Inventory of the volatiles on comet 67P/Churyumov-Gerasimenko from Rosetta/ROSINA

Context. The ESA Rosetta spacecraft (S/C) is tracking comet 67P/Churyumov-Gerasimenko in close vicinity. This prolonged encounter enables studying the evolution of the volatile coma composition. Aims. Our work aims at comparing the diversity of the coma of 67P/Churyumov-Gerasimenko at large heliocentric distance to study the evolution of the comet during its passage around the Sun and at trying to classify it relative to other comets. Methods. We used the Double Focussing Mass Spectrometer (DFMS) of the ROSINA experiment on ESA’s Rosetta mission to determine relative abundances of major and minor volatile species. This study is restricted to species that have previously been detected elsewhere. Results. We detect almost all species currently known to be present in cometary coma with ROSINA DFMS. As DFMS measured the composition locally, we cannot derive a global abundance, but we compare measurements from the summer and the winter hemisphere with known abundances from other comets. Differences between relative abundances between summer and winter hemispheres are large, which points to a possible evolution of the cometary surface. This comet appears to be very rich in CO2 and ethane. Heavy oxygenated compounds such as ethylene glycol are underabundant at 3 AU, probably due to their high sublimation temperatures, but nevertheless, their presence proves that Kuiper belt comets also contain complex organic molecules.

Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features.

Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman's correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 - 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r = 0.35, 0.43 and 0.36; manual r = 0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55-0.77 and 0.65, CI: 0.54-0.76), comparable to manually defined volumes (0.64, CI: 0.53-0.75 and 0.63, CI: 0.52-0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research.

Evaluation of an inventory for the assessment of critical incidents involving football fans

Introduction: Fan violence is a frequent occurrence in Swiss football (Bundesamt für Polizei, 2015) leading to high costs for prevention and control (Mensch & Maurer, 2014). Various theories put forward an explanation of fan violence, such as the Elaborated Social Identity Model (Drury & Reicher, 2000)and the Aggravation Mitigation Model (Hylander & Guvå, 2010). Important observations from these theories are the multi-dimensional understanding of fan violence and the Dynamics occurring in the fan group. Nevertheless, none of them deal with critical incidents (CIs) which involve a tense atmosphere combined with a higher risk of fan violence. Schumacher Dimech, Brechbühl and Seiler (2015) tackled this gap in research and explored CIs where 43 defining criteria were identified and compiled in an integrated model of CIs. The defining criteria were categorised in four higher-order themes “antecedents” (e.g. a documented history of fan rivalry), “triggers” (e.g. the arrest of a fan), “reactions” (e.g. fans masking themselves) and “consequences” (e.g. fans avoiding communication with fan social workers). Methods: An inventory based on this model is being developed including these 43 criteria. In an exploratory phase, this inventory was presented as an online questionnaire and was completed by 143 individuals. Three main questions are examined: Firstly, the individual items are tested using descriptive analyses. An item analysis is conducted to test reliability, item difficulty and discriminatory power. Secondly, the model’s four higher-order themes are tested using exploratory factor analysis (EFA). Thirdly, differences between sub -groups are explored, such as gender and age-related differences. Results: Respondents rated the items’ importance as high and the quota of incomplete responses was not systematic. Two items were removed from the inventory because of low mean or a high rate of “don’t know”-responses. EFA produced a six-factor solution grouping items into match-related factors, repressive measures, fans’ delinquent behaviour, intra-group behaviour, communication and control and inter-group factors. The item “fans consume alcohol” could not be ordered into any category but was retained since literature accentuates this factor’s influence on fan violence. Analyses examining possible differences between groups are underway. Discussion: Results exploring the adequacy of this inventory assessing defining criteria of CIs in football are promising and thus further evaluative investigation is recommended. This inventory can be used in two ways: as a standardised instrument of assessment for experts evaluating specific CIs and as an instrument for exploring differences in perception and assessment of a CI e.g. gender and age differences, differences between interest groups and stakeholders.

What’s the best method? Comparison of different short forms oft he Pathological Narcissism Inventory

Recent research emphasizes the various facets of narcissism. As a consequence, newly developed questionnaires for narcissism have a large number of subscales and items. However, for the daily use in research and practice, short measures are crucial. In this study we compare different short forms of the Pathological Narcissism Questionnaire, a 54 item measure with seven subscales. In different samples (total N>2000) we applied different theoretical models to construct short forms of approximately 20 items. In particular, we compared IRT, item-total correlation, and factor loading based short forms and versions based on content validity and random selection. In all versions the original subscale structure was preserved. Results show that the short forms all have high correlations with the original version. Furthermore, correlations with criterion validation measures were comparable. We conclude that the item number can be reduced substantially without loosing information. Pros and cons of the different reduction methods are discussed.

