

NASA Astrophysics Data System (ADS)

Chand, Shyam; Minshull, Tim A.; Priest, Jeff A.; Best, Angus I.; Clayton, Christopher R. I.; Waite, William F.

2006-08-01

The presence of gas hydrate in marine sediments alters their physical properties. In some circumstances, gas hydrate may cement sediment grains together and dramatically increase the seismic P- and S-wave velocities of the composite medium. Hydrate may also form a load-bearing structure within the sediment microstructure, but with different seismic wave attenuation characteristics, changing the attenuation behaviour of the composite. Here we introduce an inversion algorithm based on effective medium modelling to infer hydrate saturations from velocity and attenuation measurements on hydrate-bearing sediments. The velocity increase is modelled as extra binding developed by gas hydrate that strengthens the sediment microstructure. The attenuation increase is modelled through a difference in fluid flow properties caused by different permeabilities in the sediment and hydrate microstructures. We relate velocity and attenuation increases in hydrate-bearing sediments to their hydrate content, using an effective medium inversion algorithm based on the self-consistent approximation (SCA), differential effective medium (DEM) theory, and Biot and squirt flow mechanisms of fluid flow. The inversion algorithm is able to convert observations in compressional and shear wave velocities and attenuations to hydrate saturation in the sediment pore space. We applied our algorithm to a data set from the Mallik 2L-38 well, Mackenzie delta, Canada, and to data from laboratory measurements on gas-rich and water-saturated sand samples. Predictions using our algorithm match the borehole data and water-saturated laboratory data if the proportion of hydrate contributing to the load-bearing structure increases with hydrate saturation. The predictions match the gas-rich laboratory data if that proportion decreases with hydrate saturation. We attribute this difference to differences in hydrate formation mechanisms between the two environments.

An effective medium inversion algorithm for gas hydrate quantification and its application to laboratory and borehole measurements of gas hydrate-bearing sediments

USGS Publications Warehouse

Chand, S.; Minshull, T.A.; Priest, J.A.; Best, A.I.; Clayton, C.R.I.; Waite, W.F.

2006-01-01

The presence of gas hydrate in marine sediments alters their physical properties. In some circumstances, gas hydrate may cement sediment grains together and dramatically increase the seismic P- and S-wave velocities of the composite medium. Hydrate may also form a load-bearing structure within the sediment microstructure, but with different seismic wave attenuation characteristics, changing the attenuation behaviour of the composite. Here we introduce an inversion algorithm based on effective medium modelling to infer hydrate saturations from velocity and attenuation measurements on hydrate-bearing sediments. The velocity increase is modelled as extra binding developed by gas hydrate that strengthens the sediment microstructure. The attenuation increase is modelled through a difference in fluid flow properties caused by different permeabilities in the sediment and hydrate microstructures. We relate velocity and attenuation increases in hydrate-bearing sediments to their hydrate content, using an effective medium inversion algorithm based on the self-consistent approximation (SCA), differential effective medium (DEM) theory, and Biot and squirt flow mechanisms of fluid flow. The inversion algorithm is able to convert observations in compressional and shear wave velocities and attenuations to hydrate saturation in the sediment pore space. We applied our algorithm to a data set from the Mallik 2L–38 well, Mackenzie delta, Canada, and to data from laboratory measurements on gas-rich and water-saturated sand samples. Predictions using our algorithm match the borehole data and water-saturated laboratory data if the proportion of hydrate contributing to the load-bearing structure increases with hydrate saturation. The predictions match the gas-rich laboratory data if that proportion decreases with hydrate saturation. We attribute this difference to differences in hydrate formation mechanisms between the two environments.

AVQS: attack route-based vulnerability quantification scheme for smart grid.

PubMed

Ko, Jongbin; Lim, Hyunwoo; Lee, Seokjun; Shon, Taeshik

2014-01-01

A smart grid is a large, consolidated electrical grid system that includes heterogeneous networks and systems. Based on the data, a smart grid system has a potential security threat in its network connectivity. To solve this problem, we develop and apply a novel scheme to measure the vulnerability in a smart grid domain. Vulnerability quantification can be the first step in security analysis because it can help prioritize the security problems. However, existing vulnerability quantification schemes are not suitable for smart grid because they do not consider network vulnerabilities. We propose a novel attack route-based vulnerability quantification scheme using a network vulnerability score and an end-to-end security score, depending on the specific smart grid network environment to calculate the vulnerability score for a particular attack route. To evaluate the proposed approach, we derive several attack scenarios from the advanced metering infrastructure domain. The experimental results of the proposed approach and the existing common vulnerability scoring system clearly show that we need to consider network connectivity for more optimized vulnerability quantification.

Automatic 3D segmentation of multiphoton images: a key step for the quantification of human skin.

PubMed

Decencière, Etienne; Tancrède-Bohin, Emmanuelle; Dokládal, Petr; Koudoro, Serge; Pena, Ana-Maria; Baldeweck, Thérèse

2013-05-01

Multiphoton microscopy has emerged in the past decade as a useful noninvasive imaging technique for in vivo human skin characterization. However, it has not been used until now in evaluation clinical trials, mainly because of the lack of specific image processing tools that would allow the investigator to extract pertinent quantitative three-dimensional (3D) information from the different skin components. We propose a 3D automatic segmentation method of multiphoton images which is a key step for epidermis and dermis quantification. This method, based on the morphological watershed and graph cuts algorithms, takes into account the real shape of the skin surface and of the dermal-epidermal junction, and allows separating in 3D the epidermis and the superficial dermis. The automatic segmentation method and the associated quantitative measurements have been developed and validated on a clinical database designed for aging characterization. The segmentation achieves its goals for epidermis-dermis separation and allows quantitative measurements inside the different skin compartments with sufficient relevance. This study shows that multiphoton microscopy associated with specific image processing tools provides access to new quantitative measurements on the various skin components. The proposed 3D automatic segmentation method will contribute to build a powerful tool for characterizing human skin condition. To our knowledge, this is the first 3D approach to the segmentation and quantification of these original images. © 2013 John Wiley & Sons A/S. Published by Blackwell Publishing Ltd.

An Improved Neutron Transport Algorithm for HZETRN2006

NASA Astrophysics Data System (ADS)

Slaba, Tony

NASA's new space exploration initiative includes plans for long term human presence in space thereby placing new emphasis on space radiation analyses. In particular, a systematic effort of verification, validation and uncertainty quantification of the tools commonly used for radiation analysis for vehicle design and mission planning has begun. In this paper, the numerical error associated with energy discretization in HZETRN2006 is addressed; large errors in the low-energy portion of the neutron fluence spectrum are produced due to a numerical truncation error in the transport algorithm. It is shown that the truncation error results from the narrow energy domain of the neutron elastic spectral distributions, and that an extremely fine energy grid is required in order to adequately resolve the problem under the current formulation. Since adding a sufficient number of energy points will render the code computationally inefficient, we revisit the light-ion transport theory developed for HZETRN2006 and focus on neutron elastic interactions. The new approach that is developed numerically integrates with adequate resolution in the energy domain without affecting the run-time of the code and is easily incorporated into the current code. Efforts were also made to optimize the computational efficiency of the light-ion propagator; a brief discussion of the efforts is given along with run-time comparisons between the original and updated codes. Convergence testing is then completed by running the code for various environments and shielding materials with many different energy grids to ensure stability of the proposed method.

Cystic fibrosis: terminology and diagnostic algorithms

PubMed Central

De Boeck, K; Wilschanski, M; Castellani, C; Taylor, C; Cuppens, H; Dodge, J; Sinaasappel, M

2006-01-01

There is great heterogeneity in the clinical manifestations of cystic fibrosis (CF). Some patients may have all the classical manifestations of CF from infancy and have a relatively poor prognosis, while others have much milder or even atypical disease manifestations and still carry mutations on each of the CFTR genes. It is important to distinguish between these categories of patients. The European Diagnostic Working Group proposes the following terminology. Patients are diagnosed with classic or typical CF if they have one or more phenotypic characteristics and a sweat chloride concentration of >60 mmol/l. The vast majority of CF patients fall into this category. Usually one established mutation causing CF can be identified on each CFTR gene. Patients with classic CF can have exocrine pancreatic insufficiency or pancreatic sufficiency. The disease can have a severe course with rapid progression of symptoms or a milder course with very little deterioration over time. Patients with non‐classic or atypical CF have a CF phenotype in at least one organ system and a normal (<30 mmol/l) or borderline (30–60 mmol/l) sweat chloride level. In these patients confirmation of the diagnosis of CF requires detection of one disease causing mutation on each CFTR gene or direct quantification of CFTR dysfunction by nasal potential difference measurement. Non‐classic CF includes patients with multiorgan or single organ involvement. Most of these patients have exocrine pancreatic sufficiency and milder lung disease. Algorithms for a structured diagnostic process are proposed. PMID:16384879

Cystic fibrosis: terminology and diagnostic algorithms.

