dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  M. Lichten  and  P. Hsieh  for critical feedback on the manuscript, and  Florencia Pratto  for many helpful discussions and suggestions. This study used the high-performance computational capabilities of the Biowulf Linux cluster at the  National Institutes of Health  (http://biowulf. nih. gov). This research was supported by  National Institutes of Health  grant  1R01GM084104  from the  National Institute of General Medical Sciences  (G. V. P. ),  March of Dimes Foundation  grant  1-FY13-506  (G. V. P. ), and the  National Institute of Diabetes and Digestive and Kidney Diseases Intramural Research Program (R. D. C. -O. ).
0  [79] , is a Matlab implementation that uses in a non-intrusive way a finite element code to compute the spatiotemporal sensitivity fields (Fig. 2a ) in addition to the current estimates of the displacement fields and reaction forces. The inputs to such simulations are the measured displacements of some boundaries of the ROI. In the present case, the commercial finite element package Abaqus/Standard is used with its built-in constitutive laws. C++ kernels compute all the other data needed to perform DVC analyses, namely, the DVC matrix [M], the instantaneous voxel residuals ρ(x, t), and the instantaneous nodal residual vector {b(t)}. Binary MEX files are generated and called in the Matlab environment to compute the Hessians and residual vectors to be utilized in the Gauss-Newton schemes introduced above.
Bacterial growth and DNA extraction were performed as previously described  (9, 10). Genomic DNA was sequenced using HiSeq 2000 technology (Illumina, Inc. , San Diego, CA, USA), and the paired-end library was built using the MGX facility of the CNRS in Montpellier, France. There is a total of 13,785,510 highquality reads giving an average 720-fold coverage of the genome, which was assembled using CLC Genomics Workbench version 6 software (http://www. clcbio. com). The resulting assembly consisted of 31 contigs, which were oriented on the basis of available sequences of GAS. After reassembling, 23 gaps persisted, which were filled by PCR followed by Sanger sequencing. Genome annotation was performed in parallel using the RAST server  (11)  and the NCBI PGAP (http://ncbi. nlm. nih. gov/genome/annotation _prok).
Interestingly, further analysis identified a single mutation in ropB coding for the stand-alone regulator RopB, resulting in a premature stop codon. This null allele may be associated with an absence of SpeB activity or, as described recently, may incur an overall fitness cost for GAS, preventing its fixation in the population  (15). The annotated sequence of this rare GAS emm66 genotype and newly emerged clone provides a genetic basis for comparison in order to enhance the understanding of clone emergence and hopefully control measures and vaccine design strategies. Nucleotide sequence accession number. The complete genome sequence of strain STAB13021 has been deposited in the NCBI GenBank under the accession number CP014278, as part of Bioproject PRJNA310843.
This paper presents the characterization of the in-flight beams, the beam window functions, and the associated uncertainties for the Planck Low Frequency Instrument (LFI). The structure of the paper is similar to that presented in the 2013 Planck release; the main differences concern the beam normalization and the delivery of the window functions to be used for polarization analysis. The in-flight assessment of the LFI main beams relies on measurements performed during observations of Jupiter. By stacking data from seven Jupiter transits, the main beam profiles are measured down to -25 dB at 30 and 44 GHz, and down to -30 dB at 70 GHz. It has been confirmed that the agreement between the simulated beams and the measured beams is better than 1% at each LFI frequency band (within the 20 dB contour from the peak, the rms values are 0.
Introduction This paper, one of a set associated with the 2015 release of data from the Planck 1  mission, describes the beams and window functions of the Low Frequency Instrument (LFI). The structure of the paper is similar to that presented in Planck Collaboration IV (2014); the main differences concern the beam normalization and the delivery of the window functions to be used for polarization analysis. We summarize here the general framework and the nomenclature adopted, which is the same as that used in Planck Collaboration IV  (2014). The LFI optical layout is composed of an array of 11 corrugated feed horns, each coupled to an orthomode transducer (OMT), which splits the incoming electromagnetic wave into two orthogonal, linearly polarized components. Planck Collaboration: LFI beams and window functions Thus, the LFI observed the sky with 11 pairs of beams, associated with 22 pseudo-correlation radiometers.
