A lanthanide complex, named CLaNP (caged lanthanide NMR probe) has been developed for the characterisation of proteins by paramagnetic NMR spectroscopy. The probe consists of a lanthanide chelated by a derivative of DTPA (diethylenetriaminepentaacetic acid) with two thiol reactive functional groups. The CLaNP molecule is attached to a protein by two engineered, surface-exposed, Cys residues in a bidentate manner. This drastically limits the dynamics of the metal relative to the protein and enables measurements of pseudocontact shifts. NMR spectroscopy experiments on a diamagnetic control and the crystal structure of the probe-protein complex demonstrate that the protein structure is not affected by probe attachment. The probe is able to induce pseudocontact shifts to at least 40 A from the metal and causes residual dipolar couplings due to alignment at a high magnetic field. The molecule exists in several isomeric forms with different paramagnetic tensors; this provides a fast way to obtain long-range distance restraints.

When Genetic Programming is used to evolve decision trees for data classification, search spaces tend to become extremely large. We present several methods using techniques from the field of machine learning to refine and thereby reduce the search space sizes for decision tree evolvers. We will show that these refinement methods improve the classification performance of our algorithms.

In the popular computer game of Tetris, the player is given a sequence of tetromino pieces and must pack them into a rectangular gameboard initially occupied by a given configuration of filled squares; any completely filled row of the gameboard is cleared and all filled squares above it drop by one row. We prove that in the offline version of Tetris, it is [Formula: see text]-complete to maximize the number of cleared rows, maximize the number of tetrises (quadruples of rows simultaneously filled and cleared), minimize the maximum height of an occupied square, or maximize the number of pieces placed before the game ends. We furthermore show the extreme inapproximability of the first and last of these objectives to within a factor of p1-ε, when given a sequence of p pieces, and the inapproximability of the third objective to within a factor of 2-ε, for any ε>0. Our results hold under several variations on the rules of Tetris, including different models of rotation, limitations on player agility, and restricted piece sets.

International audienceIn this paper, we propose a new algorithm that computes the radius and the diameter of a weakly connected digraph G=(V,E), by finding bounds through heuristics and improving them until they are validated. Although the worst-case running time is O(|V||E|), we will experimentally show that it performs much better in the case of real-world networks, finding the radius and diameter values after 10–100 BFSs instead of |V| BFSs (independently of the value of |V|), and thus having running time O(|E|) in practice. As far as we know, this is the first algorithm able to compute the diameter of weakly connected digraphs, apart from the naive algorithm, which runs in time Ω(|V||E|) performing a BFS from each node. In the particular cases of strongly connected directed or connected undirected graphs, we will compare our algorithm with known approaches by performing experiments on a dataset composed by several real-world networks of different kinds. These experiments will show that, despite its generality, the new algorithm outperforms all previous methods, both in the radius and in the diameter computation, both in the directed and in the undirected case, both in average running time and in robustness. Finally, as an application example, we will use the new algorithm to determine the solvability over time of the “Six Degrees of Kevin Bacon” game, and of the “Six Degrees of Wikipedia” game. As a consequence, we will compute for the first time the exact value of the radius and the diameter of the whole Wikipedia digraph

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