dataesr/scientific-paragraphs-categorization huggingface dataset:
Kepler photometry Given the unprecedented quality of the photometric data provided by the Kepler space mission, binaries can be detected by searching for four different features. First, there are the classical eclipsing binary systems, which can be detected through the periodic eclipses. A second class of binaries are the ellipsoidal variables, in which the flux modulation originates from tidal interaction [e. g. [41] [42] [43]. A special case of these ellipsoidal variables are the eccentric cases, in which the flux modulation only occurs during the periastron passage. This leads to a periodic modulation of the observed object brightness. The characteristic shape of the light curve at periastron triggered the colloquial but controversial nick name of 'heartbeat stars'. This term, coined by  [43] , was debated during this conference.
Long period flux modulation A careful rereduction of the full available Kepler data set (Q0-Q17), as described in  [56, 57] , reveals that this system exhibits strong flux modulations (see top panel of Figure  3 ). Among the red-giant stars observed with Kepler, photometric rotational variability is found only for a small fraction of stars. In a very recent study, only ∼300 redgiant stars were found to exhibit surface modulation in the range of a few to several hundred days  [58]. Applying optimised techniques  [59, 60]  to the light curve of this system, such as the wavelet analysis depicted in Figure  4 , we find the period of the modulation to be ∼130 d. The photometric period, likely originating from stellar rotation of the primary component is close to the orbital period of ∼120 d. By comparing with values of solar and anti-solar differentially surface rotation reported in the literature [e.
This indicates that the primary contributes substantially more flux to the integrated brightness of the system than the secondary. This indicates that the fundamental parameters of both stars are quite different for these stars with a mass ratio close to unity. The final orbital solution was used to extract a 20 nm wide spectral segment around the H β line as well as the Mg i triplet which were subsequently used to derive the spectroscopic analysis with the Grid Search in Stellar Parameters (gssp 1  ) software package by  [67]. In such an analysis the contribution of both components to the total flux need to be taken into account and correctly normalised to the individual continuum flux. Although the mass ratio derived from the solution of spectral disentangling indicates that the masses of the two stars is very similar the spectroscopic analysis finds that both components differ substantially in terms of the effective temperature with a difference of 600 K.
Acknowledgements. We acknowledge the work of the team behind Kepler. Funding for the Kepler Mission is provided by  NASA's Science Mission Directorate. We thank the technical team as well as the observers of the Hermes spectrograph and Mercator Telescope, operated on the island of  La Palma  by the  Flemish Community , at the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias. PGB, RAG and BM acknowledge the  ANR (Agence Nationale de la Recherche, France)  program  IDEE  (n •  ANR-12-BS05-0008 ) " Interaction Des Etoiles et des Exoplanetes". PGB, RAG, StM  and BM also received funding from the  CNES  grants at  CEA. KP was supported by the  Croatian Science Foundation  grant  2014-09-8656. E. C. is funded by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement N • 312844 (SPACEINN).
The discrete spatial biharmonic problems in the basic preconditioner ( §4. 1) and on the coarsest level of the multigrid preconditioner ( §4. 2) are solved in Matlab with "backslash". Temporal mesh refinements Temporal mesh refinements Temporal mesh refinements Temporal mesh refinements   The eigenvalue λ ω max in the scaled mass matrix smoother in Section 4. 2 is obtained from λ max = λ 0 max + ω 2 , where λ 0 max is approximated using the Matlab eigs routine on M -1 C 0 with flag 'LM', the options opt. isreal = 1, opt. issym = 0, and with the default tolerance (the matrices are assembled in the B-spline basis with a modification at the boundary ensuring the homogeneous Neumann boundary condition). Unless specified otherwise, we use the W cycle with n smooth = 5 pre-and post-smoothing steps on each level.
The minimizer of (  48 ) is approximated by a Newton iteration on the derivative of the argument with 20 iterations initializing with the values of the prior. The use of the Newton iteration (rather than the implementation of the proximal operator as in  [9, Appendix] ) is justified because the second derivative of the functional is still continuous in our examples. This allows to vectorize the Newton iteration in our Matlab implementation, so that Step B takes significantly less time than Step A. The minimization problem (49) is solved approximately by a Hessian trust-region algorithm implemented in the Matlab routine fminunc, and the result is used as the starting value for the same procedure until the relative improvement in the • V norm is less than 10 -10.
