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Estimating RNA-quality using GeneChip microarrays
Microarrays are a powerful tool for transcriptome analysis. Best results are obtained using high-quality RNA samples for preparation and hybridization. Issues with RNA integrity can lead to low data quality and failure of the microarray experiment.
Microarray intensity data contains information to estimate the RNA quality of the sample. We here study the interplay of the characteristics of RNA surface hybridization with the effects of partly truncated transcripts on probe intensity. The 3′/5′ intensity gradient, the basis of microarray RNA quality measures, is shown to depend on the degree of competitive binding of specific and of non-specific targets to a particular probe, on the degree of saturation of the probes with bound transcripts and on the distance of the probe from the 3′-end of the transcript. Increasing degrees of non-specific hybridization or of saturation reduce the 3′/5′ intensity gradient and if not taken into account, this leads to biased results in common quality measures for GeneChip arrays such as affyslope or the control probe intensity ratio. We also found that short probe sets near the 3′-end of the transcripts are prone to non-specific hybridization presumable because of inaccurate positional assignment and the existence of transcript isoforms with variable 3′ UTRs. Poor RNA quality is associated with a decreased amount of RNA material hybridized on the array paralleled by a decreased total signal level. Additionally, it causes a gene-specific loss of signal due to the positional bias of transcript abundance which requires an individual, gene-specific correction. We propose a new RNA quality measure that considers the hybridization mode. Graphical characteristics are introduced allowing assessment of RNA quality of each single array ('tongs plot' and 'degradation hook'). Furthermore, we suggest a method to correct for effects of RNA degradation on microarray intensities.
The presented RNA degradation measure has best correlation with the independent RNA integrity measure RIN, and therefore presents itself as a valuable tool for quality control and even for the study of RNA degradation. When RNA degradation effects are detected in microarray experiments, a correction of the induced bias in probe intensities is advised.
Measurement of gene expression is based on the assumption that an analyzed RNA sample closely represents the amount of transcripts in vivo. Several effects can distort the abundance of RNA transcripts during extraction and preparation before RNA analytics using, e.g., microarrays: The first problem concerns the degradation of the RNA in vitro
The screening of nearly three thousand public available GeneChip array data suggests that there is noticeable degradation effect in the majority data files and that 2% of the files were even so severely degraded that their worth was questionable
Several RNA quality measures are established based on conventional wet lab techniques such as gel optical density measurement or denaturating agarose gel-electrophoresis (see refs.
Importantly, microarray intensity data itself contains information about the RNA quality used for hybridization due to the 3′/5′-gradient of transcript abundance
Although proven in many applications, these measures are based on probe intensities which, in general, are non-linear functions of transcript abundance
This publication addresses the following tasks to overcome these problems: Firstly, we adapt non-linear hybridization theory based on a physico-chemical model of probe/target binding to the special case of truncated transcripts due to RNA degradation. We will show that our approach consistently explains previous observations such as the effect of RNA quality on transcript intensity level
Additional file 1
Supplementary text addressing the probe positional characteristics of different gene chip types; the apparent degradation index for combined specific and non-specific probe signals and the correction of the 3′/5′ bias of pobe intensities and gene expression values (L-versus-k positional scaling).
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Results and discussion
3′-biased transcript coverage of microarray probes after RNA amplification and degradation
Affymetrix expression microarrays typically use a 3′-biased probe location which is motivated by the specifics of target preparation prior to hybridization. The preparation step applies in vitro transcription (IVT) protocols according to the Eberwein method
First-strand cDNA polymerization is primed at the 3′-end of mRNA and proceeds towards the 5′-end (see Figure
Figure 1
The 3′-bias of transcript abundance can be caused by in vitro transcription (left part) and degradation (right part) of source mRNA
The 3′-bias of transcript abundance can be caused by in vitro transcription (left part) and degradation (right part) of source mRNA. Left part: Specific targets hybridize to the probes along the interrogated transcript with decreasing frequency due to incomplete amplification starting at the primers attached to the 3′-poly-A motif of source mRNA. In contrast, cross-hybridization of non-specific targets is not associated with the 3′-end of the transcripts giving rise to uniform coverage. Right part: Degradation of source mRNA due to RNases from both ends (a and b) and/or fragmentation at randomly chosen positions (c) also result in a 3′-enriched length distribution of amplified RNA giving rise to a similar coverage of the probes as shown in the left part. aRNA fragments are shown in 3′ → 5′ direction (from left to right) in contrast to convention to agree with the probe numbering used (k = 1, 2…) and the intensity decays introduced below
Importantly, the decaying coverage of the probes is expected to apply to specific (S) but not to non-specific (N) hybridization. In the S-hybridization mode the probes bind the aRNA fragments of complementary sequence transcribed from mRNA transcripts which they intend to detect. In the N-hybridization mode the probes bind aRNA fragments of partly complementary sequence originating however from mRNA transcripts not referring to the interrogated gene. Trivially, these non-specific transcripts lack a common start position with respect to the intended target and, as a consequence, they, on the average, uniformly cover the probes of each probe set (see Figure
Also degradation of mRNA, e.g. upon storage, can produce 3′-biased probe coverages of fragmented aRNA by endonuclease activity that cuts RNA internally, or by means of exonucleases
Probing transcript abundance using genechip arrays
With only 25 base-pairs, Affymetrix has considerably shorter probe sequence lengths than competing manufacturers of high-density microarrays such as Illumina or Agilent, who employ probe lengths between 50 and 80 bp. Small probe lengths reduce the specificity for probe/target binding with negative consequences for the signal to noise ratio of the probe intensities. Affymetrix compensates the decrease in single probe signal quality by targeting each transcript with several probes forming a so-called probe set. We here focus on Affymetrix 3′-expression arrays which use typically 11 to 16 probes per set to interrogate transcripts preferentially near their 3′-end (see Figure
The probes of each set cover transcript lengths which largely exceed the length of the individual probes. This design is well suited to study length-dependent alterations of transcript abundance due to RNA degradation and imperfect amplification. Figure
Figure 2
Probe and probe set characteristics of the RAE230 GeneChip array
Probe and probe set characteristics of the RAE230 GeneChip array: Panel a correlates the position of the 11th (nearest the 5′ end of the transcripts) and of the 1th (nearest the 3′ end) probe of each probe set and shows the respective number distributions. Most probe sets accumulate in the LH (low L1, high L11) and LL ranges whereas only a few sets are found in the HH range. Panel b shows the coverage size of the probe sets (ΔL = L11- L1) as a function of the position of the 11th probe set together with the respective number distributions. The mean ΔL value nearly linearly increases until k11 ≈ 600 and then it remains virtually constant with ΔL ≈ 460. The most probe sets cover a transcript range of 400 – 550 nucleotides. The open circles refer to the 3′- and 5′-control probe sets. The boxplot in part c correlates the probe index k with the probe position L. The median position per index (see the horizontal bar in each box) nearly linearly increases with k. The slope provides the < ΔL > −value of the array which characterizes the probe sensitivity per index increment (~ 50 nucleotide positions per index increment)
The mean position of all probes on the array with a given index k = 1…11 linearly correlates with k to a good approximation (Figure
<… > array denotes averaging over all probes of the array. The approximation in the right part assumes a vanishing intercept in good agreement with the data (see Figure
Affymetrix GeneChip arrays include a small number of control probe sets designed to estimate the RNA quality in terms of the 3′/5′ bias. They target the 3′ end, the 5′-end and the middle (m) of relatively long transcripts coding, e.g., beta-actin and glyceraldehyde-3-phosphate dehydrogenase (GADPH) using 20 probes per set. Figure
Intensity-based degradation metrics
In this section we discuss the consequences of the 3′-enriched probe coverage on the observed probe intensities. In the following we will subsume the 3′-bias of probe coverage as 'degradation effect' independent of its origin (IVT amplification or degradation) for the sake of convenience. Let us first define the probe-specific and the mean degradation ratio averaged over all probes of the array,
