dataesr/scientific-paragraphs-categorization huggingface dataset:
hargravesii were only found at the latter location. Leptocylindrus hargravesii is also much rarer in the GoN than L. danicus  [7]. Yet both species were also first observed along the North-western Atlantic coast  [7, 19] , while GenBank sequences from that area, also obtained from environmental DNA, only revealed the presence of L. aporus. Indeed, lack of observation cannot be translated in absence for plankton microbes, and this may be true for NGS data as well, despite the sequence depth allowed by this approach. Leptocylindrus convexus also seems to be widespread based on GenBank sequences, although it was not retrieved at the Roscoff sampling site. As this species can also be identified in the light microscope based on the typically convex valve shape, data on its geographic range will probably accumulate also independently from metabarcoding in future years.
The large distribution ranges of the four above-mentioned species agree with the concept that microorganisms, including prokaryotes, unicellular eukaryotes, and small multicellular organisms, are cosmopolitan, which forms the basis for the hypothesis that ''everything is everywhere, but the environment selects''  [41, 42]. However recent studies have demonstrated that microorganisms may exhibit biogeographic patterns, although the rates of the underlying processes vary more widely than for macroorganisms  [43] [44] [45]. Indeed the remaining two known species to be discussed, L. minimus and Tenuicylindrus belgicus were virtually absent from the GenBank dataset (only one sequence of the former from Roscoff) and also missing at several of the BioMarKs sites. Tenuicylindrus belgicus was absent from the Gijon (North Atlantic) and Varna (Black Sea) stations, whereas L.
minimus was only found at the Oslo Fjord, Roscoff and Black Sea stations. In the case of L. minimus, a GenBank sequence and previous observations  [7, 19, 46]  show that it is also present along the North West US coast. Leptocylindrus minimus was the only species never found in the GoN in the previous survey of the genus  [7] , and it did not show up at any of the Mediterranean Sea sites in this first NGS survey either. Therefore, the species is probably restricted to colder waters at least in Europe. Its presence in the Black Sea could seem contradictory, yet in the latter area there are other examples of cold-water relict species, while quite unequivocal drawings confirm that it was also recognised in light microscopy  [47]. Similarly, the possibly new species identified in this paper also appeared only in sequences from Baffin Bay (GenBank) and Oslo Fjord (BioMarKs).
belgicus in late summer and autumn, whereas L. minimus is not found at all. The BioMarKs V4-and V9-data collected in autumn in the GoN agree with these observations, as they show L. aporus dominating in the October 2009 sample and also reveal the presence of L. danicus/ hargravesii, L. convexus and T. belgicus on that date. The May 2010 plankton samples was expected to contain only L. danicus/ hargravesii and L. convexus but the V9 datasets also showed many sequences of L. aporus and of T. belgicus, although the latter in very low numbers. Therefore, the sequencing depth in this case seemed to compensate the marked seasonality observed for these species through microscopy and strain isolation. By contrast, in the case of Oslo Fjord, all the six known species and the new Baffin Bay taxon were detected in the autumn 2009, but only L.
Conclusions The present NGS-based DNA-metabarcoding study provides an overview of the species diversity of Leptocylindraceae and offers a glimpse into the biogeographic distribution of these species in European coastal waters. Additional information from Gen-Bank showed the presence of several of these species also at sites outside Europe. A 454-sequencing exercise of a V4 fragment revealed clearly defined clades of leptocylindracean species, low sequence diversity within these clades, and restricted distribution patterns of the individual species. Instead, more massive Illumina- sequencing of a V9 fragment revealed higher sequence variation, but the clades were less well-delineated. Yet, the V9 dataset permitted detection of species at sites where the V4 data failed to do so. Nonetheless, the number of new species detected using NGS data did not increase markedly, supporting the idea that the family Leptocylindraceae, which occupies a basal position in the phylogeny of the diatoms, is species poor.