Planet data The LFI in-flight main beam reconstruction is based on the same method adopted in the past release (Planck Collaboration IV 2014). In Fig. 3  the LFI footprint on the sky is shown for both polarization arms. In contrast to the analogous figure reported in Planck Collaboration IV (2014), here the beams are plotted down to -30 dB at 70 GHz, and -25 dB at 30 and 44 GHz. To assess the beam properties, we used seven Jupiter transits  (Planck Collaboration II 2015). The first four transits (J1 to J4) occurred in nominal scan mode (spin shift 2 arcmin, 1 degree per day), and the last three scans (J5 to J7) in deep mode (shift of the spin axis between rings of 0. 5 arcmin, 15 arcmin per day). Figure  2  shows two Jupiter scans at 70 GHz: the first one, in nominal mode; and the seventh, in deep mode.
Conclusions In this paper we discussed: (i) the improvement in the LFI main beam reconstruction with respect to the 2013 release; (ii) the beam normalization convention adopted in the LFI pipeline; (iii) the temperature and polarized beam window functions; and (iv) the error budgets on the beam parameters and window functions. The in-flight assessment of the LFI main beams relied mainly on the measurements performed during seven Jupiter crossings, the first four transits occurring in nominal scan mode and the last three scans in deep mode. The calibrated data from the Jupiter scans were used to determine the scanning beams: the signal-to-noise ratio for these data makes it possible to follow the LFI beams profile down to -30 dB. These measurements have been used to further validate the beam model presented in 2013 (GRASP beams properly smeared to take into account the satellite motion).
It does not pretend to be inclusive of all contributions. The Planck-LFI project is developed by an International Consortium led by Italy and involving Canada, Finland, Germany, Norway, Spain, Switzerland, UK, and USA. The Italian contribution to Planck is supported by the Italian Space Agency (ASI) and INAF. This work was supported by the Academy of Finland grants 253204, 256265, 257989, and 283497. We acknowledge that the results of this research have been achieved using the PRACE-3IP project (FP7 RI-312763) resource Sisu based in Finland at CSC. We thank CSC -IT Center for Science Ltd (Finland) for computational resources. We acknowledge financial support provided by the Spanish Ministerio de Ciencia e Innovaciõn through the Plan Nacional del Espacio y Plan Nacional de Astronomia y Astrofisica.
We acknowledge the Max Planck Institute for Astrophysics Planck Analysis Centre (MPAC), funded by the Space Agency of the German Aerospace Center (DLR) under grant 50OP0901 with resources of the German Federal Ministry of Economics and Technology, and by the Max Planck Society. This work has made use of the Planck satellite simulation package (Level-S), which is assembled by the Max Planck Institute for Astrophysics Planck Analysis Centre (MPAC). We acknowledge financial support provided by the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U. S. Department of Energy under Contract No. DE-AC02-05CH11231. Some of the results in this paper have been derived using the HEALPix package. The Planck Collaboration acknowledges the support of: ESA; CNES and CNRS/INSU-IN2P3-INP (France); ASI, CNR, and INAF (Italy); NASA and DoE (USA); STFC and UKSA (UK); CSIC, MINECO, JA, and RES (Spain); Tekes, AoF, and CSC (Finland); DLR and MPG (Germany); CSA (Canada); DTU Space (Denmark); SER/SSO (Switzerland); RCN (Norway); SFI (Ireland); FCT/MCTES (Portugal); ERC and PRACE (EU).
Acknowledgements. Planck is too large a project to allow full acknowledgement of all contributions by individuals, institutions, industries, and funding agencies. The main entities involved in the mission operations are as follows. The  European Space Agency (ESA) operates the satellite via its Mission Operations Centre  located at  ESOC (Darmstadt, Germany ) and coordinates scientific operations via the Planck Science Office located at  ESAC (Madrid, Spain). Two Consortia , comprising around 50 scientific institutes within Europe, the  USA ,.
