Measurement of the Proton Spin Structure Function g1p with a Pure Hydrogen Target

A measurement of the proton spin structure function g1p(x,Q^2) in deep-inelastic scattering is presented. The data were taken with the 27.6 GeV longitudinally polarised positron beam at HERA incident on a longitudinally polarised pure hydrogen gas target internal to the storage ring. The kinematic range is 0.021<x<0.85 and 0.8 GeV^2<Q^2<20 GeV^2. The integral Int_{0.021}^{0.85} g1p(x)dx evaluated at Q0^2 of 2.5 GeV^2 is 0.122+/-0.003(stat.)+/-0.010(syst.).Comment: 7 pages, 3 figures, 1 table, RevTeX late

Measurement of the Neutron Spin Structure Function g1ng_1^n with a Polarized ^3He Target

Results are reported from the HERMES experiment at HERA on a measurement of the neutron spin structure function $g_1^n(x,Q^2)$ in deep inelastic scattering using 27.5 GeV longitudinally polarized positrons incident on a polarized $^3$He internal gas target. The data cover the kinematic range $0.023<x<0.6$ and $1 (GeV/c)^2 < Q^2 <15 (GeV/c)^2$. The integral $\int_{0.023}^{0.6} g_1^n(x) dx$ evaluated at a fixed $Q^2$ of $2.5 (GeV/c)^2$ is $-0.034\pm 0.013(stat.)\pm 0.005(syst.)$. Assuming Regge behavior at low $x$, the first moment $\Gamma_1^n=\int_0^1 g_1^n(x) dx$ is $-0.037\pm 0.013(stat.)\pm 0.005(syst.)\pm 0.006(extrapol.)$.Comment: 4 pages TEX, text available at http://www.krl.caltech.edu/preprints/OAP.htm

Observation of a Coherence Length Effect in Exclusive Rho^0 Electroproduction

Exclusive incoherent electroproduction of the rho^0(770) meson from 1H, 2H, 3He, and 14N targets has been studied by the HERMES experiment at squared four-momentum transfer Q**2>0.4 GeV**2 and positron energy loss nu from 9 to 20 GeV. The ratio of the 14N to 1H cross sections per nucleon, known as the nuclear transparency, was found to decrease with increasing coherence length of quark-antiquark fluctuations of the virtual photon. The data provide clear evidence of the interaction of the quark- antiquark fluctuations with the nuclear medium.Comment: RevTeX, 5 pages, 3 figure

Determination of the Deep Inelastic Contribution to the Generalised Gerasimov-Drell-Hearn Integral for the Proton and Neutron

The virtual photon absorption cross section differences [sigma_1/2-sigma_3/2] for the proton and neutron have been determined from measurements of polarised cross section asymmetries in deep inelastic scattering of 27.5 GeV longitudinally polarised positrons from polarised 1H and 3He internal gas targets. The data were collected in the region above the nucleon resonances in the kinematic range nu < 23.5 GeV and 0.8 GeV**2 < Q**2 < 12 GeV**2. For the proton the contribution to the generalised Gerasimov-Drell-Hearn integral was found to be substantial and must be included for an accurate determination of the full integral. Furthermore the data are consistent with a QCD next-to-leading order fit based on previous deep inelastic scattering data. Therefore higher twist effects do not appear significant.Comment: 6 pages, 3 figures, 1 table, revte

Flavor Decomposition of the Polarized Quark Distributions in the Nucleon from Inclusive and Semi-inclusive Deep-inelastic Scattering

Spin asymmetries of semi-inclusive cross sections for the production of positively and negatively charged hadrons have been measured in deep-inelastic scattering of polarized positrons on polarized hydrogen and 3He targets, in the kinematic range 0.023<x<0.6 and 1 GeV^2<Q^2<10 GeV^2. Polarized quark distributions are extracted as a function of x for up $(u+u_bar) and down (d+d_bar) flavors. The up quark polarization is positive and the down quark polarization is negative in the measured range. The polarization of the sea is compatible with zero. The first moments of the polarized quark distributions are presented. The isospin non-singlet combination Delta_q_3 is consistent with the prediction based on the Bjorken sum rule. The moments of the polarized quark distributions are compared to predictions based on SU(3)_f flavor symmetry and to a prediction from lattice QCD.Comment: 14 pages, 6 figures (eps format), 10 tables in Latex New version contains tables of asymmetries and correlation matri

