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PMC1127068 | 15850479 | 6 | 1471-2105-6-106-6 | null | Figure 6 Examples of genes with the same LU regression pattern but in different k-means clusters . a. Bzrp is an example from k-means cluster K2; b. Aqp1 is an example from k-means cluster K5; c. Prg is an example from k-means cluster K6; d. Hnrpl is an example from k-means cluster K8. These 4 genes are all identified to have the LU regression pattern, but in 4 different k-means clusters. The LU regression pattern is clearly a good fit to the temporal expression profiles of these 4 genes. The horizontal axis is the log transformation of time. The blue dots are the signals. The green line is the connection of the mean signal at each time point. The red line is the LU regression pattern. | CC BY | no | 2022-01-12 14:35:10 | BMC Bioinformatics. 2005 Apr 25; 6:106 |
PMC1127068 | 15850479 | 7 | 1471-2105-6-106-7 | null | Figure 7 Examples of genes with similar expression patterns in terms of mean signal and regression . a. Sfpi1 is an example from k-means cluster K2; b. Anxa2 is an example from k-means cluster K8; c. Clu is an example from k-means cluster K5; d. D17H6S56E-5 is an example from k-means cluster K6. a and b are examples of genes with the same up-down-up-up pattern (up-regulated at the second time point, down-regulated at the third time point, then up-regulated at the last two time points) in terms of mean transformed signals (green lines). They also have the same LU regression pattern, but are in different k-means clusters. c and d are examples of genes with the same down-up-down-up pattern in terms of mean transformed signals (green lines). They also have the same QLVU regression pattern, but are in different k-means clusters. Clearly, the regression method provides better classification of the temporal expression profiles of these genes than the k-means clustering method. The horizontal axis is the log transformation of time. The blue dots are the signals. The green line is the connection of the mean signal at each time point. The red line or curve is the fitted regression pattern. | CC BY | no | 2022-01-12 14:35:10 | BMC Bioinformatics. 2005 Apr 25; 6:106 |
PMC1127068 | 15850479 | 8 | 1471-2105-6-106-8 | null | Figure 8 Examples of genes with the same regression pattern but different onset of differential expression . a. Psmb6 is an example in k-means cluster K8; b. Adora2b is an example in k-means cluster K5. Adora2b clearly starts differential expression later than Psmb6 . After the onset point (first time point for Psmb6 and second time point for Adora2b ), these two genes show similar upward regulation. The regression method classifies these two genes into the same group (QLCU regression pattern), but k-means clustering method does not. The horizontal axis is the log transformation of time. The blue dots are the signals. The green line is the connection of the mean signal at each time point. The red curve is the QLCU regression pattern. | CC BY | no | 2022-01-12 14:35:10 | BMC Bioinformatics. 2005 Apr 25; 6:106 |
PMC1127068 | 15850479 | 9 | 1471-2105-6-106-9 | null | Figure 9 Reliability curves of regression patterns, Peddada et al.'s profiles, and k-means clusters . The horizontal axis is the reliability (percentage of agreement of the bootstrap results with the original result), and the vertical axis is the corresponding percentage of genes. The regression patterns show the highest reliability, and k-means clusters show the lowest reliability. | CC BY | no | 2022-01-12 14:35:10 | BMC Bioinformatics. 2005 Apr 25; 6:106 |
PMC1127107 | 15850491 | 1 | 1471-2105-6-107-1 | null | Figure 1 Composition of redefined Affymetrix probe-sets based on overlap with cDNA clone insert sequence. Stacked histograms show the distribution of probe-set size for sets consisting of a single Affymetrix-defined probe-set (black) and for those comprised of probes originally grouped into separate probe-sets by Affymetrix (gray). A , NCI-60 10 k cDNA microarray to HuFL alternative CDF. B , Breast cancer 8 k cDNA microarray to HuFL alternative CDF. C , Breast cancer 8 k cDNA microarray to HG-U95Av2 alternative CDF. D , Lung cancer 22 k cDNA microarray to HG-U95Av2 alternative CDF. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 25; 6:107 |
PMC1127107 | 15850491 | 2 | 1471-2105-6-107-2 | null | Figure 2 Sequence-overlapping probes give greater cross-platform concordance for the NCI-60 panel. ( A ) Pearson correlation coefficient was calculated for each gene between its expression values measured on the Affymetrix Hu6800 platforms and its expression values measured on the Stanford cDNA microarray across sixty cell lines of the NCI-60 panel. The figure shows the cumulative distribution of the Pearson correlation coefficients for all genes analyzed. The five different curves reflect the level of cross-platform consistency of probe sets with various levels of overlap between the two microarray platforms. Matched gene measurements across the two platforms showed higher correlation when greater numbers of probes in the Affymetrix probe sets overlapped the insert region of the cDNA clone. The highest correlation was attained when only those Affymetrix probes overlapping the insert-sequence of a given cDNA clone were retained. Measurements for which the probes targeted the same transcript as the cDNA clone, but did not overlap the clone sequence, showed the lowest correlation. ( B ), Pearson correlation coefficient was calculated across all genes for each matched sample pair profiled by the Affymetrix Hu6800 platform and by the Stanford cDNA microarray. The figure shows the cumulative distribution of the Pearson correlation coefficients for the sixty cell lines of the NCI-60 panel. Matched cell-line measurements showed identical stratification of correlation levels by feature-matching criteria. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 25; 6:107 |
PMC1127107 | 15850491 | 3 | 1471-2105-6-107-3 | null | Figure 3 Effect of standard deviation filtering on cross-platform NCI-60 concordance. Genes are filtered removing those with low standard deviations across the 60 cell-lines (methods.) Matching features are determined and concordance assessed as in Figure 1. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 25; 6:107 |
PMC1127107 | 15850491 | 4 | 1471-2105-6-107-4 | null | Figure 4 Conserved clustering pattern of the NCI-60 cell lines profiled using cDNA microarray and Affymetrix gene chips. Data was normalized as described (methods). Average linkage Pearson correlation hierarchical clustering was computed for each dataset. Cell line names are colored according to cancer type. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 25; 6:107 |
PMC1127107 | 15850491 | 5 | 1471-2105-6-107-5 | null | Figure 5 Improved hierarchical clustering of combined NCI-60 cell-lines profiled by Affymetrix gene-chip and cDNA microarray by sequence-overlapping probe measurements. The gene expression profiles obtained for the sixty cell lines by the Affymetrix gene chips and the Stanford cDNA microarray platform were pooled after data transformation as described in the text. Gene expression data by the two different platforms were matched by either Unigene ID matching or by redefining the Affymetrix probe sets based on the sequence overlap criteria of the probes. The pooled gene expression profiles were subjected to average linkage hierarchical clustering. Matched cell-lines from the two platforms which cluster together are marked by red branches in the dendrogram. ( A ) Unigene-matched measurements tended to cluster the cell-lines by measurement platform, and produced only 28 instances of matched cell-lines clustering together. ( B ) Sequence-overlapping probe measurements produced more (43) instances of matched cell-lines from each platform clustering together. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 25; 6:107 |
