Isolating Individual Signals from a Composite Data Set

ABSTRACT Background Retinol isotope dilution (RID) and model-based compartmental analysis are recognized techniques for assessing vitamin A (VA) status. Recent studies have shown that RID predictions of VA total body stores (TBS) can be improved by using modeling and that VA kinetics and TBS in children can be effectively studied by applying population modeling (“super-child” approach) to a composite data set. Objectives The objectives were to model whole-body retinol kinetics and predict VA TBS in a group of Mexican preschoolers using the super-child approach and to use model predictions of RID coefficients to estimate TBS by RID in individuals. Methods Twenty-four healthy Mexican children (aged 3–6 y) received an oral dose (2.96 μmol) of [13C10]retinyl acetate in corn oil. Blood samples were collected from 8 h to 21 d after dosing, with each child sampled at 4 d and at 1 other time. Composite data for plasma labeled retinol compared with time were analyzed using a 6-component model to obtain group retinol kinetic parameters and pool sizes. Model-predicted TBS was compared with mean RID predictions at 4 d; RID estimates at 4 d were compared with those calculated at 7–21 d. Results Model-predicted TBS was 1097 μmol, equivalent to ∼2.4 y-worth of VA; using model-derived coefficients, group mean RID-predicted TBS was 1096 μmol (IQR: 836–1492 μmol). TBS at 4 d compared with a later time was similar (P = 0.33). The model predicted that retinol spent 1.5 h in plasma during each transit and recycled to plasma 13 times before utilization. Conclusions The super-child modeling approach provides information on whole-body VA kinetics and can be used with RID to estimate TBS at any time between 4 and 21 d postdose. The high TBS predicted for these children suggests positive VA balance, likely due to large-dose VA supplements, and warrants further investigation.

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MW-ACGAN: Generating Multiscale High-Resolution SAR Images for Ship Detection

Sensors ◽

10.3390/s20226673 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6673

Author(s):

Lichuan Zou ◽

Hong Zhang ◽

Chao Wang ◽

Fan Wu ◽

Feng Gu

Keyword(s):

High Resolution ◽

Small Sample ◽

Generative Adversarial Networks ◽

Detection Accuracy ◽

Data Set ◽

Ship Detection ◽

Multi Scale ◽

Adversarial Networks ◽

Sar Data ◽

Composite Data

In high-resolution Synthetic Aperture Radar (SAR) ship detection, the number of SAR samples seriously affects the performance of the algorithms based on deep learning. In this paper, aiming at the application requirements of high-resolution ship detection in small samples, a high-resolution SAR ship detection method combining an improved sample generation network, Multiscale Wasserstein Auxiliary Classifier Generative Adversarial Networks (MW-ACGAN) and the Yolo v3 network is proposed. Firstly, the multi-scale Wasserstein distance and gradient penalty loss are used to improve the original Auxiliary Classifier Generative Adversarial Networks (ACGAN), so that the improved network can stably generate high-resolution SAR ship images. Secondly, the multi-scale loss term is added to the network, so the multi-scale image output layers are added, and multi-scale SAR ship images can be generated. Then, the original ship data set and the generated data are combined into a composite data set to train the Yolo v3 target detection network, so as to solve the problem of low detection accuracy under small sample data set. The experimental results of Gaofen-3 (GF-3) 3 m SAR data show that the MW-ACGAN network can generate multi-scale and multi-class ship slices, and the confidence level of ResNet18 is higher than that of ACGAN network, with an average score of 0.91. The detection results of Yolo v3 network model show that the detection accuracy trained by the composite data set is as high as 94%, which is far better than that trained only by the original SAR data set. These results show that our method can make the best use of the original data set, improve the accuracy of ship detection.

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Dissecting random and systematic differences between noisy composite data sets

Acta Crystallographica Section D Structural Biology ◽

10.1107/s2059798317000699 ◽

2017 ◽

Vol 73 (4) ◽

pp. 286-293 ◽

Cited By ~ 23

Author(s):

Kay Diederichs

Keyword(s):

Correlation Coefficient ◽

Unit Sphere ◽

Dimensional Space ◽

Scaling Analysis ◽

Data Sets ◽

Random Errors ◽

Experimental Conditions ◽

Data Set ◽

Pairwise Correlation ◽

Composite Data

Composite data sets measured on different objects are usually affected by random errors, but may also be influenced by systematic (genuine) differences in the objects themselves, or the experimental conditions. If the individual measurements forming each data set are quantitative and approximately normally distributed, a correlation coefficient is often used to compare data sets. However, the relations between data sets are not obvious from the matrix of pairwise correlations since the numerical value of the correlation coefficient is lowered by both random and systematic differences between the data sets. This work presents a multidimensional scaling analysis of the pairwise correlation coefficients which places data sets into a unit sphere within low-dimensional space, at a position given by their CC* values [as defined by Karplus & Diederichs (2012),Science,336, 1030–1033] in the radial direction and by their systematic differences in one or more angular directions. This dimensionality reduction can not only be used for classification purposes, but also to derive data-set relations on a continuous scale. Projecting the arrangement of data sets onto the subspace spanned by systematic differences (the surface of a unit sphere) allows, irrespective of the random-error levels, the identification of clusters of closely related data sets. The method gains power with increasing numbers of data sets. It is illustrated with an example from low signal-to-noise ratio image processing, and an application in macromolecular crystallography is shown, but the approach is completely general and thus should be widely applicable.

