Synthesis and enzymatic evaluation of the guanosine analogue 2-amino-6-mercapto-7-methylpurine ribonucleoside (MESG): insights into the phosphorolysis reaction mechanism based on the blueprint transition state: SN1 or SN2?

A modified experimental procedure for the synthesis of MESG (2-amino-6-mercapto-7-methylpurine ribonucleoside) 1 has been successfully performed and its full characterization is presented. High resolution ESI(+)-MSMS indicates both the nucleoside bond cleavage as the main fragmentation in the gas phase and a possible SN1 mechanism. Ab initio transition state calculations based on the blue print transition state support this mechanistic rationale and discard an alternative SN2 mechanism. Assays using purine nucleoside phosphorylase (PNP) enzyme (human and M. tuberculosis sources) indicate its efficiency in the phosphorolysis of MESG and allow the quantitative determination of inorganic phosphate in real time assay.

Upgrading automation for nuclear fuel in-core management: from the symbolic generation of configurations to the neural adaptation of heuristics

FUELCON is an expert system in nuclear engineering. Its task is optimized refueling-design, which is crucial to keep down operation costs at a plant. FUELCON proposes sets of alternative configurations of fuel-allocation; the fuel is positioned in a grid representing the core of a reactor. The practitioner of in-core fuel management uses FUELCON to generate a reasonably good configuration for the situation at hand. The domain expert, on the other hand, resorts to the system to test heuristics and discover new ones, for the task described above. Expert use involves a manual phase of revising the ruleset, based on performance during previous iterations in the same session. This paper is concerned with a new phase: the design of a neural component to carry out the revision automatically. Such an automated revision considers previous performance of the system and uses it for adaptation and learning better rules. The neural component is based on a particular schema for a symbolic to recurrent-analogue bridge, called NIPPL, and on the reinforcement learning of neural networks for the adaptation.

Seven 3-methylidene-1H-indol-2(3H)-ones related to the multiple-receptor tyrosine kinase inhibitor sunitinib

The solid-state structures of a series of seven substituted 3-methylidene-1H-indol-2(3H)-one derivatives have been determined by single-crystal X-ray diffraction and are compared in detail. Six of the structures {(3Z)-3-(1H-pyrrol-2- ylmethylidene)-1H-indol-2(3H)-one, C13H10N2O, (2a); (3Z)-3-( 2-thienylmethylidene)-1H-indol-2(3H)-one, C13H9NOS, (2b); (3E)-3-(2-furylmethylidene)-1H-indol-2(3H)-one monohydrate, C13H9NO2 center dot H2O, (3a); 3-(1-methylethylidene)-1H-indol- 2(3H)-one, C11H11NO, (4a); 3-cyclohexylidene-1H-indol- 2(3H)-one, C14H15NO, (4c); and spiro[1,3-dioxane-2,3'-indolin]- 2'-one, C11H11NO3, (5)} display, as expected, intermolecular hydrogen bonding (N-H center dot center dot center dot O=C) between the 1H-indol-2(3H)-one units. However, methyl 3-(1-methylethylidene)- 2-oxo-2,3-dihydro-1H-indole-1-carboxylate, C13H13NO3, (4b), a carbamate analogue of (4a) lacking an N-H bond, displays no intermolecular hydrogen bonding. The structure of (4a) contains three molecules in the asymmetric unit, while (4b) and (4c) both contain two independent molecules.