Modeling, optimization, and computer control of the cephalosporin C fermentation process

1988 ◽  
Vol 32 (3) ◽  
pp. 277-288 ◽  
Author(s):  
Wey-Bang Z. Chu ◽  
Alkis Constantinides
Keyword(s):  
Fermentation Process ◽  
Computer Control ◽  
Cephalosporin C

Computer Control of a Protein Fermentation Process

1979 ◽  
Vol 12 (3) ◽  
pp. 133-137
Author(s):  
A. Halme
Keyword(s):  
Fermentation Process ◽  
Computer Control

A Study of Beer Fermentation Control System Based on the Technology of Computer Control

2015 ◽  
Vol 713-715 ◽  
pp. 719-722
Author(s):  
Jun Hai Jiang ◽  
Qian Zhang Zhuang
Keyword(s):  
Computer Technology ◽  
Fermentation Process ◽  
Production Management ◽  
System Analysis ◽  
Computer Control ◽  
Real Time Processing ◽  
Beer Fermentation ◽  
Fermentation Control ◽  
Data Signal ◽  
Control And Management

By analyzing the structure characteristics of fermented beer and has the function of system analysis, introduced in the fermentation process of beer, the use of computer technology, the real-time processing of beer fermentation temperature, pressure, the collection of data signal level etc.. The computer system to realize the automatic control and management on the beer fermentation process of beer fermentation, production management and cost will be greatly improved.

Influence of the regime characteristics of the fermentation process on the biosynthesis of cephalosporin C

1985 ◽  
Vol 19 (10) ◽  
pp. 724-726
Author(s):  
E. A. Maksimova ◽  
A. M. Bagdasarova ◽  
M. V. Laishevskaya ◽  
N. N. Falkov ◽  
E. S. Bylinkina
Keyword(s):  
Fermentation Process ◽  
Cephalosporin C

COMPUTER CONTROL OF A PROTEIN FERMENTATION PROCESS

1979 ◽  
pp. 133-137
Author(s):  
A. Halme
Keyword(s):  
Fermentation Process ◽  
Computer Control

Study of a single-cell protein fermentation process for computer control

1977 ◽  
Vol 10 (16) ◽  
pp. 407-415 ◽  
Author(s):  
A. Halme ◽  
H. Kiviranta ◽  
M. Kiviranta
Keyword(s):  
Single Cell ◽  
Fermentation Process ◽  
Computer Control ◽  
Single Cell Protein ◽  
Cell Protein

Microdensitometer—computer correlation analysis of ultrastructural periodicity

1978 ◽  
Vol 36 (2) ◽  
pp. 32-33
Author(s):  
D.R. Ensor ◽  
C.G. Jensen ◽  
J.A. Fillery ◽  
R.J.K. Baker
Keyword(s):  
Correlation Analysis ◽  
Background Noise ◽  
Computer Control ◽  
Optical Diffraction ◽  
Screw Thread ◽  
Straight Line ◽  
Computer Storage ◽  
Correlation Process ◽  
Electron Microscope Image ◽  
Fixed Beam

Because periodicity is a major indicator of structural organisation numerous methods have been devised to demonstrate periodicity masked by background “noise” in the electron microscope image (e.g. photographic image reinforcement, Markham et al, 1964; optical diffraction techniques, Horne, 1977; McIntosh,1974). Computer correlation analysis of a densitometer tracing provides another means of minimising "noise". The correlation process uncovers periodic information by cancelling random elements. The technique is easily executed, the results are readily interpreted and the computer removes tedium, lends accuracy and assists in impartiality.A scanning densitometer was adapted to allow computer control of the scan and to give direct computer storage of the data. A photographic transparency of the image to be scanned is mounted on a stage coupled directly to an accurate screw thread driven by a stepping motor. The stage is moved so that the fixed beam of the densitometer (which is directed normal to the transparency) traces a straight line along the structure of interest in the image.

Defocus ramp correction in spot-scan imaging

1992 ◽  
Vol 50 (1) ◽  
pp. 130-131
Author(s):  
Kenneth H. Downing
Keyword(s):  
Computer Control ◽  
Three Dimensional ◽  
Sufficient Accuracy ◽  
Objective Lens ◽  
Electron Crystallography ◽  
Contrast Transfer Function ◽  
Tilt Axis ◽  
Specimen Height ◽  
The Difference ◽  
Envelope Functions

Three-dimensional structures of a number of samples have been determined by electron crystallography. The procedures used in this work include recording images of fairly large areas of a specimen at high tilt angles. There is then a large defocus ramp across the image, and parts of the image are far out of focus. In the regions where the defocus is large, the contrast transfer function (CTF) varies rapidly across the image, especially at high resolution. Not only is the CTF then difficult to determine with sufficient accuracy to correct properly, but the image contrast is reduced by envelope functions which tend toward a low value at high defocus.We have combined computer control of the electron microscope with spot-scan imaging in order to eliminate most of the defocus ramp and its effects in the images of tilted specimens. In recording the spot-scan image, the beam is scanned along rows that are parallel to the tilt axis, so that along each row of spots the focus is constant. Between scan rows, the objective lens current is changed to correct for the difference in specimen height from one scan to the next.

Applications of CCEM to Environmental Health Problems

1985 ◽  
Vol 43 ◽  
pp. 102-103
Author(s):  
R. J. Lee ◽  
J. S. Walker
Keyword(s):  
Electron Microscopy ◽  
Image Analysis ◽  
Environmental Health ◽  
Computer Control ◽  
External Agent ◽  
External Agents ◽  
Computer Controlled ◽  
Primitive State ◽  
Textural Changes ◽  
General Aspects

Electron microscopy (EM), with the advent of computer control and image analysis techniques, is rapidly evolving from an interpretative science into a quantitative technique. Electron microscopy is potentially of value in two general aspects of environmental health: exposure and diagnosis.In diagnosis, electron microscopy is essentially an extension of optical microscopy. The goal is to characterize cellular changes induced by external agents. The external agent could be any foreign material, chemicals, or even stress. The use of electron microscopy as a diagnostic tool is well- developed, but computer-controlled electron microscopy (CCEM) has had only limited impact, mainly because it is fairly new and many institutions lack the resources to acquire the capability. In addition, major contributions to diagnosis will come from CCEM only when image analysis (IA) and processing algorithms are developed which allow the morphological and textural changes recognized by experienced medical practioners to be quantified. The application of IA techniques to compare cellular structure is still in a primitive state.

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