Automatic Mesh Generation of Hybrid Mesh on Valves in Multiple Positions in Feedline Systems

Fluid flow simulations through a valve often require evaluation of the valve in multiple opening positions. A mesh has to be generated for the valve for each position and compounding. The problem is the fact that the valve is typically part of a larger feedline system. In this paper, we propose to develop a system to create meshes for feedline systems with parametrically controlled valve openings. Herein we outline two approaches to generate the meshes for a valve in a feedline system at multiple positions. There are two issues that must be addressed. The first is the creation of the mesh on the valve for multiple positions. The second is the generation of the mesh for the total feedline system including the valve. For generation of the mesh on the valve, we will describe the use of topology matching and mesh generation parameter transfer. For generation of the total feedline system, we will describe two solutions that we have implemented. In both cases the valve is treated as a component in the feedline system. In the first method the geometry of the valve in the feedline system is replaced with a valve at a different opening position. Geometry is created to connect the valve to the feedline system. Then topology for the valve is created and the portion of the topology for the valve is topology matched to the standard valve in a different position. The mesh generation parameters are transferred and then the volume mesh for the whole feedline system is generated. The second method enables the user to generate the volume mesh on the valve in multiple open positions external to the feedline system, to insert it into the volume mesh of the feedline system, and to reduce the amount of computer time required for mesh generation because only two small volume meshes connecting the valve to the feedline mesh need to be updated

Radio Astronomy

Contains reports on sixteen research projects.National Science Foundation (Grant AST81-21416)National Science Foundation (Grant AST80-22864)National Aeronautics and Space Administration (Contract S-10665-C)National Aeronautics and Space Administration (Contract NAGW373)National Science Foundation (Grant AST79-19553)National Oceanic and Atmospheric Administration (Grant 04-8-M01-1)National Aeronautics and Space Administration (Grant NAG5-10)National Aeronautics and Space Administration (Contract NAS5-22929)Defense Advanced Research Projects Agency (Contract MDA 903-82-K-0521)Intelsat (Contract Intel-188)Joint Services Electronics Program (Contract DAAG29-80-C-0104)Lockheed Missiles and Space Company (Contract LS90B4860F

Radio Astronomy

Contains reports on eleven research projects.National Science Foundation (Grant AST79-25075)National Science Foundation (Grant AST79-20984)National Science Foundation (Grant AST79-19553)U.S. Navy - Office of Naval Research (Contract N00014-80-C-0348)National Aeronautics and Space Administration (Grant NAG2-50)MIT Sloan Fund for Basic ResearchJoint Services Electronics Program(Contract DAAG80-C-0104)Lockheed Aircraft Corporation (Contract LS90B4860F)National Aeronautics and Space Administration (Grant NAG5-10)National Aeronautics and Space Administration (Contract NAS5-22929)U.S. Department of Commerce, National Oceanic and Atmospheric Administration (Grant 04-8-MO1-1)California Institute of Technology Jet Propulsion Laboratory (Contract LZ-727891)California Institute of Technology Jet Propulsion Laboratory Subcontract 956059California Institute of Technology Jet Propulsion Laboratory Task Order RD-15

Impaired perception of facial motion in autism spectrum disorder

Copyright: © 2014 O’Brien et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This article has been made available through the Brunel Open Access Publishing Fund.Facial motion is a special type of biological motion that transmits cues for socio-emotional communication and enables the discrimination of properties such as gender and identity. We used animated average faces to examine the ability of adults with autism spectrum disorders (ASD) to perceive facial motion. Participants completed increasingly difficult tasks involving the discrimination of (1) sequences of facial motion, (2) the identity of individuals based on their facial motion and (3) the gender of individuals. Stimuli were presented in both upright and upside-down orientations to test for the difference in inversion effects often found when comparing ASD with controls in face perception. The ASD group’s performance was impaired relative to the control group in all three tasks and unlike the control group, the individuals with ASD failed to show an inversion effect. These results point to a deficit in facial biological motion processing in people with autism, which we suggest is linked to deficits in lower level motion processing we have previously reported

The APEX Quantitative Proteomics Tool: Generating protein quantitation estimates from LC-MS/MS proteomics results

