| 641. |
- Schirmer, M. D., et al.
(författare)
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White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study
- 2019
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Ingår i: Neuroimage-Clinical. - : Elsevier BV. - 2213-1582. ; 23
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Tidskriftsartikel (refereegranskat)abstract
- White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of -2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.
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| 642. |
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| 643. |
- Vogel, L., et al.
(författare)
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Early in-flight detection of SO2 via Differential Optical Absorption Spectroscopy: a feasible aviation safety measure to prevent potential encounters with volcanic plumes
- 2011
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Ingår i: Atmospheric Measurement Techniques. - : Copernicus GmbH. - 1867-1381 .- 1867-8548. ; 4:9, s. 1785-1804
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Tidskriftsartikel (refereegranskat)abstract
- Volcanic ash constitutes a risk to aviation, mainly due to its ability to cause jet engines to fail. Other risks include the possibility of abrasion of windshields and potentially serious damage to avionic systems. These hazards have been widely recognized since the early 1980s, when volcanic ash provoked several incidents of engine failure in commercial aircraft. In addition to volcanic ash, volcanic gases also pose a threat. Prolonged and/or cumulative exposure to sulphur dioxide (SO(2)) or sulphuric acid (H(2)SO(4)) aerosols potentially affects e. g. windows, air frame and may cause permanent damage to engines. SO(2) receives most attention among the gas species commonly found in volcanic plumes because its presence above the lower troposphere is a clear proxy for a volcanic cloud and indicates that fine ash could also be present. Up to now, remote sensing of SO(2) via Differential Optical Absorption Spectroscopy (DOAS) in the ultraviolet spectral region has been used to measure volcanic clouds from ground based, airborne and satellite platforms. Attention has been given to volcanic emission strength, chemistry inside volcanic clouds and measurement procedures were adapted accordingly. Here we present a set of experimental and model results, highlighting the feasibility of DOAS to be used as an airborne early detection system of SO(2) in two spatial dimensions. In order to prove our new concept, simultaneous airborne and ground-based measurements of the plume of Popocatepetl volcano, Mexico, were conducted in April 2010. The plume extended at an altitude around 5250 m above sea level and was approached and traversed at the same altitude with several forward looking DOAS systems aboard an airplane. These DOAS systems measured SO(2) in the flight direction and at +/- 40 mrad (2.3 degrees) angles relative to it in both, horizontal and vertical directions. The approaches started at up to 25 km distance to the plume and SO(2) was measured at all times well above the detection limit. In combination with radiative transfer studies, this study indicates that an extended volcanic cloud with a concentration of 10(12) molecules cm(-3) at typical flight levels of 10 km can be detected unambiguously at distances of up to 80 km away. This range provides enough time (approx. 5 min) for pilots to take action to avoid entering a volcanic cloud in the flight path, suggesting that this technique can be used as an effective aid to prevent dangerous aircraft encounters with potentially ash rich volcanic clouds.
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| 644. |
- Wu, O., et al.
(författare)
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Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
- 2019
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Ingår i: Stroke. - : Ovid Technologies (Wolters Kluwer Health). - 0039-2499 .- 1524-4628. ; 50:7, s. 1734-1741
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Tidskriftsartikel (refereegranskat)abstract
- Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (rho=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm(3) (0.9-16.6 cm(3)). Patients with small artery occlusion stroke subtype had smaller lesion volumes (P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.
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| 645. |
- Wu, Ona, et al.
(författare)
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Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
- 2019
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Ingår i: Stroke. - 1524-4628. ; 50:7, s. 1734-1741
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Tidskriftsartikel (refereegranskat)abstract
- Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.
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| 646. |
- Anghinolfi, F., et al.
(författare)
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Hadron beam test of a scintillating fibre tracker system for elastic scattering and luminosity measurement in ATLAS
- 2007
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Ingår i: Journal of Instrumentation. - 1748-0221. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- A scintillating fibre tracker is proposed to measure elastic proton scattering at very small angles in the ATLAS experiment at CERN. The tracker will be located in so-called Roman Pot units at a distance of 240 m on each side of the ATLAS interaction point. An initial validation of the design choices was achieved in a beam test at DESY in a relatively low energy electron beam and using slow off-the-shelf electronics. Here we report on the results from a second beam test experiment carried out at CERN, where new detector prototypes were tested in a high energy hadron beam, using the first version of the custom designed front-end electronics. With a spatial resolution of 25 mu m an adequate tracking performance was obtained, under conditions which are similar to the situation at the LHC. In addition, the alignment method using so-called overlap detectors was studied and shown to have the expected precision.
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| 647. |
- Beecken, Jörg, 1982, et al.
(författare)
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Performance assessment of state-of-the-art and novel methods for remote compliance monitoring of sulfur emissions from shipping
- 2023
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Ingår i: Atmospheric Measurement Techniques. - 1867-1381 .- 1867-8548. ; 16:23, s. 5883-5895
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Tidskriftsartikel (refereegranskat)abstract
- The fuel sulfur content (FSC) of ocean-going and inland vessels was measured simultaneously by eight different state-of-the-art and novel monitoring systems during a 6-week campaign at the Elbe River, at a distance of about 10 km to the port of Hamburg, Germany. Both stationary and airborne systems on unoccupied aerial vehicles (UAVs) were operated by four participating partners in a side-by-side measurement setup to measure the emission factors of the same emission sources. A novel laser spectrometer, with significantly better-precision specifications as compared with the other instruments, was used for the first time for emission monitoring regarding the International Convention for the Prevention of Pollution from Ships (MARPOL) Annex VI regulations. The comparison took place in the North Sea sulfur emission control area (SECA), where the allowed FSC is limited to 0.10 %Sm/m. The unit %Sm/m relates to the percentage of mass sulfur per mass combusted fuel. In total, 966 plumes that originated from 436 different vessels were analysed in this study. At the same time, fuel samples obtained from 34 different vessels and bunker delivery notes (BDNs) from five frequently monitored vessels were used as a reference to assess the uncertainties of the different systems. Seven of the eight measurement systems tended to underestimate the FSC found from fuel samples and BDNs. A possible relation between underestimation and high relative humidities (above 80 %) was observed. The lowest systematic deviations were observed for the airborne systems and the novel laser spectrometer. The two UAV-borne systems showed total uncertainties of 0.07 %Sm/m and 0.09 %Sm/m (confidence level: 95 %). The novel laser spectrometer showed the lowest total uncertainty of 0.05 %Sm/m compared with other stationary sniffer systems, whose total uncertainties range from 0.08 %Sm/m to 0.09 %Sm/m. It was concluded that non-compliant vessels, with an actual FSC of the combusted fuel above 0.15 %Sm/m to 0.19 %Sm/m, can be detected by the compared systems with 95 % confidence.
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| 648. |
- Bernatsky, Sasha, et al.
(författare)
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Lupus-related single nucleotide polymorphisms and risk of diffuse large B-cell lymphoma
- 2017
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Ingår i: Lupus Science and Medicine. - : BMJ. - 2053-8790. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Determinants of the increased risk of diffuse large B-cell lymphoma (DLBCL) in SLE are unclear. Using data from a recent lymphoma genome-wide association study (GWAS), we assessed whether certain lupus-related single nucleotide polymorphisms (SNPs) were also associated with DLBCL. Methods: GWAS data on European Caucasians from the International Lymphoma Epidemiology Consortium (InterLymph) provided a total of 3857 DLBCL cases and 7666 general-population controls. Data were pooled in a random-effects meta-analysis. Results: Among the 28 SLE-related SNPs investigated, the two most convincingly associated with risk of DLBCL included the CD40 SLE risk allele rs4810485 on chromosome 20q13 (OR per risk allele=1.09, 95% CI 1.02 to 1.16, p=0.0134), and the HLA SLE risk allele rs1270942 on chromosome 6p21.33 (OR per risk allele=1.17, 95% CI 1.01 to 1.36, p=0.0362). Of additional possible interest were rs2205960 and rs12537284. The rs2205960 SNP, related to a cytokine of the tumour necrosis factor superfamily TNFSF4, was associated with an OR per risk allele of 1.07, 95% CI 1.00 to 1.16, p=0.0549. The OR for the rs12537284 (chromosome 7q32, IRF5 gene) risk allele was 1.08, 95% CI 0.99 to 1.18, p=0.0765. Conclusions: These data suggest several plausible genetic links between DLBCL and SLE.
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| 649. |
- Berndt, Sonja I., et al.
(författare)
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Genome-wide association study identifies multiple risk loci for chronic lymphocytic leukemia
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:8, s. 868-U202
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWAS) have previously identified 13 loci associated with risk of chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL). To identify additional CLL susceptibility loci, we conducted the largest meta-analysis for CLL thus far, including four GWAS with a total of 3,100 individuals with CLL (cases) and 7,667 controls. In the meta-analysis, we identified ten independent associated SNPs in nine new loci at 10q23.31 (ACTA2 or FAS (ACTA2/FAS), P = 1.22 x 10(-14)), 18q21.33 (BCL2, P = 7.76 x 10(-11)), 11p15.5 (C11orf21, P = 2.15 x 10(-10)), 4q25 (LEF1, P = 4.24 x 10(-10)), 2q33.1 (CASP10 or CASP8 (CASP10/CASP8), P = 2.50 x 10(-9)), 9p21.3 (CDKN2B-AS1, P = 1.27 x 10(-8)), 18q21.32 (PMAIP1, P = 2.51 x 10(-8)), 15q15.1 (BMF, P = 2.71 x 10(-10)) and 2p22.2 (QPCT, P = 1.68 x 10(-8)), as well as an independent signal at an established locus (2q13, ACOXL, P = 2.08 x 10(-18)). We also found evidence for two additional promising loci below genome-wide significance at 8q22.3 (ODF1, P = 5.40 x 10(-8)) and 5p15.33 (TERT, P = 1.92 x 10(-7)). Although further studies are required, the proximity of several of these loci to genes involved in apoptosis suggests a plausible underlying biological mechanism.
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| 650. |
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