PMC1127107 | 15850491 | 6 | 1471-2105-6-107-6 | null | Figure 6 Increased efficiency of breast cancer subtype classification transfer from cDNA microarray to Affymetrix HuFL gene-chip tumor-profiles by sequence-overlapping probe measurements. Tumor samples profiled on the Affymetrix platform were classified according to their correlation with the set of subtype median-centroids derived from cDNA microarray measurements (see methods). The classified samples were then hierarchically clustered using Pearson correlation and average-linkage agglomeration. Affymetrix measurements matched to cDNA centroids by sequence-overlap of probe features produced more coherent classifications than those obtained in the original transfer (Sørlie), specifically, more coherent Luminal A and ERBB2+ subtype clusters. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 25; 6:107 |
PMC1127107 | 15850491 | 7 | 1471-2105-6-107-7 | null | Figure 7 Increased efficiency of breast cancer subtype classification transfer from cDNA microarray to Affymetrix HG-U95Av2 gene-chip tumor-profiles by sequence-overlapping probe measurements. Tumor samples profiled on the Affymetrix platform were classified according to their correlation with the set of subtype median-centroids derived from cDNA microarray measurements (see methods). The classified samples were then hierarchically clustered using Pearson correlation and average-linkage agglomeration. ( A ), Affymetrix measurements matched to the cDNA centroids by Unigene identifier. ( B ), Affymetrix measurements matched to cDNA centroids by sequence-overlap of probe features produced more coherent classifications. In particular, the large ERbB2+ subtype cluster (upper left) is mostly absent from the unigene-based classification. The significance of this cluster is supported by the observation that all tumors in this cluster for which Her-2 amplification was assessed by immunohistochemistry were designated positive. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 25; 6:107 |
PMC1127107 | 15850491 | 8 | 1471-2105-6-107-8 | null | Figure 8 Increased cross-platform similarity of normal lung samples by sequence-overlapping probe measurements. Shown are the cumulative distributions of the 5 × 17 cross-platform sample correlations (see methods.) substantially greater similarity is observed when only sequence-overlapping probe measurements are retained (black curve.) | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 25; 6:107 |
PMC1127108 | 15857505 | 1 | 1471-2105-6-109-1 | null | Figure 1 Histogram of similar words for the knirps cis-regulatory module . An example of a distribution of similar 5-mer words for the knirps cis-regulatory module Drosophila melanogaster . Note that the sequence contains an exceptionally large number (37) of lists with an exceptionally large number (137) of similar words. The Y axis shows the number of lists, the X axis is for list size. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:109 |
PMC1127108 | 15857505 | 2 | 1471-2105-6-109-2 | null | Figure 2 Histogram of similar words for the knirps cis-regulatory module, after shuffling . The frequency distribution of similar words for one randomly shuffled version of the knirps cis-regulatory region, Drosophila melanogaster . The Y axis shows the number of lists, the X axis is for list size. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:109 |
PMC1127108 | 15857505 | 3 | 1471-2105-6-109-3 | null | Figure 3 Cumulative histograms . Cumulative histograms for the data in Figures 1 and 2: solid line: original data from Figure 1, dotted line: randomised data from Figure 2. The X axis shows the size of lists of similar words, the Y axis is the number of lists. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:109 |
PMC1127108 | 15857505 | 4 | 1471-2105-6-109-4 | null | Figure 4 Fluffy-tailed knirps distribution . (Left) The distribution of the original regulatory knirps sequence: (solid line); the distribution of 10 randomised sequences (dotted lines). (Right) The same distributions in cumulative form. The X axis shows the size of lists of similar words, the Y axis is the number of lists. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:109 |
PMC1127108 | 15857505 | 5 | 1471-2105-6-109-5 | null | Figure 5 Histograms for regulatory (green), coding (cyan) and NCNR (magenta) sequences . The word length is 5, mismatch is 1, r is 50. The X axis shows the fluffiness coefficient F, the Y axis is the number of sequences in the set with this F. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:109 |
PMC1127108 | 15857505 | 6 | 1471-2105-6-109-6 | null | Figure 6 Separation of regulatory DNA . Separation of regulatory DNA (column 2) from coding (column 1) and non-coding, non-regulatory (column 3) due to the fluffiness coefficient F (Y-axis). Box-plot of the Fluffiness (Y-axis) index for the three functional regions. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:109 |
PMC1127108 | 15857505 | 7 | 1471-2105-6-109-7 | null | Figure 7 Spatial distribution of similar words in MSW L . F airly uniform spatial distribution of start locations for words in the MSWL (n = 137, see Fig.1) of the knirps cis- regulatory region of Drosophila melanogaster . The X axis shows the positions of each word start in the sequence, the Y axis is the rank of this position in the list. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:109 |
PMC1127108 | 15857505 | 8 | 1471-2105-6-109-8 | null | Figure 8 Histogram for exon cg3201 3 . Distribution of similar words for the exon cg3201 3 of Drosophil a (solid line) compared to the histograms of the randomly shuffled versions (dotted lines) in direct (left) and cumulative (right) forms. The X axis shows the size of lists of similar words, the Y axis is the number of lists. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:109 |
PMC1127108 | 15857505 | 9 | 1471-2105-6-109-9 | null | Figure 9 Histogram for non-coding presumed non-regulatory sequence . Distribution of similar words for a non-coding, non-regulatory sequence, randomly picked from chromosome 3L has significant tail because of simple repeats. The X axis shows the size of lists of similar words, the Y axis is the number of lists. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:109 |
PMC1127108 | 15857505 | 10 | 1471-2105-6-109-10 | null | Figure 10 Coefficient of variation in spatial cluster size for four types of DNA: exons (1), non-fluffy NCNR (2), fluffy NCNR (3), regulatory regions (4); Vertical bars denote 95% confidence intervals. The Y axis shows coefficient of variation, the X axis is for four DNA type. We calculated CV based on spatial clustering coefficient k = 1. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:109 |
PMC1127108 | 15857505 | 11 | 1471-2105-6-109-11 | null | Figure 11 Non-coding presumed non-regulatory sequence before and after repeat-masking . For a non-coding, non-regulatory sequence, randomly picked from chromosome 3L. Panels (a,b,c) show results before repeat-masking; panels (d,e,f) show results after repeat-masking. Panels (a,d) show histograms of similar words (solid: original data; dotted: after random shuffling) as in Figure 1; panels (b,e) show the same data in cumulative form as in Figure 3; panels (c,f) show start locations of similar words as in Figure 7. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:109 |
PMC1127109 | 15877815 | 1 | 1471-2105-6-112-1 | null | Figure 1 Empirical and background distribution of correlation values . For each interaction pair and for each dataset we calculated the correlation of expression levels of genes encoding interacting protein pairs. The graph shows slightly higher correlation values in the datasets (empirical distribution) than the correlation in the case of random protein pairs (background distribution). | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 May 6; 6:112 |
