24,086 research outputs found
SVM-based texture classification in optical coherence tomography
This paper describes a new method for automated texture classification for glaucoma detection using high resolution retinal Optical Coherence Tomography (OCT). OCT is a non-invasive technique that produces cross-sectional imagery of ocular tissue. Here, we exploit information from OCT im-ages, specifically the inner retinal layer thickness and speckle patterns, to detect glaucoma. The proposed method relies on support vector machines (SVM), while principal component analysis (PCA) is also employed to improve classification performance. Results show that texture features can improve classification accuracy over what is achieved using only layer thickness as existing methods currently do. Index Terms — classification, support vector machine, optical coherence tomography, texture 1
On the structure of the scalar mesons and
We investigate the structure of the scalar mesons and
within realistic meson-exchange models of the and
interactions. Starting from a modified version of the J\"ulich model for
scattering we perform an analysis of the pole structure of the
resulting scattering amplitude and find, in contrast to existing models, a
somewhat large mass for the ( MeV,
MeV). It is shown that our model provides a description of
data comparable in quality with those of
alternative models. Furthermore, the formalism developed for the
system is consistently extended to the interaction leading to a
description of the as a dynamically generated threshold effect
(which is therefore neither a conventional state nor a
bound state). Exploring the corresponding pole position the
is found to be rather broad ( MeV,
MeV). The experimentally observed smaller width results from the influence of
the nearby threshold on this pole.Comment: 25 pages, 15 Postscript figure
Microlensing of the Lensed Quasar SDSS0924+0219
We analyze V, I and H band HST images and two seasons of R-band monitoring
data for the gravitationally lensed quasar SDSS0924+0219. We clearly see that
image D is a point-source image of the quasar at the center of its host galaxy.
We can easily track the host galaxy of the quasar close to image D because
microlensing has provided a natural coronograph that suppresses the flux of the
quasar image by roughly an order of magnitude. We observe low amplitude,
uncorrelated variability between the four quasar images due to microlensing,
but no correlated variations that could be used to measure a time delay. Monte
Carlo models of the microlensing variability provide estimates of the mean
stellar mass in the lens galaxy (0.02 Msun < M < 1.0 Msun), the accretion disk
size (the disk temperature is 5 x 10^4 K at 3.0 x 10^14 cm < rs < 1.4 x 10^15
cm), and the black hole mass (2.0 x 10^7 Msun < MBH \eta_{0.1}^{-1/2}
(L/LE)^{1/2} < 3.3 x 10^8 Msun), all at 68% confidence. The black hole mass
estimate based on microlensing is consistent with an estimate of MBH = 7.3 +-
2.4 x 10^7 Msun from the MgII emission line width. If we extrapolate the
best-fitting light curve models into the future, we expect the the flux of
images A and B to remain relatively stable and images C and D to brighten. In
particular, we estimate that image D has a roughly 12% probability of
brightening by a factor of two during the next year and a 45% probability of
brightening by an order of magnitude over the next decade.Comment: v.2 incorporates referee's comments and corrects two errors in the
original manuscript. 28 pages, 10 figures, published in Ap
The Spatial Structure of An Accretion Disk
Based on the microlensing variability of the two-image gravitational lens
HE1104-1805 observed between 0.4 and 8 microns, we have measured the size and
wavelength-dependent structure of the quasar accretion disk. Modeled as a power
law in temperature, T proportional to R^-beta, we measure a B-band (0.13
microns in the rest frame) half-light radius of R_{1/2,B} = 6.7 (+6.2 -3.2) x
10^15 cm (68% CL) and a logarithmic slope of beta=0.61 (+0.21 -0.17) for our
standard model with a logarithmic prior on the disk size. Both the scale and
the slope are consistent with simple thin disk models where beta=3/4 and
R_{1/2,B} = 5.9 x 10^15 cm for a Shakura-Sunyaev disk radiating at the
Eddington limit with 10% efficiency. The observed fluxes favor a slightly
shallower slope, beta=0.55 (+0.03 -0.02), and a significantly smaller size for
beta=3/4.Comment: 5 pages, 4 figures, submitted to Ap
Orbit transfer rocket engine integrated control and health monitoring system technology readiness assessment
The objectives of this task were to: (1) estimate the technology readiness of an integrated control and health monitoring (ICHM) system for the Aerojet 7500 lbF Orbit Transfer Vehicle engine preliminary design assuming space based operations; and (2) estimate the remaining cost to advance this technology to a NASA defined 'readiness level 6' by 1996 wherein the technology has been demonstrated with a system validation model in a simulated environment. The work was accomplished through the conduct of four subtasks. In subtask 1 the minimally required functions for the control and monitoring system was specified. The elements required to perform these functions were specified in Subtask 2. In Subtask 3, the technology readiness level of each element was assessed. Finally, in Subtask 4, the development cost and schedule requirements were estimated for bringing each element to 'readiness level 6'
A Fast and Efficient Python Library for Interfacing with the Biological Magnetic Resonance Data Bank
Background: The Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis.
