Subgraph spotting in graph representations of comic book images

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset.University of La Rochelle (France

Fluoroalkyl and Alkyl Chains Have Similar Hydrophobicities in Binding to the “Hydrophobic Wall” of Carbonic Anhydrase

This paper describes the performance of junctions based on self-assembled monolayers (SAMs) as the functional element of a half-wave rectifier (a simple circuit that converts, or rectifies, an alternating current (AC) signal to a direct current (DC) signal). Junctions with SAMs of 11-(ferrocenyl)-1-undecanethiol or 11-(biferrocenyl)-1-undecanethiol on ultraflat, template-stripped Ag (AgTS) bottom electrodes, and contacted by top electrodes of eutectic indium–gallium (EGaIn), rectified AC signals, while similar junctions based on SAMs of 1-undecanethiol—SAMs lacking the ferrocenyl terminal group—did not. SAMs in these AC circuits (operating at 50 Hz) remain stable over a larger window of applied bias than in DC circuits. AC measurements, therefore, can investigate charge transport in SAM-based junctions at magnitudes of bias inaccessible to DC measurements. For junctions with SAMs of alkanethiols, combining the results from AC and DC measurements identifies two regimes of bias with different mechanisms of charge transport: (i) low bias (|V| 1.3 V), at which Fowler–Nordheim (FN) tunneling dominates. For junctions with SAMs terminated by Fc moieties, the transition to FN tunneling occurs at |V| ≈ 2.0 V. Furthermore, at sufficient forward bias (V > 0.5 V), hopping makes a significant contribution to charge transport and occurs in series with direct tunneling (V 2.0 V) until FN tunneling activates (V 2.0 V). Thus, for Fc-terminated SAMs at forward bias, three regimes are apparent: (i) direct tunneling (V = 0–0.5 V), (ii) hopping plus direct tunneling (V ≈ 0.5–2.0 V), and (iii) FN tunneling (V 2.0 V). Since hopping does not occur at reverse bias, only two regimes are present over the measured range of reverse bias. This difference in the mechanisms of charge transport at forward and reverse bias for junctions with Fc moieties resulted in large rectification ratios (R > 100) and enabled half-wave rectification.Chemistry and Chemical Biolog

Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project

© 2015, The Author(s). Objective: Quantitative estimates of air pollution health impacts have become an increasingly critical input to policy decisions. The WHO project “Health risks of air pollution in Europe—HRAPIE” was implemented to provide the evidence-based concentration–response functions for quantifying air pollution health impacts to support the 2013 revision of the air quality policy for the European Union (EU). Methods: A group of experts convened by WHO Regional Office for Europe reviewed the accumulated primary research evidence together with some commissioned reviews and recommended concentration–response functions for air pollutant–health outcome pairs for which there was sufficient evidence for a causal association. Results: The concentration–response functions link several indicators of mortality and morbidity with short- and long-term exposure to particulate matter, ozone and nitrogen dioxide. The project also provides guidance on the use of these functions and associated baseline health information in the cost–benefit analysis. Conclusions: The project results provide the scientific basis for formulating policy actions to improve air quality and thereby reduce the burden of disease associated with air pollution in Europe

Le FORUM, Vol. 35 No. 4

https://digitalcommons.library.umaine.edu/francoamericain_forum/1032/thumbnail.jp

Le Forum, Vol. 44 #4

https://digitalcommons.library.umaine.edu/francoamericain_forum/1106/thumbnail.jp

Structural basis for receptor activity-modifying protein-dependent selective peptide recognition by a G protein-coupled receptor

Association of receptor activity-modifying proteins (RAMP1-3) with the G protein-coupled receptor (GPCR) calcitonin receptor-like receptor (CLR) enables selective recognition of the peptides calcitonin gene-related peptide (CGRP) and adrenomedullin (AM) that have diverse functions in the cardiovascular and lymphatic systems. How peptides selectively bind GPCR:RAMP complexes is unknown. We report crystal structures of CGRP analog-bound CLR:RAMP1 and AM-bound CLR:RAMP2 extracellular domain heterodimers at 2.5 and 1.8 Å resolutions, respectively. The peptides similarly occupy a shared binding site on CLR with conformations characterized by a β-turn structure near their C termini rather than the α-helical structure common to peptides that bind related GPCRs. The RAMPs augment the binding site with distinct contacts to the variable C-terminal peptide residues and elicit subtly different CLR conformations. The structures and accompanying pharmacology data reveal how a class of accessory membrane proteins modulate ligand binding of a GPCR and may inform drug development targeting CLR:RAMP complexes

Response to "Quantifying the health impacts of ambient air pollutants: methodological errors must be avoided".

We thank Morfeld and Erren for their interest in our recent publication on “Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project” (Héroux et al. 2015). Morfeld and Erren claim that there are potential problems with the statistical approach used in our paper to measure the impact on mortality from air pollution. In fact, they state that “Greenland showed that a calculation based on RR estimates, as performed in the EU research project, does estimate excess cases numbers—but it does not estimate the number of premature cases or etiological cases” (Greenland 1999).Peer reviewe

Clustering of Unhealthy Behaviors in the Aerobics Center Longitudinal Study

Background Clustering of unhealthy behaviors has been reported in previous studies; however the link with all-cause mortality and differences between those with and without chronic disease requires further investigation. Objectives To observe the clustering effects of unhealthy diet, fitness, smoking, and excessive alcohol consumption in adults with and without chronic disease and to assess all-cause mortality risk according to the clustering of unhealthy behaviors. Methods Participants were 13,621 adults (aged 20–84) from the Aerobics Center Longitudinal Study. Four health behaviors were observed (diet, fitness, smoking, and drinking). Baseline characteristics of the study population and bivariate relations between pairs of the health behaviors were evaluated separately for those with and without chronic disease using cross-tabulation and a chi-square test. The odds of partaking in unhealthy behaviors were also calculated. Latent class analysis (LCA) was used to assess clustering. Cox regression was used to assess the relationship between the behaviors and mortality. Results The four health behaviors were related to each other. LCA results suggested that two classes existed. Participants in class 1 had a higher probability of partaking in each of the four unhealthy behaviors than participants in class 2. No differences in health behavior clustering were found between participants with and without chronic disease. Mortality risk increased relative to the number of unhealthy behaviors participants engaged in. Conclusion Unhealthy behaviors cluster together irrespective of chronic disease status. Such findings suggest that multi-behavioral intervention strategies can be similar in those with and without chronic disease