Design study of a device to simulate the dynamic environment encountered under condi- tions of reduced or zero gravity final report

Design study of reduced or zero gravity environment simulation devic

Lunar Outgassing, Transient Phenomena and The Return to The Moon, I: Existing Data

Herein the transient lunar phenomena (TLP) report database is subjected to a discriminating statistical filter robust against sites of spurious reports, and produces a restricted sample that may be largely reliable. This subset is highly correlated geographically with the catalog of outgassing events seen by the Apollo 15, 16 and Lunar Prospector alpha-particle spectrometers for episodic Rn-222 gas release. Both this robust TLP sample and even the larger, unfiltered sample are highly correlated with the boundary between mare and highlands, as are both deep and shallow moonquakes, as well as Po-210, a long-lived product of Rn-222 decay and a further tracer of outgassing. This offers another significant correlation relating TLPs and outgassing, and may tie some of this activity to sagging mare basalt plains (perhaps mascons). Additionally, low-level but likely significant TLP activity is connected to recent, major impact craters (while moonquakes are not), which may indicate the effects of cracks caused by the impacts, or perhaps avalanches, allowing release of gas. The majority of TLP (and Rn-222) activity, however, is confined to one site that produced much of the basalt in the Procellarum Terrane, and it seems plausible that this TLP activity may be tied to residual outgassing from the formerly largest volcanic ffusion sites from the deep lunar interior. With the coming in the next few years of robotic spacecraft followed by human exploration, the study of TLPs and outgassing is both promising and imperiled. We will have an unprecedented pportunity to study lunar outgassing, but will also deal with a greater burden of anthropogenic lunar gas than ever produced. There is a pressing need to study lunar atmosphere and its sources while still pristine. [Abstract abridged.]Comment: 35 pages, 3 figures, submitted to Icarus. Other papers in series found at http://www.astro.columbia.edu/~arlin/TLP

Intramolecular diffusive motion in alkane monolayers studied by high-resolution quasielastic neutron scattering and molecular dynamics simulations

URL:http://link.aps.org/doi/10.1103/PhysRevLett.92.046103 DOI:10.1103/PhysRevLett.92.046103Molecular dynamics simulations of a tetracosane (n-C24H50) monolayer adsorbed on a graphite basal-plane surface show that there are diffusive motions associated with the creation and annihilation of gauche defects occurring on a time scale of ~0.1-4 ns. We present evidence that these relatively slow motions are observable by high-energy-resolution quasielastic neutron scattering (QNS) thus demonstrating QNS as a technique, complementary to nuclear magnetic resonance, for studying conformational dynamics on a nanosecond time scale in molecular monolayers.This work was supported by the NSF under Grants No. DMR-9802476 and No. DMR-0109057, by the Chilean government under FONDECYT Grant No. 1010548, and by the U.S. Department of Energy through Grant No. DE-FG02-01ER45912. The neutron scattering facilities in this work are supported in part by the National Science Foundation under Agreement No. DMR-0086210

Association of NCF2, IKZF1, IRF8, IFIH1, and TYK2 with Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a complex trait characterised by the production of a range of auto-antibodies and a diverse set of clinical phenotypes. Currently, ∼8% of the genetic contribution to SLE in Europeans is known, following publication of several moderate-sized genome-wide (GW) association studies, which identified loci with a strong effect (OR>1.3). In order to identify additional genes contributing to SLE susceptibility, we conducted a replication study in a UK dataset (870 cases, 5,551 controls) of 23 variants that showed moderate-risk for lupus in previous studies. Association analysis in the UK dataset and subsequent meta-analysis with the published data identified five SLE susceptibility genes reaching genome-wide levels of significance (Pcomb<5×10−8): NCF2 (Pcomb = 2.87×10−11), IKZF1 (Pcomb = 2.33×10−9), IRF8 (Pcomb = 1.24×10−8), IFIH1 (Pcomb = 1.63×10−8), and TYK2 (Pcomb = 3.88×10−8). Each of the five new loci identified here can be mapped into interferon signalling pathways, which are known to play a key role in the pathogenesis of SLE. These results increase the number of established susceptibility genes for lupus to ∼30 and validate the importance of using large datasets to confirm associations of loci which moderately increase the risk for disease

