dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, Vogler et al. [31]  proposed an MLVA10 assay, comprising Ft-M2, Ft-M3, Ft-M4, Ft-M5, Ft-M6, Ft-M10, Ft-M20, Ft-M22, Ft-M23, Ft-M24. Table  5  shows the composition of the three MLVA assays. The three assays can be compared using the Johansson et al. investigation  [25] , which constitutes the most complete dataset. Whereas 119 genotypes are resolved among the 192 strains when using all 25 loci, MLVA6, MLVA9 Obolensk and MLVA10 resolve 111, 101 and 116 genotypes, respectively. Determination of subspecies by single primer amplification. We previously developed a F. tularensis typing assay using a single-primer PCR (Fig  5 ). This method which is essentially a Random Amplified Polymorphic DNA (RAPD) assay  [32]  is used for the rapid determination of the subspecies of strains arriving in the SCRAMB collection.
Phylogeny by whole genome SNP analysis of F. tularensis including one mediasiatica M. II representative In order to confirm the clustering suggested by MLVA analysis, we sequenced one representative strain from the mediasiatica M. II group, strain A554. Additional public whole genome sequence data including full genomes, contigs, scaffold and reads was downloaded from the European Nucleotide Archive (ENA). Data from 65 strains was retained after removal of datasets of insufficient quality, or representing variants of laboratory strains or duplicates. This final set includes 31 holarctica, 31 tularensis and three mediasiatica strains (S2 Table ). The mediasiatica strains are A554 from M. II (this report) and FSC147 (alias GIEM 543) and FSC148 belonging to M. I, both from Kazakhstan. A total of 9,737 core genome SNPs is retained by the SNPs calling pipeline.
We hope that the simple genotyping assays proposed here will help develop procedures that can be applied on site at low cost on clinical as well as environmental samples. MLVA is especially interesting as compared to typing of a selection of SNPs because it is an unbiased approach, which will identify a previously unknown clade. The online Francisella tularensis database hosted on the Microbes Genotyping web site provides an easily accessible depository of reference data for comparisons. Whole genome sequencing will help investigate more precisely the population structure of the M. II group, as illustrated by the investigation of other F. tularensis foci  [40]  and the evolutionary relationships between subsp. mediasiatica, holarctica and tularensis. Wang et al  [41]  recently described the presence of all basal holarctica lineages in Western China.
This work is supported by the  Sectoral Scientific Program «Problem- oriented research in the epidemiological surveillance of infectious and parasitic diseases ( 2016-2020 )» of the  Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor)  (http://www. rospotrebnadzor. ru/). Research by  DC  and GV is supported by grant soil or water [ 1][2][3][4][5][6 ]. ANR-14-ASMA-0002  from the  French Agence Nationale de la Recherche (ANR)  project  MicroType  (http://www. agence-nationalerecherche. fr/en/project-based-funding-to-advancefrench-research/).
ACKNOWLEDGMENTS Thanks to two anonymous reviewers for the comments and corrections that greatly improve the final manuscript. We are very grateful to  M. Batallés  for the photographs in Figure 2, to  M. S. Bargo  for photograph of Scelidotherium in Figure 5 and to  L. M. Pérez  for photograph of  Megatherium in Figure 5. Comisión Sectorial de Investigación Científica (CSIC) of the Universidad de la República  partially supported this research.
Specifically, individuals with low call rate (o95%) as well as all individuals evaluated to be of non-European ancestry (using the HapMap version 2 CEU, JPT/CHB and YRI populations as a reference) were excluded. For apparent first-degree relative pairs, we removed the control from a case-control pair; otherwise, we excluded the individual with the lower call rate. SNPs with a call rate o95% were excluded as were those with a MAF o0. 01 or displaying significant deviation from Hardy-Weinberg equilibrium (that is, Po10 À 6 ). GWAS data were imputed to 410 million SNPs with the IMPUTE2 v2. 3 software 32 using a merged reference panel consisting of data from 1000 Genomes Project (phase 1 integrated release 3 March 2012)  10  and UK10K (ref. 11). Genotypes were aligned to the positive strand in both imputation and genotyping.