PubMed

De Boeck, K; Wilschanski, M; Castellani, C; Taylor, C; Cuppens, H; Dodge, J; Sinaasappel, M

2006-07-01

There is great heterogeneity in the clinical manifestations of cystic fibrosis (CF). Some patients may have all the classical manifestations of CF from infancy and have a relatively poor prognosis, while others have much milder or even atypical disease manifestations and still carry mutations on each of the CFTR genes. It is important to distinguish between these categories of patients. The European Diagnostic Working Group proposes the following terminology. Patients are diagnosed with classic or typical CF if they have one or more phenotypic characteristics and a sweat chloride concentration of >60 mmol/l. The vast majority of CF patients fall into this category. Usually one established mutation causing CF can be identified on each CFTR gene. Patients with classic CF can have exocrine pancreatic insufficiency or pancreatic sufficiency. The disease can have a severe course with rapid progression of symptoms or a milder course with very little deterioration over time. Patients with non-classic or atypical CF have a CF phenotype in at least one organ system and a normal (<30 mmol/l) or borderline (30-60 mmol/l) sweat chloride level. In these patients confirmation of the diagnosis of CF requires detection of one disease causing mutation on each CFTR gene or direct quantification of CFTR dysfunction by nasal potential difference measurement. Non-classic CF includes patients with multiorgan or single organ involvement. Most of these patients have exocrine pancreatic sufficiency and milder lung disease. Algorithms for a structured diagnostic process are proposed.

HYPOTrace: image analysis software for measuring hypocotyl growth and shape demonstrated on Arabidopsis seedlings undergoing photomorphogenesis.

PubMed

Wang, Liya; Uilecan, Ioan Vlad; Assadi, Amir H; Kozmik, Christine A; Spalding, Edgar P

2009-04-01

Analysis of time series of images can quantify plant growth and development, including the effects of genetic mutations (phenotypes) that give information about gene function. Here is demonstrated a software application named HYPOTrace that automatically extracts growth and shape information from electronic gray-scale images of Arabidopsis (Arabidopsis thaliana) seedlings. Key to the method is the iterative application of adaptive local principal components analysis to extract a set of ordered midline points (medial axis) from images of the seedling hypocotyl. Pixel intensity is weighted to avoid the medial axis being diverted by the cotyledons in areas where the two come in contact. An intensity feature useful for terminating the midline at the hypocotyl apex was isolated in each image by subtracting the baseline with a robust local regression algorithm. Applying the algorithm to time series of images of Arabidopsis seedlings responding to light resulted in automatic quantification of hypocotyl growth rate, apical hook opening, and phototropic bending with high spatiotemporal resolution. These functions are demonstrated here on wild-type, cryptochrome1, and phototropin1 seedlings for the purpose of showing that HYPOTrace generated expected results and to show how much richer the machine-vision description is compared to methods more typical in plant biology. HYPOTrace is expected to benefit seedling development research, particularly in the photomorphogenesis field, by replacing many tedious, error-prone manual measurements with a precise, largely automated computational tool.

Transparent volume imaging

NASA Astrophysics Data System (ADS)

Wixson, Steve E.

1990-07-01

Transparent Volume Imaging began with the stereo xray in 1895 and ended for most investigators when radiation safety concerns eliminated the second view. Today, similiar images can be generated by the computer without safety hazards providing improved perception and new means of image quantification. A volumetric workstation is under development based on an operational prototype. The workstation consists of multiple symbolic and numeric processors, binocular stereo color display generator with large image memory and liquid crystal shutter, voice input and output, a 3D pointer that uses projection lenses so that structures in 3 space can be touched directly, 3D hard copy using vectograph and lenticular printing, and presentation facilities using stereo 35mm slide and stereo video tape projection. Volumetric software includes a volume window manager, Mayo Clinic's Analyze program and our Digital Stereo Microscope (DSM) algorithms. The DSM uses stereo xray-like projections, rapidly oscillating motion and focal depth cues such that detail can be studied in the spatial context of the entire set of data. Focal depth cues are generated with a lens and apeture algorithm that generates a plane of sharp focus, and multiple stereo pairs each with a different plane of sharp focus are generated and stored in the large memory for interactive selection using a physical or symbolic depth selector. More recent work is studying non-linear focussing. Psychophysical studies are underway to understand how people perce ive images on a volumetric display and how accurately 3 dimensional structures can be quantitated from these displays.

{sup 90}Y -PET imaging: Exploring limitations and accuracy under conditions of low counts and high random fraction

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

Carlier, Thomas, E-mail: thomas.carlier@chu-nantes.fr; Willowson, Kathy P.; Fourkal, Eugene

Purpose: {sup 90}Y -positron emission tomography (PET) imaging is becoming a recognized modality for postinfusion quantitative assessment following radioembolization therapy. However, the extremely low counts and high random fraction associated with {sup 90}Y -PET may significantly impair both qualitative and quantitative results. The aim of this work was to study image quality and noise level in relation to the quantification and bias performance of two types of Siemens PET scanners when imaging {sup 90}Y and to compare experimental results with clinical data from two types of commercially available {sup 90}Y microspheres. Methods: Data were acquired on both Siemens Biograph TruePointmore » [non-time-of-flight (TOF)] and Biograph microcomputed tomography (mCT) (TOF) PET/CT scanners. The study was conducted in three phases. The first aimed to assess quantification and bias for different reconstruction methods according to random fraction and number of true counts in the scan. The NEMA 1994 PET phantom was filled with water with one cylindrical insert left empty (air) and the other filled with a solution of {sup 90}Y . The phantom was scanned for 60 min in the PET/CT scanner every one or two days. The second phase used the NEMA 2001 PET phantom to derive noise and image quality metrics. The spheres and the background were filled with a {sup 90}Y solution in an 8:1 contrast ratio and four 30 min acquisitions were performed over a one week period. Finally, 32 patient data (8 treated with Therasphere{sup ®} and 24 with SIR-Spheres{sup ®}) were retrospectively reconstructed and activity in the whole field of view and the liver was compared to theoretical injected activity. Results: The contribution of both bremsstrahlung and LSO trues was found to be negligible, allowing data to be decay corrected to obtain correct quantification. In general, the recovered activity for all reconstruction methods was stable over the range studied, with a small bias appearing at extremely high random fraction and low counts for iterative algorithms. Point spread function (PSF) correction and TOF reconstruction in general reduce background variability and noise and increase recovered concentration. Results for patient data indicated a good correlation between the expected and PET reconstructed activities. A linear relationship between the expected and the measured activities in the organ of interest was observed for all reconstruction method used: a linearity coefficient of 0.89 ± 0.05 for the Biograph mCT and 0.81 ± 0.05 for the Biograph TruePoint. Conclusions: Due to the low counts and high random fraction, accurate image quantification of {sup 90}Y during selective internal radionuclide therapy is affected by random coincidence estimation, scatter correction, and any positivity constraint of the algorithm. Nevertheless, phantom and patient studies showed that the impact of number of true and random coincidences on quantitative results was found to be limited as long as ordinary Poisson ordered subsets expectation maximization reconstruction algorithms with random smoothing are used. Adding PSF correction and TOF information to the reconstruction greatly improves the image quality in terms of bias, variability, noise reduction, and detectability. On the patient studies, the total activity in the field of view is in general accurately measured by Biograph mCT and slightly overestimated by the Biograph TruePoint.« less

USACM Thematic Workshop On Uncertainty Quantification And Data-Driven Modeling.

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

Stewart, James R.