Mass spectrometry (MS) based label-free protein quantitation has mainly focused on analysis of ion peak heights and peptide spectral counts. Most analyses of tandem mass spectrometry (MS/MS) data begin with an enzymatic digestion of a complex protein mixture to generate smaller peptides that can be separated and identified by an MS/MS instrument. Peptide spectral counting techniques attempt to quantify protein abundance by counting the number of detected tryptic peptides and their corresponding MS spectra. However, spectral counting is confounded by the fact that peptide physicochemical properties severely affect MS detection resulting in each peptide having a different detection probability. Lu et al. (2007) described a modified spectral counting technique, Absolute Protein Expression (APEX), which improves on basic spectral counting methods by including a correction factor for each protein (called O(i) value) that accounts for variable peptide detection by MS techniques. The technique uses machine learning classification to derive peptide detection probabilities that are used to predict the number of tryptic peptides expected to be detected for one molecule of a particular protein (O(i)). This predicted spectral count is compared to the protein's observed MS total spectral count during APEX computation of protein abundances. Results: The APEX Quantitative Proteomics Tool, introduced here, is a free open source Java application that supports the APEX protein quantitation technique. The APEX tool uses data from standard tandem mass spectrometry proteomics experiments and provides computational support for APEX protein abundance quantitation through a set of graphical user interfaces that partition thparameter controls for the various processing tasks. The tool also provides a Z-score analysis for identification of significant differential protein expression, a utility to assess APEX classifier performance via cross validation, and a utility to merge multiple APEX results into a standardized format in preparation for further statistical analysis. Conclusion: The APEX Quantitative Proteomics Tool provides a simple means to quickly derive hundreds to thousands of protein abundance values from standard liquid chromatography-tandem mass spectrometry proteomics datasets. The APEX tool provides a straightforward intuitive interface design overlaying a highly customizable computational workflow to produce protein abundance values from LC-MS/MS datasets.National Institute of Allergy and Infectious Diseases (NIAID) N01-AI15447National Institutes of HealthNational Science Foundation, the Welsh and Packard FoundationsInternational Human Frontier Science ProgramCenter for Systems and Synthetic Biolog

Transforming Ovarian Cancer Care by Targeting Minimal Residual Disease

Frontline treatment and resultant cure rates in patients with advanced ovarian cancer have changed little over the past several decades. Here, we outline a multidisciplinary approach aimed at gaining novel therapeutic insights by focusing on the poorly understood minimal residual disease phase of ovarian cancer that leads to eventual incurable recurrences

Developmental Regulation of Hepatitis B Virus Biosynthesis by Hepatocyte Nuclear Factor 4α

The host cellular factors that promote persistent viral infections in vivo are, in general, poorly understood. Utilizing the hepatitis B virus (HBV) transgenic mouse model of chronic infection, we demonstrate that the nuclear receptor, hepatocyte nuclear factor 4α (HNF4α, NR2A1), is essential for viral biosynthesis in the liver. The dependency of HBV transcription on HNF4α links viral biosynthesis and persistence to a developmentally regulated transcription factor essential for host viability

JAK2/IDH-mutant–driven myeloproliferative neoplasm is sensitive to combined targeted inhibition

Patients with myeloproliferative neoplasms (MPNs) frequently progress to bone marrow failure or acute myeloid leukemia (AML), and mutations in epigenetic regulators such as the metabolic enzyme isocitrate dehydrogenase (IDH) are associated with poor outcomes. Here, we showed that combined expression of Jak2V617Fand mutant IDH1R132Hor Idh2R140Q induces MPN progression, alters stem/progenitor cell function, and impairs differentiation in mice. Jak2V617FIdh2R140Q–mutant MPNs were sensitive to small-molecule inhibition of IDH. Combined inhibition of JAK2 and IDH2 normalized the stem and progenitor cell compartments in the murine model and reduced disease burden to a greater extent than was seen with JAK inhibition alone. In addition, combined JAK2 and IDH2 inhibitor treatment also reversed aberrant gene expression in MPN stem cells and reversed the metabolite perturbations induced by concurrent JAK2 and IDH2 mutations. Combined JAK2 and IDH2 inhibitor therapy also showed cooperative efficacy in cells from MPN patients with both JAK2mutand IDH2mutmutations. Taken together, these data suggest that combined JAK and IDH inhibition May offer a therapeutic advantage in this high-risk MPN subtype.Damon Runyon Cancer Research Foundation (DRG-2241-15)Howard Hughes Medical Institute (Faculty Scholars Award)Stand Up To CancerNational Cancer Institute (U.S.) (P50CA165962)National Cancer Institute (U.S.) (P30CA14051)Koch Institute for Integrative Cancer Research ( Dana-Farber Harvard Cancer Center Bridge Project)Leukemia & Lymphoma Society of America. Specialized Center of Research (SCOR) ProgramNational Institutes of Health (U.S.) (grant U54OD020355-01)National Institutes of Health (U.S.) (grant NCI R01CA172636)National Institutes of Health (U.S.) (grant R35CA197594)National Cancer Institute (U.S.) (Cancer Center Support Grant (P30 CA008747)

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

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