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Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC)

Journal of Official Statistics ◽

10.1515/jos-2017-0044 ◽

2017 ◽

Vol 33 (4) ◽

pp. 921-962 ◽

Cited By ~ 8

Author(s):

Laura Boeschoten ◽

Daniel Oberski ◽

Ton de Waal

Keyword(s):

Multiple Imputation ◽

Latent Class ◽

Latent Class Model ◽

Class Model ◽

Data Set ◽

Public Data ◽

Classification Errors ◽

Composite Data ◽

Latent Class Modelling

Abstract Both registers and surveys can contain classification errors. These errors can be estimated by making use of a composite data set. We propose a new method based on latent class modelling to estimate the number of classification errors across several sources while taking into account impossible combinations with scores on other variables. Furthermore, the latent class model, by multiply imputing a new variable, enhances the quality of statistics based on the composite data set. The performance of this method is investigated by a simulation study, which shows that whether or not the method can be applied depends on the entropy R2 of the latent class model and the type of analysis a researcher is planning to do. Finally, the method is applied to public data from Statistics Netherlands.

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Genetic diversity among Curtobacterium flaccumfaciens pv. flaccumfaciens populations in the American High Plains

Canadian Journal of Microbiology ◽

10.1139/w2012-052 ◽

2012 ◽

Vol 58 (6) ◽

pp. 788-801 ◽

Cited By ~ 18

Author(s):

I.V. Agarkova ◽

P.A. Lambrecht ◽

A.K. Vidaver ◽

R.M. Harveson

Keyword(s):

Cluster Analysis ◽

Population Heterogeneity ◽

High Plains ◽

Positive Bacterium ◽

Data Set ◽

Yellow Orange ◽

Polymerase Chain ◽

Extracellular Pigment ◽

Composite Data ◽

Color Variants

Curtobacterium flaccumfaciens pv. flaccumfaciens is a Gram-positive bacterium and has reemerged as an incitant of bacterial wilt in common (dry, edible) beans in western Nebraska, eastern Colorado, and southeastern Wyoming. Curtobacterium flaccumfaciens pv. flaccumfaciens is diverse phenotypically and genotypically and is represented by several different pathogen color variants. The population structure of 67 strains collected between 1957 and 2009, including some isolated from alternate hosts, was determined with 3 molecular typing techniques: amplified fragment length polymorphism (AFLP), repetitive extragenic palindromic polymerase chain reaction (rep-PCR), and pulsed-field gel electrophoresis (PFGE). All 3 typing techniques showed a great degree of population heterogeneity, but they were not congruent in cluster analysis of the C. flaccumfaciens pv. flaccumfaciens populations. Cluster analysis of a composite data set (AFLP, PFGE, and rep-PCR) using averages from all experiments yielded 2 distinct groups: cluster A included strains with colonies of yellow, orange, and pink pigments, and cluster B had strains of only yellow pigment. Strains producing purple extracellular pigment were assigned to both clusters. Thus, C. flaccumfaciens pv. flaccumfaciens is diverse phenotypically and genotypically.

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Rotational Characteristics of the Green Solar Corona: 1947-1991

International Astronomical Union Colloquium ◽

10.1017/s0252921100025197 ◽

1994 ◽

Vol 144 ◽

pp. 139-141 ◽

Cited By ~ 2

Author(s):

J. Rybák ◽

V. Rušin ◽

M. Rybanský

Keyword(s):

Solar Corona ◽

World Wide ◽

Rotation Period ◽

Original Data ◽

Coronal Emission ◽

Time Intervals ◽

Data Set ◽

The World ◽

Coronal Emission Line ◽

Homogeneous Data

AbstractFe XIV 530.3 nm coronal emission line observations have been used for the estimation of the green solar corona rotation. A homogeneous data set, created from measurements of the world-wide coronagraphic network, has been examined with a help of correlation analysis to reveal the averaged synodic rotation period as a function of latitude and time over the epoch from 1947 to 1991.The values of the synodic rotation period obtained for this epoch for the whole range of latitudes and a latitude band ±30° are 27.52±0.12 days and 26.95±0.21 days, resp. A differential rotation of green solar corona, with local period maxima around ±60° and minimum of the rotation period at the equator, was confirmed. No clear cyclic variation of the rotation has been found for examinated epoch but some monotonic trends for some time intervals are presented.A detailed investigation of the original data and their correlation functions has shown that an existence of sufficiently reliable tracers is not evident for the whole set of examinated data. This should be taken into account in future more precise estimations of the green corona rotation period.