The USACM Thematic Workshop on Uncertainty Quantification and Data-Driven Modeling was held on March 23-24, 2017, in Austin, TX. The organizers of the technical program were James R. Stewart of Sandia National Laboratories and Krishna Garikipati of University of Michigan. The administrative organizer was Ruth Hengst, who serves as Program Coordinator for the USACM. The organization of this workshop was coordinated through the USACM Technical Thrust Area on Uncertainty Quantification and Probabilistic Analysis. The workshop website (http://uqpm2017.usacm.org) includes the presentation agenda as well as links to several of the presentation slides (permission to access the presentations was granted by eachmore » of those speakers, respectively). Herein, this final report contains the complete workshop program that includes the presentation agenda, the presentation abstracts, and the list of posters.« less

On the predictivity of pore-scale simulations: Estimating uncertainties with multilevel Monte Carlo

NASA Astrophysics Data System (ADS)

Icardi, Matteo; Boccardo, Gianluca; Tempone, Raúl

2016-09-01

A fast method with tunable accuracy is proposed to estimate errors and uncertainties in pore-scale and Digital Rock Physics (DRP) problems. The overall predictivity of these studies can be, in fact, hindered by many factors including sample heterogeneity, computational and imaging limitations, model inadequacy and not perfectly known physical parameters. The typical objective of pore-scale studies is the estimation of macroscopic effective parameters such as permeability, effective diffusivity and hydrodynamic dispersion. However, these are often non-deterministic quantities (i.e., results obtained for specific pore-scale sample and setup are not totally reproducible by another ;equivalent; sample and setup). The stochastic nature can arise due to the multi-scale heterogeneity, the computational and experimental limitations in considering large samples, and the complexity of the physical models. These approximations, in fact, introduce an error that, being dependent on a large number of complex factors, can be modeled as random. We propose a general simulation tool, based on multilevel Monte Carlo, that can reduce drastically the computational cost needed for computing accurate statistics of effective parameters and other quantities of interest, under any of these random errors. This is, to our knowledge, the first attempt to include Uncertainty Quantification (UQ) in pore-scale physics and simulation. The method can also provide estimates of the discretization error and it is tested on three-dimensional transport problems in heterogeneous materials, where the sampling procedure is done by generation algorithms able to reproduce realistic consolidated and unconsolidated random sphere and ellipsoid packings and arrangements. A totally automatic workflow is developed in an open-source code [1], that include rigid body physics and random packing algorithms, unstructured mesh discretization, finite volume solvers, extrapolation and post-processing techniques. The proposed method can be efficiently used in many porous media applications for problems such as stochastic homogenization/upscaling, propagation of uncertainty from microscopic fluid and rock properties to macro-scale parameters, robust estimation of Representative Elementary Volume size for arbitrary physics.

Construction of Polarimetric Radar-Based Reference Rain Maps for the Iowa Flood Studies Campaign

NASA Technical Reports Server (NTRS)

Petersen, Walter; Wolff, David; Krajewski, Witek; Gatlin, Patrick

2015-01-01

The Global Precipitation Measurement (GPM) Mission Iowa Flood Studies (IFloodS) campaign was conducted in central and northeastern Iowa during the months of April-June, 2013. Specific science objectives for IFloodS included quantification of uncertainties in satellite and ground-based estimates of precipitation, 4-D characterization of precipitation physical processes and associated parameters (e.g., size distributions, water contents, types, structure etc.), assessment of the impact of precipitation estimation uncertainty and physical processes on hydrologic predictive skill, and refinement of field observations and data analysis approaches as they pertain to future GPM integrated hydrologic validation and related field studies. In addition to field campaign archival of raw and processed satellite data (including precipitation products), key ground-based platforms such as the NASA NPOL S-band and D3R Ka/Ku-band dual-polarimetric radars, University of Iowa X-band dual-polarimetric radars, a large network of paired rain gauge platforms, and a large network of 2D Video and Parsivel disdrometers were deployed. In something of a canonical approach, the radar (NPOL in particular), gauge and disdrometer observational assets were deployed to create a consistent high-quality distributed (time and space sampling) radar-based ground "reference" rainfall dataset, with known uncertainties, that could be used for assessing the satellite-based precipitation products at a range of space/time scales. Subsequently, the impact of uncertainties in the satellite products could be evaluated relative to the ground-benchmark in coupled weather, land-surface and distributed hydrologic modeling frameworks as related to flood prediction. Relative to establishing the ground-based "benchmark", numerous avenues were pursued in the making and verification of IFloodS "reference" dual-polarimetric radar-based rain maps, and this study documents the process and results as they pertain specifically to efforts using the NPOL radar dataset. The initial portions of the "process" involved dual-polarimetric quality control procedures which employed standard phase and correlation-based approaches to removal of clutter and non-meteorological echo. Calculation of a scale-adaptive KDP was accomplished using the method of Wang and Chandrasekar (2009; J. Atmos. Oceanic Tech.). A dual-polarimetric blockage algorithm based on Lang et al. (2009; J. Atmos. Oceanic Tech.) was then implemented to correct radar reflectivity and differential reflectivity at low elevation angles. Next, hydrometeor identification algorithms were run to identify liquid and ice hydrometeors. After the quality control and data preparation steps were completed several different dual-polarimetric rain estimation algorithms were employed to estimate rainfall rates using rainfall scans collected approximately every two to three minutes throughout the campaign. These algorithms included a polarimetrically-tuned Z-R algorithm that adjusts for drop oscillations (via Bringi et al., 2004, J. Atmos. Oceanic Tech.), and several different hybrid polarimetric variable approaches, including one that made use of parameters tuned to IFloodS 2D Video Disdrometer measurements. Finally, a hybrid scan algorithm was designed to merge the rain rate estimates from multiple low level elevation angle scans (where blockages could not be appropriately corrected) in order to create individual low-level rain maps. Individual rain maps at each time step were subsequently accumulated over multiple time scales for comparison to gauge network data. The comparison results and overall error character depended strongly on rain event type, polarimetric estimator applied, and range from the radar. We will present the outcome of these comparisons and their impact on constructing composited "reference" rainfall maps at select time and space scales.

Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry.

PubMed

Abbatiello, Susan E; Mani, D R; Keshishian, Hasmik; Carr, Steven A

2010-02-01

Multiple reaction monitoring mass spectrometry (MRM-MS) of peptides with stable isotope-labeled internal standards (SISs) is increasingly being used to develop quantitative assays for proteins in complex biological matrices. These assays can be highly precise and quantitative, but the frequent occurrence of interferences requires that MRM-MS data be manually reviewed, a time-intensive process subject to human error. We developed an algorithm that identifies inaccurate transition data based on the presence of interfering signal or inconsistent recovery among replicate samples. The algorithm objectively evaluates MRM-MS data with 2 orthogonal approaches. First, it compares the relative product ion intensities of the analyte peptide to those of the SIS peptide and uses a t-test to determine if they are significantly different. A CV is then calculated from the ratio of the analyte peak area to the SIS peak area from the sample replicates. The algorithm identified problematic transitions and achieved accuracies of 94%-100%, with a sensitivity and specificity of 83%-100% for correct identification of errant transitions. The algorithm was robust when challenged with multiple types of interferences and problematic transitions. This algorithm for automated detection of inaccurate and imprecise transitions (AuDIT) in MRM-MS data reduces the time required for manual and subjective inspection of data, improves the overall accuracy of data analysis, and is easily implemented into the standard data-analysis work flow. AuDIT currently works with results exported from MRM-MS data-processing software packages and may be implemented as an analysis tool within such software.

Automated Detection of Inaccurate and Imprecise Transitions in Peptide Quantification by Multiple Reaction Monitoring Mass Spectrometry

PubMed Central

Abbatiello, Susan E.; Mani, D. R.; Keshishian, Hasmik; Carr, Steven A.