PMC1127109 | 15877815 | 2 | 1471-2105-6-112-2 | null | Figure 2 Distribution of mutual information . For each interaction pair and for each dataset we calculated the mutual information of expression levels of genes encoding interacting protein pairs. The graph shows empirical and background distribution to be very similar. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 May 6; 6:112 |
PMC1127109 | 15877815 | 3 | 1471-2105-6-112-3 | null | Figure 3 Correlations and p-values of the expression dataset from Chi . The diagram contains the contains the box-and-whisker plots and the p-values of the twenty GO-classes that yield the most significant results for the respective dataset and of the GO-class biological process . It shows for different GO-classes, how strongly the expression levels of genes that encode interacting proteins from this common GO-class are correlated. The GO-classes along the x-axis are ordered by the corresponding p-value. This p-value gives the probability to get the depicted correlation results using random interacting protein pairs from the respective GO-class. For comparison the GO-class 'biological process', which comprises all interaction pairs (except the self-interactions), has been added. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 May 6; 6:112 |
PMC1127109 | 15877815 | 4 | 1471-2105-6-112-4 | null | Figure 4 Correlations and p-values of the expression dataset from Higgins . Analogous to figure 3 | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 May 6; 6:112 |
PMC1127109 | 15877815 | 5 | 1471-2105-6-112-5 | null | Figure 5 Correlations and p-values of the expression dataset from Pathan . Analogous to figure 3 | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 May 6; 6:112 |
PMC1127109 | 15877815 | 6 | 1471-2105-6-112-6 | null | Figure 6 Correlations and p-values of the expression dataset from Zhang . Analogous to figure 3 | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 May 6; 6:112 |
PMC1127109 | 15877815 | 7 | 1471-2105-6-112-7 | null | Figure 7 Correlations and p-values of the expression dataset from Zhao . Analogous to figure 3 | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 May 6; 6:112 |
PMC1127110 | 15819976 | 1 | 1471-2105-6-91-1 | null | Figure 1 Homology structures of HPr from E. coli determined by PERMOL . Ensemble of the 10 homology structures with the lowest pseudo-energy out of 200 structures calculated with DYANA. (left) A superimposition of the C α atom traces is shown. (right) A cartoon representation of the mean structure of the 10 models is displayed. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 8; 6:91 |
PMC1127110 | 15819976 | 2 | 1471-2105-6-91-2 | null | Figure 2 Comparison of the model structure of Ppar γ from human with the corresponding X-ray structure . Overall good agreement between the bundle of final model structures (helices in red and yellow, β -strands in blue and loops in grey) and the X-ray structure (orange) is obtained. Deviations are mainly seen in larger loop regions, the unstructured N-terminus and at the C-terminal end. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 8; 6:91 |
PMC1127110 | 15819976 | 3 | 1471-2105-6-91-3 | null | Figure 3 Comparison of the model structure of HPr from E. coli with the corresponding X-ray and NMR structures . A comparison of the modeled HPr homology structure with the structures experimentally determined by NMR spectroscopy (1HDN) and X-ray crystallography (1POH). The structures are shown in the same orientation as in Fig. 1 with the radius of the backbone splines indicating the RMSD of the C α atom positions in the respective structures. (A) Overall good agreement between the model structure (yellow) and the X-ray structure (blue) is obtained. Deviations are mainly seen in loop regions and in the orientation of helices a and b. RMSD values for the C α atom positions of the X-ray structure 1POH have been derived from the crystallographic B-factors, f B , using the Debye-Waller equation where isotropic displacement from the mean atom positions was assumed. (B) Comparison of the model (yellow) and the NMR structure (red). Deviations are seen in the same regions as before. (C) X-ray (blue) and NMR (red) structures superimpose well. Interestingly, deviations between them are mainly observed in regions where the two structures also diverge from the homology model. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 8; 6:91 |
PMC1127110 | 15819976 | 4 | 1471-2105-6-91-4 | null | Figure 4 Importance of torsion angle restraints exemplified on HPr from Streptococcus faecalis . On the left hand side the model structure calculated with PERMOL using 427 torsion angle restraints and 41 hydrogen bonds is displayed, while on the right hand side the target X-ray structure 1PTF is shown. The RMSD value for the heavy atoms of the two structures is 0.328 nm. Restraints for torsion angles and hydrogen bonds were directly generated from the X-ray structure 1PTF. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 8; 6:91 |
PMC1127111 | 15850481 | 1 | 1471-2180-5-19-1 | null | Figure 1 Symbol key . These symbols are used in the Figures 2 through 7 to link pathogen names to their originating Additional Files. | CC BY | no | 2022-01-12 14:35:56 | BMC Microbiol. 2005 Apr 25; 5:19 |
PMC1127111 | 15850481 | 2 | 1471-2180-5-19-2 | null | Figure 2 Bacterial pathogens . Bacterial phylogeny is based on work by Hugenholtz et al. [69]. Bacterial phyla are shown on the leaves, with the Firmicutes and the Proteobacteria subdivided to the class level (taxa are shown in italics). Their phylogeny is further broken down to the order level with the use of smaller branches. These two phyla account for roughly three quarters of the described infectious bacterial species (with the noticeable exception of deltaproteobacteria pathogens). Proteobacteria contain both important plant and animal pathogens. Proteobacterial plant pathogens are not clustered, but are observed in alpha, beta, and gamma subdivisions. Some plant and animal pathogen species share the same genus classification (e.g. Ralstonia and Pseudomonas ), and for at least one species, Burkholderia cepacia , different subspecies are plant and human pathogens. Of the 20 bacterial phyla currently recognized in the NCBI taxonomy, thirteen are not present in Figure 2 due to the absence of noteworthy pathogens. Some of these missing phyla are relatively important, like the Cyanobacteria, but most of the remaining phyla are restricted to a handful of species and/or environmental niches. Also of interest is the relative weight of the infectious agents within their respective phylum: While virtually all Spirochaetes and Chlamydiae constitute potential infectious agents due to their parasitic lifestyle, the phylum Actinobacteria has few pathogens relative to the overall diversity of the phylum. It should be cautioned that our current view of bacterial diversity is biased towards cultivatable organisms, which represent a small fraction of bacterial diversity [69]. Therefore, this compilation represents the well-recognized infectious bacterial agents, but should in no way be considered exhaustive or complete. | CC BY | no | 2022-01-12 14:35:56 | BMC Microbiol. 2005 Apr 25; 5:19 |
PMC1127111 | 15850481 | 3 | 1471-2180-5-19-3 | null | Figure 3 Eukaryotic pathogens . Eukaryotic pathogen life forms are clearly dominated by the fungi and protists. Within the Fungi, the phylum Ascomycota has many human, animal, and plant pathogens and is a major source of toxins. Protist species are responsible for globally important diseases such as the malaria-causing Plasmodium species and the Leishmania and Trypanosoma species that cause significant mortality in the developing world. The eukaryotic phylogeny tree is based on ribosomal and housekeeping gene sequence analysis [70], but different taxonomy levels are simultaneously represented as the topology has not yet been reliably determined. | CC BY | no | 2022-01-12 14:35:56 | BMC Microbiol. 2005 Apr 25; 5:19 |
