scholarly journals Characteristics of Lightning Within Electrified Snowfall Events Using Lightning Mapping Arrays

2018 ◽  
Vol 123 (4) ◽  
pp. 2347-2367 ◽  
Author(s):  
Christopher J. Schultz ◽  
Timothy J. Lang ◽  
Eric C. Bruning ◽  
Kristin M. Calhoun ◽  
Sebastian Harkema ◽  
...  
Keyword(s):  
Mapping Arrays

Quantitative OCT image correction using Fermat's principle and mapping arrays

2002 ◽  
Author(s):  
Volker Westphal ◽  
Sunita Radhakrishnan ◽  
Andrew M. Rollins ◽  
Joseph A. Izatt
Keyword(s):  
Image Correction ◽  
Fermat’S Principle ◽  
Fermat's Principle ◽  
Mapping Arrays ◽  
Oct Image

Abstract 14839: High-resolution Spatiotemporal Changes in Dominant Frequency and Structural Organization During Persistent Atrial Fibrillation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Terrence Pong ◽  
Joy Aparicio Valenzuela ◽  
Kevin J Cyr ◽  
Cody Carlton ◽  
Sasank Sakhamuri ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
High Resolution ◽  
Structural Organization ◽  
Porcine Model ◽  
Dominant Frequency ◽  
Patient Specific ◽  
Structural Remodeling ◽  
Atrial Activity ◽  
Spatiotemporal Changes ◽  
Mapping Arrays

Introduction: Spatiotemporal differences in atrial activity are thought to contribute to the maintenance of atrial fibrillation (AF). While recent evidence has identified changes in dominant frequency (DF) during the transition from paroxysmal to persistent AF, little is known about the frequency characteristics of the epicardium during this transition. The purpose of this study was to perform high-resolution mapping of the atrial epicardium and to characterize changes in frequency activity and structural organization during the transition from paroxysmal to persistent AF. Hypothesis: In a porcine model of persistent AF, we tested the hypothesis that the epicardium undergoes spatiotemporal changes in atrial activity and structural organization during persistent AF. Methods: Paroxysmal and persistent AF was induced in adult Yorkshire swine by atrial tachypacing. Atrial morphology was segmented from magnetic resonance imaging and high-resolution patient-specific flexible mapping arrays were 3D printed to match the epicardial contours of the atria. Epicardial activation and DF mapping was performed in four paroxysmal and four persistent AF animals using personalized mapping arrays. Histological analysis was performed to determine structural differences between paroxysmal and persistent AF. Results: The left atrial epicardium was associated with a significant increase in DF between paroxysmal and persistent AF (6.5 ± 0.2 vs. 7.4 ± 0.5 Hz, P = 0.03). High-resolution spatiotemporal mapping identified organized clusters of DF during paroxysmal AF which were lost during persistent AF. The development of persistent AF led to structural remodeling with increased atrial epicardial fibrosis. The organization index (OI) significantly decreased during persistent AF in both the left atria (0.3 ± 0.03 vs. 0.2 ± 0.03, P = 0.01) and right atria (0.33 ± 0.04 vs. 0.23 ± 0.02, P = 0.02). Conclusions: In the porcine model of persistent AF, the epicardium undergoes structural remodeling with increased epicardial fibrosis, reflected by changes in atrial organization index and dominant frequency.

Sub-Classification of Hyperdiploid Myeloma Using Global Gene Expression Profiling and SNP-Based Mapping Arrays.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3390-3390
Author(s):  
Brian A. Walker ◽  
Paola E. Leone ◽  
Matthew W. Jenner ◽  
David C. Johnson ◽  
David Gonzalez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hierarchical Clustering ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Poor Response ◽  
Response To Therapy ◽  
Genomic Change ◽  
Group 5 ◽  
Mapping Arrays ◽  
Group 1