respectively, which characterize the decrease of the transcript concentration due to the degradation effect. [Sg]target is the (true) expression degree of a selected gene g given as the total concentration of the target transcripts in the hybridization solution independent of their length. It refers to the target concentration in the absence of degradation and presumes that RNA processing proceeds without 3′/5′ bias. Contrarily, [Sg,k] denotes the apparent expression degree reported by probe with index k = 1,…,Npset designed to interrogate target g. It is given as the concentration of the RNA fragments which specifically bind to this probe. It consequently refers to the probe coverage which decays with increasing distance of the probe to the 3′-end of the target. Angular brackets < … > all probes denote averaging over all probes of the array. One expects [S]g,k ≤ [Sg]target and thus dg,k ≤ 1 owing to the 3′-enrichment after incomplete amplification and degradation of the fragments. The probe specific degradation index, dg,k, thus characterizes the loss of mRNA material at a given probe position along the transcribed region of the gene. The mean degradation index d averages the single probe effects over all probes. It estimates the total loss of RNA probed by the microarray in a given preparation prior to hybridization.
The probe intensity measured in the microarray experiment is given to a good approximation by the hyperbolic function
where M is the maximum intensity upon complete saturation of the probes and O is the optical background. The probe index p ≡ g,k subsumes the gene and probe index explicitly used in Eq. (2). The superscript P = PM, MM specifies the probe type and h = S, N the hybridization mode (specific and non-specific, see below). wp
P,h is the survival factor characterizing the removal of probe-bound targets in the post-hybridization washing step
respectively. Kp
P,S and Kp
P,N are the respective equilibrium constants for target/probe binding. The degradation factors in Eq. (4) consider the reduction of the concentrations [Sg
target and [N]chip after incomplete amplification and/or degradation (see Eq. (2)). Non-specific hybridization is related to the total amount of RNA used for hybridization
The probe-specific degradation index dp defines the decrease of transcript concentration after amplification and degradation (Eq. (2)). In the next step we define the apparent degradation index as the intensity ratio of probes located at different position along the target sequence, for example near its 5′- and 3′-end of one selected target,
where the intensities are given by Eqs. (3) and (4) with the respective degradation ratios d5' and d3', respectively.
Let us consider two special cases if the probes hybridize either far from saturation in the linear range
respectively. The lower case x defines the hybridization strengths at 'ideal' transcript concentrations (see Eq. (4) with d = dp = 1:
On the other hand, two intensity-based degradation measures are well established for quality control of GeneChip arrays: (i) The slope of a linear function fitted to the so-called 'RNA degradation plot', r5'/3'
slope. This RNA degradation plot displays the mean logged intensity averaged over all probes with the same index k, taken from one array, as a function of k
In view of the discussed problems of intensity-based degradation measures we will revise these estimates and judge their suitability for determining RNA quality. Large values of r5′/3′ slope and/or r5′/3′ control near unity are generally thought to indicate small degradation bias and thus good RNA quality. Note that reciprocal values of these measures are often used in practice estimating the respective 3′/5′-ratios. Here we consequently use 5′/3′-ratios to ensure direct comparability between the various measures.
In summary, probes located nearer to the 3′-end of the interrogated transcripts potentially shine brighter than more distant probes due to the 3′-enrichment of probe coverage giving rise to expected 'true' intensity ratios r5′/3′ < 1. However, this rule applies only to conditions of specific hybridization far from saturation. RNA quality measures based on the 5′/3′-intensity ratio consequently require consideration and evaluation of the hybridization mode of the chosen probes. Moreover, the potential dependence of the probe intensities on the degree of degradation gives rise to systematic errors of the estimated expression degree of the transcripts which requires appropriate correction.
The 3′-intensity bias depends on the hybridization-mode
The so-called hook method transforms the PM and MM intensities of GeneChip arrays into a smoothed delta-versus-sigma summary plot of characteristic shape where delta and sigma are the difference and the sum of the logged probe intensities after affinity correction. Its visual inspection allows the simple and straightforward detection of five hybridization regimes with increasing sigma (see
Figure 3
Hook- and degradation hook (above) and tongs-plot (below) of two selected chip hybridization taken form the human body index data set (muscle, GEO accession numbers GSM176301 in part a and skin, GSM175967 in part b) referring to large and smaller degradation effects, respectively
Hook- and degradation hook (above) and tongs-plot (below) of two selected chip hybridization taken form the human body index data set (muscle, GEO accession numbers GSM176301 in part a and skin, GSM175967 in part b) referring to large and smaller degradation effects, respectively. Note that all plots use the same abscissa scaling (Σ, see Eq. (7)) which is related to the expression degree of the respective probes. The hook curve reveals the changing hybridization mode with increasing sigma: non-specific (N), mixed N and S (mix), specific (S), saturation (sat) and asymptotic (as) ranges. The degradation hook and the tongs-plot reveal the mean 3′/5′-intensity bias of the probes. The three branches of the tongs plot refer to three probes nearest to the 3′-end (upper branch), nearest to the 5′-end (lower branch) and located in the middle in-between (middle branch). Note that the different branches split maximally in the S-range of hybridization whereas no bias is observed in the N-range as predicted by theory (lines, see Eqs. (16) and (21) for the hook and tongs plot, respectively). The theoretical curves are calculated using the formulae given in the methodical section using the parameters given in the figure. The hook dimensions (α, 'height' of the hook, see Eq. (18); β, 'width' of the hook; Σ(0), 'start' point; M, 'end'-point) are very similar for both arrays whereas the logarithmic 3′- and 5′-degradation levels (Eq. (24)) are markedly different. The size of the moving window is decreased towards the right end of the tongs plot to compensate the reduced number of probe sets in saturation range. As a consequence, the part of the curves beyond of the maximum is prone to increasing error
We here complement the hook representation by two modifications allowing to assess the degradation of the RNA-transcripts in a chip-specific fashion. These so-called 'degradation hook' and 'tongs plot' estimate the 3′-enrichment of the probes and thus their degradation level in dependence on the hybridization mode. They depict differences between the Sigma-values of selected subsets of probes taken from the 3′- and 5′-ends of the probe set,
as a function of sigma,
Interestingly, the different hybridization modes analogously affect the intensity differences in the standard and the degradation hook as well. For example, upon non-specific hybridization both, the PM/MM difference and the 3′-bias essentially disappear because both effects, the MM-penalty and the 3′/5′-bias, require duplexing of the probes with the intended targets. Non-specific binding doesn't meet this criterion because the binding of non-specific transcripts is indifferent with respect to the mismatched pairing of the middle base of the MM probes and with respect to the degradation bias as well (see also Figure
Note that the two standard hook plots shown in panel a and b of Figure
The tongs plots explicitly estimate the intensity bias at three positions of the probe sets and thus it illustrates the progression of degradation with increasing probe index. The ΔΣs curves of all three subsets (s = 3′, 5′ and m) degenerate in the N-hybridzation range indicating the absence of the 3′-bias for non-specific binding as discussed above (see also Eq. (6) for xS < <xN). In the mix-range the ΔΣs-curves split into three branches which progressively diverge with increasing sigma and thus with increasing contribution of specific hybridization. The 'opening of the tongs', i.e. the split between the 3′- and 5′-branches, reaches its maximum in the S-range of hybridization in parallel with the maximum of the hook curve and of the degradation hook. Subsequently, the different branches start to converge as predicted for the range of saturation (see Eq. (6)). Both, the experimental degradation hook and the tongs plot are well described by theoretical curves based on the Langmuir-model of array hybridization (see Materials and Methods section, Eq. (21)). The split parameter Δγ3′/5′ characterizes the height of the degradation hook, or equivalently, the 'tongs opening' serving as a measure of the maximum vertical difference between the 5′- and the 3′-branches of the tongs, respectively. Δγ3′/5′ estimates the 5′-depletion of probe coverage in terms of the logged concentration increment between the targets covering the 5′- and 3′-probes (Eq. (24)). The examples shown in Figure
Figure 4
Collection of tongs plots taken from the ratQC data set
Collection of tongs plots taken from the ratQC data set. The RNA was extracted from liver samples either after ex vivo incubation of fresh tissue (panel a, incubation time 0, 210 and 300 min) or after thawing frozen tissue (b, incubation time 0, 40 and 60 min). The plots in panel c and d show the tongs-opening and the dk parameter of both series as a function of the incubation time, respectively. RNA prepared from frozen samples degrades much faster than RNA from fresh samples. The insert in part b correlates the dk and tongs opening parameters. Their relation follows a logarithmic function
In summary, the 3′/5′-bias of probe intensities essentially disappears for probes which hybridize predominantly non-specifically and it markedly decreases for probes which are strongly saturated with specific transcripts. The 3′/5′-bias consequently provides a suited metrics for RNA-quality only in the linear range of specific hybridization in agreement with the theoretical predictions made in the previous subsection.
Short 3′-probe sets are prone to non-specific′ hybridization
In the next step we selected the probe sets from the non-specific and specific hybridization ranges of the hook curve and calculated their frequency histograms as a function of L1 and Lmax, the position of the nearest and of the most distant probe from the 3′-end in each probe set.
Figure 5