The PPDB is an automatically built database which contains millions of paraphrases in different languages. Paraphrases in this resource are associated with features that serve to their ranking and reflect paraphrase quality. This context-unaware ranking captures the semantic similarity of paraphrases but cannot serve to estimate their adequacy in specific contexts. We propose to use vector-space semantic models for selecting PPDB paraphrases that preserve the meaning of specific text fragments. This is the first work that addresses the substitutability of PPDB paraphrases in context. We show that vector-space models of meaning can be successfully applied to this task and increase the benefit brought by the use of the PPDB resource in applications.
Introduction Paraphrases are alternative ways to convey the same information and can improve natural language processing by making systems more robust to language variability and unseen words. The paraphrase database (PPDB)  (Ganitkevitch et al. , 2013)  contains millions of automatically acquired paraphrases in 21 languages associated with features that serve to their ranking. In PPDB's most recent release (2. 0), such features include natural logic entailment relations, distributional and word embedding similarities, formality and complexity scores, and scores assigned by a supervised ranking model  (Pavlick et al. , 2015b). These features serve to identify good quality paraphrases but do not say much about their substitutability in context. To judge the adequacy of paraphrases for specific instances of words or phrases, the surrounding context needs to be considered.
This can be done using vector-space models of semantics which calculate the meaning of word occurrences in context based on distributional representations  (Mitchell and Lapata, 2008; Erk and Padó, 2008; Dinu and Lapata, 2010; Thater et al. , 2011). These models capture the influence of the context on the meaning of a target word through vector composition. More precisely, they represent the contextualised meaning of a target word w in context c by a vector obtained by combining the vectors of w and c using some operation such as component-wise multiplication or addition  (Thater et al. , 2011). We use this kind of representations to rank the PPDB paraphrases in context and retain the ones that preserve the semantics of specific text fragments. We evaluate the vector-based ranking models on data hand-annotated with lexical variants and compare the obtained ranking to confidence estimates available in the PPDB, highlighting the importance of context filtering for paraphrase selection.
3 Paraphrase candidates for a target word are then ranked according to the cosine similarity of their basic vector representation to the contextualised vector of the target. 4 3 Experimental Set-up stances manually annotated with single and multiword substitutes. The manual annotations serve to evaluate the performance of the vector-space models on the task of ranking PPDB paraphrases. For each annotated English target word (noun, verb, adjective or adverb) in COINCO, we collect the lexical paraphrases (P = {p 1 , p 2 ,. , p n }) available for the word in each PPDB package (from S to XXL). 5  We do not filter by syntactic label as annotations often include substitutes of different grammatical categories. Table  1  shows the number of COINCO tokens with paraphrases in each PPDB package and the average size of the retained paraphrase sets.
AgigaSim reflects the distributional similarity of the paraphrases in the Annotated Gigaword corpus  (Napoles et al. , 2011). As noted by  Kremer et al. (2014) , the whole-document annotation in COINCO faces the natural skewed distribution towards predominant senses which favors non-contextualised baseline models. The good performance of Ppdb2 is due to the use of a supervised scoring model trained on human judgments of paraphrase quality. The human judgments were used to fit a regression to the features available in PPDB 1. 0 plus numerous new features including cosine word embedding similarity, lexical overlap features, WordNet features and distributional similarity features. 8 The small difference observed between the Ppdb2 and the syntactic models score in the XXL package is highly significant.
For the moment, Ppdb2 scores are available in the PPDB only for English. Since the vector-space methodology is unsupervised and language independent, it could be easily applied to paraphrase ranking in other languages. The performance of the models remains high with the XL package which contains paraphrase sets of reasonable size (about 10 paraphrases per word) and ensures a high coverage, and lowers in XXL which contains 185 paraphrases in average per word (cf. Table  1 ). To use this package more efficiently, one could initially reduce the number of erroneous paraphrases on the basis of the Ppdb2 score which provides a good ranking of the XXL package contents before applying the vector-based models. The increase in GAP score observed when words with one paraphrase are considered shows that these paraphrases are often correct.
Genetic Analysis Thirty-two families with adRP were enrolled in the study: three Arab-Muslim families of Palestinian origin and 29 Israeli Jewish families. Genomic DNA was extracted from peripheral blood of the participants by using FlexiGene DNA kit (QIAGEN, Hilden, Germany). Nine samples underwent an in-house whole exome sequencing (WES) analysis (see Supplementary Methods). DNA samples of the 32 index cases were screened for mutations in RHO (NCBI Reference Sequence NM_ 000539. 3) exons (and flanking intronic sequences) with PCR followed by Sanger sequencing. Primers (Supplementary Table  S1 ) were designed by using the PRIMER3 program (http://frodo. wi. mit. edu/; provided in the public domain by the Whitehead Institute for Biomedical Research, Cambridge, MA, USA). For fluorescent PCR of RHO-exon 3, the forward primer was 5 0 labeled with Fam.
The exponential growth of studies on the biological response to ocean acidification over the last few decades has generated a large amount of data. To facilitate data comparison, a data compilation hosted at the data publisher PANGAEA was initiated in 2008 and is updated on a regular basis (doi:10. 1594/PANGAEA. 149999). By January 2015, a total of 581 data sets (over 4 000 000 data points) from 539 papers had been archived. Here we present the developments of this data compilation 5 years since its first description by  Nisumaa et al. (2010). Most of the study sites from which data have been archived are in the Northern Hemisphere and the number of archived data from studies from the Southern Hemisphere and polar oceans is still relatively low. Data from 60 studies that investigated the response of a mix of organisms or natural communities were all added after 2010, indicating a welcome shift from the study of individual organisms to communities and ecosystems.
, 2014)  but key uncertainties remain in our understanding of the impacts on organisms, life histories and ecosystems. The number of papers addressing biological responses to ocean acidification has increased steeply in the past decade, from 18 papers per year in 2004 to 365 papers in 2014  (Gattuso and Hansson, 2011; OA-ICC bibliographic database, www. tinyurl. com/oaicc-biblio). It is challenging to compare and synthesize the results of these papers for two reasons. First, data are not easily discoverable and accessible because they are either not archived or archived in different data repositories in varying formats. Second, the carbonate Published by Copernicus Publications. chemistry and ancillary data are often reported in different units and scales, and calculated using different sets of constants, making the carbonate chemistry across studies inconsistent.
For example, it is crucial to report the pH scale used, since pH reported on the free scale is about 0. 11 to 0. 12 units higher than on the total and seawater scales, respectively  (Zeebe and Wolf-Gladrow, 2001). A compilation of ocean acidification biological response data was initiated by the European Network of Excellence for Ocean Ecosystems Analysis (EUR-OCEANS) and the European Project on Ocean Acidification (EPOCA) in 2008  (Nisumaa et al. , 2010). This effort ended in 2012 when the EPOCA project came to an end but was resumed in the framework of the Ocean Acidification International Coordination Centre (OA-ICC) in 2013, in close collaboration with Xiamen University and the world data centre PANGAEA. The goal of the OA-ICC data compilation is to gather data on the biological response to ocean acidification (carbonate chemistry, biogeochemical processes and ancillary data) from published articles and to make them available in a coherent format to the scientific community.
As part of the effort, the carbonate system variables are recalculated in a consistent way. Data from papers that report at least two carbonate chemistry parameters, as well as temperature and salinity, are included in the compilation. The data compilation has been very well received by the ocean acidification community. It has been used in three meta-analyses  (Kroeker et al. , 2010 (Kroeker et al. , , 2013;; Liu et al. , 2010) , a modelling study  (Muller and Nisbet, 2014)  and cited in six other publications  (Fiorini et al. , 2011; Hendriks and Duarte, 2010; Hoppe et al. , 2012; Koeve and Oschlies, 2012; Meyer and Riebesell, 2014; Rokitta, 2012). We report here on the developments of this database 5 years since its first description by  Nisumaa et al. (2010). This update is timely, since 500 data sets from 439 papers (81 % of the total number included in the compilation) have been archived since the publication of  Nisumaa et al.
