Off-Shell Axial Anomaly via the \gamma^* \pi^0 -> \gamma Transition

The $\gamma^* \pi^0 \rightarrow \gamma$ form factor, including the extension off the pion mass-shell, is obtained from a generalized impulse approximation within a QCD-based model field theory known to provide an excellent description of the pion charge form factor. This approach implements dressing of the vertex functions and propagators consistent with dynamical chiral symmetry breaking, gauge invariance, quark confinement and perturbative QCD. Soft nonperturbative behavior, dictated by the axial anomaly, is found to evolve to the perturbative QCD limit only for \mbox{$Q^2 \geq 20~{\rm GeV}^2$}.Comment: 10 Pages, 3 figures (uuencoded and appended), REVTE

Factors Influencing Trust in Agencies That Disseminate Tobacco Prevention Information

Several health-related agencies administer national and targeted public education campaigns to provide health information and change health-related behaviors. The trust the public has in these agencies as the source of the message impacts the effectiveness of their communication campaigns. In this study, we explore the perceived trust of agencies that communicate health messages in the tobacco control field. As part of a larger tobacco regulatory science study, we conducted six 90-min focus groups comprising 41 participants. Five main themes emerged pertinent to the agency: (1) its integrity, (2) its competence, (3) its motives, (4) how it is portrayed in the media, and (5) skepticism and mistrust about it. Given the significant resources spent on health messaging to the public and potential benefits offered by this communication, an understanding of public trust in the agencies as the source of health messages is important. Findings suggest health information may be ignored or discounted when there is mistrust in the agency sending those messages

Uric acid: an old actor for a new role

The role of uric acid as an independent risk factor for cardiovascular events is still debated. In fact, other confounding factors such as glucose intolerance, obesity, dyslipidaemia, hypertension, use of diuretics and insulin resistance may play a role in determining the increased vascular risk associated to elevated uric acid concentrations. These factors (including high uric acid) have been mentioned in one or more definitions of the metabolic syndrome. Recently, much attention has been paid to the metabolic syndrome due to its possible role as a risk factor for the development of type 2 diabetes and cardiovascular disease. The worldwide increase in the prevalence of obesity and diabetes is a reason not only for the increasing prevalence of the metabolic syndrome but also of hyperuricaemia. A better understanding of the role of uric acid in health and in disease states may help physicians to improve their performance in preventing and treating cardiovascular disease

Forecasting U.S. Home Foreclosures with an Index of Internet Keyword Searches

Finding data to feed into financial and risk management models can be challenging. Many analysts attribute a lack of data or quality information as a contributing factor to the worldwide financial crises that seems to have begun in the U.S. subprime mortgage market. In this paper, a new source of data, key word search statistics recently available from Google, are applied in a experiment to develop a short-term forecasting model for the number of foreclosures in the U.S. housing market. The keyword search data significantly improves forecast of foreclosures, suggesting that this data can be useful for financial risk management. More generally, the new data source shows promise for a variety of financial and market analyses

Backward pion-nucleon scattering

A global analysis of the world data on differential cross sections and polarization asymmetries of backward pion-nucleon scattering for invariant collision energies above 3 GeV is performed in a Regge model. Including the $N_\alpha$, $N_\gamma$, $\Delta_\delta$ and $\Delta_\beta$ trajectories, we reproduce both angular distributions and polarization data for small values of the Mandelstam variable $u$, in contrast to previous analyses. The model amplitude is used to obtain evidence for baryon resonances with mass below 3 GeV. Our analysis suggests a $G_{39}$ resonance with a mass of 2.83 GeV as member of the $\Delta_{\beta}$ trajectory from the corresponding Chew-Frautschi plot.Comment: 12 pages, 16 figure

The scintillation and ionization yield of liquid xenon for nuclear recoils

XENON10 is an experiment designed to directly detect particle dark matter. It is a dual phase (liquid/gas) xenon time-projection chamber with 3D position imaging. Particle interactions generate a primary scintillation signal (S1) and ionization signal (S2), which are both functions of the deposited recoil energy and the incident particle type. We present a new precision measurement of the relative scintillation yield \leff and the absolute ionization yield Q_y, for nuclear recoils in xenon. A dark matter particle is expected to deposit energy by scattering from a xenon nucleus. Knowledge of \leff is therefore crucial for establishing the energy threshold of the experiment; this in turn determines the sensitivity to particle dark matter. Our \leff measurement is in agreement with recent theoretical predictions above 15 keV nuclear recoil energy, and the energy threshold of the measurement is 4 keV. A knowledge of the ionization yield \Qy is necessary to establish the trigger threshold of the experiment. The ionization yield \Qy is measured in two ways, both in agreement with previous measurements and with a factor of 10 lower energy threshold.Comment: 8 pages, 9 figures. To be published in Nucl. Instrum. Methods

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

Expansion and further delineation of the SETD5 phenotype leading to global developmental delay, variable dysmorphic features, and reduced penetrance

Diagnostic exome sequencing (DES) has aided delineation of the phenotypic spectrum of rare genetic etiologies of intellectual disability (ID). A SET domain containing 5 gene (SETD5) phenotype of ID and dysmorphic features has been previously described in relation to patients with 3p25.3 deletions and in a few individuals with de novo sequence alterations. Herein, we present additional patients with pathogenic SETD5 sequence alterations. The majority of patients in this cohort and previously reported have developmental delay, behavioral/psychiatric issues, and variable hand and skeletal abnormalities. We also present an apparently unaffected carrier mother of an affected individual and a carrier mother with normal intelligence and affected twin sons. We suggest that the phenotype of SETD5 is more complex and variable than previously presented. Therefore, many features and presentations need to be considered when evaluating a patient for SETD5 alterations through DES

Novel Druggable Hot Spots in Avian Influenza Neuraminidase H5N1 Revealed by Computational Solvent Mapping of a Reduced and Representative Receptor Ensemble

The influenza virus subtype H5N1 has raised concerns of a possible human pandemic threat because of its high virulence and mutation rate. Although several approved anti-influenza drugs effectively target the neuraminidase, some strains have already acquired resistance to the currently available anti-influenza drugs. In this study, we present the synergistic application of extended explicit solvent molecular dynamics (MD) and computational solvent mapping (CS-Map) to identify putative ‘hot spots’ within flexible binding regions of N1 neuraminidase. Using representative conformations of the N1 binding region extracted from a clustering analysis of four concatenated 40-ns MD simulations, CS-Map was utilized to assess the ability of small, solvent-sized molecules to bind within close proximity to the sialic acid binding region. Mapping analyses of the dominant MD conformations reveal the presence of additional hot spot regions in the 150- and 430-loop regions. Our hot spot analysis provides further support for the feasibility of developing high-affinity inhibitors capable of binding these regions, which appear to be unique to the N1 strain