Difference in Enzyme Activity and Conformation of Glucose Oxidase Before and After Purification

EEnzyme activity of commercial glucose oxidase was enhanced after purification through a strong anionic exchange resin. In order to get a better insight into this phenomenon, surface pressure–area ( –A) isotherms and surface pressure–time ( –t) isotherms was used to study the interaction and the absorption at different pH values of the subphases between octadecylamine and glucose oxidase purified by a styrene system quaternary ammonium type strongly basic anionic exchange resin. Circular dichroism (CD), electrophoresis and enzyme activity measurements were conducted to study these phenomena. A preliminary hypothesis has been suggested to explain why the enzyme activity of purified glucose oxidase was higher than that of the commercial one. © 2002 Elsevier Science B.V. All rights reserved.

Fast Hands-free Writing by Gaze Direction

We describe a method for text entry based on inverse arithmetic coding that relies on gaze direction and which is faster and more accurate than using an on-screen keyboard. These benefits are derived from two innovations: the writing task is matched to the capabilities of the eye, and a language model is used to make predictable words and phrases easier to write.

Fast Computing For Lurr Of Earthquake Prediction

The LURR theory is a new approach for earthquake prediction, which achieves good results in earthquake prediction within the China mainland and regions in America, Japan and Australia. However, the expansion of the prediction region leads to the refinement of its longitude and latitude, and the increase of the time period. This requires increasingly more computations, and the volume of data reaches the order of GB, which will be very difficult for a single CPU. In this paper, a new method was introduced to solve this problem. Adopting the technology of domain decomposition and parallelizing using MPI, we developed a new parallel tempo-spatial scanning program.

Probabilistic inference for fast learning in control

A novel framework is provided for very fast model-based reinforcement learning in continuous state and action spaces. It requires probabilistic models that explicitly characterize their levels of condence. Within the framework, exible, non-parametric models are used to describe the world based on previously collected experience. It demonstrates learning on the cart-pole problem in a setting where very limited prior knowledge about the task has been provided. Learning progressed rapidly, and a good policy found after only a small number of iterations.