dataesr/scientific-paragraphs-categorization huggingface dataset:
, , 2016)). RSP statistics of r e and k (calculated from v e using equation (  13 )) for liquid-containing clouds observed during various campaigns are given in Figure  12. Future opportunities for space-based multiangle polarimetry for cloud retrievals include the 3MI instruments on ESA's METOP series that are based on POLDER  (Marbach et al. , 2013) , as well as instruments on the Ukrainian Aerosol-UA satellite  (Milinevsky et al. , 2016). Furthermore, the Hyper Angular Rainbow Polarimeter (HARP) is a cube sat mission due to be launched in 2018 that will provide data for selected targets during its 3-month lifetime  (Martins, 2016). In addition, a version of HARP is planned to be on the NASA Plankton, Aerosol, Clouds, Ocean Ecosystem (PACE) mission to be launched after 2022. HARP will have a spatial sampling of about 4 × 4 km 2 and an angular sampling sufficient to retrieve DSDs on a pixel level.
3 • offset from the best fit position of HESS J1741-302. In that study, the authors suggested a possible association between the HE source and both the pulsars PSR B1737-30 and PSR J1739-3023 since the curved spectrum of the new HE source resembles γ-ray pulsars, although the spectral parameters could not be tightly constrained. In this paper, the analysis of a large 145-hour VHE dataset on HESS J1741-302, obtained in part due to a relative proximity to the deep observed Galactic Center, are presented with the aim to constrain the nature of this "hidden accelerator". Section 2 describes the details of VHE data analysis and presents the results. Section 3 describes a dedicated analysis of 12 CO and HI data from the interstellar medium along the line of sight toward HESS J1741-302, which has been performed to investigate the possible hadronic origin of the observed emission.
Detection and Morphological Analysis The observations of the field of view around HESS J1741-302 were carried out between 2004 and 2013, corresponding to an acceptance corrected live-time of 145 h of H. E. S. S. phase I data after the application of the quality selection criteria  (Aharonian et al. 2006 ). The data have been analyzed with the H. E. S. S. analysis package for shower reconstruction and the multivariate analysis technique  (Ohm et al. 2009 ) has been applied for providing an improved discrimination between hadrons and γ-rays. Using this dataset, HESS J1741-302 is detected with a statistical significance of 7. 8σ pre-trials  (Li & Ma 1983) , which corresponds to a 5. 4σ post-trial. In order to provide improved angular resolution and reduce contamination from the bright and nearby source HESS J1745-303  (Aharonian et al.
Pulsar Name Position   For the morphology analysis, various models, including symmetric and asymmetric 2D Gaussian functions and a combination of these, convolved with the H. E. S. S. point spread function (PSF), were fitted to the excess map by using the Sherpa fitting package  (Freeman et al. 2001 ). The H. E. S. S. PSF used in the morphology analysis was modelled as a weighted sum of three 2D Gaussian functions. The morphology models were compared using a log-likelihood ratio test (LLRT). The results of the morphology analysis are given in Table  2  along with the tested morphology models. The tested morphology models include three 2D Gaussians: one for describing the contamination coming from HESS J1745-303 (tagged as "Contamination" in Table  2 ), one for HESS J1741-302 and the other one for compensating the hotspot structure seen in the analysis residual map after fitting the previous two components (Model A).
g. Hare et al. (2016)  and  Hui et al. (2016). cm -2 s -1 and corresponding to ∼1% of the Crab Nebula flux. The integrated energy flux F γ (> 0. 4 TeV) is ∼1. 2 × 10 -12 erg cm -2 s -1. A power-law function with an exponential cut-off does not statistically improve the fit (0. 4σ from LLRT with respect to the power-law function). However, one can not exclude a cut-off in the γ-ray spectrum because of the limited statistics above 10 TeV. Since this source does not show any significant extension, a variability analysis was performed to test its potential as a binary. The integral flux is found to be constant (p-value of 0. 74 for the monthly light curve) within the H. E. S. S. dataset. Variability could be seen neither from run-wise (∼30 min. , p-value of 0. 99) nor from year-wise (p-value of 0.
