dataesr/scientific-paragraphs-categorization huggingface dataset:
The overlap of the red (M1) and blue (lagoon) circles denotes the amount of transcripts present in both locations during the respective phase. The diagrams were reduced to show only major nodes and thus raise no claim to completeness. Yet, each node contains the information from all its children nodes, also those not shown. Archaea are scarcely represented in the current RefSeq protein database, thus their transcript abundances are underestimated here. on day 8 (from 8 to 26 %) and in the lagoon on day 16 (from 5 to 28 %;  Pfreundt et al. , 2016).
Specific analysis of relevant transcripts The 100 most highly expressed nonribosomal transcripts, as identified by highest mean expression in all samples, are presented in the Supplementary Table  S1. Of them, 24 could not be classified with the NCBI nucleotide or protein databases and remain unknown. The most abundant transcript overall, both inside M1 and outside, was the non-protein-coding RNA (ncRNA) Yfr103, discussed in Sect. 3. 4. All classified transcripts on the top 9 ranks plus 28 additional transcripts in M1 were related to Synechococcus and encoded mainly photosynthetic proteins or represented ncRNAs. The transcripts following on ranks 10-12 in M1 plus five additional transcripts were affiliated with the recently defined new class of marine Euryarchaeota Candidatus Thalassoarchaea  (Martin-Cuadrado et al.
Gene expression relevant for nitrogen assimilation To investigate gene-specific expression patterns, we analyzed genes of interest (GOIs) from specific genera. Transcripts mapping to the respective genes from different organisms were extracted, searched against NCBI's nonredundant protein database, and the hits were manually curated. This analysis was only performed for the M1 samples. Genes indicative of different nitrogen utilization strategies are shown in Fig. 6. The selected GOIs were related to nitrogen fixation, nitrate and nitrite reduction, the uptake and assimilation of ammonia (transporter AmtA and glutamine synthetase, glnA gene product), and the assimilation of urea. Signal transducer PII (glnB gene product) and NtcA (nitrogen control transcription factor) were chosen as examples for the most important regulatory factors  (Forchhammer, 2008; Huergo et al.
ATCC 51142. The nifH and nifD protein sequences of the R. gibba en- Figure 6. Expression of selected genes indicative for different nitrogen acquisition strategies. TPM counts were summed per taxonomic family, the names of which are denoted to the right of each line. The maximum TPM value for each group is written below the name of that group. For plotting, values were scaled within each functional group, but not for each line, resulting in the maximum color density always representing the maximum TPM. After the name, in brackets, is additional annotation information, if deviant from the name of the functional group. The nifHDK transcript counts were summarized for each taxon to avoid possible classification biases due to multicistronic transcripts (i. e. , a multicistronic transcript might be classified as either of these depending on the best BLAST match).
Data availability All raw sequencing data can be downloaded from NCBI's Sequence Read Archive (SRA) under the accession number PRJNA304389  (Pfreundt et al. , 2015). The Supplement related to this article is available online at doi:10. 5194/bg-13-4135-2016-supplement. Author contributions. Sophie Bonnet conceived and designed the experiment. Ilana Berman-Frank took part in experimental planning, preparation, and implementation. Ulrike Pfreundt, Wolfgang R. Hess, Dina Spungin, and Ilana Berman-Frank participated in experiment and sampled. Ulrike Pfreundt analyzed samples and prepared all figures. Wolfgang R. Hess and Ulrike Pfreundt wrote the manuscript and all authors contributed and revised the manuscript.
Figure1. Flowchart describing the major steps in the bioinformatics workflow. Preprocessing of RNA-Seq reads was done separately for each data set, leading to 22 data sets of nonribosomal paired-end reads. These were binned and used as input for de-novo assembly of transcripts. The nonribosomal reads were mapped back onto the assembled transcripts with bowtie (Langmead, 2010)  to infer each transcripts abundance in each sample using RSEM (Li and Dewey, 2011). Raw abundances were used for differential expression (DE) analysis and cluster analysis with edgeR on the M1 and lagoon count matrices separately, to find transcripts which changed significantly over time. To enable direct in-between sample comparison of transcript abundances, raw abundances were converted to TPM (transcripts per kilobase million) and TMM normalized (trimmed mean of M values) in RSEM, creating the final count matrix used for all figures showing transcript abundances.
