The electrochemistry of redox enzymes

The work presented in this thesis is of two types. Firstly methods for the electrochemical immobilisation of redox enzymes in organic polymers are described. The electrochemical monitoring of the immobilised enzyme reaction by detection of one of the enzyme's products is discussed, and the results obtained for such a system under a variety of experimental conditions are presented. A good understanding of the way in which such a system operates' was obtained by using a specially developed kinetic model., This model is explained fully in the theory chapter of this thesis. A variety of organic polymers were used in the electrochemical immobilisation process, with varying degrees of success. The flexibility of this approach is demonstrated by the use of a variety of immobilisation matrices and also by the development of bienzyme and bilayer devices. The final experimental chapter presents work on the covalent modification of redox enzymes with a variety of, redox centres based. on ferrocene. Although attempts to electrochemically immobilise a modified enzyme were not successful, some interesting information about the kinetic behaviour and stability of a series Of modified enzymes was obtained. An indication of possible work forming an extension to this thesis is given in the final part of this thesis. The electrochemical immobilisation techniques and the procedure for covalently modifying, enzymes using electroactive, groups are relatively recent ideas. Much work remains to be done before a better understanding of these systems is gained

Fertilizing corn during dry years : results of experimentation during three dry years at McCredie Claypan Experiment Station

Caption title."Corn Belt Branch, Soil and Water Conservation Research Division, Agricultural Research Service, U.S. Department of Agriculture, Columbia, Missouri in cooperation with the Soils Department of the Missouri Agricultural Experiment Station"--Page 2.Includes bibliographical references (page 11)

Fossil deposit in cave in St. Louis

46 p. : ill., map ; 24 cm.Includes bibliographical references (p. 44-46)

Min and max scorings for two-sample ordinal data

Journal of the American Statistical Association, March 1992, Vol. 87, No. 417, Theory and MethodsTo analyze two-sample ordinal data, one must often assign some increasing numerical scores to the ordinal categories. The choice of appropriate scores in these types of analyses is often problematic. This article presents a new approach for reporting the results of such analyses. Using techniques of order-restricted inference, we obtain the minimum and maximum of standard two-sample test statistics over all possible assignments of increasing scores. If the range of the min and max values does not include the critical value for the test statistics, then we can immediately conclude that the result of the analysis remains the same no matter what choice of increasing scores is used. On the other hand, if the range includes a critical value, the choice of scores used in the analysis must be carefully justified. Numerous examples are given to clarify our approach

Through-Ice Acoustic Source Tracking Using Vision Transformers with Ordinal Classification

Ice environments pose challenges for conventional underwater acoustic localization techniques due to theirmultipath and non-linear nature. In this paper, we compare different deep learning networks, such as Transformers, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Vision Transformers (ViTs), for passive localization and tracking of single moving, on-ice acoustic sources using two underwater acoustic vector sensors. We incorporate ordinal classification as a localization approach and compare the results with other standard methods. We conduct experiments passively recording the acoustic signature of an anthropogenic source on the ice and analyze these data. The results demonstrate that Vision Transformers are a strong contender for tracking moving acoustic sources on ice. Additionally, we show that classification as a localization technique can outperform regression for networks more suited for classification, such as the CNN and ViTs