We appreciate the excellent work of the technical support staff in  Berlin, Durham, Hamburg, Heidelberg, Palaiseau, Paris, Saclay , and in Namibia in the construction and operation of the equipment. This work benefited from services provided by the  H. E. S. S. Virtual Organisation , supported by the national resource providers of the  EGI Federation. This research has made use of software provided by the  Chandra X-ray Center (CXC)  in the application packages CIAO, ChIPS, and  Sherpa. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This research has made use of the ATNF pulsar catalog database (http://www. atnf. csiro. au/research/pulsar/psrcat/). The  NANTEN  project is based on the mutual agreement between  Nagoya University  and the  Carnegie Institution of Washington.
Data analysis Data from the questionnaires were coded and analysed with SPSS® (version 21). Contingency tables with Chi-Square test for independence (with Yates Continuity Correction); and Pearson-Chi-Square value were used for correlation statistics. A p-value of < 0. 05 was considered statistically significant. For all percentages presented in this article, the denominator corresponds to the number of participants who supplied an answer to the relevant question, and therefore may not correspond to the entire group of participants, as not all participants answered all the questions.
Acknowledgements The authors thank all patients, family members, and others participating in the study. We further thank:  Yusman Kamaleri PhD  at  National Support for Health Registries, South-East Region, Norway  for contributing to the preliminary analyses and interpretation of data,  Richard Xiaoran Lai ,  Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Science, University of Oslo, Norway  for help with analyses and interpretation of data;  Renaud Touraine, MD  and  The French Association of Tuberous Sclerosis  for distribution of the questionnaire in France, The French Reference Center for TSC for initiating the study,  Nils-Roe Fjørtoft  from The  Norwegian Association of Tuberous Sclerosis  for contributing to the translation and adapting the study to Norwegian,  Arnt Egil Trelsgård  from The  Norwegian Association of Tuberous Sclerosis  for pilot testing the questionnaire in Norway,  Professor Lucy Robertson  for providing final writing service.
On 19 June (not shown) Minorca was on the western edge of the plume that had shifted eastward. Figure  5  shows time series of products from the WALI from late 15 June to the end of 17 June. The high extinction areas below 2. 5 km until 13:00 16 June, are not or are weekly depolarising. Chazette et al. (2016;  see their Fig. 7 ) could infer from those data that the dominant aerosol was of marine nature around 500 m in altitude within the atmospheric boundary layer, dust above 2. 5 km, and pollution-related in between during the night of 15 to 16 June. Five LDB flights were also conducted from Minorca Island during the 28 June-2 July period. Figure  6  shows maps of the daytime mean AOD over the western Mediterranean on 29 June and 2 July. On 27-29 June, the Minorca region was impacted by turbid air masses arriving from the northwest  (Chazette et al.
22;  Chazette et al. , 2016). A BLPB flight was conducted on 2 July; the mean MODIS AOD was of 0. 15 during this period. Lidar data indicate that dust dominated between about 2 and 4. 8 km in altitude  (Chazette et al. , 2016). The aerosol in lower layers could not be typified. Two other LDB flights were conducted from the Ile du Levant during a dust event on 27-28 July. Twin BLPB flights were also performed in the upper edge of the plume. Finally, two LDB flights and one BLPB flight were conducted during a last dust event on 3-4 August. For those two events, daytime mean MSG-SEVIRI-derived AOD at 550 nm are up to about 0. 30-0. 35 off Ile du Levant (Fig. 6 ). From all those drifting balloon flights, it is possible to study the vertical extent of dust plumes and the temporal evolution of the dust particle size-segregated concentration at a given altitude during transport.
The French VOLTAIRE Labex (Laboratoire d'Excellence ANR-10-LABX-100-01) also provided a couple of LOAC instruments. The PHOTONS (http://loaphotons. univ-lille1. fr/) and AERONET (https://aeronet. gsfc. nasa. gov/) teams are acknowledged for our sun photometer calibration and data processing, respectively. Patrick Chazette and Julien Totems are acknowledged for lidar data from Minorca, and Paola Formenti for her contribution to airborne data acquisition and reference suggestions. We also thank Oleg Dubovik for his comments on the AERONET inversion limitation for large particles. Edited by: Evangelos Gerasopoulos Reviewed by: two anonymous referees.
Data availability. ChArMEx LOAC balloon and other measurement data are available on the ChArMEx database (http://mistrals. sedoo. fr/ChArMEx/). Finally, CALIOP data have been retrieved through the ICARE Data and Services Center (http://www. icare. univ-lille1. fr). The doi reference for the data is https://doi. org/10. 6096/MISTRALS-ChArMEx. 1017  (Renard, 2013).
