Amino acid sequence data from 57 different enzymes were used to determine the divergence times of the major biological groupings. Deuterostomes and protostomes split about 670 million years ago and plants, animals, and fungi last shared a common ancestor about a billion years ago. With regard to these protein sequences, plants are slightly more similar to animals than are the fungi. In contrast, phylogenetic analysis of the same sequences indicates that fungi and animals shared a common ancestor more recently than either did with plants, the greater difference resulting from the fungal lineage changing faster than the animal and plant lines over the last 965 million years. The major protist lineages have been changing at a somewhat faster rate than other eukaryotes and split off about 1230 million years ago. If the rate of change has been approximately constant, then prokaryotes and eukaryotes last shared a common ancestor about 2 billion years ago, archaebacterial sequences being measurably more similar to eukaryotic ones than are eubacterial ones.

The evolutionary spread of 22 fibronectin type III (Fn3) sequences among a dozen bacterial enzymes has been traced by searching databases with the non-Fn3 parts of the enzyme sequences. Numerous homologues were found that lacked the Fn3 domains. In each case the related sequences were aligned, phylogenetic trees were constructed, and the occurrences of Fn3 units on the trees were noted. Comparison with phylogenetic trees prepared from the Fn3 segments themselves allowed inferences to be made about when the Fn3 units were shuffled into their present positions.

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Pathologists have had increasing responsibility for quantitating immunohistochemistry (IHC) biomarkers with the expectation of high between-reader reproducibility due to clinical decision-making especially for patient therapy. Digital imaging-based quantitation of IHC clinical slides offers a potential aid for improvement; however, its clinical adoption is limited potentially due to a conventional field-of-view annotation approach. In this study, we implemented a novel solely morphology-based whole tumor section annotation strategy to maximize image analysis quantitation results between readers. We first compare the field-of-view image analysis annotation approach to digital and manual-based modalities across multiple clinical studies (~120 cases per study) and biomarkers (ER, PR, HER2, Ki-67, and p53 IHC) and then compare a subset of the same cases (~40 cases each from the ER, PR, HER2, and Ki-67 studies) using whole tumor section annotation approach to understand incremental value of all modalities. Between-reader results for each biomarker in relation to conventional scoring modalities showed similar concordance as manual read: ER field-of-view image analysis: 95.3% (95% CI 92.0-98.2%) vs digital read: 92.0% (87.8-95.8%) vs manual read: 94.9% (91.4-97.8%); PR field-of-view image analysis: 94.1% (90.3-97.2%) vs digital read: 94.0% (90.2-97.1%) vs manual read: 94.4% (90.9-97.2%); Ki-67 field-of-view image analysis: 86.8% (82.1-91.4%) vs digital read: 76.6% (70.9-82.2%) vs manual read: 85.6% (80.4-90.4%); p53 field-of-view image analysis: 81.7% (76.4-86.8%) vs digital read: 80.6% (75.0-86.0%) vs manual read: 78.8% (72.2-83.3%); and HER2 field-of-view image analysis: 93.8% (90.0-97.2%) vs digital read: 91.0 (86.6-94.9%) vs manual read: 87.2% (82.1-91.9%). Subset implementation and analysis on the same cases using whole tumor section image analysis approach showed significant improvement between pathologists over field-of-view image analysis and manual read (HER2 100% (97-100%), P=0.013 field-of-view image analysis and 0.013 manual read; Ki-67 100% (96.9-100%), P=0.040 and 0.012; ER 98.3% (94.1-99.5%), p=0.232 and 0.181; and PR 96.6% (91.5-98.7%), p=0.012 and 0.257). Overall, whole tumor section image analysis significantly improves between-pathologist's reproducibility and is the optimal approach for clinical-based image analysis algorithms.

zontal gene transfer or lineage fusion (4) is reflected in the dating and not the age of the last common ancestor.2) As Doolittle et al. recognize (1), proteins are conserved over billions of years because natural selection eliminates amino acid substitutions that significantly reduce functionality. Sites involved in catalysis or substrate binding often do not vary at all, while others may vary considerably without affecting protein functionality, leading to variation among sites in substitution rates. Doolittle et al. consider the influence of among-site rate variation on their molecular clock calibration, but significantly underestimate its effect. Rate variation among sites appears to be much greater than can be accounted for by irreplaceable residues alone and much greater than the "extreme distribution of probabilities" that Doolittle et al. assumed in their simulations.The gamma distribution has been widely used in analyses of nucleotide sequence data to examine the effects of among-site rate variation on phylogenetic analysis and molecular distance estimates. The shape parameter, ot, is inversely related to the amount of rate variation among sites. Low values of a correspond to large differences among sites in underlying rates of substitution, while high values correspond to small differences. Estimates of the shape parameter obtained from aligned amino acid sequences for the 70-kD heat shock protein family, the triose phosphate isomerases, and V-ATPase catalytic subunits range from 0.57 to 1.21 (7). A model assuming an invariant class of sites and a gamma distribution of rates among remaining sites provides a significantly better fit to the data and gives estimates of the shape parameter ranging from 2.42 to 3.29 with between 18 and 21% of the sites invariant ( Fig. 1).At low levels of difference, where