Data The compilation process described in  Nisumaa et al. (2010)  was followed to maintain consistency. Briefly, papers on the biological response to ocean acidification were identified by searching the OA-ICC news stream (http:// news-oceanacidification-icc. org/) or through the OA-ICC bibliographic database for older papers. Papers were excluded from the compilation when they only used levels of partial pressure of carbon dioxide (pCO 2 ) or pH which are not consistent with present-day levels or future scenarios. For example, data collected with a control condition with pCO 2 values below about 100 µatm or above 1700 µatm or pH T (on the total scale) values below about 7. 5 or above 8. 5 were excluded unless they are environmentally relevant at the study location. Data were requested from the authors by email.
Data were either provided by authors, or ex-Figure  1. Cumulative number of papers for which data have been included in the compilation ("archived"), papers for which data could not be obtained ("not obtained"), papers which reported less than two carbonate system parameters ("incomplete") and papers for which the data have been lost ("lost"). tracted from tables in the original paper, or downloaded from other data repositories such as the Biological and Chemical Oceanography Data Management Office (BCO-DMO), the British Oceanographic Data Centre (BODC) and the Australian Antarctic Data Centre (AADC). All the data sets archived in the framework of the OA-ICC (since 2013) as well as the projects EPOCA/EUR-OCEANS (2008-2012) have been given the tag "Ocean Acidification International Coordination Centre (OA-ICC)" in PANGAEA.
Between the beginning of this activity in 2008 and January 2015, 1026 relevant papers were identified. 581 data sets (over 4 000 000 data points) were archived from 539 of these papers. Data from a paper can be archived as several data sets (e. g. http://doi. pangaea. de/10. 1594/PANGAEA. 777725), which explains why out of 539 papers, there were 581 data sets archived. Data of the remaining 487 papers could not be added to the compilation for the following reasons (Fig. 1):  (1) less than two carbonate system parameters were measured in 176 papers, preventing the recalculation of the carbonate chemistry; (2) data from 295 papers could not be obtained from the authors; and (3) data from 16 papers were lost by authors. The first papers having investigated a biological response to decreased pH (e.
g. Vernon, 1895)  predate the definition of the pH scale by  Sørensen (1909)  and are obviously not amenable to be included in the data compilation. The earliest data included in the compilation were published in 1967  (Traganza, 1967). No data from the papers published during 1968-1993 could be archived because data were lost or could not be obtained. Data from 41 papers published during 1994 and 2006 were archived. The quantity of data archived for a given year follows the increase in publication rate of bio-logical response papers, with data from 11 papers archived for 2007 to 110 papers for 2014. More than 90 % of the data archived in the compilation come from papers published after 2007. All the references of papers included in the data compilation have been tagged with the keyword "OAICCdb" in the OA-ICC bibliographic database (www.
tinyurl. com/ oaicc-biblio). The OA-ICC bibliographic database was used to retrieve statistical information on the type of papers from which the data archived originated. Keywords describing the geographical region, type of organism and biological process studied, other factors manipulated, as well as the country of affiliation of the first author were extracted from the bibliographic database for statistical analysis. Results are presented as the percentage of papers from which data were archived. Information on salinity, temperature and carbonate chemistry data archived as part of the data compilation were extracted from the PANGAEA data warehouse. The analyses of these parameters were based on the percentage of data sets or count of data points.
Geographical coverage In the OA-ICC data compilation, the location of study sites indicates where the studied organisms were collected or the location of the natural communities investigated. The geographical location is not always clearly indicated in the papers, and the bibliographic database does not provide geographical information for experiments using organisms which have been cultured for a long time in the laboratory or collected from commercial hatcheries. The geographical areas which are best covered are the North Atlantic Ocean, North Pacific Ocean, South Pacific Ocean and Mediterranean Sea (33, 19, 17 and 10 %, respectively, of the papers indicating a geographical location). The Arctic Ocean, Baltic Sea, Southern Ocean, Red Sea, Indian Ocean and South Atlantic Ocean collectively represent only 21 % of the papers.