dataesr/scientific-paragraphs-categorization huggingface dataset:
Alpha diversities and community comparisons across spatial scales As the morphological survey consisted of only macrofauna, for the purposes of accurate comparisons, alpha diversity analyses were performed against molecular surveys including and excluding the 0. 5mm fraction. Both molecular datasets consistently exhibited much higher values (mostly five to ten times higher) than that of morphology, and when including only assigned MOTUs for the propose of being conservative, 18S exhibited much higher Chao1 values for Sainte Marguerite and Arradon compared to COI (Table  3  and Fig. 6 , see S5 Dataset, S6 Dataset and S7 Dataset for all diversity values). Additionally, comparisons of alpha diversity by meadow did not show strict correlation across morphological and molecular surveys (Fig. 6 , S8 Table ), with meadows having the highest species richness values differing depending on the survey method implemented.
Advantages and disadvantages of both survey approaches Advantages and disadvantages of both morphologic and molecular identification approaches have been discussed in detail recently  [24, 26, 43]. A notable advantage of morphological screenings includes the ability to view whole specimens and to differentiate between biological footprints of transient organisms and those contemporarily present into the community. Further, the details provided by morphological screenings of organisms provide better taxonomic identifications for particular lineages. Morphological inventories also provide actual species abundance counts, whereas metabarcoding datasets are still limited to presence/absence data, given that technical factors likely influence the number of times a sequence is observed more than the abundance of the corresponding taxa  [24].
The synergistic relationship between molecular and morphological identifications Molecular surveys (a combination of both genes) uncovered 36% common species in a single survey, suggesting that the other 64% were either i) not present in the sediment sampled at that particular snapshot in time, ii) were present, but the tissue contained degraded DNA, iii) were not amplified with the primers employed, or iv) were not recognized through the assignment as adequately homologous to taxa present in reference databases. We also note that the low number of common species barcodes we obtained to add to the reference databases (14 for COI and 33 for 18S) could be due to mismatch in the primers or low quality of DNA from morphologically identified specimens that had been preserved part time in formaldehyde, which may have impaired the retrieval of common taxa among inventories.
3  [15, 60, 61]. Another reason for the lack of common species uncovered could be due to the analyses of only 10g of sediment taken from the sieved samples. Though our initial goal was to identify species present in a snapshot of sediment at a given time, replicate testing of sediment within each sample for consistency may determine if more species were present in the overall sample. However, in an analysis of earthworm communities, no differences were found between distinct DNA extractions performed on the same environmental sample  [56]. Prior research described the problem of deriving accurate taxonomy using current databases, given the low reliability of publicly available taxonomic assignments past the class or family level, especially for organisms in smaller class sizes  [6, 25, 26, 43].
As our sequence barcodes were taxonomically identified prior to being added to the reference database libraries, the inability to detect some of the common species if indeed present in the sediment, is not likely due to curation issues or misidentifications in the libraries used for assignments but more likely to technical pitfalls described above (the quality of DNA or primer matching). However, given metabarcoding's reliance upon reference libraries for taxonomic assignments, our results also bring to light additional evidence for the absolute need for properly curated (i. e. taxonomically and molecularly identified) libraries to yield a higher likelihood of correct taxonomic assignment. This is the required condition to obtain inventories that are not only quantitatively superior for work on the spatial and temporal distribution of diversity, but also qualitatively reliable for understanding the taxonomic composition of communities and their evolution under differing environmental conditions.
We therefore suggest that pilot studies, including parallel morphological and molecular surveys, should be performed to i) formally test the efficiency of metabarcoding using the best protocols and genetic markers for the ecosystem studied, ii) provide a baseline for a temporal survey that could implement the blind metabarcode method once taxonomic coverage and assignment are enhanced as well as the number of replicates determined for a reliable comparison, and iii) provide molecular databases against which future blind metabarcode datasets could be compared, in order to survey the temporal evolution of a representative subset of the community. This would allow a baseline strategy with necessary parameters determined, prior to performing fully "blind" metabarcode studies. Ultimately, refining the reference dataset to target specific groups would be useful in ecosystem investigations where the goals are to provide diversity estimates and community composition to the genus or species level based on reliable taxonomic inferences.