Distribution of probe sets hybridized predominantly with specific and non-specific transcripts as a function of the position of the first (left part) and last (right part) probe of each probe set
Distribution of probe sets hybridized predominantly with specific and non-specific transcripts as a function of the position of the first (left part) and last (right part) probe of each probe set. The graphs show that 'short' probe sets located nearer to the 3′-end of the transcript are more prone to bind non-specific transcripts at (L1 < 100 and Lmax < 500) than specific ones. The part in the middle shows the difference between the respective fractions of probe sets whereas the part above normalizes this difference with respect to the mean fraction of probe sets in both, S- and N-groups. Accordingly, the differential binding refers to about 50% of all probe sets. The distributions are calculated using the ratQC data set
We suspect that the increased fraction of non specific hybridization towards the 3′ end of the transcripts is caused by inaccurate assignment of the 3′-transcript end upon probe design and/or by variations of the 3′-end of the transcripts, e.g. due to effects such as alternative polyadenylation as discussed previously
An alternative option that potentially explains the increase of the relative contribution of N-hybridization near the 3′-end can be sought in depletion of the respective targets in solution due to the high number of probes with partly overlapping probe sequences in this L-range. In consequence, a larger number of probes can be thought to compete for each transcript-fragment than at larger distances L. This competition for the same target can deplete its concentration in solution. Such target depletion effectively reduces the binding affinity of the respective probes for specific binding
In summary, we found a biased distribution of specific and non-specific hybridization along the targeted transcripts: Non-specific binding is more heavily weighted near the 3′-end, presumably owing to its inaccurate assignment and to transcript isoforms with variable 3′ UTR lengths.
Positional dependent intensity decays
The degradation hook and the tongs plot shown in Figure
respectively. The angular brackets denote averaging either over all probes with the same index k or over all probes with the same absolute position within a window L-δL < Lp ≤ L + δL.
Figure 6
Positional dependent intensity decays in relative and absolute scale
Positional dependent intensity decays in relative and absolute scale. Panel a) Mean intensity decays of specifically and non-specifically hybridized probes (Eq. (8)) referring to the data shown in Figure
This approach attributes the flattening of the decay near the 3′-end to the saturation of the probes with bound transcripts. However this effect becomes relevant usually at large intensity values only (log I(3′)~ log M > 4; see Figure
To account for this effect we pursue a simple approach which neglects saturation and normalizes the decays with respect to their maximum intensity level near the 3′ end of the transcripts,
The obtained degradation index due to non-specific hybridization is given by a constant, dN(x) ≈ 1, to a good approximation (Figure
as illustrated by the dotted curves in Figure
Panel b and c of Figure
The L-decays of specifically hybridized probes obviously behave differently for L > 600 showing a less pronounced loss of intensity than for L < 600. The origin of this difference is unknown. The standard error of the experimental decays roughly agrees with the symbol size (k-dependencies, left part of Figure
Our fits show that the values of the decay parameters systematically depend on the chosen decay function and strongly correlate each with another. To illustrate this correlation we show fits with variable d∞
L but constant λL = 150 in Figure
To obtain a robust decay characteristic we substitute the exponential fit functions (Eq. (10)) by a simple two-point estimate
This logged degradation ratio characterizes the intensity decay in the index-range kstart- kend =2-10, or equivalently, in the positional range Lstart-Lend = <L > 1,2 - < L > 10,11 ~ 150–550 which comprises the majority of more than 95% of all probes. The degradation ratio can be transformed into estimates of the decay length of the exponential decays:
Please note that the decay function defined in Eq. (10) estimates the fold change of transcript abundance at position x and x0, to a good approximation, i.e.
The degradation ratio (Eq. (12)) consequently estimates the mean fold change of transcript abundance reported by the probes positioned near the 5′- and 3′-ends of the probed range. It represents an alternative estimate of the tongs opening parameter introduced above,
In summary, the effect of degradation can be described as a function of the probe position in terms of a 'shifted exponential decay plus constant'-function using either the probe index or the 'absolute' probe position as argument. This information can be further condensed into a single degradation ratio parameter characterizing the fold change of transcript abundance over the length of the DNA region interrogated by the probes.
3′/5′-controls are affected by the hybridization mode
It was previously shown that the 3′/5′ intensity ratios of special control probe sets interrogating long transcripts such as GADPH and beta-actin might not represent a sufficient measure of the degradation bias at small expression degrees because non-specific binding leads to an underestimation of the 3′/5′-bias
In the first step we estimated the hybridization regime of the GADPH and beta-actin controls of the rat-QC and tissue data sets using modified hook plots (Figure
Figure 7
Hybridization and RNA-quality characteristics of the GADPH and beta-actin control probe sets in the tissue (left) and rat-QC (right) data sets
Hybridization and RNA-quality characteristics of the GADPH and beta-actin control probe sets in the tissue (left) and rat-QC (right) data sets. Each data point refers to one array of the respective series. The abscissa provides the degradation level in units of the logged 3′/5′- mean intensity ratio of the respective control data sets. The vertical axes plot either sigma coordinates of the 3′- (green dots) and 5′- (blue dots) probe sets of the controls, or their mean (dark blue circles). The red and black dots mark the respective sigma-levels of non-specific binding and of saturation, respectively. The vertical orange lines indicate the constant quality threshold separating good (to the left) and poor (to the right) apparent RNA quality. The 'threshold' hooks (orange) refer to the same quality threshold. They however explicitly consider its decrease in the N- and sat-ranges of hybridization. Application of the constant threshold thus produces false positives together with true positives and true negatives (see also Figure
To judge the hybridization mode we also depict the sigma coordinates of the non-specific background intensity (N, red dots) and of the asymptotic saturation level (as, black dots) obtained from the standard hook analysis of each of the arrays. Recall that the sigma-values of the N- and the as-mode limit the range of possible probe intensities. They consequently constitute an intrinsic metric allowing to assign the probes to one of the five possible hybridization modes as indicated in the figure (see also Figure
It has been recommended that good-quality samples should have a 3′/5′ signal ratio for GADPH and beta-actin of no more than three, or in our notation of Δ3′/5′
control < log(3) = 0.47
The 3′/5′-intensity ratio of the probe sets is however not a constant for a given RNA-quality level. Instead it depends on the hybridization mode (see above and Eq. (6)). Particularly, the 3′/5′-intensity ratio referring to a constant RNA-quality level follows the degradation hook shown in Figure
For example, a large fraction of the GADPH- and beta-actin intensity ratios of the tissue data set meet the constant quality criterion, Δ3′/5′
control < threshold = 0.47, indicating apparently good RNA quality (Figure
The mean sigma coordinates (Σ3′+5′
control) of the Rat-QC data set are found approximately halfway between the respective N- and as- levels indicating that the controls are predominantly hybridized in the S-range (Figure
The sigma (i.e. logged mean intensity values) values of both data sets studied clearly indicate the decrease of the mean intensity of the controls with decreasing RNA quality due to the loss of material assumed, e.g. in Eq. (13). In consequence, the hybridization regime of the controls can shift with changing RNA-quality. Note also that GADPH is associated with slightly larger probe signals than beta-actin in both data sets. Beta-actin controls are consequently less prone to saturation than GADPH controls.
In summary, control probes can overestimate RNA-quality if one uses a constant threshold criterion because the true threshold level strongly decays for saturated probes. The problem can be fixed either by using an intensity dependent 'threshold' hook or by using alternative RNA-quality estimates such as the degradation ratio (see above).
Affy-slope is affected by absent probes
A widely applied metric for RNA quality is the 'RNA degradation plot' provided with the R package
However, the affy-slope degradation plot is virtually identical with the reciprocal positional dependent degradation index introduced above in Eq. (9) (dh(k)-1) except the fact that it considers all probes of the array whereas our approach separately averages over the N- and S-subensembles referring either to the S- or N-hybridization regimes, respectively. The affy-slope estimates are expected to underestimate the degradation level owing to the inclusion of predominantly non-specifically hybridized probes (so-called absent probes) which do not respond to RNA quality as shown above. More importantly, the chip-to-chip variability of the fraction of absent probes (%N; as determined by methods such as MAS5 or hook; see ref.
To illustrate this effect, a series of affy-slope curves referring to different degradation levels are shown in Figure
Figure 8
RNA degradation plot of all probes (panel a) and degradation profile of specifically hybridized probes (b) for microarrays selected from the human tissue data set
RNA degradation plot of all probes (panel a) and degradation profile of specifically hybridized probes (b) for microarrays selected from the human tissue data set. Panel a shows the plots obtained using the affy package
