Broadcast spawning in Porites lutea at Reunion Island (Western Indian Ocean)

This study report in situ observations of broadcast spawning of P. lutea on a shallow reef flat of Reunion Island

Dynamic Bayesian decision network to represent growers' adaptive pre-harvest burning decisions in a sugarcane supply chain

Sugarcane growers usually burn their cane to facilitate its harvesting and transportation. Cane quality tends to deteriorate after burning, so it must be delivered as soon as possible to the mill for processing. This situation is dynamic and many factors, including weather conditions, delivery quotas and previous decisions taken, affect when and how much cane to burn. A dynamic Bayesian decision network (DBDN) was developed, using an iterative knowledge engineering approach, to represent sugarcane growers' adaptive pre-harvest burning decisions. It was evaluated against five different scenarios which were crafted to represent the range of issues the grower faces when making these decisions. The DBDN was able to adapt reactively to delays in deliveries, although the model did not have enough states representing delayed delivery statuses. The model adapted proactively to rain forecasts, but only adapted reactively to high wind forecasts. The DBDN is a promising way of modelling such dynamic, adaptive operational decisions

On the Easy Use of Scientific Computing Services for Large Scale Linear Algebra and Parallel Decision Making with the P-Grade Portal

International audienceScientific research is becoming increasingly dependent on the large-scale analysis of data using distributed computing infrastructures (Grid, cloud, GPU, etc.). Scientific computing (Petitet et al. 1999) aims at constructing mathematical models and numerical solution techniques for solving problems arising in science and engineering. In this paper, we describe the services of an integrated portal based on the P-Grade (Parallel Grid Run-time and Application Development Environment) portal (http://www.p-grade.hu) that enables the solution of large-scale linear systems of equations using direct solvers, makes easier the use of parallel block iterative algorithm and provides an interface for parallel decision making algorithms. The ultimate goal is to develop a single sign on integrated multi-service environment providing an easy access to different kind of mathematical calculations and algorithms to be performed on hybrid distributed computing infrastructures combining the benefits of large clusters, Grid or cloud, when needed

Rituximab in B-Cell Hematologic Malignancies: A Review of 20 Years of Clinical Experience

Rituximab is a human/murine, chimeric anti-CD20 monoclonal antibody with established efficacy, and a favorable and well-defined safety profile in patients with various CD20-expressing lymphoid malignancies, including indolent and aggressive forms of B-cell non-Hodgkin lymphoma. Since its first approval 20 years ago, intravenously administered rituximab has revolutionized the treatment of B-cell malignancies and has become a standard component of care for follicular lymphoma, diffuse large B-cell lymphoma, chronic lymphocytic leukemia, and mantle cell lymphoma. For all of these diseases, clinical trials have demonstrated that rituximab not only prolongs the time to disease progression but also extends overall survival. Efficacy benefits have also been shown in patients with marginal zone lymphoma and in more aggressive diseases such as Burkitt lymphoma. Although the proven clinical efficacy and success of rituximab has led to the development of other anti-CD20 monoclonal antibodies in recent years (e.g., obinutuzumab, ofatumumab, veltuzumab, and ocrelizumab), rituximab is likely to maintain a position within the therapeutic armamentarium because it is well established with a long history of successful clinical use. Furthermore, a subcutaneous formulation of the drug has been approved both in the EU and in the USA for the treatment of B-cell malignancies. Using the wealth of data published on rituximab during the last two decades, we review the preclinical development of rituximab and the clinical experience gained in the treatment of hematologic B-cell malignancies, with a focus on the well-established intravenous route of administration. This article is a companion paper to A. Davies, et al., which is also published in this issue

On the Preprocessing of Sparse Unassembled Linear Systems for Efficient Solution Using Element-by-element Preconditioners

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Element-by-Element Preconditioners for Large Partially Separable Optimization Problems

International audienceno abstrac

Subspace-by-subspace preconditioners for structured linear systems

We consider the iterative solution of symmetric positive-definite linear systems whose coefficient matrix may be expressed as the outer-product of low-rank terms. We derive suitable preconditioners for such systems, and demonstrate their effectiveness on a number of test examples. We also consider combining these methods with existing techniques to cope with the commonly-occuring case where the coefficient matrix is the linear sum of elements, some of which are of very low rank. 1 Introduction We consider the solution of n by n real linear systems of equations Ax = b; (1.1) where A is symmetric positive-definite and has the form A = e X i=1 A i A T i ; (1.2) and where A i is an n by n i real matrix. Systems of this form arise naturally in a number of ways. 1. Normal equations for least squares (see, for instance, Bjorck, 1996). 2. The Schur complement following partial elimination in augmented systems (see, for example, Duff, 1994). 3. Newton equations for partially separable op..

User's guide to PAREBE: parallel element-by-element preconditioners for the conjugate gradient algorithm

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