Superconductivity and Quantum Spin Disorder in Cuprates

A fundamental connection between superconductivity and quantum spin fluctuations in underdoped cuprates, is revealed. A variational calculation shows that {\em Cooper pair hopping} strongly reduces the local magnetization $m_0$. This effect pertains to recent neutron scattering and muon spin rotation measurements in which $m_0$ varies weakly with hole doping in the poorly conducting regime, but drops precipitously above the onset of superconductivity

Local Magnetic Order vs. Superconductivity in a Layered Cuprate

We report on the phase diagram for charge-stripe order in La(1.6-x)Nd(0.4)Sr(x)CuO(4), determined by neutron and x-ray scattering studies and resistivity measurements. From an analysis of the in-plane resistivity motivated by recent nuclear-quadrupole-resonance studies, we conclude that the transition temperature for local charge ordering decreases monotonically with x, and hence that local antiferromagnetic order is uniquely correlated with the anomalous depression of superconductivity at x = 1/8. This result is consistent with theories in which superconductivity depends on the existence of charge-stripe correlations.Comment: 4 pages, 4 figures; introduction revised, Fig. 3 removed, last figure replace

Excitation spectrum of the homogeneous spin liquid

We discuss the excitation spectrum of a disordered, isotropic and translationally invariant spin state in the 2D Heisenberg antiferromagnet. The starting point is the nearest-neighbor RVB state which plays the role of the vacuum of the theory, in a similar sense as the Neel state is the vacuum for antiferromagnetic spin wave theory. We discuss the elementary excitations of this state and show that these are not Fermionic spin-1/2 `spinons' but spin-1 excited dimers which must be modeled by bond Bosons. We derive an effective Hamiltonian describing the excited dimers which is formally analogous to spin wave theory. Condensation of the bond-Bosons at zero temperature into the state with momentum (pi,pi) is shown to be equivalent to antiferromagnetic ordering. The latter is a key ingredient for a microscopic interpretation of Zhang's SO(5) theory of cuprate superconductivityComment: RevTex-file, 16 PRB pages with 13 embedded eps figures. Hardcopies of figures (or the entire manuscript) can be obtained by e-mail request to: [email protected]

Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification

Motivation: Tandem mass spectrometry (MS/MS) is an indispensable technology for identification of proteins from complex mixtures. Proteins are digested to peptides that are then identified by their fragmentation patterns in the mass spectrometer. Thus, at its core, MS/MS protein identification relies on the relative predictability of peptide fragmentation. Unfortunately, peptide fragmentation is complex and not fully understood, and what is understood is not always exploited by peptide identification algorithms

SAMPI: Protein Identification with Mass Spectra Alignments

BACKGROUND: Mass spectrometry based peptide mass fingerprints (PMFs) offer a fast, efficient, and robust method for protein identification. A protein is digested (usually by trypsin) and its mass spectrum is compared to simulated spectra for protein sequences in a database. However, existing tools for analyzing PMFs often suffer from missing or heuristic analysis of the significance of search results and insufficient handling of missing and additional peaks. RESULTS: We present an unified framework for analyzing Peptide Mass Fingerprints that offers a number of advantages over existing methods: First, comparison of mass spectra is based on a scoring function that can be custom-designed for certain applications and explicitly takes missing and additional peaks into account. The method is able to simulate almost every additive scoring scheme. Second, we present an efficient deterministic method for assessing the significance of a protein hit, independent of the underlying scoring function and sequence database. We prove the applicability of our approach using biological mass spectrometry data and compare our results to the standard software Mascot. CONCLUSION: The proposed framework for analyzing Peptide Mass Fingerprints shows performance comparable to Mascot on small peak lists. Introducing more noise peaks, we are able to keep identification rates at a similar level by using the flexibility introduced by scoring schemes

A mass accuracy sensitive probability based scoring algorithm for database searching of tandem mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become one of the most used tools in mass spectrometry based proteomics. Various algorithms have since been developed to automate the process for modern high-throughput LC-MS/MS experiments.</p> <p>Results</p> <p>A probability based statistical scoring model for assessing peptide and protein matches in tandem MS database search was derived. The statistical scores in the model represent the probability that a peptide match is a random occurrence based on the number or the total abundance of matched product ions in the experimental spectrum. The model also calculates probability based scores to assess protein matches. Thus the protein scores in the model reflect the significance of protein matches and can be used to differentiate true from random protein matches.</p> <p>Conclusion</p> <p>The model is sensitive to high mass accuracy and implicitly takes mass accuracy into account during scoring. High mass accuracy will not only reduce false positives, but also improves the scores of true positive matches. The algorithm is incorporated in an automated database search program MassMatrix.</p

"Hook"-calibration of GeneChip-microarrays: Theory and algorithm

Abstract Background: The improvement of microarray calibration methods is an essential prerequisite for quantitative expression analysis. This issue requires the formulation of an appropriate model describing the basic relationship between the probe intensity and the specific transcript concentration in a complex environment of competing interactions, the estimation of the magnitude these effects and their correction using the intensity information of a given chip and, finally the development of practicable algorithms which judge the quality of a particular hybridization and estimate the expression degree from the intensity values. Results: We present the so-called hook-calibration method which co-processes the log-difference (delta) and -sum (sigma) of the perfect match (PM) and mismatch (MM) probe-intensities. The MM probes are utilized as an internal reference which is subjected to the same hybridization law as the PM, however with modified characteristics. After sequence-specific affinity correction the method fits the Langmuir-adsorption model to the smoothed delta-versus-sigma plot. The geometrical dimensions of this so-called hook-curve characterize the particular hybridization in terms of simple geometric parameters which provide information about the mean non-specific background intensity, the saturation value, the mean PM/MM-sensitivity gain and the fraction of absent probes. This graphical summary spans a metrics system for expression estimates in natural units such as the mean binding constants and the occupancy of the probe spots. The method is single-chip based, i.e. it separately uses the intensities for each selected chip. Conclusion: The hook-method corrects the raw intensities for the non-specific background hybridization in a sequence-specific manner, for the potential saturation of the probe-spots with bound transcripts and for the sequence-specific binding of specific transcripts. The obtained chip characteristics in combination with the sensitivity corrected probe-intensity values provide expression estimates scaled in natural units which are given by the binding constants of the particular hybridization.</p