Acknowledgements. The authors thank the captain and crew of the  R/V Alis  for their great support during the cruise, to  Eyal Rahav  throughout the experimental campaign, to  Brian Haas  (The  Broad Institute ) and  Ben Fulton  ( Indiana University ) for their patient help with the Trinity de-novo transcriptome assembler,  Craig Nelson  ( University of Hawaii at Manoa ) for his valuable advice on multivariate statistics,  Björn Grüning  ( University of Freiburg ) for his immediate actions regarding tool updates and bugs on the local Galaxy server, and  Steffen Lott  ( University of Freiburg ) for his help with the visualization tool CoVennTree. The participation of  Ulrike Pfreundt ,  Wolfgang R. Hess ,  Dina Spungin , and  Ilana Berman-Frank  in the VAHINE experiment was supported by the  German-Israeli Research Foundation (GIF) , project number  1133-13.
Statistical analysis Data analysis was carried out using a software from Statistical Package for the Social Science (SPSS) version 17. 0 (USA). Chi 2 tests were used to study the characteristics of the population in terms of gender, age (transformed in two equally distributed classes) and smoking status. Based on the Kolmogorov-Smirnov test, none of the variables were normally distributed even after log 10 transformation attempts. Mann-Whitney tests were used to compare the different continuous variables between the different groups. Associations between variables were examined using the Kendall rank correlation coefficient τ. Leftcensored values below detection limits (and positivity thresholds) were substituted by half the detection limits, mainly due to the fact that non-parametric tests were used.
A multivariable model was attempted to concurrently adjust for all covariates that may predict NP scattered intensity in EBC. Moreover it was verified that the same statistical results were found when using all left-censored data. Biomarker concentrations in EBC are presented with raw data but standardization attempts, dividing EBC concentrations by EBC volume, total protein or Na concentrations, were performed to study the differences between the groups. The p value p < 0. 05 was considered statistically significant.
Observations of particles in EBC SEM-EDS explorations of EBC revealed a sparse population of particles around 500 nm that could correspond to the main peak of the DLS size distributions. Interestingly, similarly to what was observed in the air, sulphur was found on some particles in EBC (figure  3 ), as well as elements representative of a biological content such as Calcium and Potassium. Subjects with 2nd peak, n (%) 25 (  12 ) 18  (7)  2nd peak, nm 107 (41) 109 (  29 ) a Data are expressed as median (min-max).
Discussion We have found that EBC allows the detection of a particulate content in airport workers airways, even if the particulate intensity measured in EBC could not be linked to air PNC. Among the three investigated metals, Cd was significantly elevated in workers of the apron. However the detection rate of this metal in EBC was globally very low. The apron group was exposed to high concentrations, of the 10 4 -10 5 range with peak concentrations of the 10 7 range, which is elevated compared to urban concentrations. The mean measured PNC at the roadside in European cities is 3. 1 ± 1. 6 × 10 4 part cm -3  [27]. On the opposite, the administrative group was exposed to lower PNC, with values of the 10 3 -10 4 range that are reported for city backgrounds or even rural zones  [28]. However, peak concentrations of the 10 4 -10 5 range occurred episodically in the offices, when the doors of the building were open for instance (data not shown).
Accordingly, it has already been reported that EBC volume was directly dependent of the ventilation mode  [39]. In contrast, total proteins and Na concentration seemed to be independent on such intrinsic parameters and found to be correlated to one another. On the contrary to EBC volume, the same statistical differences between the groups were found when these two standardizers were used (data not shown), indicating that they might be useful standardization tools. However, this study did not allow us to conclude firmly on specific indications for the use of one standardization strategy. Protein concentration is most certainly dependent of other factors than the dilution factor, as illustrated by the significant influence of the smoking status, also reported elsewhere  [40].
