Problems with Using the Normal Distribution – and Ways to Improve Quality and Efficiency of Data Analysis

Background: The Gaussian or normal distribution is the most established model to characterize quantitative variation of original data. Accordingly, data are summarized using the arithmetic mean and the standard deviation, by x 6 SD, or with the standard error of the mean, x 6 SEM. This, together with corresponding bars in graphical displays has become the standard to characterize variation. Methodology/Principal Findings: Here we question the adequacy of this characterization, and of the model. The published literature provides numerous examples for which such descriptions appear inappropriate because, based on the ‘‘95 % range check’’, their distributions are obviously skewed. In these cases, the symmetric characterization is a poor description and may trigger wrong conclusions. To solve the problem, it is enlightening to regard causes of variation. Multiplicative causes are by far more important than additive ones, in general, and benefit from a multiplicative (or log-) normal approach. Fortunately, quite similar to the normal, the log-normal distribution can now be handled easily and characterized at the level of the original data with the help of both, a new sign, x /, times-divide, and notation. Analogous to x 6 SD, it connects the multiplicative (or geometric) mean x * and the multiplicative standard deviation s * in the form x * x /s*, that is advantageous and recommended. Conclusions/Significance: The corresponding shift from the symmetric to the asymmetric view will substantially increas

Temporal variability and effect of environmental variables on airborne bacterial communities in an urban area of Northern Italy

Despite airborne microorganisms representing a relevant fraction of atmospheric suspended particles, only a small amount of information is currently available on their abundance and diversity and very few studies have investigated the environmental factors influencing the structure of airborne bacterial communities. In this work, we used quantitative PCR and Illumina technology to provide a thorough description of airborne bacterial communities in the urban area of Milan (Italy). Forty samples were collected in 10-day sampling sessions, with one sessionper season.Themeanbacterialabundancewasabout104 ribosomal operons perm3 of air andwas lower inwinter than in the other seasons. Communitieswere dominated by Actinobacteridae, Clostridiales, Sphingobacteriales and fewproteobacterial orders (Burkholderiales, Rhizobiales, Sphingomonadales andPseudomonadales).Chloroplastswere abundant in all samples. Ahigher abundanceof Actinobacteridae,which are typical soil-inhabiting bacteria, and a lower abundance of chloroplasts in samples collected on cold days were observed. The variation in community composition observed within seasons was comparable to that observed between seasons, thus suggesting that airborne bacterial communities showlarge temporal variability, even between consecutive days. The structure of airborne bacterial communities therefore suggests that soil and plants are the sources which contribute most to the airborne communities of Milan atmosphere, but the structure of the bacterial community seems to depend mainly on the source of bacteria that predominates in a given period of time

Santé au travail : approche moléculaire pour évaluer le risque de l'exposition aux bioaérosols sur les lieux de travail

RESUME : Dans de nombreux environnements professionnels, des travailleurs sont exposés à des bioaérosols, que ce soit des bactéries, champignons, virus ou fragments de microorganismes. Ces bioaérosols peuvent être responsables de maladies infectieuses (p.ex. légionellose), ou de maladies non infectieuses (touchant principalement les voies respiratoires). Cependant, pour une majorité des bioaérosols, les relations entre une exposition à une certaine dose et les effets sur la santé humaine sont peu connues. Ce manque de connaissances étant dû principalement à une absence de méthodes adéquates permettant de quantifier cette exposition. La real-time quantitative PCR (Q-PCR) est un outil basé sur la quantification du DNA dont le potentiel de quantification des bioaérosols dans des environnements professionnels n'a pas été exploré. Le but de ce travail est de développer une méthode de Q-PCR permettant de quantifier des bioaérosols - en particulier des bactéries - et d'appliquer ces techniques pour des mesures préventives sur les lieux de travail. Dans ce travail, la Q-PCR a été appliquée à 1a quantification de pathogènes, de groupes taxonomiques spécifiques et de la charge bactérienne totale dans des environnements de travail, stations d'épuration et élevages industriels de volailles. Nous avons montré que la Q-PCR : 1) est capable de quantifier des pathogènes difficilement cultivables si ceux-ci sont présents en concentration importante, 2) a le potentiel pour être un outil performant dans l'étude des communautés bactériennes présentes dans l'air d'environnements professionnels, 3) est aussi performante que le comptage total des bactéries par DAPI pour quantifier 1a charge bactérienne totale et est donc une alternative prometteuse aux techniques culture-dépendantes. La Q-PCR pourrait être utilisée afin d'établir des relations doses-réponses pour la charge bactérienne ; soit dans des populations de travailleurs hautement exposés (p.ex. les éleveurs de volailles), soit en exposant des cellules à des concentrations de bioaérosols mesurées par Q-PCR. ABSTRACT : Many workers are exposed to bioaerosols such as bacteria, fungi, viruses or fragments of microorganisms. These bioaerosols can be responsible of infectious (e.g. legionellosis) or non infectious diseases (mainly respiratory symptoms). However, for a majority of them, the relationship between exposure and effects on human health is not clearly established. This is mainly due to the lack of valid quantitative assessment methods. Real-time quantitative PCR (Q-PCR) is a tool based on the quantification of DNA, of which the potential for the quantification of bioaerosols in work environments has not yet been explored. The aim of this work was to develop a Q-PCR method permitting to quantify bioaerosols -mainly bacteria and to apply those techniques in occupational environments. In this work, Q-PCR was applied to the quantification of pathogens, of specific taxonomic groups and of the total bacterial load in two different occupational settings, namely wastewater treatment plants and poultry houses. We showed that Q-PCR : 1) is capable of quantifying difficult to cultivate pathogens; when they are present at high concentrations, 2) has the potential to be a useful tool for studying bacterial communities in the air of work environments, 3) is as efficient as epifluorescence for the quantification of total bacterial load, and is a promising alternative to the culture-dependent methods. Q-PCR could be used to establish doses-responses relationships for bacterial load, either in populations of highly exposed workers such as poultry farmers, or by exposing cells to concentrations of bioaerosols quantified with Q-PCR

Spatio-temporal variability of airborne bacterial communities and their correlation with particulate matter chemical composition across two urban areas

The study of spatio-temporal variability of airborne bacterial communities has recently gained importance due to the evidence that airborne bacteria are involved in atmospheric processes and can affect human health. In this work, we described the structure of airborne microbial communities in two urban areas (Milan and Venice, Northern Italy) through the sequencing, by the Illumina platform, of libraries containing the V5-V6 hypervariable regions of the 16S rRNA gene and estimated the abundance of airborne bacteria with quantitative PCR (qPCR). Airborne microbial communities were dominated by few taxa, particularly Burkholderiales and Actinomycetales, more abundant in colder seasons, and Chloroplasts, more abundant in warmer seasons. By partitioning the variation in bacterial community structure, we could assess that environmental and meteorological conditions, including variability between cities and seasons, were the major determinants of the observed variation in bacterial community structure, while chemical composition of atmospheric particulate matter (PM) had a minor contribution. Particularly, Ba, SO4 (2-) and Mg(2+) concentrations were significantly correlated with microbial community structure, but it was not possible to assess whether they simply co-varied with seasonal shifts of bacterial inputs to the atmosphere, or their variation favoured specific taxa. Both local sources of bacteria and atmospheric dispersal were involved in the assembling of airborne microbial communities, as suggested, to the one side by the large abundance of bacteria typical of lagoon environments (Rhodobacterales) observed in spring air samples from Venice and to the other by the significant effect of wind speed in shaping airborne bacterial communities at all sites