Comparison between COI and 18S barcoding markers Results identified the18S gene as uncovering higher numbers of common species as well as greater species diversity at the Sainte Marguerite and Arradon meadows, when compared to COI. We do not suggest that 18S is a unilaterally better marker, but in this case, had a better curated reference database and likely better primer matching owing to the composition of the communities studied, which were characterized by a high prevalence of taxa having better affinity to 18S primers. Further, we consider that the shorter fragment targeted by 18S (450bp versus 710bp with COI primers) may also explain a higher success when DNA quality is limited as with environmental samples. Both COI and 18S surveys still exhibited different and somehow complementary taxonomic specific biases, which pleads for multi-gene barcoding approaches.
Community structure in morphological vs. molecular surveys A similar pattern of clustering and meadow based community differentiation obtained with both survey methods was illustrated through multidimensional analysis (Fig. 7 ). While we do find some overlap of taxa within the seagrass communities, the stronger clustering of meadows shown by the molecular survey is likely the result of the weight given to rare taxa via the implementation of presence/absence analyses. Additionally, the molecular survey did identify higher levels of biodiversity than the morphological survey, and this more complete inventory provided a more holistic picture of community structure at Z. marina meadows. Furthermore, the analyses of 18S datasets from Sainte Marguerite and Arradon in 2010 also identify community differentiation amongst these two meadows  (S2 Fig.
Acknowledgments We would like to extend our thanks to  Olivier Soubigou ,  Julien Chopelet  and  Christian Hily  for technical help and useful discussions throughout the course of this project. We also thank  Lenaick Menot  and  Ronan Becheler  for their contributions to the field sampling for the study. Finally, we would like to thank the  Rebent-Bretagne  and particularly the  Region Bretagne , the  European Regional Development Fund, Ifremer , and the  Agence de l'Eau Loire Bretagne  for funding the morphological identifications survey.
Spoligotyping All M. tuberculosis complex strains were assayed by spoligotyping using standard protocols  [10]  and following manufacturer's instructions (reagents from Ocimum Biosolution, custom MasterMix from ABgene). The presence of spacers was visualized on film as black squares after incubation with streptavidin-peroxidase and ECL chemiluminescence detection reagents (RPN 2105 Amersham, GE Healthcare Bio-sciences). The spacer hybridization patterns were translated into binary and octal format as previously described  [17]. The 43-digit binary code was converted to 15-digit octal code (base 8, having the digits 0-7)  [17]. The binary codes of the isolates were entered into the SITVIT2 database of the Pasteur Institute of Guadeloupe and assigned specific shared international spoligotype signatures (SIT) according to the SITVIT2 data base  [18].
Phylogenetic analysis The evolutionary relationships between the spoligotypes of the 121 isolates was studied by (a) a Minimum Spanning Tree (MST) constructed on all isolates, (b) a spoligoforest tree drawn as a hierarchical layout and (c) a spoligoforest tree drawn using the Fruchterman-Reingold algorithm. Both spoligoforests were drawn using the spolTools software (http://www. emi. unsw. edu. au/spoltools/) [22, 23]. The worldwide distribution of clustered spoligotypes found in this study was further investigated using the SITVIT2 database, and was recorded for regions and countries representing ≥3 % of a given SIT as compared to their total number in the SITVIT2 database. The various macro-geographical regions and subregions were defined according to the specifications of the United Nations (http://en.
Data analysis Clinical-demographic data was entered into the computer using Epidata 3. 1 software and then exported to SPSS version 21 for analysis. The spoligotyping data were digitized and analyzed with the BioNumerics software, version 5. 0 (Applied Maths, Kortrijk, Belgium). Statistical association between the dominant strain types and HIV sero-status were analyzed in a 2 × 2 contingency table using Fisher's exact test. A p value of <0. 05 was considered statistically significant. The independent T test was used to determine whether there was a statistically significant difference between the female and male mean ages.
