dataesr/scientific-paragraphs-categorization huggingface dataset:
(2015)  calculated the Lyα properties of high-z progenitors of a local Milky Way-like galaxy, and showed that the filamentary accreting gas produces Lyα photons efficiently via the excitation cooling process at z > 6. However, the mesh-based schemes of Lyα radiative transfer inevitably smooth out highly-resolved structures in SPH simulations, and might lead to a lack of accuracy in the Lyα photon transfer. In this paper, we develop a novel Monte Carlo scheme for Lyα radiative transfer adapted for SPH simulations, SEURAT (SPH scheme Extended with Ultraviolet line RAdiative Transfer). Some basic parts of SEURAT are the extension of the mesh-based Lyα radiation transfer code LICORICE  (Semelin et al. 2007). In SEURAT, SPH particles themselves are directly used to solve the radiation transfer unlike in the previous mesh-based codes.
Ray-tracing in SEURAT The method for integrating optical depths in our code is fairly distinct from those in the previous Monte Carlo schemes of line transfer, since our ray-tracing algorithm is optimized for using SPH particles directly The ray-tracing algorithm in SEURAT is similar to that in  Susa (2006)  except that rays are not always cast towards SPH particles. Fig. 1  shows the principle of the ray-tracing algorithm in SEURAT. After determining the direction of the light ray from a radiation source, we search the particle closest to the light ray (particle 1 in Fig. 1 ) from the list of neighbour particles of the source. Then we create a virtual grid point at the position where the perpendicular line intersects with the ray, and directly assign the physical quantities of the particle such as the density, the temperature and the velocity component (d • v) to the grid point (grid point s1 in Fig.
Comparison between SEURAT and LICORICE In this section, we compare the transfer in the Lyα lines with SEURAT to that with LICORICE which is a mesh-based RT code  (Semelin et al. 2007). col α = CLyαnH I ne, ( 24 ) where fα is the average number of the Lyα photons generated via the Case B recombination, (fα = 0. 68,  Osterbrock & Ferland 2006) , αB is the Case B recombination coefficient  (Hui & Gnedin 1997) , CLyα is the collisional excitation coefficient, CLyα = 3. 7 × 10 -17 exp(-hνα/kBT )T -1/2 erg s -1 cm 3  (Osterbrock & Ferland 2006 ). We cast 10 5 photon packets.
To solve Lyα radiative transfer in highly inhomogeneous media, special attention has been paid for the neighbour list construction. The neighbour list of the neighbour particle allows us to properly treat local optically thick regions. Furthermore, a scatter list is also required to perform the ray-tracing in systems having large density gradients. We have demonstrated that SEURAT reliably searches the neighbour particles, and correctly performs the ray-tracing in significantly clumpy media. As a result, we have confirmed that SEURAT is successfully designed to manage extremely optically thick and highly inhomogeneous media. Finally, we have compared the SEURAT to the meshbased code LICORICE. We have applied the codes to a model galaxy and calculated the emergent spectra. We have shown that for a static system the two methods produce very similar spectra.
ACKNOWLEDGEMENTS We are grateful to  A. Inoue  for fruitful discussions. The numerical simulations have been performed with COMA provided by  Interdisciplinary Computational Science Program in Center for Computational Sciences, University of Tsukuba , with the K computer provided by the  RIKEN Advanced Institute for Computational Science  and with Cray XC30 at  Center for Computational Astrophysics, NAOJ. This research was supported in part by  Grant-in-Aid for Scientific Research  (B) No. 15H03638  ( MU ),  Grant-in-Aid for Scientific Research  (A) No. 17H01110  (KH), and  Grantin-Aid for Young Scientists  (A) No. 17H04827  (HY) through  Japan Society for the Promotion of Science.
1 kit (Applied Biosystems, Foster City, CA, USA), according to producer's recommendations, and precipitated with 96% ethanol. Sequence reading was performed using Applied Biosystems equipment. Sequences were analyzed using BLASTn algorithm. The dendrogram was calculated using MEGA 4 software package  (Tamura et al. 2007 ) using F. oxysporum (GenBank sequence) and F. nygamai KF 337 (home made sequence) as an outgroup. Maximum parsimony heuristics were used with Bfill gaps^function. Bootstrapping was done using 1000 iterations. Only branches with at least 50% of support were considered and presented.
6A ). The IPA analysis of the molecular and cellular functions of these age-related lncRNAs showed that they were significantly (P < 0. 05) involved in lipid metabolism, small molecule biochemistry, molecular transport, carbohydrate metabolism and cell cycle (Supplementary Figure  S2 ). Finally, using qRT-PCR approach, we assessed the expression pattern of four lncRNAs (IL10RB-AS1, APOA1-AS, PSMB8-AS1 and LINC00548) in independent samples [five CC samples from younger patients (age <35 years) and five CC samples from older patients (age >40 years)]. The obtained results were in agreement with the sequencing data (Fig. 6B ).
RNA-seq data selection and analysis. Publicly available RNA-seq data were downloaded from the European Nucleotide Archive (ENA) database. We included 135 human samples: 10 MII oocytes, 10 cumulus granulosa samples and 97 pre-implantation embryos. In addition, we used 9 cumulus cells (CC) samples from younger patients and 9 CC from older patients. The accession numbers and detailed information on the RNA-seq data are listed in Supplementary Table  S8. Data were processed using the open Galaxy platform GalaxEast (http://galaxeast. fr). Raw RNA-seq reads were mapped to the human reference genome (GRCh37/hg19) using TopHat2  89  and reads were counted with HTSeq 90 using the galaxeast default parameters, as reported  91. We used Ensembl annotations (http://www. ensembl. org/ index. html) for protein-coding genes and long non-coding transcripts that included lncRNAs, long intergenic non-coding RNAs (lincRNAs), sense and antisense overlapping transcripts and pseudogenes.
Small non-coding RNAs, such as microRNAs, and ribosomal RNAs, which are not classified as lncRNAs, were removed from the analysis. Transcripts with a read detection in less than 50% of samples for each sample category were also removed. Dendrograms and heat maps were produced by using the Cluster and Treeview software packages  92. The average linkage method and the branching pattern in which the similarity between the lncRNA expression profiles could be visually assessed were used. Significant Analysis of Microarrays (SAM) (http://statweb. stanford. edu/~tibs/SAM/) with the Wilcoxon test and sample label permutation (n = 300) was performed to identify differentially expressed genes (lncRNAs and protein-coding mRNAs) in MII oocyte and cumulus granulosa samples. Genes were considered as differentially expressed at FDR < 0.
05 and with an absolute FC > 2. R studio (https://www. rstudio. com/) was used to run custom R scripts for the principal component analysis (PCA). Scatter plots were generated using Excel, based on the differentially expressed gene analysis and the color was determined according to the filtering criteria. Gene ontology (GO) and network analysis. LncRNA and mRNA functions were assessed with Gene ontology (GO) and the PANTHER tool (http://pantherdb. org) and the Ingenuity Pathway Analysis (IPA) platform (http://www. ingenuity. com). LncRNA and mRNA data were integrated with molecular interactions using IPA. LncRNAs and mRNAs (with known gene symbols) and their corresponding expression values were uploaded in the IPA software. Each gene symbol was mapped to the corresponding gene object in the Ingenuity Pathways Knowledge Base.
Gene networks were algorithmically generated based on their connectivity and assigned a score. The score is a numerical value used to rank networks according to how relevant they are to the genes in the input dataset, but may not be an indication of the network quality or significance. The score takes into account the number of focus genes in the network and the network size to approximate how relevant the network is to the original list of focus genes. Prediction of binding sites on lncRNA targets. The Single Tissue cis-eQTL data were downloaded from the GTEx website (v6p GTEx release, GTEx_Analysis_v6p_eQTL. tar)  29  using the UCSC liftover tool. Then, eQTL SNPs were located in lncRNAs and mRNAs using BedTools  93. Fisher's exact tests were performed using R.
Oocyte and CC samples for RT-PCR. For data validation by RT-PCR, human oocyte and CC samples were collected from women (without endometriosis and polycystic ovary syndrome) who underwent conventional IVF or intracytoplasmic sperm injection (ICSI) at Laboratoire de Biologie de la Reproduction, François Mitterrand Hospital, Dijon, France. All patients signed an informed consent form. Moreover, the material used in the present study would have been discarded. This study was approved by the Ethical Committee of DIJON (authorization number #DC-2011-1332) and the methods were carried out in accordance with the approved guidelines. Oocytes were retrieved transvaginally 36 h after human chorionic gonadotropin (hCG) injection under ultrasound guidance. Immature and unfertilized MII oocytes were collected 24 h post-insemination and CCs were mechanically removed from MII oocytes before ICSI, as previously described  28, 33.
9 ('GRS-363'), SNPs representing distinct loci  9  ('GRS-377'), and directly-genotyped All participants were of >90% European ancestry according to genetic markers of ancestry. a OCAC is an international collaboration of largely case-control studies. See Supplementary Table  1  for study names and references. To maximise power, nine case-only studies were grouped for analysis with case-control studies from the same region: HSK combined with GER; GRR with HOP; PVD with MAL; RMH, SOC, SRO, UKR with SEA and UKO ('UK group'); ORE with DOV; LAX with UCI. b Cases had primary ovarian (n = 15,636), fallopian tube (n = 180) or peritoneal (n = 552) cancer or ovarian/tubal/peritoneal tumours of undetermined site (n = 27). c Usual adult height. Height is summarised for 22 studies (20 case-control studies) where >50% participants had data available (AUS, BAV, DOV, GER, HAW, HOP, HSK, MAL, MAY, MCC, NCO, NEC, NJO, NTH, POL, PVD, SEA, STA, TOR, UCI, UKO, USC).
Sixteen of these 22 studies were also used in conventional height analyses, as they provided data on potential confounders (age, parity, use of oral contraceptives, education, and age at menarche) for >50% of participants (AUS, DOV, GER, HAW, HOP, MAL, NCO, NEC, NJO, NTH, POL, STA, TOR, UCI, UKO, USC). OCAC Ovarian Cancer Association Consortium, SD standard deviation SNPs ('GRS-92'). We examined whether potential confounders of the association in observational studies were associated with the GRS. To assess robustness to pleiotropy (where SNPs may influence risk via pathways not mediated through height), we conducted MR-Egger regression  15  and assessed smaller GRSs excluding SNPs with the highest probability of acting via other pathways from GRS to outcome (SNPs associated with ovarian/ other hormonal cancers (breast, prostate), hormone levels and in/ near tumour initiation/growth genes).
We identified these potentially pleiotropic, pathway-specific SNPs via the NHGRI GWAS Catalog,  16  the UCSC Genome/Table Browsers  17, 18  and from lists of SNPs nominated for iCOGS genotyping by ovarian, breast and prostate cancer researchers (to capture SNPs of interest unpublished at the time of analysis). Secondary analyses defined cases by histologic subtype/ behaviour. Among 16 studies with height/confounder data, we conducted conventional analysis (adjusted for parity, OC use, education, menarche age; stratified by study, 5-year age group) and compared results with MR-estimates among the same women. Analyses were performed using SAS 9. 2 (SAS Institute Inc. , Cary, NC) and Stata 13. 0 (StataCorp LP, College Station, TX). This work and each contributing study was approved by the appropriate institutional review board/ethics committee.
DISCUSSION We used a 609-SNP GRS to examine the relationship between height and ovarian cancer risk for women of European ancestry. Our data indicate a modest positive association between genetically predicted height and ovarian cancer risk, which may be stronger for borderline cancers. Height may be relevant to cancer risk as a marker for lifetime growth-factor levels (e. g. IGF-1) and/or early-life exposures (socio-economic/environmental/nutritional). 3, 21, 22  O ervational studies are subject to biases (reverse causality, selection bias, differential/non-differential reporting, confounding) which cannot be ruled out as possible explanations for observed associations. By using genotype, the MR technique can overcome some of these biases, given three assumptions. We confirmed the two verifiable assumptions: the GRS was associated with height, and not with most known confounders.
ACKNOWLEDGEMENTS We thank all the individuals who took part in this study and all the researchers, clinicians, and technical and administrative staff who have made possible the many studies contributing to this work. In particular, for their contribution to the design and conduct of the individual studies that contributed to the analysis, we thank:  D. Bowtell ,  A. deFazio ,  D. Gertig ,  A. Green ,  P. Parsons ,  N. Hayward  and  D. Whiteman  (AUS);  G. Peuteman ,  T. Van Brussel  and  D. Smeets  (BEL);  U. Eilber  (GER);  S. Reckemeyer ,  A. Korotkaia  and  S. Reina-Campanon  (HJO);  J. Meyer ,  C. Hilker ,  S. Windebank , and  J. Vollenweider  (MAY); the  Victorian Cancer Registry  and the  Australian Institute of Health and Welfare for ascertaining cases and vital status (MCC) ;  I. Orlow ,  L.
For each event, hadronic jets are clustered from the PF reconstructed particles using the infrared and collinear-safe anti-k T algorithm  [71, 72]  with a distance parameter of 0. 4. The jet momentum is determined as the vectorial sum of all particle momenta in the jet, and is found in the simulation to be within 5 to 10% of its true value over the whole p T spectrum and detector acceptance. Jet energy corrections are derived from simulation to bring the measured response of jets to that of particle level jets on average. In situ measurements of the momentum balance in the dijet, multijet, photon+jet, and leptonically decaying Z+jet events are used to account for any residual difference in jet energy scale in data and simulation  [75]. Additional quality criteria are applied to reject spurious jets from detector noise.