Information from studies in different parts of the word is needed to increase the resolution of geographic patterns and to search for additional diversity. Understanding the diversity, biogeography and ecological role of protists depends on the degree of correlation between morphological and molecular characters. Morphological analysis involves extensive observation, measurement, comparison and documentation whereas estimating and interpreting species richness from the molecular data obtained through NGS of environmental samples depends on the choice made in term of molecular marker as well as on the cut-off values used for delineating OTUs. Among the variety of choices available for genetic markers selection to estimate the diversity of species in environmental samples, the widely used SSU rDNA, V4 and V9 regions provided similar taxon discrimination, but V4 produced more reliable distance trees, while V9 showed a higher detection power in the case of the target diatoms of this study.
Acknowledgments We thank the  BioMarKs -Biodiversity of Marine euKaryotes ;  EU ERA-Net program  BiodivERsA #2008-6530 -consortium for having generated the data, from sampling to NGS protocols to sequencing, and in particular  Colomban de Vargas  for overall coordination. Results of this study are within the scope of the project  MIUR-FIRB-BIODIVERSITALIA.
INTRODUCTION Since the discovery of the molecular mechanisms underlying Hutchinson-Gilford progeria syndrome (HGPS) (OMIM no. 176670), three independent clinical trials have been proposed to treat the patients affected with this accelerated aging disorder: lonafarnib (ClinicalTrials. gov identifier NCT00425607)  [1] , pravastatin and zoledronate (ClinicalTrials. gov identifier NCT00731016), and their combination (ClinicalTrials. gov identifier NCT00916747). Although some patients are currently treated with these drugs, little is known regarding each of their specific effects on different cell defects observed in progeria. In this study, we have taken advantage of the unique potential of induced pluripotent stem (iPS) cells to address that issue. Progeria is a rare genetic disorder caused by a point mutation in the LMNA gene that leads to the production and accumulation of a truncated form of lamin A called "progerin"  [2, 3].
Cell Proliferation Assay Cell proliferation analysis of HGPS MSCs was performed using the Click-iT 5-ethynyl-29-deoxyuridine (EdU) flow cytometry assay (Life Technologies, Rockville, MD, http://www. lifetech. com), according to the manufacturer's protocol. Cells were treated with 10 mM EdU for 2 hours and then fixed, permeabilized, and stained with propidium iodide. The proportion of cells in S phase was determined by magnetic affinity cell sorting flow cytometry (Miltenyi Biotec, Bergisch Gladbach, Germany, http://www. miltenyibiotec. com) using FlowJo software. The number of events analyzed for each experiment was 10,000. Three independent experiments were performed for each condition.
Flow Cytometry Cells were detached from culture plates using trypsinethylenediaminetetraacetic acid (Invitrogen) and fixed in 4% paraformaldehyde (15 minutes at room temperature). After a PBS wash, cells were stained with CD29-PE Mouse IgG1 1:6 (BD Biosciences, San Diego, CA, http://www. bdbiosciences. com), CD44-PE Mouse IgG1 1:6 (BD Biosciences), CD73-PE Mouse IgG1 1:2 (BD Biosciences), CD105-PE Mouse IgG1 1:20 (Invitrogen), and CD166-PE mouse IgG1 1:2 (BD Biosciences), diluted in PBS containing 5% fetal bovine serum (30 minutes at room temperature). Negative controls were unstained cells and isotype-control stained cells (mouse IgG1-PE). Cells were analyzed on a magnetic affinity cell sorting flow cytometry (Miltenyi Biotec) using FlowJo software. The number of events analyzed for each experiment was 10,000.
After 24 days, a final reduction in the number of cells was 700,000 times smaller compared with untreated HGPS cells. Finally, the analysis of energy metabolism was monitored through the quantification of ATP production using CellTracker Green labeling. Although comparison between WT MSCs and HGPS MSCs revealed no statistically significant difference (Fig. 2H ), pharmacological experiments highlighted differences among the treatments. In data normalized on the number of cells to avoid the bias introduced by the differential cell proliferation, ZoPra exhibited the most beneficial effect, with the amount of ATP almost doubling per cell (Fig. 4F ). Rapa had a positive although much reduced effect, with a 30% increase in ATP content per cell, whereas FTI had no effect on ATP content per cell (Fig.