Spoligotypes All of the 121 isolates had spoligotypes characteristic of M. tuberculosis. The spoligotype octal code outcome results were compared to the SIT and lineages/sublineages described in the SITVIT2 database. Spoligotyping revealed 52 distinct spoligotype patterns, of which 32 patterns matched a pre-existing SIT in the SITVIT2 database, whereas 4 SITs were newly created either within the present study or after a match with an orphan in the database (Table  1 ). Sixteen patterns were true orphans (Additional file 1: Table  S1 ). The T2 sublineage was predominant comprising 55 (46 %) isolates, of which 46 were T2-Uganda, characterized by the absence of spacers 33-36 and spacer 40 and 43  [24] , while 2 isolates had the controversial signature T2/LAM3. The T2 sublineage was followed in frequency by the T1 sublineage with 19 (16 %) isolates.
The Central Asian Strain (CAS) family comprised 16 (18 %) isolates, of which CAS1-Dehli was the most common with 10 isolates followed by CAS1-Kili with 5 isolates. Other sublineages were EAI5 with 3 isolates, Beijing 3, LAM11-ZWE 3, MANU3 3 and LAM1 2. Seven spoligotypes could not be assigned to any sublineage. The two predominant spoligotypes in our sample were SIT420 (T2-Uganda) with 13 (10. 7 %) and SIT53 (T1) with 11 (19. 8 %) isolates. Other significant spoligotypes identified in the sample were SIT128 (T2-Uganda) with 9 (7. 4 %) and SIT135 (T2-Uganda) with 10 (8. 3 %) isolates (Table  1 ). Sixteen isolates had no assigned SIT according to the SITVIT2 database and were true orphans (Additional file 1: Table  S1 ). None of the predominant spoligotypes SIT420, SIT53 and SIT135 was significantly associated with HIV infection.
RD analysis RD9 analysis confirmed that all isolates were M. tuberculosis, while RD4 and RD14 analysis ruled out M. bovis. RD105 analysis identified three Beijing isolates. RD724 analysis revealed 55 (46 %) isolates to be Uganda genotypes, that is all the 55 isolates with a T2 spoligotype signature, lacking spacers 33-36 and spacer 40 and/or 43. Two of the isolates, designated SIT125, which were deleted for RD724, were initially assigned to the LAM lineage according to the SITVITWEB database, although the spoligotype signature was a typical T2. Their designation in the SITVIT2 database has now been changed to T2/LAM3.
Discussion In this study of the genotypic diversity of M. tuberculosis strains causing EPTB in Uganda, EPTB was found to be caused exclusively by M. tuberculosis. The Uganda genotype was the predominant genotype comprising 46 % of the isolates. The Uganda genotype is defined by the RD724 deletion in combination with the lack of spacers 33-36 and spacer 40 and/or 43 by spoligotyping  [20, 21]. Interestingly all a Newly created SITs are marked by lowercase alphabet a. Country distribution for newly created SITs was as follows: SIT4059 a , n = 2 this study; SIT4060 a , n = 1 this study, n = 1 PRT; SIT4061 a n = 2 this study; SIT4062 a n = 1 this study, n = 1 ZMB b Lineage designations according to SITVIT2; "unknown" designates patterns with signatures that do not belong to any of the major lineages described in the database c Clustered strains correspond to a similar spoligotype pattern shared by 2 or more strains "within this study"; as opposed to unique strains harboring a spoligotype pattern that does not match with another strain from this study.
Unique strains matching a preexisting pattern in the SITVIT2 database are classified as SITs, whereas in case of no match, they are designated as "orphan" isolates of the T2 sublineage were of the Uganda genotype, indicating that the RD724 deletion may be a marker of a majority of the T2 sublineage, a matter that needs to be further explored. In a phylogenetic analysis SIT420 (T-2 Uganda), the most common spoligotype in this study was at the tip of a long descendant branch of spoligotypes belonging to the T2-Uganda sublineage, and this supports the ongoing evolution of this sublineage in Uganda. The fact that SIT420 is only reported in three other countries (USA, Tanzania and Belgium) in the SITVIT2 database supports the theory that Uganda is at the center of this evolution. Following SIT420, SIT53 was the most prevalent spoligotype.