Acknowledgments None of the authors have any competing interests in the manuscript. VCM, CM-D and MD take responsibility for the content of the manuscript, for the integrity of the data, and the accuracy of the data analysis, including and especially any adverse effects. LT, MK, PC, NM and IV contributed to the design of the study. MK was responsible for the acceptance of the study by the  Air France company. LT contributed substantially to the inclusion of the workers. CM-D and VCM contributed to the data analysis and writing of the manuscript. MD, LT, LL, MK, NM, IV and PC contributed substantially to the revision of the manuscript. EZ and CD were responsible for the atmospheric measurements and SEM-EDS analyses. CM-D, MD and MG-M were responsible for the ICP-MS and DLS analyses and data analysis.
Introduction Identifying the genetic basis of adaptive variation is challenging in non-model organisms. With the recent advance of next generation sequencing, we now have a facilitated access to genomic and transcriptomic datasets which often implicate a large number of candidate genes. Functional characterization is still required to pinpoint the causative genes among these candidates. Although genetic transformation methods provide the gold standard in linking genes to phenotypic effect, in systems where this remains difficult or impossible, indirect techniques such as quantitative reverse transcriptase PCR (RT-qPCR) permit the association of gene expression patterns or splicing variation with phenotypic variation  (Kunte et al. 2014). RT-qPCR is a widely used method for fast, accurate, sensitive and cost-effective gene expression quantification when candidate-genes have been identified  (Bustin 2002; Derveaux et al.
2014) , and polyA-binding protein (polyABP) gene in rat  (Vesentini et al. 2012). To find Heliconius orthologues, these sequences were blasted against the reference genome, H. melpomene primary v1. 1  (Altschul et al. 1997; Dasmahapatra et al. 2012 ). H. melpomene orthologues were then blasted against H. numata sequence. H. numata sequences are available in the Sequence Read Archive under the project number PRJNA317526. Specific DNA oligonucleotide primers were designed using sequences from H. numata coupled with H. melpomene's genome annotation to identify exonic regions (Table  1 ). To distinguish cDNA amplicons from amplicons produced from residual traces of genomic DNA, we preferentially designed primers on either side of an intron. However, eF1a, actin, and polyUBQ, presented uncertainties in position of introns, or lacked introns, so primers were designed across a single exon.
Validation of Reference Genes To evaluate whether the tested reference genes were expressed at similar relative level across different samples, we compared outputs obtained from three different tools commonly used for reference gene validation: (1) The geNorm software  (Vandesompele et al. 2002 ) calculates relative quantities from the Cq values and then gene expression stability measure (M) for a reference gene as the average pairwise variation, V, for that gene with all other tested reference genes in a given sample panel. Gene expression is considered stable when the stability parameter M is below 1. 5. The lowest M values are produced by genes with the most stable expression; stepwise exclusion of the gene with the highest M value is then used to rank the tested genes according to their expression stability  (Vandesompele et al.
Efficiency (E) qPCR efficiency was then calculated from three-point standard curve with the cDNA mix for the eight candidate reference genes. Each point is the mean value obtained from the two technical replicates. PCR efficiency ranged from 57. 1% (polyUBQ) to 110. 8% (tubulin) (Table  2 ). The PCR efficiency and the correlation coefficient R 2 , measuring how well the data fit the standard curve, reflects the linearity of the standard curve and the inhibition of PCR. Reference genes with at least 80% efficiency and R 2 > 0. 99 are generally considered suitable for further RT-qPCR analysis  (Bustin et al. 2009). We thus applied these thresholds to our candidate reference genes: the analysis of our calibration curves show that in seven out of our eight candidate reference genes, no significant inhibition of PCR was observed (Table  2 ).