ACKNOWLEDGMENTS We thank  M élody Mazon ,  Kristell Lebozec ,  Karine Girault-Triboult ,  Pauline Poydenot ,  Pauline Georges ,  Johana Tournois , and  Drs. Delphine Peric  and  Delphine Laustriat  for their part in certain experiments;  Yves Maury  and  Dr. Alexandra Benchoua  for their expertise in high-content imaging; and  Drs. Gisele Bonne ,  Walter Habeler ,  Alexandre M éjat ,  Pierre Cau ,  Odile Rigault , and  Mich èle Martin  for helpful discussions. This work was supported by the  Institut National de la Santé et de la Recherche M édicale, University Evry Val d'Essonne, Association Française contre les Myopathies , and  Genopole.
16S sequence analysis All sequences are available on MG-RAST server (MG-RAST IDs: 4539915. 3). The data were analyzed in two steps. First, CLC Genomics Workbench 3. 1 (CLC Bio, Aarhus, Denmark CLC workbench BIO) was used to trim sequences for quality, recover and remove the primers' sequences and tags (minimum average quality score: 35 for a window of 50, number of differences to the primer sequence = 0, maximum number of differences to the barcode sequence = 0, number of ambiguous base calls = 0, maximum homopolymer length = 8). In a second step, preprocessing and analysis were performed using the MOTHUR software  [27]. All datasets were checked for chimeras with the chimera slayer algorithm implemented in MOTHUR. Standardization of the different samples was done by using the zscore which calculates the normalized abundance as follows: normalized abundance = (abundance -mean) / standard deviation  [3, 28, 29 ].
An alternative method for normalization was also tested, which consisted of subsampling equal number of reads from each sample  [30]. This method greatly reduced the numbers of sequences (224 sequences per samples), but gave the same results as zscore (Figure  S2 , Figure  S3 , Table  S1 ). Therefore we preferred to keep zscore normalization to base our conclusions on a larger dataset. We used the Operational Taxonomic Unit-based method described by  Costello et al. (2009)  because it is not biased towards a predefined taxonomy. One index was retained to assess the quality of pyrosequencing: the sequence coverage index (Good's estimator). The sequence coverage index is a metric used to estimate the quality of the depth sequencing. All sequences were clustered into Operational Taxonomic Unit (OTU) using a 97% identity threshold and OTU were classified from phylum to genus using the program MOTHUR with the default settings.
For all interesting OTUs (explaining the inter-individual variation or linked to a genetic region), we extracted all the sequences classified in that OTU and used the single best BLAST hit to identify the different species. Statistical differences in the abundance of each OTU were calculated with the software for metagenomic analysis (Metastats). To visualize potential differences across host progenies in terms of mucus bacterial community structure, distances between those communities were computed using the Yue & Clayton measure of dissimilarity (Thetayc). This index developed by Yue and Clayton (2005) is a measure of dissimilarity between the structures of two communities, meaning that this calculator takes in account the abundances of each OTU. Then, Distances were represented using a dendrograms based on this index and statistical robustness of the dendrogram was determined by a Unifrac weighted test.
This test allows determining whether any of the samples have a significantly different structure than the other samples. A random (Monte Carlo) permutation test was performed to test whether or not the distance between two communities was greater than expected by chance alone. To visualize the distances between communities, we also applied a Principal Coordinate Analysis (PCoA) based on the Yue & Clayton measure of dissimilarity (Thetayc) using an eigenvectorbased approach to represent multidimensional data in as few dimensions as possible. This method allows determining which OTUs are responsible for shifting the samples along the PCoA axes by measuring the correlation of the relative abundance of each OTU clustered in genera with the axes of the PCoA dataset. Statistical differences in the abundance of each OTU was calculated to highlight which genera were the causative agents of the differentiation between groups obtained in the dendrogram based upon the TethaYC index  [31].
QTL detection QTL analysis was carried out using the [R] package J/qtl [v. 1. 3. 1, August 2012, http://churchill. jax. org/software/jqtl. shtml/]. QTL were projected on the consensus brook charr linkage map (see  Sauvage et al. , 2012)  according to the following steps. (1) A single QTL analysis was performed using the Haley-Knott (HK) regression method (10,000 permutations) to reveal which LGs were carrying QTL. The most probable position of the QTL was defined at the position giving the largest logarithm of odds (LOD) score indicating the QTL was fixed. (2) A two QTL model based upon the Haley-Knott regression was used to refine the QTL detection across the genome with a resolution of 5 cM and eventually to detect two QTL on a single LG linked to a particular trait. (3)  The best model fitting our data was used to compute the percent variance explained (PVE expressed in %) by the QTL.