To generalize this result we studied the mean 5′/3′-intensity ratios of different subsets of probes taken from the 677 array hybridizations of the human tissue data set ( Additional file
Hence, the apparent degradation ratio derived from the simple affy-slope intensity measures is strongly modulated by the fraction of non-specifically hybridized 'absent' probes leading potentially to the systematic overestimation of RNA quality. Contrarily, the proposed use of specifically hybridized probes largely removes this bias from the data and provides a reliable measure of the degradation degree which can be consistently compared between different arrays.
Array-degradation metrics correlate with RIN
The RNA Integrity Number (RIN) provides a numerical value for the assessment of RNA quality based on the electropherogram trace of a RNA sample captured with the Agilent Bioanalyzer
Figure 9
Comparison of microarray degradation measures (dk and 5′/3′-ratio of the hybridization controls) with the RNA integrity number (RIN, panel a and c) and with the mean length of the transcripts (panel b) obtained in the ratQC experiment
Comparison of microarray degradation measures (dk and 5′/3′-ratio of the hybridization controls) with the RNA integrity number (RIN, panel a and c) and with the mean length of the transcripts (panel b) obtained in the ratQC experiment.
In panel b of Figure
The regression line between the dk parameter and the 5′/3′-intensity ratio of the controls allows to transform the quality threshold of the latter ratio into a dk threshold (Figure
Degradation reduces total transcript abundance
We so far estimated RNA quality in relative units using suited 5′/3′-intensity metrics which reflect the decrease of transcript abundance with increasing distance from the 3′-end. Trivially, this effect is expected to reduce the total amount of mRNA used for hybridization. The decrease of the mean intensity of the control probe sets with increasing degradation ratio as shown in Figure
The hook method enables the independent estimation of the mean levels of non-specific 'background' hybridization and that of specific expression using the simple summary measures β (see Eq. (18)) and ϕ (Eq. (19)) which are based on large ensembles of probe sets on each array. Particularly, the width of the hook curve β has been shown to relate to the total amount of RNA material
Figure 10
Hook-hybridization characteristics of the arrays of the ratQC data set
Hook-hybridization characteristics of the arrays of the ratQC data set. (a) The width of the hook curves β increases with progressive degradation indicating the decrease of the non-specific background due to the loss of material (see Eq. (18)). Log d is the mean degradation index (Eq. (2)) and K the slope of the regression line. The mean level of specific hybridization changes only weakly with degradation (b). The fraction of absent probes is virtually unaffected by degradation (c). All parameters are estimated using the hook method
The degree of specific binding drops upon degradation, however to a considerable smaller degree than the amount of non-specific binding (Figure
Part c of Figure
RNA-quality scaling of gene expression
It has been previously found that, although moderate levels of RNA degradation are tolerated by differential expression analysis, beyond a threshold especially long targets provide erroneous expression results
Considering also correlations between 3′/5′-quality measures and signal values (called LEV, 'labeling extension values'), Lee et al.
The final aim is therefore to use the degradation model for correcting the 3′-probe intensity bias to provide (largely) unbiased probe signals for downstream analysis. One expects that the loss of RNA material in general and particularly, RNA-fragments probed far away from the 3′-end, systematically decreases the apparent expression degree extracted from microarray probe intensities. In the Methods-section we propose a simple algorithm which corrects the raw probe intensities for the positional-dependent 3′/5′-bias. It makes use of the mean intensity decay-function of specifically hybridized probes and of the degree of specific hybridization of the particular probe which it intends to correct. Our approach corrects the 3′/5′-bias on the level of raw probe intensities.
In the supplementary text ( Additional file
Amplification of RNA-material using primed in-vitro transcription protocols and degradation of RNA during extraction, storage and processing of the samples affects RNA-quality in microarray experiments with consequences for expression estimates and their interpretation. We systematically analysed the effect of varying RNA quality on microarray probe intensities using a physico-chemical hybridization model and propose (i) new measures to assess RNA quality and (ii), a simple method to correct probe intensities for the degradation bias.
Particularly, it is shown that
– poor RNA quality is associated with a 3′-bias of transcript abundance which affects only the probe signal due to specific hybridization;
– estimation and correction of the signal bias of each particular probe requires consideration of its hybridization mode (specific, non-specific or a superposition of both) and of the positional effect of probe intensity along the respective gene due to truncated transcripts. The former issue is solved by applying a modified 'hook'-approach of data analysis based on Langmuir hybridization theory. The latter effect is taken into account by estimating the mean positional intensity decay on each array as a function of either the probe index or the probe's distance to the 3′ end of its target transcript.
– RNA quality is estimated in terms of the 3′/5′-intensity gradient of specifically hybridized probes. In addition to appropriate quality numbers (such as 'tongs opening'-parameter and the degradation ratio) we introduce graphical characteristics allowing assessment of RNA quality of each single array ('tongs plot' and 'degradation hook'). The parameters have a well defined physical meaning related to the fold change of transcript abundance along the genes. 'Poor' RNA quality is characterized roughly by a decay of the mean specific signal by a factor of less than 0.5 between probes near the 3′- end and probes located about 600 nt away.
– our approach improves established RNA-integrity measures such as 'affyslope' and the 3′/5′-intensity ratio of degradation control probe sets. Both methods are prone to overestimate RNA quality if the signals are dominated by non-specific hybridization (affyslope) and/or saturation (controls). Our microarray-based quality estimate correlates well with the RNA integrity number (RIN) which, in addition, is affected by more complex properties of RNA degradation not uniquely related to transcript length.
– short probe sets near the 3′-end are prone to non-specific hybridization presumable because of uncertainties in 3′UTR length owing to inaccurate assignment of the 3′-end and transcript isoforms.
– poor RNA quality is associated with a decreased amount of RNA material hybridized on the array paralleled by a decreased total signal level. Additionally, it causes a gene-specific loss of signal due to the positional bias of transcript abundance which requires an individual, gene-specific correction. The former total effect can decrease the overall signal level of an array by the factor of 0.5 -0.7 in the case of poor RNA quality (RIN < 7). The latter local effect can be more pronounced with a penalty in expression measures by a factor of 0.3-0.4 or even less in worst cases.
Our study proposes new degradation measures to judge RNA quality. The basic difference to previous ones is the explicit consideration of the hybridization mode to largely reduce the influence of non-specific hybridization. Based on the physico-chemical mechanisms of probe hybridization we propose a correction method which aims at removing the degradation bias and thus enables to use the full ensemble of probes in downstream analysis without loss of information.
The tongs visualizations and the degradation measures are implemented in the Bioconductor package
Affymetrix microarray raw intensity data (CEL-file format) were downloaded from the public repositories Gene expression omnibus (
Probe set definitions were applied as proposed by Affymetrix by using standard CDF-files. Absolute probe positions with respect to the intended 3′-end of the transcript, Lp, (as used, e.g. in Eq. (8)) were determined from the target sequences provided by Affymetrix for each transcript (see
Degradation hook and tongs plot
The so-called hook method aims at characterizing the hybridization of a particular microarray in terms of quality control and expression analysis (see
<… > pset denotes averaging over each probe set of usually 11 PM/MM probe pairs addressing the same transcript (log ≡ log10 is the decadic logarithm) and < … > mov is the moving average over a sliding window of about 1000 probes for smoothing. Plotting the data into Δ-versus-Σ coordinates provides the hook curve which enables decomposition of the probe signals into contributions due to specific and non-specific hybridization by simple visual inspection.
In analogy with Eq. (14) we define the modified ordinate values
where < … > s denotes averaging over subsets of three probes from the respective probe set located either nearer its 3′-end (s = 3′), 5′-end (s = 5′) or in the middle (s = m), and subsequent smoothing using a sliding window of appropriate size. The so-called tongs-plot shows the three positional-dependent values ΔΣ3′, ΔΣ5′ and optionally ΔΣm as a function of the hook-abscissa Σ whereas the 'degradation hook' plots ΔΣ3′/5′-versus- Σ.
The two-species Langmuir hybridization isotherm predicts the theoretical hook-curve which was previously used to fit the experimental curves and to extract characteristic chip-related parameters. It provides a 'mean' hybridization isotherm implicitly characterizing the concentration dependence of the probe signals. Here we modify the hook formalism to take into account the effect of incomplete transcript amplification and degradation in terms of the degradation ratios defined in Eq. (2). For the detailed derivation of the hook equation in the absence of degradation and the detailed discussion of the used parameters see refs.