2010-01-01

BACKGROUND Multiple reaction monitoring mass spectrometry (MRM-MS) of peptides with stable isotope–labeled internal standards (SISs) is increasingly being used to develop quantitative assays for proteins in complex biological matrices. These assays can be highly precise and quantitative, but the frequent occurrence of interferences requires that MRM-MS data be manually reviewed, a time-intensive process subject to human error. We developed an algorithm that identifies inaccurate transition data based on the presence of interfering signal or inconsistent recovery among replicate samples. METHODS The algorithm objectively evaluates MRM-MS data with 2 orthogonal approaches. First, it compares the relative product ion intensities of the analyte peptide to those of the SIS peptide and uses a t-test to determine if they are significantly different. A CV is then calculated from the ratio of the analyte peak area to the SIS peak area from the sample replicates. RESULTS The algorithm identified problematic transitions and achieved accuracies of 94%–100%, with a sensitivity and specificity of 83%–100% for correct identification of errant transitions. The algorithm was robust when challenged with multiple types of interferences and problematic transitions. CONCLUSIONS This algorithm for automated detection of inaccurate and imprecise transitions (AuDIT) in MRM-MS data reduces the time required for manual and subjective inspection of data, improves the overall accuracy of data analysis, and is easily implemented into the standard data-analysis work flow. AuDIT currently works with results exported from MRM-MS data-processing software packages and may be implemented as an analysis tool within such software. PMID:20022980

Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms.

PubMed

Jaffe, Jacob D; Feeney, Caitlin M; Patel, Jinal; Lu, Xiaodong; Mani, D R

2016-11-01

Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques. Graphical Abstract ᅟ.

A new surface fractal dimension for displacement mode shape-based damage identification of plate-type structures

NASA Astrophysics Data System (ADS)

Shi, Binkai; Qiao, Pizhong

2018-03-01

Vibration-based nondestructive testing is an area of growing interest and worthy of exploring new and innovative approaches. The displacement mode shape is often chosen to identify damage due to its local detailed characteristic and less sensitivity to surrounding noise. Requirement for baseline mode shape in most vibration-based damage identification limits application of such a strategy. In this study, a new surface fractal dimension called edge perimeter dimension (EPD) is formulated, from which an EPD-based window dimension locus (EPD-WDL) algorithm for irregularity or damage identification of plate-type structures is established. An analytical notch-type damage model of simply-supported plates is proposed to evaluate notch effect on plate vibration performance; while a sub-domain of notch cases with less effect is selected to investigate robustness of the proposed damage identification algorithm. Then, fundamental aspects of EPD-WDL algorithm in term of notch localization, notch quantification, and noise immunity are assessed. A mathematical solution called isomorphism is implemented to remove false peaks caused by inflexions of mode shapes when applying the EPD-WDL algorithm to higher mode shapes. The effectiveness and practicability of the EPD-WDL algorithm are demonstrated by an experimental procedure on damage identification of an artificially-induced notched aluminum cantilever plate using a measurement system of piezoelectric lead-zirconate (PZT) actuator and scanning laser Doppler vibrometer (SLDV). As demonstrated in both the analytical and experimental evaluations, the new surface fractal dimension technique developed is capable of effectively identifying damage in plate-type structures.

Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Jaffe, Jacob D.; Feeney, Caitlin M.; Patel, Jinal; Lu, Xiaodong; Mani, D. R.

2016-11-01

Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques.

A Surrogate-based Adaptive Sampling Approach for History Matching and Uncertainty Quantification

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

Li, Weixuan; Zhang, Dongxiao; Lin, Guang

A critical procedure in reservoir simulations is history matching (or data assimilation in a broader sense), which calibrates model parameters such that the simulation results are consistent with field measurements, and hence improves the credibility of the predictions given by the simulations. Often there exist non-unique combinations of parameter values that all yield the simulation results matching the measurements. For such ill-posed history matching problems, Bayesian theorem provides a theoretical foundation to represent different solutions and to quantify the uncertainty with the posterior PDF. Lacking an analytical solution in most situations, the posterior PDF may be characterized with a samplemore » of realizations, each representing a possible scenario. A novel sampling algorithm is presented here for the Bayesian solutions to history matching problems. We aim to deal with two commonly encountered issues: 1) as a result of the nonlinear input-output relationship in a reservoir model, the posterior distribution could be in a complex form, such as multimodal, which violates the Gaussian assumption required by most of the commonly used data assimilation approaches; 2) a typical sampling method requires intensive model evaluations and hence may cause unaffordable computational cost. In the developed algorithm, we use a Gaussian mixture model as the proposal distribution in the sampling process, which is simple but also flexible to approximate non-Gaussian distributions and is particularly efficient when the posterior is multimodal. Also, a Gaussian process is utilized as a surrogate model to speed up the sampling process. Furthermore, an iterative scheme of adaptive surrogate refinement and re-sampling ensures sampling accuracy while keeping the computational cost at a minimum level. The developed approach is demonstrated with an illustrative example and shows its capability in handling the above-mentioned issues. Multimodal posterior of the history matching problem is captured and are used to give a reliable production prediction with uncertainty quantification. The new algorithm reveals a great improvement in terms of computational efficiency comparing previously studied approaches for the sample problem.« less

Validation of an integrated software for the detection of rapid eye movement sleep behavior disorder.

PubMed

Frauscher, Birgit; Gabelia, David; Biermayr, Marlene; Stefani, Ambra; Hackner, Heinz; Mitterling, Thomas; Poewe, Werner; Högl, Birgit

2014-10-01

Rapid eye movement sleep without atonia (RWA) is the polysomnographic hallmark of REM sleep behavior disorder (RBD). To partially overcome the disadvantages of manual RWA scoring, which is time consuming but essential for the accurate diagnosis of RBD, we aimed to validate software specifically developed and integrated with polysomnography for RWA detection against the gold standard of manual RWA quantification. Academic referral center sleep laboratory. Polysomnographic recordings of 20 patients with RBD and 60 healthy volunteers were analyzed. N/A. Motor activity during REM sleep was quantified manually and computer assisted (with and without artifact detection) according to Sleep Innsbruck Barcelona (SINBAR) criteria for the mentalis ("any," phasic, tonic electromyographic [EMG] activity) and the flexor digitorum superficialis (FDS) muscle (phasic EMG activity). Computer-derived indices (with and without artifact correction) for "any," phasic, tonic mentalis EMG activity, phasic FDS EMG activity, and the SINBAR index ("any" mentalis + phasic FDS) correlated well with the manually derived indices (all Spearman rhos 0.66-0.98). In contrast with computerized scoring alone, computerized scoring plus manual artifact correction (median duration 5.4 min) led to a significant reduction of false positives for "any" mentalis (40%), phasic mentalis (40.6%), and the SINBAR index (41.2%). Quantification of tonic mentalis and phasic FDS EMG activity was not influenced by artifact correction. The computer algorithm used here appears to be a promising tool for REM sleep behavior disorder detection in both research and clinical routine. A short check for plausibility of automatic detection should be a basic prerequisite for this and all other available computer algorithms. © 2014 Associated Professional Sleep Societies, LLC.

Abdominal adipose tissue quantification on water-suppressed and non-water-suppressed MRI at 3T using semi-automated FCM clustering algorithm

NASA Astrophysics Data System (ADS)

Valaparla, Sunil K.; Peng, Qi; Gao, Feng; Clarke, Geoffrey D.

2014-03-01

Accurate measurements of human body fat distribution are desirable because excessive body fat is associated with impaired insulin sensitivity, type 2 diabetes mellitus (T2DM) and cardiovascular disease. In this study, we hypothesized that the performance of water suppressed (WS) MRI is superior to non-water suppressed (NWS) MRI for volumetric assessment of abdominal subcutaneous (SAT), intramuscular (IMAT), visceral (VAT), and total (TAT) adipose tissues. We acquired T1-weighted images on a 3T MRI system (TIM Trio, Siemens), which was analyzed using semi-automated segmentation software that employs a fuzzy c-means (FCM) clustering algorithm. Sixteen contiguous axial slices, centered at the L4-L5 level of the abdomen, were acquired in eight T2DM subjects with water suppression (WS) and without (NWS). Histograms from WS images show improved separation of non-fatty tissue pixels from fatty tissue pixels, compared to NWS images. Paired t-tests of WS versus NWS showed a statistically significant lower volume of lipid in the WS images for VAT (145.3 cc less, p=0.006) and IMAT (305 cc less, p<0.001), but not SAT (14.1 cc more, NS). WS measurements of TAT also resulted in lower fat volumes (436.1 cc less, p=0.002). There is strong correlation between WS and NWS quantification methods for SAT measurements (r=0.999), but poorer correlation for VAT studies (r=0.845). These results suggest that NWS pulse sequences may overestimate adipose tissue volumes and that WS pulse sequences are more desirable due to the higher contrast generated between fatty and non-fatty tissues.

