Analysing spatio-temporal change by use of national historical Geographical Information Systems: Population change during and after the Great Irish Famine

Several countries have made large investments in building historical Geographical Information Systems (GIS) databases containing census and other quantitative statistics over long periods of time. Making good use of these databases requires approaches that explore spatial and temporal change.

The inactivation of bovine cathepsin B by novel N-chloro-acetyl-dipeptides. Application of the Houghten 'tea bag' methodology to inhibitor synthesis

In this study, a series of N-chloro-acetylated dipeptides were synthesised by the application of Houghten's methodology of multiple analog peptide syntheses. The peptides, all of which contain a C-terminal free acid, were tested as inactivators of bovine cathepsin B, in an attempt at exploiting the known and, amongst the cysteine proteinases, unique carboxy dipeptidyl peptidase activity of the protease. We have succeeded in obtaining a number of effective inactivators, the most potent of which-chloroacetyl-Leu-Leu-OH, inactivates the enzyme with an apparent second-order rate constant of 3.8 x 10(4) M-1 min(-1). In contrast, the esterified analog, chloroacetyl-Leu-Leu-OMe, inactivates the enzyme some three orders of magnitude less efficiently, lending credence to our thesis that a free carboxylic acid moiety is an important determinant for inhibitor effectiveness. This preliminary study has highlighted a number of interesting features about the specificity requirements of the bovine proteinase and we believe that our approach has great potential for the rapid delineation of the subsite specificities of cathepsin B-like proteases from various species. (c) 2005 Elsevier Inc. All rights reserved.

Error sensitive historical GIS: Identifying areal interpolation errors in time series data

Historical GIS has the potential to re-invigorate our use of statistics from historical censuses and related sources. In particular, areal interpolation can be used to create long-run time-series of spatially detailed data that will enable us to enhance significantly our understanding of geographical change over periods of a century or more. The difficulty with areal interpolation, however, is that the data that it generates are estimates which will inevitably contain some error. This paper describes a technique that allows the automated identification of possible errors at the level of the individual data values.