Different point mutations in the nucleolar protein fibrillarin (Nop1p in Saccharomyces cerevisiae) can inhibit different steps in ribosome synthesis. A screen for mutations that are synthetically lethal (sl) with the nop1-5 allele, which inhibits pre-rRNA processing, identified NOP56. An independent sl mutation screen with nop1-3, which inhibits pre-rRNA methylation, identified a mutation in NOP58. Strikingly, Nop56p and Nop58p are highly homologous (45% identity). Both proteins were found to be essential and localized to the nucleolus. A temperature-sensitive lethal mutant allele, nop56-2, inhibited many steps in pre-rRNA processing, particularly on the pathway of 25S/5.8S rRNA synthesis, and led to defects in 60S subunit assembly. Epitope-tagged constructs show that both Nop56p and Nop58p are associated with Nop1p in complexes, Nop56p and Nop1p exhibiting a stoichiometric association. These physical interactions presumably underlie the observed sl phenotypes. Well-conserved homologs are present in a range of organisms, including humans (52% identity between human hNop56p and yeast Nop56p), suggesting that these complexes have been conserved in evolution.Most steps of ribosome biogenesis occur in the nucleolus, a specialized subnuclear structure (for reviews, see references 14, 34, 39, 45, and 53). In eukaryotes including Saccharomyces cerevisiae and humans, a large precursor rRNA transcript (prerRNA) is processed into the mature 18S, 5.8S, and 25S/28S rRNAs. During transcription and processing, these rRNAs associate with approximately 80 ribosomal proteins and with the 5S rRNA. In addition, the mature rRNA regions of the prerRNA undergo extensive covalent nucleotide modification, mainly base modification of uridine to pseudouridine and methylation of the ribose 2Ј-hydroxyl (2Ј-O methylation) (reviewed in reference 27). The large number of concerted reactions occurring during rRNA processing and ribosome assembly has made it difficult to analyze single steps in ribosome synthesis. Over recent years, the analysis of yeast mutants defective in ribosome biogenesis has proved to be a powerful approach (reviewed in reference 51), particularly when combined with in vitro analyses using purified components (8,26,30). Despite this progress, our understanding of the detailed mechanisms of eukaryotic rRNA processing remains poor.The small nucleolar RNAs (snoRNAs) play important roles in the covalent processing of the pre-rRNAs (reviewed in references 2, 28, and 48). With the exception of RNase MRP, which is an endonuclease structurally related to RNase P, the very large numbers of snoRNAs present in eukaryotes can be divided into two groups based on conserved sequence and structural features (3, 12; reviewed in reference 48). Most of the box CϩD snoRNAs direct the site-specific 2Ј-O methylation of the pre-rRNA (19), while most of the box HϩACA snoRNAs select the sites of pseudouridine formation (6,11,33). In addition, a few members of each group of snoRNAs do not appear to select sites of pre-rRNA modification but are re...

This paper presents the main features of the Signal language and its compiler. Designed to provide safe real time system programming, the Signal language is based on the synchronous principles. Its semantics is de ned via a mathematical model of multiple-clocked ows of data and events. Signal programs describe relations on such objects, so that it is possible to program a real time application via constraints. The compiler calculates the solutions of the system and may thus be used as a proof system. Moreover, the equational approach is a natural way to derive multiprocessor executions of a program. Finally, this approach meets the intuition through a graphical interface of block-diagram style, and the system is illustrated on a speech recognition application.

The histone variant H2A.Bbd appeared to be associated with active chromatin, but how it functions is unknown. We have dissected the properties of nucleosome containing H2A.Bbd. Atomic force microscopy (AFM) and electron cryo-microscopy (cryo-EM) showed that the H2A.Bbd histone octamer organizes only approximately 130 bp of DNA, suggesting that 10 bp of each end of nucleosomal DNA are released from the octamer. In agreement with this, the entry/exit angle of the nucleosomal DNA ends formed an angle close to 180 degrees and the physico-chemical analysis pointed to a lower stability of the variant particle. Reconstitution of nucleosomes with swapped-tail mutants demonstrated that the N-terminus of H2A.Bbd has no impact on the nucleosome properties. AFM, cryo-EM and chromatin remodeling experiments showed that the overall structure and stability of the particle, but not its property to interfere with the SWI/SNF induced remodeling, were determined to a considerable extent by the H2A.Bbd docking domain. These data show that the whole H2A.Bbd histone fold domain is responsible for the unusual properties of the H2A.Bbd nucleosome.

Abstract-Most recent HPC platforms have heterogeneous nodes composed of multi-core CPUs and accelerators, like GPUs. Programming such nodes is typically based on a combination of OpenMP and CUDA/OpenCL codes; scheduling relies on a static partitioning and cost model.We present the XKaapi runtime system for data-flow task programming on multi-CPU and multi-GPU architectures, which supports a data-flow task model and a localityaware work stealing scheduler. XKaapi enables task multiimplementation on CPU or GPU and multi-level parallelism with different grain sizes. We show performance results on two dense linear algebra kernels, matrix product (GEMM) and Cholesky factorization (POTRF), to evaluate XKaapi on a heterogeneous architecture composed of two hexa-core CPUs and eight NVIDIA Fermi GPUs.Our conclusion is two-fold. First, fine grained parallelism and online scheduling achieve performance results as good as static strategies, and in most cases outperform them. This is due to an improved work stealing strategy that includes locality information; a very light implementation of the tasks in XKaapi; and an optimized search for ready tasks. Next, the multi-level parallelism on multiple CPUs and GPUs enabled by XKaapi led to a highly efficient Cholesky factorization. Using eight NVIDIA Fermi GPUs and four CPUs, we measure up to 2.43 TFlop/s on double precision matrix product and 1.79 TFlop/s on Cholesky factorization; and respectively 5.09 TFlop/s and 3.92 TFlop/s in single precision.