Multidimensional en-face OCT imaging of the retina.

Fast T-scanning (transverse scanning, en-face) was used to build B-scan or C-scan optical coherence tomography (OCT) images of the retina. Several unique signature patterns of en-face (coronal) are reviewed in conjunction with associated confocal images of the fundus and B-scan OCT images. Benefits in combining T-scan OCT with confocal imaging to generate pairs of OCT and confocal images similar to those generated by scanning laser ophthalmoscopy (SLO) are discussed in comparison with the spectral OCT systems. The multichannel potential of the OCT/SLO system is demonstrated with the addition of a third hardware channel which acquires and generates indocyanine green (ICG) fluorescence images. The OCT, confocal SLO and ICG fluorescence images are simultaneously presented in a two or a three screen format. A fourth channel which displays a live mix of frames of the ICG sequence superimposed on the corresponding coronal OCT slices for immediate multidimensional comparison, is also included. OSA ISP software is employed to illustrate the synergy between the simultaneously provided perspectives. This synergy promotes interpretation of information by enhancing diagnostic comparisons and facilitates internal correction of movement artifacts within C-scan and B-scan OCT images using information provided by the SLO channel

Assessment of Waste Treatment Plant Lab C3V (LB-S1) Stack Sampling Probe Location for Compliance with ANSI/HPS N13.1-1999

This report documents a series of tests used to assess the proposed air sampling location in the Hanford Tank Waste Treatment and Immobilization Plant (WTP) Lab C3V (LB-S1) exhaust stack with respect to the applicable criteria regarding the placement of an air sampling probe. Federal regulations require that an air sampling probe be located in the exhaust stack in accordance with the criteria of American National Standards Institute/Health Physics Society (ANSI/HPS) N13.1-1999, Sampling and Monitoring Releases of Airborne Radioactive Substances from the Stack and Ducts of Nuclear Facilities. These criteria address the capability of the sampling probe to extract a sample that represents the effluent stream

Impact of quality of evidence on the strength of recommendations: an empirical study

<p>Abstract</p> <p>Background</p> <p>Evidence is necessary but not sufficient for decision-making, such as making recommendations by clinical practice guideline panels. However, the fundamental premise of evidence-based medicine (EBM) rests on the assumed link between the quality of evidence and "truth" and/or correctness in making guideline recommendations. If this assumption is accurate, then the quality of evidence ought to play a key role in making guideline recommendations. Surprisingly, and despite the widespread penetration of EBM in health care, there has been no empirical research to date investigating the impact of quality of evidence on the strength of recommendations made by guidelines panels.</p> <p>Methods</p> <p>The American Association of Blood Banking (AABB) has recently convened a 12 member panel to develop clinical practice guidelines (CPG) for the use of fresh-frozen plasma (FFP) for 6 different clinical indications. The panel was instructed that 4 factors should play a role in making recommendation: quality of evidence, uncertainty about the balance between desirable (benefits) and undesirable effects (harms), uncertainty or variability in values and preferences, and uncertainty about whether the intervention represents a wise use of resources (costs). Each member of the panel was asked to make his/her final judgments on the strength of recommendation and the overall quality of the body of evidence. "Voting" was anonymous and was based on the use of GRADE (Grading quality of evidence and strength of recommendations) system, which clearly distinguishes between quality of evidence and strength of recommendations.</p> <p>Results</p> <p>Despite the fact that many factors play role in formulating CPG recommendations, we show that when the quality of evidence is higher, the probability of making a strong recommendation for or against an intervention dramatically increases. Probability of making strong recommendation was 62% when evidence is "moderate", while it was only 23% and 13% when evidence was "low" or "very low", respectively.</p> <p>Conclusion</p> <p>We report the first empirical evaluation of the relationship between quality of evidence pertinent to a clinical question and strength of the corresponding guideline recommendations. Understanding the relationship between quality of evidence and probability of making (strong) recommendation has profound implications for the science of quality measurement in health care.</p

Meeting report: a hard look at the state of enamel research.

