Search

To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis detection from phase contrast images of individual cells within iPSC colonies. The analysis uses a 2D convolutional neural network (U-Net) plus a 3D U-Net applied on time lapse images to detect and segment nuclei, mitotic events, and daughter nuclei to enable tracking of large numbers of individual cells over long times in culture. The analysis uses fluorescence data to train models for segmenting nuclei in phase contrast images. The use of classical image processing routines to segment fluorescent nuclei precludes the need for manual annotation. We optimize and evaluate the accuracy of automated annotation to assure the reliability of the training. The model is generalizable in that it performs well on different datasets with an average F1 score of 0.94, on cells at different densities, and on cells from different pluripotent cell lines. The method allows us to assess, in a non-invasive manner, rates of mitosis and cell division which serve as indicators of cell state and cell health. We assess these parameters in up to hundreds of thousands of cells in culture for more than 36 hours, at different locations in the colonies, and as a function of excitation light exposure., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

A reconstruction algorithm for partially coherent x-ray computed tomography (XCT) including Fresnel diffraction is developed and applied to an optical fiber. The algorithm is applicable to a high-resolution tube-based laboratory-scale x-ray tomography instrument. The computing time is only a few times longer than the projective counterpart. The algorithm is used to reconstruct, with projections and diffraction, a tilt series acquired at the micrometer scale of a graded-index optical fiber using maximum likelihood and a Bayesian method based on the work of Bouman and Sauer. The inclusion of Fresnel diffraction removes some reconstruction artifacts and use of a Bayesian prior probability distribution removes others, resulting in a substantially more accurate reconstruction.

Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019., (© 2019 International Society for Magnetic Resonance in Medicine.)

Fundamental limits for the calculation of scattering corrections within X-ray computed tomography (CT) are found within the independent atom approximation from an analysis of the cross sections, CT geometry, and the Nyquist sampling theorem, suggesting large reductions in computational time compared to existing methods. By modifying the scatter by less than 1 %, it is possible to treat some of the elastic scattering in the forward direction as inelastic to achieve a smoother elastic scattering distribution. We present an analysis showing that the number of samples required for the smoother distribution can be greatly reduced. We show that fixed forced detection can be used with many fewer points for inelastic scattering, but that for pure elastic scattering, a standard Monte Carlo calculation is preferred. We use smoothing for both elastic and inelastic scattering because the intrinsic angular resolution is much poorer than can be achieved for projective tomography. Representative numerical examples are given.

Using a commercial X-ray tomography instrument, we have obtained reconstructions of a graded-index optical fiber with voxels of edge length 1.05 µm at 12 tube voltages. The fiber manufacturer created a graded index in the central region by varying the germanium concentration from a peak value in the center of the core to a very small value at the core-cladding boundary. Operating on 12 tube voltages, we show by a singular value decomposition that there are only two singular vectors with significant weight. Physically, this means scans beyond two tube voltages contain largely redundant information. We concentrate on an analysis of the images associated with these two singular vectors. The first singular vector is dominant and images of the coefficients of the first singular vector at each voxel look are similar to any of the single-energy reconstructions. Images of the coefficients of the second singular vector by itself appear to be noise. However, by averaging the reconstructed voxels in each of several narrow bands of radii, we can obtain values of the second singular vector at each radius. In the core region, where we expect the germanium doping to go from a peak value at the fiber center to zero at the core-cladding boundary, we find that a plot of the two coefficients of the singular vectors forms a line in the two-dimensional space consistent with the dopant decreasing linearly with radial distance from the core center. The coating, made of a polymer rather than silica, is not on this line indicating that the two-dimensional results are sensitive not only to the density but also to the elemental composition.

Purpose: This paper lays the groundwork for linking Hounsfield unit measurements to the International System of Units (SI), ultimately enabling traceable measurements across X-ray CT (XCT) machines. We do this by characterizing a material basis that may be used in XCT reconstruction giving linear combinations of concentrations of chemical elements (in the SI units of mol/m3) which may be observed at each voxel. By implication, linear combinations not in the set are not observable., Methods and Materials: We formulated a model for our material basis with a set of measurements of elemental powders at four tube voltages, 80 kV, 100 kV, 120 kV, and 140 kV, on a medical XCT. The samples included 30 small plastic bottles of powders containing various compounds spanning the atomic numbers up to 20, and a bottle of water and one of air. Using the chemical formulas and measured masses, we formed a matrix giving the number of Hounsfield units per (mole per cubic meter) at each tube voltage for each of 13 chemical elements. We defined a corresponding matrix in units we call molar Hounsfield unit (HU) potency, the difference in HU values that an added mole per cubic meter in a given voxel would add to the measured HU value. We built a matrix of molar potencies for each chemical element and tube voltage and performed a singular value decomposition (SVD) on these to formulate our material basis. We determined that the dimension of this basis is two. We then compared measurements in this material space with theoretical measurements, combining XCOM cross section data with the tungsten anode spectral model using interpolating cubic splines (TASMICS), a one-parameter filter, and a simple detector model, creating a matrix similar to our experimental matrix for the first 20 chemical elements. Finally, we compared the model predictions to Hounsfield unit measurements on three XCT calibration phantoms taken from the literature., Results: We predict the experimental HU potency values derived from our scans of chemical elements with our theoretical model built from XCOM data. The singular values and singular vectors of the model and powder measurements are in substantial agreement. Application of the Bayesian Information Criterion (BIC) shows that exactly two singular values and singular vectors describe the results over four tube voltages. We give a good account of the HU values from the literature, measured for the calibration phantoms at several tube voltages for several commercial instruments, compared with our theoretical model without introducing additional parameters., Conclusions: We have developed a two-dimensional material basis that specifies the degree to which individual elements in compounds effect the HU values in XCT images of samples with elements up to atomic number Z = 20. We show that two dimensions is sufficient given the contrast and noise in our experiment. The linear combination of concentrations of elements that can be observed using a medical XCT have been characterized, providing a material basis for use in dual-energy reconstruction. This approach provides groundwork for improved reconstruction and for the link of Hounsfield units to the SI., Competing Interests: The Corresponding Author, ZHL, discloses the following patents on which they are a co-inventor: US Patent (10,073,026) "Optical Particle Sorter," US Patent 7,967,507 "A Dimensional Reference for Tomography," US Patent 7,163,649 "Minimizing Spatial-Dispersion-Induced Birefringence" and US Patent 6,389,101 "Parallel X-ray Nanotomography." The rights to these patents have been assigned to the United States Government. The other authors report that they have read the PLOS ONE financial disclosure rules and have nothing to disclose with the following exception: Author ADH took a job with Ball Aerospace after the conclusion of this project. Author ADH had been a postdoctoral fellow during his association with us. The other authors have declared that no competing interests exist. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.

The goal of this study was to compare volumetric analysis in computed tomography (CT) with the length measurement prescribed by the Response Evaluation Criteria in Solid Tumors (RECIST) for a system with known mass and unknown shape. We injected 2 mL to 4 mL of water into vials of sodium polyacrylate and into disposable diapers. Volume measurements of the sodium polyacrylate powder were able to predict both mass and proportional changes in mass within a 95 % prediction interval of width 12 % and 16 %, respectively. The corresponding figures for RECIST were 102 % and 82 %.

We present a case study in which we use natural language processing and machine learning techniques to automatically select candidate scientific articles that may contain new experimental thermophysical property data from thousands of articles available in five different relevant journals. The National Institute of Standards and Technology (NIST) Thermodynamic Research Center (TRC) maintains a large database of available thermophysical property data extracted from articles that are manually selected for content. Over time the number of articles requiring manual inspection has grown and assistance from machine-based methods is needed. Previous work used topic modeling along with classification techniques to classify these journal articles into those with data for the TRC database and those without. These techniques have produced classifications with accuracy between 85 % and 90 %. However, the TRC does not want to lose data from the misclassified articles that contain relevant information. In this study, we start with these topic modeling and classification techniques, and then enhance the model using information relevant to the TRC's selection process. Our goal is to minimize the number of articles that require manual selection without missing articles of importance. Through a series of selection methods, we eliminate those articles for which we can determine a rejection criterion. We can reduce the number of articles that are not of interest by 70.8 % while retaining 98.7 % of the articles of interest. We have also found that topic model classification improves when the corpus of words is derived from specific sections of the articles rather than the entire articles, and we improve on our classification by using a combination of topic models from different sections of the article. Our best classification used only the Experimental and Literature Cited sections.