Data on the geographic distribution of the 24 species were compiled from the European Forest Genetic Resources Programme database (EUFORGEN, http://www. euforgen. org/), from published floras, from scientific publications containing syntheses of compiled data, and from the database of the Centre for Applied Research in Agroforestry Development  (IDAF, Spain). The EUFORGEN data consisted of shapefiles defining distribution areas and the IDAF data consisted of geographical points of occurrence. Floras and other publications provided most of the distribution maps we used, in various image formats  (Aytar et al. 2011; Bohbot et al. 2005; de Bolòs & Vigo 1984 -2001; Boulos 1999; Browičz & Zielinski 1982 ; Committee for Mapping the Flora of  Europe 1972 Europe -2013;; Davis 1965 Davis -1988;; Emberger 1939; FAO 2012; Fennane 1987 Fennane , 1999;; Gounot & Schoenenberger 1966 , 1967; Lebanese Ministry of Agriculture 1965; Médail 2012; Quézel & Médail 2003; Quézel & Santa 1962 -1963; Turkish Ministry of Forests and Water Affairs 2013; Yaltırık 1984).
Due to the difficulty of gaining access rights to country-level institutional databases, raw data from national forest inventories were not used, with the exception of the data from the Algerian national forest inventory that were used to locally refine the geographical distribution of some species. Although scientific publications in the field of ecology and forestry may report on the occurrence of the targeted tree species, no systematic review of these publications was made due to the high number of references (e. g. a search in the Web of Science using Pinus halepensis as keyword yielded over 1500 journal references) and to their redundancy with existing synthesis. Information on the countries where the species are considered as native was collected from four databases: the Catalogue of Life (http://www.
catalogueoflife. org/), the EURO+MED Plantbase (http://ww2. bgbm. org/EuroPlusMed/query. asp), the Kew World Checklist (http://apps. kew. org/wcsp/home. do), and the Med-Checklist (http://ww2. bgbm. org/mcl/query. asp). Country definition in these databases does not necessary match the administrative boundaries of countries but can correspond to biogeographically important regions within countries or to groups of countries. In the Med-Checklist for example, Italy as a country was split into Sicily, Sardinia and continental Italy while Lebanon and Syria, on the contrary, were grouped together to report on native species. Building the distribution maps Maps were produced using existing digital distribution maps and digitizing by eye from published maps, hand-drawn maps on paper and other compilations.
) was often not documented. This is one of the reasons we did not use the occurrence data of the Global Biodiversity Information Facility database (GBIF, http://www. gbif. org). When inconsistencies were found, countries of native distribution were checked with botanist experts and corrections incorporated. When uncertainties remained on how to solve these inconsistencies, no correction was made. Points of occurrence were turned into areas of distribution using alpha-shapes. Alpha-shapes extend the concept of convex hull to recover the shape of a point cloud allowing this shape to be non-convex, multi-part, or with holes inside (Pateiro-López & Rodríguez-Casal 2010, Capinha & Pateiro-López 2014). Alpha-shapes depend on a parameter α that defines how wide or narrow the hull is. Specifically, two points will be allocated to the alpha-shape if there exists a circle of radius α with both points on its boundary, and which contains no other points.
Access to data and metadata description For each species, known distribution localities were compiled into a vector shapefile (ESRI format) with point geometry. All species except Cedrus atlantica and Chamaerops humilis have such a shapefile with point locations. Countries (or regions within countries) or native distribution areas were compiled into a vector shapefile with polygon geometry. All species have a shapefile of country distribution. The presumed areas of native distribution were compiled into a vector shapefile with polygon geometry. All species except Acer hyrcanum subsp. tauricolum and Juniperus drupacea have such a shapefile of distribution area. We produced 68 shapefiles totalling 31 249 geometric objects (points or polygons). Each shapefile has a table of attributes that provides details on: i) scientific name, ii) common name, iii) country where the species have been reported, iv) data source, v) additional comments (Table  2 ).
