Swelling of Transported Smoke from Savanna Fires over the Southeast Atlantic Ocean

We use the recently released Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Version 4.1 (V4) lidar data to study the smoke plumes transported from Southern African biomass burning areas. Significant improvements in the CALIPSO V4 Level 1 calibration and V4 Level 2 algorithms lead to a better representation of their optical properties, with the aerosol subtype improvements being particularly relevant to smoke over this area. For the first time, we show evidence of smoke particles increasing in size, evidenced in their particulate color ratios, as they are transported over the South Atlantic Ocean from the source regions over Southern Africa. We hypothesize that this is due to hygroscopic swelling of the smoke particles and is reflected in the higher relative humidity in the middle troposphere for profiles with smoke. This finding may have implications for radiative forcing estimates over this area and is also relevant to the ORACLES field mission

Swelling of Transported Smoke from Savanna fires over the Southeast Atlantic Ocean

We use the recently released Version 4 (V4) lidar data products from CALIPSO to study the smoke plumes transported from Southern African biomass burning areas. The significant improvements in CALIPSO V4 Level 1 calibration and the V4 Level 2 aerosol subtyping algorithms, the latter being particularly relevant to biomass burning smoke over this area, lead to a better representation of their optical properties. For the first time, we show evidence of smoke particles increasing in size, evidenced in their particulate color ratios, as they are transported over the South Atlantic Ocean from the source regions over Southern Africa. This is likely due to hygroscopic swelling of the smoke particles and is reflected in the higher relative humidity in the middle troposphere for profiles with smoke. This finding may have implications for radiative forcing estimates over this area and is relevant to the ORACLES field mission that is currently underway

Application of high-dimensional fuzzy <i>k</i>-means cluster analysis to CALIOP/CALIPSO version 4.1 cloud–aerosol discrimination

This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94&thinsp;% of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.</p

A New Approach for Checking and Complementing CALIPSO Lidar Calibration

We have been studying the backscatter ratio of the two CALIPSO wavelengths for 3 different targets. We are showing the ratio of integrate attenuated backscatter coefficient for cirrus clouds, ocean surface and liquid. Water clouds for one month of nightime data (left:July,right:December), Only opaque cirrus classified as randomly oriented ice[1] are used. For ocean and water clouds, only the clearest shots, determined by a threshold on integrated attenuated backscatter are used. Two things can be immediately observed: 1. A similar trend (black dotted line) is visible using all targets, the color ratio shows a tendency to be higher north and lower south for those two months. 2. The water clouds average value is around 15% lower than ocean surface and cirrus clouds. This is due to the different multiple scattering at 532 nm and 1064 nm [2] which strongly impact the water cloud retrieval. Conclusion: Different targets can be used to improve CALIPSO 1064 nm calibration accuracy. All of them show the signature of an instrumental calibration shift. Multiple scattering introduce a bias in liquid water cloud signal but it still compares very well with all other methods and should not be overlooked. The effect of multiple scattering in liquid and ice clouds will be the subject of future research. If there really is a sampling issue. Combining all methods to increase the sampling, mapping the calibration coefficient or trying to reach an orbit per orbit calibration seems an appropriate way

Initial results of secukinumab drug survival in patients with psoriasis: A multicentre daily practice cohort study

Interleukin 17-antagonist secukinumab demonstrated high efficacy for treatment of psoriasis in randomized controlled trials. However, performance in daily practice may differ from trials. Drug survival is a comprehensive outcome covering effectiveness and safety, suitable for analyses of daily practice. The aim of this study was to evaluate drug survival of secukinumab in a daily practice psoriasis cohort. Data were collected from 13 hospitals. Drug survival was analysed using Kaplan–Meier survival curves, split for reason of discontinuation. In total, 196 patients were included (83% biologic experienced). Overall, 12 and 18 months drug survival of secukinumab was 76% and 67%, respectively, and was mostly determined by ineffectiveness. There was a trend towards shorter drug survival in women and in biologic experienced patients. Thirteen percent of patients experienced at least one episode of fungal infection. This is one of the first studies of drug survival of secukinumab in patients with psoriasis treated in daily practice