In short, insertion of Eqs. (3) and (4) with P = PM and MM and wp P,h = 1 into Eqs. (14) and (15) provides the theoretical expressions of the hook coordinates for the subset s of probes taken from the probe sets,
with the saturation terms
Eq. (16) expresses the hook-coordinates as a function of the probe-specific S/N-ratio,
It scales with
The vertical and horizontal dimensions of the hook curve and its start coordinates are defined as
respectively. Note that the width and the start coordinate of the hook curve, β and Σstart, change with the mean degradation index d (see also Eq. (2)) whereas the height of the hook α doesn't depend on degradation.
The mean expression index characterizes the mean expression level of present probes of the chip,
The ordinate values of the degradation plots are obtained by inserting Eq. (16) into Eq. (15),
One gets after explicit consideration of Eq. (16)
where ΔΣs1(R) refers to the special case rs2 = 1. The parameters
define the 3′-bias of transcript abundance (see also Eqs. (17) and (6)). Particularly,
if one assumes uniformly distributed probes along the relevant transcript regions. With
Hence, the mean amount of RNA (Eq. (2)) is directly related to the 3′/5′-difference of transcript abundance.
Typical examples of the hook curve (Δ-versus-Σ, Eq. (16), thick curves), the degradation hook (ΔΣ3′/5′-versus-Σ, Eq. (21), thin curves) and the tongs-plot ((ΔΣ-versus-Σ, Eq. (21), panel above) as predicted by theory are shown in Figure
Figure 11
Theoretical hook curve (Eq. (16), thick curves), degradation hook (thin curves) and tongs plot (panel above; Eqs. (20) and (21)) for different degradation levels log d
Theoretical hook curve (Eq. (16), thick curves), degradation hook (thin curves) and tongs plot (panel above; Eqs. (20) and (21)) for different degradation levels log d. With increasing degradation the positive and negative amplitudes of the tongs plot (the tongs opening Δγ3′/5′) and the height of the degradation hook increase, accompanied by the shift of its increasing branch towards the left which widens the curves (parameter β). The curves are calculated with γ3′ = −γ5′ = 0.1, 0.3 and 0.5, respectively. The dotted curves in the part above are calculated neglecting the saturation term in Eq. (21). The geometrical meaning of selected parameters is indicated by arrows (see text)
Threshold hook
The threshold hook represents a special version of the degradation hook described in the previous subsection. It defines a threshold of the 3′/5-intensity ratio of the probe sets used to assess RNA-quality such a GADPH or beta-actin. The threshold hook accounts for the fact that the probe signals are affected by non-specific binding and by saturation. Both effects give rise to an intensity-dependent threshold for estimating good RNA-quality.
In the first step one transforms the intensity values of the control probes into hook coordinates
In contrast to the standard hook (Eq. (14)) we here use only the intensities of PM-probes.
In the second step, one calculates the threshold hook as a Δ3′/5′-vs-Σ3′/5′-plot under the condition that both, the 5′- and 3′-probes hybridize according to the hyperbolic Langmuir isotherm (see Eq. (3) with wp P = 1), however using different specific binding activities due to the different degradation indices of the respective transcripts, d5′ < d3′ (Eq. (4)). The delta-value is expressed as a function of sigma using the degradation hook-formalism described in the previous subsection after neglecting the MM-probes (use Eq. (16) with α = −∞). The start and end point of the threshold hook are taken from the standard hook analysis which provides these data with relatively high accuracy.
The obtained hook curve thus describes the 'trajectory' of a pairing of 3′/5-probe sets upon changing expression degree of the respective transcript (see Figure
Figure 12
Threshold hook for estimating good RNA quality using control probe sets
Threshold hook for estimating good RNA quality using control probe sets. (a) Constant (apparent) and variable (threshold hook) RNA quality threshold. The true threshold depends on the hybridization regime and vanished upon non-specific hybridization and upon saturation. (b) Error estimates of GADPH-controls taken from the tissue data set (see text)
The application of a constant threshold instead of the variable one will cause false quality estimates. We estimated the error of the 3′/5′-intensity ratio of the GADPH-control taken from the tissue data set as example: Figure
Correcting the 3′/5′ bias of pobe intensities
RNA quality affects the probe intensities of each probe by two factors: (i) the probe position with respect to the nominal 3′-end of the transcript, L (or, alternatively, the probe index in the probe set, k) and (ii), the hybridization regime. The specific hybridization regime below saturation is particularly prone to biased intensities as opposed to non-specific hybridization and specific hybridization in the asymptotic saturation range. Our approach aims at correcting raw probe intensities which subsequently can be preprocessed using any method available.
Firstly, we estimate the degree of specific, unsaturated hybridization in terms of the relative amplitude of the degradation hook with respect to its maximum reached in the S-range of hybridization,
Then, the correction function
is defined as weighted sum of the decay functions due to specific and non-specific hybridization where the latter is set to unity, dN(x) = 1. Note that for saturated probes beyond the maximum of the degredation hook dN(x) = 1 can be interpreted as the apparently absent 3′/5′-bias in the asymptotic range of hybridization.
The biased probe intensities are corrected according to
Hence, each probe intensity is rescaled according to its position (x = k or L) and the hybridization mode of its probe set according to y. Consequently, probe intensities taken from the non-specific hybridization range remain uncorrected. With increasing degree of specific hybridiztaion the probes are progressively scaled up with increasing distance from the 3′-end of the transcript. The maximum correction applies to probe sets in the S-hybridzation range. MM probe intensities are scaled using the mean logged MM-intensity of the probe set as argument. In the supplementary material ( Additional file
The 3′/5′ correction algorithm is implemented in R and builds upon the popular affy package
Competing interests
Both authors declare that they have no competing interests.
Authors' contributions
HB and MF: Conceived and designed this study, performed data analysis; HB: developed theory and wrote large parts of the manuscript. MF: Wrote the programs and performed the calculations. Both authors read and approved the final manuscript.
This publication is supported by LIFE - Leipzig Research Center for Civilization Diseases, Universität Leipzig. This project was funded by means of the European Social Fund and the Free State of Saxony.
Effects of RNA degradation on gene expression analysis of human postmortem tissues
Impact of RNA degradation on gene expression profiles: Assessment of different methods to reliably determine RNA quality
Evaluation of quality-control criteria for microarray gene expression analysis
Effect of RNA quality on transcript intensity levels in microarray analysis of human post-mortem brain tissues
RNA integrity and the effect on the real-time qRT-PCR performance
Chipping away at the chip bias: RNA degradation in microarray analysis
Big results from small samples: evaluation of amplification protocols for gene expression profiling
In vitro transcription amplification and labeling methods contribute to the variability of gene expression profiling with DNA microarrays
On the causes of outliers in Affymetrix GeneChip data
Characterization of the effect of sample quality on high density oligonucleotide microarray data using progressively degraded rat liver RNA
Effect of agonal and postmortem factors on gene expression profile: quality control in microarray analyses of postmortem human brain
Quality control for microarray analysis of human brain samples: the impact of postmortem factors, RNA characteristics, and histopathology
The RIN: an RNA integrity number for assigning integrity values to RNA measurements
Amplified RNA degradation in T7-amplification methods results in biased microarray hybridizations
Simpleaffy: a bioconductor package for affymetrix quality control and data analysis
affy–analysis of Affymetrix GeneChip data at the probe level
Modeling of DNA microarray data by using physical properties of hybridization
Statistical analysis of adsorption models for oligonucleotide microarrays
The sensitivity of microarray oligonucleotide probes - variability and the effect of base composition
Thermodynamics of competitive surface adsorption on DNA microarrays
Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips
Amplified RNA synthesized from limited quantities of heterogeneous cDNA
The highways and byways of mRNA decay
Sensitivity, specificity, and the hybridization isotherms of DNA Chips
Absolute mRNA concentrations from sequence-specific calibration of oligonucleotide arrays
Adsorption models of hybridisation and post-hybridisation behaviour on oligonucleotide microarrays
GeneChip microarrays - signal intensities, RNA concentrations and probe sequences
Relationship between gene expression and observed intensities in DNA microarrays—a modeling study
Washing scaling of microarray expression
Non-specific hybridization scaling of microarray expression estimates - a physico-chemical approach for chip-to-chip normalization
Expression profiling — best practices for data generation and interpretation in clinical trials
Quality control in microarray assessment of gene expression in human airway epithelium
"Hook" calibration of GeneChip-microarrays: theory and algorithm
"Hook" calibration of GeneChip-microarrays: chip characteristics and expression measures
Probe-level analysis of expression microarrays characterizes isoform-specific degradation during mouse oocyte maturation
Interpretation of multiple probe sets mapping to the same gene in Affymetrix GeneChips
Physico-chemical modelling of target depletion during hybridisation on oligonulceotide microarrays
Impact of RNA degradation on gene expression profiling
An application for assessing quality of RNA hybridized to Affymetrix GeneChips
Assessing quality of hybridized RNA in affymetrix GeneChip experiments using mixed-effects models
Calibration of microarray gene-expression data


