Statistical analysis of ELISA data on plasma A-SAA levels Two statistical tests were performed on transformed data from the sample time-courses from the five control non-HIV-infected plasma donors and the time points prior to D-15 in the timecourses from HIV-infected plasma donors: a Wilcoxon test for a two-group comparison (control vs. HIV-infected, p = 0.28) and an ANOVA test in a linear mixed model for the group difference (p = 0.4). As no significant differences were found between the controls and pre-D-15 data from the HIV-infected subjects, both were used to determine baseline A-SAA levels in the HIV-infected subjects. Linear mixed-effects models were fit to these data to estimate the subject-specific baseline level of A-SAA in each HIVinfected individual. A-SAA values above the 90% upper prediction bound was considered significantly elevated (see Fig. 3A and S2A). A two-sided Binomial test was conducted to examine whether the first elevation of A-SAA occurred significantly before T 0 . Additional tests of associations between the timing of first A-SAA elevation and three different parameters of viral replication (viral reproductive rate R 0 , slope of viral ramp-up and the highest recorded viral load) were also performed using linear models. Figure S2 A-SAA levels as measured by ELISA in plasma panels from 5 control plasma donors, 3 plasma donors acquiring HBV infection and 3 donors acquiring HCV infection. (A). A-SAA levels in plasma panels from five control non-HIV-infected plasma donors. The solid circles show A-SAA protein levels. Subjectspecific background levels of A-SAA (dotted grey lines) and the 90% prediction interval (threshold for defining a significant elevation, black lines) were calculated as described in the methods section. (B). A-SAA levels in plasma panels from three HBVinfected donors. In each graph, time is plotted relative to T200 (first time point where DNA levels are above 200 copies ml 21 ). A-SAA protein levels are indicated by black squares and solid black lines and HBV DNA levels by black triangles and dotted black lines. (C). A-SAA levels in plasma panels from three HCV-infected donors. In each graph, time is plotted relative to T600 (first time point where RNA levels are above 600 copies ml 21 ). A-SAA protein levels are indicated by black squares and solid black lines and HCV RNA levels by black triangles and dotted black lines.  Synthesis and Pharmacological Evaluation of Schiff Bases of 4-(2-Aminophenyl)-Morpholines In the present study, a novel series of 4-(2-aminophenyl)morpholines were synthesized and characterized by IR, (1)H-NMR, (13)C NMR and mass spectral analysis. The synthesized compounds were screened for analgesic (100 and 200 mg/kg), antiinflammatory (200 and 400 mg/kg), antibacterial (Bacillus subtilis, Bacillus cereus, Staphylococcus epidermidis, Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa and Escherichia coli) and antifungal (Candida albicans and Aspergillus niger) activities. The minimum inhibitory concentrations of the compounds were also ascertained by agar streak dilution method. N-benzylidine-2-morpholoino benzenamine (1) and N-(3-nitro benzylidine)-2-morpholino benzenamine (3) exhibited significant analgesic, antiinflammatory and antimicrobial activities.  Proper Distance Metrics for Phylogenetic Analysis Using Complete Genomes without Sequence Alignment A shortcoming of most correlation distance methods based on the composition vectors without alignment developed for phylogenetic analysis using complete genomes is that the “distances” are not proper distance metrics in the strict mathematical sense. In this paper we propose two new correlation-related distance metrics to replace the old one in our dynamical language approach. Four genome datasets are employed to evaluate the effects of this replacement from a biological point of view. We find that the two proper distance metrics yield trees with the same or similar topologies as/to those using the old “distance” and agree with the tree of life based on 16S rRNA in a majority of the basic branches. Hence the two proper correlation-related distance metrics proposed here improve our dynamical language approach for phylogenetic analysis. Whole genome sequences are generally accepted as excellent tools for studying evolutionary relationships [1] . Traditional distance methods with multiple alignment or various sequence evolutionary models for phylogenetic analysis are not directly applicable to the analysis of complete genomes.