Study the effect of reservoir spatial heterogeneity on CO2 sequestration under an uncertainty quantification (UQ) software framework

NASA Astrophysics Data System (ADS)

Fang, Y.; Hou, J.; Engel, D.; Lin, G.; Yin, J.; Han, B.; Fang, Z.; Fountoulakis, V.

2011-12-01

In this study, we introduce an uncertainty quantification (UQ) software framework for carbon sequestration, with the focus of studying being the effect of spatial heterogeneity of reservoir properties on CO2 migration. We use a sequential Gaussian method (SGSIM) to generate realizations of permeability fields with various spatial statistical attributes. To deal with the computational difficulties, we integrate the following ideas/approaches: 1) firstly, we use three different sampling approaches (probabilistic collocation, quasi-Monte Carlo, and adaptive sampling approaches) to reduce the required forward calculations while trying to explore the parameter space and quantify the input uncertainty; 2) secondly, we use eSTOMP as the forward modeling simulator. eSTOMP is implemented using the Global Arrays toolkit (GA) that is based on one-sided inter-processor communication and supports a shared memory programming style on distributed memory platforms. It provides highly-scalable performance. It uses a data model to partition most of the large scale data structures into a relatively small number of distinct classes. The lower level simulator infrastructure (e.g. meshing support, associated data structures, and data mapping to processors) is separated from the higher level physics and chemistry algorithmic routines using a grid component interface; and 3) besides the faster model and more efficient algorithms to speed up the forward calculation, we built an adaptive system infrastructure to select the best possible data transfer mechanisms, to optimally allocate system resources to improve performance, and to integrate software packages and data for composing carbon sequestration simulation, computation, analysis, estimation and visualization. We will demonstrate the framework with a given CO2 injection scenario in a heterogeneous sandstone reservoir.

Second-order standard addition for deconvolution and quantification of fatty acids of fish oil using GC-MS.

PubMed

Vosough, Maryam; Salemi, Amir

2007-08-15

In the present work two second-order calibration methods, generalized rank annihilation method (GRAM) and multivariate curve resolution-alternating least square (MCR-ALS) have been applied on standard addition data matrices obtained by gas chromatography-mass spectrometry (GC-MS) to characterize and quantify four unsaturated fatty acids cis-9-hexadecenoic acid (C16:1omega7c), cis-9-octadecenoic acid (C18:1omega9c), cis-11-eicosenoic acid (C20:1omega9) and cis-13-docosenoic acid (C22:1omega9) in fish oil considering matrix interferences. With these methods, the area does not need to be directly measured and predictions are more accurate. Because of non-trilinear conditions of GC-MS data matrices, at first MCR-ALS and GRAM have been used on uncorrected data matrices. In comparison to MCR-ALS, biased and imprecise concentrations (%R.S.D.=27.3) were obtained using GRAM without correcting the retention time-shift. As trilinearity is the essential requirement for implementing GRAM, the data need to be corrected. Multivariate rank alignment objectively corrects the run-to-run retention time variations between sample GC-MS data matrix and a standard addition GC-MS data matrix. Then, two second-order algorithms have been compared with each other. The above algorithms provided similar mean predictions, pure concentrations and spectral profiles. The results validated using standard mass spectra of target compounds. In addition, some of the quantification results were compared with the concentration values obtained using the selected mass chromatograms. As in the case of strong peak-overlap and the matrix effect, the classical univariate method of determination of the area of the peaks of the analytes will fail, the "second-order advantage" has solved this problem successfully.

Novel quantitative real-time LCR for the sensitive detection of SNP frequencies in pooled DNA: method development, evaluation and application.

PubMed

Psifidi, Androniki; Dovas, Chrysostomos; Banos, Georgios

2011-01-19

Single nucleotide polymorphisms (SNP) have proven to be powerful genetic markers for genetic applications in medicine, life science and agriculture. A variety of methods exist for SNP detection but few can quantify SNP frequencies when the mutated DNA molecules correspond to a small fraction of the wild-type DNA. Furthermore, there is no generally accepted gold standard for SNP quantification, and, in general, currently applied methods give inconsistent results in selected cohorts. In the present study we sought to develop a novel method for accurate detection and quantification of SNP in DNA pooled samples. The development and evaluation of a novel Ligase Chain Reaction (LCR) protocol that uses a DNA-specific fluorescent dye to allow quantitative real-time analysis is described. Different reaction components and thermocycling parameters affecting the efficiency and specificity of LCR were examined. Several protocols, including gap-LCR modifications, were evaluated using plasmid standard and genomic DNA pools. A protocol of choice was identified and applied for the quantification of a polymorphism at codon 136 of the ovine PRNP gene that is associated with susceptibility to a transmissible spongiform encephalopathy in sheep. The real-time LCR protocol developed in the present study showed high sensitivity, accuracy, reproducibility and a wide dynamic range of SNP quantification in different DNA pools. The limits of detection and quantification of SNP frequencies were 0.085% and 0.35%, respectively. The proposed real-time LCR protocol is applicable when sensitive detection and accurate quantification of low copy number mutations in DNA pools is needed. Examples include oncogenes and tumour suppressor genes, infectious diseases, pathogenic bacteria, fungal species, viral mutants, drug resistance resulting from point mutations, and genetically modified organisms in food.

Novel Quantitative Real-Time LCR for the Sensitive Detection of SNP Frequencies in Pooled DNA: Method Development, Evaluation and Application

PubMed Central

Psifidi, Androniki; Dovas, Chrysostomos; Banos, Georgios

2011-01-01

Background Single nucleotide polymorphisms (SNP) have proven to be powerful genetic markers for genetic applications in medicine, life science and agriculture. A variety of methods exist for SNP detection but few can quantify SNP frequencies when the mutated DNA molecules correspond to a small fraction of the wild-type DNA. Furthermore, there is no generally accepted gold standard for SNP quantification, and, in general, currently applied methods give inconsistent results in selected cohorts. In the present study we sought to develop a novel method for accurate detection and quantification of SNP in DNA pooled samples. Methods The development and evaluation of a novel Ligase Chain Reaction (LCR) protocol that uses a DNA-specific fluorescent dye to allow quantitative real-time analysis is described. Different reaction components and thermocycling parameters affecting the efficiency and specificity of LCR were examined. Several protocols, including gap-LCR modifications, were evaluated using plasmid standard and genomic DNA pools. A protocol of choice was identified and applied for the quantification of a polymorphism at codon 136 of the ovine PRNP gene that is associated with susceptibility to a transmissible spongiform encephalopathy in sheep. Conclusions The real-time LCR protocol developed in the present study showed high sensitivity, accuracy, reproducibility and a wide dynamic range of SNP quantification in different DNA pools. The limits of detection and quantification of SNP frequencies were 0.085% and 0.35%, respectively. Significance The proposed real-time LCR protocol is applicable when sensitive detection and accurate quantification of low copy number mutations in DNA pools is needed. Examples include oncogenes and tumour suppressor genes, infectious diseases, pathogenic bacteria, fungal species, viral mutants, drug resistance resulting from point mutations, and genetically modified organisms in food. PMID:21283808

Novel Digital Features Discriminate Between Drought Resistant and Drought Sensitive Rice Under Controlled and Field Conditions.