text:
Smart car parent company Daimler has launched a new app that helps owners determine how to best pack parcels in their car's diminutive cargo space, reports CTV News.
The new Pactris app takes its name from the popular 1980s video game Tetris, which it mimics in order to help smart owners with effectively packing objects as closely together as possible.
Pactris uses a database of package dimensions to determine if a given package will fit in the rear cargo hold of a smart fortwo or forfour with the rear hatch closed. Users simply scan the bar code on the physical package, and the app will determine if the package will fit or not.
The highly useful app will also determine if a number of packages will fit, and if so, in what arrangement, all based on the bar-code database it relies upon for dimensional information. The app also stores information on standard-size items such as 24-packs of beer, wine boxes and banking boxes.
While the Pactris app is an obvious fit for the smart car – a vehicle limited to only 350 litres of trunk space – it would no doubt appeal to owners of nearly any vehicle used for shopping expeditions.
(Relaxnews via CTV News)

subset: commoncrawl


text:
The New Macroeconomics: Imperfect Markets and Policy Effectiveness
€ 41,99
Bisher € 52,49
Lieferbar innert 2 Wochen
Mai 2004
BeschreibungThis book focuses on the rapidly growing research field of imperfect competition, asymmetric information, and other market imperfections in a macroeconomic context. It brings together leading researchers from the U.S. and Europe to examine the implications for macroeconomic policy of market imperfections in output, labor and financial markets. All the contributions are original, and include several general surveys and expository chapters that synthesize the larger literature, along with some state-of-the-art research at the frontier of the discipline. This is the first volume to focus exclusively on this literature. It should be a valuable resource for graduate students and researchers in macroeconomics.
InhaltsverzeichnisIntroduction Huw Dixon and Neil Rankin; Part I. Overviews and Perspectives: 1. Classical and Keynesian features in macroeconomic models with imperfect competition Jean-Pascal Benassy; 2. Imperfect competition and macroeconomics: a survey Huw Dixon and Neil Rankin; 3. Notes on imperfect competition and new Keynesian economics Richard Startz; Part II. Goods Market Imperfections: 4. Optimal labour contracts and imperfect competition: a framework for analysis Russell Cooper; 5. Market power, coordination failures and endogenous fluctuations Claude d'Aspremont, Rodolphe Dos Santos Ferreira, and Louis-Andre Gerard-Varet; 6. Macroeconomic externalities Andrew John; Part III. Labour Market Imperfections: 7. Demand uncertainty and unemployment in a monopoly union model Omar Licandro; 8. Efficiency wages as a persistence mechanism Gilles Saint-Paul; 9. Efficiency, enforceability and acyclical wages Christian Schultz; 10. Business fluctuations, worker moral hazard and optimal environment policy Jon Strand; Part IV. Financial Market Imperfections: 11. The stock market and equilibrium recessions Jeff Frank; 12. Asymmetric information, investment finance and real business cycles Brian Hillier and Tim Worrall; Part V. Nominal Rigidities and Bounded Rationality: 13. Hedging, multiple equilibria and nominal contracts Daron Acemoglu; 14. Information acquisition and nominal price adjustment Torben M. Andersen and Morten Hviid; 15. Expectation calculation, hyperinflation and currency collapse George W. Evans and Garey Ramey; 16. Menu costs and aggregate price dynamics Alan Sutherland; Bibliography.
Untertitel: New. Sprache: Englisch.
Verlag: CAMBRIDGE UNIV PR
Erscheinungsdatum: Mai 2004
Seitenanzahl: 404 Seiten
Format: kartoniert


text:
Tickets: Sold out
Facts: Fulham have conceded just two goals in their last six games. Walsall have lost only two of eight games against London opposition so far this season - both defeats were inflicted by Watford.
Recommended pub: King George V (Wallows Lane)
Woeful Walsall, beaten 3-0 at home by struggling Sheffield Wednesday last weekend, surely have little chance against Fulham. In fact, should The Saddlers win tomorrow, I am prepared to have a bet on Keith Vaz being named Parliamentarian of the Year. It would be good to see Fulham have a real run in the FA Cup, although the thought of their chairman cavorting around Cardiff does temper the enthusiasm a bit.
Verdict: FULHAM IN THE SIXTH ROUND DRAW
- More about:
- Cardiff City Fc
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text:
Weight gain is a known side effect of prednisone use and perhaps the most frustrating. The pounds seem to pack on quickly. Attempts to lose the extra weight can seem futile, leaving patients depressed and feeling hopeless.

Prednisone belongs to a class of drugs known as corticosteroids. Steroids, as they are more commonly called, can be very effective medications for controlling inflammation. Many people with inflammatory types of arthritis use prednisone or a similar corticosteroid medication to bring a flareup of symptoms under control. Others may include one of the steroid medications as part of their usual medication regimen.

There are several side effects associated with prednisone and the other corticosteroid drugs. People who use steroids are advised to take the lowest effective dose for the shortest duration possible to decrease the risk of adverse side effects. High doses and long-term use is linked to more severe side effects.

Experts suggest that there are things you can do to manage weight gain while on prednisone. First, you should understand how corticosteroids work and why weight gain occurs. Then, consider if suggested changes to your diet or lifestyle might help.

How Corticosteroids Work

Corticosteroids, natural or synthetic, are a group of chemically similar hormones that have important functions within the body. Steroids play a role in regulating metabolism, immune function, inflammation and response to stress and injury. Naturally-occurring steroids, like cortisol, are produced by your adrenal glands. Normally, the adrenal glands produce steroids that would be the equivalent of a low dose (about 5 mg) of prednisone daily. When corticosteroid drugs are prescribed at higher doses, they can help control inflammatory diseases.

How Prednisone Causes Weight Gain

Not everyone will gain the same amount of weight while on prednisone but most will have some degree of weight gain. Additionally, prednisone can cause a redistribution of fat, especially in the face, abdomen and back of the neck.

According to experts at The University of California San Francisco Medical Center (UCSFMC), weight gain associated with prednisone use is typically caused by fluid retention and higher calorie intake due to increased appetite. That's in addition to being less physically active because of pain and other consequences of chronic inflammatory disease.

You may think there is nothing you can do about those factors—fluid retention, increased appetite and decreased physical activity. Think again. Let's consider what the experts recommend to help you manage weight gain from prednisone.

Manage Fluid Retention

A low-sodium, high-potassium diet can help control fluid retention. According to UCSFMC, you should not ingest more than 2,000 mg of sodium per day. You can achieve these dietary goals by avoiding high-sodium foods, especially processed foods, while increasing your intake of potassium-rich foods.

Tips for Cutting Sodium From Your Diet

Low Sodium Diet

15 Potassium-Rich Foods

Control Your Appetite

If increases in appetite have been subtle, you may not even realize this is an issue. Being aware of your increased appetite is the first step. Pay attention to your calorie intake and the nutritional value of the foods you eat.

Avoid processed sugars and simple carbohydrates, the so-called empty calories. These are digested quickly and just leave you hungry soon after. Instead, eat small but frequent healthy meals. Some recommend low-calorie, low-fat diets while others pitch low-carb, high-protein diets. Try both recommended courses and see what works for you.

Top 10 Foods Good for Low-Fat and Low-Carb Diets

Increase Physical Activity

Joint pain and other symptoms of inflammatory types of arthritis will make it difficult to exercise and keep activity at a high level. Most people with arthritis believe that they can't do enough to burn calories. It is important to do something rather than nothing, though.

With the advice of your doctor or physical therapist, structure a daily exercise routine that takes into account your abilities. To whatever level is possible, include aerobic exercise and some strength training, which will help burn calories, build strength and slow bone loss. Make an effort each day.

Hold Yourself Accountable

Once you establish a diet and exercise regimen that will help you manage your weight, track your daily progress. You can do it by writing in a journal or keeping a chart on your computer. How you track your diet, exercise, and weight is up to you—but make sure you do it. Accountability is a major factor that will help you successfully manage your weight while on prednisone.

Sources:

ILD Nutrition Manual: Prednisone and Weight Gain. UCSF Medical Center.

Prednisone Information. The Johns Hopkins Vasculits Center.


text:
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text:
U.N. urges Olympic social inclusion amid Russian gay rights storm
By Michelle Nichols
UNITED NATIONS (Reuters) - The U.N. General Assembly called on Wednesday for an international truce during the 2014 Winter Olympics in Russia and urged Moscow "to promote social inclusion without discrimination" amid outrage over the country's ban on gay "propaganda" among minors.
The 193-member General Assembly approved by consensus a resolution calling for an end to fighting around the world from seven days before the Winter Olympics start in Sochi in February until seven days after the Paralympics end in March.
For the first time, according to the United States, the resolution - which has been adopted regularly by the General Assembly for 20 years - called "upon host countries to promote social inclusion without discrimination of any kind."
Russia triggered angry criticism and even calls to boycott the Sochi games when it adopted in June a ban on homosexual "propaganda" among minors. Critics denounced the law as discriminatory and a curb on rights to free speech and assembly.
"Sport embraces all segments of society and is instrumental in empowering people with diverse backgrounds, while fostering tolerance and respect for all people no matter what they look like, where they come from, where they worship or whom they love," U.S. delegate Elizabeth Cousens told the United Nations.
Sochi Games chief Dmitry Chernyshenko said that everyone was welcome at the games at Russia's Black Sea resort.
"During the games we guarantee that there will be no discrimination whether by religious or sexual or gender," he told reporters at the United Nations. "The president of the country three times repeated there will be no discrimination."
Russian President Vladimir Putin said last month that everything was being done "so that participants and guests feel comfortable in Sochi, regardless of nationality, race or sexual orientation. Continued...