PMC1127111 | 15850481 | 4 | 1471-2180-5-19-4 | null | Figure 4 DNA viruses . The relationships for large genome DNA viruses on this chart was derived from the work of Iyer, Aravind & Koonin, who showed the common ancestry of four large DNA virus families [72]. Noteworthy in this figure are the number of viruses that may be readily bioengineered. | CC BY | no | 2022-01-12 14:35:56 | BMC Microbiol. 2005 Apr 25; 5:19 |
PMC1127111 | 15850481 | 5 | 1471-2180-5-19-5 | null | Figure 5 Single-stranded negative strand RNA viruses . The common origin of RNA viruses and their tentative relationships as indicated on this chart are based on an extensive analysis of their RNA-dependent RNA- or DNA-polymerases (C.M. manuscript in preparation). The branching of double-stranded RNA viruses is unresolved in light of their apparent polyphyly [73]. Virtually all known branches of ssRNA(-) viruses harbor pathogens and thus are represented in this panel. Only the rhabdoviridae family harbors both animal and plant pathogens. Particularly noteworthy are the deadly Ebola and Influenza viruses, the latter has claimed tens of millions of human lives in the past century. | CC BY | no | 2022-01-12 14:35:56 | BMC Microbiol. 2005 Apr 25; 5:19 |
PMC1127111 | 15850481 | 6 | 1471-2180-5-19-6 | null | Figure 6 Single-stranded positive strand RNA viruses . The Togaviridae and Flaviviridae , and to a lesser extent the Coronaviridae and Picornaviridae , families are the most prominent human pathogens. Important plant viruses affect major cereal grains causing severe crop damages worldwide and affecting the nutrition of entire populations. | CC BY | no | 2022-01-12 14:35:56 | BMC Microbiol. 2005 Apr 25; 5:19 |
PMC1127111 | 15850481 | 7 | 1471-2180-5-19-7 | null | Figure 7 Retroid viruses and double-stranded RNA viruses . The most prominent include the two HIV viruses and the Hepatitis B virus, and the main family of double-stranded RNA viruses, the Orbiviridae. The latter family is an example of a virus family that includes both animal and plant pathogens. The plant pathogens primarily infect rice cultures. | CC BY | no | 2022-01-12 14:35:56 | BMC Microbiol. 2005 Apr 25; 5:19 |
PMC1129022 | 15571637 | 1 | 1475-2840-3-9-1 | null | Figure 1 Study flow schema. CVD = cardiovascular disease. | CC BY | no | 2022-01-12 14:35:57 | Cardiovasc Diabetol. 2004 Dec 1; 3:9 |
PMC1131883 | 15898833 | 1 | pbio.0030193.g001 | null | Figure 1 Isolation with Migration Models (A) The basic IM model. The demographic terms are effective population sizes (N 1 , N 2 , and N A ), gene flow rates (m 1 and m 2 ), and population splitting time (t). Also shown are parameters scaled by the neutral mutation rate (u) , as they are actually used in the model fitting. Terms for basic demographic parameters, including N, m, t, and u, are not italicized. Note that the migration parameters are identified by the source of migrants as time goes backward in the coalescent. In other words, the migration rate from population 1 to population 2 (i.e., m 1 ) actually corresponds to the movement of genes from population 2 to population 1 as time moves forward. (B) The IM model with changing population size. An additional parameter, s, is the fraction of N A that forms N 1 (i.e., the fraction 1 − s gives rise to N 2 ) | CC BY | no | 2022-01-13 00:01:43 | PLoS Biol. 2005 Jun 24; 3(6):e193 |
PMC1131883 | 15898833 | 2 | pbio.0030193.g002 | null | Figure 2 Approximate Geographic Locations, and Sample Sizes per location, for Each Locus Listed in Table 1
In some cases locations are based on actual geographic locations, in other cases the locations are the approximate center of the geographic region occupied by ethnic groups identified in the original references ( Table 1 ). | CC BY | no | 2022-01-13 00:01:43 | PLoS Biol. 2005 Jun 24; 3(6):e193 |
PMC1131883 | 15898833 | 3 | pbio.0030193.g003 | null | Figure 3 Marginal Posterior Probability Densities Probability densities for each of the parameters described in Figure 1 are shown, as follows: (A) θ 1 ; (B) θ 2 ; (C) θ A ; (D) t (i.e., t/u); (E) t shown on a scale of years over the range corresponding to a maximum t value of 0.2; (F) s; (G) m
1 ; and (H) m
2 . The analysis in which a high upper limit on the prior distribution for t was used is identified as “high t upper ,” while those analyses with a smaller upper limit on the prior distribution of t are identified as “low t upper .” Each curve is based upon the results of multiple simulations over millions of Markov chain updates (see Materials and Methods ), and is plotted over the specified prior range of that parameter. | CC BY | no | 2022-01-13 00:01:43 | PLoS Biol. 2005 Jun 24; 3(6):e193 |
PMC1131883 | 15898833 | 4 | pbio.0030193.g004 | null | Figure 4 The Marginal Densities Obtained by Fitting the Model with Population Size Change to Simulated Data The input parameters for the simulations were as follows: (A) θ 1 = 10; (B) θ 2 = 10; (C) θ A = 10; (D) t =2.5, (E) s = 0.2, (F) m
1 = 0.04; (G) m
2 = 0.2 ; and t = 5 (t/2N A = 0.5). For each simulated dataset, coalescent simulations were done for each of 20 loci with identical mutation rates under an infinite sites mutation model, each with sample sizes of 10 for each of the two populations. Each simulated dataset was analyzed using wide uniform prior distributions for each parameter. Each analysis began with a burn-in period of 300,000 steps followed by a primary chain of 3 million to 10 million steps. The curves for parameters θ 1 through m 2 are shown in (A) through (G), respectively. For each figure, the true parameter value used in the simulations is shown as a black vertical bar, and the mean of the estimates for the 20 simulations (based on peak locations) is shown as a gray vertical bar. | CC BY | no | 2022-01-13 00:01:43 | PLoS Biol. 2005 Jun 24; 3(6):e193 |
PMC1131884 | 0 | 1 | pbio.0030212.g001 | null | A gene involved in V(D)J recombination—which allows immune cells to recognize an unlimited number of antigens by reshuffling immune receptor gene segments—evolved from an ancient gene-transposing enzyme | CC BY | no | 2022-01-13 00:01:42 | PLoS Biol. 2005 Jun 24; 3(6):e212 |
PMC1131885 | 0 | 1 | pbio.0030227.g001 | null | Data from nine different regions in the human genome chart the journey of the first immigration to the New World | CC BY | no | 2022-01-13 00:01:42 | PLoS Biol. 2005 Jun 24; 3(6):e227 |
PMC1131887 | 15877818 | 1 | 1742-6405-2-4-1 | null | Figure 1 Flowchart depicting study selection and inclusion/exclusion. | CC BY | no | 2022-01-12 14:36:10 | AIDS Res Ther. 2005 May 6; 2:4 |
PMC1131887 | 15877818 | 2 | 1742-6405-2-4-2 | null | Figure 2 Meta-analysis of MTCT. | CC BY | no | 2022-01-12 14:36:10 | AIDS Res Ther. 2005 May 6; 2:4 |
PMC1131887 | 15877818 | 3 | 1742-6405-2-4-3 | null | Figure 3 Meta-analysis of pre-term delivery. | CC BY | no | 2022-01-12 14:36:10 | AIDS Res Ther. 2005 May 6; 2:4 |
PMC1131888 | 15850477 | 1 | 1471-2105-6-105-1 | null | Figure 1 Hydrophobicity Profiles generated before preprocessing for PAK4 & PAK5 . Hydrophobicity profiles of the sequences of kinases PAK4 and PAK5 generated by substituting the amino acid characters with their respective property value (hydrophobicity values given in table 1). The two sequences are known to be closely similar. These profiles would subsequently be divided in equal segments and the neighborhood around the maximum peak in each segment would be converted to an orthogonal plane using Fast Fourier Transformation. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 23; 6:105 |