All maps are available in electronic format as shapefiles at: https://doi. org/10. 5281/zenodo. 1308577 (Wazen et al. 2018). Associated metadata are available at: http://www. fao. org/geonetwork/srv/en/metadata. show?id=56996. Maps (pdf format) showing the three geometries (localities, countries, area) of the species distribution are available as figures in this article and can be downloaded at http://www. fao. org/forestry/89249/en/. Polygons only: In some references, the area of occurrence is described with multiple species. In some cases the species of concern are mentioned as the 2nd, 3rd or 4th species; other times 2 or more species of concern appear in the same polygon and therefore are mentioned as 1st and/or 2nd and/or 3rd and/or 4th species.
Shorter values than 50 km for α were not appropriate because points of occurrence were sometimes distributed along a grid and α < 50 km failed to connect the points across this grid. Longer values than 50 km for α tended to create unrealistically wide distribution areas. As an example, Figure  1  shows the distribution area computed as the alpha-shape of points of occurrence of Acer hyrcanum subsp. tauricolum using α values ranging from 30 to 100 km. All computations were made using the R statistical environment (www. r-project. org) and the alphahull package to compute alpha-shapes (Pateiro-López & Rodríguez-Casal 2010). The maps where the point geometry of localities were converted into polygons using the alphashapes and merged with areas, are shown in Figure  2. tauricolum using α values ranging from 30 to 100 km.
We did not digitize all available published chorological maps and the works of  Meusel & Jäger (1965 -78-92), of Hultén & Fries (1986)  or of  Critchfield & Little (1966)  for example, could be added to our dataset and might refine some distribution limits. The diversity of data resolutions used for drawing the maps, however, does not make it possible to downscale the information as is possible with resources made from occurrence data from inventories such as the European atlas of forest tree species  (San-Miguel-Ayanz et al. 2016 ). Our maps are not maps of exact occurrence at all spatial scales either. Geographic data compiled from published sources are provided both as a basis of knowledge and as a basis for further discussion and questioning on the distribution of the species. During our quality check procedure (see  Assante et al.
Legal and ethical authorizations were granted by the French Research Ministry (n° DC-2008-402), the National Commission for Computerized Information and Liberties (CNIL, n° 1520735 v 0), and the local ethical committee (GNEDS). Patients suffering from non-atherosclerotic peripheral arterial disease, thrombosis or restenosis were excluded. Demographic and clinical data were collected, including age, gender, treatment, CVRF (high blood pressure, diabetes mellitus, dyslipidemia, tobacco use (active/past user) and obesity (BMI ≥ 30 kg/m 2 )), and Cockcroft creatinine clearance. Prior to surgery, blood specimens were collected for lipid balance and phospho-calcic metabolism assessments. CVRF were defined as follows. Regarding smoking, we unfortunately do not know whether smoking was still active at the sampling time.
Gene expression analysis. Samples for RNA processing were harvested and immediately snap-frozen in liquid nitrogen or stored in All-protect Tissue Reagent (Qiagen). Total RNA was extracted from 31 healthy arteries (10 CA, 11 FA, and 10 IPA) and 65 atherosclerotic arteries (27 CA, 25 FA, 13 IPA) using Macherey Nagel NucleoSpin columns (Macherey Nagel, Düren, Germany). RNA was hybridized to Agilent Human Gene Expression Microarrays. Fluorescence values corresponding to raw expression data were extracted using Feature Extraction Software (Agilent). Positive and negative control probes were removed. Non-linear effects, such as background or saturation, were corrected by Lowess against a median profile of all samples. Values of replicate probes were averaged, and the data matrix was filtered to 20,000 probes based on highest median expression values.
Microarray data have been deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE100927. Clusters of co-expressed genes were identified using the partitional clustering method k-means (k = 10)  29  on natural-log-transformed and gene-median-centered data with uncentered correlation as a similarity metric in Gene Cluster 3. 0  30. Hierarchical clustering was performed using Gene Cluster 3. 0, and heatmaps were displayed using Java Treeview  31. Clusters separating the different arterial beds were selected, and a collective p was calculated. For each sample, a mean expression value of all genes from the cluster was calculated. The hereby obtained mean values of different arterial beds were compared. This strategy, which was based on strong correlations of gene expression, allowed us to avoid multitesting.
Annotations with FDR < 0. 05 were considered significant. GO terms enriched in gene lists obtained after SAM analysis were filtered to remove redundancy, and the top scoring elements were visualized using the web-based tool REVIGO (http:// revigo. irb. hr/)  35. For atherosclerotic arteries, only GO terms with FDR = 0% were used as input in REVIGO. For healthy arteries, GO terms with FDR < 0. 05 were used. The parameters were as follows: Allowed similarity = Small (0. 5); Homo sapiens database; and Simrel as semantic similarity measure.
