The New Horizons Spacecraft

The New Horizons spacecraft was launched on 19 January 2006. The spacecraft was designed to provide a platform for seven instruments that will collect and return data from Pluto in 2015. The design drew on heritage from previous missions developed at The Johns Hopkins University Applied Physics Laboratory (APL) and other missions such as Ulysses. The trajectory design imposed constraints on mass and structural strength to meet the high launch acceleration needed to reach the Pluto system prior to the year 2020. The spacecraft subsystems were designed to meet tight mass and power allocations, yet provide the necessary control and data handling finesse to support data collection and return when the one-way light time during the Pluto flyby is 4.5 hours. Missions to the outer solar system require a radioisotope thermoelectric generator (RTG) to supply electrical power, and a single RTG is used by New Horizons. To accommodate this constraint, the spacecraft electronics were designed to operate on less than 200 W. The spacecraft system architecture provides sufficient redundancy to provide a probability of mission success of greater than 0.85, even with a mission duration of over 10 years. The spacecraft is now on its way to Pluto, with an arrival date of 14 July 2015. Initial inflight tests have verified that the spacecraft will meet the design requirements.Comment: 33 pages, 13 figures, 4 tables; To appear in a special volume of Space Science Reviews on the New Horizons missio

Genome-wide linkage screen for testicular germ cell tumour susceptibility loci

A family history of disease is a strong risk factor for testicular germ cell tumour (TGCT). In order to identify the location of putative TGCT susceptibility gene(s) we conducted a linkage search in 237 pedigrees with two or more cases of TGCT. One hundred and seventy-nine pedigrees were evaluated genome-wide with an average inter-marker distance of 10 cM. An additional 58 pedigrees were used to more intensively investigate several genomic regions of interest. Genetic linkage analysis was performed with the ALLEGRO software using two model-based parametric analyses and a non-parametric analysis. Six genomic regions on chromosomes 2p23, 3p12, 3q26, 12p13-q21, 18q21-q23 and Xq27 showed heterogeneity LOD (HLOD) scores of greater than 1, with a maximum HLOD of 1.94 at 3q26. Genome-wide simulation studies indicate that the observed number of HLOD peaks greater than one does not differ significantly from that expected by chance. A TGCT locus at Xq27 has been previously reported. Of the 237 pedigrees examined in this study, 66 were previously unstudied at Xq27, no evidence for linkage to this region was observed in this new pedigree set. Overall, the results indicate that no single major locus can account for the majority of the familial aggregation of TGCT, and suggests that multiple susceptibility loci with weak effects contribute to the diseas

Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Background: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions: Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses

Multifactorial Analysis of Differences Between Sporadic Breast Cancers and Cancers Involving BRCA1 and BRCA2 Mutations

Background: We have previously demonstrated that breast cancers associated with inherited BRCA1 and BRCA2 gene mutations differ from each other in their histopathologic appearances and that each of these types differs from breast cancers in patients unselected for family history (i.e., sporadic cancers). We have now conducted a more detailed examination of cytologic and architectural features of these tumors. Methods: Specimens of tumor tissue (5-µm-thick sections) were examined independently by two pathologists, who were unaware of the case or control subject status, for the presence of cell mitosis, lymphocytic infiltration, continuous pushing margins, and solid sheets of cancer cells; cell nuclei, cell nucleoli, cell necrosis, and cell borders were also evaluated. The resulting data were combined with previously available information on tumor type and tumor grade and further evaluated by multifactorial analysis. All statistical tests are two-sided. Results: Cancers associated with BRCA1 mutations exhibited higher mitotic counts (P = .001), a greater proportion of the tumor with a continuous pushing margin (P<.0001), and more lymphocytic infiltration (P = .002) than sporadic (i.e., control) cancers. Cancers associated with BRCA2 mutations exhibited a higher score for tubule formation (fewer tubules) (P = .0002), a higher proportion of the tumor perimeter with a continuous pushing margin (P<.0001), and a lower mitotic count (P = .003) than control cancers. Conclusions: Our study has identified key features of the histologic phenotypes of breast cancers in carriers of mutant BRCA1 and BRCA2 genes. This information may improve the classification of breast cancers in individuals with a family history of the disease and may ultimately aid in the clinical management of patients. [J Natl Cancer Inst 1998;90:1138-45

Automatically Harnessing Sparse Acceleration

Sparse linear algebra is central to many scientific programs, yet compilers fail to optimize it well. High-performance libraries are available, but adoption costs are significant. Moreover, libraries tie programs into vendor-specific software and hardware ecosystems, creating non-portable code. In this paper, we develop a new approach based on our specification Language for implementers of Linear Algebra Computations (LiLAC). Rather than requiring the application developer to (re)write every program for a given library, the burden is shifted to a one-off description by the library implementer. The LiLAC-enabled compiler uses this to insert appropriate library routines without source code changes. LiLAC provides automatic data marshaling, maintaining state between calls and minimizing data transfers. Appropriate places for library insertion are detected in compiler intermediate representation, independent of source languages. We evaluated on large-scale scientific applications written in FORTRAN; standard C/C++ and FORTRAN benchmarks; and C++ graph analytics kernels. Across heterogeneous platforms, applications and data sets we show speedups of 1.1$\times$ to over 10$\times$ without user intervention.Comment: Accepted to CC 202