The SPARC water vapour assessment II: Profile-to-profile comparisons of stratospheric and lower mesospheric water vapour data sets obtained from satellites

This work is distributed under the Creative Commons Attribution 4.0 License. Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), profile-to-profile comparisons of stratospheric and lower mesospheric water vapour were performed by considering 33 data sets derived from satellite observations of 15 different instruments. These comparisons aimed to provide a picture of the typical biases and drifts in the observational database and to identify data-set-specific problems. The observational database typically exhibits the largest biases below 70 hPa, both in absolute and relative terms. The smallest biases are often found between 50 and 5 hPa. Typically, they range from 0.25 to 0.5 ppmv (5 % to 10 %) in this altitude region, based on the 50 % percentile over the different comparison results. Higher up, the biases increase with altitude overall but this general behaviour is accompanied by considerable variations. Characteristic values vary between 0.3 and 1 ppmv (4 % to 20 %). Obvious data-set-specific bias issues are found for a number of data sets. In our work we performed a drift analysis for data sets overlapping for a period of at least 36 months. This assessment shows a wide range of drifts among the different data sets that are statistically significant at the 2σ uncertainty level. In general, the smallest drifts are found in the altitude range between about 30 and 10 hPa. Histograms considering results from all altitudes indicate the largest occurrence for drifts between 0.05 and 0.3 ppmv decade-1. Comparisons of our drift estimates to those derived from comparisons of zonal mean time series only exhibit statistically significant differences in slightly more than 3 % of the comparisons. Hence, drift estimates from profile-to-profile and zonal mean time series comparisons are largely interchangeable. As for the biases, a number of data sets exhibit prominent drift issues. In our analyses we found that the large number of MIPAS data sets included in the assessment affects our general results as well as the bias summaries we provide for the individual data sets. This is because these data sets exhibit a relative similarity with respect to the remaining data sets, despite the fact that they are based on different measurement modes and different processors implementing different retrieval choices. Because of that, we have by default considered an aggregation of the comparison results obtained from MIPAS data sets. Results without this aggregation are provided on multiple occasions to characterise the effects due to the numerous MIPAS data sets. Among other effects, they cause a reduction of the typical biases in the observational database

Socioeconomic inequalities in food insecurity and malnutrition among under-five children: within and between-group inequalities in Zimbabwe

Background Food insecurity and malnutrition in children are pervasive public health concerns in Zimbabwe. Previous studies only identified determinants of food insecurity and malnutrition with very little efforts done in assessing related inequalities and decomposing the inequalities across household characteristics in Zimbabwe. This study explored socioeconomic inequalities trend in child health using regression decomposition approach to compare within and between group inequalities. Methods The study used Demographic Health Survey (DHS) data sets of 2010\11 and 2015. Food insecurity in under-five children was determined based on the WHO dietary diversity score. Minimum dietary diversity was defined by a cut- off point of > 4 therefore, children with at least 3 of the 13 food groups were defined as food insecure. Malnutrition was assessed using weight for age (both acute and chronic under-nutrition) Z-scores. Children whose weight-for-age Z-score below minus two standard deviations (- 2 SD) from the median were considered malnourished. Concentration curves and indices were computed to understand if malnutrition was dominant among the poor or rich. The study used the Theil index and decomposed the index by population subgroups (place of residence and socioeconomic status). Results Over the study period, malnutrition prevalence increased by 1.03 percentage points, while food insecurity prevalence decreased by 4.35 percentage points. Prevalence of malnutrition and food insecurity increased among poor rural children. Theil indices for nutrition status showed socioeconomic inequality gaps to have widened, while food security status socioeconomic inequality gaps contracted for the period under review. Conclusion The study concluded that unequal distribution of household wealth and residence status play critical roles in driving socioeconomic inequalities in child food insecurity and malnutrition. Therefore, child food insecurity and malnutrition are greatly influenced by where a child lives (rural/urban) and parental wealth