The Influence of Polygenic Risk Scores on Heritability of Anti-CCP Level in RA

Objective: To study genetic factors that influence quantitative anti-cyclic citrullinated peptide (anti-CCP) antibody levels in RA patients. Methods: We carried out a genome wide association study (GWAS) meta-analysis using 1,975 anti-CCP+ RA patients from 3 large cohorts, the Brigham Rheumatoid Arthritis Sequential Study (BRASS), North American Rheumatoid Arthritis Consortium (NARAC), and the Epidemiological Investigation of RA (EIRA). We also carried out a genome-wide complex trait analysis (GCTA) to estimate the heritability of anti-CCP levels. Results: GWAS-meta analysis showed that anti-CCP levels were most strongly associated with the human leukocyte antigen (HLA) region with a p-value of 2×10−11 for rs1980493. There were 112 SNPs in this region that exceeded the genome-wide significance threshold of 5×10−8, and all were in linkage disequilibrium (LD) with the HLA- DRB1*03 allele with LD r2 in the range of 0.25-0.88. Suggestive novel associations outside of the HLA region were also observed for rs8063248 (near the GP2 gene) with a p-value of 3×10−7. None of the known RA risk alleles (~52 loci) were associated with anti-CCP level. Heritability analysis estimated that 44% of anti-CCP variation was attributable to genetic factors captured by GWAS variants. Conclusions: Anti-CCP level is a heritable trait. HLA-DR3 and GP2 are associated with lower anti-CCP levels

Risk Alleles for Systemic Lupus Erythematosus in a Large Case-Control Collection and Associations with Clinical Subphenotypes

Systemic lupus erythematosus (SLE) is a genetically complex disease with heterogeneous clinical manifestations. Recent studies have greatly expanded the number of established SLE risk alleles, but the distribution of multiple risk alleles in cases versus controls and their relationship to subphenotypes have not been studied. We studied 22 SLE susceptibility polymorphisms with previous genome-wide evidence of association (p<5×10−8) in 1919 SLE cases from 9 independent Caucasian SLE case series and 4813 independent controls. The mean number of risk alleles in cases was 15.1 (SD 3.1) while the mean in controls was 13.1 (SD 2.8), with trend p = 4×10−128. We defined a genetic risk score (GRS) for SLE as the number of risk alleles with each weighted by the SLE risk odds ratio (OR). The OR for high-low GRS tertiles, adjusted for intra-European ancestry, sex, and parent study, was 4.4 (95% CI 3.8–5.1). We studied associations of individual SNPs and the GRS with clinical manifestations for the cases: age at diagnosis, the 11 American College of Rheumatology classification criteria, and double-stranded DNA antibody (anti-dsDNA) production. Six subphenotypes were significantly associated with the GRS, most notably anti-dsDNA (ORhigh-low = 2.36, p = 9e−9), the immunologic criterion (ORhigh-low = 2.23, p = 3e−7), and age at diagnosis (ORhigh-low = 1.45, p = 0.0060). Finally, we developed a subphenotype-specific GRS (sub-GRS) for each phenotype with more power to detect cumulative genetic associations. The sub-GRS was more strongly associated than any single SNP effect for 5 subphenotypes (the above plus hematologic disorder and oral ulcers), while single loci are more significantly associated with renal disease (HLA-DRB1, OR = 1.37, 95% CI 1.14–1.64) and arthritis (ITGAM, OR = 0.72, 95% CI 0.59–0.88). We did not observe significant associations for other subphenotypes, for individual loci or the sub-GRS. Thus our analysis categorizes SLE subphenotypes into three groups: those having cumulative, single, and no known genetic association with respect to the currently established SLE risk loci