Therapeutic Development in Neurofibromatosis

Although neurofibromatosis (NF) was initially recognized in the nineteenth century, only in the past two decades we have witnessed a paradigm shift in therapeutics. This progress is driven by the increasing understanding of the natural history of the NF-associated tumors and understanding of the molecular landscape of these disorders. Multiple clinical trials have been launched evaluating non-surgical treatment modalities and more studies are in the pipeline. Recently, the NF community has adopted standardized endpoints recommended by the Response Evaluation in Neurofibromatosis and Schwannomatosis (REiNS) International Collaboration established in 2011. Such collaborations among academic, regulatory and supporting communities are crucial for providing the infrastructure needed for advancing the therapeutic development in the field of NF

Planning When Goals Change: A Moving Target Search Approach

International audienceDevising intelligent robots or agents that interact with humans is a major challenge for artificial intelligence. In such contexts, agents must constantly adapt their decisions according to human activities and modify their goals. In this paper, we tackle this problem by introducing a novel planning approach, called Moving Goal Planning (MGP), to adapt plans to goal evolutions. This planning algorithm draws inspiration from Moving Target Search (MTS) algorithms. In order to limit the number of search iterations and to improve its efficiency, MGP delays as much as possible triggering new searches when the goal changes over time. To this purpose, MGP uses two strategies: Open Check (OC) that checks if the new goal is still in the current search tree and Plan Follow (PF) that estimates whether executing actions of the current plan brings MGP closer to the new goal. Moreover, MGP uses a parsimonious strategy to update incrementally the search tree at each new search that reduces the number of calls to the heuristic function and speeds up the search. Finally, we show evaluation results that demonstrate the effectiveness of our approach

Additive Pattern Database Heuristics

We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases, which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heuristics, however, we partition our problems into disjoint subproblems, so that the costs of solving the different subproblems can be added together without overestimating the cost of solving the original problem. Previously, we showed how to statically partition the sliding-tile puzzles into disjoint groups of tiles to compute an admissible heuristic, using the same partition for each state and problem instance. Here we extend the method and show that it applies to other domains as well. We also present another method for additive heuristics which we call dynamically partitioned pattern databases. Here we partition the problem into disjoint subproblems for each state of the search dynamically. We discuss the pros and cons of each of these methods and apply both methods to three different problem domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and finding an optimal vertex cover of a graph. We find that in some problem domains, static partitioning is most effective, while in others dynamic partitioning is a better choice. In each of these problem domains, either statically partitioned or dynamically partitioned pattern database heuristics are the best known heuristics for the problem

Phase Transition in the Number Partitioning Problem

Number partitioning is an NP-complete problem of combinatorial optimization. A statistical mechanics analysis reveals the existence of a phase transition that separates the easy from the hard to solve instances and that reflects the pseudo-polynomiality of number partitioning. The phase diagram and the value of the typical ground state energy are calculated.Comment: minor changes (references, typos and discussion of results

GC skew is a conserved property of unmethylated CpG island promoters across vertebrates.

GC skew is a measure of the strand asymmetry in the distribution of guanines and cytosines. GC skew favors R-loops, a type of three stranded nucleic acid structures that form upon annealing of an RNA strand to one strand of DNA, creating a persistent RNA:DNA hybrid. Previous studies show that GC skew is prevalent at thousands of human CpG island (CGI) promoters and transcription termination regions, which correspond to hotspots of R-loop formation. Here, we investigated the conservation of GC skew patterns in 60 sequenced chordates genomes. We report that GC skew is a conserved sequence characteristic of the CGI promoter class in vertebrates. Furthermore, we reveal that promoter GC skew peaks at the exon 1/ intron1 junction and that it is highly correlated with gene age and CGI promoter strength. Our data also show that GC skew is predictive of unmethylated CGI promoters in a range of vertebrate species and that it imparts significant DNA hypomethylation for promoters with intermediate CpG densities. Finally, we observed that terminal GC skew is conserved for a subset of vertebrate genes that tend to be located significantly closer to their downstream neighbors, consistent with a role for R-loop formation in transcription termination

Random Costs in Combinatorial Optimization

The random cost problem is the problem of finding the minimum in an exponentially long list of random numbers. By definition, this problem cannot be solved faster than by exhaustive search. It is shown that a classical NP-hard optimization problem, number partitioning, is essentially equivalent to the random cost problem. This explains the bad performance of heuristic approaches to the number partitioning problem and allows us to calculate the probability distributions of the optimum and sub-optimum costs.Comment: 4 pages, Revtex, 2 figures (eps), submitted to PR

Embarrassingly Parallel Search

International audienceWe propose the Embarrassingly Parallel Search, a simple and efficient method for solving constraint programming problems in parallel. We split the initial problem into a huge number of independent subproblems and solve them with available workers (i.e., cores of machines). The decomposition into subproblems is computed by selecting a subset of variables and by enumerating the combinations of values of these variables that are not detected inconsistent by the propagation mechanism of a CP Solver. The experiments on satisfaction problems and on optimization problems suggest that generating between thirty and one hundred subproblems per worker leads to a good scalability. We show that our method is quite competitive with the work stealing approach and able to solve some classical problems at the maximum capacity of the multi-core machines. Thanks to it, a user can parallelize the resolution of its problem without modifying the solver or writing any parallel source code and can easily replay the resolution of a problem