dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by grants from the  Polish National Science Centre  (# N N303 808840  ://www. nauka. gov. pl/), the  French National Research Agency  (collaborative projects  ANR-10-BLAN-1603  " GENOMAC " and  ANR-12-BSV6-0017  " INFERNO ", http://www. agence-nationale-recherche. fr/), the  ARC Foundation for Cancer Research  (# SFI20121205487  to MB, http://www. fondation-arc. org/) and the  Centre National de la Recherche Scientifique  (CNRS; an  ATIP Plus  grant to SD, http:// www. cnrs. fr/). KMO, CDW and MB received exchange grants from the  CNRS  and the  Polish Academy of Sciences  (http://www. pan. pl/) in the framework of the  CNRS GDRE "Paramecium Genome Dynamics and Evolution ". KMO was supported by  PhD  fellowship from the  School of Molecular Biology-Institute of Biochemistry and Biophysics, PAS  (http://www.
The TFIIS4-RNAi DNA-seq and RNA-seq datasets have been deposited in the GenBank Sequence Read Archive (accession numbers SRP047508 and SRX1022957). IES retention scores may be obtained via ParameciumDB (http://paramecium. cgm. cnrs-gif. fr/ download/). The data concerning microarray transcriptome analysis upon TFIIS4 silencing have been deposited at the Gene Expression Omnibus.
Implementation The current FA/BRCA network is available through the supplementary file FAnetwork. r this file has been tested using R (v3. 1. 1) package BoolNet (v1. 63)  [66]. Additionally, the SBML-qual implementation of the model obtained by using the toSBML() function of BoolNet is provided as the supplementary file FAnetwork. sbml. The generated file was validated using the online service at http://sbml. org/Facilities/Validator/. [49, 52, [55] [56] [57]  ATR ←(ICL ∨ ATM) ∧ ¬ CHKREC  [58] [59] [60]  ATM ← (ATR ∨ DSB) ∧ ¬ CHKREC ∨ FAcore  [61, 62]  P53 ← ((ATR ∨ ATM) ∨ NHEJ) ∧ ¬ CHKREC  [58, 63, 64]  CHKREC ← ((TLS ∨ NHEJ ∨ FAHRR ∨ HRR2) ∧ ¬ DSB ) ∨  [52, 53] ; FANCD2I ← FAcore ∧ ((ATR ∨ ATM) ∨ ((ATR ∨ ATM) ∧ DSB)) ∧ [47-49] ¬ (CHKREC) NUC1 ←ICL ∧ FANCD2I [50]; [38] NUC2 ← (ICL ∧ (ATR ∨ ATM) ∧ ¬ (FAcore ∧ FANCD2I)) ∨ [51]; [38] (ICL ∧ NUC1 ∧ p53 ∧ ¬(FAcore ∧ FANCD2I)) ADD ← (NUC1 ∨ NUC2 ∨ (NUC1 ∧ NUC2)) ∧ ¬ (TLS) [47, 50, 51] DSB ← (NUC1 ∨ NUC2) ∧ ¬ (NHEJ ∨ FAHRR ∨ HRR2) [50, 52] TLS ← (ADD ∨ (ADD ∧ FAcore)) ∧ ¬ (CHKREC) [53, 54] FAHRR ← DSB ∧ FANCD2I ∧ ¬ (NHEJ ∧ CHKREC) [53, 54] HRR2 ← (DSB ∧ NUC2 ∧ NHEJ ∧ ICL ∧ ¬ (FAHRR ∨ CHKREC)) ∨ [38] (DSB ∧ NUC2 ∧ TLS ∧ ¬ (NHEJ ∨ FAHRR ∨ CHKREC)) NHEJ ← (DSB ∧ NUC2 ∧ ¬ (FAHRR ∨ HRR2 ∨ CHKREC)) ((¬ ADD) ∧ (¬ ICL) ∧ (¬ DSB) ∧ ¬ (CHKREC))  [38, 65]  Key references are included.
Acknowledgements Authors thank our colleagues at  Dr. Frías' laboratory  for insightful discussions and  Anet Rivera Osorio  for invaluable assistance with experiments. This work was supported in part by grants from  Universidad Nacional Autónoma de México   PAPIIT IN200514  and  IA201713 , and  Instituto Nacional de Pediatría-SSA , Fondos Federales  043-12. AR received the 346717 scholarship from  Consejo Nacional de Ciencia y Tecnología (CONACyT). We thank  S Becerra  and  J Yañez  from  IBT UNAM  for primer synthesis.
journaux@idele. fr: (FGE, France Génetique Elevage, Interprofession de l'Amélioration Génétique de Ruminants, 149 rue de Bercy, Paris, France) and rachel. rupp@toulouse. inra. fr (corresponding author of this publication). The 207 variations identified in the QTL region are available from the public NCBI databases (dbSNP Build143; http://www. ncbi. nlm. nih. gov/projects/SNP/snp_ss. cgi? subsnp_id=1553223136; ss references in S2 Table ). The sequence data is fully available on the public SRA database (http://www. ebi. ac. uk/ena/data/view/ PRJEB9911). All other relevant data are within the paper and its Supporting Information files. mammary gland in response to infection. Measured on a monthly basis, SCC can be interpreted as indicating the consequences of infection and repeatedly high SCC can be associated with the presence of chronic mastitis.
Out of the total of 1543 SNPs found in a region of 0. 5 Mb, 207 SNPs were retained that were heterozygous (0/1 genotype) for the sire and homozygous, with different genotypes (0/0 or 1/1) for the sons and annotated with the current gtf file for sheep genome (ftp://ftp. ensembl. org/pub/current_ gtf/ovis_aries/) (S2 Table ). The distribution of the 207 SNP over the OAR3 QTL region is given in Fig 1C. These 207 variations were then submitted to the NCBI databases, dbSNP and dbVar. SNPs were released in the dbSNP Build (B143). Sixty-six SNPs were homozygous for the reference genome base in the susceptible son (0/0), whereas 141 SNPs were homozygous for the alternate base in the susceptible son (1/1). We focused our interest on the variants located in coding or untranslated regions (UTR). Two SNPs were located in the 3'UTR of CRADD gene.
Discovery population and phenotypic measurements For association mapping, data from a total of 1009 commercial French dairy rams were used. These 1009 males were distributed in 33 half-sib families in a so called grand-daughter design. Family size averaged 30. 2 (±9. 7) sons and ranged from to 18 to 54. The phenotypic measurement was the milk somatic cell count (SCC) measured in lactating ewes and available from the national data base, (Centre de Traitement de l'Information Génétique, CTIG, Jouy en Josas, France), as well as pedigree information, as part of the official data system for livestock (ministerial order NOR: AGRT1431011A, 24 th March 2015, Ministry of Agriculture, France). Sampling methods and SCC analyses followed standard recommendations by the IDF (International Dairy Federation) and ICAR (International Committee for Animal Recording).
SCC was measured on average three times per lactation in first and second parity. SCC was then log-transformed to somatic cell score [SCS = log2 (SCC/100) +3] to normalize the data distribution and averaged per lactation to compute the analyzed trait LSCS as described in  [9]. For association mapping we used twice the daughter yield deviation (DYD)  [45]  for LSCS from the national genetic evaluation procedure  [46]. DYDs correspond to the average performance of the daughters of a ram, corrected for the environmental effects and the genetic value of the dams.
Genome-wide SNP genotyping All 1009 rams were genotyped using the Illumina Ovine SNP50 BeadChip assay. DNA extraction from blood samples and genotyping were performed at the Laboratoire d'Analyses Génétiques pour les Espèces Animales, Jouy en Josas, France (LABOGENA; www. labogena. fr). Blood samples were collected as part of the official national preservation collection and the coded samples stored at LABOGENA. Data were cleaned using in-house pipelines automated for the whole dairy sheep population genotyping data. In brief, any individual with a call rate below 98% or showing pedigree inconsistency had been previously discarded (less than 3%). SNP quality control included the following inclusion criteria: call rate above 97%, minor allele frequency above 1% and Hardy-Weinberg P-value above 10 -6.