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Difference Fourier Analysis of Glucose Embedded and Frozen Hydrated Purple Membrane

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100053164 ◽

1982 ◽

Vol 40 ◽

pp. 74-75

Author(s):

Jules S. Jaffe ◽

Robert M. Glaeser

Keyword(s):

Purple Membrane ◽

Data Set ◽

High Resolution Data ◽

X Ray ◽

X Ray Crystallography ◽

Fourier Techniques ◽

Versus Protein ◽

The Difference ◽

Difference Fourier ◽

Ideal Method

Although difference Fourier techniques are standard in X-ray crystallography it has only been very recently that electron crystallographers have been able to take advantage of this method. We have combined a high resolution data set for frozen glucose embedded Purple Membrane (PM) with a data set collected from PM prepared in the frozen hydrated state in order to visualize any differences in structure due to the different methods of preparation. The increased contrast between protein-ice versus protein-glucose may prove to be an advantage of the frozen hydrated technique for visualizing those parts of bacteriorhodopsin that are embedded in glucose. In addition, surface groups of the protein may be disordered in glucose and ordered in the frozen state. The sensitivity of the difference Fourier technique to small changes in structure provides an ideal method for testing this hypothesis.

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Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100139627 ◽

1995 ◽

Vol 53 ◽

pp. 650-651

Author(s):

D. E. Becker

Keyword(s):

Image Analysis ◽

High Resolution ◽

Laser Scanning ◽

Cell Counting ◽

Wide Area ◽

Data Set ◽

Computational Synthesis ◽

Automated Cell Counting ◽

Partial View ◽

The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

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Merging of electron-diffraction intensities acquired with a slow-scan CCD camera of crotoxin complex crystals to 3.5Å resolution

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100168256 ◽

1994 ◽

Vol 52 ◽

pp. 104-105

Author(s):

Jaap Brink ◽

Wah Chiu

Keyword(s):

Electron Diffraction ◽

Ccd Camera ◽

Crystal Thickness ◽

Local Contrast ◽

Camera Model ◽

Data Set ◽

3 Dimensional ◽

Diffraction Mode ◽

Diffraction Patterns ◽

Complex Crystals

Crotoxin complex is the principal neurotoxin of the South American rattlesnake, Crotalus durissus terrificus and has a molecular weight of 24 kDa. The protein is a heterodimer with subunit A assigneda chaperone function. Subunit B carries the lethal activity, which is exerted on both sides ofthe neuro-muscular junction, and which is thought to involve binding to the acetylcholine receptor. Insight in crotoxin complex’ mode of action can be gained from a 3 Å resolution structure obtained by electron crystallography. This abstract communicates our progress in merging the electron diffraction amplitudes into a 3-dimensional (3D) intensity data set close to completion. Since the thickness of crotoxin complex crystals varies from one crystal to the other, we chose to collect tilt series of electron diffraction patterns after determining their thickness. Furthermore, by making use of the symmetry present in these tilt data, intensities collected only from similar crystals will be merged.Suitable crystals of glucose-embedded crotoxin complex were searched for in the defocussed diffraction mode with the goniometer tilted to 55° of higher in a JEOL4000 electron cryo-microscopc operated at 400 kV with the crystals kept at -120°C in a Gatan 626 cryo-holder. The crystal thickness was measured using the local contrast of the crystal relative to the supporting film from search-mode images acquired using a 1024 x 1024 slow-scan CCD camera (model 679, Gatan Inc.).

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Three-dimensional image analysis and visualization in confocal light microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100146631 ◽

1993 ◽

Vol 51 ◽

pp. 158-159

Author(s):

J. K. Samarabandu ◽

R. Acharya ◽

D. R. Pareddy ◽

P. C. Cheng

Keyword(s):

Depth Perception ◽

Computer Graphic ◽

Three Dimensional ◽

Cellular Organization ◽

Data Set ◽

Computational Burden ◽

Interactive Operation ◽

Cell Organization ◽

Confocal Images ◽

3D Digitization

In the study of cell organization in a maize meristem, direct viewing of confocal optical sections in 3D (by means of 3D projection of the volumetric data set, Figure 1) becomes very difficult and confusing because of the large number of nucleus involved. Numerical description of the cellular organization (e.g. position, size and orientation of each structure) and computer graphic presentation are some of the solutions to effectively study the structure of such a complex system. An attempt at data-reduction by means of manually contouring cell nucleus in 3D was reported (Summers et al., 1990). Apart from being labour intensive, this 3D digitization technique suffers from the inaccuracies of manual 3D tracing related to the depth perception of the operator. However, it does demonstrate that reducing stack of confocal images to a 3D graphic representation helps to visualize and analyze complex tissues (Figure 2). This procedure also significantly reduce computational burden in an interactive operation.

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