Results:To aid in the use of this public resource, we have developed a package called nmrstarlib in the popular open-source programming language Python. The nmrstarlib’s implementation is very efficient, both in design and execution. The library has facilities for reading and writing both NMR-STAR version 2.1 and 3.1 formatted files, parsing them into usable Python dictionary- and list-based data structures, making access and manipulation of the experimental data very natural within Python programs (i.e. “saveframe” and “loop” records represented as individual Python dictionary data structures). Another major advantage of this design is that data stored in original NMR-STAR can be easily converted into its equivalent JavaScript Object Notation (JSON) format, a lightweight data interchange format, facilitating data access and manipulation using Python and any other programming language that implements a JSON parser/generator (i.e., all popular programming languages). We have also developed tools to visualize assigned chemical shift values and to convert between NMR-STAR and JSONized NMR-STAR formatted files. Full API Reference Documentation, User Guide and Tutorial with code examples are also available. We have tested this new library on all current BMRB entries: 100% of all entries are parsed without any errors for both NMR-STAR version 2.1 and version 3.1 formatted files. We also compared our software to three currently available Python libraries for parsing NMR-STAR formatted files: PyStarLib, NMRPyStar, and PyNMRSTAR.
Conclusions: The nmrstarlib package is a simple, fast, and efficient library for accessing data from the BMRB. The library provides an intuitive dictionary-based interface with which Python programs can read, edit, and write NMR-STAR formatted files and their equivalent JSONized NMR-STAR files. The nmrstarlib package can be used as a library for accessing and manipulating data stored in NMR-STAR files and as a command-line tool to convert from NMR-STAR file format into its equivalent JSON file format and vice versa, and to visualize chemical shift values. Furthermore, the nmrstarlib implementation provides a guide for effectively JSONizing other older scientific formats, improving the FAIRness of data in these formats
European Starling Use of Nest Boxes Relative to Human Disturbance
European starling (Sturnus vulgaris; starling) nesting poses debris hazards within airport hangars and to engine and flight surfaces of moored aircraft. We questioned whether consistent removal of nest material would negatively affect use of a nest site, measured by a reduction in material accumulation. We conducted our study on a 2,200-ha site in Erie County, Ohio, USA (41° 22’ N, 82° 41’ W), from April 15 through June 2, 2020. We used 120 wooden nest boxes on utility poles, protected by an aluminum predator guard below the box. Our treatments included (1) twice weekly, repeated nest material removal (RMR; n = 40 nest boxes); (2) complete nest removal, but only after nest construction and ≥1 starling egg was laid (CNR; n = 40 nest boxes); and (3) a control; n = 40 nest boxes; N = 120 nest boxes). Starlings deposited approximately 50% greater mass of nest material and eggs at RMR than CNR nest boxes, indicating that consistent disturbance failed to dissuade use. Predator guard protection of nest boxes at our site reduced nest predation of starlings; the current starling population is likely adapted to selecting these sites. Similar selection toward low nest-predation risk associated with anthropogenic structures and moored aircraft is also possible. Aside from covering moored aircraft and closing hangar doors, actions not necessarily feasible, removal of starling nesting material more than twice weekly would be necessary to maintain minimum control over material deposition that could affect aircraft function and safety
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