Mutational status. IGVH mutation status was determined according to the BIOMED-2 protocols as described previously  44. Sequence analysis was conducted using the Chromas software version 2. 23 (Applied Biosystems) and the international immunogenetics information system database. In accordance with published criteria, we classified sequences with a germline identity of Z98% as unmutated and those with an identity of o98% as mutated. Association between genotype and patient outcome. To examine the relationship between SNP genotype and patient outcome, we analysed two patient series: (1) 356 patients from the UK Leukaemia Research Fund (LRF) CLL-4 trial  45  , which compared the efficacy of fludarabine, chlorambucil and the combination of fludarabine plus cyclophosphamide; (2) 377 newly diagnosed patients from Mayo Clinic who were prospectively followed.
Cox-regression analysis was used to estimate genotype-specific hazard ratios and 95% CIs with overall survival. Statistical analyses were undertaken using R version 2. 5. 0. eQTL analysis. eQTL analyses were performed by examining the gene expression profiles of 452 CLL cases (Affymetrix Human Genome U219 Array)  46. Additional data were obtained by querying publicly available eQTL mRNA expression data using MuTHER  47  , the Blood eQTL browser  48  and data from the GTEx consortium  49. MuTHER contains expression data on LCLs, skin and adipose tissue from 856 healthy twins. The Blood eQTL browser contains expression data from 5,311 non-transformed peripheral blood samples. We used the whole-blood RNA-seq data from GTEx, which consisted of data from 338 individuals. Functional annotation. Novel risk SNPs and their proxies (that is, r 2 40.
8 and D 0 40. 8) was determined, and these SNPs were termed the associated variant set (AVS). TF ChIP-seq uniform peak data were obtained from ENCODE for the GM12878 cell line, which included data for 82 TF and 11 histone marks. For each of these marks, the overlap of the SNPs in the AVS and the binding sites was determined to produce a mapping tally. A null distribution was produced by randomly selecting SNPs with the same characteristics as the risk-associated SNPs, and the null mapping tally calculated. This process was repeated 10,000 times, and approximate P-values were calculated as the proportion of permutations where null mapping tally was greater or equal to the AVS mapping tally. An enrichment score was calculated by normalizing the tallies to the median of the null distribution.
006 by SEER (http://seer. cancer. gov/statfacts/html/ clyl. html). The variance of the risk distribution due to the identified risk loci was calculated as described by Pharoah et al. 57  , assuming that the relative risk when a first-degree relative has CLL is 8. 5 (ref. 1). Pathway analysis. To investigate the interaction between the gene products of the GWAS hits, we performed a pathway analysis. We selected the closest coding genes for the lead-associated SNPs and then performed pathway analysis using the LENS tool  25  , which identifies gene product and protein-protein interactions from HPRD  58  and BioGRID  59. Enrichment of pathways was assessed using Fisher's exact test, comparing the overlap of the genes in the network with the genes in the pathway. Pathway data were obtained from REACTOME  60.
Acknowledgements In the  United Kingdom, Bloodwise  provided funding for the study ( LRF05001 ,  LRF06002  and  LRF13044 ) with additional support from  Cancer Research UK  ( C1298/A8362  supported by the  Bobby Moore Fund)  and the  Arbib Fund. G. P. S. is in receipt of a PhD studentship from  The Institute of Cancer Research. The  NCI/InterLymph NHL GWAS initiative  was supported by the  intramural programme of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, US National Institutes of Health. ATBC-This research was supported in part by the  Intramural Research Program  of the  NIH  and the  National Cancer Institute. In addition, this research was supported by  U. S. Public Health Service  contracts  N01-CN-45165 ,  N01-RC-45035 ,  N01-RC-37004  and  HHSN261201000006C  from the  National Cancer Institute, Department of Health and Human Services.
We assume full responsibility for analyses and interpretation of these data. Iowa- Mayo SPORE-NCI Specialized Programs of Research Excellence (SPORE)  in  Human Cancer  ( P50 CA97274 );  National Cancer Institute  ( P30 CA086862 ,  P30 CA15083 );  Henry J. Predolin Foundation. Italian GxE-Italian Association for Cancer Research  ( AIRC, Investigator Grant   11855 ; PC);  Fondazione Banco di Sardegna   2010-2012  and  Regione Autonoma della Sardegna  ( LR7 CRP-59812/2012 ; MGE). Mayo Clinic Case-Control- National Institutes of Health  ( R01 CA92153 );  National Cancer Institute  ( P30 CA015083 ). MCCS-The Melbourne Collaborative Cohort Study recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further supported by Australian  NHMRC  grants  209057 ,  251553  and  504711  and by infrastructure provided by  Cancer Council Victoria.
The ideas and opinions expressed herein are those of the authors, and endorsement by the  State of California, the California Department of Health Services , the  National Cancer Institute  or the  Centers for Disease Control and Prevention  or their contractors and subcontractors is not intended nor should be inferred. UTAH-National Institutes of Health  CA134674. Partial support for data collection at the Utah site was made possible by the  Utah Population Database (UPDB)  and the  Utah Cancer Registry (UCR). Partial support for all data sets within the UPDB is provided by the  Huntsman Cancer Institute (HCI)  and the  HCI Comprehensive Cancer Center   Support grant ,  P30 CA42014. The UCR is supported in part by  NIH  contract  HHSN261201000026C  from the  National Cancer Institute SEER Program  with additional support from the  Utah State Department of Health  and the  University of Utah.
The Spanish replication study was supported by the  Spanish Ministry of Economy and Competitiveness  through the  Instituto de Salud Carlos III  ( FIS PI13/01136 ;  International Cancer Genome Consortium-Chronic Lymphocytic Leukemia Genome Project ). We thank  L. Padyukov  ( Karolinska Institutet ) and the  Epidemiological Investigation of Rheumatoid Arthritis (EIRA) group  for providing control samples from the  Swedish population  for the Swedish replication study. MCCS cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further supported by Australian  NHMRC  grants  209057 ,  251553  and  504711 , and by infrastructure provided by  Cancer Council Victoria. Cases and their vital status were ascertained through the  Victorian Cancer Registry (VCR)  and the  Australian Institute of Health and Welfare (AIHW) , including the  National Death Index  and the Australian Cancer Database.
Data availability. Genotype data that support the findings of this study have been deposited in the database of Genotypes and Phenotypes (dbGAP) under accession code phs000802. v2. p1 and in the European Genome-phenome Archive (EGA) under accession codesEGAS00001000090, EGAD00001000195, EGAS00001000108, EGAD00000000022 and EGAD00000000024. Transcriptional profiling data from the MuTHER consortium that support the findings of this work have been deposited in the European Bioinformatics Institute (Part of the European Molecular Biology Laboratory, EMBL-EBI) under accession code E-TABM-1140. Data from Blood eQTL have been deposited in the EBI-EMBL under accession codes E-TABM-1036, E-MTAB-945 and E-MTAB-1708. GTEx data are deposited in dbGaP under accession code phs000424. v6. p1. The remaining data are contained within the paper and its Supplementary files or are available from the authors upon reasonable request.
Over the past decade, sea-ice freeboard has been monitored with various satellite altimetric missions with the aim of producing long-term time series of ice thickness. While recent studies have demonstrated the capacity of the CryoSat-2 mission (2010-present) to provide accurate freeboard measurements, the current estimates obtained with the Envisat mission (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) still require some large improvements. In this study, we first estimate Envisat and CryoSat-2 radar freeboard by using the exact same processing algorithms. We then analyse the freeboard difference between the two estimates over the common winter periods (November 2010-April 2011 and November 2011-March 2012). The analysis of along-track data and gridded radar freeboard in conjunction with Envisat pulse-peakiness (PP) maps suggests that the discrepancy between the two sensors is related to the surface properties of sea-ice floes and to the use of a threshold retracker.
Based on the relation between the Envisat pulse peakiness and the radar freeboard difference between Envisat and CryoSat-2, we produce a monthly CryoSat-2-like version of Envisat freeboard. The improved Envisat data set freeboard displays a similar spatial distribution to CryoSat-2 (RMSD = 1. 5 cm) during the two ice growth seasons and for all months of the period of study. The comparison of the altimetric data sets with in situ ice draught measurements during the common flight period shows that the improved Envisat data set (RMSE = 12-28 cm) is as accurate as CryoSat-2 (RMSE = 15-21 cm) and much more accurate than the uncorrected Envisat data set (RMSE = 178-179 cm). The comparison of the improved Envisat radar freeboard data set is then extended to the rest of the Envisat mission to demonstrate the validity of PP correction from the calibration period.
The common flight period of Envisat and CryoSat-2 (November 2010-March 2012) represents an unique opportunity to analyse the bias in the Envisat freeboard fields. In  Schwegmann et al. (2016) , Envisat and CryoSat-2 radar freeboard data sets are compared over Antarctic sea ice. While the spatial and temporal distributions are consistent, it is shown that CryoSat-2 radar freeboard is thicker (thinner) for thick (thin) freeboard values. The authors conclude that the discrepancy between the two data sets is likely to be related to the difference in footprint characteristics between pulselimited and SAR altimetry. Over sea ice, conventional pulse-limited altimeters such as Envisat have a typical footprint of 2-10 km  (Connor et al. , 2009). Unlike pulse-limited altimetry, the SAR technology and the Doppler post-processing allow the along-track position of each scatterer to be identified and offer SIRAL an along-track footprint of ∼ 300 m (the across-track being unchanged).