Estimation of Expression Level Quantitative PCR was assayed on the seven remaining genes using SYBR Green to monitor levels of transcripts in wing tissue during the two developmental stages of different morphs of H. numata. A suitable reference gene should display a constant expression level among tested samples; therefore we used the Cq values to compare the expression levels of the tested reference genes. This reveals that the annexin gene was the least expressed, while tubulin was the most expressed. Cq values ranged from 15. 33 to 29. 01 cycles across samples. eF1a showed lowest levels of variation across the entire dataset, while RPS3A gene showed the highest level of variation (Fig. 2 ).
geNorm In our samples, six out of seven genes had an M value below 1. 0, which indicates high expression stability (Table  3 ). Comparison of pairwise variation in stability values among the seven genes indicated that RpL3 and eF1a had the two most stable expressions in wing discs of H. numata. The geNorm program highlights the minimum number of reference genes for accurate normalization by calculating the pairwise variation V n/nþ1 between two sequential normalization factors ranked by stability. This tool is used to estimate the benefit from adding a supplementary reference gene from the list  (Vandesompele et al. 2002). The lowest V value was found to be 0. 080 for V3 =4 and is lower than the cut-off value of 0. 15, suggesting that the first three reference genes are sufficient to accurately normalize gene expression data in these samples (RpL3, eF1a, and PolyABP) (Fig.
Acknowledgements This work was supported by the  French National Agency for Research (ANR)  grant DOMEVOL attributed to V. L. ( ANR-13-JSV7-0003-01 ). Molecular work was performed with the support of the  BoEM laboratory at the Museum National d'Histoire Naturelle, Paris, France. The 'Service de Systematique Moleculaire'  ( UMS7200 ) at the  Museum National d'Histoire Naturelle, Paris, France  lent us the  CFX96 Touch Real-Time PCR detection system (BIO-RAD ) for experiments.
Statistical analyses. Development time was analysed using Cox's proportional hazard mixed effect models with sex and larval diet and their interaction coded as fixed factors, and replicate as a random factor. Wing length was compared after log-transformation using a Linear Mixed Model with a Gaussian distribution (after confirming data normality). Larval diet, sex and their interaction were coded as fixed factors and replicate as a random factor. Fecundity. The proportion of gravid females was compared using a Generalized Linear Mixed Model (GLMM) with a binomial error structure. Larval diet, female wing size (after being reduced and centered), infection status and gametocyte carrier and biologically relevant interactions (wing size*infection status, larval diet*infection status, gametocyte carrier*infection status, wing size*gametocyte carrier, larval diet*gametocyte carrier) were coded as fixed factors and replicate was coded as a random factor.
Egg number and egg volume were assessed in one replicate on a data subset of gravid females only and thus analysed using General Linear Models with a quasi-Poisson error structure (to account for overdispersion) and a Gaussian distribution (after confirming data normality), respectively. In these models, larval diet, wing size, infection status and their interactions were coded as fixed factors. Longevity data were analysed using Cox's proportional hazard models with infection status, larval diet, gametocyte carrier and their interactions coded as fixed factors. Competence. Parasite prevalence and intensity were assessed using GLMMs with a binomial and a negative binomial error structure (to deal with overdispersion), respectively. Parasite intensity was assessed on a data subset that included infected females only.
We then explored how different ecological contexts may impact the relationship between nutritional regimes and outbreak size. We assumed different ratios (r) of mosquito/human densities (Nm/Nh; low: r = 1, medium: r = 10 and high: r = 100). Since we only varied the mosquito and not the human densities, we refer to mosquito densities for the sake of clarity. We explored the parameter space through a Latin Hypercube Sampling with 10,000 replicates 62 using Matlab software.
Acknowledgements This study was funded by the  ANR  grant  11-PDOC-006-01  to TL. We would like to thank all of the children and their parents who participated in this study, as well as the local authorities for their support, and  Andrea Yockey-Dejean  for proofreading the paper. We are very grateful to the  IRSS  staff in  Burkina Faso  for technical assistance.
IFT is a mechanism for flagella construction and is considered to be conserved in almost all eukarytotes (reviewed in  [73] ). The Bastin laboratory (Pasteur Institute, Paris, France) used PC trypanosome IFT knockdown mutants to specifically ask whether there are dependency relationships between the FP, FPC and flagellum. Through knocking down important IFT proteins and preventing flagellum formation they showed that a new FP and FPC can still be formed and segregated from the mother structures, but the FP is smaller and distorted and the FPC is less defined when probed with anti-TbBILBO1 antibody  [74] , (TbBILBO1 is a important protein of the FPC, and is described in detail below). After IFT knockdown, the FPC also appeared to be thinner when viewed by electron microscopy. This data indicates that there is no dependency relationship between the FP or the FPC that require the flagellum because both can be newly synthesized and localized to the expected position in the cell in the absence of a new flagellum  [74].