Metagenomic and Metatranscriptomic Analysis of Microbial Community Structure and Gene Expression of Activated Sludge

The present study applied both metagenomic and metatranscriptomic approaches to characterize microbial structure and gene expression of an activated sludge community from a municipal wastewater treatment plant in Hong Kong. DNA and cDNA were sequenced by Illumina Hi-seq2000 at a depth of 2.4 Gbp. Taxonomic analysis by MG-RAST showed bacteria were dominant in both DNA and cDNA datasets. The taxonomic profile obtained by BLAST against SILVA SSUref database and annotation by MEGAN showed that activated sludge was dominated by Proteobacteria, Actinobacteria, Bacteroidetes, Firmicutes and Verrucomicrobia phyla in both DNA and cDNA datasets. Global gene expression annotation based on KEGG metabolism pathway displayed slight disagreement between the DNA and cDNA datasets. Further gene expression annotation focusing on nitrogen removal revealed that denitrification-related genes sequences dominated in both DNA and cDNA datasets, while nitrifying genes were also expressed in relative high levels. Specially, ammonia monooxygenase and hydroxylamine oxidase demonstrated the high cDNA/DNA ratios in the present study, indicating strong nitrification activity. Enzyme subunits gene sequences annotation discovered that subunits of ammonia monooxygenase (amoA, amoB, amoC) and hydroxylamine oxygenase had higher expression levels compared with subunits of the other enzymes genes. Taxonomic profiles of selected enzymes (ammonia monooxygenase and hydroxylamine oxygenase) showed that ammonia-oxidizing bacteria present mainly belonged to Nitrosomonas and Nitrosospira species and no ammonia-oxidizing Archaea sequences were detected in both DNA and cDNA datasets

Nicotiana benthamiana as a Production Platform for Artemisinin Precursors

Background Production of pharmaceuticals in plants provides an alternative for chemical synthesis, fermentation or natural sources. Nicotiana benthamiana is deployed at commercial scale for production of therapeutic proteins. Here the potential of this plant is explored for rapid production of precursors of artemisinin, a sesquiterpenoid compound that is used for malaria treatment. Methodology/Principal Findings Biosynthetic genes leading to artemisinic acid, a precursor of artemisinin, were combined and expressed in N. benthamiana by agro-infiltration. The first committed precursor of artemisinin, amorpha-4,11-diene, was produced upon infiltration of a construct containing amorpha-4,11-diene synthase, accompanied by 3-hydroxy-3-methylglutaryl-CoA reductase and farnesyl diphosphate synthase. Amorpha-4,11-diene was detected both in extracts and in the headspace of the N. benthamiana leaves. When the amorphadiene oxidase CYP71AV1 was co-infiltrated with the amorphadiene-synthesizing construct, the amorpha-4,11-diene levels strongly decreased, suggesting it was oxidized. Surprisingly, no anticipated oxidation products, such as artemisinic acid, were detected upon GC-MS analysis. However, analysis of leaf extracts with a non-targeted metabolomics approach, using LC-QTOF-MS, revealed the presence of another compound, which was identified as artemisinic acid-12-ß-diglucoside. This compound accumulated to 39.5 mg.kg-1 fwt. Apparently the product of the heterologous pathway that was introduced, artemisinic acid, is further metabolized efficiently by glycosyl transferases that are endogenous to N. benthamiana. Conclusion/Significance This work shows that agroinfiltration of N. bentamiana can be used as a model to study the production of sesquiterpenoid pharmaceutical compounds. The interaction between the ectopically introduced pathway and the endogenous metabolism of the plant is discussed