PubMed

Duan, Lingfeng; Han, Jiwan; Guo, Zilong; Tu, Haifu; Yang, Peng; Zhang, Dong; Fan, Yuan; Chen, Guoxing; Xiong, Lizhong; Dai, Mingqiu; Williams, Kevin; Corke, Fiona; Doonan, John H; Yang, Wanneng

2018-01-01

Dynamic quantification of drought response is a key issue both for variety selection and for functional genetic study of rice drought resistance. Traditional assessment of drought resistance traits, such as stay-green and leaf-rolling, has utilized manual measurements, that are often subjective, error-prone, poorly quantified and time consuming. To relieve this phenotyping bottleneck, we demonstrate a feasible, robust and non-destructive method that dynamically quantifies response to drought, under both controlled and field conditions. Firstly, RGB images of individual rice plants at different growth points were analyzed to derive 4 features that were influenced by imposition of drought. These include a feature related to the ability to stay green, which we termed greenness plant area ratio (GPAR) and 3 shape descriptors [total plant area/bounding rectangle area ratio (TBR), perimeter area ratio (PAR) and total plant area/convex hull area ratio (TCR)]. Experiments showed that these 4 features were capable of discriminating reliably between drought resistant and drought sensitive accessions, and dynamically quantifying the drought response under controlled conditions across time (at either daily or half hourly time intervals). We compared the 3 shape descriptors and concluded that PAR was more robust and sensitive to leaf-rolling than the other shape descriptors. In addition, PAR and GPAR proved to be effective in quantification of drought response in the field. Moreover, the values obtained in field experiments using the collection of rice varieties were correlated with those derived from pot-based experiments. The general applicability of the algorithms is demonstrated by their ability to probe archival Miscanthus data previously collected on an independent platform. In conclusion, this image-based technology is robust providing a platform-independent tool for quantifying drought response that should be of general utility for breeding and functional genomics in future.

Uncertainty Quantification Techniques for Population Density Estimates Derived from Sparse Open Source Data

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

Stewart, Robert N; White, Devin A; Urban, Marie L

2013-01-01

The Population Density Tables (PDT) project at the Oak Ridge National Laboratory (www.ornl.gov) is developing population density estimates for specific human activities under normal patterns of life based largely on information available in open source. Currently, activity based density estimates are based on simple summary data statistics such as range and mean. Researchers are interested in improving activity estimation and uncertainty quantification by adopting a Bayesian framework that considers both data and sociocultural knowledge. Under a Bayesian approach knowledge about population density may be encoded through the process of expert elicitation. Due to the scale of the PDT effort whichmore » considers over 250 countries, spans 40 human activity categories, and includes numerous contributors, an elicitation tool is required that can be operationalized within an enterprise data collection and reporting system. Such a method would ideally require that the contributor have minimal statistical knowledge, require minimal input by a statistician or facilitator, consider human difficulties in expressing qualitative knowledge in a quantitative setting, and provide methods by which the contributor can appraise whether their understanding and associated uncertainty was well captured. This paper introduces an algorithm that transforms answers to simple, non-statistical questions into a bivariate Gaussian distribution as the prior for the Beta distribution. Based on geometric properties of the Beta distribution parameter feasibility space and the bivariate Gaussian distribution, an automated method for encoding is developed that responds to these challenging enterprise requirements. Though created within the context of population density, this approach may be applicable to a wide array of problem domains requiring informative priors for the Beta distribution.« less

A statistical kinematic source inversion approach based on the QUESO library for uncertainty quantification and prediction

NASA Astrophysics Data System (ADS)

Zielke, Olaf; McDougall, Damon; Mai, Martin; Babuska, Ivo

2014-05-01

Seismic, often augmented with geodetic data, are frequently used to invert for the spatio-temporal evolution of slip along a rupture plane. The resulting images of the slip evolution for a single event, inferred by different research teams, often vary distinctly, depending on the adopted inversion approach and rupture model parameterization. This observation raises the question, which of the provided kinematic source inversion solutions is most reliable and most robust, and — more generally — how accurate are fault parameterization and solution predictions? These issues are not included in "standard" source inversion approaches. Here, we present a statistical inversion approach to constrain kinematic rupture parameters from teleseismic body waves. The approach is based a) on a forward-modeling scheme that computes synthetic (body-)waves for a given kinematic rupture model, and b) on the QUESO (Quantification of Uncertainty for Estimation, Simulation, and Optimization) library that uses MCMC algorithms and Bayes theorem for sample selection. We present Bayesian inversions for rupture parameters in synthetic earthquakes (i.e. for which the exact rupture history is known) in an attempt to identify the cross-over at which further model discretization (spatial and temporal resolution of the parameter space) is no longer attributed to a decreasing misfit. Identification of this cross-over is of importance as it reveals the resolution power of the studied data set (i.e. teleseismic body waves), enabling one to constrain kinematic earthquake rupture histories of real earthquakes at a resolution that is supported by data. In addition, the Bayesian approach allows for mapping complete posterior probability density functions of the desired kinematic source parameters, thus enabling us to rigorously assess the uncertainties in earthquake source inversions.

Applicability of digital analysis and imaging technology in neuropathology assessment.

PubMed

Dunn, William D; Gearing, Marla; Park, Yuna; Zhang, Lifan; Hanfelt, John; Glass, Jonathan D; Gutman, David A

2016-06-01

Alzheimer's disease (AD) is a progressive neurological disorder that affects more than 30 million people worldwide. While various dementia-related losses in cognitive functioning are its hallmark clinical symptoms, ultimate diagnosis is based on manual neuropathological assessments using various schemas, including Braak staging, CERAD (Consortium to Establish a Registry for Alzheimer's Disease) and Thal phase scoring. Since these scoring systems are based on subjective assessment, there is inevitably some degree of variation between readers, which could affect ultimate neuropathology diagnosis. Here, we report a pilot study investigating the applicability of computer-driven image analysis for characterizing neuropathological features, as well as its potential to supplement or even replace manually derived ratings commonly performed in medical settings. In this work, we quantitatively measured amyloid beta (Aβ) plaque in various brain regions from 34 patients using a robust digital quantification algorithm. We next verified these digitally derived measures to the manually derived pathology ratings using correlation and ordinal logistic regression methods, while also investigating the association with other AD-related neuropathology scoring schema commonly used at autopsy, such as Braak and CERAD. In addition to successfully verifying our digital measurements of Aβ plaques with respective categorical measurements, we found significant correlations with most AD-related scoring schemas. Our results demonstrate the potential for digital analysis to be adapted to more complex staining procedures commonly used in neuropathological diagnosis. As the efficiency of scanning and digital analysis of histology images increases, we believe that the basis of our semi-automatic approach may better standardize quantification of neuropathological changes and AD diagnosis, ultimately leading to a more comprehensive understanding of neurological disorders and more efficient patient care. © 2015 Japanese Society of Neuropathology.

Applicability of digital analysis and imaging technology in neuropathology assessment

PubMed Central

Dunn, William D.; Gearing, Marla; Park, Yuna; Zhang, Lifan; Hanfelt, John; Glass, Jonathan D.; Gutman, David A.

2017-01-01

Alzheimer’s disease (AD) is a progressive neurological disorder that affects more than 30 million people worldwide. While various dementia-related losses in cognitive functioning are its hallmark clinical symptoms, ultimate diagnosis is based on manual neuropathological assessments using various schemas, including Braak staging, CERAD (Consortium to Establish a Registry for Alzheimer’s Disease) and Thal phase scoring. Since these scoring systems are based on subjective assessment, there is inevitably some degree of variation between readers, which could affect ultimate neuropathology diagnosis. Here, we report a pilot study investigating the applicability of computer-driven image analysis for characterizing neuropathological features, as well as its potential to supplement or even replace manually derived ratings commonly performed in medical settings. In this work, we quantitatively measured amyloid beta (Aβ) plaque in various brain regions from 34 patients using a robust digital quantification algorithm. We next verified these digitally derived measures to the manually derived pathology ratings using correlation and ordinal logistic regression methods, while also investigating the association with other AD-related neuropathology scoring schema commonly used at autopsy, such as Braak and CERAD. In addition to successfully verifying our digital measurements of Aβ plaques with respective categorical measurements, we found significant correlations with most AD-related scoring schemas. Our results demonstrate the potential for digital analysis to be adapted to more complex staining procedures commonly used in neuropathological diagnosis. As the efficiency of scanning and digital analysis of histology images increases, we believe that the basis of our semi-automatic approach may better standardize quantification of neuropathological changes and AD diagnosis, ultimately leading to a more comprehensive understanding of neurological disorders and more efficient patient care. PMID:26577803

Statistical modeling and MAP estimation for body fat quantification with MRI ratio imaging

NASA Astrophysics Data System (ADS)

Wong, Wilbur C. K.; Johnson, David H.; Wilson, David L.

