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Keywords: pastoral care ; spiritual care ; Clearance Enjoy The North Face Storm III Winter WP Sale Get Authentic Clearance With Credit Card wBaVHA3i

Baumgartner I (2010) Seelsorge in Hospiz- und Palliativdiensten – ein Modell für die pastorale Entwicklung. In: Aigner ME, Bucher R, Hable I, Ruckenbauer H–W (Hg.) Räume des Aufatmens. Pastoralpsychologie im Risiko der Anerkennung. Festschrift zu Ehren von Karl Heinz Ladenhauf. Münster: Lit. 362–376.

Baumgartner I, Pfrang C, Haslbeck B (2009) Ambulante Palliativversorgung und Seelsorge. Universität Passau: Lehrstuhl für Christliche Gesellschaftslehre und Caritaswissenschaften.

Borck S (2006) Seelsorge in der Palliativmedizin. Bundesgesundheitsblatt – Gesundheitsforschung – Gesundheitsschutz 49:1122–1131.

Buser K, Amelung VE, Schneider N (2008) Interviews mit Gemeindepastoren zur Versorgung von Patienten am Lebensende: Eine explorative Studie. Wege zum Menschen 60:503–511.

Charbonnier R (2008) Seelsorge in der Palliativversorgung. Konzeptionelle, kommunikative und organisatorische Aspekte einer berufsübergreifenden Zusammenarbeit. Wege zum Menschen 60:512–528.

Eisenmann C, Klein C, Swhajor-Biesemann A, Drexelius U, Keller B, Streib H (2016) Dimensions of “spirituality”: the semantics of subjective definitions. In: Streib H, Hood RW jr. (Hg.) Semantics and psychology of spirituality. Heidelberg: Springer. 125–151.

Engelhardt H, Delkeskamp-Hayes C (2009) Der Geist der Wahrheit und die „Legion” der Spiritualitäten. Ein orthodoxer Blick auf die Klinikseelsorge im religiösen Pluralismus. In: Frick E, Roser T (Hg.) Spiritualität und Medizin. Gemeinsame Sorge für den kranken Menschen. Stuttgart: Kohlhammer. 72–79.

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Höhn H–J (2004) Auf dem Weg in eine postsäkulare Kultur? Herausforderungen einer kritischen Phänomenologie der Religion. In: Zulehner PM (Hg.) Spiritualität – mehr als ein Megatrend. Gedenkschrift für Kardinal DDr. Franz König. Ostfildern: Schwabenverlag. 15–28.

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Kreisel-Liebermann H, Ärztekammer Niedersachsen (2003) „Laßt mich ... aber laßt mich nicht allein!“ Seelsorge in einem palliativmedizinischen Modellprojekt. Hannover: Lutherisches Verlagshaus.

We examined the properties of three different normalization strategies: global median normalization, variance stabilizing normalization and a spike-in based normalization. Global median normalization (3) has been previously used for normalizing microRNA data (4). Variance stabilizing normalization (VSN) (5) is another method widely used for microarray data. In both cases, we only use the human microRNAs in the normalization, as the remainder are unlikely to be expressed in our samples. The third method (spike-in VSN normalization) is described next.

Spike-in VSN normalization

One way to assess whether or not normalization is needed is to plot the raw log intensity values of each spike-in control against their median across all arrays. If no normalization is needed we expect these points to all fall along the diagonal line . This is not quite true ( Churchs Brogue Shoes On Sale Dark Asphalt Grey Leather 2017 10 8 9 95 Free Shipping Cheap Clearance twf7J6zLW
), but the departures are small, and can be accounted for by an affine transformation. This observation suggests a normalization procedure where all values on an array are transformed so that the spike-ins become approximately aligned. We find such a per-array transformation using VSN, restricting the model fit to the spiked-in spots. Normalized intensities for all microRNAs are then obtained by applying the resulting transformation to all spots of interest on the array. We will henceforth refer to this procedure as ‘spike-in VSN normalization’. One limitation of this approach is that we can only expect reliable results for intensities within the range covered by the spike-ins, which excludes targets that are not expressed. To address this problem, we augmented the list of spike-ins used for the initial VSN fit with 15 randomly chosen probes that correspond to rice () microRNA targets and have no known human counterparts. Further details are given in the Supplementary Material .


Assessing different normalization schemes is somewhat problematic, and is an issue that is difficult to adequately address due to the lack of references where the true values of some of the features (in our case microRNAs) are known. The approach taken in (6), for Affymetrix arrays, was to make use of spike-in datasets, where concentrations were known for a small number of mRNAs. While such an experiment has not yet been performed for microRNAs, our use of internal spike-ins provides a reasonable metric for assessing the performance of different normalization schemes; the variability in intensity for a given spike-in, across arrays, should be small, and consequently, normalization methods that tend to reduce that variability should be preferred over those that do not.

However, the reduction of variability alone does not make a good normalization method. One must also ensure that the signal is maintained. For that purpose we used an external method, qRT-PCR, to assess the levels of 17 microRNAs ( Supplementary Table S2 ). These measurements can be used to assess the performance of various normalization methods; we expect the normalized expressions to correlate well with the qRT-PCR measurements, and better normalization schemes to have stronger correlation. In practice, for this dataset, the observed correlations are fairly strong for most microRNAs, both for unnormalized and normalized expressions, with no significant differences between methods. Details can be found in the Supplementary Material .

Differential Expression

Differential expression was assessed using an empirical Bayes approach, as implemented in the software package limma (7), available from the Bioconductor project (8). Comparison was between the serous and endometrioid subtypes of ovarian cancer.

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As noted previously, Figure 3 and other plots designed to emphasize potential batch effects can be used to assess success of normalization. Another useful diagnostic can be derived from the spike-ins. By design, the observed intensities for the spike-ins should cover the range of expressed microRNAs, and each spike-in control should have essentially the same intensity in every array. Thus, after normalization, these spots should have low variability across arrays in the red channel. In Figure 4 we plot, for each human probe and spike-in, a measure of spread (MAD) across arrays against a measure of location (median), before and after normalization. For successful normalization, we expect the spike-ins to have lower variability than regular probes with similar median intensities.

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