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We present the ability to conduct single micrometer-sized uranium particle age-dating measurements on particles that are younger, smaller, and less enriched in 235 U content than previously reported. Specifically, we use large geometry secondary ion mass spectrometry (LG-SIMS) to precisely measure the 230 Th/ 234 U radiochronometer, combined with a systematic treatment of relevant parameters such as particle size, enrichment, and age, to achieve this development. We describe the necessary requirements for instrument background, interference rejection, abundance sensitivity, and other instrumental conditions that allow for this advance in single-particle uranium age-dating. We introduce the use of statistics developed by Feldman and Cousins to generate 95% confidence intervals in particle age, even when 230 Th daughter ions are not detected. For particles where counts are limited and are of identical isotopic signatures, we provide an option for aggregating individual measurements of single particles to reduce measurement uncertainty, as if the measurement had been performed on one larger particle. The methodology is validated on a range of certified reference materials and 'real-world' samples, ranging in age from 15 to 60 years, and on individual particles ranging in equivalent size from 0.6 to 6.8 micrometers. Additionally, we provide model age calculations for particles ranging in size from 1.0 to 3.0 micrometers across enrichments ranging from natural uranium to highly-enriched uranium and on ages ranging from 0 to 60 years. Experimental results compare well with the predicted model ages, providing realistic guidance for expectations of single micrometer-sized uranium particle age-dating measurements. The age-dating capabilities described herein are directly relevant to the International Atomic Energy Agency (IAEA) and its mission to safeguard nuclear materials and monitor member state nuclear programs.

We present Direct Assessment, a method for manually assessing the quality of automatically-generated captions for video. Evaluating the accuracy of video captions is particularly difficult because for any given video clip there is no definitive ground truth or correct answer against which to measure. Metrics for comparing automatic video captions against a manual caption such as BLEU and METEOR, drawn from techniques used in evaluating machine translation, were used in the TRECVid video captioning task in 2016 but these are shown to have weaknesses. The work presented here brings human assessment into the evaluation by crowd sourcing how well a caption describes a video. We automatically degrade the quality of some sample captions which are assessed manually and from this we are able to rate the quality of the human assessors, a factor we take into account in the evaluation. Using data from the TRECVid video-to-text task in 2016, we show how our direct assessment method is replicable and robust and scales to where there are many caption-generation techniques to be evaluated including the TRECVid video-to-text task in 2017., Competing Interests: The authors have declared that no competing interests exist and Dakota Consulting, Inc does not alter our adherence to PLOS ONE policies on sharing data and materials.

Light field cameras are an emerging imaging device for acquiring 3-D information of a scene by capturing a field of light rays traveling in space. As light field cameras become portable, hand-held, and affordable, their potential as a 3-D measurement instrument is growing in many applications, including 3-D evidence imaging in crime scene investigations. We evaluated the lateral resolution of commercially available light field cameras, which is one of the fundamental specifications of imaging instruments. For the evaluation of the camera's lateral resolution, we imaged Siemens stars under various imaging configurations and experimental conditions, including changes in distance between the camera and the resolution target plate, illumination, zoom level, location of the Siemens star in the camera's field-of-view, and cameras of the same model. The analysis results from a full factorial experiment showed that (i) when a lower zoom level of the camera was used, the lateral resolution tended not to be affected by distance; however, when a higher zoom level was used, it tended to decrease significantly with respect to the distance, (ii) the center region of the camera's field-of-view provided a better lateral resolution than the peripheral regions, (iii) a higher zoom level yielded a higher lateral resolution, (iv) the two cameras of the same model used in the study did not show a significant difference in the lateral resolution, and (v) changes in illumination did not affect the lateral resolution of the cameras.

Background: Cell-scaffold contact measurements are derived from pairs of co-registered volumetric fluorescent confocal laser scanning microscopy (CLSM) images (z-stacks) of stained cells and three types of scaffolds (i.e., spun coat, large microfiber, and medium microfiber). Our analysis of the acquired terabyte-sized collection is motivated by the need to understand the nature of the shape dimensionality (1D vs 2D vs 3D) of cell-scaffold interactions relevant to tissue engineers that grow cells on biomaterial scaffolds., Results: We designed five statistical and three geometrical contact models, and then down-selected them to one from each category using a validation approach based on physically orthogonal measurements to CLSM. The two selected models were applied to 414 z-stacks with three scaffold types and all contact results were visually verified. A planar geometrical model for the spun coat scaffold type was validated from atomic force microscopy images by computing surface roughness of 52.35 nm ±31.76 nm which was 2 to 8 times smaller than the CLSM resolution. A cylindrical model for fiber scaffolds was validated from multi-view 2D scanning electron microscopy (SEM) images. The fiber scaffold segmentation error was assessed by comparing fiber diameters from SEM and CLSM to be between 0.46% to 3.8% of the SEM reference values. For contact verification, we constructed a web-based visual verification system with 414 pairs of images with cells and their segmentation results, and with 4968 movies with animated cell, scaffold, and contact overlays. Based on visual verification by three experts, we report the accuracy of cell segmentation to be 96.4% with 94.3% precision, and the accuracy of cell-scaffold contact for a statistical model to be 62.6% with 76.7% precision and for a geometrical model to be 93.5% with 87.6% precision., Conclusions: The novelty of our approach lies in (1) representing cell-scaffold contact sites with statistical intensity and geometrical shape models, (2) designing a methodology for validating 3D geometrical contact models and (3) devising a mechanism for visual verification of hundreds of 3D measurements. The raw and processed data are publicly available from https://isg.nist.gov/deepzoomweb/data/ together with the web -based verification system.