PMC1131888 | 15850477 | 2 | 1471-2105-6-105-2 | null | Figure 2 Preprocessing of Inputs in a single property plane . The property profile of one of the input sequences in a plane is subjected to segmentation of equal sizes. Maximum peak in each segmented is identified by simple comparison of the heights of the peaks and the a neighborhood of size F around the position containing the peak is taken. Each neighborhood is then collectively subjected to fourier transformation. This preprocessing is implemented in each plane of the property profile. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 23; 6:105 |
PMC1131888 | 15850477 | 3 | 1471-2105-6-105-3 | null | Figure 3 Matching of segments using dynamic programming . Matching of the Sequence vectors generated through Dynamic Programming. The method used is a version of the N-W Algorithm. A penalty of β is imposed on each non matching of segments while for an accepted match the distance score is increased by the dissimilarity measure between the segments. A matching is defined as an ordered map between the two ordered sets of segments. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 23; 6:105 |
PMC1131888 | 15850477 | 4 | 1471-2105-6-105-4 | null | Figure 4 3D matching for PAKd PAKe using SPDBV magic fit . 3D images of fit obtained by using SPDBV [30, 31] software's "magic fit" tools. The first value in the bracket is the SSS for the subsequence and second refers to rms value obtained by the tool in 0 A . Color red is used for PKCd and yellow for PKCe. The subsequences in the figures are (a)MKEALSTE & DDSRIGQT (b) ANQPFCAV & QTFLLDPY (c) GKAEFWLD & ANCTIQFE (d) QAKVLMSV & RVYVIIDL (e) RVIQIVLM & RKIELAVF belonging to PKCd and PKCe respectively. All subsequences are completely dissimilar using character based approaches but are found to be similar using SSS. Appreciably low rms values confirms that the subsequences in fig 4a-4e are similar subsequences. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 23; 6:105 |
PMC1131888 | 15850477 | 5 | 1471-2105-6-105-5 | null | Figure 5 3D matching for xyna-theau xynz-clotm using SPDBV magic fit . 3D images of fit obtained by using SPDBV [30, 31] software's "magic fit" tools. The first value in the bracket is the SSS for the subsequence and second refers to rms value obtained by the tool in 0 A Color red is used for xyna-theau and yellow for xynz-clotm. The two proteins are similar proteins with high BLAST score and overlapping 3D structures. SSS however is still able to catch subsequences that are left as dissimilar by BLAST, and low rms values for captured subsequences confirm the findings. The subsequences in the figures are (a) SCVGITVM & NCNTFVMW (b) GITVWGVA & TFVMWGFT (c) RVKQWRAA & MIKSMKER (d) EDGSLRQT & SGNGLRSS belonging to xyna-theau and xynz-clotm respectively. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 23; 6:105 |
PMC1131888 | 15850477 | 6 | 1471-2105-6-105-6 | null | Figure 6 3D matching for xyna-psefl xynz-clotm using SPDBV magic fit . 3D images of fit obtained by using SPDBV [30, 31] software's "magic fit" tools. The first value in the bracket is the SSS for the subsequence and second refers to rms value obtained by the tool in 0 A . Color red is used for xyna-psefl and green for xynz-clotm. The subsequences for which structures are shown are (a) NCNTFVMW & RRGGITVW (b) RDSLLAVM & ENGAKTTA (c) YNSILQRE & RQSVFYRQ belonging to xynz-clotm and xyna-psefl respectively. All the subsequences found to be similar are left by traditional algorithms as dissimilar (or unidentical). Interestingly, the subsequences paired up in fig 6b and 6c are not aligned by BLAST but were still found to be similar by SSS and are captured by the same. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 23; 6:105 |
PMC1131888 | 15850477 | 7 | 1471-2105-6-105-7 | null | Figure 7 3D matching for xyna-theau xyna-strli using SPDBV magic fit . 3D images of fit obtained by using SPDBV [30, 31] software's "magic fit" tools. The first value in the bracket is the SSS for the subsequence and second refers to rms value obtained by the tool in 0 A . Color red is used for xyna-theau and green for xyna-strli. Subsequences for which structures are shown are (a) TTPLLFDG & QTPLLFNN (b) SQTHLSAG & FQSHFNSG (c) VLQALPLL & YNSNFRTT belonging to xyna-theau and xyna-strli respectively. Fig 7a shows a structure refering to matching subsequences that shows that SSS is able to capture subsequences like traditional algorithms also, though it is also capable of picking subsequences like in fig 7c that are not similar on the basis of amino acid characters. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 23; 6:105 |
PMC1131889 | 15857510 | 1 | 1471-2105-6-108-1 | null | Figure 1 Stratified accuracy analysis of Scoredist and ML . To illustrate how estimated distance depends on the model, the average deviation is plotted as a function of true distance for two evolutionary models, Dayhoff and Mueller-Vingron. For each evolutionary distance between 1 and 200 PAM, 10 alignments were generated. For each alignment, the deviation was calculated as the difference between the estimated distance and the true distance used for data generation by ROSE [16]. The average of the 10 deviations was plotted using a running average with a window of 10 residues. Note that positive and negative deviations at the same true distance can cancel each other out – the curve only shows the average deviation and not the variability. The values in Table 1 measure the accuracy more correctly by using RMSD of every datapoint. The testset data was created with the matrices given by Dayhoff (A) or Müller-Vingron (B). In both cases, the estimators using the same evolutionary model as the testset data perform well. However, when switching the model in the estimator, Scoredist diverges less than ML, indicating that Scoredist is more robust. The curves show that ML-MV is more different from ML-Dayhoff than Scoredist -MV is from Scoredist -Dayhoff, particularly for the MV dataset in (B). The less difference between estimates using different models, the more robust is the method. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:108 |
PMC1131889 | 15857510 | 2 | 1471-2105-6-108-2 | null | Figure 2 The Belvu multiple sequence alignment viewer . Belvu is a multiple sequence alignment viewer that implements the Scoredist distance estimator. The alignment window (A) shows a subset of the Pfam family DNA_pol_A (PF00476). Uniprot IDs are shown throughout. A sequence with known structure is included (DPO1_ECOLI) – the SA line showing surface accessibility and the SS line showing secondary structure. The neighbour-joining tree in (B) used uncorrected distances (observed differences), while the tree in (C) used Scoredist correction. Belvu assigns a colour to each species if provided with species markup information. The distance correction mainly affects the longer branches, and affects the tree topology in some cases, e.g . the placement of DPOQ_HUMAN. Structural markup and taxonomic information were embedded in the Stockholm format alignment provided by the Pfam database. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:108 |
PMC1131889 | 15857510 | 3 | 1471-2105-6-108-3 | null | Figure 3 Estimation of the calibration factor c in Scoredist . This factor rescales the raw distance d r to optimally fit true evolutionary distances. The plot shows how c is estimated by least-squares fitting of raw distances d r to true distances for 2000 artificially produced sequence alignments, using the Dayhoff matrix series. The linear relationship between the raw distance d r and the true distance of the sequence samples justifies the introduction of the calibration factor c , which was here determined to c Dayhoff = 1.3370 (See Table 2). | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 Apr 27; 6:108 |