Abstract The translocation/cyclin classification system in myeloma does not neatly define subgroups of hyperdiploidy (HRD) and we sought a more definitive sub-classification. Using 131 pre-treatment samples (49 HRD with no split IgH locus by FISH) we defined subgroups using both supervised and unsupervised hierarchical clustering of gene expression profiles. RNA was purified from CD138+ cells, amplified using a 2-cycle IVT and hybridised onto U133 Plus 2 GeneChips. On 30 of the 49 HRD samples we also performed 500K SNP mapping arrays to define the true extent of the genomic change in HRD. The most common trisomic chromosomes were 15 (97%), 9 (86%), 19 (80%), 5 (77%), 11 (74%), 3 (64%), 21 (54%) and 7 (54%). There was no association between HRD and any of the major genetic abnormalities (1p, 1q, 6q, 8p, 13, 16q and 17p) compared to the non-HRD (NHRD) group. Many interstitial deletions were seen in all HRD samples, on both odd and even numbered chromosomes. However, using gene mapping alone it was not possible to globally sub-classify HRD myeloma. We compared NHRD and HRD sample gene expression profiles, removing differences between t(4;14) and t(11;14) cases in the NHRD group. This analysis showed that HRD samples segregate into 2 groups; one with a pattern distinct to NHRD samples and another containing genes that are up-regulated in both HRD and NHRD samples. In this analysis 176 genes were up-regulated in the HRD samples and were predominantly located on the trisomic chromosomes, especially 19, 11, 9 and 5. These genes showed a predominant upregulation of HGF and TRAIL, and down-regulation of TRAIL-R2 compared to NHRD samples. Unsupervised hierarchical clustering split the HRD samples into 5 distinct groups suggesting that there are distinct pathological entities. Group 1 overexpressed 90 genes including BCL2, CCNL1 (cyclin L1) and CDK6, consistent with a proliferation signature. Group 2 overexpressed interferon inducible genes including IFI6, IFI27, IFIT1 as well as TRAIL. Group 3 upregulated genes included IL8, MMP9 and TIMP2. Group 4 upregulated transcripts include neurexophilin 3. Group 5 was less well defined but contained transcripts for CCND2, WNT5A and CXCR4. To define clinically relevant subgroups the HRD samples were clustered comparing response or no response to induction chemotherapy. Analysis showed that Group 1 cases cluster together and were either non or minimal responders. This is consistent with the Group 1 cases over-expressing cell-cycle and proliferation related genes. Group 5 clustered together and were either complete or partial responders, and had a low expression of the genes over expressed by Group 1. The non-responder group overexpressed 58 genes and include MMSET-like 1 (in a region on 8p paralogous to 4p containing FGFR1), DVL3 (dishevelled homolog 3) and CCNL1. 23 genes were over expressed in the complete response group including caspase 1 and manic fringe homolog. The unsupervised HRD cluster and the supervised response cluster shared 10 genes, including CCNL1 and ASS. We have used both genetic and expression data to further define the HRD sub-group in terms of gene expression signatures and response to therapy and have identified 5 groups, of which Group 1 has a proliferation signature and poor response to induction therapy.

scholarly journals Deletion mapping in Xp21 for patients with complex glycerol kinase deficiency using SNP mapping arrays

2007 ◽  
Vol 28 (3) ◽  
pp. 235-242 ◽  
Author(s):  
Christopher M. Stanczak ◽  
Zugen Chen ◽  
Yao-Hua Zhang ◽  
Stanley F. Nelson ◽  
Edward R.B. McCabe
Keyword(s):  
Glycerol Kinase ◽  
Deletion Mapping ◽  
Glycerol Kinase Deficiency ◽  
Snp Mapping ◽  
Mapping Arrays

scholarly journals Genomic Variation by Whole-Genome SNP Mapping Arrays Predicts Time-to-Event Outcome in Patients with Chronic Lymphocytic Leukemia

2013 ◽  
Vol 15 (2) ◽  
pp. 196-209 ◽  
Author(s):  
Carmen D. Schweighofer ◽  
Kevin R. Coombes ◽  
Tadeusz Majewski ◽  
Lynn L. Barron ◽  
Susan Lerner ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Lymphocytic Leukemia ◽  
Genomic Variation ◽  
Whole Genome ◽  
Time To Event ◽  
Snp Mapping ◽  
Mapping Arrays

scholarly journals Timing Calibration and Windowing Technique Comparison for Lightning Mapping Arrays

2021 ◽  
Author(s):  
B. M. Hare ◽  
H. Edens ◽  
P. Krehbiel ◽  
W. Rison ◽  
O. Scholten ◽  
...  
Keyword(s):  
Windowing Technique ◽  
Mapping Arrays ◽  
Technique Comparison

Two Methods for Correcting Range-Dependent Limitations of Lightning Mapping Arrays

2018 ◽  
Vol 35 (6) ◽  
pp. 1273-1282 ◽  
Author(s):  
Stephanie A. Weiss ◽  
Donald R. MacGorman ◽  
Eric C. Bruning ◽  
Vanna C. Chmielewski
Keyword(s):  
Detection Efficiency ◽  
Sorting Algorithm ◽  
Source Detection ◽  
Very High Frequency ◽  
Future Studies ◽  
Large Sets ◽  
Source Data ◽  
Using Data ◽  
Mapping Arrays ◽  
Very High

AbstractLightning Mapping Arrays (LMAs) detect very high frequency (VHF) radiation produced by lightning as it propagates; however, VHF source detection efficiency drops off rapidly with range from the centers of the arrays, which results in a maximum of source points over the center of the network for large datasets. Using data from nearly one billion detected sources of various powers, an approximation of VHF source detection efficiency (relative to the number of sources detected within 25 km of the center of the array) for the Oklahoma LMA is calculated for different ranges and source powers. The calculated source detection efficiencies are then used to normalize the VHF source data out to a range of 125 km, as a method for correcting the detection efficiency drop-off with range. The data are also sorted into flashes using a popular flash-sorting algorithm in order to compare how well flash sorting corrects for detection efficiency drop-off with range compared to the normalization method. Both methods produce similar patterns and maxima of the lightning location, but the differences between them are identified and highlighted. The use of a flash-sorting algorithm is recommended for future studies involving large sets of data.

An Integrated Pharmacogenomic Strategy for the Definition of Thalidomide Response Signatures in Presenting Cases of Multiple Myeloma.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 2493-2493
Author(s):  
Gareth J. Morgan ◽  
Matthew W. Jenner ◽  
Brian A. Walker ◽  
David C. Johnson ◽  
Paola A. Leone ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Mapping ◽  
Candidate Genes ◽  
Copy Number ◽  
Copy Number Change ◽  
Plasma Cells ◽  
Poor Response ◽  
Expression Arrays ◽  
Intensive Group ◽  
Mapping Arrays

Abstract Whilst gene expression signatures have been defined that correspond to poor overall survival, the mechanism for deregulation of such genes is often elusive. We and others have described acquired copy number change as one potential mechanism of gene deregulation in myeloma. Other potential mechanisms exist that may influence the expression of myeloma-associated genes such as inherited SNPs and copy number variation (CNV). We have therefore embarked upon an integrated pharmacogenomic strategy to determine the importance of acquired and inherited genetic changes in determining response to therapy. We have carried out gene expression analysis on CD 138 selected bone marrow plasma cells from 231 newly diagnosed myeloma cases using Affymetrix U133 Plus 2.0 expression arrays and copy number analysis using 500K Gene Mapping arrays on a subset of 90 cases. Peripheral blood DNA has been genotyped using Affymetrix 500K Gene Mapping arrays and the BOAC chips. Cytogenetics was available in the majority of cases. Younger, fitter patients received either cyclophosphamide, thalidomide and dexamethasone (CTD) or cyclohosphamide-VAD (C-VAD), followed by high dose melphalan (HDM). Older, less fit patients received attenuated dose CTD or MP. Response was assessed before and after HDM in the intensive group and on completion of therapy in the non-intensive group using EBMT criteria plus the category of VGPR. We used a supervised approach to define a gene expression signature corresponding to high level response (CR, VGPR or PR) against poor response (NC, PD or MR) overall and for each of the three induction strategies, CTD/CTDA, CVAD and MP. We have combined the data from expression arrays together with mapping data from tumor DNA and 2 different SNP arrays performed on germline DNA. We defined a poor response expression signature initially and then identified the genomic loci of these genes and how they were affected by acquired copy number change. For each candidate gene we also examined the constitutional DNA to see if each fell within a region of inherited CNV and how this could be affected by acquired copy number change. In a similar fashion, we used the BOAC chip to define genes and SNPs associated with response. This is different as it utilized mostly functional cSNPs in candidate genes. We then looked at how CNV affected these genes. Although not all genes in which functional cSNPs are present would necessarily be expected to be expressed in plasma cells, this approach is a vital step in identifying the clinical relevance of such cSNPs in myeloma. We also took the alternate approach and designed an algorithm able to correlate acquired copy number change with paraprotein response. We then identified differentially expressed genes in these loci and their impact on response, narrowing the candidate genes down to define a signature which could be validated. Using this approach has allowed us to identify genes important in determining response and their relation to tumor-associated copy number change and inherited CNV. Overall, this methodology provides significant insight in to the factors that predict response to different chemotherapy regimens. Preliminary data will be presented.

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