Evaluation of an inventory for the assessment of critical incidents involving football fans

Introduction: Fan violence is a frequent occurrence in Swiss football (Bundesamt für Polizei, 2015) leading to high costs for prevention and control (Mensch & Maurer, 2014). Various theories put forward an explanation of fan violence, such as the Elaborated Social Identity Model (Drury & Reicher, 2000) and the Aggravation Mitigation Model (Hylander & Guvå, 2010). Important observations from these theories are the multi-dimensional understanding of fan violence and the dynamics occurring in the fan group. Nevertheless, none of them deal with critical incidents (CIs) which involve a tense atmosphere combined with a higher risk of fan violence. Schumacher Dimech, Brechbühl and Seiler (2015) tackled this gap in research and explored CIs where 43 defining criteria were identified and compiled in an integrated model of CIs. The defining criteria were categorised in four higher-order themes “antecedents” (e.g. a documented history of fan rivalry), “triggers” (e.g. the arrest of a fan), “reactions” (e.g. fans masking themselves) and “consequences” (e.g. fans avoiding communication with fan social workers). Methods: An inventory based on this model is being developed including these 43 criteria. In an exploratory phase, this inventory was presented as an online questionnaire and was completed by 143 individuals. Three main questions are examined: Firstly, the individual items are tested using descriptive analyses. An item analysis is conducted to test reliability, item difficulty and discriminatory power. Secondly, the model’s four higher-order themes are tested using exploratory factor analysis (EFA). Thirdly, differences between sub-groups are explored, such as gender and agerelated differences. Results: Respondents rated the items’ importance as high and the quota of incomplete responses was not systematic. Two items were removed from the inventory because of low mean or a high rate of “don’t know”-responses. EFA produced a six-factor solution grouping items into match-related factors, repressive measures, fans’ delinquent behaviour, intra-group behaviour, communication and control and inter-group factors. The item “fans consume alcohol” could not be ordered into any category but was retained since literature accentuates this factor’s influence on fan violence. Analyses examining possible differences between groups are underway. Discussion: Results exploring the adequacy of this inventory assessing defining criteria of CIs in football are promising and thus further evaluative investigation is recommended. This inventory can be used in two ways: as a standardised instrument of assessment for experts evaluating specific CIs and as an instrument for exploring differences in perception and assessment of a CI e.g. gender and age differences, differences between interest groups and stakeholders. References: Bundesamt für Polizei. (2015). Jahresbericht 2014. Kriminalitätsbekämpfung Bund. Lage, Massnahmen und Mittel [Electronic Version]. Drury, J., & Reicher, S. (2000). Collective action and psychological change. The emergence of new social identities. British Journal of Social Psychology, 39, 579-604. Hylander, I., & Guvå, G. (2010). Misunderstanding of out-group behaviour: Different interpretations of the same crowd events among police officers and demonstrators. Nordic Psychology, 62, 25-47. Schumacher-Dimech, A., Brechbühl, A. &, Seiler, R. (2016). Dynamics of critical incidents with potentially violent outcomes involving ultra fans: an explorative study. Sport in Society. Advance online publication. doi: 10.1080/17430437.2015.1133597

Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling

Atmospheric inverse modelling has the potential to provide observation-based estimates of greenhouse gas emissions at the country scale, thereby allowing for an independent validation of national emission inventories. Here, we present a regional-scale inverse modelling study to quantify the emissions of methane (CH₄) from Switzerland, making use of the newly established CarboCount-CH measurement network and a high-resolution Lagrangian transport model. In our reference inversion, prior emissions were taken from the "bottom-up" Swiss Greenhouse Gas Inventory (SGHGI) as published by the Swiss Federal Office for the Environment in 2014 for the year 2012. Overall we estimate national CH₄ emissions to be 196 ± 18 Gg yr⁻¹ for the year 2013 (1σ uncertainty). This result is in close agreement with the recently revised SGHGI estimate of 206 ± 33 Gg yr⁻¹ as reported in 2015 for the year 2012. Results from sensitivity inversions using alternative prior emissions, uncertainty covariance settings, large-scale background mole fractions, two different inverse algorithms (Bayesian and extended Kalman filter), and two different transport models confirm the robustness and independent character of our estimate. According to the latest SGHGI estimate the main CH₄ source categories in Switzerland are agriculture (78 %), waste handling (15 %) and natural gas distribution and combustion (6 %). The spatial distribution and seasonal variability of our posterior emissions suggest an overestimation of agricultural CH₄ emissions by 10 to 20 % in the most recent SGHGI, which is likely due to an overestimation of emissions from manure handling. Urban areas do not appear as emission hotspots in our posterior results, suggesting that leakages from natural gas distribution are only a minor source of CH₄ in Switzerland. This is consistent with rather low emissions of 8.4 Gg yr⁻¹ reported by the SGHGI but inconsistent with the much higher value of 32 Gg yr⁻¹ implied by the EDGARv4.2 inventory for this sector. Increased CH₄ emissions (up to 30 % compared to the prior) were deduced for the north-eastern parts of Switzerland. This feature was common to most sensitivity inversions, which is a strong indicator that it is a real feature and not an artefact of the transport model and the inversion system. However, it was not possible to assign an unambiguous source process to the region. The observations of the CarboCount-CH network provided invaluable and independent information for the validation of the national bottom-up inventory. Similar systems need to be sustained to provide independent monitoring of future climate agreements.