In particular, we seek to analyse the impact of surface specularity on the freeboard retrieval. In Sect. 2, we introduce the data sets used in the present study as well as the freeboard retrieval algorithms. Then, we compare CryoSat-2 and Envisat waveform echoes (Sect. 3. 1), along-track freeboard estimates (Sect. 3. 2) and gridded freeboard estimates (Sect. 3. 3). In Sect. 3. 4, we build a Cryosat-2-like version of the Envisat freeboard based on the relation between the freeboard discrepancy of Envisat and CryoSat-2 and the Envisat pulse peakiness (PP). Finally, in Sect. 3. 5, we convert CryoSat-2, Envisat and the corrected Envisat freeboard data sets to ice draught fields and compare each data set to in situ measurements. 2 Data and methodology.
Envisat Envisat was launched in 2002 by ESA and was set on the same orbit as the ERS-1/2 satellites, providing coverage of the Arctic Ocean up to 81. 5 • N. The RA-2 altimeter on board Envisat includes a Ku-band pulse-limited radar altimeter with a bandwidth of 320 MHz. To derive the Envisat freeboard fields, we use the Sensor Geophysical Data Record (SGDR) product from ESA (https://earth. esa. int/web/guest/ -/ra-2-sensor-data-record-1471) that is converted to NetCDF files by the Centre of Topography of Oceans and Hydrosphere (CTOH). The netcdfs contain geolocated 20 Hz waveform echoes, orbit parameters, ionospheric (model) and tropospheric (model) corrections. In addition to the parameters provided in the ESA sgdr product, the CTOH product also provides the DTU15 mean sea surface correction  (Andersen and Knudsen, 2015)  as well as the FES14 tide correction  (Carrere et al.
Ancillary data Daily sea-ice extent and multiyear ice (MYI) age fields are available at a 12. 5 km resolution from the National Snow and Ice Data Center (NSIDC) and are derived from AMSR-E and SSMIS passive microwave observations  (Anderson et al. , 2014). The Beaufort Gyre Exploration Project (BGEP) moorings provide high-frequency (0. 5 Hz) measurements of seaice draught since August 2003 due to a network of two to four moorings located in the Beaufort Sea (http://www. whoi. edu,  Melling et al. , 1995). The buoys are equipped, along with other instruments, on an upward-looking sonar (ULS), which measures the distance from the mooring to the bottom of the ice with a 420 kHz beam sonar. Sea-ice draught is then estimated by removing the depth of the mooring that is deduced from pressure measured every 40 s.
Freeboard retrievals In this section we describe the methodology that is applied to CryoSat-2 and Envisat to derive radar freeboard. While the different measurement modes (pulse limited and SAR) are expected to cause differences in the radar freeboard estimates, we try to keep the retrieval algorithms as similar as possible between the two sensors in order to reduce the impact of the processing chain on each data set. The procedure we apply to retrieve Envisat and CryoSat-2 radar freeboard is described in detail in the ESA SI-CCI project  (Ridout et al. , 2012)  and is based on the already published method used for ERS-2  (Peacock and Laxon, 2004; Laxon et al. , 2003). Radar freeboard is obtained by measuring the difference in range between the ocean and sea-ice floes. The discrimination between sea-ice and ocean observations is performed through the analysis of the radar waveform shape.
org/data/docs/daac/ nsidc0611-sea-ice-age/,  Anderson et al. , 2014). In this product version, the ice age is calculated by tracking the ice by comparing adjacent passive microwave images as well as using wind forcing and buoy displacement information. In addition to not making the assumption of a constant correlation between ice age and ice roughness, this product has the advantage of being available over the entire Envisat mission  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012). In the present study, monthly snow depth fields are therefore derived as follows: first, each ice pixel with ice older than 1 year is considered as MYI and pixels with ice younger than 1 year is considered FYI. Then, we apply an average filter with a radius of 100 km (similarly to the freeboard data sets) and estimate the ratio of MYI observation within the filtering area to build MYI concentration maps.