Purpose: To assess the ability of a recent, anatomically designed breast phantom incorporating T1 and diffusion elements to serve as a quality control device for quantitative comparison of apparent diffusion coefficient (ADC) measurements calculated from diffusion-weighted MRI (DWI) within and across MRI systems., Materials and Methods: A bilateral breast phantom incorporating multiple T1 and diffusion tissue mimics and a geometric distortion array was imaged with DWI on 1.5 Tesla (T) and 3.0T scanners from two different manufacturers, using three different breast coils (three configurations total). Multiple measurements were acquired to assess the bias and variability of different diffusion weighted single-shot echo-planar imaging sequences on the scanner-coil systems., Results: The repeatability of ADC measurements was mixed: the standard deviation relative to baseline across scanner-coil-sequences ranged from low variability (0.47, 95% confidence interval [CI]: 0.22-1.00) to high variability (1.69, 95% CI: 0.17-17.26), depending on material, with the lowest and highest variability from the same scanner-coil-sequence. Assessment of image distortion showed that right/left measurements of the geometric distortion array were 1 to 16% larger on the left coil side compared with the right coil side independent of scanner-coil systems, diffusion weighting, and phase-encoding direction., Conclusion: This breast phantom can be used to measure scanner-coil-sequence bias and variability for DWI. When establishing a multisystem study, this breast phantom may be used to minimize protocol differences (e.g., due to available sequences or shimming technique), to correct for bias that cannot be minimized, and to weigh results from each system depending on respective variability. J. Magn. Reson. Imaging 2016. J. MAGN. RESON. IMAGING 2016;44:846-855., (© 2016 International Society for Magnetic Resonance in Medicine.)

RECIST (Response Evaluation Criteria in Solid Tumors) is a linear measure intended to predict tumor size in medical computed tomography (CT). In this work, using purely geometrical considerations, we estimate how well RECIST can predict the volume of randomly-oriented tumor models, each composed of the union of ellipsoids. The principal conclusion is that RECIST is likely to work less well for realistic tumors than for ellipsoids.

Purpose: The authors investigate the extent to which Response Evaluation Criteria in Solid Tumors (RECIST) can predict tumor volumes in ideal geometric settings and using clinical data., Methods: The authors consider a hierarchy of models including uniaxial ellipsoids, general ellipsoids, and composites of ellipsoids, using both analytical and numerical techniques to show how well RECIST can predict tumor volumes in each case. The models have certain features that are compared to clinical data., Results: The principal conclusion is that a change in the reported RECIST value needs to be a factor of at least 1.2 to achieve a 95% confidence that one ellipsoid is larger than another assuming the ratio of maximum to minimum diameters is no more than 2, an assumption that is reasonable for some classes of tumors. There is a significant probability that RECIST will select a tumor other than the largest due to orientation effects of nonspherical tumors: in previously reported malignoma data, RECIST would have selected a tumor other than the largest in 9% of the cases. Also, the widely used spherical model connecting RECIST values for a single tumor to volumes overestimates these volumes., Conclusions: RECIST imposes a limit on the ability to determine tumor volumes, which is greater than the limit imposed by modem medical computed tomography machines. It is also likely the RECIST limit is above natural biological variability of stable lesions. The authors recommend the study of such natural variability as a fruitful avenue for further study.

We present methods for measuring errors in the rendering of three-dimensional points, line segments, and polygons in pixel-based computer graphics systems. We present error metrics for each of these three cases. These methods are applied to rendering with OpenGL on two common hardware platforms under several rendering conditions. Results are presented and differences in measured errors are analyzed and characterized. We discuss possible extensions of this error analysis approach to other aspects of the process of generating visual representations of synthetic scenes.

This is the third in a series of articles that describe, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing, visualization, and machine learning to accelerate scientific discovery. In this article we focus on the use of high performance computing and visualization for simulations of nanotechnology.

This paper describes a set of tools for performing measurements of objects in a virtual reality based immersive visualization environment. These tools enable the use of the immersive environment as an instrument for extracting quantitative information from data representations that hitherto had be used solely for qualitative examination. We provide, within the virtual environment, ways for the user to analyze and interact with the quantitative data generated. We describe results generated by these methods to obtain dimensional descriptors of tissue engineered medical products. We regard this toolbox as our first step in the implementation of a virtual measurement laboratory within an immersive visualization environment.

This is the second in a series of articles describing a wide variety of projects at NIST that synergistically combine physical science and information science. It describes, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing, visualization, and machine learning to accelerate research. The examples include scientific collaborations in the following areas: (1) High Precision Energies for few electron atomic systems, (2) Flows of suspensions, (3) X-ray absorption, (4) Molecular dynamics of fluids, (5) Nanostructures, (6) Dendritic growth in alloys, (7) Screen saver science, (8) genetic programming.

An adaptive simulated annealing optimization algorithm is used to derive laser rate equation and waveguiding models with which the best design for a diode-pumped fiber-coupled, Yb:Er glass waveguide laser can be determined. Material parameters that correspond to commercially available laser-glass and diode-pump sources are used in this study. Given a continuous-wave 300-mW pump at 977 nm, approximately 48 mW of power at 1540 nm can be coupled into the LP(01) mode of an optical fiber. Fabrication and alignment tolerance analyses are presented.

We investigated an Er(3+)/Yb(3+) codoped silicate glass as a host material for waveguide lasers operating near 1.5 microm. Spectroscopic properties of the glass are reported. Waveguide lasers were fabricated by K(+)-ion exchange from a nitrate melt. The waveguides support a single transverse mode at 1.5 microm. An investigation of the laser performance as a function of the Yb:Er ratio was performed, indicating an optimal ratio of approximately 5:1. Slope efficiencies of as great as 6.5% and output powers as high as 19.6 mW at 1.54 microm were realized. The experimental results are compared with a waveguide laser model that is used to extract the Er(3+) upconversion coefficients and the Yb(3+)-Er(3+) cross-relaxation coefficients. The results indicate the possibility of obtaining high-performance waveguide lasers from a durable silicate host glass.

We present a new numerical technique for modeling the flow around multiple objects moving in a fluid. The method tracks the dynamic interaction between each particle and the fluid. The movements of the fluid and the object are directly coupled. A background mesh is designed to fit the geometry of the overall domain. The mesh is designed independently of the presence of the particles except in terms of how fine it must be to track particles of a given size. Each particle is represented by a geometric figure that describes its boundary. This figure overlies the mesh. Nodes are added to the mesh where the particle boundaries intersect the background mesh, increasing the number of nodes contained in each element whose boundary is intersected. These additional nodes are then used to describe and track the particle in the numerical scheme. Appropriate element shape functions are defined to approximate the solution on the elements with extra nodes. The particles are moved through the mesh by moving only the overlying nodes defining the particles. The regular finite element grid remains unchanged. In this method, the mesh does not distort as the particles move. Instead, only the placement of particle-defining nodes changes as the particles move. Element shape functions are updated as the nodes move through the elements. This method is especially suited for models of moderate numbers of moderate-size particles, where the details of the fluid-particle coupling are important. Both the complications of creating finite element meshes around appreciable numbers of particles, and extensive remeshing upon movement of the particles are simplified in this method.

Previous studies have implicated glutamine synthetase (L-glutamate:ammonia ligase [adenosine diphosphate for-ing], EC 6.6.1.2) as a major controlling element of the nitrogen fixation (nif) genes in Klebsiella pneumoniae. We report here the isolation of a new class of K. pneumoniae mutants which exhibit altered patterns of nif and hut (histidine utlization) regulation. The expression of nif in these mutants, which were isolated as Gln+ (glutamine nonrequiring) revertants of a particular glnA mutation, is extremely sensitive to ammonia repression. These mutants have a Nif- Hut- phenotype at external ammonia concentrations at which wild-type strains are Nif+ Hut+. On the other hand, these mutants can be fully derepressed for nif at very low ammonia concentrations. We adopted the nomenclature "GlnR- (Nif- Hut-)" to facilitate discussion of the phenotype of these mutant strains. The mutations in these strains which confer the GlnR- phenotype map at or near glnA, the structural gene for glutamine synthetase.