scholarly journals Coxsackievirus B4 Exposure Results in Variable Pattern Recognition Response in the Kidneys of Female Non-Obese Diabetic Mice Before Establishment of Diabetes

2020 ◽  
Vol 33 (7) ◽  
pp. 494-506
Author(s):  
Debra L. Walter ◽  
Sarah E. Benner ◽  
Rosemary J. Oaks ◽  
Jean R. Thuma ◽  
Ramiro Malgor ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Diabetic Mice ◽  
Coxsackievirus B4 ◽  
Variable Pattern ◽  
Recognition Response

Abstract 1960: Mechanisms of therapeutic synergy between pattern recognition response agonists and cdk4 inhibitors

2021 ◽  
Author(s):  
Victoria Roulstone ◽  
Joan Kyula ◽  
Richard Elliot ◽  
Christopher J. Lord ◽  
Nik Matthews ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Recognition Response

Pancreatic D-cell disorder in Coxsackievirus B4-induced diabetic mice

1989 ◽  
Vol 67 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Nando K. Chatterjee ◽  
Ivan Gerling ◽  
Catherine Nejman
Keyword(s):  
Diabetic Mice ◽  
Coxsackievirus B4 ◽  
D Cell ◽  
Cell Disorder

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

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