2008-03-01

We are developing small animal imaging techniques to characterize the kinetics of lipid accumulation/reduction of fat depots in response to genetic/dietary factors associated with obesity and metabolic syndromes. Recently, we developed an MR ratio imaging technique that approximately yields lipid/{lipid + water}. In this work, we develop a statistical model for the ratio distribution that explicitly includes a partial volume (PV) fraction of fat and a mixture of a Rician and multiple Gaussians. Monte Carlo hypothesis testing showed that our model was valid over a wide range of coefficient of variation of the denominator distribution (c.v.: 0-0:20) and correlation coefficient among the numerator and denominator (ρ 0-0.95), which cover the typical values that we found in MRI data sets (c.v.: 0:027-0:063, ρ: 0:50-0:75). Then a maximum a posteriori (MAP) estimate for the fat percentage per voxel is proposed. Using a digital phantom with many PV voxels, we found that ratio values were not linearly related to PV fat content and that our method accurately described the histogram. In addition, the new method estimated the ground truth within +1.6% vs. +43% for an approach using an uncorrected ratio image, when we simply threshold the ratio image. On the six genetically obese rat data sets, the MAP estimate gave total fat volumes of 279 +/- 45mL, values 21% smaller than those from the uncorrected ratio images, principally due to the non-linear PV effect. We conclude that our algorithm can increase the accuracy of fat volume quantification even in regions having many PV voxels, e.g. ectopic fat depots.

Large-Scale Uncertainty and Error Analysis for Time-dependent Fluid/Structure Interactions in Wind Turbine Applications

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

Alonso, Juan J.; Iaccarino, Gianluca

2013-08-25

The following is the final report covering the entire period of this aforementioned grant, June 1, 2011 - May 31, 2013 for the portion of the effort corresponding to Stanford University (SU). SU has partnered with Sandia National Laboratories (PI: Mike S. Eldred) and Purdue University (PI: Dongbin Xiu) to complete this research project and this final report includes those contributions made by the members of the team at Stanford. Dr. Eldred is continuing his contributions to this project under a no-cost extension and his contributions to the overall effort will be detailed at a later time (once his effortmore » has concluded) on a separate project submitted by Sandia National Laboratories. At Stanford, the team is made up of Profs. Alonso, Iaccarino, and Duraisamy, post-doctoral researcher Vinod Lakshminarayan, and graduate student Santiago Padron. At Sandia National Laboratories, the team includes Michael Eldred, Matt Barone, John Jakeman, and Stefan Domino, and at Purdue University, we have Prof. Dongbin Xiu as our main collaborator. The overall objective of this project was to develop a novel, comprehensive methodology for uncertainty quantification by combining stochastic expansions (nonintrusive polynomial chaos and stochastic collocation), the adjoint approach, and fusion with experimental data to account for aleatory and epistemic uncertainties from random variable, random field, and model form sources. The expected outcomes of this activity were detailed in the proposal and are repeated here to set the stage for the results that we have generated during the time period of execution of this project: 1. The rigorous determination of an error budget comprising numerical errors in physical space and statistical errors in stochastic space and its use for optimal allocation of resources; 2. A considerable increase in efficiency when performing uncertainty quantification with a large number of uncertain variables in complex non-linear multi-physics problems; 3. A solution to the long-time integration problem of spectral chaos approaches; 4. A rigorous methodology to account for aleatory and epistemic uncertainties, to emphasize the most important variables via dimension reduction and dimension-adaptive refinement, and to support fusion with experimental data using Bayesian inference; 5. The application of novel methodologies to time-dependent reliability studies in wind turbine applications including a number of efforts relating to the uncertainty quantification in vertical-axis wind turbine applications. In this report, we summarize all accomplishments in the project (during the time period specified) focusing on advances in UQ algorithms and deployment efforts to the wind turbine application area. Detailed publications in each of these areas have also been completed and are available from the respective conference proceedings and journals as detailed in a later section.« less

Automated recognition of the pericardium contour on processed CT images using genetic algorithms.

PubMed

Rodrigues, É O; Rodrigues, L O; Oliveira, L S N; Conci, A; Liatsis, P

2017-08-01

This work proposes the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart using Computed Tomography (CT) images. We assume that each slice of the pericardium can be modelled by an ellipse, the parameters of which need to be optimally determined. An optimal ellipse would be one that closely follows the pericardium contour and, consequently, separates appropriately the epicardial and mediastinal fats of the human heart. Tracing and automatically identifying the pericardium contour aids in medical diagnosis. Usually, this process is done manually or not done at all due to the effort required. Besides, detecting the pericardium may improve previously proposed automated methodologies that separate the two types of fat associated to the human heart. Quantification of these fats provides important health risk marker information, as they are associated with the development of certain cardiovascular pathologies. Finally, we conclude that GA offers satisfiable solutions in a feasible amount of processing time. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automated selected reaction monitoring software for accurate label-free protein quantification.

PubMed

Teleman, Johan; Karlsson, Christofer; Waldemarson, Sofia; Hansson, Karin; James, Peter; Malmström, Johan; Levander, Fredrik

2012-07-06

Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.

Comparison of K-means and fuzzy c-means algorithm performance for automated determination of the arterial input function.

PubMed

Yin, Jiandong; Sun, Hongzan; Yang, Jiawen; Guo, Qiyong

2014-01-01

The arterial input function (AIF) plays a crucial role in the quantification of cerebral perfusion parameters. The traditional method for AIF detection is based on manual operation, which is time-consuming and subjective. Two automatic methods have been reported that are based on two frequently used clustering algorithms: fuzzy c-means (FCM) and K-means. However, it is still not clear which is better for AIF detection. Hence, we compared the performance of these two clustering methods using both simulated and clinical data. The results demonstrate that K-means analysis can yield more accurate and robust AIF results, although it takes longer to execute than the FCM method. We consider that this longer execution time is trivial relative to the total time required for image manipulation in a PACS setting, and is acceptable if an ideal AIF is obtained. Therefore, the K-means method is preferable to FCM in AIF detection.

Comparison of K-Means and Fuzzy c-Means Algorithm Performance for Automated Determination of the Arterial Input Function

PubMed Central

Yin, Jiandong; Sun, Hongzan; Yang, Jiawen; Guo, Qiyong

2014-01-01

The arterial input function (AIF) plays a crucial role in the quantification of cerebral perfusion parameters. The traditional method for AIF detection is based on manual operation, which is time-consuming and subjective. Two automatic methods have been reported that are based on two frequently used clustering algorithms: fuzzy c-means (FCM) and K-means. However, it is still not clear which is better for AIF detection. Hence, we compared the performance of these two clustering methods using both simulated and clinical data. The results demonstrate that K-means analysis can yield more accurate and robust AIF results, although it takes longer to execute than the FCM method. We consider that this longer execution time is trivial relative to the total time required for image manipulation in a PACS setting, and is acceptable if an ideal AIF is obtained. Therefore, the K-means method is preferable to FCM in AIF detection. PMID:24503700

OPAD-EDIFIS Real-Time Processing

NASA Technical Reports Server (NTRS)

Katsinis, Constantine

1997-01-01

The Optical Plume Anomaly Detection (OPAD) detects engine hardware degradation of flight vehicles through identification and quantification of elemental species found in the plume by analyzing the plume emission spectra in a real-time mode. Real-time performance of OPAD relies on extensive software which must report metal amounts in the plume faster than once every 0.5 sec. OPAD software previously written by NASA scientists performed most necessary functions at speeds which were far below what is needed for real-time operation. The research presented in this report improved the execution speed of the software by optimizing the code without changing the algorithms and converting it into a parallelized form which is executed in a shared-memory multiprocessor system. The resulting code was subjected to extensive timing analysis. The report also provides suggestions for further performance improvement by (1) identifying areas of algorithm optimization, (2) recommending commercially available multiprocessor architectures and operating systems to support real-time execution and (3) presenting an initial study of fault-tolerance requirements.

Advantages and Limitations of Wearable Activity Trackers: Considerations for Patients and Clinicians.

