A new synthetic route to a 3'-fluoro-3'-deoxytetrose adenine phosphonate has been developed. The synthesis starts from l-xylose and key steps include the stereospecific introduction of the phosphonomethoxy group and adenine. In addition, a regioselective fluorination reaction allows access to the desired 3'-fluoro-3'-deoxytetrose moiety. This methodology allows the straightforward synthesis of a 3'-fluoro-3'-deoxytetrose adenine phosphonate and can be expanded toward the synthesis of other types of 3'-fluoro nucleoside phosphonates.
A number of synthetically useful transformations have been developed to generate novel 5'-peptidyl nucleoside monophosphate analogues that incorporate sensitive phosphoaminal, -hemiaminal or -hemithioaminal functionalities. The strategies adopted entailed the coupling between dipeptides, which enclose a reactive Cα-functionalized glycine residue and phosphate or phosphorothioate moieties. These developments led to potentially powerful and general methodologies for the preparation of α-phosphorylated pseudopeptides as well as nucleoside monophosphate mimics. The resulting conjugates are of interest for a variety of important applications, which range from drug development to synthetic biology, as pronucleotides or artificial building blocks for the enzymatic synthesis of xenobiotic information systems. The potential of all dipeptide-TMP conjugates as pyrophosphate mimics in the DNA polymerization reaction was tested, and the influence of the nature of the linker was evaluated by in vitro chain elongation assay in the presence of wild-type microbial DNA polymerases.
Dynamic spatial Bayesian (DSB) models are proposed for the analytical modelling of radioactivity deposition after a nuclear accident. The proposed models are extensions of the multi-variate time-series dynamic linear models of West and Harrison (1997) to Markov random field processes. They combine the outputs from a long-range atmospheric dispersal model with measured data (and prior information) to provide improved deposition prediction in space and time. Two versions of a Gaussian DSB model were applied to the radioactivity deposition in Bavaria over a 15 days period during the Chernobyl nuclear accident. One version had fixed functional forms for its spatial variances and covariances while the other allowed those to adapt and 'learn' from data in the conjugate Bayesian paradigm. There were two main sources of information for radioactivity deposition in our application: radioactivity measurements at a sparse set of 13 monitoring stations, and the numerical deposition evaluation of the atmospheric dispersal K-model for the points of a 64 · 64 regular grid. We have analysed the temporal predictions (one-step-ahead forecasting) of those DSB models to show that the dispersal K-model tended in general to underestimate the deposition levels at all times while the DSB models corrected for that although with different degrees of adjustment.
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