Results Mucus samples were obtained from 86 F2 fish progenies (44 males and 42 females) issued from the same family. A total of 87,940 high-quality, classifiable sequences were obtained with an average number of 10226540 per sample, which were subsequently classified in OTUs  [1]. All of these sequences were successfully clustered into 9520 OTUs with 97% identity. A final trimming step was performed to focus on the most abundant OTU (which are represented by at least 10 sequences for the whole project) and result in a dataset containing 71,719 sequences (81% of the initial data set) clustered in 192 OTUs. The depth of Groups are defined with the Weighted Unifrac distance. The first and second groups are composed of closely related bacterial communities. The third group is an assemblage of dissimilar communities and is considered as an outgroup.
(EPS) Table  S1  Differentiation between the three groups of OTU calculated with an analysis of similarity (ANOSIM, statistic R). The q-values represent p-values corrected with Bonferroni and considered as significant when p,0. 05. Note: The analysis was performed on a dataset normalized by two methods: zscore and subsampling. Both analyses gave the same results, each group being significantly different from the two others. (DOC).
To illustrate how this has can be done, the European Centre for Disease Prevention and Control developed the European Environment and Epidemiology (E3) Network to help monitor environmental conditions related to infectious disease threats  (121). The initial building-block of the E3 Network is data from the Emerging Diseases in a Changing European Environment project (EDEN), a research initiative funding by the Directorate-General for Research and Innovation of the European Commission. 16  The aggregation of these data sets, and those continuously acquired from other sources, with regular outputs from advanced scientific analysis, serve as a starting point for analysis of disease risk and vulnerable regions. This approach has been used to predict the environmental suitability of malaria transmission in Greece  (124).
Malaria was eradicated from Greece in 1974, but in 2009 (and subsequent years), locally acquired cases were identified. Remotely sensed data were used to describe the environmental and climatic conditions where future transmission could happen in Greece. Sea-level altitude and the mean and annual variation of land-surface temperature, both for daytime and night-time, were predictors in this model. Defining the areas of high risk helped guide the public health responses and to integrate preparedness and response activities, including targeted epidemiological and entomological surveillance, vector control activities, and awareness rising among the general population and health workers, in the areas environmentally suitable for transmission. In another example, the E3 Network hosts a real-time model for the environmental suitability of Vibrio spp.
Study site and evaluation strategy Our study site is the experimental site of Col du Lac Blanc near the Alpe d'Huez ski resort, French Alps (Fig. 3 ). Due to the surrounding topography, the pass may be considered as a natural wind tunnel where 90 % of observed wind blows from two directions: northeast and south  (Vionnet et al. , 2013). In the following sections, we propose a first evaluation of Meso-NH/Crocus using data collected at Col du Lac Blanc during two northern blowing snow events in 2011. Their main characteristics are given in Table  1. In-situ measurements collected during both events include: (i) meteorological conditions (wind speed and direction, air temperature) at three automatic weather stations (AWS) located around the pass, (ii) vertical profile (up to 3. 5 m) of wind speed on a meteorological mast at the pass and (iii) vertical profiles of fluxes and radius of blown snow particles using three Snow Particles Counters (SPC,  Sato et al.
, 1993)  at the pass. Additionally, the evolution of snow depth was followed for the first event using data from a Terrestrial Laser Scanner (TLS,  Prokop, 2008). Snow depths were measured over an area of 0. 54 km 2 around the pass before (17 February 2011) and after (28 February 2011) the first event. Note that TLS measurements are not available for the second event. The evaluation of Meso-NH/Crocus follows two steps. Results of 1-D simulations are firstly compared with vertical profiles of radius and fluxes of blown snow particles measured by the SPC during the 22-26 February event (Sect. 5). This allows us to discuss some of the model's features. 3-D simulations of the 18-19 March event are then presented and evaluated (Sect. 6). Figure  4  describes the meteorological conditions observed at Col du Lac Blanc for the 18-19 March event.