Both spoligoforests were drawn using the spolTools software (http://www. emi. unsw. edu. au/spoltools/). Loss of spacers is represented by directed edges between nodes, and the arrowheads point to descendant spoligotypes. The heuristic used selects a single inbound edge with a maximum weight using a Zipf model. Solid black lines link patterns that are very similar, i. e. loss of one spacer only (maximum weight being 1. 0), while dashed lines represent links of weight comprised between 0. 5 and 1, and dotted lines a weight less than 0. 5 isolates compared to 46 % seen in this current study. A more recent longitudinal study of sputum isolates in central Uganda also showed predominance of the Uganda genotype of M. tuberculosis at 63 % over a period from 1992 to 2009  [29] , and in a study of sputum isolates done in Southwestern Uganda, 59.
2 % of the isolates were of Uganda genotype  [19]. In another study done on PTB and EPTB subjects in a rural central Uganda district hospital, 23 different spoligotypes were detected comprising predominantly T2 with subtypes Uganda I and Uganda II  [27] , which are both characterized by lack of hybridization to spacer 40, while in addition, strains of genotype Uganda I also lack spacer 43  [19]. The combination of spoligotyping and deletion analysis revealed one incongruence: two isolates, designated SIT125 (signature being loss of spacers 1-24, 33-36 and 40) which were deleted for RD724, were originally assigned to the LAM lineage according to the SITVIT2 database. By spoligotyping, the Euro-American lineage consists of the sublineages T, Haarlem, LAM, S and X. The spoligotype patterns of isolates from different lineages may converge by deletion of single or contiguous spacers in the direct repeat (DR) locus  [20].
Although the phenomenon of homoplasy in the DR locus leading to convergent evolution to identical spoligotypes is considered a rare event  [21] , it argues against the use of spoligotyping for establishing phylogenetic relationships within the Euro-American lineage  [30]. Indeed SIT125 with its characteristically 'abridged' profile could be linked either to T2 or LAM3 as well as an underlying S-lineage signature. It was initially classified as T2 in the SpolDB4 database and later reclassified as LAM3 in SITVITWEB, both being feasible based strictly on spoligotyping signatures. Although its definitive classification remains controversial, parallel data show its high phylogeographical specificity for Bulgaria  [31]. In that paper, a possible relatedness with the SIT34 spoligotype (prototype of the S family) was suggested.
Furthermore, an absence of LAM-specific IS6110 insertion, which suggested that SIT125 (at least those from Bulgaria) were most probably not LAM. However, a study from Brazil on RDRio  [32]  showed that although 4/5 The three letter country codes are according to http://en. wikipedia. org/wiki/ISO_3166-1_alpha-3; countrywide distribution is only shown for SITs with ≥3 % of a given SITs as compared to their total number in the SITVIT2 database. Note that FXX code designates Metropolitan France SIT125 strains were not LAM, 1/5 strain was LAM/RD-Rio. The SIT125 designation in the SITVIT2 database has now been changed to the controversial signature of T2/ LAM3, while the exact phylogenetic position of SIT125 is debated and possible homoplasy in spoligotyping defined lineages is considered. For this reason, in this current study we have chosen to assign the two SIT125 isolates to the T2 lineage.
Worldwide distribution is reported for regions with more than 3 % of a given SITs as compared to their total number in the SITVIT2 database. The definition of macro-geographical regions and sub-regions (http://unstats. un. org/unsd/methods/m49/m49regin. htm) is according to the United Nations; Regions: AFRI (Africa), AMER (Americas), ASIA (Asia), EURO (Europe), and OCE (Oceania), subdivided in: E (Eastern), M (Middle), C (Central), N (Northern), S (Southern), SE (South-Eastern), and W (Western). Furthermore, CARIB (Caribbean) belongs to Americas, while Oceania is subdivided in four sub-regions, AUST (Australasia), MEL (Melanesia), MIC (Micronesia), and POLY (Polynesia). Note that in our classification scheme, Russia has been attributed a new sub-region by itself (Northern Asia) instead of including it among rest of the Eastern Europe.
6 µm, and because large particles are unlikely to be transported over long distances. During the campaign, airborne aerosol lidar profiles were measured below or above the aircraft by the LNG instrument (lidar LEANDRE Nouvelle Génération)  (Flamant and Pelon, 1996; de Villiers et al. , 2010; Ancellet, 2014). Specifically, the LNG instrument measured aerosol optical properties at two wavelengths (532 and 1064 nm) providing information about the location of aerosol layers vertically (in our case below the aircraft). The vertical resolution of the data presented is 30 m (four-point average) and the horizontal resolution is 450 m (average of 100 lidar profiles). In this work, we use the LNG measurements to study the spatial structure of aerosol layers below the aircraft and to analyze the representation of these aerosol layers in regional chemical transport modeling.