PMC1131890 | 15869708 | 1 | 1471-2105-6-111-1 | null | Figure 1 Index file A and data file L for sequence s 1 : CAATTACGAGCTCTGCCTACAATGAT. The format for and are discussed in the text. To demonstrate how different regions map to different genes, the first 13 bases map to the gene with PID = 1234 and the last 13 bases map to the gene with PID = 5678. We add leading zeroes to each location so that all numbers in are four bytes and we record this as numbersize in each line in . Keys in this example are made from two bases of sequence so there are 4 2 = 16 lines in ranging from m(AA) = 5 through m(CC) = 20. Key number m(GT) = 11 and number m(GG) = 15 are not present in the sequence. For clarity, each offset in is repeated in the correct position above the line in and each PID is underlined. Two arrows map two different lines from into by pointing to two bubbles that show the content of two hash bins. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 May 3; 6:111 |
PMC1131890 | 15869708 | 2 | 1471-2105-6-111-2 | null | Figure 2 Database search pseudo code . The length of the query sequence Q determines which block of code will execute. Lines 3 – 18 execute for | Q | < k (wordsize) while lines 19 – 29 execute for | Q | = k . If k < | Q | < 2 k then Q is divided into two k length pieces for recursive calls to Search in lines 32 – 35 and the results from these calls are further tested in lines 38 – 41 to obtain the final answer in line 42. If | Q | > = 2 k , a similar block of code is executed in lines 44 – 57. However, a different comparison is made in line 54 as compared to line 40. | CC BY | no | 2022-01-12 14:24:23 | BMC Bioinformatics. 2005 May 3; 6:111 |
PMC1131893 | 15850478 | 1 | 1472-6750-5-10-1 | null | Figure 1 Cationic polythiophene transducer for the fluorometric detection of hybridization on microarrays. A) Schematic depiction of the interaction between cationic polymers and a) single-stranded DNA, b) double-stranded DNA, c) single-stranded PNA and d) PNA-DNA duplex. Fluorescent cationic polymer is shown in yellow, DNA probes are shown in green and PNA probes are shown in red. B) Experimental results for fluorometric detection on microarray when cationic polythiophene transducer is reacted with a) single-stranded DNA, b) double-stranded DNA, c) single-stranded PNA and d) PNA-DNA duplex. Results are shown in triplicate. C) Graphs showing the fluorescence intensity with standard deviation for each triplicate shown in B. | CC BY | no | 2022-01-12 14:24:27 | BMC Biotechnol. 2005 Apr 25; 5:10 |
PMC1131893 | 15850478 | 2 | 1472-6750-5-10-2 | null | Figure 2 Specificity of oligodeoxyribonucleotide hybridization to PNA probes. Hybridizations were performed at room temperature with a concentration of 7.5 × 10 10 targets per μL using the fluorescent cationic polymer for detection. Hybridization of PNA probes to perfectly complementary, or complementary oligonucleotides presenting a terminal mismatch, a central mismatch, or two mismatches were performed in triplicate. Fluorescence intensities from hybridized probes were corrected by substraction of background fluorescence intensity. | CC BY | no | 2022-01-12 14:24:27 | BMC Biotechnol. 2005 Apr 25; 5:10 |
PMC1131894 | 15862127 | 1 | 1471-2407-5-44-1 | null | Figure 1 1A . Pre- (dark bars) and post-hydralazine treatment (light bars). The bars represent the number of patients that showed methylation for each studied gene from each of the 16 patients. 1B . Representative cases of genes (M methylated, U unmethylated; pre/post): M/M; M/U; U/U, M/U; MU/U; M/ U-M. 1C . Percentage of demethylation after treatment according to the dose. Percentage was calculated considering 100% methylation the total number of pre-treatment methylated genes in each cohort of 4 patients | CC BY | no | 2022-01-12 14:24:31 | BMC Cancer. 2005 Apr 29; 5:44 |
PMC1131894 | 15862127 | 2 | 1471-2407-5-44-2 | null | Figure 2 Representative cases correlating methylation and re-expression before and after hydralazine treatment. 2A is a patient treated with 75 mg/day that demethylated and re-expressed the DAPK gene. 2B corresponds to a patient receiving 150 mg/day who showed only the methylated band pre-treatment, but both bands after treatment, which correlated with re-expression of MGMT. 2C is a 50 mg/day patient which failed to demethylate the DAPK gene and therefore lacked expression. 2D represents the distribution of informative cases. From the 128 genes/cases, 116 were RT-PCR positive regardless of the methylation status, hence were not informative. In the remaining 12 cases, nine demethylated and re-expressed the gene. | CC BY | no | 2022-01-12 14:24:31 | BMC Cancer. 2005 Apr 29; 5:44 |
PMC1131894 | 15862127 | 3 | 1471-2407-5-44-3 | null | Figure 3 3A . Photomicrography of a representative set of pre and post-treatment tumor biopsies showing that the malignant component represents almost the half of the tumor. 3B . Methylation analysis of the DAPK gene in the four fragments of the tumor biopsy of an untreated cervical cancer patient. Despite all fragments contained different proportions of malignant cells and stroma the four samples show methylated and unmethylated bands. | CC BY | no | 2022-01-12 14:24:31 | BMC Cancer. 2005 Apr 29; 5:44 |
PMC1131894 | 15862127 | 4 | 1471-2407-5-44-4 | null | Figure 4 DNA methylation analysis obtained from peripheral mononuclear cells of genes "imprinted or normally methylated". Clone 1.2 remained methylated in all cases whereas for the imprinted H19 gene, the pattern of U and M alleles did not change. | CC BY | no | 2022-01-12 14:24:31 | BMC Cancer. 2005 Apr 29; 5:44 |
PMC1131894 | 15862127 | 5 | 1471-2407-5-44-5 | null | Figure 5 5A . Capillary electrophoretic analysis of global methylation. Relative methylation showed no variation in percentage of m C after treatment (37.3% versus 36.3%). 5B is a electropherogram showing the separation of C and m C. 5C is the percent increase in radiolabeled incorporation pre and post-treatment as compared to the control of undigested DNA. | CC BY | no | 2022-01-12 14:24:31 | BMC Cancer. 2005 Apr 29; 5:44 |
PMC1131895 | 15854225 | 1 | 1471-2121-6-22-1 | null | Figure 1 Fluorescence intensity images of GFP-PKC and DsRed-cav expressed in CHO cells. GFP-PKC and/or DsRed-cav were transiently expressed in CHO cells on glass coverslips by culturing for 48 hr. The cells were then treated with Ca 2+ -ionophore (1 nM) or TPA (100 nM) for 3 min before the cells were fixed on microscope slides as described under Methods. Images were acquired using a Olympus IX-70 inverted epifluorescence microscope with a 60× objective and GFP/DsRed filter sets and a Olympus C3030 camera. (a) a representative image before and (b) after Ca 2+ -induced translocation by ionophore of GFP-PKC to the peripheral membrane and to discrete regions in the perinuclear region. (c) image of DsRed-cav and (d) GFP-PKC, after treatment with 100 nM TPA. | CC BY | no | 2022-01-12 14:36:20 | BMC Cell Biol. 2005 Apr 26; 6:22 |
PMC1131895 | 15854225 | 2 | 1471-2121-6-22-2 | null | Figure 2 Fluorescence lifetime imaging of GFP-PKC expressed in CHO cells. GFP-PKC was transiently expressed in CHO cells as described in the legend in Figure 1 and detailed under Methods. Images were acquired using a Nikon 2000 inverted epifluorescence microscope with a 60× objective and GFP/DsRed filter sets and an ICAM camera. The 2P-FLIM images were collected using the TCSPC fast scanning imaging mode as described under Methods. (a) Conventional epifluorescence image of GFP-PKC distribution in a resting CHO cell, (b) lifetime image of the same cell with the analysis area enclosed by the red line (cytosol) shown (with colour coding) in the inset and giving an average lifetime of ~2.2 ns and (c) with the analysis area enclosed by the red line (nucleus) shown in the inset giving an average lifetime of ~2.0 ns. Cells shown are representative images from replicate experiments. | CC BY | no | 2022-01-12 14:36:20 | BMC Cell Biol. 2005 Apr 26; 6:22 |
PMC1131895 | 15854225 | 3 | 1471-2121-6-22-3 | null | Figure 3 Fluorescence lifetime imaging of GFP-PKC expressed in CHO cells: effect of TPA. 2P-FLIM images were collected as described in the legend to Figure 2 except cells were treated with TPA (100 nM) for 3 min. Treatment with the phorbol ester did not affect the fluorescence lifetime of GFP attached to PKC. (a) Fluorescence lifetime image with the analysis area enclosed by the red line (nucleus) shown in inset giving an average lifetime of ~2.1 to 2.2 ns. (b) Fluorescence lifetime image with the analysis area enclosed by the red line (cytosol) shown in inset giving an average lifetime of ~2.2 ns. Insets: lifetime distributions with colour coding. | CC BY | no | 2022-01-12 14:36:20 | BMC Cell Biol. 2005 Apr 26; 6:22 |
PMC1131895 | 15854225 | 4 | 1471-2121-6-22-4 | null | Figure 4 Fluorescence lifetime imaging of GFP-PKC co-expressed with DsRed-cav in CHO cells. 2P-FLIM images were collected as described in the legend to Figure 2. Co-expression of the GFP-PKC with DsRed-cav does not affect the lifetime of the GFP showing that in the unstimulated state PKC is not associated with caveolin. Epifluorescence images for excitation of DsRed (a) and GFP along with DsRed (b) showing that the PKC and caveolin co-distributed in the cytosol. Fluorescence lifetime images with the analysis area enclosed by the red line, (cytosol) (c) or nucleus both essentially showing a lifetime as for Figs 2–3 centred around ~2.2 ns. Cells shown are representative images from replicate experiments. | CC BY | no | 2022-01-12 14:36:20 | BMC Cell Biol. 2005 Apr 26; 6:22 |
PMC1131895 | 15854225 | 5 | 1471-2121-6-22-5 | null | Figure 5 Lifetime imaging of GFP-PKC co-expressed with DsRed-cav in CHO cells: effect of Ca2+-ionophore. 2P-FLIM images were collected as described in the legend to Figure 2. Cells were treated with ionophore for 3 min before mounting and fixation as described in Methods. The epifluorescence image in the inset shows the DsRed-cav distribution (cytoplasmic) which was not affected by the Ca 2+ ionophore. When the cytoplasmic area was analysed, (a) as shown by the area within the red line, both orange and green/blue areas are seen indicating the presence of both GFP-PKC and quenched GFP-PKC – note that only GFP lifetime can be observed in the lifetime images. This indicates that DsRed-cav was sufficiently close to the PKC-GFP to induce a quenching of the GFP by the DsRed, i.e. the PKC is translocating to caveolin containing areas. By contrast, in the nucleus (b) only GFP-PKC was expressed and the lifetime was unquenched (~2.2 ns). This is the same as the lifetime for GFP-PKC when only the latter is expressed (see Figure 2). Cells shown are representative images from replicate experiments. | CC BY | no | 2022-01-12 14:36:20 | BMC Cell Biol. 2005 Apr 26; 6:22 |
PMC1131895 | 15854225 | 6 | 1471-2121-6-22-6 | null | Figure 6 Lifetime imaging of GFP-PKC co-expressed with DsRed-cav in CHO cells: effect of phorbol ester. Epifluorescence and 2P-FLIM images were collected as described in the legend to Figure 2. Cells were treated with TPA (100 nM) for 3 min before mounting and fixation as described in Methods. (a) left: GFP-PKC and DsRed-cav co-distribution revealed in a green and red epifluorescence image (red and green filters) and right: DsRed-cav visualised using a red filter, the caveolin was mainly restricted to the perinuclear region with PKC more widely distributed. Three distinct GFP lifetimes are discernable in the lifetime image and were separately analysed. For the lifetime images shown in (b, c and d), representative single point analyses within the regions enclosed by dashed white lines are analysed in (f, g and h) respectively, as follows: (b and f) peripheral regions (τ avg: 1.8 ns; single point 1.87 ns [χ 2 1.00]); (c and g) the nuclear region (τ avg: 2.0 ns; single point 2.00 [χ 2 1.00]); (d and h) the cytoplasm (τ avg: 1.5 ns; single point 1.48 ns [χ 2 1.56]). (e) example of derivation of average lifetime for one of the three areas, shown for the cytoplasm, with the analysis for the area enclosed in red (lifetime colour coding shown in the inset) and other similar areas in the cytoplasm indicated by white arrows. Cells shown are representative images from replicate experiments. | CC BY | no | 2022-01-12 14:36:20 | BMC Cell Biol. 2005 Apr 26; 6:22 |
PMC1131895 | 15854225 | 7 | 1471-2121-6-22-7 | null | Figure 7 Lifetime imaging of GFP-PKC co-expressed with DsRed-cav in CHO cells: effect of bradykinin. 2P-FLIM images were collected as described in the legend to Figure 2. Cells were treated with 5 nM bradykinin for 3 min before mounting and fixation as described in Methods. The area in the cytoplasm outlined in red was analysed as shown in the colour coded inset (τ avg: 1.6 ns), showing PKC interaction with caveolin. | CC BY | no | 2022-01-12 14:36:20 | BMC Cell Biol. 2005 Apr 26; 6:22 |
PMC1131898 | 15869704 | 1 | 1471-2296-6-19-1 | null | Figure 1 General practitioners (GPs) and patients in study | CC BY | no | 2022-01-12 14:32:58 | BMC Fam Pract. 2005 May 3; 6:19 |
PMC1131899 | 15819990 | 1 | 1471-2156-6-18-1 | null | Figure 1 Contour plot of minimum number of cases needed to maintain constant asymptotic power of 95% at a 5% significance level in the presence of phenotype misclassification for Alzheimer's disease ApoE example . We compute the increase in minimum cases ( ) needed to maintain constant 95% asymptotic power at the 5% significance level (using a central χ 2 distribution with 5 degrees of freedom) in the presence of errors. Sample sizes are computed using equation (3). The affected and unaffected genotype frequencies are taken from a previous publication [9, 14]. In that work, the marker locus considered was ApoE and the disease phenotype was Alzheimer's disease. We use the LRT ae estimates from table 5 of that work [9]. Six genotypes are observed in most populations. The frequencies we use to perform the sample size calculations in figure 1 are presented in the Methods section (Minimum sample size requirements in presence of phenotype misclassification – Alzheimer's Disease ApoE example). We assume that equal numbers of cases and controls are collected. Also, we specify a prevalence K = 0.02, which is consistent with recent published reports for Alzheimer's Disease in the U. S. [32]. Sample sizes are calculated for each misclassification parameter θ , φ ranging from 0.0 to 0.15 in increments of 0.01. The number of cases ranges from 484 when θ = φ = 0 to 10,187 when θ = φ = 0.15. In this figure, each (approximately) horizontal line represents a constant sample size as a function of the misclassification parameters θ and φ . For two consecutive horizontal lines, the values in between those lines (represented by different colors) have sample sizes that are between the sample sizes indicated by the two horizontal lines. | CC BY | no | 2022-01-12 14:36:03 | BMC Genet. 2005 Apr 8; 6:18 |
PMC1131899 | 15819990 | 2 | 1471-2156-6-18-2 | null | Figure 2 Power to detect association for two different settings of prevalence when only one phenotype misclassification parameter is non-zero . In this figure, the horizontal axis refers to the misclassification probability for one parameter when the second parameter is 0. For example, the graphs labeled " φ = 0" provide power calculations at two settings of disease prevalence ( K = 0.05, K = 0.01) as a function of θ values ranging from 0.0 to 0.15 on the horizontal axis. Similarly, the graphs labeled " θ = 0" provide power calculations at two settings of disease prevalence ( K = 0.05, K = 0.01) as a function of φ ranging from 0.0 to 0.15 on the horizontal axis. | CC BY | no | 2022-01-12 14:36:03 | BMC Genet. 2005 Apr 8; 6:18 |
PMC1131900 | 15860127 | 1 | 1472-6963-5-31-1 | null | Figure 1 Comparison of odds ratios for age-sex effects on the likelihood of ever hospitalisation in public hospitals between administrative and survey data for year 1999. | CC BY | no | 2022-01-12 14:33:58 | BMC Health Serv Res. 2005 Apr 28; 5:31 |
