151 research outputs found

    Frozen magma lenses below the oceanic crust

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    Author Posting. © The Authors, 2005. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature 436 (2005): 1149-1152, doi:10.1038/nature03944.The Earth's oceanic crust crystallizes from magmatic systems generated at mid-ocean ridges. Whereas a single magma body residing within the mid-crust is thought to be responsible for the generation of the upper oceanic crust, it remains unclear if the lower crust is formed from the same magma body, or if it mainly crystallizes from magma lenses located at the base of the crust. Thermal modelling, tomography, compliance and wide-angle seismic studies, supported by geological evidence, suggest the presence of gabbroic-melt accumulations within the Moho transition zone in the vicinity of fast- to intermediate-spreading centres. Until now, however, no reflection images have been obtained of such a structure within the Moho transition zone. Here we show images of groups of Moho transition zone reflection events that resulted from the analysis of approximately 1,500 km of multichannel seismic data collected across the intermediate-spreading-rate Juan de Fuca ridge. From our observations we suggest that gabbro lenses and melt accumulations embedded within dunite or residual mantle peridotite are the most probable cause for the observed reflectivity, thus providing support for the hypothesis that the crust is generated from multiple magma bodies

    Preamplification techniques for real-time RT-PCR analyses of endomyocardial biopsies

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    <p>Abstract</p> <p>Background</p> <p>Due to the limited RNA amounts from endomyocardial biopsies (EMBs) and low expression levels of certain genes, gene expression analyses by conventional real-time RT-PCR are restrained in EMBs. We applied two preamplification techniques, the TaqMan<sup>Ÿ </sup>PreAmp Master Mix (T-PreAmp) and a multiplex preamplification following a sequence specific reverse transcription (SSRT-PreAmp).</p> <p>Results</p> <p>T-PreAmp encompassing 92 gene assays with 14 cycles resulted in a mean improvement of 7.24 ± 0.33 Ct values. The coefficients for inter- (1.89 ± 0.48%) and intra-assay variation (0.85 ± 0.45%) were low for all gene assays tested (<4%). The PreAmp uniformity values related to the reference gene CDKN1B for 91 of the investigated gene assays (except for CD56) were -0.38 ± 0.33, without significant differences between self-designed and ABI inventoried Taqman<sup>Ÿ </sup>gene assays. Only two of the tested Taqman<sup>Ÿ </sup>ABI inventoried gene assays (HPRT-ABI and CD56) did not maintain PreAmp uniformity levels between -1.5 and +1.5. In comparison, the SSRT-PreAmp tested on 8 self-designed gene assays yielded higher Ct improvement (9.76 ± 2.45), however was not as robust regarding the maintenance of PreAmp uniformity related to HPRT-CCM (-3.29 ± 2.40; p < 0.0001), and demonstrated comparable intra-assay CVs (1.47 ± 0.74), albeit higher inter-assay CVs (5.38 ± 2.06; p = 0.01). Comparing EMBs from each 10 patients with dilated cardiomyopathy (DCM) and inflammatory cardiomyopathy (DCMi), T-PreAmp real-time RT-PCR analyses revealed differential regulation regarding 27 (30%) of the investigated 90 genes related to both HPRT-CCM and CDKN1B. Ct values of HPRT and CDKN1B did not differ in equal RNA amounts from explanted DCM and donor hearts.</p> <p>Conclusion</p> <p>In comparison to the SSRT-PreAmp, T-PreAmp enables a relatively simple workflow, and results in a robust PreAmp of multiple target genes (at least 92 gene assays as tested here) by a mean Ct improvement around 7 cycles, and in a lower inter-assay variance in RNA derived from EMBs. Preliminary analyses comparing EMBs from DCM and DCMi patients, revealing differential regulation regarding 30% of the investigated genes, confirm that T-PreAmp is a suitable tool to perform gene expression analyses in EMBs, expanding gene expression investigations with the limited RNA/cDNA amounts derived from EMBs. CDKN1B, in addition to its function as a reference gene for the calculation of PreAmp uniformity, might serve as a suitable housekeeping gene for real-time RT-PCR analyses of myocardial tissues.</p

    Relative impact of key sources of systematic noise in Affymetrix and Illumina gene-expression microarray experiments

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    <p>Abstract</p> <p>Background</p> <p>Systematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA.</p> <p>Results</p> <p>A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependant upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content.</p> <p>Conclusions</p> <p>The magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies.</p

    In vivo hippocampal subfield volumes in bipolar disorder—A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group

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    The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta‐Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1‐weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed‐effects models and mega‐analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = −0.20), cornu ammonis (CA)1 (d = −0.18), CA2/3 (d = −0.11), CA4 (d = −0.19), molecular layer (d = −0.21), granule cell layer of dentate gyrus (d = −0.21), hippocampal tail (d = −0.10), subiculum (d = −0.15), presubiculum (d = −0.18), and hippocampal amygdala transition area (d = −0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non‐users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD

    Search for neutral B meson decays to two charged leptons

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    The decays Bd0, Bs0→e+e−, Ό+Ό−, e±Ό∓\mathrm{B_d^0,\,B_s^0 \rightarrow e^+e^-,\,\mu^+\mu^-,\, e^\pm\mu^\mp} are searched for in 3.5 million hadronic Z{\mathrm{Z}} events, which constitute the full LEP I data sample collected by the L3 detector. No signals are observed, therefore upper limits at the 90\%(95\%) confidence levels are set on the following branching fractions: % \begin{center}% {\setlength{\tabcolsep}{2pt} \begin{tabular}{lccccclcccc}% % Br(Bd0→e+e−)({\mathrm{B_d^0 \rightarrow {\mathrm{e^+e^-}}}}) & << & 1.4(1.8)1.4(1.8) & ×\times & 10−5 10^{-5}; & \hspace*{5mm} & Br(Bs0→e+e−)({\mathrm{B_s^0 \rightarrow {\mathrm{e^+e^-}}}}) & << & 5.4(7.0)5.4(7.0) & ×\times & 10−5 10^{-5}; \\% Br(Bd0→Ό+Ό−)({\mathrm{B_d^0 \rightarrow \mu^+\mu^-}}) & << & 1.0(1.4)1.0(1.4) & ×\times & 10−5 10^{-5}; & \hspace*{5mm} & Br(Bs0→Ό+Ό−)({\mathrm{B_s^0 \rightarrow \mu^+\mu^-}}) & << & 3.8(5.1)3.8(5.1) & ×\times & 10−5 10^{-5}; \\% Br(Bd0→e±Ό∓)({\mathrm{B_d^0 \rightarrow {\mathrm{e^\pm\mu^\mp}}}}) & << & 1.6(2.0)1.6(2.0) & ×\times & 10−5 10^{-5}; & \hspace*{5mm} & Br(Bs0→e±Ό∓)({\mathrm{B_s^0 \rightarrow {\mathrm{e^\pm\mu^\mp}}}}) & << & 4.1(5.3)4.1(5.3) & ×\times & 10−5 10^{-5}. \\% % \end{tabular}% } \end{center}% % The results for Bs0→e+e−{\mathrm{B_s^0\rightarrow{\mathrm{e^+e^-}}}} and Bs0→e±Ό∓{\mathrm{B_s^0 \rightarrow {\mathrm{e^\pm\mu^\mp}}}} are the first limits set on these decay modes

    Measurement of energetic single-photon production at LEP

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