Gain of 20q11.21 in human pluripotent stem cells impairs TGF-β-dependent neuroectodermal commitment

Gain of 20q11.21 is one of the most common recurrent genomic aberrations in human pluripotent stem cells. Although it is known that overexpression of the antiapoptotic gene Bcl-xL confers a survival advantage to the abnormal cells, their differentiation capacity has not been fully investigated. RNA sequencing of mutant and control hESC lines, and a line transgenically overexpressing Bcl-xL, shows that overexpression of Bcl-xL is sufficient to cause most transcriptional changes induced by the gain of 20q11.21. Moreover, the differentially expressed genes in mutant and Bcl-xL overexpressing lines are enriched for genes involved in TGF-beta- and SMAD-mediated signaling, and neuron differentiation. Finally, we show that this altered signaling has a dramatic negative effect on neuroectodermal differentiation, while the cells maintain their ability to differentiate to mesendoderm derivatives. These findings stress the importance of thorough genetic testing of the lines before their use in research or the clinic

Scheduling internal audit activities:A stochastic combinatorial optimization problem

The problem of finding the optimal timing of audit activities within an organisation has been addressed by many researchers. We propose a stochastic programming formulation with Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) certainty-equivalent models. In experiments neither approach dominates the other. However, the CP approach is orders of magnitude faster for large audit times, and almost as fast as the MILP approach for small audit times. This work generalises a previous approach by relaxing the assumption of instantaneous audits, and by prohibiting concurrent auditin

Constraint-level advice for shaving

International audienceThis work concentrates on improving the robustness of con- straint solvers by increasing the propagation strength of constraint mod- els in a declarative and automatic manner. Our objective is to efficiently identify and remove shavable values during search. A value is shavable if as soon as it is assigned to its associated variable an inconsistency can be detected, making it possible to refute it. We extend previous work on shaving by using different techniques to decide if a given value is an interesting candidate for the shaving process. More precisely, we exploit the semantics of (global) constraints to suggest values, and reuse both the successes and failures of shaving later in search to tune shaving fur- ther. We illustrate our approach with two important global constraints, namely alldifferent and sum, and present the results of an experimen- tation obtained for three problem classes. The experimental results are quite encouraging: we are able to significantly reduce the number of search nodes (even by more than two orders of magnitude), and improve the average execution time by one order of magnitude

POOC - a Platform for Object-Oriented Constraint Programming

In this paper, we describe an implementation-independent object-oriented interface for commercial and academic Constraint Solvers

Faster Algorithms for Bound-Consistency of the Sortedness and the Alldifferent Constraint

We present narrowing algorithms for the sortedness and the alldifferent constraint which achieve bound-consistency. The algorithm for the sortedness constraint takes as input 2n intervals X1 , ..., Xn , Y1 , ..., Yn from a linearly ordered set D. Let S denote the set of all tuples t 2 X1 Xn Y1 Yn such that the last n components of t are obtained by sorting the first n components. Our algorithm determines whether S is non-empty and if so reduces the intervals to bound-consistency. The running time of the algorithm is asymptotically the same as for sorting the interval endpoints. In problems where this is faster than O(n log n), this improves upon previous results. The algorithm for the alldifferent constraint takes as input n integer intervals Z1 , ..., Zn . Let T denote all tuples t 2 Z1 Zn where all components are pairwise different. The algorithm checks whether T is non-empty and if so reduces the ranges to bound-consistency. The running time is also asymptotically the same as for sorting the interval endpoints. When the constraint is for example a permutation constraint, i.e. Z i [1; n] for all i, the running time is linear. This also improves upon previous results

Extending forward checking

Abstract. Among backtracking based algorithms for constraint satisfaction problems (CSPs), algorithms employing constraint propagation, like forward checking (FC) and MAC, have had the most practical impact. These algorithms use constraint propagation during search to prune inconsistent values from the domains of the uninstantiated variables. In this paper we present a general approach to extending constraint propagating algorithms, especially forward checking. In particular, we provide a simple yet flexible mechanism for pruning domain values, and show that with this in place it becomes easy to utilize new mechanisms for detecting inconsistent values during search. This leads to a powerful and uniform technique for designing new CSP algorithms: one simply need design new methods for detecting inconsistent values and then interface them with the domain pruning mechanism. Furthermore, we also show that algorithms following this design can proved to be correct in a simple and uniform way. To demonstrate the utility of these ideas five “new ” CSP algorithms are presented.

A CSP search algorithm with responsibility sets and kernels

A CSP search algorithm, like FC or MAC, explores a search tree during its run. Every node of the search tree can be associated with a CSP created by the refined domains of unassigned variables. If the algorithm detects that the CSP associated with a node is insoluble, the node becomes a dead-end. A strategy of pruning “by analogy” states that the current node of the search tree can be discarded if the CSP associated with it is “more constrained” than a CSP associated with some dead-end node. In this paper we present a method of pruning based on the above strategy. The information about the CSPs associated with dead-end nodes is kept in the structures called responsibility sets and kernels. We term the method that uses these structures for pruning RKP, which is abbreviation of Responsibility set, Kernel, Propagation. We combine the pruning method with algorithms FC and MAC. We call the resulting solvers FC-RKP and MAC-RKP, respectively. Experimental evaluation shows that MAC-RKP outperforms MAC-CBJ on random CSPs and on random graph coloring problems. The RKP-method also has theoretical interest. We show that under certain restrictions FC-RKP simulates FC-CBJ. It follows from the fact that intelligent backtracking implicitly uses the strategy of pruning “by analogy.