Ultra high-speed transaxial image reconstruction of the heart, lungs, and circulation via numerical approximation methods and optimized processor architecture

A high temporal resolution scanning multiaxial tomography unit, the Dynamic Spatial Reconstructor (DSR), presently under development will be capable of recording multiangular X-ray projection data of sufficient axial range to reconstruct a cylindrical volume consisting of up to 240 contiguous 1-mm thick cross sections encompassing the intact thorax. At repetition rates of up to 60 sets of cross sections per second, the DSR will thus record projection data sufficient to reconstruct as many as 14 400 cross-sectional images during each second of operation. Use of this system in a clinical setting will be dependent upon the development of software and hardware techniques for carrying out X-ray reconstructions at the rate of hundreds of cross sections per second. A conceptual design, with several variations, is proposed for a special purpose hardware reconstruction processor capable of completing a single cross section reconstruction within 1 to 2 msec. In addition, it is suggested that the amount of computation required to execute the filtered back-projection algorithm may be decreased significantly by the utilization of approximation equations, formulated as recursions, for the generation of internal constants required by the algorithm. The effects on reconstructed image quality of several different approximation methods are investigated by reconstruction of density projections generated from a mathematically simulated model of the human thorax, assuming the same source-detector geometry and X-ray flux density as will be employed by the DSR. These studies have indicated that the prudent application of numerical approximations for the generation of internal constants will not cause significant degradation in reconstructed image quality and will in fact require substantially less auxiliary memory and computational capacity than required by direct execution of mathematically exact formulations of the reconstruction algorithm.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/23631/1/0000595.pd

Error-analysis and comparison to analytical models of numerical waveforms produced by the NRAR Collaboration

The Numerical-Relativity-Analytical-Relativity (NRAR) collaboration is a joint effort between members of the numerical relativity, analytical relativity and gravitational-wave data analysis communities. The goal of the NRAR collaboration is to produce numerical-relativity simulations of compact binaries and use them to develop accurate analytical templates for the LIGO/Virgo Collaboration to use in detecting gravitational-wave signals and extracting astrophysical information from them. We describe the results of the first stage of the NRAR project, which focused on producing an initial set of numerical waveforms from binary black holes with moderate mass ratios and spins, as well as one non-spinning binary configuration which has a mass ratio of 10. All of the numerical waveforms are analysed in a uniform and consistent manner, with numerical errors evaluated using an analysis code created by members of the NRAR collaboration. We compare previously-calibrated, non-precessing analytical waveforms, notably the effective-one-body (EOB) and phenomenological template families, to the newly-produced numerical waveforms. We find that when the binary's total mass is ~100-200 solar masses, current EOB and phenomenological models of spinning, non-precessing binary waveforms have overlaps above 99% (for advanced LIGO) with all of the non-precessing-binary numerical waveforms with mass ratios <= 4, when maximizing over binary parameters. This implies that the loss of event rate due to modelling error is below 3%. Moreover, the non-spinning EOB waveforms previously calibrated to five non-spinning waveforms with mass ratio smaller than 6 have overlaps above 99.7% with the numerical waveform with a mass ratio of 10, without even maximizing on the binary parameters.Comment: 51 pages, 10 figures; published versio

Endothelial ether lipids link the vasculature to blood pressure, behavior, and neurodegeneration

Vascular disease contributes to neurodegeneration, which is associated with decreased blood pressure in older humans. Plasmalogens, ether phospholipids produced by peroxisomes, are decreased in Alzheimer's disease, Parkinson's disease, and other neurodegenerative disorders. However, the mechanistic links between ether phospholipids, blood pressure, and neurodegeneration are not fully understood. Here, we show that endothelium-derived ether phospholipids affect blood pressure, behavior, and neurodegeneration in mice. In young adult mice, inducible endothelial-specific disruption of PexRAP, a peroxisomal enzyme required for ether lipid synthesis, unexpectedly decreased circulating plasmalogens. PexRAP endothelial knockout (PEKO) mice responded normally to hindlimb ischemia but had lower blood pressure and increased plasma renin activity. In PEKO as compared with control mice, tyrosine hydroxylase was decreased in the locus coeruleus, which maintains blood pressure and arousal. PEKO mice moved less, slept more, and had impaired attention to and recall of environmental events as well as mild spatial memory deficits. In PEKO hippocampus, gliosis was increased, and a plasmalogen associated with memory was decreased. Despite lower blood pressure, PEKO mice had generally normal homotopic functional connectivity by optical neuroimaging of the cerebral cortex. Decreased glycogen synthase kinase-3 phosphorylation, a marker of neurodegeneration, was detected in PEKO cerebral cortex. In a co-culture system, PexRAP knockdown in brain endothelial cells decreased glycogen synthase kinase-3 phosphorylation in co-cultured astrocytes that was rescued by incubation with the ether lipid alkylglycerol. Taken together, our findings suggest that endothelium-derived ether lipids mediate several biological processes and may also confer neuroprotection in mice

Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the Initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter-estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses.Comment: 56 pages, 25 figures; various clarifications; accepted to CQ

Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions

Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (G×E) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (Pv), G×E interaction effects (with smoking and physical activity), and marginal genetic effects (Pm). Correlations between Pv and Pm were stronger for SNPs with established marginal effects (Spearman’s ρ = 0.401 for triglycerides, and ρ = 0.236 for BMI) compared to all SNPs. When Pv and Pm were compared for all pruned SNPs, only BMI was statistically significant (Spearman’s ρ = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the Pv distribution (Pbinomial <0.05). SNPs from the top 1% of the Pm distribution for BMI had more significant Pv values (PMann–Whitney= 1.46×10−5), and the odds ratio of SNPs with nominally significant (<0.05) Pm and Pv was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G×E interaction P-values (Pint<0.05) were enriched with nominally significant Pv values (Pbinomial = 8.63×10−9 and 8.52×10−7 for SNP × smoking and SNP × physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for G×E, and variance-based prioritization can be used to identify them

Submicron Structures Technology and Research

Contains reports on ten research projects.Joint Services Electronics Program (Contract DAAG29-83-K-0003)Joint Services Electronics Program (Contract DAAL03-86-K-0002)National Science Foundation (Grant ECS82-05701)National Science Foundation (Grant ECS85-06565)Lawrence Livermore Laboratory (Subcontract 2069209)National Science Foundation (Grant ECS85-03443)U.S. Air Force - Office of Scientific Research (Grant AFOSR-85-0154)National Aeronautics and Space Administration (Grant NGL22-009-638)National Science Foundation (through KMS Fusion, Inc.)U.S. Navy - Office of Naval Research (Contract N00014-79-C-0908

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin