dataesr/scientific-paragraphs-categorization huggingface dataset:
Level-2A processing is applied to Top-Of-Atmosphere (TOA) Level-1C ortho-image reflectance products. Level-2A main output is the Bottom-Of-Atmosphere (BOA) corrected reflectance product. Additional outputs are an Aerosol Optical Thickness (AOT) map, a Water Vapour (WV) map and a Scene Classification (SC) map with Quality Indicators for cloud and snow probabilities. Level-2A BOA, AOT and WV outputs are calibrated and validated using ground-based data of automatic operating stations and data of in-situ campaigns. Scene classification is validated by the visual inspection of test datasets and cross-sensor comparison, supplemented by meteorological data, if available. Contributions of external in-situ campaigns would enlarge the reference dataset and enable extended validation exercise. Therefore, we are highly interested in and welcome external contributors.
The algorithm uses the reflective properties of scene features to establish the presence or absence of clouds in a scene. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters, either as input for the further processing steps below or for being valuable input for processing steps of higher levels. The Atmospheric Correction (AC) is performed using a set of Look-Up tables generated via libRadtran  (Mayer and Kylling, 2005). Baseline processing is the rural/continental aerosol type. Other Look-Up tables can also be used according to the scene geographic location and climatology. The Atmospheric Correction module is a porting and adaptation of the ATCOR software into Python.
The proper activity of fine tuning consists in manually slightly varying the SC thresholds to improve the Scene Classification based on these three different cases (easy, medium, and difficult) 5. Run of Sen2Cor processor using a new set of updated SC parameters 6. A quantitative comparison exercise performed between the results of the updated baseline and the results of the current baseline to assess the impact of the algorithm. i. e. , the absolute and relative variation of the number of pixels per class, the class origin for the new classified pixels. A calibration dataset of Sentinel-2 Level-1C products is constituted, covering different land cover types (e. g. snow, rocks, desert, urban, vegetation, grass, forest, cropland, vineyard, irrigated crops, rivers, lakes, sand, costal area, wetlands, ocean water) and different atmospheric conditions (e.
Even at the stable land surface it is required for each time step to perform groups of at least 10 scans with each instrument for the final data analysis. 3. Processing of Microtops measurements using a coupled analysis of sunphotometer and ozonometer measurements  (Pflug, 2013). First, ozonometer data are used for computation of vertical column ozone content  [cmSTP]. Actual cmSTP is used to calculate vertical column AOT-spectra. AOT spectra allow computation of the vertical column Ångstroem exponent α, which contains information about aerosol particle size respectively aerosol type. Spectral dependency of AOT given with the AOT-spectra is also used to compute vertical column water vapour content [cm precipitable water column] and to interpolate the AOT at 550 nm. 4. Direct comparison of Sen2Cor outputs with groundtruth from in-situ data and statistical analysis.
Cloud Screening & Classification Validation: The main objectives include verification of the cloud screening/masking and classification accuracy to limit L2A products and final products uncertainties. The Scene Classification Validation is limited due to the lack of "ground truth" data sets. Therefore, validation of classification map relies on the visual inspection, supplemented by the meteorological data, if available. Validation is performed using a set of test sites (Figure  4 ) representative for a large range of land cover types and weather condition (e. g. sun position, elevation, cloud cover). The Cloud Screening and Scene Classification Validation involves following steps: 1. Selection of a reference database based on a stratified random sampling, in order to guarantee statistical consistency (validity), and avoid exclusion of spatially limited classes from the validation.
2. Visual inspection of the samples using ENVI © software. In case of difficulties in class identification as for example distinction of dark water and shadow pixel, or between cloud and snow pixel, additional tools like TimeSync © software  (Cohen et al. , 2010)  and open data archive Google Earth are involved. Visual inspection is provided for following classes: no data, saturated or defective pixels, dark pixels and clouds shadows, vegetation, bare soils, water, cloud probabilities (low, medium, high), thin cirrus, and snow. Besides of existing 11 class labels, one additional label for non-classified samples is introduced. The Cloud Screening Validation is performed based on the visual inspection of original images and on-ground clouds observations, available from the meteorological stations.
Phase E2: Similarly to activities in Phase E1, the objectives are acquisition, quality assurance and archiving of Aeronet data, Microtops and surface reflectance measurements required to perform the Sen2Cor processor and products calibration and validation. Processing of the corresponding satellite data will be continued respectively Sentinel-2 data will be reprocessed with a revised radiometric calibration. As far as available, the Aeronet Level 1. 5 data will be replaced by Level 2. 0 data. Level 2. 0 Aeronet data are pre and post field calibrated, automatically cloud cleared and manually inspected. The full calibration and validation of the Sen2Cor Cloud Screening and Classification algorithm is performed using the calibration and validation datasets enlarged by new acquisitions, considering different solar/viewing angles and various atmospheric conditions.
Pre-ranking Annotators Our annotation voting exploits annotators' precision on an annotated reference set in order to weight the annotations produced by each annotator (details in 3. 3 below). It is not viable to create a reference set for each new corpus that we need to perform entity linking on. To help overcome this issue, we adopt the following approach: We have ranked the annotators for precision on two reference sets: AIDA/CONLL Test B  (Hoffart et al. , 2011) , and IITB  (Kulkarni et al. , 2009). The IITB dataset contains annotations for category Others, i. e. entities that are not a person, organization or location, whereas AIDA/CONLL B does not contain such annotations. The proportion of annotations in a corpus that fall into the Others category is a strong predictor of annotators' performance on that corpus, according to a study on how different dataset features correlate with annotators' results, available on the GERBIL platform 8   (Usbeck et al.
Datasets: The workflow was tested on four golden sets. First, the two datasets that had also been used as reference sets in order to obtain the weights to vote annotations with (see Section 3. 2). These two datasets were AIDA/CONLL B (231 documents with 4485 annotations; 1039 characters avg. , news and sports topics) and IITB (103 documents with 11245 annotations; 3879 characters avg. , topics from news, science and others). In order to test whether the annotator weights obtained from those two corpora can improve results when applied to annotator combination on other corpora, we tested on two additional datasets: MSNBC  (Cucerzan, 2007) , with 20 documents and 658 annotations (3316 characters avg. , news topics) and AQUAINT  (Milne and Witten, 2008b) , with 50 documents and 727 annotations (1415 characters avg.
, news topics). The AQUAINT dataset contains annotations for common noun entities (besides Person, Location, Organization). For this reason, according to the procedure described in 3. 2 above, its annotations were weighted according to annotators' ranking on the IITB corpus, which also contains commonnoun annotations. The MSNBC dataset does not contain common-noun annotations, so the annotator ranking for the AIDA/CONLL test-set was used in order to combine annotations in MSNBC.
Regarding snow types, we obtained somewhat higher concentrations in the old snow samples (average 15 ppb, median 3 ppb) compared to the fresh snow samples, with average and median concentrations of 5 and 1. 3 ppb. However, these differences as well as differences between the sampling sites remain questionable because they are small compared to the uncertainty of the rBC concentrations. Lower rBC concentrations were measured by  Kaspari et al. (2011)  in an ice core from the East Rongbuk Glacier at 6500 m a. s. l. close to Mt. Everest using the same analytical method. They found average concentrations of (0. 7 ± 0. 1) ppb for the period 1975 to 2000 and a maximum of 32 ppb. However, the same uncertainty in the measured rBC concentrations as for our samples due to the sample handling applies to the data reported by  Kaspari et al.
Acknowledgements. This work was supported by the  "Agence Nationale de la Recherche"  under contracts  ANR-09-CEP-005-01/PAPRIKA ,  ANR-09-CEP-005-02/PAPRIKA ,  ANR 2011 Blanc SIMI 5-6 021 04 , and by the  Investissements d'avenir Labex OSUG@2020  under grant  ANR10 LABX56. The authors thank EvK2CNR and the  Nepalese technical staff of Pyramid Laboratory , the  SHARE (Stations at High Altitude for Research on the Environment)  project, and the  Nepal Academy of Science and Technology. S. Lim  acknowledges a government scholarship by the  Korean Ministry of Education and Science Technology. Edited by:  A. Klein.
Results and Discussion Genome sequencing of M. pneumoniae strains  ). Genomic DNAs of the strains were sequenced by Illumina GAII. We obtained ~six million reads per sequenced genome, representing average genome coverage of ~1500 (2x100 per read/genome size). After filtering the initial raw reads, the genomes were mapped against the reference genomes: M. pneumoniae M129 and M. pneumoniae FH strains (NCBI Reference Sequences: NC_000912. 1 and NC_017504. 1; S1 Table ). As shown in S2 Table, the fraction of reads mapping to the reference genomes is always higher than 98% (data not shown), and their orientation preserved in more than 96. 5% for M129 and 95. 9% for FH of read pairs, thus suggesting that within the strains different degrees of rearrangements exist. We assembled the filtered reads (see methods section), obtaining a number of scaffolds for every strain, ranging from 19 up until 45, with an N50 bigger than 80 kb and an E-size bigger than 100 kb (S2 Table ).
6 and p-value = 0. 02 for COG L; S9 Table ). Interestingly, mpn535 and mpn536 encoding for RuvA and RuvB proteins, respectively, are NE in vitro yet became essential in the infection process. In M. genitalium, the deletion of ruvA and ruvB genes impaires its ability to generate antigenic variation of the MgpB and MgpC adhesins  [49]. Additionaly, RuvA and RuvB have been described to have a key role in the infectivity of Borrelia bugdorferi by promoting the recombination of the VlsE, a gene which encodes a surface-exposed lipoprotein  [50]. All these data taken together suggest that recombination by RuvAB could be an essential mechanism for the pathogenicity of M. pneumoniae. We evaluated the antigenic variation by estimating the non-synonymous density in all ORFs and calculating the probabilities of finding the observed number of mutations (or higher) in a given gene, considering its size.
Genomic DNA library preparations Genomic DNA was collected using the Illustrabacteria genomic Kit (GE Healthcare) and sheared to 100 bp fragments using a Covaris S2 device. Paired-end Illumina libraries were created as described by Bentley et al. [61]  and the size selected to be between 200 and 400 bp. The resulting libraries were quantified on an Agilent Bioanalyzer chip (Agilent Technologies). Double-stranded templates were cluster amplified and sequenced on an Illumina GAII. The raw data of DNAseq was submitted to have been upload to SRA with the accession number SRP061659. The sequences of the assembled genomes have been deposited at DDBJ/EMBL/ GenBank with the indicated accession names: LHPO00000000, LHPP00000000, LHPQ00000000, LHPR00000000, LHPS00000000, LHPU00000000, LHPO00000000, LHPV00000000, LHPW00000000, LHPX00000000, LHPZ00000000, LHPO00000000, LHQA00000000, LHQB00000000, LHQC00000000, LHQE00000000, LHQA00000000, LHQF00000000, LHQG00000000, LHQH00000000, LHQI00000000, LHQJ00000000 and LHQK00000000.
De novo assembly, variant calling and bioinformatics analysis Raw reads were analyzed by using the FastQC tool (website: http://www. bioinformatics. babraham. ac. uk/projects/fastqc/ Key: citeulike:11583827) for assessing the quality and the presence of adapters, and then filtered according to the following criteria: i) reads containing the adapter were removed by the program tagdust  [62] ; ii) unchaste reads were removed; iii) the latest 5 bases at the 3' end were trimmed; iv) mean phred quality of the trimmed read > = 20; v) a maximum of two low quality bases (phred quality<5) was allowed. Filtered reads corresponding to a genome coverage of 150X were assembled by using the Abyss program  [63]. Briefly, reads were fragmented in k-mers and connected depending on whether they shared the same sequence of length k-1.
Ambiguities were solved by counting the number of reads connecting different edges, and scaffolds were produced by considering the mate pairs information. For each assembly we calculated the N-50 and the E-size  [64]  by using the abyss-fac tool from the Abyss package and we mapped back the original reads to the scaffolds by using bowtie2 with options-very-sensitive-X 1000 (version 2. 2. 5)  [65]  to estimate the quality of the assembly (S2 Table ; column "remapping"). Subsequently, the resulting scaffolds were mapped to the reference genomes (M129 and FH) by using bwa mem tool (version 0. 7. 12-r1039)  [66]   [67]. Variant calling was performed by using samtools and bcftools (version 0. 1. 18) [68]  on both alignments obtained by mapping scaffolds and reads the references. Variations (indels and SNPs) were kept only if they are found from both procedures.
Variations were annotated using the Variant Effect Predictor tool  [69]  with annotations from Ensembl Bacteria database for FH strain (GCA_000143945. 1) and HomoConTrans19 for M129  [47]. Heat-maps were generated by using the heatmap. 2 function in the gplots package  [70]. A binary matrix indicating the presence or absence of each variation in given samples is used to calculate the Euclidean distance between the samples and then fed to the heatmap. 2 function for clustering and displaying. Genes from M129 and FH strains were considered orthologous when detected by the Protei-nOrtho tool  [71]  imposing 50% of coverage and identity and using the synteny option.
Fragment ion spectra produced via collision induced dissociation (CID) were acquired in the ion trap mass analyzer. In each cycle of data-dependent analysis, following each survey scan the top twenty most intense ions with multiple charged ions above a threshold ion count of 5000 were selected for fragmentation at a normalized collision energy of 35%. All data were acquired with Xcalibur 2. 1 software. In addition, 20 μg of the total extract was digested and desalted and 1 μg of the resulting peptides analyzed on an Orbitrap Velos Pro in the same conditions as the fractions but with a longer gradient (120 min). A total of three biological replicates were done, as well as two technical replicates for each strain in two independent experiments. The spectra were assigned to peptides by using Mascot and a customized database comprising all the ORFs longer than 19 amino acid.
RS-200-0012, Illumina) according to the manufacturer's protocol. In summary, 3' adapters and subsequently 5' adapters were ligated to the RNA. cDNA was synthesized using reverse transcriptase (SuperScript II, ref. 18064-014, Invitrogen) and a specific primer (RNA RT Primer) complementary to the 3' RNA adapter. cDNA was further amplified by PCR using indexed adapters supplied in the kit. Finally, size selection of the libraries was performed using 6% Novex 1 TBE Gels (ref. EC6265BOX, Life Technologies). Fragments with insert sizes of 100 to 130 bp were cut from the gel, and cDNA was precipitated and eluted in 10 μl of elution buffer. Double-stranded templates were cluster amplified and sequenced on an Illumina HiSeq 2000. The raw data of RNAseq was submited to the GEO database with the accession number GSE71467.
Acknowledgments We thank the  Proteomics and Genomics Core Facilities  at the  CRG  for fruitful discussions. Also, we would like to acknowledge  Tony Ferrar  for English corrections and  Toni Hermoso from the Bioinformatics Core. We are greatly indebted to  Dr A. Ben Hassen ,  Dr R Dumke ,  Dr J. S. Jensen ,  Dr M. Messeguer , and  Dr T. Sasaki  for the gift of Tunisian, German,  Danish ,  Spanish , and Japanese strains.
Introduction: A second generation HPV vaccine has been developed for the prevention of anogenital cancers and precancerous lesions of the cervix, vulva, vagina, anus and of genital warts due to nine HPV types. We estimated the annual burden of these diseases attributable to the nine HPV types compared to HPV types from first generation vaccines in women and men in Europe. Material and methods: Incidence rates from the IARC database, cancer registries, the literature and Eurostat population data were used. The burden attributable to the HPV types targeted by both vaccines was estimated by applying the relative contribution of the respective HPV types from epidemiological studies. Results: In 2013, the number of new anogenital HPV-attributable cancers was 44,480 with 39,494 of these cases related to second vs.
Estimation of the annual burden of cancer in Europe The Cancer Incidence in Five Continents (CI5) database, available on the IARC website  [12] , contains worldwide data on cancer incidence rates classified by International Classification of Diseases 10th Revision (ICD-10) codes. These data are obtained from regional or national registries, depending on the country, but to be included in CI5 these registries must meet the IARC's quality criteria, i. e. , they must have reliable cancer registry data. The present report includes cancer incidence data from CI5 Volume X, which were collected from 2003 throughout 2007. We selected a total of 32 countries: all 31 countries covered by the European Medicines Agency (Austria, Belgium, Bulgaria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Liechtenstein, Lithuania, Luxemburg, Malta, the Netherlands, Norway, Poland, Portugal, Romania, Slovenia, Slovakia, Spain, Sweden, and the United Kingdom) plus Switzerland.
The information in CI5 Volume X was obtained from national cancer registries for Belgium, Bulgaria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, Iceland, Ireland, Latvia, Lithuania, Malta, the Netherlands, Norway, Slovenia, Slovakia, and Sweden. The information in CI5 Volume X was obtained from regional cancer registries for Austria, France, Germany, Italy, Poland, Portugal, Spain, Switzerland, and the United Kingdom (Fig. 1 ). To ensure that national populations were adequately represented in countries where only regional cancer registries exist, we assessed the geographical coverage and distribution of these registries. Five of the 32 countries selected did not have data available in CI5 Volume X, as they did not have reliable cancer registry data: Greece, Hungary, Liechtenstein, Luxemburg, and Romania.
We excluded Lichtenstein from our analysis, but for the remaining four countries we extrapolated the age-specific average cancer incidence rates of neighboring countries, or from cancer registries in the same area as the countries selected. The choice of countries used for extrapolation was the same as that in the Globocan database  [13]. Thus for Greece data from Bulgaria, Cyprus, and Central Serbia were used; for Hungary data from Austria, Croatia, Central Serbia, Slovakia, and Slovenia were used; for Luxemburg data from French and German cancer registries were used; and for Romania data from Bulgaria, Slovakia, and one regional registry in Romania were used. In conclusion, our results are referring to a geographical region of 31 European countries.
Estimation of the annual number of new selected HPV-related cancers in Europe The following HPV-related cancer sites were selected: cervix (ICD-10 code C53), vulva (C51), vagina (C52), and anus (C21, both sexes). We estimated the mean annual number of new cancers at these sites in the selected countries based on the sex-and agespecific cancer incidence data available in CI5 Volume X  [14] , and extrapolated to the population of each country using 2013 Eurostat population data (for Italy we used 2012 population data, as 2013 data were not yet available)  [15]  as follows: where AIR is the age-and sex-specific annual incidence rate, and population is the age-and sex-specific population. The estimated numbers of new HPV-related cancer cases for all the selected countries were then summed to obtain the overall European burden.
Sex-specific data were available for anal cancer. However, as preference was given to European data, sex-specific data were not used, as they were not considered robust enough due to the small sample size. When adjusted and crude data were available, preference was given to adjusted data. The number of new cancers attributable to HPV overall, to HPV16/18, and to HPV16/18/31/33/45/52/58 was then estimated by applying the corresponding cancer site-specific HPV prevalence. Data on HPV prevalence were extracted from the most relevant published cancer site-specific data. To avoid overestimating the prevalence of individual HPV types due to multiple infections, different adjustment methods, which are described in the cited paper for each cancer site, were used. There are other cancers known to be associated with HPV, including penile cancer in men and a subset of head and neck cancers in both sexes.
Estimation of the annual number of new genital warts cases in Europe To estimate the annual number of new genital warts cases we used the methodology previously described  [21]. Briefly, two European publications were identified that, based on their design, provided the most robust incidence data for Europe  [22, 23]. Both are retrospective cohort studies carried out using databases, including very large samples of routinely collected data. We extrapolated the value from each publication to the 2013 population of the 31 European countries to provide an estimated range of the annual number of new genital warts cases in women and men in Europe. The estimated prevalence of HPV6/11 of 90% in genital warts was then applied to estimate the number of cases attributable to the HPV types targeted by the nine-valent and quadrivalent vaccines.
Strengths and limitations A short-term prediction method was used to estimate the number of new cancer cases in 2013 from the most recent data collected from 2003 to 2007. Therefore these estimates were accurate only if the incidence rates of the cancers under study remained stable over time. In the case of increasing incidence, they would slightly underestimate the expected number of cases, and the opposite would be true in the case of decreasing incidence. As mentioned above, the CI5 database contains national cancer incidence rates for 19 European countries. Eight of the countries included in this report had only regional incidence rates available, which were extrapolated to the entire country. Although we assessed the geographical coverage and distribution of these regional registries, other factors could vary and influence regional incidence rates.