EMEP ground-based measurements The EMEP network of ground-based measurements includes both aerosol PM 2. 5 mass and aerosol chemical composition (available online from the EMEP database -http://www. nilu. no/projects/ccc/). Stations from the EMEP network are typically outside of urban centers and are intended to represent air free of recent pollution sources. We use the EMEP measurements of PM 2. 5 , as well as chemical composition in SO = 4 , organic carbon (OC), black carbon (BC), NH + 4 , and NO - 3 to evaluate model aerosols from 1 April to 11 April 2008, using data from stations with either daily or hourly data. Stations are excluded if they have less than 75 % data coverage during this period, and OC and BC measurements are excluded because of the lack of spatial coverage of measurements (four stations for BC, five for OC).
In situ measurements during other campaigns generally indicate less nitrate and   2011 ) found (excluding sea salt and black carbon) 30 % OM, 60 % SO = 4 and 1 % NO - 3 in aerosols found in the Scandinavian marine boundary layer. This comparison with other POLARCAT data also indicates that in our simulations, nitrate aerosols might have been formed at the expense of organic matter, probably due to the lack of a SOA mechanism. The proportion of black carbon modeled in the present study is 2. 5 % in anthropogenic air masses (2. 6 % for submicron particles), and 3 % in mixed plumes (3. 1 % for submicron particles). These values are comparable with results from the study of  Brock et al. (2011) , which found on average 2. 4 % submicron mass of BC in anthropogenic plumes and 3. 5 % in fire plumes in the Alaskan Arctic during spring 2008.
, 2011; Quinn et al. , 2007). These radiative effect values do not include the impacts of cloud-aerosol interactions, which could be significant due to the extensive cloud cover in northern Scandinavia during this transport event. The indirect effect could offset the warming effect of European aerosols over snow and icecovered surfaces we have shown here. Moreover, the indi-rect aerosol effect is still uncertain, especially in the Arctic, and further work is needed to estimate its magnitude. During POLARCAT-France, the ATR-42 aircraft also sampled an intense Asian plume that was not investigated in this study, which focuses on European aerosols. The contribution of Asian sources to Arctic pollution is an active area of research, and the POLARCAT-France data set, as well as the other POLARCAT data sets, could be the basis of a focused study on the transport of such plumes to the Arctic.
Acknowledgements. This study was supported by the  French research agency ANR Climate Impacts of Short-Lived Pollutants and Methane in the Arctic (CLIMSLIP)  project and  CNRS/LEFE. We also acknowledge support from the  ANR  " Programme d'Investissements d'Avenir " (grant no. ANR-10-LABX-0018 ). Financial support for this work was provided by  Total. The  UMS SAFIRE  is acknowledged for supporting the ATR-42 aircraft deployment and for providing the aircraft meteorological data. The  POLARCAT-FRANCE project was  supported by  ANR ,  CNES ,  CNRS/INSU , and  IPEV. We thank  Jerome Brioude  ( CIRES ) for the development of FLEXPART-WRF. We thank the EDGAR team for compiling the HTAPv2 emissions (http://edgar. jrc. ec. europa. eu/htap_v2/index. php?SECURE=123). We acknowledge the E-OBS data set from the  EU-FP6  project  ENSEMBLES  (http://ensembles-eu.
Microarray and Gene Expression Analysis Five mice per group were used to carry out microarray experiments (1 week aldosterone treatment). Total RNA was prepared from ventricle samples with the RNeasy Mini Kit (Qiagen), according to the manufacturer's instructions, and its quality was assessed with the 2100 Bioanalyzer (Agilent Technologies). Microarray experiments were carried out using the mouse GE 4x44K v2 microarray kit (Agilent Technologies). Labeling of the RNAs, hybridization (1 animal per array), and scanning of the microarrays were performed according to the manufacturer's instructions. Data were analyzed using GeneSpring software (Agilent Technologies). The false discovery rate and foldchange threshold retained were 1% and ≥1. 5, respectively. Quantitative real-time polymerase chain reaction was carried out, as described previously.