text:
KANSAS CITY, Kan. – On Tuesday, Peter Vermes promised that Sporting Kansas City would take matters into their own hands in the home leg of the Eastern Conference Semifinals.
Sporting's hard-nosed, intense manager wouldn't elaborate on what he meant by that. Watch and see, he said.
On Wednesday night, his club made good on the promise – not with rough play, but with a dominating, dynamic performance in a 3-1 extratime victory over New England and a 4-3 win on aggregate in the two-match series.
And that, Vermes said, is what he meant all along.
"If you watch tonight's game, you can't leave here and say you weren't entertained," he said in the postmatch news conference. "Our players managed the game extremely well in all the pieces of the game, and on top of that they also entertained."
The sellout crowd at Sporting Park certainly got their money's worth, from Sporting's early pressing to Claudio Bieler's series-winner in the 23rd minute of extratime, and they returned that with loud, passionate support.
"It was an unbelievable environment," Vermes said. "I've always said that the interesting thing for me here at Sporting Park is that there's a connection between the fans and the players, and the players and the fans. And that is the thing that needs to be replicated all over this league as the league continues to grow.
"We want to play a high-tempo, entertaining style of game, and when we're given the chance to do that, it's entertaining it not only for the fans watching it live but also for the fans watching on TV."
Vermes felt his club didn't get that chance in their 2-1 away-leg loss this past Saturday, so they went out and took it on Wednesday.
"The difference is that we went out and said, 'This is what we're going to do, no matter what the other team does, we're going to play this way, and we're going to take certain situations into our own hands, and we're going to make sure that we force things to happen the way that we want to, and that's a credit to the players," he explained.
Sporting outshot the visitors 32-5, had 64.3 percent of the possession, won 98 duels to New England's 69 – and committed 17 fouls to 27 for the Revolution.
"There's not a guy on our team that ever cheap-shots anyone," Vermes said. "If they did, I'm the first guy to step in and take care of that, because that's not the way we want to play. We play hard, but we play fair, and we want to play to make sure that we're always entertaining as well.
"We want results – don't get me wrong, results are first and foremost – but we want to do that in conjunction with entertaining."
Steve Brisendine covers Sporting Kansas City for MLSsoccer.com.


text: Hi I have a drive, that shows reallocated sectors. I have taken this drive, and low level format/Write Zeroes, ran stress test for several days, the drive is performing well. Have done several error scans, all is well. I need to clear the Glist/Plist, within the drives firmware. i don't trust the manufacturers notifications about reallocated sectors, I would like to clear them, and then retest the disk. Is there a freeware method of doing this? There are forensic utilities that can clear the list, but they cost a fortune. As mentioned I don't trust the manufacturers, they just want you to buy a new HD. Thx!


text:
By Brian Truitt, USA TODAY
He's the star of the Fox action series Human Target, and Mark Valley has made himself a human target of sorts in real life — most recently in his hometown of Ogdensburg, N.Y.
To commemorate the 250th anniversary of the Battle of the Thousand Islands, the last battle of the French and Indian War where 11,000 British soldiers defeated nearly 400 French Marines, Valley was one of several townsfolk who were part of an epic re-enactment.
"It was ugly. But the re-enactment was fun!" says Valley, who played one of the losing French Marines and is always up for a return trip to where he grew up.
"I go home enough where I'm not a hometown hero. No one really has any compunction against just coming up and talking to me like I'm just Mark Valley. Jenny from the Block, so to speak."
Valley is a man of high action on screen, too, as Christopher Chance, the former assassin and now security expert and bodyguard-for-hire of Human Target, which co-stars Chi McBride and Jackie Earle Haley. New to the 13-episode second season of the show? two new female cast members, including the team's new boss (Indira Varma).
Its star, for one, approves. "I love to mix it up. I'm really enjoying working with Indira," Valley says. "It's like this little tennis match we have going back and forth. Actually, faster than tennis — it's more like ping-pong. Like Olympic ping-pong."
That female influence won't turn Human Target into Grey's Anatomy, he promises. There will still be lots of Chance jumping, shooting, punching and all sorts of stunt-filled goodness.
"That's the great thing about an action show: An awful lot is revealed about who a character is based on what he does," Valley, 45, explains. "And that's something I like about playing Chance. I like to be doing things and be physically engaged. It's invigorating."
The crew at times teases him when he engages in one of his impromptu workouts, he says. "I'll do push-ups or squats in between takes to limber up. Chance just might have to take off sprinting somewhere."
Valley says his own military service helps with the physicality and the responsibility of playing Chance. After graduating from West Point, he was in the Gulf War as a member of the U.S. Army as well as in Berlin for five years during the Cold War.
Before he wanted to be a soldier, though, Valley wanted to be a fireman at a young age in Ogdensburg, where he often does charity work for veterans programs. And he became interested in spies during college, where he read Jason Bourne novels and watched James Bond films.
His curiosity in espionage grew while in Germany, where Valley would visit the same bridge in Potsdam where spies were headed over and the Brandenburg canals where they would sneak from West to East Berlin.
Valley recalls going to a bar one time with a friend in communist East Berlin when the Berlin Wall was still up. "We took our hats off so you couldn't tell we were Americans. We still had our black jackets on over our Class A uniforms, which looked like suits. I could speak German at the time, and some of the people in this bar thought that we were Russian soldiers," he says, laughing. "That was exciting. I was like, 'Oh my God! I feel like a spy!'
"Then when the Wall came down, we all went to the Russian officers' club just to see how that would go down in Potsdam. We weren't really too well accepted right off the bat, let's just put it that way. It was a little tense."
While his on-screen life is pretty action-packed, Valley tends to prefer the solitude of his home life in Vancouver, where he films Human Target. He studies martial arts on the weekends, including Muay Thai kickboxing and Krav Maga, and spends time with his 3-year-old rescue dog Rudy, a mixed breed of German Shepherd, pit bull, Jack Russell and Labrador.
"He's a swimmer, he likes to run, I bring him to the set and he guards my trailer, but he doesn't like teamsters," Valley says. "Maybe it's a union thing, maybe it's his political stance toward organized labor, I don't know what it is, but he doesn't really like the Local 155 up here."
Most importantly for a single guy like Valley, Rudy loves women. "Yeah, that would put a damper on my social life, wouldn't it?" says Valley, who separated from his wife, Fringe star Anna Torv, earlier this year. "Sorry, I would date you, but my dog doesn't like women."
Unfortunately, these days Valley sees more women on set rather than off.
"Dude, this is my social life right here, this conversation," he says with a chuckle. "I better be enjoying it because it's all I'm going to get until episode 13."
Guidelines: You share in the USA TODAY community, so please keep your comments smart and civil. Don't attack other readers personally, and keep your language decent. Use the "Report Abuse" button to make a difference. Read more.


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- JD, University of Iowa College of Law, 2001
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- Candidate, United States House of Representatives, District 2, 2014
- Candidate, United States House of Representatives, District 2, 2012
- Candidate, Minnesota State House of Representatives, District 38B, 2006, 2006, 2010
- Representative, Minnesota State House of Representatives, 2008-2010
- No caucus information on file.
- Criminal Prosecutor
- Director, College Dining Service/Catering Business, Mount Marty College
- Associate, Winthrop and Weinstine, Practicing Attorneys
- Member, Dakota County Regional Chamber of Commerce
- Board Member, Eagan Art Festival
- Member, Knights of Columbus
- Member, Saint Thomas Becket Catholic Church
- Volunteer, Youth Eagan Athletic Association


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Sheraton Grande Island Villas is one of five integrated resorts in the Laguna Phuket complex. Visitors looking to sell, buy or rent can choose from 30 restaurants and bars, and practice their golf game at the Laguna Phuket Golf Club. Resales will enjoy a resort that is set in a landscaped garden highlighted by a free-form swimming pool complete with waterfall and hot tub.
Other on-site activities include waterskiing, sailing, and scuba diving. Complimentary transportation is available throughout the complex via shuttle bus or pontoon boat.


