Further evidence for a variable fine-structure constant from Keck/HIRES QSO absorption spectra

[Abridged] We previously presented evidence for a varying fine-structure constant, alpha, in two independent samples of Keck/HIRES QSO spectra. Here we present a detailed many-multiplet analysis of a third Keck/HIRES sample containing 78 absorption systems. We also re-analyse the previous samples, providing a total of 128 absorption systems over the redshift range 0.2<z_abs<3.7. All three samples separately yield consistent, significant values of da/a. The analyses of low- and high-z systems rely on different ions/transitions with very different dependencies on alpha, yet they also give consistent results. We identify additional random errors in 22 high-z systems characterized by transitions with a large dynamic range in apparent optical depth. Increasing the statistical errors on da/a for these systems gives our fiducial result, a weighted mean da/a=(-0.543+/-0.116)x10^-5, representing 4.7-sigma evidence for a smaller weighted mean alpha in the absorption clouds. Assuming that da/a=0 at z_abs=0, the data marginally prefer a linear increase in alpha with time: dota/a=(6.40+/-1.35)x10^-16 yr^-1. The two-point correlation function for alpha is consistent with zero over 0.2-13 Gpc comoving scales and the angular distribution of da/a shows no significant dipolar anisotropy. We therefore have no evidence for spatial variations in da/a. We extend our previous searches for possible systematic errors, identifying atmospheric dispersion and isotopic structure effects as potentially the most significant. However, overall, known systematic errors do not explain the results. Future many-multiplet analyses of QSO spectra from different telescopes and spectrographs will provide a now crucial check on our Keck/HIRES results.Comment: 31 pages, 25 figures (29 EPS files), 8 tables. Accepted by MNRAS. Colour versions of Figs. 6, 8 & 10 and text version of Table 3 available at http://www.ast.cam.ac.uk/~mim/pub.htm

Network Compression as a Quality Measure for Protein Interaction Networks

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients