Static Load-Balancing Techniques For Iterative Computations On Heterogeneous Clusters

This paper is devoted to static load balancing techniques for mapping iterative algorithms onto heterogeneous clusters. The application data is partitioned over the processors. At each iteration, independent calculations are carried out in parallel, and some communications take place. The questio

Data Redistribution Algorithms For Heterogeneous Processor Rings

We consider the problem of redistributing data on homogeneous and heterogeneous ring of processors. The problem arises in several applications, each time after that a load-balancing mechanism is invoked (but we do not discuss the load-balancing mechanism itself). We provide algorithms that aim at optimizing the data redistribution, both for unidirectional and bi-directional rings, and we give complete proofs of correctness. One major contribution of the paper is that we are able to prove the optimality of the proposed algorithms in all cases except that of a bi-directional heterogeneous ring, for which the problem remains open

TractLearn: A geodesic learning framework for quantitative analysis of brain bundles

International audienceDeep learning-based convolutional neural networks have recently proved their efficiency in providing fast segmentation of major brain fascicles structures, based on diffusion-weighted imaging. The quantitative analysis of brain fascicles then relies on metrics either coming from the tractography process itself or from each voxel along the bundle. Statistical detection of abnormal voxels in the context of disease usually relies on univariate and multivariate statistics models, such as the General Linear Model (GLM). Yet in the case of high-dimensional low sample size data, the GLM often implies high standard deviation range in controls due to anatomical variability, despite the commonly used smoothing process. This can lead to difficulties to detect subtle quantitative alterations from a brain bundle at the voxel scale. Here we introduce TractLearn, a unified framework for brain fascicles quantitative analyses by using geodesic learning as a data-driven learning task. TractLearn allows a mapping between the image high-dimensional domain and the reduced latent space of brain fascicles using a Riemannian approach. We illustrate the robustness of this method on a healthy population with test-retest acquisition of multi-shell diffusion MRI data, demonstrating that it is possible to separately study the global effect due to different MRI sessions from the effect of local bundle alterations. We have then tested the efficiency of our algorithm on a sample of 5 age-matched subjects referred with mild traumatic brain injury. Our contributions are to propose: 1/ A manifold approach to capture controls variability as standard reference instead of an atlas approach based on a Euclidean mean. 2/ A tool to detect global variation of voxels' quantitative values, which accounts for voxels' interactions in a structure rather than analyzing voxels independently. 3/ A ready-to-plug algorithm to highlight nonlinear variation of diffusion MRI metrics. With this regard, TractLearn is a ready-to-use algorithm for precision medicine

Brassica orthologs from BANYULS belong to a small multigene family, which is involved in procyanidin accumulation in the seed.

International audienceAs part of a research programme focused on flavonoid biosynthesis in the seed coat of Brassica napus L. (oilseed rape), orthologs of the BANYULS gene that encoded anthocyanidin reductase were cloned in B. napus as well as in the related species Brassica rapa and Brassica oleracea. B. napus genome contained four functional copies of BAN, two originating from each diploid progenitor. Amino acid sequences were highly conserved between the Brassicaceae including B. napus, B. rapa, B. oleracea as well as the model plant Arabidopsis thaliana. Along the 200 bp in 5′ of the ATG codon, Bna.BAN promoters (ProBna.BAN) were conserved with AtANR promoter and contained putative cis-acting elements. In addition, transgenic Arabidopsis and oilseed rape plants carrying the first 230 bp of ProBna.BAN fused to the UidA reporter gene were generated. In the two Brassicaceae backgrounds, ProBna.BAN activity was restricted to the seed coat. In B. napus seed, ProBna.BAN was activated in procyanidin-accumulating cells, namely the innermost layer of the inner integument and the micropyle-chalaza area. At the transcriptional level, the four Bna.BAN genes were expressed in the seed. Laser microdissection assays of the seed integuments showed that Bna.BAN expression was restricted to the inner integument, which was consistent with the activation profile of ProBna.BAN. Finally, Bna.BAN genes were mapped onto oilseed rape genetic maps and potential co-localisations with seed colour quantitative trait loci are discussed