Association mapping For discovery association, both linkage analyses (LA) and genome-wide association study (GWAS) were applied to the data using the QTLMap software (Elsen et al. , 1999; http://dga7. jouy. inra. fr/qtlmap/). For LA, interval mapping  [47]  was performed by likelihood ratio test (LRT) using within-sire linear regression  [48]. The QTL effect (average substitution effect) was expressed in deviation units (SD) for the trait. GWAS was based on a regression analysis of the phenotypes on founder sires' haplotypes for every haplotype of 4 consecutive SNPs along the chromosome  [49]. Chromosome-wise significance levels were calculated with QTLMap, using the current family structure and phenotypes. For LA, the empirical 5% and 1% chromosomewise significance levels of the test statistics were estimated from 1000 within-family permutations  [50]  for each chromosome.
For GWAS, the empirical chromosome-wise significance level of the test statistics was estimated from 1000 simulations for each chromosome. The genome-wise thresholds were obtained by applying the Bonferroni correction: Pgenomewise = (1 -Pchromosomewise) n , where n is the number of chromosomes, i. e. , 26 in sheep  [51]. The 95% confidence intervals of the QTL locations were estimated by logarithm of odds drop-off  [47]  implemented in the QTLMap software. In practice, the bounds of the interval were the 2 locations where the likelihood was equal to the maximum likelihood minus 3. 84 ð¼ w 2 ð1;0:05Þ Þ.
Association of Socs2 genotype with LSCS and milk production traits For the 468 rams from the discovery population that were genotyped with the KASPar test, data from the national data base CTIG were used to quantify the effect of the Socs2 genotype on milk production traits. A single SNP test of association by analysis of variance (ANOVA) was performed using the mixed procedure of the statistical analysis system (SAS) programs (9. 1) for the 468 genotyped animals. The dependent variables were DYD for LSCS and production traits (Milk Yield, Fat and Protein content) available from the national genetic evaluation procedure. The three possible Socs2 genotypes were fitted as a fixed explanatory variable and a significance threshold of p<0. 05 was selected. The varcomp procedure of SAS was used to fit the genotype effect as random and estimate the proportion of variance explained by the genotype.
, 2003; Rignot et al. , 2003; Rivera et al. , 2000 Rivera et al. , , 2005;; Masiokas et al. , 2008 Masiokas et al. , , 2009;; Le Quesne et al. , 2009; Pellicciotti et al. , 2014). Quantitative assessments of regional glacier mass-balance changes and glacier-climate relationships are, however, seriously hampered by the scarcity and short length of in situ glacier massbalance data and proximal climate records within the Andes. The latest publication of the World Glacier Monitoring Service (WGMS, 2013) reports annual mass-balance measurements for seven extratropical Andean glaciers (five in Argentina, two in Chile). Four of these records start in 2010 and are for small glaciers and glacierets located ca. 29. 30 • S, two records are located between 32 and 34 • S and start in the midto-late 1970s and the remaining record from Tierra del Fuego (54.
e. , the mass balance that would have been observed if the glacier topography had not changed over the study period; see  Cogley et al. , 2011). Mass-balance data from PIL from 1979 to 2002 and shorter time series from small glaciers and glacierets further north in this region are also available from the WGMS database  (Leiva et al. (2007) ,  Rabatel et al. (2011) , WGMS (2013); see Fig. 1a  and Table  1 ). Here we compare the cumulative annual mass-balance records of these glaciers as independent validation measures of the main patterns and temporal trends observed in the measured and modeled massbalance series from ECH.
2 ). The glacier mass-balance reconstructions are based on simple linear regression models where the predictand is the 1975-2013 ECH annual mass-balance series and the predictors are, alternatively, the regional 1951-2014 snowpack and 1909-2013 streamflow records depicted in Fig. 2. Given the relative shortness of the common period between the predictor and predictand series (39 years), the reconstruction models were also developed using a leave-one-out crossvalidation procedure  (Michaelsen, 1987). Here, linear regression models for each year were successively calibrated on the remaining 38 observations and then used to estimate the predictand's value for the year omitted at each step. A simple linear regression model based on the full calibration data set  was finally used to reconstruct the mass-balance values over the complete period covered by the regional time series.
Acknowledgements. This work was funded by  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina) ,  FONDECYT  Grant  1121106  and  FONDAP Grant 15110009  (Chile). We greatly acknowledge the  World Glacier Monitoring Service  (http://www. wgms. ch), Dirección General de Aguas  (http://www. dga. cl), Dirección Meteorológica de Chile  (http://www. meteochile. gob. cl) and  Subsecretaría de Recursos Hídricos  (http://www. hidricosargentina. gov. ar) for providing the data used in this study. ERA-Interim reanalysis data and correlation maps were provided by the freely available Climate Explorer online application maintained by  Geert Jan van Oldenborgh  at the  Royal Netherlands Meteorological Institute (KNMI ; http://climexp. knmi. nl/). E. Berthier  acknowledges support from the  French Space Agency (CNES)  through his  TOSCA program ,  S.
From this adaptation, the first commercial automated DNA sequencer that used capillary electrophoresis rather than a slab gel, became available in 1996. With these new automated machines the accumulation of DNA sequence data accelerated rapidly. The early pioneer of sequence databases was Margaret Dayhoff who had previously established a protein sequence database and published the first collection of nucleotide sequence information in 1981  (Dayhoff et al. , 1981). Shortly thereafter, GenBank was created in 1982 by the National Institute of Health, USA. GenBank provided a timely, centralized, accessible repository for genetic sequences  (Bilofsky et al. , 1986). In parallel with the growth of databases, methods to prepare robust sequence alignments for detailed comparison soon became the rate-limiting step in the analysis of sequence data.
The development of rapid search programs such as FASTA  (Pearson and Lipman, 1988)  and BLAST  (Altschul et al. , 1990 ) made it practical to identify genes in a new sequence by comparison with sequences already in the databases. Bioinformatics which was in its infancy suddenly became essential for the interpretation of these sequence data and for the generation of testable hypotheses arising from the data. The first sequence analysis software then began to appear such as MEGA which offered the possibility of producing phylogenetic trees from nucleotide and amino acid sequence alignments  (Kumar et al. , 1994). The production of software for analysis of sequence data is now a major growth industry, with new and improved methods appearing frequently. The automated Sanger DNA sequencing method is recognized as the ''first-generation'' technology, and newer DNA sequencing methods are referred to as ''next-generation'' sequencing (NGS).
, 2013). Then properties over the Svalbard region are investigated and compared to those at the regional scale. Section 2 describes the DAR-DAR retrieval algorithm and presents the data set used and the method applied to detect MPC and determine cloud occurrences. In this section, uncertainties and limitations of DARDAR retrievals are analysed. In Sect. 3, total cloud occurrence determined from DARDAR products is first compared to previous works in order to validate the methodology. Then Sect. 4 focuses on mixed-phase clouds variability at regional scale and over the region of Svalbard. In Sect. 5, regional and local MPC properties are compared and differences observed between the whole Arctic and the Svalbard region are discussed.
Data and method Cloud thermodynamic phase distributions over the Arctic region are studied using CALIPSO and CLOUDSAT measurements. Active remote sensing observations are expected to provide a detailed characterisation of the cloud vertical structure in the Arctic. Indeed, compared to passive instruments, active remote sensing techniques are less disturbed by the important Arctic ice cover throughout the year or the lack of sunlight during the polar night  (Chan and Comiso, 2013; Liu et al. , 2010; Wang and Key, 2005).
Data set and methodology In this study, mixed-phase clouds are defined based on DAR-DAR classification. All DARDAR pixels satisfying one of the following two conditions are assumed to belong to MPCs: -they are classified as a mixing of ice and supercooled water (class 2, see Table  1 ); -they are classified as supercooled only (class 4, see Table  1 ) associated with the presence of ice or ice and supercooled water mixing in the three vertical adjacent pixels. Note that "isolated" supercooled water pixels are not classified as mixed-phase clouds. The annual and seasonal variability of the total cloud fraction and MPC is investigated using CALIPSO/CLOUDSAT measurements performed continuously from 2007 to 2010. In this study the Arctic region is defined by all latitudes between 60 • N  (Liu et al.
, 2012)  and 82 • N (upper limit of CALIPSO/CLOUDSAT measurements) and the Svalbard region is defined as the area between 0 • and 30 • E of longitude and 75 • and 80 • N of latitude. The spatial frequency of occurrence and vertical distributions are determined for both total cloud and MPC. The spatial distribution of clouds is studied for altitudes spanning from 500 m up to 12 km (according to the study of  Liu et al. , 2012)  as well as for low-level altitudes ranging between 500 m and 3 km (according to  Zygmuntowska et al. , 2012). A minimum altitude of 500 m is chosen to avoid surface contamination in the CLOUDSAT data  (Marchand et al. , 2008)  and also to avoid false water detection (due to the lidar high backscattered signal from the ground) in the DARDAR algorithm. These instrument shortcomings will be discussed further in Sect.
Ground contamination on CLOUDSAT data As described in Sect. 2. 2. , a minimum altitude threshold of 500 m is chosen to compute cloud occurrence. However, ground contamination could also affect CLOUDSAT cloud detection measurements above an altitude of 500 m. We recall that the DARDAR MASK algorithm is based on the CLOUDSAT cloud mask (and also the CALIPSO lidar mask) to classify pixels as clouds  (Delanoë and Hogan, 2010). Regarding CLOUDSAT measurements, a pixel is considered as cloud if its radar mask reports a value of 30 or greater (indicating a high confidence in the cloud detection, according to  Mace, 2007). Values smaller than this threshold are considered inaccurate and possibly contaminated by ground echoes. Therefore, DARDAR MASK algorithm excludes data contaminated by ground. This implies that the data set is reduced between 500 m and 1000 m.
Open water facilitates the vertical transfer of moisture, responsible for an increase of cloud formation as compared to sea ice, which could explain the prevalence of MPCs during this period in the Western Arctic. In an attempt to strengthen this assumption and to bring perspective to this work, a short analysis of the variability of sea ice extent is done and its potential link with humidity, temperature and cloud occurrence is investigated. Sea ice concentration data included in CALIOP Level 1 data from the National Snow and Ice Data Center (NSIDC) are used. Two specific areas are selected: -over the Greenland Sea, between 70 • and 80 • N and between 10 • W and 20 • E; -over the Western Arctic (Chukchi and Beaufort seas), between 70 • and 80 • N and between 150 and 180 • W. Figure  10a  displays the annual variability of the specific humidity (top panel) and the 2 m temperature (bottom panel) in solid lines between 2007 and 2010 for the whole Arctic region (black), over the Greenland Sea (blue) and over the Western Arctic (orange).
Acknowledgements. This work is part of the  French scientific community EECLAT project (Expecting EarthCare, Learning from A-Train ) and is supported by the  French Centre National des Etudes Spatiales (CNES). This research used resources from the  ICARE  centre for space remote sensing data processing. This work was also supported by the  French ANR   CLIMSLIP  project  (Climate Impacts of Short-Lived Pollutants and Methane in the Arctic ). We thank the anonymous reviewers who made important comments that helped to strengthen the manuscript. Edited by:  M. Krämer.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/jacs. 5b06630. Crystallographic files are also deposited in the CCDC, numbers 1062064-1062066. Experimental raw data are openly available through the University of Edinburgh's DataShare archive, DOI:10. 7488/ds/289. X-ray crystallographic data for 1c, 2b, and 3a (CIF) Experimental procedures and characterization data for the real and computed complexes (PDF).
The authors declare no competing financial interest. ■ ACKNOWLEDGMENTS P. L. A. thanks the  Technische Universitaẗ Munchen -Institute for Advanced Study , funded by the  German Excellence Initiative ;  PLA  and  NLB  thank the  University of Edinburgh , the  Engineering and Physical Sciences Research Council EPSRC , and  Dr Christos Apostolidis ,  Institute of Transuranic Elements, ITU KIT, Germany ; LM thanks the  Institut Universitaire de France ,  Humboldt Foundation , CINES, and CalMip.
This article presents the In-service Aircraft of a Global Observing System (IAGOS) developed for operations on commercial long-range Airbus aircraft (A330/A340) for monitoring the atmospheric composition. IAGOS is the continuation of the former Measurement of OZone and water vapour on Airbus In-service airCraft (MOZAIC) programme (1994Á2014) with five aircraft operated by European airlines over 20 yr. MOZAIC has provided unique scientific database used worldwide by the scientific community. In continuation of MOZAIC, IAGOS aims to equip a fleet up to 20 aircraft around the world and for operations over decades. IAGOS started in July 2011 with the first instruments installed aboard a Lufthansa A340-300, and a total of six aircraft are already in operation. We present the technical aircraft system concept, with basic instruments for O 3 , CO, water vapour and clouds; and optional instruments for measuring either NO y , NO x , aerosols or CO 2 /CH 4.
In this article, we focus on the O 3 and CO instrumentation while other measurements are or will be described in specific papers. O 3 and CO are measured by optimised but well-known methods such as UV absorption and IR correlation, respectively. We describe the data processing/validation and the data quality control for O 3 and CO. Using the first two overlapping years of MOZAIC/IAGOS, we conclude that IAGOS can be considered as the continuation of MOZAIC with the same data quality of O 3 and CO measurements.
MOZAIC consisted in automatic and regular measurements, by five long-range passenger airliners, of O 3 and water vapour since 1994 and CO (as one of the main O 3 precursors and pollution tracer) since end of 2001. Additional NO y measurements (nitrogen reservoir to further understand the O 3 budget) was made on one aircraft for 5 yr (2001Á2005). A large database of measurements (38 494 flights over 1994Á2014, see map in Fig. 1 ) allows studies of chemical and physical processes in the atmosphere  (Ne´de´lec et al. , 2005) , validations of global chemistry transport models  (Stohl et al. , 2003)  and satellite retrievals along with studies on climatologies and trends  (Thouret et al. , 1998b). About 10 yr ago, back in 2004Á2005, the historical MOZAIC consortium has started the transition from a 'three successive phases' research programme into a research infrastructure preparatory phase with objectives of (1) making a long-term deployment on a 20 aircraft fleet ( !20 yr) and (2) making near real-time transmission of data.
The objective of this article is to present the methodology and performances of this new O3ÁCO instrumentation within the so-called Package1. The other associated instruments such as the water vapour sensor and the cloud droplet detector are described elsewhere  (Helten et al. , 1998; Beswick et al. , 2014; Neis et al. , 2014). IAGOS aims to achieve a level of data quality that other measurement methods are unable to attain deploying all over the world identical systems with regular quality assessment procedures, including calibration against reference instruments based on Global Atmospheric Watch (GAW) standard procedures. Therefore, we aim to demonstrate that the longest time series of O 3 (and CO) recorded since 1994  (and 2001 ) can be considered as uninterrupted using equivalent devices with the same maintenance/calibration procedures and under the responsibility of the same group.