Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

Making Social Science Matter presents an exciting new approach to social science, including theoretical argument, methodological guidelines, and examples of practical application. Why has social science failed in attempts to emulate natural science and produce normal theory? Bent Flyvbjerg argues that the strength of social science is in its rich, reflexive analysis of values and power, essential to the social and economic development of any society. Richly informed, powerfully argued, and clearly written, this book provides essential reading for all those in the social and behavioral sciences.

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Making Social Science Matter: Why Social Inquiry Fails and How It Can Succeed Again

We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.

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Markov logic networks

The new class of amplifiers described is based on a load network synthesized to have a transient response which maximizes power efficiency even if the active device switching times are substantial fractions of the a.c. cycle. The new class of amplifiers, named `Class E,' is defined and is illustrated by a detailed description and a set of design equations for one simple member of the class. For that circuit the authors measured 96 percent transistor efficiency at 3.9 MHz at 26-W output from a pair of Motorola 2N3735 TO-5 transistors. Advantages of Class E are unusually high efficiency, a priori designability, large reduction in second-breakdown stress, low sensitivity to active-device characteristics, and potential for high-efficiency operation at higher frequencies than previously published Class-D circuits.

Class E-A new class of high-efficiency tuned single-ended switching power amplifiers

A partir d'une analyse des aspects philosophiques et ideologiques de la mecanique quantique et de la relativite generale, l'A. etudie le developpement scientifique que constitue l'emergence des nouvelles theories de la gravite quantique et en mesure les consequences culturelles et politiques

/pdf/transgressing-the-boundaries-toward-a-transformative-hqegctd8fg.pdf

Transgressing the Boundaries: Toward a Transformative Hermeneutics of Quantum Gravity

The pivot algorithm is a dynamic Monte Carlo algorithm, first invented by Lal, which generates self-avoiding walks (SAWs) in a canonical (fixed-N) ensemble with free endpoints (hereN is the number of steps in the walk). We find that the pivot algorithm is extraordinarily efficient: one “effectively independent” sample can be produced in a computer time of orderN. This paper is a comprehensive study of the pivot algorithm, including: a heuristic and numerical analysis of the acceptance fraction and autocorrelation time; an exact analysis of the pivot algorithm for ordinary random walk; a discussion of data structures and computational complexity; a rigorous proof of ergodicity; and numerical results on self-avoiding walks in two and three dimensions. Our estimates for critical exponents areυ=0.7496±0.0007 ind=2 andυ= 0.592±0.003 ind=3 (95% confidence limits), based on SAWs of lengths 200⩽N⩽10000 and 200⩽N⩽ 3000, respectively.

The pivot algorithm: A highly efficient Monte Carlo method for the self-avoiding walk

We give a simple explanation of the Swendsen-Wang algorithm for Potts models in terms of a joint model of Potts spin variables interacting with bond occupation variables. We then show how to generalize this representation, as well as the corresponding Monte Carlo algorithm, to arbitrary models. We give initial results of tests of the new algorithm on the two-dimensional XY model.

Generalization of the Fortuin-Kasteleyn-Swendsen-Wang representation and Monte Carlo algorithm.

These notes are an updated version of lectures given at the Cours de Troisieme Cycle de la Physique en Suisse Romande (Lausanne, Switzerland) in June 1989. We thank the Troisieme Cycle de la Physique en Suisse Romande and Professor Michel Droz for kindly giving permission to reprint these notes.

Alan D. Sokal

Papers

Class E-A new class of high-efficiency tuned single-ended switching power amplifiers

Transgressing the Boundaries: Toward a Transformative Hermeneutics of Quantum Gravity

The pivot algorithm: A highly efficient Monte Carlo method for the self-avoiding walk

Generalization of the Fortuin-Kasteleyn-Swendsen-Wang representation and Monte Carlo algorithm.

Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms