Relationship between ATSR fire counts and CO vertical column densities retrieved from SCIAMACHY onboard ENVISAT

SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric ChartographY) is the first instrument to allow retrieval of CO by measuring absorption in the near infrared from reflected and scattered sunlight instead of from thermal emission. Thus, in contrast to thermal-infrared satellites (MOPITT), SCIAMACHY is highly sensitive to the lower layers of the troposphere where the sources, such as biomass burning, are located, and where the bulk of the CO is usually found. In many regions of the world, the burning of vegetation has a repeating seasonal pattern, but the amount of CO emitted from biomass burning varies considerably from place to place. Here we present a study on the relationship between fire counts and CO vertical column densities (VCD) in different regions. These results are compared with the CO VCD from MOPITT, aerosol index, and NO_2 tropospheric VCD (TVCD) from SCIAMACHY, and the coupled chemistry climate model (CCM) ECHAM5/MESSY

Satellite measurements of formaldehyde linked to shipping emissions

International shipping is recognized as a pollution source of growing importance, in particular in the remote marine boundary layer. Nitrogen dioxide originating from ship emissions has previously been detected in satellite measurements. This study presents the first satellite measurements of formaldehyde (HCHO) linked to shipping emissions as derived from observations made by the Global Ozone Monitoring Experiment (GOME) instrument. <br><br> We analyzed enhanced HCHO tropospheric columns from shipping emissions over the Indian Ocean between Sri Lanka and Sumatra. This region offers good conditions in term of plume detection with the GOME instrument as all ship tracks follow a single narrow track in the same east-west direction as used for the GOME pixel scanning. The HCHO signal alone is weak but could be clearly seen in the high-pass filtered data. The line of enhanced HCHO in the Indian Ocean as seen in the 7-year composite of cloud free GOME observations clearly coincides with the distinct ship track corridor from Sri Lanka to Indonesia. The observed mean HCHO column enhancement over this shipping route is about 2.0×10<sup>15</sup> molec/cm<sup>2</sup>. <br><br> Compared to the simultaneously observed NO<sub>2</sub> values over the shipping route, those of HCHO are substantially higher; also the HCHO peaks are found at larger distance from the ship routes. These findings indicate that direct emissions of HCHO or degradation of emitted NMHC cannot explain the observed enhanced HCHO values. One possible reason might be increased CH<sub>4</sub> degradation due to enhanced OH concentrations related to the ship emissions, but this source is probably too weak to fully explain the observed values. <br><br> The observed HCHO pattern also agrees qualitatively well with results from the coupled earth system model ECHAM5/MESSy applied to atmospheric chemistry (EMAC). However, the modelled HCHO values over the ship corridor are two times lower than in the GOME high-pass filtered data. This might indicate uncertainties in the satellite data and used emission inventories and/or that the in-plume chemistry taking place in the narrow path of the shipping lanes are not well represented at the rather coarse model resolution

Boreal forest fires in 1997 and 1998: a seasonal comparison using transport model simulations and measurement data

Forest fire emissions have a strong impact on the concentrations of trace gases and aerosols in the atmosphere. In order to quantify the influence of boreal forest fire emissions on the atmospheric composition, the fire seasons of 1997 and 1998 are compared in this paper. Fire activity in 1998 was very strong, especially over Canada and Eastern Siberia, whereas it was much weaker in 1997. According to burned area estimates the burning in 1998 was more than six times as intense as in 1997. Based on hot spot locations derived from ATSR (Along Track Scanning Radiometer) data and official burned area data, fire emissions were estimated and their transport was simulated with a Lagrangian tracer transport model. Siberian and Canadian forest fire tracers were distinguished to investigate the transport of both separately. The fire emissions were transported even over intercontinental distances. Due to the El Niño induced meteorological situation, transport from Siberia to Canada was enhanced in 1998. Siberian fire emissions were transported towards Canada and contributed concentrations more than twice as high as those due to Canada's own CO emissions by fires. In 1998 both tracers arrive at higher latitudes over Europe, which is due to a higher North Atlantic Oscillation (NAO) index in 1998. The simulated emission plumes are compared to CMDL (Climate Monitoring and Diagnostics Laboratory) CO₂ and CO data, Total Ozone Mapping Spectrometer (TOMS) aerosol index (AI) data and Global Ozone Monitoring Experiment (GOME) tropospheric NO₂ and HCHO columns. All the data show clearly enhanced signals during the burning season of 1998 compared to 1997. The results of the model simulation are in good agreement with ground-based as well as satellite-based measurements

Characterization of Antibiotic and Biocide Resistance Genes and Virulence Factors of Staphylococcus Species Associated with Bovine Mastitis in Rwanda

The present study was conducted from July to August 2018 on milk samples taken at dairy farms in the Northern Province and Kigali District of Rwanda in order to identify Staphylococcus spp. associated with bovine intramammary infection. A total of 161 staphylococcal isolates originating from quarter milk samples of 112 crossbred dairy cattle were included in the study. Antimicrobial susceptibility testing was performed and isolates were examined for the presence of various resistance genes. Staphylococcus aureus isolates were also analyzed for the presence of virulence factors, genotyped by spa typing and further phenotypically subtyped for capsule expression using Fourier Transform Infrared (FTIR) spectroscopy. Selected S. aureus were characterized using DNA microarray technology, multi-locus sequence typing (MLST) and whole-genome sequencing. All mecA-positive staphylococci were further genotyped using dru typing. In total, 14 different staphylococcal species were detected, with S. aureus being most prevalent (26.7%), followed by S. xylosus (22.4%) and S. haemolyticus (14.9%). A high number of isolates was resistant to penicillin and tetracycline. Various antimicrobial and biocide resistance genes were detected. Among S. aureus, the Panton–Valentine leukocidin (PVL) genes, as well as bovine leukocidin (LukM/LukF-P83) genes, were detected in two and three isolates, respectively, of which two also carried the toxic shock syndrome toxin gene tsst-1 bovine variant. t1236 was the predominant spa type. FTIR-based capsule serotyping revealed a high prevalence of non-encapsulated S. aureus isolates (89.5%). The majority of the selected S. aureus isolates belonged to clonal complex (CC) 97 which was determined using DNA microarray based assignment. Three new MLST sequence types were detected

NEAT: An efficient network enrichment analysis test

Background: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. Results: We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. Conclusions: NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat )

Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization

Background: The recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations. Methodology and Principal Findings: We inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets. Conclusions: The inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters

Global Monitoring of Atmospheric Trace Gases, Clouds and Aerosols from UV/vis/NIR Satellite Instruments: Currents Status and Near Future Perspectives

A new generation of UV/vis/near‐IR satellite instruments like GOME (since 1995), SCIAMACHY (since 2002), OMI (since 2004), and GOME‐2 (since 2006) allows to measure several important stratospheric and tropospheric trace gases like O_3, NO_2, OClO, HCHO, SO_2, BrO, and H_2O as well as clouds and aerosols from space. Because of its extended spectral range, the SCIAMACHY instrument also allows the retrieval of Greenhouse gases (CO_2, CH_4) and CO in the near IR. Almost all of the tropospheric trace gases are observed by these instruments for the first time. From satellite data it is possible to investigate the temporal and spatial variation. Also different sources can be characterised and quantified. The derived global distributions can serve as input and for the validation of atmospheric models. Here we give an overview on the current status of these new instruments and data products and their recent applications to various atmospheric and oceanic phenomena