Suicide reporting in the Swiss print media: Frequency, form and content of articles

All the Swiss newspapers were screened for the target words ‘suicide' and ‘attempted suicide' and all relevant articles were analysed for form and content over a period of 8 months. In 151 out of 208 articles suiclde/attempted suicide was the major topic. Large differences between the newspapers regarding the frequency and format of the articles (e.g. placement on front page) were found. In a few newspapers with a large circulation suicide is clearly treated as a major topic of news sensation. The majority of articles reported cases of completed suicide, mainly shooting and hanging by young persons. Attempted suicide was only reported for persons with celebrity status. This survey shows that there is reason for concern in view of the danger of imitation effect

The record of the magnetic storm on 15 May 1921 in Stará Ďala (present-day Hurbanovo) and its compliance with the global picture of this extreme event

This paper deals with the most intense magnetic storm of the 20th century, which took place on 13–15 May 1921. Part of this storm was observed in the magnetic declination and vertical intensity at Stará Ďala, currently known as Hurbanovo. However, the sensitivity of the magnetometer was not determined there in the years when the storm occurred. Here, we estimated the sensitivity scale values on the basis of data from before and after the studied event. The resulting digitized Stará Ďala’s data for 13–15 May 1921 are the main contribution of this work. The data were also put into the context of the records from other observatories. The overall picture of the geomagnetic field variations compiled from the observations by worldwide observatories, including Stará Ďala, suggests that the auroral oval got close to Stará Ďala and other European mid-latitude observatories in the morning hours on 15 May 1921.</p

Seasonal trends in concentrations and fluxes of volatile organic compounds above central London

Concentrations and fluxes of seven volatile organic compounds (VOCs) were measured between August and December 2012 at a roof-top site in central London as part of the ClearfLo project (Clean Air for London). VOC concentrations were quantified using a proton transfer reaction-mass spectrometer and fluxes were calculated using a virtual disjunct eddy covariance technique. The median VOC fluxes, including aromatics, oxygenated compounds and isoprene, ranged from 0.07 to 0.33 mg m−2 h−1 and mixing ratios were 7.27 ppb for methanol (m / z 33) and <1 ppb for the remaining compounds. Strong relationships were observed between most VOC fluxes and concentrations with traffic density, but also with photosynthetically active radiation (PAR) and temperature for the oxygenated compounds and isoprene. An estimated 50–90 % of aromatic fluxes were attributable to traffic activity, which showed little seasonal variation, suggesting boundary layer effects or possibly advected pollution may be the primary causes of increased concentrations of aromatics in winter. PAR and temperature-dependent processes accounted for the majority of isoprene, methanol and acetaldehyde fluxes and concentrations in August and September, when fluxes and concentrations were largest. Modelled biogenic isoprene fluxes using the G95 algorithm agreed well with measured fluxes in August and September, due to urban vegetation. Comparisons of estimated annual benzene emissions from the London and National Atmospheric Emissions Inventory agreed well with measured benzene fluxes. Flux footprint analysis indicated emission sources were localized and that boundary layer dynamics and source strengths were responsible for temporal and spatial VOC flux and concentration variability during the measurement period

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Geomagnetic storm dependence on the solar flare class

Content. Solar flares are often used as precursors of geomagnetic storms. In particular, Howard and Tappin (2005) recently published in A&A a dependence between X-ray class of solar flares and Ap and Dst indexes of geomagnetic storms which contradicts to early published results. Aims. We compare published results on flare-storm dependences and discuss possible sources of the discrepancy. Methods. We analyze following sources of difference: (1) different intervals of observations, (2) different statistics and (3) different methods of event identification and comparison. Results. Our analysis shows that magnitude of geomagnetic storms is likely to be independent on X-ray class of solar flares.Comment: 3 pages, 1 tabl

Extensive molecular tinkering in the evolution of the membrane attachment mode of the Rheb GTPase

Rheb is a conserved and widespread Ras-like GTPase involved in cell growth regulation mediated by the (m)TORC1 kinase complex and implicated in tumourigenesis in humans. Rheb function depends on its association with membranes via prenylated C-terminus, a mechanism shared with many other eukaryotic GTPases. Strikingly, our analysis of a phylogenetically rich sample of Rheb sequences revealed that in multiple lineages this canonical and ancestral membrane attachment mode has been variously altered. The modifications include: (1) accretion to the N-terminus of two different phosphatidylinositol 3-phosphate-binding domains, PX in Cryptista (the fusion being the first proposed synapomorphy of this clade), and FYVE in Euglenozoa and the related undescribed flagellate SRT308; (2) acquisition of lipidic modifications of the N-terminal region, namely myristoylation and/or S-palmitoylation in seven different protist lineages; (3) acquisition of S-palmitoylation in the hypervariable C-terminal region of Rheb in apusomonads, convergently to some other Ras family proteins; (4) replacement of the C-terminal prenylation motif with four transmembrane segments in a novel Rheb paralog in the SAR clade; (5) loss of an evident C-terminal membrane attachment mechanism in Tremellomycetes and some Rheb paralogs of Euglenozoa. Rheb evolution is thus surprisingly dynamic and presents a spectacular example of molecular tinkering

Canopy-scale flux measurements and bottom-up emission estimates of volatile organic compounds from a mixed oak and hornbeam forest in northern Italy