05 or seemed to be confounding (more than 10% change in estimate). All two-way interactions between pairs of predictors in the model were tested one at a time. p < 0. 05 was considered statistically significant when no Bonferroni correction was applied. Data were analyzed with SAS packages (SAS Institute Inc. version 9. 4, Cary, NC). CVRF and calcifications were analyzed by comparing the arterial territories (CA, FA and IPA) in pathological arteries.
Acknowledgements We thank  Carine Montagne ,  Flavien Gautron , and  Manon Pondjikli , who were in charge of bio-collection. We are most grateful to the  GenoBIRD Core Facility  for its technical support. This work was funded by an  Allocation Nationale de Recherche (ANR)  for physiopathology and by an inter-regional  Programme Hospitalier de Recherche Clinique (PHRC). T. Quillard received financial support from the  European commission (Marie Sklodowska-Curie Actions, Individual Fellowships) , the  Fondation de l' Avenir (Paris, France) , the  University Hospital of Nantes (CHU Nantes, Nantes, France) , and the  Fédération Francaise de Cardiologie (Paris, France).
e. photochemical production and downward transport from the stratosphere). Our study uses the ratio between the enhancements of O 3 and CO to characterise the Lagrangian production of ozone during transport across East Asia, derived for the first time from ozone satellite data sensitive in the LMT. First, the paper presents the datasets used in the study and a quality assessment of the IASI + GOME2 ozone observations by comparing them with in situ measurements performed by ozonesondes and also by surface stations (Sect. 2). This comparison illustrates the unprecedented capacity of IASI + GOME2 to observe from space the variability of surface ozone concentrations. Section 3 describes the regional distribution of ozone plumes and the meteorological conditions during each day of the pollution outbreak.
aeris-data. fr and http://cds-espri. ipsl. fr). Table  1. Validation of IASI + GOME2 ozone retrievals in the LMT against ozonesondes measurements from 46 stations distributed worldwide (and over East Asia in the region 25-45 • N, 110-150 • E, i. e. 3 Japanese stations) launched in 2009 and 2010, during all seasons. We account for the satellite retrieval sensitivity by smoothing ozonesonde profiles with averaging kernels of IASI + GOME2 pixels with centres collocated within ±1 • of latitude and longitude from the station launching the sonde. Ozonesonde-derived LMT ozone columns are calculated by vertical integration and compared with the average of IASI + GOME2 collocated retrievals. Biases and rms differences are given in Dobson units (DU), and percentage in parentheses. Scatter plots of these datasets are provided in Fig.
We consider in situ measurements at 10:00 Japan Local Time (JLT) and the average of collocated satellite retrievals ±1 • of latitude and longitude. We only account for coincidences with both IASI + GOME2 and IASI retrievals. Biases and rms differences are given in ppb mixing ratio and percentage in parentheses. Scatter plots of these datasets are provided in Fig. 2. A selection of the data with limited gradient (lower than 10 ppb km -1 in absolute value) of ozone between the surface and 2 km (according to CHASER analyses) is considered. mated as the root-mean-squared difference between the two datasets; see Fig. 1a ). These good results are very similar to those obtained in a first validation exercise over Europe during the summer of 2009, with practically the same correlation, precision, variability and weak bias  (Cuesta et al.
Other satellite observations In order to analyse the origin and evolution of ozone pollution plumes, the following correlative datasets are used: CO and O 3 retrievals from IASI, and NO 2 and CH 2 O observations derived from GOME-2 and OMI. Morning time (around 09:30 local time) datasets from IASI and GOME-2 are derived from the same spectra as those used in synergism by IASI + GOME2. OMI overpass occurs in the early afternoon (near 13:30 local time). The CO retrievals used in the present paper are derived from IASI radiances using the FORLI algorithm (Fast Optimal Retrievals on Layers for IASI;  Hurtmans et al. , 2012) , from the Université Libre de Bruxelles (ULB) and the Laboratoire Atmosphères, Milieux, Observations Spatiales (LAT-MOS). This approach uses pre-calculated lookup tables of absorbance cross sections at various pressures and temperatures, and an optimal estimation method for the inverse scheme.