Blood/plasma secretome and microvesicles

A major but hitherto overseen component of the blood/plasma secretome is that of extracellular vesicles (EVs) which are shed from all blood cell types. These EVs are made up of microvesicles (MVs) and exosomes. MVs, 100nm-1μm in diameter, are released from the cell surface, and are a rich source of non-conventionally secreted proteins lacking a conventional signal peptide, and thus not secreted by the classical secretory pathways. Exosomes are smaller vesicles (≤100nm) having an endocytic origin and released upon multivesicular body fusion with the plasma membrane. Both vesicle types play major roles in intercellular cross talk and constitute an important component of the secretome especially in the area of biomarkers for cancer. The release of EVs, which are found in all the bodily fluids, is enhanced in cancer and a major focus of cancer proteomics is therefore targeted at EVs. The blood/plasma secretome is also a source of EVs, potentially diagnostic of infectious disease, whether from EVs released from infected cells or from the pathogens themselves. Despite the great excitement in this field, as is stated here and in other parts of this Special issue entitled: An Updated Secretome, much of the EV research, whether proteomic or functional in nature, urgently needs standardisation both in terms of nomenclature and isolation protocols. This article is part of a Special Issue entitled: An Updated Secretome

Étude de la qualité des milieux aquatiques : caractérisation des impacts écotoxiques

National audienc

The RovA regulons of Yersinia enterocolitica and Yersinia pestis are distinct: evidence that many RovA-regulated genes were acquired more recently than the core genome.

RovA is a transcriptional activator of Yersinia invasin, an outer membrane protein involved in bacterial attachment and invasion across the intestinal epithelium. In Y. enterocolitica, a rovA mutant is attenuated for virulence compared with either wild-type or inv mutant strains, indicating that RovA may regulate additional virulence factors. Here, we used microarray analysis to define the RovA regulon. Curiously, there was little overlap between the RovA regulons of Y. enterocolitica and Y. pestis despite the fact that RovA itself is highly conserved between the two species. Some of these differences are explained by the observation that a number of RovA-regulated loci in Y. enterocolitica do not have orthologues in Y. pestis and vice versa, suggesting that RovA established regulatory control over genetic material acquired after the divergence of the species. Electromobility shift assays demonstrated that 15 of these RovA-regulated loci directly interact with RovA, and 11 of these promoters had similar affinity as observed for the inv promoter. H-NS and YmoA are believed to form a transcriptional repression complex on the inv promoter, and several studies indicate that RovA and H-NS have overlapping DNA binding sites. H-NS and YmoA regulated a subset of the RovA-regulated loci. Furthermore, H-NS directly bound to 14 of the 15 promoters bound by RovA. From these data, we hypothesize that RovA generally behaves as an anti-H-NS factor to alleviate transcriptional repression in Y. enterocolitica. A number of recent studies have presented data and a model suggesting that H-NS functions as a transcriptional silencer of horizontally acquired genes. This repression can be selectively relieved by regulators such as RovA, and the observation that nearly all RovA-activated genes are repressed by H-NS is consistent with this model

Maximum growth rates and possible life strategies of different bacterioplankton groups in relation to phosphorus availability in a freshwater reservoir

We investigated net growth rates of distinct bacterioplankton groups and heterotrophic nanoflagellate (HNF) communities in relation to phosphorus availability by analysing eight in situ manipulation experiments, conducted between 1997 and 2003, in the canyon-shaped Rimov reservoir (Czech Republic). Water samples were size-fractionated and incubated in dialysis bags at the sampling site or transplanted into an area of the reservoir, which differed in phosphorus limitation (range of soluble reactive phosphorus concentrations - SRP, 0.7-96 mu g l(-1)). Using five different rRNA-targeted oligonucleotide probes, net growth rates of the probe-defined bacterial groups and HNF assemblages were estimated and related to SRP using Monod kinetics, yielding growth rate constants specific for each bacterial group. We found highly significant differences among their maximum growth rates while insignificant differences were detected in the saturation constants. However, the latter constants represent only tentative estimates mainly due to insufficient sensitivity of the method used at low in situ SRP concentrations. Interestingly, in these same experiments HNF assemblages grew significantly faster than any bacterial group studied except for a small, but abundant cluster of Betaproteobacteria (targeted by the R-BT065 probe). Potential ecological implications of different growth capabilities for possible life strategies of different bacterial phylogenetic lineages are discussed