Symposium: THE MULTINATIONAL ENTERPRISE AS GLOBAL CORPORATE CITIZEN

The word globalization finds its way into many commentaries these days, in different ways and different connotations. Sometimes it is thought to be beneficial, and sometimes it\u27s thought to be harmful. We have here today four people highly qualified to talk about this topic. The focus on the multinational enterprise, which is one of the terms that\u27s used for a company that does business in many jurisdictions, is the focus of today\u27s discussion, and whether and how multinational enterprises function as global corporate citizens

Production and calibration of the first HEFT hard x-ray optics module

Complete hard X-ray optics modules are currently being produced for the High Energy Focusing Telescope (HEFT), a balloon born mission that will observe a wide range of objects including young supernova remnants, active galactic nuclei, and galaxy clusters at energies between 20 and 70 keV. Large collecting areas are achieved by tightly nesting layers of grazing incidence mirrors in a conic approximation Wolter-I design. The segmented layers are made of thermally-formed glass substrates coated with depth-graded multilayer films for enhanced reflectivity. Our novel mounting technique involves constraining these mirror segments to successive layers of precisely machined graphite spacers. We report the production and calibration of the first HEFT optics module

A synthesis is emerging between biodiversity–ecosystem function and ecological resilience research: reply to Mori

Letter

Computational Analysis of Whole-Genome Differential Allelic Expression Data in Human

Allelic imbalance (AI) is a phenomenon where the two alleles of a given gene are expressed at different levels in a given cell, either because of epigenetic inactivation of one of the two alleles, or because of genetic variation in regulatory regions. Recently, Bing et al. have described the use of genotyping arrays to assay AI at a high resolution (∼750,000 SNPs across the autosomes). In this paper, we investigate computational approaches to analyze this data and identify genomic regions with AI in an unbiased and robust statistical manner. We propose two families of approaches: (i) a statistical approach based on z-score computations, and (ii) a family of machine learning approaches based on Hidden Markov Models. Each method is evaluated using previously published experimental data sets as well as with permutation testing. When applied to whole genome data from 53 HapMap samples, our approaches reveal that allelic imbalance is widespread (most expressed genes show evidence of AI in at least one of our 53 samples) and that most AI regions in a given individual are also found in at least a few other individuals. While many AI regions identified in the genome correspond to known protein-coding transcripts, others overlap with recently discovered long non-coding RNAs. We also observe that genomic regions with AI not only include complete transcripts with consistent differential expression levels, but also more complex patterns of allelic expression such as alternative promoters and alternative 3′ end. The approaches developed not only shed light on the incidence and mechanisms of allelic expression, but will also help towards mapping the genetic causes of allelic expression and identify cases where this variation may be linked to diseases

Copy Number Variation in Familial Parkinson Disease

Copy number variants (CNVs) are known to cause Mendelian forms of Parkinson disease (PD), most notably in SNCA and PARK2. PARK2 has a recessive mode of inheritance; however, recent evidence demonstrates that a single CNV in PARK2 (but not a single missense mutation) may increase risk for PD. We recently performed a genome-wide association study for PD that excluded individuals known to have either a LRRK2 mutation or two PARK2 mutations. Data from the Illumina370Duo arrays were re-clustered using only white individuals with high quality intensity data, and CNV calls were made using two algorithms, PennCNV and QuantiSNP. After quality assessment, the final sample included 816 cases and 856 controls. Results varied between the two CNV calling algorithms for many regions, including the PARK2 locus (genome-wide p = 0.04 for PennCNV and p = 0.13 for QuantiSNP). However, there was consistent evidence with both algorithms for two novel genes, USP32 and DOCK5 (empirical, genome-wide p-values<0.001). PARK2 CNVs tended to be larger, and all instances that were molecularly tested were validated. In contrast, the CNVs in both novel loci were smaller and failed to replicate using real-time PCR, MLPA, and gel electrophoresis. The DOCK5 variation is more akin to a VNTR than a typical CNV and the association is likely caused by artifact due to DNA source. DNA for all the cases was derived from whole blood, while the DNA for all controls was derived from lymphoblast cell lines. The USP32 locus contains many SNPs with low minor allele frequency leading to a loss of heterozygosity that may have been spuriously interpreted by the CNV calling algorithms as support for a deletion. Thus, only the CNVs within the PARK2 locus could be molecularly validated and associated with PD susceptibility