Comparison of CryoSat-2 and Envisat waveform echoes As a first analysis, we compare mean CryoSat-2 and Envisat waveforms echoes for observations originating from leads, MYI and FYI. Leads and ice floes are identified as described in Sect. 2. 4 and MYI and FYI observations are distinguished using the NSIDC ice age data set. More specifically, ice floes echoes located in areas with more than 70 % of MYI are classified as MYI and ice floes located in areas with less than 30 % are classified as FYI. To average CryoSat-2 and Envisat waveform echoes, we use the same methodology as in the study by  Zygmuntowska et al. (2013). All waveform echoes are normalised and shifted so their maximum power is located in the same sampling bin (the 67th bin here). Then, all waveform echoes are averaged to produce a mean echo.
Comparison of along-track radar freeboard estimates As CryoSat-2 and Envisat have a different orbit configuration, it is unfortunately not possible to compare the radar freeboard over the exact same track. Thus, all one can do is to compare the radar freeboard within the same Arctic region. In Fig. 3 , we show ground tracks of Envisat and CryoSat-2 missions used to analyse typical along-track freeboard estimates. Both tracks are located in the in the western part of the Arctic Ocean and were acquired on 24 February 2011. The ice type classification (grey tons) shows that both MYI and FYI observations are found below each track. Figure  3b  and c shows the elevation of floes (grey) and leads (black) for each satellite mission. The first striking difference between the two data sets is the dispersion of surface elevation.
Over sea-ice leads, CryoSat-2 displays a relatively low standard deviation of surface elevation (4. 2 cm) when compared to Envisat (7. 8 cm). This result clearly demonstrates that CryoSat-2 provides more consistent sea level estimates than Envisat. The standard deviation of the floes surface elevation is also lower for CryoSat-2 (8. 6 cm) than for Envisat (18. 6 cm). This discrepancy between the two data sets is most likely to be related to the larger footprint size of Envisat radar altimeter and to its higher sensitivity of sea-ice properties. This result will be further discussed in the following section. Surprisingly, the elevation of Envisat leads is found above the average elevation of sea-ice floes, which causes the radar freeboard to be negative (Fig. 3d ). In the literature, this phenomenon is attributed to the large difference between leads and ice floes waveform echoes  (Laxon, 1994; Giles et al.
The resulting Envisat/PP freeboard estimates display similar patterns to CryoSat-2 over the entire period of study (RMSD = ∼ 1. 5 cm) and offers a more realistic seasonal cycle (∼ 2. 0 to 3. 0 cm) than the uncorrected Envisat freeboard. The comparison of each altimetric data set with in situ ice draught observations during the common flight period reveals that Envisat/PP ice draught is much more accurate (RMSE = 12-28 cm) than the uncorrected Envisat data set (RMSE = 178-179 cm) and as accurate as CryoSat-2 (RMSE = 15-21 m). To further assess the potential of the PP correction, the Envisat and Envisat/PP ice draught data sets are extended to the 2003-2010 period and are similarly compared to BGEP ice draught measurements. As for the 2010-2012 period, the agreement with in situ measurements is higher for Envisat/PP (RMSE = 13-32 cm) than for Envisat (RMSE = 165-186 cm), which demonstrates the potential of the PP correction to provide accurate freeboard estimates over the entire Envisat mission lifetime.
Acknowledgements. This research is supported by the  French CNES TOSCA SICKAyS  and the  ESA CryoSeaNice project. We thank the  Center for Topographic studies of the Oceans and Hydrosphere (CTOH)  at  LEGOS  for providing the SGDR Envisat and CryoSat-2 data. Edited by: Christian Haas Reviewed by:  Eero Rinne  and two anonymous referees.
The International Rare Diseases Research Consortium (IRDiRC) has agreed on IRDiRC Policies and Guidelines, following extensive deliberations and discussions in 2012 and 2013, as a first step towards improving coordination of research efforts worldwide. The 25 funding members and 3 patient umbrella organizations (as of early 2013) of IRDiRC, a consortium of research funders that focuses on improving diagnosis and therapy for rare disease patients, agreed in Dublin, Ireland in April 2013 on the Policies and Guidelines that emphasize collaboration in rare disease research, the involvement of patients and their representatives in all relevant aspects of research, as well as the sharing of data and resources. The Policies and Guidelines provide guidance on ontologies, diagnostics, biomarkers, patient registries, biobanks, natural history, therapeutics, models, publication, intellectual property, and communication.