Novel methodology for the preparation and purification of oligonucleotides incorporating phosphorothiolate termini

A novel phosphoramidite; N,N-diisopropylamino-2-cyanoethyl-ortho-methylbenzylphosphoramidite 1, was prepared. The reaction of 1 with DMTrT and subsequent derivatisation of the phosphite triester product under solution-phase, Michaelis–Arbuzov conditions was investigated. Coupling of 1 with the terminal hydroxyl groups of support-bound oligodeoxyribonucleotides and subsequent reaction with an activated disulfide yielded oligonucleotides bearing a terminal, phosphorothiolate-linked, lipophilic moiety. The oligomers were readily purified using RP-HPLC. Silver(I)-mediated cleavage of the phosphorothiolate linkage and desalting of the oligonucleotides were performed readily in one step to yield cleanly the corresponding phosphate monester-terminated oligomers.

Addressing Bed Costs for the Elderly: A New Methodology for Modelling Patient Outcomes and Length of Stay

The proportion of elderly in the population has dramatically increased and will continue to do so for at least the next 50 years. Medical resources throughout the world are feeling the added strain of the increasing proportion of elderly in the population. The effective care of elderly patients in hospitals may be enhanced by accurately modelling the length of stay of the patients in hospital and the associated costs involved. This paper examines previously developed models for patient length of stay in hospital and describes the recently developed conditional phase-type distribution (C-Ph) to model patient duration of stay in relation to explanatory patient variables. The Clinics data set was used to demonstrate the C-Ph methodology. The resulting model highlighted a strong relationship between Barthel grade, patient outcome and length of stay showing various groups of patient behaviour. The patients who stay in hospital for a very long time are usually those that consume the largest amount of hospital resources. These have been identified as the patients whose resulting outcome is transfer. Overall, the majority of transfer patients spend a considerably longer period of time in hospital compared to patients who die or are discharged home. The C-Ph model has the potential for considering costs where different costs are attached to the various phases or subgroups of patients and the anticipated cost of care estimated in advance. It is hoped that such a method will lead to the successful identification of the most cost effective case-mix management of the hospital ward.

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.

Many ways to say 'no', different ways to say 'yes': Applying Q-Methodology to understand public acceptance of wind farm proposals

This paper explores the nature of public acceptance of wind farms by investigating the discourses of support and objection to a proposed offshore scheme. It reviews research into opposition to wind farms, noting previous criticisms that this has tended to provide descriptive rather than explanatory insights and as a result, has not effectively informed the policy debate. One explanation is that much of this research has been conceived within an unreflective positivist research frame, which is inadequate in dealing with the subjectivity and value-basis of public acceptance of wind farm development. The paper then takes a case study of an offshore wind farm proposal in Northern Ireland and applies Q-Methodology to identify the dominant discourse of support and objection. It is argued that this provides new insights into the nature of wind farm conflicts, points to a number or recommendations for policy functions of an example of how this methodology can act as a potential bridge between positivist and post-positivist approaches to policy analysis.

Design Methodology for Real-Time FPGA-Based Sound Synthesis

Explicit finite difference (FD) schemes can realise highly realistic physical models of musical instruments but are computationally complex. A design methodology is presented for the creation of FPGA-based micro-architectures for FD schemes which can be applied to a range of applications with varying computational requirements, excitation and output patterns and boundary conditions. It has been applied to membrane and plate-based sound producing models, resulting in faster than real-time performance on a Xilinx XC2VP50 device which is 10 to 35 times faster than general purpose and DSP processors. The models have developed in such a way to allow a wide range of interaction (by a musician) thereby leading to the possibility of creating a highly realistic digital musical instrument.

System oriented neural networks - Problem formulation, methodology and application

A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful