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- Alm, Erik, 1980-, et al.
(författare)
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A solution to the 1D NMR alignment problem using an extended generalized fuzzy Hough transform and mode support
- 2009
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Ingår i: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 395:1, s. 213-223
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Tidskriftsartikel (refereegranskat)abstract
- This paper approaches the problem of intersample peak correspondence in the context of later applying statistical data analysis techniques to 1D 1H-nuclear magnetic resonance (NMR) data. Any data analysis methodology will fail to produce meaningful results if the analyzed data table is not synchronized, i.e., each analyzed variable frequency (Hz) does not originate from the same chemical source throughout the entire dataset. This is typically the case when dealing with NMR data from biological samples. In this paper, we present a new state of the art for solving this problem using the generalized fuzzy Hough transform (GFHT). This paper describes significant improvements since the method was introduced for NMR datasets of plasma in Csenki et al. (Anal Bioanal Chem 389:875-885, 15) and is now capable of synchronizing peaks from more complex datasets such as urine as well as plasma data. We present a novel way of globally modeling peak shifts using principal component analysis, a new algorithm for calculating the transform and an effective peak detection algorithm. The algorithm is applied to two real metabonomic 1H-NMR datasets and the properties of the method are compared to bucketing. We implicitly prove that GFHT establishes the objectively true correspondence. Desirable features of the GFHT are: (1) intersample peak correspondence even if peaks change order on the frequency axis and (2) the method is symmetric with respect to the samples.
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- Csenki, Leonard, et al.
(författare)
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Proof of principle of a generalized fuzzy Hough transform approach to peak alignment of one-dimensional 1H NMR data
- 2007
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Ingår i: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 389:3, s. 875-885
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Tidskriftsartikel (refereegranskat)abstract
- In metabolic profiling, multivariate data analysis techniques are used to interpret one-dimensional (1D) 1H NMR data. Multivariate data analysis techniques require that peaks are characterised by the same variables in every spectrum. This location constraint is essential for correct comparison of the intensities of several NMR spectra. However, variations in physicochemical factors can cause the locations of the peaks to shift. The location prerequisite may thus not be met, and so, to solve this problem, alignment methods have been developed. However, current state-of-the-art algorithms for data alignment cannot resolve the inherent problems encountered when analysing NMR data of biological origin, because they are unable to align peaks when the spatial order of the peaks changes—a commonly occurring phenomenon. In this paper a new algorithm is proposed, based on the Hough transform operating on an image representation of the NMR dataset that is capable of correctly aligning peaks when existing methods fail. The proposed algorithm was compared with current state-of-the-art algorithms operating on a selected plasma dataset to demonstrate its potential. A urine dataset was also processed using the algorithm as a further demonstration. The method is capable of successfully aligning the plasma data but further development is needed to address more challenging applications, for example urine data.
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- Glund, S., et al.
(författare)
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Interleukin-6 directly increases glucose metabolism in resting human skeletal muscle
- 2007
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Ingår i: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 56:6, s. 1630-7
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Tidskriftsartikel (refereegranskat)abstract
- Interleukin (IL)-6 is a proinflammatory cytokine shown to modify insulin sensitivity. Elevated plasma levels of IL-6 are observed in insulin-resistant states. Interestingly, plasma IL-6 levels also increase during exercise, with skeletal muscle being the predominant source. Thus, IL-6 has also been suggested to promote insulin-mediated glucose utilization. In this study, we determined the direct effects of IL-6 on glucose transport and signal transduction in human skeletal muscle. Skeletal muscle strips were prepared from vastus lateralis biopsies obtained from 22 healthy men. Muscle strips were incubated with or without IL-6 (120 ng/ml). We found that IL-6 increased glucose transport in human skeletal muscle 1.3-fold (P < 0.05). A 30-min pre-exposure to IL-6 did not affect insulin-stimulated glucose transport. IL-6 also increased skeletal muscle glucose incorporation into glycogen, as well as glucose oxidation (1.5- and 1.3-fold, respectively; P < 0.05). IL-6 increased phosphorylation of STAT3 (signal transducer and activator of transcription 3; P < 0.05), AMP-activated protein kinase (P = 0.063), and p38 mitogen-activated protein kinase (P < 0.05) and reduced phosphorylation of S6 ribosomal protein (P < 0.05). In contrast, phosphorylation of protein kinase B/Akt, AS160 (Akt substrate of 160 kDa), and GSK3alpha/beta (glycogen synthase kinase 3alpha/beta) as well as insulin receptor substrate 1-associated phosphatidylinositol 3-kinase activity remained unaltered. In conclusion, acute IL-6 exposure increases glucose metabolism in resting human skeletal muscle. Insulin-stimulated glucose transport and insulin signaling were unchanged after IL-6 exposure.
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- Stolt, Ragnar, et al.
(författare)
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Second-Order Peak Detection for Multicomponent High-Resolution LC/MS Data
- 2006
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Ingår i: Analytical Chemistry. - : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 78:4, s. 975-83
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Tidskriftsartikel (refereegranskat)abstract
- The first step when analyzing multicomponent LC/MS data from complex samples such as biofluid metabolic profiles is to separate the data into information and noise via, for example, peak detection. Due to the complex nature of this type of data, with problems such as alternating backgrounds and differing peak shapes, this can be a very complex task. This paper presents and evaluates a two-dimensional peak detection algorithm based on raw vector-represented LC/MS data. The algorithm exploits the fact that in high-resolution centroid data chromatographic peaks emerge flanked with data voids in the corresponding mass axis. According to the proposed method, only 4‰ of the total amount of data from a urine sample is defined as chromatographic peaks; however, 94% of the raw data variance is captured within these peaks. Compared to bucketed data, results show that essentially the same features that an experienced analyst would define as peaks can automatically be extracted with a minimum of noise and background. The method is simple and requires a priori knowledge of only the minimum chromatographic peak widtha system-dependent parameter that is easily assessed. Additional meta parameters are estimated from the data themselves. The result is well-defined chromatographic peaks that are consistently arranged in a matrix at their corresponding m/z values. In the context of automated analysis, the method thus provides an alternative to the traditional approach of bucketing the data followed by denoising and/or one-dimensional peak detection. The software implementation of the proposed algorithm is available at http://www.anchem.su.se/peakd as compiled code for Matlab.
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