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While Germaine Mason aims for gold in London in 2012 having won a silver medal in the high jump at Beijing, his teenage brother Andre is beginning a life sentence for the murder of an innocent student.
Germaine escaped the ghetto to pursue his sporting dream, while Andre fell through the net and into a life of gangland violence.
Andre was still only 14 when he and a pack of youths "hunted" down and murdered Yasin Abdirahman, 22, stabbing him in the head and chest, one of the blows piercing his skull, the knife entering his brain.
Mr Abdirahman, who was studying pharmacy at London Metropolitan University, died of brain injuries eight days after the attack, close to his home in Southall, in September 2007.
His youngest attacker, found guilty of manslaughter, was just 12.
This week, Andre, now aged 16, was sentenced to life in prison and ordered to serve a minimum 13 years behind bars. Germaine, said to be "shocked" and "saddened" by the murder and his brother's part in it, had made a desperate plea to reduce the punishment. He wrote to the judge in the case, Peter Beaumont QC, insisting his brother had acted entirely out of character and appealing for as much clemency as a life sentence with a minimum term could permit. The boys' mother Carol felt she "had lost a son".
How Andre Mason went so badly wrong is a terrible tale of our times. He was born in London but went to live in Jamaica, his mother's birthplace, when he was two months old. Aged 13, he returned to the UK, winning trophies at 100m in junior competitions and with hopes to emulate his brother, who was already winning senior championships. Andre too dreamed of university. But back in London, where he attended Acton High School, he appears to have gone off the rails, lured in by a notorious local gang known as MDP - standing for "Murder Dem Pussies" or "Money, Drugs, Power" - known for its use of knives and guns.
Mr Abdirahman was picked upon either because he was mistaken for a member of a rival gang or else as the randomly chosen victim of an initiation test. In sentencing Mason at the Old Bailey this week, Judge Beaumont warned that the young killer had turned his back on his family.
"You come from a good family but the boy they describe stands oddly with the boy the evidence reveals," he said. "Even after your conviction you continue to regard your co-defendants and members of the MDP as a group of supportive friends."
Germaine, meanwhile, is in Jamaica training with great dedication for the athletics season ahead and with 2012 in his sights.
He fell in love with the high jump at the age of nine and by the age of 17, he was Jamaican champion. In 2002 he first met Usain Bolt, the world's fastest man.
The pair have developed a firm and long standing friendship and it was Bolt who was one of the first to congratulate Germaine on his Olympic silver medal.
A fall-out with coach Stephen Francis and persuasion from his mother led him in 2006 to take up the option offered by his dual nationality and represent Great Britain.
The 26-year-old, a member of the Birmingham-based Birchfield Harriers, still spends winter in warm weather training in Jamaica.
"I feel very British. Britain is my home and that's where it's going to be forever," he has said. "But I also feel at home in Jamaica." In contrast, his brother's home for the next 13 years will be the inside of a jail cell.
- More about:
- Athletics, Track And Field
- Beijing
- Ghetto
- Prison
- Punishment
- Teenagers


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on gracie's style
i knew a young lady who worked in a law office and she read first blog which was a much better blog before i moved to bc and everything changed. gracie never capitalized anything and her writing was much like a stream of consciosness that rambled coherently and i loved reading it and i scorned her by choosing the girl in BC which is not so bad because gracie and i still talk and we did an exercise where we wrote 1500 words describing each other and she was really astounded with mine and i wasn't surprised, it hought it was pretty good too, and then eight months later, two months ago i got an email from her about never being one not to repay a debt and it was 1500 words about me from her perspective and i nearly cried it was so precise not like dumb horoscope precise but intuitive this is who you are precise.
the law student was jealous of gracie though she denies it but acting jealous and being jealous are the same which was funny because her, the law student and I had never met and that always freaks me out that people like me in the first place and im still not sure if im insecure or humble but the more things fall over the more im thinking insecure which sucks because, yeah whatever, so i finally meet the law student and she's lovely and intelligent and we can talk for hours except the bottom just completelt fell out of my life and it seems the worst time to fall in love.


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Steve Rubel reviews the Office 2003 online campaign and identifies an opportunity for the Office marketing team to use the power of blogging as part of the mix.
"Someone please post a link to this blog post on the Redmond Sharepoint server. Here are three ways Microsoft could use our agency's "find, listen, engage and empower" approach to encourage business users to upgrade to Office 2003…
- Find referenceable users and empower them to blog on your site – Use PubSub, Feedster, MSN Search and customer data, etc. and find the most vocal Office 2003 enthusiasts in the blogosphere. Initiate a dialogue with them and, if they're interested, give them blogs where they can regularly chronicle how they use Office 2003 to improve their daily productivity. Let the customers tell your story.
- Have loyal Office 2000 and 2003 users debate each other head-to-head – Find two users of the Office suite – one a loyal 2000 user, another a 2003 fan – and let them them debate each other on the merits of their choice of suite via a shared blog.
- Give out free upgrades – Seed 500 bloggers with free upgrades to Office 2003 and index their posts via an RSS-powered portal."
In response, one Marc Orchant (warning, traffic Mark's site, office.webloginc.com seems to nailing the server), a self-described Office 2003 advocate volunteers through a comment on Steve's post:
"Great ideas Steve – I hereby volunteer to be the Office 2003 advocate if Microsoft picks up on your debate idea. I've found it a worthy upgrade in every respect.
But you're right – they continually miss opportunities to get it right. They ought to follow the example set by Buzz Bruggeman in engaging bloggers to spread the word about the goodness of their products."
Personally, I think Steve is spot on in highlighting this opportunity. In my view, every media/pr campaign should at least consider the role online 'conversational media' has to play, even if it to consciously reject it as a tactic.. The truth is it will take a while for the marketing agencies, PR in particular – (it is fair to say they have a reputation for laggard-like tendencies) to realise the power of online consumer-created content and pitch the ideas. That said, it really is up to the client to brief this requirement in. So, if anyone if the Office marketing team is listening…at least consider Steve's idea.


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Coastal Inn Digby
No prices found through our partners. Please contact the business directly or check some of our recommended alternatives.
- Suites
- Free High-Speed Internet
- Continental Breakfast
- Kitchenette
- Free parking
- Pets allowed
Photos of Coastal Inn Digby
- Free High-Speed Internet
- Kitchenette
- Free parking
- Pets allowed
- Suites
- Continental Breakfast
TripAdvisor Reviews Coastal Inn Digby
Travel Blogs from Digby
Back to Clare
... the kitchen party. Plus more beer for Norm and Renette. Stopped off at the tourist bureau to get a map for Margot so she can map the trip for the family. Headed for the French Shore about four o'clock since it's a 90 minute drive. Went directly to the golf course to see if we could find Betty 's gold earring she lost when the hornets got her. We took two carts, dean and I in the first one. I put my purse in the front compartment for safe keeping. ...
First day of golf
... she recovered quickly and was able to play 17 and 18. We stopped off to see Renette and Norm at the b & b where we stayed last time we were here. Hope to get back there for a "kitchen party" before we go home. All the restaurants were closed because it is Monday but we found a place called Roadside Grill in Belliveaus Cove where we had some fried haddock. The canned peas left something to be desired but otherwise a good ...
We woke up to a beautiful, sunny day. We took a ride over to the ferry dock to see the ferry and to see the loading process. I can't believe we are going to drive our RV onto this boat. Yikes! We were amazed to watch 3 RVs, a truck with a boat on a trailer, and at least 3 or 4 semis drive onto the boat. Add to that 3 or 4 long lines of cars. John is very excited about doing this tomorrow, me, not so much.
We then took a ride up the coast a ...
... of the Oak Island which is filmed there. Apparently there is a belief that there is a huge buried treasure on the island. However, many people, including the government of Canada have searched and found nothing. The story is that some boys found a big manmade hole in the ground in 1795. They dug some more and found a bunch of artifacts and something that indicated there was treasure buried there.
We kept driving along ...
Headin down
... all the black loyalists were given land and freedom. (9 of them had their freedom). There is a wonderful interpretative center that is well worth visiting. The original Book of Negroes is there see. If anyone has read the Book of Negroes it really touches you to see this.
Drove down to Yarmouth and found a campsite, then off to Digby ...
Other places to stay in Digby
Mountain Gap Inn217 Highway 1 Smiths Cove, Digby | 2 star hotelfrom $107


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Melinda M Calvert (firstname.lastname@example.com)Nationwide USA
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- 2.5"
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Some manufacturers place restrictions on how details of their products may be communicated.
Slow when data is loaded 02/27/2014
This review is from: Kingston SSDNow V300 Series 2.5" 120GB SATA III Internal Solid State Drive (SSD) SV300S37A/120G
Solid construction
Running on AMD 990fx
HDtune short stroke 1GB read pass with:
no data loaded: ~300mb/s
with data loaded @ 80% full: ~120mb/s
Other Thoughts:
I understand drives respond slower with data loaded but this is ridiculous.
Manufacturer Response:
Hello Brandon,
I'm Jewel with Kingston Technical Support. The base read speed seems to be slightly low as the drive should perform at 450MB/s read and write out-of-box. There may be some settings that could be affecting the speed of the drive such as the SATA port not being in AHCI mode or if you are using a 3rd party SATA port/controller like Marvell or AsMedia. We are here to help and can explore your set up and see what may be the issue and work towards a proper resolution. Please call us at 1-800-435-0640 (USA and Canada only) M - F 6am - 6pm PT and I or another available Technician will assist you. Please be sure to have the part in question on hand when you call.
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9 people found this review helpful. Did you? Yes / No Thanks for the valuable feedback you provided!


text: Now that you've unwrapped all your gifts, we want to know what some of your favorites are.Sunday, local residents stopped to tell us about the most popular gifts in their house."A lot of books. I got a crock pot, which was nice. I'll do a little cooking for myself, which will be good."" Toys. Toys were great. And I made some hand painted items, ornaments. A shovel for my daughter-in-law and a gnome for my daughter.""My favorite gift would've been twenty dollars to Borders here. I just went and got myself some books.""Dogs and video games and air soft guns""A microphone.""A microphone? What do you want to do with it?""Sing.""Air soft guns."" It was video girl."Video girl, what's that?"" It's this barbie video girl that video tapes some stuff.""I had all my family here, so that's what counts."