PMC1131900 | 15860127 | 2 | 1472-6963-5-31-2 | null | Figure 2 Comparison of odds ratios for age-sex effects on the likelihood of ever hospitalisation in public hospitals between administrative and survey data for year 2001. | CC BY | no | 2022-01-12 14:33:58 | BMC Health Serv Res. 2005 Apr 28; 5:31 |
PMC1131900 | 15860127 | 3 | 1472-6963-5-31-3 | null | Figure 3 Comparison of odds ratios for age-sex effects on the likelihood of ever hospitalisation in public hospitals between administrative and survey data for year 2002. | CC BY | no | 2022-01-12 14:33:58 | BMC Health Serv Res. 2005 Apr 28; 5:31 |
PMC1131903 | 15882458 | 1 | 1472-6920-5-14-1 | null | Figure 1 The number of students distributed on intervals of points. | CC BY | no | 2022-01-12 14:33:39 | BMC Med Educ. 2005 May 9; 5:14 |
PMC1131904 | 15882456 | 1 | 1471-2180-5-24-1 | null | Figure 1 AP-PCR profiles of S. maltophilia strins including statistical analysis and dendrogram showing the genetic relationship between strains. | CC BY | no | 2022-01-12 14:24:40 | BMC Microbiol. 2005 May 9; 5:24 |
PMC1131905 | 15854221 | 1 | 1471-2474-6-21-1 | null | Figure 1 Phases of the motor act in stepping down movement (A). Reference times were measured from 2 curves (B). Top , The reference times plotted on the vertical velocity curve of the malleolus marker of the leading leg (T2) and of the supporting leg (T3) correspond respectively to the onset and offset of the movement phase. Bottom : lateral CP curve (T1) corresponds to the onset of CP change and Tbal to the end of the ballistic CP shift. | CC BY | no | 2022-01-12 14:34:04 | BMC Musculoskelet Disord. 2005 Apr 26; 6:21 |
PMC1131905 | 15854221 | 2 | 1471-2474-6-21-2 | null | Figure 2 Schema of the horizontal shift of the center of mass (CM) and associated center of pressure (CP) ( left part ) and description of the M/L and A/P CP curves ( right part ). The dotted lines show the time-relationships between each component. Note that the M/L thrust (T1-Peak) coincides with the first backward CP shift, and that during the unloading component of the M/L CP shift, the second backward shift occurs, which corresponds to heel off (T2). | CC BY | no | 2022-01-12 14:34:04 | BMC Musculoskelet Disord. 2005 Apr 26; 6:21 |
PMC1131905 | 15854221 | 3 | 1471-2474-6-21-3 | null | Figure 3 Schema of the vertical ground reaction force recorded on the landing force platform. Weight acceptance was from the ground contact to the peak and was calculated in percentage relative to the body weight to normalize the data for all the subjects. | CC BY | no | 2022-01-12 14:34:04 | BMC Musculoskelet Disord. 2005 Apr 26; 6:21 |
PMC1131905 | 15854221 | 4 | 1471-2474-6-21-4 | null | Figure 4 Kinetic and rectified EMG patterns recording with one control subject. The EMGs were recorded at a proximal level (VL, Vastus lateralis) for both sides. Note the supporting and leading VL activity prior to the ground contact. | CC BY | no | 2022-01-12 14:34:04 | BMC Musculoskelet Disord. 2005 Apr 26; 6:21 |
PMC1131905 | 15854221 | 5 | 1471-2474-6-21-5 | null | Figure 5 Dynamic profiles of VL activation recorded on the forthcoming landing leg before and after surgery. EMG data are windowed each 150 ms from 300 ms before ground contact to 300 ms after ground contact (Arbitrary Units, AU). | CC BY | no | 2022-01-12 14:34:04 | BMC Musculoskelet Disord. 2005 Apr 26; 6:21 |
PMC1131906 | 15813973 | 1 | 1471-2210-5-10-1 | null | Figure 1 Kaplan-Meier analysis of 10 days survival among 70 rats classified into vehicle (group I, n= 10; solid line), isoproterenol (group II, n = 15; thick dotted intermediate curve), isoproterenol/ sildenafil (group III, n = 15; thick dashed curve), sildenafil/vehicle(group IV, n = 15; solid line) and sildenafil/isoproternol/ Nω-nitro-L-arginine (group V, n = 15; thin dash-dot line to the left). Significant improvement in survival rate was found by log-rank test in group III compared to groups II and V (P < 0.05). | CC BY | no | 2022-01-12 14:34:19 | BMC Pharmacol. 2005 Apr 6; 5:10 |
PMC1131906 | 15813973 | 2 | 1471-2210-5-10-2 | null | Figure 2 Linear regression curve for myocardial cGMP level and heart coefficient. | CC BY | no | 2022-01-12 14:34:19 | BMC Pharmacol. 2005 Apr 6; 5:10 |
PMC1131906 | 15813973 | 3 | 1471-2210-5-10-3 | null | Figure 3 Linear regression curve for myocardial cGMP and myocardial creatine kinase (CK) activity. | CC BY | no | 2022-01-12 14:34:19 | BMC Pharmacol. 2005 Apr 6; 5:10 |
PMC1131906 | 15813973 | 4 | 1471-2210-5-10-4 | null | Figure 4 Linear regression curve for myocardial cGMP and serum level of cardiac troponin T (cTnT). | CC BY | no | 2022-01-12 14:34:19 | BMC Pharmacol. 2005 Apr 6; 5:10 |
PMC1131907 | 15877811 | 1 | 1472-6793-5-7-1 | null | Figure 1 The cord dorsum potential in response to phrenic nerve afferent stimulation in the C5 segment. The trace was the average of 32 stimulus epochs for the middle C5 segment from one animal. The enlarged area expands the large initial peak of the CDP to better illustrate the N1 and N2 peaks. The first negative wave indicates the electrical stimulus used as the zero time point for peak latency analysis. The stimulus artifact is followed by three negative peaks N1, N2, and N3. The amplitudes of the negative peaks were measured from the voltage difference between the peak voltage and the averaged voltage of the 5 ms period before the stimulus onset. | CC BY | no | 2022-01-12 14:24:45 | BMC Physiol. 2005 May 6; 5:7 |
PMC1131907 | 15877811 | 2 | 1472-6793-5-7-2 | null | Figure 2 Representative illustration of phrenic nerve afferents stimulation related cord dorsum potential recorded at C4, C5, C6, and C7 spinal segments in one animal. The traces were the average of 32 stimulus epochs for each spinal segment. The N1 peak was recorded in all cervical spinal segments (C4 to C7); whereas N2 and N3 CDP peaks were identified only in C5 and C6 spinal segments. The amplitudes of the negative peaks were measured from the voltage difference between the peak voltage and the averaged voltage of the 5 ms period before the stimulus onset. | CC BY | no | 2022-01-12 14:24:45 | BMC Physiol. 2005 May 6; 5:7 |
PMC1131907 | 15877811 | 3 | 1472-6793-5-7-3 | null | Figure 3 Histogram of the CDP peak latencies in different regions of the cervical spinal segments (C4 to C7) after phrenic nerve afferents stimulation in cats. The intensity of the stimulation was 500 μA. N1 is the first peak latency. N2 and N3 are the second and third peak latencies found in C5 and C6 only. * indicates a significant differences between N1 and N2 peaks. † indicates a significant difference between N2 and N3 peaks in the C5 and C6 segmental locations. # indicates a significant difference between N1 and N3 peaks in the C5 and C6 segmental locations. | CC BY | no | 2022-01-12 14:24:45 | BMC Physiol. 2005 May 6; 5:7 |
PMC1131907 | 15877811 | 4 | 1472-6793-5-7-4 | null | Figure 4 Histogram of the CDP peak amplitudes in the cervical spinal segments (C4 to C7) after phrenic nerve afferent stimulation in cats. N1 is the first peak amplitude observed in the C4 to C7 spinal segments; N2 and N3 are the second and third peak amplitudes only observed in the C5 and C6 spinal segments. * indicates a significant differences between N1 and N2 peaks in the C5 and C6 segmental locations. # indicates a significant difference between N1 and N3 peaks in the C5 and C6 segmental locations. ∞ indicates significant differences between the spinal segments for N1 peak. | CC BY | no | 2022-01-12 14:24:45 | BMC Physiol. 2005 May 6; 5:7 |
PMC1131908 | 15865623 | 1 | 1471-2482-5-10-1 | null | Figure 1 Kaplin-Meyer survival curve for 504 LVH CABG patients. Bars = standard error. | CC BY | no | 2022-01-12 14:32:32 | BMC Surg. 2005 May 2; 5:10 |
PMC1131908 | 15865623 | 2 | 1471-2482-5-10-2 | null | Figure 2 Effects of gender, age, diabetes, cerebrovascular disease, time on CPB, and operative mortality (OM) on midterm survival. P value reflects log-rank test results. | CC BY | no | 2022-01-12 14:32:32 | BMC Surg. 2005 May 2; 5:10 |