Conjunctions of Among Constraints

Many existing global constraints can be encoded as a conjunction of among constraints. An among constraint holds if the number of the variables in its scope whose value belongs to a prespecified set, which we call its range, is within some given bounds. It is known that domain filtering algorithms can benefit from reasoning about the interaction of among constraints so that values can be filtered out taking into consideration several among constraints simultaneously. The present pa- per embarks into a systematic investigation on the circumstances under which it is possible to obtain efficient and complete domain filtering algorithms for conjunctions of among constraints. We start by observing that restrictions on both the scope and the range of the among constraints are necessary to obtain meaningful results. Then, we derive a domain flow-based filtering algorithm and present several applications. In particular, it is shown that the algorithm unifies and generalizes several previous existing results.Comment: 15 pages plus appendi

Brachypodium distachyon as a model for defining the allergen potential of non-prolamin proteins

Epitope databases and the protein sequences of published plant genomes are suitable to identify some of the proteins causing food allergies and sensitivities. Brachypodium distachyon, a diploid wild grass with a sequenced genome and low prolamin content, is the closest relative of the allergen cereals, such as wheat or barley. Using the Brachypodium genome sequence, a workflow has been developed to identify potentially harmful proteins which may cause either celiac disease or wheat allergy-related symptoms. Seed tissue-specific expression of the potential allergens has been determined, and intact epitopes following an in silico digestion with several endopeptidases have been identified. Molecular function of allergen proteins has been evaluated using Gene Ontology terms. Biologically overrepresented proteins and potentially allergen protein families have been identified. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10142-012-0294-z) contains supplementary material, which is available to authorized users

Characterization of beta-fructosidase, an extracellular glycoprotein of carrot cells

International audienc

Learning a functional control for high-frequency finance

We use a deep neural network to generate controllers for optimal trading on high frequency data. For the first time, a neural network learns the mapping between the preferences of the trader, i.e. risk aversion parameters, and the optimal controls. An important challenge in learning this mapping is that in intraday trading, trader's actions influence price dynamics in closed loop via the market impact. The exploration--exploitation tradeoff generated by the efficient execution is addressed by tuning the trader's preferences to ensure long enough trajectories are produced during the learning phase. The issue of scarcity of financial data is solved by transfer learning: the neural network is first trained on trajectories generated thanks to a Monte-Carlo scheme, leading to a good initialization before training on historical trajectories. Moreover, to answer to genuine requests of financial regulators on the explainability of machine learning generated controls, we project the obtained "blackbox controls" on the space usually spanned by the closed-form solution of the stylized optimal trading problem, leading to a transparent structure. For more realistic loss functions that have no closed-form solution, we show that the average distance between the generated controls and their explainable version remains small. This opens the door to the acceptance of ML-generated controls by financial regulators

The Snf1-related protein kinases SnRK2.4 and SnRK2.10 are involved in maintenance of root system architecture during salt stress

The Sucrose non-fermenting-1 related protein kinase 2 (SnRK2) family represents a unique family of plant specific-protein kinases implicated in cellular signalling in response to osmotic stress. In our studies, we observed that two class 1 SnRK2 kinases, SnRK2.4 and SnRK2.10, are rapidly and transiently activated in Arabidopsis roots after exposure to salt. Under saline conditions, snrk2.4 knockout mutants had a reduced primary root length, while snrk2.10 mutants exhibited a reduction in the number of lateral roots. The reduced lateral root density was found to be a combinatory effect of a decrease in the amount of lateral root primordia and an increase in the amount of arrested lateral root primordia. The phenotypes were in agreement with the observed expression patterns of genomic YFP-fusions of SnRK2.10 and 2.4, under control of their native promoter sequences. SnRK2.10 was found to be expressed in the vascular tissue at the base of a developing lateral root, whereas SnRK2.4 was expressed throughout the root, with higher expression in the vascular system. Salt stress triggered a rapid re-localization of SnRK2.4-YFP from the cytosol to punctate structures in root epidermal cells. Differential centrifugation experiments of isolated Arabidopsis root proteins, confirmed recruitment of endogenous SnRK2.4/2.10 to membranes upon exposure to salt, supporting their observed binding affinity for the phospholipid phosphatidic acid. Together, our results reveal a role for SnRK2.4 and 2.10 in root growth and architecture in saline conditions. (c) 2012 The Authors. The Plant Journal (c) 2012 Blackwell Publishing Ltd

Genetic differences in omega-gliadins involved in two different immediate food hypersensitivities to wheat

Background: Anti-gliadin IgE are expressed in patients with food allergy associated to skin immediate hypersensitivity to hydrolyzed wheat proteins (IHHWP). It is not known if they react with omega 5-gliadins, the major allergens in wheat dependant exercise-induced food anaphylaxis (WDEIA), encoded on wheat chromosomes 1B. Methods: Unmodified gliadins from 14 wheat varieties expressing most of the 1B omega-gliadin alleles, were immunoprobed after SDS-PAGE and blotting, with four sera from patients with IHHWP, and two with WDEIA. Gliadins reacting with IgE were visualized using chemiluminescence and identified according to their mobility and typical SDS-PAGE pattern. The resulting signal was also measured to compare their IgE reactivity. Results: IHHWP and WDEIA sera exhibited distinct patterns of reactivity. IgE of patients with IHHWP reacted mainly with all omega-gliadins alleles and one gamma-gliadin encoded respectively on chromosomes 1D and 1B, but not with any omega 5-gliadins alleles as for WDEIA. A few other reactive alleles of omega-gliadins were encoded on chromosomes 1A. Unassigned additional bands of the whole gliadin pattern were also reactive. The four patients with IHHWP exhibited almost the same pattern of reactivity. Main differences concerned band reactivity which modulated the overall reactivity of each wheat variety. Conclusions: The IgE epitopes involved in IHHWP and WDEIA are different. This suggests that the protein state and the route of exposure to very similar gluten structures, probably orientate the pattern of epitope reactivity and the wheat food allergy manifestations

Cp(graph): Introducing a graph computation domain in constraint programming

Abstract. In an increasing number of domains such as bioinformatics, combinatorial graph problems arise. We propose a novel way to solve these problems, mainly those that can be translated to constrained subgraph finding. Our approach extends constraint programming by introducing CP(Graph), a new computation domain focused on graphs including a new type of variable: graph domain variables as well as constraints over these variables and their propagators. These constraints are subdivided into kernel constraints and additional constraints formulated as networks of kernel constraints. For some of these constraints a dedicated global constraint and its associated propagator are sketched. CP(Graph) is integrated with finite domain and finite sets computation domains, allowing the combining of constraints of these domains with graph constraints. A prototype of CP(Graph) built over finite domains and finite sets in Oz is presented. And we show that a problem of biochemical network analysis can be very simply described and solved within CP(Graph).