A Protein-Protein Interaction Map of the Trypanosoma brucei Paraflagellar Rod

We have conducted a protein interaction study of components within a specific sub-compartment of a eukaryotic flagellum. The trypanosome flagellum contains a para-crystalline extra-axonemal structure termed the paraflagellar rod (PFR) with around forty identified components. We have used a Gateway cloning approach coupled with yeast two-hybrid, RNAi and 2D DiGE to define a protein-protein interaction network taking place in this structure. We define two clusters of interactions; the first being characterised by two proteins with a shared domain which is not sufficient for maintaining the interaction. The other cohort is populated by eight proteins, a number of which possess a PFR domain and sub-populations of this network exhibit dependency relationships. Finally, we provide clues as to the structural organisation of the PFR at the molecular level. This multi-strand approach shows that protein interactome data can be generated for insoluble protein complexes

The Regulation of Photosynthetic Structure and Function during Nitrogen Deprivation in Chlamydomonas reinhardtii

The accumulation of carbon storage compounds by many unicellular algae after nutrient deprivation occurs despite declines in their photosynthetic apparatus. To understand the regulation and roles of photosynthesis during this potentially bioenergetically valuable process, we analyzed photosynthetic structure and function after nitrogen deprivation in the model alga Chlamydomonas reinhardtii. Transcriptomic, proteomic, metabolite, and lipid profiling and microscopic time course data were combined with multiple measures of photosynthetic function. Levels of transcripts and proteins of photosystems I and II and most antenna genes fell with differing trajectories; thylakoid membrane lipid levels decreased, while their proportions remained similar and thylakoid membrane organization appeared to be preserved. Cellular chlorophyll (Chl) content decreased more than 2-fold within 24 h, and we conclude from transcript protein and 13C labeling rates that Chl synthesis was down-regulated both pre- and posttranslationally and that Chl levels fell because of a rapid cessation in synthesis and dilution by cellular growth rather than because of degradation. Photosynthetically driven oxygen production and the efficiency of photosystem II as well as P700+ reduction and electrochromic shift kinetics all decreased over the time course, without evidence of substantial energy overflow. The results also indicate that linear electron flow fell approximately 15% more than cyclic flow over the first 24 h. Comparing Calvin-Benson cycle transcript and enzyme levels with changes in photosynthetic 13CO2 incorporation rates also pointed to a coordinated multilevel down-regulation of photosynthetic fluxes during starch synthesis before the induction of high triacylglycerol accumulation rates

Meta-omics approaches to understand and improve wastewater treatment systems

Biological treatment of wastewaters depends on microbial processes, usually carried out by mixed microbial communities. Environmental and operational factors can affect microorganisms and/or impact microbial community function, and this has repercussion in bioreactor performance. Novel high-throughput molecular methods (metagenomics, metatranscriptomics, metaproteomics, metabolomics) are providing detailed knowledge on the microorganisms governing wastewater treatment systems and on their metabolic capabilities. The genomes of uncultured microbes with key roles in wastewater treatment plants (WWTP), such as the polyphosphate-accumulating microorganism Candidatus Accumulibacter phosphatis, the nitrite oxidizer Candidatus Nitrospira defluvii or the anammox bacterium Candidatus Kuenenia stuttgartiensis are now available through metagenomic studies. Metagenomics allows to genetically characterize full-scale WWTP and provides information on the lifestyles and physiology of key microorganisms for wastewater treatment. Integrating metagenomic data of microorganisms with metatranscriptomic, metaproteomic and metabolomic information provides a better understanding of the microbial responses to perturbations or environmental variations. Data integration may allow the creation of predictive behavior models of wastewater ecosystems, which could help in an improved exploitation of microbial processes. This review discusses the impact of meta-omic approaches on the understanding of wastewater treatment processes, and the implications of these methods for the optimization and design of wastewater treatment bioreactors.Research was supported by the Spanish Ministry of Education and Science (Contract Project CTQ2007-64324 and CONSOLIDER-CSD 2007-00055) and the Regional Government of Castilla y Leon (Ref. VA038A07). Research of AJMS is supported by the European Research Council (Grant 323009