System description The identification system we used is a simplified and partial implementation of the system proposed in  Constant and Nivre (2016) , which is in itself a mild extension of an arc-standard dependency parser  (Nivre, 2004). Constant and Nivre (2016)  proposed a parsing algorithm that jointly predicts a syntactic dependency tree and a forest of lexical units including MWEs. In particular, in line with  Nivre (2014) , this system integrates special parsing mechanisms to deal with lexical analysis. Given that the shared task focuses on the lexical task only and that datasets do not always provide syntactic annotations, we have modified the structure of the original system by removing syntax prediction, in order to use the same system for all 18 languages. A transition-based system consists in applying a sequence of actions (namely transitions) to incrementally build the expected output structure in a bottom-up manner.
Each transition is Figure  1 : Transition sequence for tagging He 1 took 2 this 3 argument 4 into 5 account 6. usually predicted by a classifier given the current state of the parser (namely configuration). A configuration in our system consists of a triplet c = (σ, β, L), where σ is a stack containing units under processing, β is a buffer containing the remaining input tokens, and L is a set of processed lexical units. The processed units correspond either to tokens or to VMWEs. When corresponding to a single token, a lexical unit is composed of one node only, whereas a unit representing a (multi-token) VMWE is represented as a binary lexical tree over the input tokens. Every unit is associated with a set of linguistic attributes (when available in the working dataset): its actual form, lemma, part-of-speech (POS) tag, syntactic head and label.
Basic Linguistic Features First of all, depending on their availability in the working dataset and on the activation of related settings (cf. G and J in Table  1 ), we extracted linguistic attributes in order to generate features such as S 0 l, S 0 p and S 0 w where p, l and w stand for the lemma, the part of speech, and the word form respectively. The same features are extracted for unigrams S 1 , B 0 and B 1 (when used) (cf. E in Table  1 ). When enabled, the bigrams features for the pair XY of elements are XpYp, XlYl, XwYw, XpYl and XlYp. The trigram-based features are extracted in the same way. Basically, the involved bigrams are S 1 S 0 , S 0 B 0 , S 1 B 0 and S 0 B 1 (cf. setting F in Table  1 ), but we also added the S 0 B 2 bigram for a few languages (cf. N in Table  1 ). For trigrams, we only used the features of the S 1 S 0 B 0 triple (cf.
I in Table  1 ). Finally, because the datasets for some languages do not provide the basic linguistic attributes such as lemmas and POS tags, we tried to bridge the gap by extracting unigram "morphological" attributes when POS tag and lemma extraction settings were disabled (cf. G and J in Table  1 ). The features of S 0 for such languages would be S 0 w, S 0 r, S 0 s where r and s stand for the last two and three letters of S 0 w respectively.
Stack-length Features Using the length of the stack as an additional feature (cf. O in Table  1 ) has also proven beneficial during our feature tuning. Finally, it is worthwhile to note that system settings (cf. Table  1 ) interact when used to generate the precise set of features. For instance if lemma extraction is disabled (code G) while bigram extraction is enabled (code F), the produced features for e. g. the S 1 S 0 bigram would not include the following features: S 1 lS 0 l, S 1 pS 0 l and S 1 lS 0 p.
Results Table  2  summarizes the results of the system performance over all the languages proposed by the shared task. Each row of the table displays its per-MWE and per-token F-scores for a given language (identified by its ISO 639-1 code) for test dataset, on top of a 5-fold cross-validation (CV) per-MWE F-score on training dataset. The system settings are represented as a sequence of codes described in Table  1. We can observe that results are very heterogeneous. For instance, five languages (CS, FA, FR, PL, RO) are above 0. 70 per-MWE F-score in the case of cross-validation, while seven languages (DE, HE, HU, IT, LT, MT, SV) are below 0. 30. In general, we can see an approximative linear correlation between the number of training VMWEs and the performance. This suggests that the size of training datasets is not large enough as systems' performance does not converge.
QTL mapping on adult plants The doubled haploid (DH) mapping populations S/M  (Kleinhofs et al. 1993 ) and OWB  (Costa et al. 2001)  were used to map QTLs for non-hypersensitive quantitative resistance at adult plant developmental stage, heading stage Z51-Z55 on Zadoks' growth scale  (Zadoks et al. 1974). The prehaustorial, nonhypersensitive resistance conferred by Rphq11 and rphq16 was confirmed by microscopic observations (data not shown). The S/M population comprises 150 DH lines, and the OWB population comprises 94 DH lines. For QTL mapping, all marker positions were extracted from the "Barley, Integrated, Marcel 2009" linkage map [linkage map and segregating marker data are available at http://wheat. pw. usda. gov/GG2/index. shtml;  (Aghnoum et al. 2010) ]. Data comprise 3561 segregating markers in S/M and 882 in OWB.