This paper reports the fluxes and mixing ratios of biogenically emitted volatile organic compounds (BVOCs) 4aEuro-m above a mixed oak and hornbeam forest in northern Italy. Fluxes of methanol, acetaldehyde, isoprene, methyl vinyl ketoneaEuro-+aEuro-methacrolein, methyl ethyl ketone and monoterpenes were obtained using both a proton-transfer-reaction mass spectrometer (PTR-MS) and a proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) together with the methods of virtual disjunct eddy covariance (using PTR-MS) and eddy covariance (using PTR-ToF-MS). Isoprene was the dominant emitted compound with a mean daytime flux of 1.9aEuro-mgaEuro-m(-2)aEuro-h(-1). Mixing ratios, recorded 4aEuro-m above the canopy, were dominated by methanol with a mean value of 6.2aEuro-ppbv over the 28-day measurement period. Comparison of isoprene fluxes calculated using the PTR-MS and PTR-ToF-MS showed very good agreement while comparison of the monoterpene fluxes suggested a slight over estimation of the flux by the PTR-MS. A basal isoprene emission rate for the forest of 1.7aEuro-mgaEuro-m(-2)aEuro-h(-1) was calculated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) isoprene emission algorithms (Guenther et al., 2006). A detailed tree-species distribution map for the site enabled the leaf-level emission of isoprene and monoterpenes recorded using gas-chromatography mass spectrometry (GC-MS) to be scaled up to produce a bottom-up canopy-scale flux. This was compared with the top-down canopy-scale flux obtained by measurements. For monoterpenes, the two estimates were closely correlated and this correlation improved when the plant-species composition in the individual flux footprint was taken into account. However, the bottom-up approach significantly underestimated the isoprene flux, compared with the top-down measurements, suggesting that the leaf-level measurements were not representative of actual emission rates.Peer reviewe

Comparison of embedded and added motor imagery training in patients after stroke: Study protocol of a randomised controlled pilot trial using a mixed methods approach

Copyright @ 2009 Schuster et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Two different approaches have been adopted when applying motor imagery (MI) to stroke patients. MI can be conducted either added to conventional physiotherapy or integrated within therapy sessions. The proposed study aims to compare the efficacy of embedded MI to an added MI intervention. Evidence from pilot studies reported in the literature suggests that both approaches can improve performance of a complex motor skill involving whole body movements, however, it remains to be demonstrated, which is the more effective one.Methods/Design: A single blinded, randomised controlled trial (RCT) with a pre-post intervention design will be carried out. The study design includes two experimental groups and a control group (CG). Both experimental groups (EG1, EG2) will receive physical practice of a clinical relevant motor task ('Going down, laying on the floor, and getting up again') over a two week intervention period: EG1 with embedded MI training, EG2 with MI training added after physiotherapy. The CG will receive standard physiotherapy intervention and an additional control intervention not related to MI.The primary study outcome is the time difference to perform the task from pre to post-intervention. Secondary outcomes include level of help needed, stages of motor task completion, degree of motor impairment, balance ability, fear of falling measure, motivation score, and motor imagery ability score. Four data collection points are proposed: twice during baseline phase, once following the intervention period, and once after a two week follow up. A nested qualitative part should add an important insight into patients' experience and attitudes towards MI. Semi-structured interviews of six to ten patients, who participate in the RCT, will be conducted to investigate patients' previous experience with MI and their expectations towards the MI intervention in the study. Patients will be interviewed prior and after the intervention period.Discussion: Results will determine whether embedded MI is superior to added MI. Findings of the semi-structured interviews will help to integrate patient's expectations of MI interventions in the design of research studies to improve practical applicability using MI as an adjunct therapy technique

Seasonality of isoprene emissions and oxidation products above the remote Amazon

The Amazon rainforest is the largest source of isoprene emissions to the atmosphere globally. Under low nitric oxide (NO) conditions (i.e. at NO mixing ratios less than about 40 pptv), isoprene reacts rapidly with hydroxyl (OH) to form isoprene-derived peroxy radicals (ISOPOO), which subsequently react with the hydroperoxyl radical (HO2) to form isoprene epoxydiols (IEPOX). IEPOX compounds are efficient precursors to the formation of secondary organic aerosols (SOA). Natural isoprene emissions, therefore, have the potential to influence cloudiness, rainfall, radiation balance and climate. Here, we present the first seasonal analysis of isoprene emissions and concentrations above the Amazon based on eddy covariance flux measurements made at a remote forest location. We reveal the forest to maintain a constant emission potential of isoprene throughout the year (6.9 mg m-2 h-1). The emission potential of isoprene is calculated by normalising the measured fluxes to a set of standard conditions (303 K and 1500 mmol m-2 s-1). During the wet season a factor of two reduction in absolute emissions was observed but this is explained entirely on the basis of meteorology and leaf area index, not by a change in isoprene emissions potential. Using an innovative analysis of the isoprene fluxes, in combination with measurements of its oxidation products and detailed chemical box-modelling, we explore whether concentrations of IEPOX follow the same seasonal cycle as the isoprene precursor. Our analysis implies that during the dry season (Sep–Jan) air pollution from regional biomass burning provides a modest increase in NO concentrations (indirectly inferred from a combination of other anthropogenic tracer measurements and box-modelling) which creates a competing oxidation pathway for ISOPOO; rather than forming IEPOX, alternative products are formed with less propensity to produce aerosol. This competition decreases IEPOX formation rates by a factor of two in the dry season compared with a scenario with no anthropogenic NO pollution, and by 30% throughout the year. The abundance of biogenic SOA precursors in the Amazon appears not to be dictated by the seasonality of natural isoprene emissions as previously thought, but is instead driven by regional anthropogenic pollution which modifies the atmospheric chemistry of isoprene

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)