The Encouraging Novel Amelogenesis Models and Ex vivo cell Lines (ENAMEL) Development workshop was held on 23 June 2017 at the Bethesda headquarters of the National Institute of Dental and Craniofacial Research (NIDCR). Discussion topics included model organisms, stem cells/cell lines, and tissues/3D cell culture/organoids. Scientists from a number of disciplines, representing institutions from across the United States, gathered to discuss advances in our understanding of enamel, as well as future directions for the field

Refined saddle-point preconditioners for discretized Stokes problems

This paper is concerned with the implementation of efficient solution algorithms for elliptic problems with constraints. We establish theory which shows that including a simple scaling within well-established block diagonal preconditioners for Stokes problems can result in significantly faster convergence when applying the preconditioned MINRES method. The codes used in the numerical studies are available online

SUPPORT Tools for evidence-informed health Policymaking (STP) 16: Using research evidence in balancing the pros and cons of policies

This article is part of a series written for people responsible for making decisions about health policies and programmes and for those who support these decision makers

Atom Lasers, Coherent States, and Coherence:II. Maximally Robust Ensembles of Pure States

As discussed in Wiseman and Vaccaro [quant-ph/9906125], the stationary state of an optical or atom laser far above threshold is a mixture of coherent field states with random phase, or, equivalently, a Poissonian mixture of number states. We are interested in which, if either, of these descriptions of $\rho_{ss}$, is more natural. In the preceding paper we concentrated upon whether descriptions such as these are physically realizable (PR). In this paper we investigate another relevant aspect of these ensembles, their robustness. A robust ensemble is one for which the pure states that comprise it survive relatively unchanged for a long time under the system evolution. We determine numerically the most robust ensembles as a function of the parameters in the laser model: the self-energy $\chi$ of the bosons in the laser mode, and the excess phase noise $\nu$. We find that these most robust ensembles are PR ensembles, or similar to PR ensembles, for all values of these parameters. In the ideal laser limit ($\nu=\chi=0$), the most robust states are coherent states. As the phase noise $\nu$ or phase dispersion $\chi$ is increased, the most robust states become increasingly amplitude-squeezed. We find scaling laws for these states. As the phase diffusion or dispersion becomes so large that the laser output is no longer quantum coherent, the most robust states become so squeezed that they cease to have a well-defined coherent amplitude. That is, the quantum coherence of the laser output is manifest in the most robust PR states having a well-defined coherent amplitude. This lends support to the idea that robust PR ensembles are the most natural description of the state of the laser mode. It also has interesting implications for atom lasers in particular, for which phase dispersion due to self-interactions is expected to be large.Comment: 16 pages, 9 figures included. To be published in Phys. Rev. A, as Part II of a two-part paper. The original version of quant-ph/9906125 is shortly to be replaced by a new version which is Part I of the two-part paper. This paper (Part II) also contains some material from the original version of quant-ph/990612

Fast interior point solution of quadratic programming problems arising from PDE-constrained optimization

Interior point methods provide an attractive class of approaches for solving linear, quadratic and nonlinear programming problems, due to their excellent efficiency and wide applicability. In this paper, we consider PDE-constrained optimization problems with bound constraints on the state and control variables, and their representation on the discrete level as quadratic programming problems. To tackle complex problems and achieve high accuracy in the solution, one is required to solve matrix systems of huge scale resulting from Newton iteration, and hence fast and robust methods for these systems are required. We present preconditioned iterative techniques for solving a number of these problems using Krylov subspace methods, considering in what circumstances one may predict rapid convergence of the solvers in theory, as well as the solutions observed from practical computations

The Effect of Class Noise on Continuous Test Case Selection: A Controlled Experiment on Industrial Data

Continuous integration and testing produce a large amount of data about defects in code revisions, which can be utilized for training a predictive learner to effectively select a subset of test suites. One challenge in using predictive learners lies in the noise that comes in the training data, which often leads to a decrease in classification performances. This study examines the impact of one type of noise, called class noise, on a learner’s ability for selecting test cases. Understanding the impact of class noise on the performance of a learner for test case selection would assist testers decide on the appropriateness of different noise handling strategies. For this purpose, we design and implement a controlled experiment using an industrial data-set to measure the impact of class noise at six different levels on the predictive performance of a learner. We measure the learning performance using the Precision, Recall, F-score, and Mathew Correlation Coefficient (MCC) metrics. The results show a statistically significant relationship between class noise and the learners performance for test case selection. Particularly, a significant difference between the three performance measures (Precision, F-score, and MCC)under all the six noise levels and at 0% level was found, whereas a similar relationship between recall and class noise was found at a level above30%. We conclude that higher class noise ratios lead to missing out more tests in the predicted subset of test suite and increases the rate of false alarms when the class noise ratio exceeds 30