FPC and flagellum. Through knocking down important IFT proteins and preventing flagellum formation they showed that a new FP and FPC can still be formed and segregated from the mother structures, but the FP is smaller and distorted and the FPC is less defined when probed with anti-TbBILBO1 antibody  [74] , (TbBILBO1 is a important protein of the FPC, and is described in detail below). After IFT knockdown, the FPC also appeared to be thinner when viewed by electron microscopy. This data indicates that there is no dependency relationship between the FP or the FPC that require the flagellum because both can be newly synthesized and localized to the expected position in the cell in the absence of a new flagellum  [74].
A BLASTp analysis, using the National Center for Biotechnology (PubMed. gov) database has identified 20 orthologs of TbBILBO1. This includes genes of non-mammalian species such as Trypanosoma grayi (parasite of crocodiles), the plant pathogen Phytomonas serpens, as well as the free living kinetoplastid Bodo sultans (B. sultans), (the B. sultans FP/FPC have not been extensively studied), suggesting that BILBO1 is essential and has been retained during speciation. Most of these genes have very similar locations in the genome with regard to their respective flanking genes, indicating that TbBILBO1 gene synteny is preserved between these pathogens. Importantly, BLAST analysis has not identified TbBILBO1 orthologs in any non-trypanosome genome. Further, TbBILBO1 orthologs are present in all sequenced kinetoplastid genomes including Leishmania (a lethal parasitic protozoan that is present in many regions around the globe) and Trypanosoma cruzi (a lethal South American pathogen)  [68, 71, 76].
However, since there is a large spectrum of life cycle modifications, host specificities and subsequent morphological phenotypes in these cells, the degree A BLASTp analysis, using the National Center for Biotechnology (PubMed. gov) database has identified 20 orthologs of TbBILBO1. This includes genes of non-mammalian species such as Trypanosoma grayi (parasite of crocodiles), the plant pathogen Phytomonas serpens, as well as the free living kinetoplastid Bodo sultans (B. sultans), (the B. sultans FP/FPC have not been extensively studied), suggesting that BILBO1 is essential and has been retained during speciation. Most of these genes have very similar locations in the genome with regard to their respective flanking genes, indicating that TbBILBO1 gene synteny is preserved between these pathogens.
Background: Asthma is a disease of varying severity and differing disease mechanisms. To date, studies aimed at stratifying asthma into clinically useful phenotypes have produced a number of phenotypes that have yet to be assessed for stability and to be validated in independent cohorts. The aim of this study was to define and validate, for the first time ever, clinically driven asthma phenotypes using two independent, severe asthma cohorts: ADEPT and U-BIOPRED. Methods: Fuzzy partition-around-medoid clustering was performed on pre-specified data from the ADEPT participants (n = 156) and independently on data from a subset of U-BIOPRED asthma participants (n = 82) for whom the same variables were available. Models for cluster classification probabilities were derived and applied to the 12-month longitudinal ADEPT data and to a larger subset of the U-BIOPRED asthma dataset (n = 397).
Subsequently, in a subset of these subjects (n = 423), 15 inflammatory cellular measures and clinical variables were included, and 4 clusters were identified  [6]. In a recent SARP report, the dataset was reduced (n = 378) to participants with exhaled nitric oxide (FENO) and bronchoalveolar lavage (BAL) fluid cell counts, and included healthy controls, resulting in 6 clusters by clustering on 112 clinical, physiologic, and inflammatory variables  [7]. Patient data from the Dose Ranging Efficacy And safety with Mepolizumab (DREAM) study was clustered on clinical and biomarker variables  [8]. Four clusters were identified that could be defined by 3 predictors (blood eosinophils, airway reversibility, and body mass index). However, neither the longitudinal stability of the described clusters nor their validation in an independent asthma cohort has reported to date.