The HERMES Spectrometer

The HERMES experiment is collecting data on inclusive and semi-inclusive deep inelastic scattering of polarised positrons from polarised targets of Il, D, and He-3. These data give information on the spin structure of the nucleon. This paper describes the forward angle spectrometer built for this purpose. The spectrometer includes numerous tracking chambers (micro-strip gas chambers, drift and proportional chambers) in front of and behind a 1.3 T.m magnetic field, as well as an extensive set of detectors for particle identification (a lead-glass calorimeter, a pre-shower detector, a transition radiation detector, and a threshold Cherenkov detector). Two of the main features of the spectrometer are its good acceptance and identification of both positrons and hadrons, in particular pions. These characteristics, together with the purity of the targets, are allowing HERMES to make unique contributions to the understanding of how the spins of the quarks contribute to the spin of the nucleon. (C) 1998 Elsevier Science B.V. All rights reserved

Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits : A Multi-Ethnic Meta-Analysis of 45,891 Individuals

J. Kaprio, S. Ripatti ja M.-L. Lokki työryhmien jäseniä.Peer reviewe

Habitat amount, not habitat configuration, best predicts population genetic structure in fragmented landscapes

Context: Landscape structure shapes the genetic structure of populations by delimiting spatial patterns of dispersal and reproduction across generations. Thus, descriptions of human-altered landscapes can be used to predict demographic and evolutionary outcomes of populations. Effectively measuring landscape structure to predict genetic structure requires that we understand the relative importance of distinct components of landscape structure (e.g., habitat amount and configuration) in creating spatial patterns of genetic variation. Objectives: We thus developed an individual-based simulation model to test predictions about the relative importance of habitat amount and configuration in producing genetic structure. We also investigated the independent relationships between components of landscape structure and the population dynamics that underlie genetic effects. Methods: We ran experiments in which we allowed gene flow and population size to vary as emergent outcomes of the interactions between hypothetical populations and heterogeneous landscapes. Results: We found that the amount of habitat in a landscape is a much better predictor of genetic structure than is habitat configuration. This pattern holds across a range of landscapes and dispersal distances and behaviors. When habitat is non-contiguous (i.e., fragmented), habitat amount mediates production of genetic differentiation by regulating both the size and isolation of habitat patches, which in turn regulate population size and gene flow. Conclusions: These results suggest that habitat amount, a simple measure that is easy to calculate, may often be the best metric for predicting population genetic structure and that when possible, measures of habitat amount and population size should be incorporated into landscape genetic studies

Relative effects of road mortality and decreased connectivity on population genetic diversity

Roads can have two important effects on populations that impact genetic variation: reduced gene flow and reduced abundance. Reduced gene flow (" barrier effects") due to road avoidance behavior or road mortality can lead to reduced genetic diversity because genetic drift is enhanced in fragmented populations. Road mortality can also reduce population abundance (" depletion effects") whenever road-caused mortality outpaces recruitment, also lowering diversity even when barrier effects are inconsequential. Although roads are expected to affect both genetic diversity and fragmentation, most research focuses only on fragmentation. Furthermore, in studies that do investigate road effects on genetic diversity, correlations are usually attributed to barrier effects and little attention is paid to the potentially confounding influence of mortality-caused depletion effects. Here we investigate the relative importance of barrier and depletion effects on genetic diversity of populations separated by a road by performing coalescent simulations wherein these two road effects are varied independently. By simulating wide ranging rates of migration and population decline, we also determine how the importance of these forces changes depending on their relative magnitude. We show that the vast majority of potential variation in genetic diversity is governed by depletion (mortality) rather than barrier effects. We also show that unless migration is sufficiently high and population decline due to mortality is sufficiently low, increasing migration across roads will generally not recoup genetic variation lost due to road mortality. We argue that the genetic effects of road-mediated mortality have been underappreciated and should be more often considered before prioritizing road-mitigation measures

What determines the spatial extent of landscape effects on species?

Context: Landscape ecologists are often interested in measuring the effects of an environmental variable on a biological response; however, the strength and direction of effect depend on the size of the area within which the environmental variable is measured. Thus a central objective is to identify the optimal spatial extent within which to measure the environmental variable, i.e. the “scale of effect”. Objectives: Our objectives are (1) to provide a comprehensive summary of the hypotheses concerning what determines the scale of effect, (2) to provide predictions that can be tested in empirical studies, and (3) to show, with a review of the literature, that most of these predictions have so far been inadequately tested. Methods: We propose 14 predictions derived from five hypotheses explaining what determines the scale of effect, and review the literature (if any) supporting each prediction. These predictions involve five types of factors: (A) species traits, (B) landscape variables, (C) biological responses (e.g. abundance vs. occurrence), (D) indirect influences, and (E) regional context of the study. We identify methodological issues that hinder estimation of the scale of effect. Results: Of the 14 predictions, only nine have been tested empirically and only five have received some empirical support. Most support is from simulation studies. Empirical evidence usually does not support predictions. Conclusions: The study of the spatial scale at which landscape variables influence biological outcomes is in its infancy. We provide directions for future research by clarifying predictions concerning the determinants of the scale of effect