Einflüsse der Umwelt auf Synthesefasern

To get more information on the long term behaviour of technical textiles and on related degradation processes, the degradation of different synthetic fibres (polyester, PYC-coated polyester, polypropylene, polyethylene, polyamide 6 and 6.6, m-aramide) were investigated under the combined influence of pollution gases and light. Laboratory weathering experiments were performed in a specially designed climatic chamber, using different atmospheres such as normal air, zero air and zero air with pollution gases (NO2, SO2, O3, NO2/O3) added. Outdoor exposure tests are in progress at eight sites in Switzerland each having its specific meteorological conditions and typical environmental load. The exposure sites were chosen at locations of the Swiss National Air Pollution Monitoring Network (NABEL), where concentrations of air pollutants and climatic data are registered continuously. Results of tensile tests on laboratory and outdoor exposed samples are given. Comparison is made between laboratory ageing and outdoor exposure

A Synthetic Study to Assess the Applicability of Full-Waveform Inversion to Infer Snow Stratigraphy from Upward-Looking Ground-Penetrating Radar Data

Snow stratigraphy and liquid water content are key contributing factors to avalanche formation. Upward-looking ground penetrating radar (upGPR) systems allow nondestructive monitoring of the snowpack, but deriving density and liquid water content profiles is not yet possible based on the direct analysis of the reflection response. We have investigated the feasibility of deducing these quantities using full-waveform inversion (FWI) techniques applied to upGPR data. For that purpose, we have developed a frequency-domain FWI algorithm in which we additionally took advantage of time-domain features such as the arrival times of reflected waves. Our results indicated that FWI applied to upGPR data is generally feasible. More specifically, we could show that in the case of a dry snowpack, it is possible to derive snow densities and layer thicknesses if sufficient a priori information is available. In case of a wet snowpack, in which it also needs to be inverted for the liquid water content, the algorithm might fail, even if sufficient a priori information is available, particularly in the presence of realistic noise. Finally, we have investigated the capability of FWI to resolve thin layers that play a key role in snow stability evaluation. Our simulations indicate that layers with thicknesses well below the GPR wavelengths can be identified, but in the presence of significant liquid water, the thin-layer properties may be prone to inaccuracies. These results are encouraging and motivate applications to field data, but significant issues remain to be resolved, such as the determination of the generally unknown upGPR source function and identifying the optimal number of layers in the inversion models. Furthermore, a relatively high level of prior knowledge is required to let the algorithm converge. However, we feel these are not insurmountable and the new technology has significant potential to improve field data analysis

Continuous monitoring of the temporal evolution of the snowpack using upward-looking ground penetrating radar technology

Snow stratigraphy and water percolation are key parameters in avalanche forecasting. It is, however, difficult to model or measure stratigraphy and water flow in a sloping snowpack. Numerical modeling results depend highly on the type and availability of input data and the parameterization of the physical processes. Furthermore, the sensors themselves may influence the snowpack or be destroyed due to snow gliding and avalanches. Radar technology allows non-destructive scanning of the snowpack and deducing internal snow properties. If the radar system is buried in the ground, it cannot be destroyed by avalanche impacts or snow creep. During the winter seasons 2010-2011 and 2011-2012 we recorded continuous data with upward-looking pulsed radar systems (upGPR) at two test sites. We demonstrate that it is possible to determine the snow height with an accuracy comparable to conventional snow depth measuring devices. We determined the bulk volumetric liquid water content and tracked the position of the first stable wetting front. Wet-snow avalanche activity increased, when melt water penetrated deeper into the snowpack

Long-Term Mortality after New-Onset Atrial Fibrillation in COVID-19

Background: Atrial fibrillation (AF) has been described as a common cardiovascular manifestation in patients suffering from coronavirus disease 2019 (COVID-19) and has been suggested to be a potential risk factor for a poor clinical outcome. Methods: In this observational study, all patients hospitalized due to COVID-19 in 2020 in the Cantonal Hospital of Baden were included. We assessed clinical characteristics, in-hospital outcomes as well as long-term outcomes with a mean follow-up time of 278 (±90) days. Results: Amongst 646 patients diagnosed with COVID-19 (59% male, median age: 70 (IQR: 59-80)) in 2020, a total of 177 (27.4%) patients were transferred to the intermediate/intensive care unit (IMC/ICU), and 76 (11.8%) were invasively ventilated during their hospitalization. Ninety patients (13.9%) died. A total of 116 patients (18%) showed AF on admission of which 34 (29%) had new-onset AF. Patients with COVID-19 and newly diagnosed AF were more likely to require invasive ventilation (OR: 3.5; p = 0.01) but did not encounter an increased in-hospital mortality. Moreover, AF neither increased long-term mortality nor the number of rehospitalizations during follow-up after adjusting for confounders. Conclusions: In patients suffering from COVID-19, the new-onset of AF on admission was associated with an increased risk of invasive ventilation and transfer to the IMC/ICU but did not affect in-hospital or long-term mortality

Increased risk of severe clinical course of COVID-19 in carriers of HLA-C*04:01

Background: Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, there has been increasing urgency to identify pathophysiological characteristics leading to severe clinical course in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human leukocyte antigen alleles (HLA) have been suggested as potential genetic host factors that affect individual immune response to SARS-CoV-2. We sought to evaluate this hypothesis by conducting a multicenter study using HLA sequencing. Methods: We analyzed the association between COVID-19 severity and HLAs in 435 individuals from Germany (n = 135), Spain (n = 133), Switzerland (n = 20) and the United States (n = 147), who had been enrolled from March 2020 to August 2020. This study included patients older than 18 years, diagnosed with COVID19 and representing the full spectrum of the disease. Finally, we tested our results by meta-analysing data from prior genome-wide association studies (GWAS). Findings: We describe a potential association of HLA-C*04:01 with severe clinical course of COVID-19. Carriers of HLA-C*04:01 had twice the risk of intubation when infected with SARS-CoV-2 (risk ratio 1.5 [95% CI 1.1-2.1], odds ratio 3.5 [95% CI 1.9-6.6], adjusted p-value = 0.0074). These findings are based on data from four countries and corroborated by independent results from GWAS. Our findings are biologically plausible, as HLA-C*04:01 has fewer predicted bindings sites for relevant SARS-CoV-2 peptides compared to other HLA alleles. Interpretation: HLA-C*04:01 carrier state is associated with severe clinical course in SARS-CoV-2. Our findings suggest that HLA class I alleles have a relevant role in immune defense against SARS-CoV-2. Funding: Funded by Roche Sequencing Solutions, Inc

Thermische und photochemisch induzierte intramolekulare, 1,3-dipolare Cycloadditionen von 4-Phenyl-3-(2-allylphenyl)-sydnon

The title compound 9 was synthesised in the usual way, starting from 2-allylaniline and ethyl 2-bromo-2-phenylacetate, via the nitrosaminacid 8 (Scheme 2). 9 reacts at room temperature with its potential azomethinimine-function in an intramolecular [3+2]-cycloaddition to give the tricyclic compound 11 (Scheme 2). On irradiation, 9 yields the dihydro-3H-pyrazolo[2,3-a]indole 10 which probably arises by intramolecular [3+2]-cycloaddition of the corresponding intermediate nitrilimine

Simulation of snow stratigraphy using full-waveform inversion applied to data from an upward-looking radar system

Snow stratigraphy is a key contributing factor for assessing avalanche danger, but so far only destructive methods can provide this kind of information. Furthermore, continuous monitoring of the temporal evolution of the snowpack is not possible with destructive methods. Radar technology provides information on the snowpack nondestructively and allows deriving internal snow properties from its signal response. In our previous work, we demonstrated that it is feasible to quantitatively derive snowpack properties relevant for avalanche formation and monitor their evolution in time using an upward-looking ground penetrating radar system (upGPR) that was buried in a wooden box underneath the snow. Reliable results could only be obtained for the time when the snow cover was dry. In addition, to determine some properties, we still needed additional information such as independently measured snow height or modeled snow density. Hence, the system was not yet able to provide information from avalanche starting zones, since this type of information is generally not available in avalanche-prone terrain. To fully exploit the information content of upGPR data, and thus to at least partially compensate for the lack of information, we applied full-waveform inversion (FWI) techniques. We refined the model of the snowpack by repeated forward modeling the waveforms and updating the model parameters to match it with recorded data. The forward model took into account both the effect of the snow density on the velocity of the electromagnetic wave, as well as the influence of snow wetness on the attenuation. This allowed the density and the liquid water content for each layer in the snowpack to be determined. As we conducted a measurement every 3 hours (every 30 minutes as soon as the snowpack became wet), we could also simulate the temporal evolution of the density and the liquid water profiles. The method worked without assumptions or external measurements, even when the snow cover was wet

Continuous snowpack monitoring using upward-looking ground-penetrating radar technology

Snow stratigraphy and water percolation are key contributing factors to avalanche formation. So far, only destructive methods can provide this kind of information. Radar technology allows continuous, non-destructive scanning of the snowpack so that the temporal evolution of internal properties can be followed. We installed an upward-looking ground-penetrating radar system (upGPR) at the Weissfluhjoch study site (Davos, Switzerland). During two winter seasons (2010/11 and 2011/12) we recorded data with the aim of quantitatively determining snowpack properties and their temporal evolution. We automatically derived the snow height with an accuracy of about 5 cm, tracked the settlement of internal layers (+-7 cm) and measured the amount of new snow (+-10 cm). Using external snow height measurements, we determined the bulk density with a mean error of 4.3% compared to manual measurements. Radar-derived snow water equivalent deviated from manual measurements by 5%. Furthermore, we tracked the location of the dry-to-wet transition in the snowpack until water percolated to the ground. Based on the transition and an independent snow height measurement it was possible to estimate the volumetric liquid water content and its temporal evolution. Even though we need additional information to derive some of the snow properties, our results show that it is possible to quantitatively derive snow properties with upGPR