It is among the most informative measures of barley partial resistance to leaf rust and is relatively easy to evaluate  (Niks et al. 2011; Parlevliet 1979). The relative latency period (RLP50A) was calculated, relative (in %) to the LP50A on Steptoe for S/M and to the LP50A on Dom for OWB. ANOVA was performed using GenStat® 14th edition (VSN International Ltd. 2011). QTLs were mapped using MapQTL®6 (van Ooijen 2009). QTL mapping was performed for each repetition independently. A permutation test with 1000 permutations was performed for each repetition to set the LOD threshold to declare a QTL. The confidence interval of a QTL is the estimated LOD-2 support interval. The data for heading date (HD) and plant height (PH) of S/M were downloaded from GrainGenes (http://wheat. pw. usda. gov/ggpages/SxM/phenotypes.
Two BC 1 plants were selected from which 52 BC 1 S 1 plants were genotyped with flanking markers and phenotyped to perform a dominance study of rphq16 (Supplementary Material, Fig. S1 ). The first leaves of the 97 F 3 seedlings for Rphq11 and 52 BC 1 S 1 seedlings for rphq16 were inoculated with Ph. 1. 2. 1 following the method of  Qi et al. (1998). Their latency period was scored (LP50S) and the relative latency period (RLP50S) calculated, relative to SusPtrit (in %). The plants were grouped according to their QTL allele, homozygous Steptoe or Dom (AA), and heterozygous (AB) and homozygous SusPtrit (BB), to estimate the effect and the dominance or recessiveness of Rphq11 and rphq16. Plants that had a recombination between the QTL flanking markers were excluded from the analysis. The data were analysed with unbalanced one-way ANOVA using GenStat® 14th edition (VSN International Ltd.
This strategy allowed more individuals to be tested per fixed QTL recombinant at each round (from 5 to 10 individuals), increasing the confidence in the phenotype. SusPtrit was included in all disease tests as susceptible reference. The order of the fixed QTL recombinants was randomised within each experimental round, but individuals from the same fixed QTL recombinant were sown next to each other. Disease tests were performed at seedling stage following the method of  Qi et al. (1998). The LP50S was measured. The relative latency period on seedlings (RLP50S) was calculated by setting SusPtrit at 100. Data from different rounds of disease test were analysed together under a linear mixed model with GenStat® 14th edition (VSN International Ltd. 2011). The significance of difference in mean RLP50S between fixed QTL recombinants and SusPtrit was determined based on the least significant difference (LSD, P < 0.
Sequence information of targeted restriction fragment length polymorphism (RFLP) markers and transcript-derived markers (TDMs) was used to design specific primer pairs. For RFLPs, sequences were downloaded from the GrainGenes database (http://wheat. pw. usda. gov/GG2/index. shtml). For TDMs, unigene sequences were downloaded from the Barley SNP Database (http://germinate. scri. ac. uk/barley_ snpdb/dbStats_contig. html)  (Potokina et al. 2008). For sequence-tagged site (STS) markers, the primer sequences were obtained directly from the GrainGenes database. The primer sequences of the simple sequence repeat (SSR) markers were obtained from literature  (Varshney et al. 2007). Sequence for the Diversity Arrays Technology (DArT) marker ctg15632 and primers for the Cleaved Amplified Polymorphic Sequence (CAPS) marker Uni19962 have been reported elsewhere  (Boyd and Horsley 2007).
Approach II Conserved microsynteny between barley, rice, and Brachypodium distachyon was also used to generate new markers closely linked to Rphq11 and rphq16. The sequences of EST-based markers mapped in the vicinity of Rphq11 and rphq16 were used for BLAST searches against rice and B. distachyon genome databases; i. e. , the Rice Genome Annotation Project BLAST search (http://rice. plantbiology. msu. edu/analyses_ search_blast. shtml) and the B. distachyon BLAST portal (http://plants. ensembl. org/Brachypodium_distachyon). Rice and B. distachyon gene sequences within the identified synteny blocks were in turn used for BLAST against the barley EST tentative consensus (TC) sequences from the barley TIGR Gene Indices database (http://compbio. dfci. harvard. edu/tgi/). Only barley TC sequences with a BLAST hit having an E-value ≤10 -15 were further considered for primer design and marker development.
To maximise the chance of developing markers that map in the target regions of the barley genome, only barley TC sequences having an homologous gene in the syntenic regions of both rice and B. distachyon were further considered for primer design and marker development. Primers were designed using the Lasergene software (DNASTAR® 8 Inc. , Madison, WI, USA). For each primer pair, a gradient PCR was performed to determine the optimal annealing temperature. Sequencecharacterized amplified region (SCAR) markers were obtained by finding length polymorphism or allelespecific amplification directly after PCR on parental lines. For primers that amplified fragments of the same size in parental lines, CAPS markers were developed. The PCR products were sent for sequencing (BaseClear, Leiden, the Netherlands).
SNPs were identified from the sequence obtained, using the Lasergene software. The dCAPS finder program [http://helix. wustl. edu/ dcaps/dcaps. html; (Neff et al. 2002) ] was then used to find discriminating restriction enzymes. Markers developed based on TDMs, synteny, and eQTL candidate genes were named with the prefix WBE for Wageningen Barley ESTs.
Acknowledgements The authors thank their colleagues in  IPK at Gatersleben, Germany , for permission to use the confidential sequences to run three SSR markers listed in Tables S1 andS3. The authors wish to thank the  University Malaysia Sarawak, Malaysia , for granting the fellowship to  FKS Yeo  to work on this PhD thesis in Wageningen and  Prof RGF Visser  for hosting FKS Yeo for that purpose at the  Laboratory of Plant Breeding. We particularly wish to thank  Anton Vels  for his careful maintenance of plant materials.
In this paper, we implement the algorithm proposed by  Aguilera et al. (2006)  to deal with many possibly correlated covariates in CR models, a method we refer to as principal component capture-recapture (P2CR). We apply this new approach to a case study on survival of Snow petrels (Pagodroma nivea) that is possibly affected by climatic conditions. In this example, the issue of multicollinearity occurs, and summarizing the set of covariates in a subset of lower dimension is also crucial to get precise survival estimates. Overall, P2CR models can be fitted with statistical programs that perform PCA and CR data analysis. The data and R code are available from GitHub at https://github. com/oliviergimenez/p2cr.
ACKNOWLEDGEMENTS We greatly acknowledge all of the wintering fieldworkers involved in the monitoring programs in Terre Adélie since 1962, and  Dominique Besson  for the management of the database. The study on petrels was supported by  Expéditions Polaires Françaises ,  Institut Paul Emile Victor  (program IPEV 109, resp. H. Weimerskirch) and  Terres Australes et Antarctiques Françaises. LITERATURE CITED Aguilera , A. M. , Escabias,  M. & Valderrama ,  M. J. ( 2006 ) Using principal components for estimating logistic regression with high-dimensional multicollinear data. Computational Statistics and Data Analysis, 50, 1905-1924. Barbraud, C. , Rolland, V. , Jenouvrier, S. , Nevoux, M. , Delord,  K. & Weimerskirch , H. (2012) Effects of climate change and fisheries bycatch on Southern Ocean seabirds: A review.
Chromatin immunoprecipitation (ChIP) and targeted qPCR assays were performed on protein-DNA complexes extracted from normal human epidermis. Interestingly, an enhancer signature, as determined by enrichment of H3K27ac and H3K4me1 marks and absence of the H3K4me3 mark, was only found for the A2, B2 and CNE12 sequences (Fig. 1e ). In these assays, a difference in amplification efficiency between primer sets constitutes a limitation for comparison of the data obtained using different primers. Luciferase reporter assays demonstrated that only the wild-type sequences encompassing the A2, B2 and CNE12 variant positions had enhancer activity in HaCaT keratinocytes (Fig. 1f ). This activity was reduced when mutated constructs were used (Fig. 1g ). Given that a subset of eRNAs are broadly transcribed  3, 4  and that the A2, B2 and CNE12 sequences bound RNA polymerase II (RNA Pol II)  5  (Fig.
Methods Reactions were performed in triplicate on an ABI Prism 7000 machine (PE Applied Biosystems). Input recovery was calculated using a ChIP-qPCR data analysis calculation shell (Sigma-Aldrich). The oligonucleotides used in these analyses are listed in Supplementary Table  1. In determining the chromatin signature, the efficiency of the primers was calculated on sonicated DNA (input) after several dilutions and small differences between efficiencies were observed between the studied sequences (A1, 87%; A2, 87%; A3, 90%; B1, 90%; B2, 91%; CNE12, 101%). Deletion of enhancer regions using the CRISPR-associated RNA-guided endonuclease Cas9. A lentiviral-based single vector (LentiCRISPR v2) that simultaneously delivers Cas9, sgRNA and a puromycin selection marker engineered by the Zhang laboratory was purchased from Addgene (http://www.
Transcriptomic analyses. Microarray experiments were performed on Affymetrix GeneChip Human Transcriptome Array 2. 0 (a genome-wide array with 70,523 probe sets). CEL files with raw data were imported into R/Bioconductor using the Oligo package. Expression levels were calculated using the RMA algorithm from the affy package, and flags were computed using a custom algorithm within R. Assuming that a maximum of 80% of genes are expressed, we set the 20% of probes with the lowest values for each microarray as background. A threshold was fixed at 2 s. d. above the mean intensity for the background. All probes with normalized intensity measures lower than the computed threshold were flagged as 0 instead of 1. An unsupervised analysis step was done by clustering before any supervised statistical comparison to identify natural groups among the tested samples and to detect potential outliers.
For each comparison, a list was created by filtering to include probes flagged as "background" for no more than half of the samples according to flagged measurements for the relevant chips. Group comparisons were performed using Student's t-tests, and lists were filtered to include probes with P value ≤5% and fold change ≥1. 2. Heat maps were created using a custom R script and the Java TreeView software. Functional analyses of the resulting lists of genes were performed using Ingenuity Pathway Analysis (http://www. ingenuity. com/). Tumor growth assays using UW-BCC1-T2 cells. Animal experiments were performed in accordance with Swiss guidelines and regulations for the care and use of laboratory animals. Adult (aged 5-7 weeks) male AGR129 mice (deficient for IFN-α/β receptor, IFN-γ receptor and RAG-2) (specific-pathogenfree (SPF)-housed homozygous 129/C57BL/6, B6.
(h) Schematic representation of the target sites of sgRNAs around the A2, B2 and CNE12 sequences. sgRNAs that created indels in enhancer regions are shown in red, and sgRNAs that failed to delete enhancer regions are shown in black. The editing efficiency in genomic DNA (gDNA cut) was measured using tracking of indels by decomposition (TIDE) software (Supplementary Figs. 6-8). The expression of ACTRT1 mRNA was measured in RT-PCR assays, and results were normalized to PGK1 mRNA levels. The experiment was performed in triplicate, and results are representative of two independent experiments. Data in c-h are expressed as the mean ± s. d. For box-and-whisker plots, midlines represent the median; upper and lower perimeters extend from the 25th to the 75th percentile; and whiskers extend from minimum to maximum.
, including statements of data availability and any associated accession codes and references, are available in the online version of the paper. Subjects and samples. A total of 48 individuals (from six BDCS families) and many of their unaffected relatives underwent a comprehensive clinical examination. All affected individuals had two or more of the following symptoms of BDCS upon clinical examination or in their personal medical history: hypotrichosis, facial milia, follicular atrophoderma and BCC. All individuals provided their written, informed consent for genetic studies. The study was approved by the local investigational review board (Hôpital Necker-Enfants Malades, Paris, France). DNA was extracted from peripheral blood lymphocytes by conventional phenol-chloroform purification. Paraffin-embedded skin tumor samples were obtained from six individuals who underwent surgical excision of biopsy-confirmed BCCs.
ACKNOWLEDGMENTS The authors wish to thank  P. Guigue ,  A. Vincent-Salomon ,  L. Fuhrmann ,  M. Oufadem ,  S. Thomas ,  S. Petit ,  F. Mouchrik ,  V. Geoffroy ,  A. Julien ,  O. Duchamp de Lageneste ,  L. Galmiche-Rolland ,  J. Cottineau ,  J. Rossignol ,  S. Saunier  and  D. Pouly  for technical assistance and advice;  M. Sahbatou  for performing linkage analysis;  M. -E. Huang  for providing cell lines for this study;  A. Schmitt  and  J. -M. Masse  at the  Cochin Imaging Facility  ( CNRS UMR 8104 ) for their assistance and help with transmission electron microscopy;  C. Bole-Feysot  at the  Genomic Platform of IMAGINE Institute  for acquiring transcriptomic data;  M. Garfa-Traoré ,  N. Goudin  and  R. Desvaux  at the  Cell Imaging Platform of IMAGINE Institute  for technical assistance and advice;  P.