scholarly journals Rutin-Degrading Enzyme Reaction in the Presence of Water-Insoluble Alcohols.

1997 ◽  
Vol 44 (2) ◽  
pp. 156-159 ◽  
Author(s):  
Toshitaka YASUDA ◽  
Hirofumi SHINOYAMA
Keyword(s):  
Enzyme Reaction ◽  
Degrading Enzyme

Triesterase and Promiscuous Diesterase Activities of a Di-CoII-Containing Organophosphate Degrading Enzyme Reaction Mechanisms

2015 ◽  
Vol 21 (9) ◽  
pp. 3736-3745 ◽  
Author(s):  
Marta E. Alberto ◽  
Gaspar Pinto ◽  
Nino Russo ◽  
Marirosa Toscano
Keyword(s):  
Reaction Mechanisms ◽  
Enzyme Reaction ◽  
Degrading Enzyme

Lead aspartate: a new en bloc stain for electron microscopy

1978 ◽  
Vol 36 (2) ◽  
pp. 34-35
Author(s):  
J.R. Walton
Keyword(s):  
Electron Microscopy ◽  
Calcium Ion ◽  
Enzyme Reaction ◽  
Lead Citrate ◽  
Reaction Products ◽  
En Bloc ◽  
Thin Sections ◽  
Lead Salts ◽  
Thin Sectioning ◽  
High Alkalinity

In electron microscopy, lead is the metal most widely used for enhancing specimen contrast. Lead citrate requires a pH of 12 to stain thin sections of epoxy-embedded material rapidly and intensively. However, this high alkalinity tends to leach out enzyme reaction products, making lead citrate unsuitable for many cytochemical studies. Substitution of the chelator aspartate for citrate allows staining to be carried out at pH 6 or 7 without apparent effect on cytochemical products. Moreover, due to the low, controlled level of free lead ions, contamination-free staining can be carried out en bloc, prior to dehydration and embedding. En bloc use of lead aspartate permits the grid-staining step to be bypassed, allowing samples to be examined immediately after thin-sectioning.Procedures. To prevent precipitation of lead salts, double- or glass-distilled H20 used in the stain and rinses should be boiled to drive off carbon dioxide and glassware should be carefully rinsed to remove any persisting traces of calcium ion.

Computer Simulation of the Response of Amperometric Biosensors in Stirred and non Stirred Solution

2003 ◽  
Vol 8 (1) ◽  
pp. 3-18 ◽  
Author(s):  
R. Baronas ◽  
F. Ivanauskas ◽  
J. Kulys
Keyword(s):  
Mathematical Model ◽  
Computer Simulation ◽  
Finite Difference ◽  
Mass Transport ◽  
Enzyme Reaction ◽  
Analyte Concentration ◽  
Amperometric Biosensors ◽  
Finite Difference Technique ◽  
Enzyme Layer ◽  
Biosensor Response

A mathematical model of amperometric biosensors has been developed to simulate the biosensor response in stirred as well as non stirred solution. The model involves three regions: the enzyme layer where enzyme reaction as well as mass transport by diffusion takes place, a diffusion limiting region where only the diffusion takes place, and a convective region, where the analyte concentration is maintained constant. Using computer simulation the influence of the thickness of the enzyme layer as well the diffusion one on the biosensor response was investigated. The computer simulation was carried out using the finite difference technique.

Beauveria bassiana xylanase: a biomass degrading enzyme from an entomopathogen

2020 ◽  
Author(s):  
Santhosh Pillai ◽  
AYODEJI EMMANUEL AMOBONYE ◽  
Prashant Bhagwat
Keyword(s):  
Beauveria Bassiana ◽  
Degrading Enzyme

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

2019 ◽  
Author(s):  
Qiannan Duan ◽  
Jianchao Lee ◽  
Jinhong Gao ◽  
Jiayuan Chen ◽  
Yachao Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Toxicity ◽  
Model Building ◽  
Low Cost ◽  
Enzyme Reaction ◽  
Chemical Effect ◽  
Technological Innovations ◽  
Acute Toxicity Test ◽  
Toxicity Prediction ◽  
Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

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