, 2010; Schneiderbauer and Prokop, 2011). However, this map is not available for our case study as mentioned in Sect. 4. Therefore, instead of a direct comparison, we propose here a qualitative analysis in term of typical patterns of erosion and deposition around Col du Lac Blanc for northern blowing snow events. For this purpose, we used the TLS data available for the 22-26 February event. This event presents similar conditions to our case study in terms of mean wind speed and direction (Table  1 ). Schirmer et al. (2011)  have shown that individual storms arriving from the same direction produce similar patterns of snow depth changes at the Wannengrat catchment (Swiss Alps). The comparison presented in this section cannot be considered as a formal evaluation of the model ability to simulate snow redistribution but aims rather at exploring what is possible with the current model resolution of 50 m.
Meso-NH/Crocus has been evaluated in alpine terrain against data collected around the experimental site of Col du Lac Blanc (2720 m a. s. l. , French Alps). Results of 1-D simulations indicate that the vertical resolution close to the ground must be sufficient to reproduce strong gradients of blowing snow concentration and compute mass exchange between the snowpack and the atmosphere. This is achieved in our model using a SBL scheme at the interface between Crocus and Meso-NH. The double-moment scheme for the suspension layer allows the model to capture satisfactorily the vertical profile of mean particle radius and to account for its consequence on sublimation rates of blowing snow. A 22 h blowing snow event without concurrent snowfall was then selected and simulated in 3-D. At an horizontal resolution of 50 m, Meso-NH is able to capture the main features of near-surface atmospheric flow in alpine terrain including crest speed-up and the formation of recirculation zones.
Acknowledgements. We thank  Ph. Puglièse  and  H. Bellot  for support at Col du  Lac Blanc  and  S. Faroux ,  G. Tanguy  and  J. Payart  for technical help with SURFEX and Meso-NH. We also thank  E. Brun  for many meaningful scientific discussions. CNRM-GAME/CEN is part of  Labex OSUG@2020  ( ANR10 LABX56 ). This work, including the field campaign, has been partially supported by the  French INSU LEFE/IDAO programme  under the  INEV  project and the  French-Austrian cooperation programme AMADEUS. In addition, we thank  M. Lehning ,  A. Winstral ,  R. Mott  and one anonymous reviewer for providing many helpful comments. Edited by:  M. Schneebeli The publication of this article is financed by  CNRS-INSU.
Predicted Cross Section The expected σ tt has been calculated to NNLO for all production channels, namely the allfermionic scattering modes (qq, qq , qq , qq → tt + X)  [36, 37] , the reaction qg → tt + X  [38] , and the dominant process gg → tt + X  [11]. In the present analysis, these calculations are used as implemented in the program TOP++ 2. 0  [39]. Soft-gluon resummation is performed at nextto-next-leading-log (NNLL) accuracy  [40, 41]. The scales µ R and µ F are set to m pole t. In order to evaluate the theoretical uncertainty of the fixed-order calculation, the missing contributions from higher orders are estimated by varying µ R and µ F up and down by a factor of 2 independently, while using the restriction 0. 5 ≤ µ F /µ R ≤ 2. These choices for the central scale and the variation procedure were suggested by the authors of the NNLO calculations and used for earlier σ tt predictions as well  [42].
8. The data were collected as part of the Second OECD Waiting Time Project in 2011-2012. Waiting times were collected for the following common elective (non-emergency) surgical procedures: hip replacement, knee replacement, cataract surgery, vaginal hysterectomy, prostatectomy, cholecystectomy, inguinal and femoral hernia, percutaneous transluminal coronary angioplasty (PTCA) and coronary bypass. To classify surgical procedures, we used ICD-9-CM codes as a reference (see Appendix). Data were collected from 1999 to 2011. For a subset of measures data are available also for 2012. Our questionnaire asked countries to provide waiting times according to four different definitions: 1. Inpatient waiting times (from specialist addition to the list) of patients treated in a given year (publicly funded patients) 10.