PubMed

Walker, Rachel K; Hickey, Amanda M; Freedson, Patty S

2016-12-01

Exercise, light physical activity, and decreased sedentary time all have been associated with health benefits following cancer diagnoses. Commercially available wearable activity trackers may help patients monitor and self-manage their behaviors to achieve these benefits. This article highlights some advantages and limitations clinicians should be aware of when discussing the use of activity trackers with cancer survivors. Limited research has assessed the accuracy of commercially available activity trackers compared to research-grade devices. Because most devices use confidential, proprietary algorithms to convert accelerometry data to meaningful output like total steps, assessing whether these algorithms account for differences in gait abnormalities, functional limitations, and different body morphologies can be difficult. Quantification of sedentary behaviors and light physical activities present additional challenges. The global market for activity trackers is growing, which presents clinicians with a tremendous opportunity to incorporate these devices into clinical practice as tools to promote activity. This article highlights important considerations about tracker accuracy and usage by cancer survivors.

Automatic variable selection method and a comparison for quantitative analysis in laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Duan, Fajie; Fu, Xiao; Jiang, Jiajia; Huang, Tingting; Ma, Ling; Zhang, Cong

2018-05-01

In this work, an automatic variable selection method for quantitative analysis of soil samples using laser-induced breakdown spectroscopy (LIBS) is proposed, which is based on full spectrum correction (FSC) and modified iterative predictor weighting-partial least squares (mIPW-PLS). The method features automatic selection without artificial processes. To illustrate the feasibility and effectiveness of the method, a comparison with genetic algorithm (GA) and successive projections algorithm (SPA) for different elements (copper, barium and chromium) detection in soil was implemented. The experimental results showed that all the three methods could accomplish variable selection effectively, among which FSC-mIPW-PLS required significantly shorter computation time (12 s approximately for 40,000 initial variables) than the others. Moreover, improved quantification models were got with variable selection approaches. The root mean square errors of prediction (RMSEP) of models utilizing the new method were 27.47 (copper), 37.15 (barium) and 39.70 (chromium) mg/kg, which showed comparable prediction effect with GA and SPA.

Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

NASA Astrophysics Data System (ADS)

Wentworth, Mami Tonoe

Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification techniques for model calibration. For Bayesian model calibration, we employ adaptive Metropolis algorithms to construct densities for input parameters in the heat model and the HIV model. To quantify the uncertainty in the parameters, we employ two MCMC algorithms: Delayed Rejection Adaptive Metropolis (DRAM) [33] and Differential Evolution Adaptive Metropolis (DREAM) [66, 68]. The densities obtained using these methods are compared to those obtained through the direct numerical evaluation of the Bayes' formula. We also combine uncertainties in input parameters and measurement errors to construct predictive estimates for a model response. A significant emphasis is on the development and illustration of techniques to verify the accuracy of sampling-based Metropolis algorithms. We verify the accuracy of DRAM and DREAM by comparing chains, densities and correlations obtained using DRAM, DREAM and the direct evaluation of Bayes formula. We also perform similar analysis for credible and prediction intervals for responses. Once the parameters are estimated, we employ energy statistics test [63, 64] to compare the densities obtained by different methods for the HIV model. The energy statistics are used to test the equality of distributions. We also consider parameter selection and verification techniques for models having one or more parameters that are noninfluential in the sense that they minimally impact model outputs. We illustrate these techniques for a dynamic HIV model but note that the parameter selection and verification framework is applicable to a wide range of biological and physical models. To accommodate the nonlinear input to output relations, which are typical for such models, we focus on global sensitivity analysis techniques, including those based on partial correlations, Sobol indices based on second-order model representations, and Morris indices, as well as a parameter selection technique based on standard errors. A significant objective is to provide verification strategies to assess the accuracy of those techniques, which we illustrate in the context of the HIV model. Finally, we examine active subspace methods as an alternative to parameter subset selection techniques. The objective of active subspace methods is to determine the subspace of inputs that most strongly affect the model response, and to reduce the dimension of the input space. The major difference between active subspace methods and parameter selection techniques is that parameter selection identifies influential parameters whereas subspace selection identifies a linear combination of parameters that impacts the model responses significantly. We employ active subspace methods discussed in [22] for the HIV model and present a verification that the active subspace successfully reduces the input dimensions.

Uncertainty quantification for PZT bimorph actuators

NASA Astrophysics Data System (ADS)

Bravo, Nikolas; Smith, Ralph C.; Crews, John

2018-03-01

In this paper, we discuss the development of a high fidelity model for a PZT bimorph actuator used for micro-air vehicles, which includes the Robobee. We developed a high-fidelity model for the actuator using the homogenized energy model (HEM) framework, which quantifies the nonlinear, hysteretic, and rate-dependent behavior inherent to PZT in dynamic operating regimes. We then discussed an inverse problem on the model. We included local and global sensitivity analysis of the parameters in the high-fidelity model. Finally, we will discuss the results of Bayesian inference and uncertainty quantification on the HEM.

Quantification of Fluorine Content in AFFF Concentrates

DTIC Science & Technology

2017-09-29

and quantitative integrations, a 100 ppm spectral window (FIDRes 0.215 Hz) was scanned using the following acquisition parameters: acquisition time ...Naval Research Laboratory Washington, DC 20375-5320 NRL/MR/6120--17-9752 Quantification of Fluorine Content in AFFF Concentrates September 29, 2017...collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources

Application of Targeted Mass Spectrometry for the Quantification of Sirtuins in the Central Nervous System

NASA Astrophysics Data System (ADS)

Jayasena, T.; Poljak, A.; Braidy, N.; Zhong, L.; Rowlands, B.; Muenchhoff, J.; Grant, R.; Smythe, G.; Teo, C.; Raftery, M.; Sachdev, P.

2016-10-01

Sirtuin proteins have a variety of intracellular targets, thereby regulating multiple biological pathways including neurodegeneration. However, relatively little is currently known about the role or expression of the 7 mammalian sirtuins in the central nervous system. Western blotting, PCR and ELISA are the main techniques currently used to measure sirtuin levels. To achieve sufficient sensitivity and selectivity in a multiplex-format, a targeted mass spectrometric assay was developed and validated for the quantification of all seven mammalian sirtuins (SIRT1-7). Quantification of all peptides was by multiple reaction monitoring (MRM) using three mass transitions per protein-specific peptide, two specific peptides for each sirtuin and a stable isotope labelled internal standard. The assay was applied to a variety of samples including cultured brain cells, mammalian brain tissue, CSF and plasma. All sirtuin peptides were detected in the human brain, with SIRT2 being the most abundant. Sirtuins were also detected in human CSF and plasma, and guinea pig and mouse tissues. In conclusion, we have successfully applied MRM mass spectrometry for the detection and quantification of sirtuin proteins in the central nervous system, paving the way for more quantitative and functional studies.

A flexible and accurate quantification algorithm for electron probe X-ray microanalysis based on thin-film element yields

NASA Astrophysics Data System (ADS)

Schalm, O.; Janssens, K.

2003-04-01

Quantitative analysis by means of electron probe X-ray microanalysis (EPXMA) of low Z materials such as silicate glasses can be hampered by the fact that ice or other contaminants build up on the Si(Li) detector beryllium window or (in the case of a windowless detector) on the Si(Li) crystal itself. These layers act as an additional absorber in front of the detector crystal, decreasing the detection efficiency at low energies (<5 keV). Since the layer thickness gradually changes with time, also the detector efficiency in the low energy region is not constant. Using the normal ZAF approach to quantification of EPXMA data is cumbersome in these conditions, because spectra from reference materials and from unknown samples must be acquired within a fairly short period of time in order to avoid the effect of the change in efficiency. To avoid this problem, an alternative approach to quantification of EPXMA data is proposed, following a philosophy often employed in quantitative analysis of X-ray fluorescence (XRF) and proton-induced X-ray emission (PIXE) data. This approach is based on the (experimental) determination of thin-film element yields, rather than starting from infinitely thick and single element calibration standards. These thin-film sensitivity coefficients can also be interpolated to allow quantification of elements for which no suitable standards are available. The change in detector efficiency can be monitored by collecting an X-ray spectrum of one multi-element glass standard. This information is used to adapt the previously determined thin-film sensitivity coefficients to the actual detector efficiency conditions valid on the day that the experiments were carried out. The main advantage of this method is that spectra collected from the standards and from the unknown samples should not be acquired within a short period of time. This new approach is evaluated for glass and metal matrices and is compared with a standard ZAF method.