Correlation Functions in 2-Dimensional Integrable Quantum Field Theories

In this talk I discuss the form factor approach used to compute correlation functions of integrable models in two dimensions. The Sinh-Gordon model is our basic example. Using Watson's and the recursive equations satisfied by matrix elements of local operators, I present the computation of the form factors of the elementary field $\phi(x)$ and the stress-energy tensor $T_{\mu\nu}(x)$ of the theory.Comment: 19pp, LATEX version, (talk at Como Conference on ``Integrable Quantum Field Theories''

An exact expression to calculate the derivatives of position-dependent observables in molecular simulations with flexible constraints

In this work, we introduce an algorithm to compute the derivatives of physical observables along the constrained subspace when flexible constraints are imposed on the system (i.e., constraints in which the hard coordinates are fixed to configuration-dependent values). The presented scheme is exact, it does not contain any tunable parameter, and it only requires the calculation and inversion of a sub-block of the Hessian matrix of second derivatives of the function through which the constraints are defined. We also present a practical application to the case in which the sought observables are the Euclidean coordinates of complex molecular systems, and the function whose minimization defines the constraints is the potential energy. Finally, and in order to validate the method, which, as far as we are aware, is the first of its kind in the literature, we compare it to the natural and straightforward finite-differences approach in three molecules of biological relevance: methanol, N-methyl-acetamide and a tri-glycine peptideComment: 13 pages, 8 figures, published versio

Fast automated cell phenotype image classification

BACKGROUND: The genomic revolution has led to rapid growth in sequencing of genes and proteins, and attention is now turning to the function of the encoded proteins. In this respect, microscope imaging of a protein's sub-cellular localisation is proving invaluable, and recent advances in automated fluorescent microscopy allow protein localisations to be imaged in high throughput. Hence there is a need for large scale automated computational techniques to efficiently quantify, distinguish and classify sub-cellular images. While image statistics have proved highly successful in distinguishing localisation, commonly used measures suffer from being relatively slow to compute, and often require cells to be individually selected from experimental images, thus limiting both throughput and the range of potential applications. Here we introduce threshold adjacency statistics, the essence which is to threshold the image and to count the number of above threshold pixels with a given number of above threshold pixels adjacent. These novel measures are shown to distinguish and classify images of distinct sub-cellular localization with high speed and accuracy without image cropping. RESULTS: Threshold adjacency statistics are applied to classification of protein sub-cellular localization images. They are tested on two image sets (available for download), one for which fluorescently tagged proteins are endogenously expressed in 10 sub-cellular locations, and another for which proteins are transfected into 11 locations. For each image set, a support vector machine was trained and tested. Classification accuracies of 94.4% and 86.6% are obtained on the endogenous and transfected sets, respectively. Threshold adjacency statistics are found to provide comparable or higher accuracy than other commonly used statistics while being an order of magnitude faster to calculate. Further, threshold adjacency statistics in combination with Haralick measures give accuracies of 98.2% and 93.2% on the endogenous and transfected sets, respectively. CONCLUSION: Threshold adjacency statistics have the potential to greatly extend the scale and range of applications of image statistics in computational image analysis. They remove the need for cropping of individual cells from images, and are an order of magnitude faster to calculate than other commonly used statistics while providing comparable or better classification accuracy, both essential requirements for application to large-scale approaches

Autism as a disorder of neural information processing: directions for research and targets for therapy

The broad variation in phenotypes and severities within autism spectrum disorders suggests the involvement of multiple predisposing factors, interacting in complex ways with normal developmental courses and gradients. Identification of these factors, and the common developmental path into which theyfeed, is hampered bythe large degrees of convergence from causal factors to altered brain development, and divergence from abnormal brain development into altered cognition and behaviour. Genetic, neurochemical, neuroimaging and behavioural findings on autism, as well as studies of normal development and of genetic syndromes that share symptoms with autism, offer hypotheses as to the nature of causal factors and their possible effects on the structure and dynamics of neural systems. Such alterations in neural properties may in turn perturb activity-dependent development, giving rise to a complex behavioural syndrome many steps removed from the root causes. Animal models based on genetic, neurochemical, neurophysiological, and behavioural manipulations offer the possibility of exploring these developmental processes in detail, as do human studies addressing endophenotypes beyond the diagnosis itself

Study of W boson production in PbPb and pp collisions at sqrt(s[NN]) = 2.76 TeV

A measurement is presented of W-boson production in PbPb collisions carried out at a nucleon-nucleon (NN) centre-of-mass energy sqrt(s[NN]) of 2.76 TeV at the LHC using the CMS detector. In data corresponding to an integrated luminosity of 7.3 inverse microbarns, the number of W to mu mu-neutrino decays is extracted in the region of muon pseudorapidity abs(eta[mu])<2.1 and transverse momentum pt[mu]>25 GeV. Yields of muons found per unit of pseudorapidity correspond to (159 +/- 10 (stat.) +/- 12 (syst.)) 10E-8 W(plus) and (154 +/- 10 (stat.) +/- 12 (syst.)) 10E-8 W(minus) bosons per minimum-bias PbPb collision. The dependence of W production on the centrality of PbPb collisions is consistent with a scaling of the yield by the number of incoherent NN collisions. The yield of W bosons is also studied in a sample of pp interactions at sqrt(s)= 2.76 TeV corresponding to an integrated luminosity of 231 inverse nanobarns. The individual W(plus) and W(minus) yields in PbPb and pp collisions are found to agree, once the neutron and proton content in Pb nuclei is taken into account. Likewise, the difference observed in the dependence of the positive and negative muon production on pseudorapidity is consistent with next-to-leading order perturbative QCD calculations.Comment: Submitted to Physics Letters