We hypothesised that biomass smoke exposure is associated with an airway-predominant chronic obstructive pulmonary disease (COPD) phenotype, while tobacco-related COPD is associated with an emphysema-predominant phenotype.In this cross-sectional study, female never-smokers with COPD and biomass exposure (n521) and female ex-cigarette smokers with COPD without biomass exposure (n522) completed computed tomography (CT) at inspiration and expiration, pulmonary function, blood gas, exercise tolerance, and quality of life measures. Two radiologists scored the extent of emphysema and air trapping on CT. Quantitative emphysema severity and distribution and airway wall thickness were calculated using specialised software.Women in the tobacco group had significantly more emphysema than the biomass group (radiologist score 2.3 versus 0.7, p50.001; emphysema on CT 27% versus 19%, p50.046; and a larger size of emphysematous spaces, p50.006). Women in the biomass group had significantly more air trapping than the tobacco group (radiologist score 2.6 and 1.5, respectively; p50.02) and also scored lower on the symptom, activities and confidence domains of the quality of life assessment and had lower oxygen saturation at rest and during exercise (p,0.05).Biomass smoke exposure is associated with less emphysema but more air trapping than tobacco smoke exposure, suggesting an airway-predominant phenotype. @ERSpublications Biomass smoke causes less emphysema but more air trapping than tobacco smoke: airwaypredominant COPD phenotype?

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We present results from an extensive analytic and numerical study of a twodimensional model of a square array of ultrasmall Josephson junctions. We include the ultrasmall self and mutual capacitances of the junctions, for the same parameter ranges as those produced in the experiments. The model Hamiltonian studied includes the Josephson, E J , as well as the charging, E C , energies between superconducting islands. The corresponding quantum partition function is expressed in different calculationally convenient ways within its path-integral representation. The phase diagram is analytically studied using a WKB renormalization group (WKB-RG) plus a self-consistent harmonic approximation (SCHA) analysis, together with non-perturbative quantum Monte Carlo simulations. Most of the results presented here pertain to the superconductor to normal (S-N) region, although some results for the insulating to normal (I-N) region are also included. We find very good agreement between the WKB-RG and QMC results when compared to the experimental data. To fit the data, we only used the experimentally determined capacitances as fitting parameters. The WKB-RG analysis in the S-N region predicts a low temperature instability i.e. a Quantum Induced Transition (QUIT). We carefully analyze the possible existence of the QUIT via the QMC simulations and carry out a finite size analysis of T QU IT as a function of the magnitude of imaginary time axis L τ . We find that for some relatively large values of α = E C E J (1 ≤ α ≤ 2.25), the L τ → ∞ limit does appear to give a non-zero T QU IT , while for α ≥ 2.5, T QU IT = 0. We use the SCHA to analytically understand the L τ dependence of the QMC results with good agreement between them. Finally, we also carried out a WKB-RG analysis in the I-N region and found no evidence of a low temperature QUIT, up to lowest order in α −1 .

This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modeled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners.