Refinement of the critical region for MCKD1 by detection of transcontinental haplotype sharing

Refinement of the critical region for MCKD1 by detection of transcontinental haplotype sharing.BackgroundAutosomal-dominant medullary cystic kidney disease type 1 (MCKD1) [OMIM 174000] is a hereditary nephropathy that leads to renal salt wasting and end-stage renal failure at a median age of 62 years. In a Welsh MCKD1 kindred we have recently demonstrated linkage to the MCKD1 locus on chromosome 1q23.1 and refined the critical MCKD1 region to <3.3Mb.MethodsIn order to refine the candidate gene region for MCKD1, high-resolution haplotype analysis in three large kindreds with MCKD1 was performed.ResultsWe report here on high-resolution haplotype analysis in this Welsh kindred, as well as in the Arizona kindred, which was used for the first definition of MCKD as a disease entity, and in a kindred from the Dutch/German border. We detected extensive haplotype sharing among all affected individuals of all three kindreds. Scrutinization of the genealogy of the Arizona kindred revealed an origin from Germany in the 17th century, thereby providing historical data for haplotype sharing by descent at the MCKD1 locus.ConclusionUnder the hypothesis of haplotype sharing by descent, we refined the critical genetic interval to <650kb, thus enabling candidate gene analysis

Physics with e<SUP>+</SUP>e<SUP>-</SUP> linear colliders

We describe the physics potential of e+e- linear colliders in this report. These machines are planned to operate in the first phase at a center-of-mass energy of 500 GeV, before being scaled up to about 1 TeV. In the second phase of the operation, a final energy of about 2 TeV is expected. The machines will allow us to perform precision tests of the heavy particles in the Standard Model, the top quark and the electroweak bosons. They are ideal facilities for exploring the properties of Higgs particles, in particular in the intermediate mass range. New vector bosons and novel matter particles in extended gauge theories can be searched for and studied thoroughly. The machines provide unique opportunities for the discovery of particles in supersymmetric extensions of the Standard Model, the spectrum of Higgs particles, the supersymmetric partners of the electroweak gauge and Higgs bosons, and of the matter particles. High precision analyses of their properties and interactions will allow for extrapolations to energy scales close to the Planck scale where gravity becomes significant. In alternative scenarios, like compositeness models, novel matter particles and interactions can be discovered and investigated in the energy range above the existing colliders up to the TeV scale. Whatever scenario is realized in Nature, the discovery potential of e+e- linear colliders and the high-precision with which the properties of particles and their interactions can be analysed, define an exciting physics programme complementary to hadron machines

Radiatively corrected shape function for inclusive heavy hadron decays

We discuss the non-perturbative and the radiative corrections to inclusive B decays from the point of view known from QED corrections to high energy e^+ e^- processes. Here the leading contributions can be implemented through the so called ``radiator function'' which corresponds to the shape function known in heavy hadron decays. In this way some new insight into the origin of the shape function is obtained. As a byproduct, a parameterization of the radiatively corrected shape function is suggested which can be implemented in Monte Carlo studies of inclusive heavy hadron decays.Comment: LaTeX, uses a4, graphicx and psfrag, 10 pages. The complete paper is also available at http://www-ttp.physik.uni-karlsruhe.de/Preprints

On-Line AdaTron Learning of Unlearnable Rules

We study the on-line AdaTron learning of linearly non-separable rules by a simple perceptron. Training examples are provided by a perceptron with a non-monotonic transfer function which reduces to the usual monotonic relation in a certain limit. We find that, although the on-line AdaTron learning is a powerful algorithm for the learnable rule, it does not give the best possible generalization error for unlearnable problems. Optimization of the learning rate is shown to greatly improve the performance of the AdaTron algorithm, leading to the best possible generalization error for a wide range of the parameter which controls the shape of the transfer function.)Comment: RevTeX 17 pages, 8 figures, to appear in Phys.Rev.

Simulation of the process e+e−↦e+e−γe^+ e^- \mapsto e^+ e^- \gamma within electroweak theory with longitudinally polarized initial electrons

We present simple analytic expressions for the distributions of the Bhabha scattering process with emission of one hard photon, including weak boson exchanges, and with longitudinal polarization of the initial electron. The results from the Monte Carlo generator BHAGEN-1PH, based on these expressions, are presented and compared, for the unpolarized case, with those existing in literature.Comment: 9 pages, plain Tex, no figures, small change in Table

Event Generators for Bhabha Scattering

The results obtained by the "Event Generators for Bhabha Scattering" working group during the CERN Workshop "Physics at LEP2" (1994/1995) are presented.Comment: 70 pages, PostScript file. To appear in the Report of the Workshop on Physics at LEP2, G. Altarelli T. Sjostrand and F. Zwirner ed

QED Corrections to Deep Inelastic Scattering with Tagged Photons at HERA

We calculate the QED corrections to deep inelastic scattering with tagged photons at HERA in the leading logarithmic approximation. Due to the special experimental setup, two large scales appear in the calculation that lead to two large logarithms of comparable size. The relation of our formalism to the conventional structure function formalism is outlined. We present some numerical results and compare with previous calculations.Comment: 7 pages, REVTeX, 2 figures; published versio

Generalizing with perceptrons in case of structured phase- and pattern-spaces

We investigate the influence of different kinds of structure on the learning behaviour of a perceptron performing a classification task defined by a teacher rule. The underlying pattern distribution is permitted to have spatial correlations. The prior distribution for the teacher coupling vectors itself is assumed to be nonuniform. Thus classification tasks of quite different difficulty are included. As learning algorithms we discuss Hebbian learning, Gibbs learning, and Bayesian learning with different priors, using methods from statistics and the replica formalism. We find that the Hebb rule is quite sensitive to the structure of the actual learning problem, failing asymptotically in most cases. Contrarily, the behaviour of the more sophisticated methods of Gibbs and Bayes learning is influenced by the spatial correlations only in an intermediate regime of $\alpha$, where $\alpha$ specifies the size of the training set. Concerning the Bayesian case we show, how enhanced prior knowledge improves the performance.Comment: LaTeX, 32 pages with eps-figs, accepted by J Phys