These events are selected by requiring three leptons and vetoing the presence of a fourth lepton passing the veto lepton requirements. A veto is applied to events with b-tagged jets to remove residual tt events. Among the three leptons, a pair of OC SF leptons with an invariant mass within 10 GeV of the Z boson mass is required to identify the Z boson. The simulation is found to describe the data well in the CRs. Based on the statistical precision of these CRs, a conservative uncertainty of 5, 10, 20, and 30% is taken for the bins 60 ≤ m T2 ( ) < 80 GeV, 80 ≤ m T2 ( ) < 100 GeV, 100 ≤ m T2 ( ) < 120 GeV, and m T2 ( ) ≥ 120 GeV, respectively. These uncertainties are applied to top quark and WW production, and treated as uncorrelated between the two types of backgrounds. Another potential source of mismodeling in the tails of the m T2 ( ) distributions arises from nonprompt leptons originating, for instance, from semileptonic decays of B hadrons in b jets or from hadronic jets accidentally passing the lepton selection.
1. • The spectrum of top quark p T in tt events has been observed to be softer in data than in simulated events  [80] [81] [82]. An uncertainty is derived from the observed variations when reweighting the tt events to the p T distribution observed in data. Finally, additional uncertainties in the modeling of signal events are taken into account, mostly related to the performance of the event reconstruction in FASTSIM. • The uncertainty in the lepton identification efficiency in events simulated with FAST-SIM, relative to the full detector simulation, is estimated to be 2%. • The analogous uncertainty in the b-tagging efficiency in FASTSIM samples ranges between 0. 2-0. 5%. • The p miss T modeling in FASTSIM events is studied by comparing the acceptances computed using the p miss T at the generator level and after the event reconstruction.
Summary A search has been presented for pair production of supersymmetric particles in events with two oppositely charged isolated leptons and missing transverse momentum. The data used consist of a sample of proton-proton collisions collected with the CMS detector during the 2016 LHC run at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35. 9 fb -1. No evidence for a deviation with respect to standard model predictions was observed in data. The results have been interpreted as upper limits on the cross sections of supersymmetric particle production for several simplified model spectra. m t. The plot on the right gives the limits for top squarks decaying into a bottom quark and a chargino, with the latter successively decaying into a W boson and a neutralino.
Measurement of stomatal closure in response to ABA Stomatal closure assays were conducted as described previously  [29]. Leaves of 4-week-old plants were floated in a solution containing 1 mM CaCl 2 , 20 mM KCl, and 5 mM MES, pH 6. 15, and exposed to light (room temperature) for 2. 5 h. Subsequently, 10 µM ABA or ethanol control was added to the buffer and incubated for another 2. 5 h. Then the abaxial epidermal strips were quickly peeled to make slides and photographed in random sequence with a microscope  [29]. The stomatal opening widths and lengths were measured using the ImageJ program and the width/length ratio was used as index of stomatal opening.
We study the automatic generation of syntactic paraphrases using four different models for generation: data-to-text generation, textto-text generation, text reduction and text expansion, We derive training data for each of these tasks from the WebNLG dataset and we show (i) that conditioning generation on syntactic constraints effectively permits the generation of syntactically distinct paraphrases for the same input and (ii) that exploiting different types of input (data, text or data+text) further increases the number of distinct paraphrases that can be generated for a given input.
Related Work Previous work on paraphrase generation falls into three main groups. Based mainly on monolingual data, earlier approaches use data-driven,  (Lin and Pantel, 2001) , grammar-or thesaurus-based methods  (Madnani et al. , 2007; McKeown, 1983; Hassan et al. , 2007; Kozlowski et al. , 2003; Quirk et al. , 2004; Zhao et al. , 2008). In contrast, the pivot-based approach exploits bilingual data and machine translation methods to extract and generate paraphrases  (Callison-Burch, 2008; Ganitkevitch and Callison-Burch, 2014; Ganitkevitch et al. , 2011). Finally, neural approaches build upon the encoder-decoder architecture to learn paraphrase generation models  (Mallinson et al. , 2017; Prakash et al. , 2016). (Prakash et al. , 2016)  uses a stacked residual LSTM network with residual connections between LSTM layers and show that their model outperforms sequence to sequence, attention-based, and bi-directional LSTM model on three datasets (PPDB, WikiAnswers, and MSCOCO).
Training and Test Data Training data. The WEBNLG dataset  (Gardent et al. , 2017)  associates sets of RDF triples with one or more texts verbalising these sets of triples. We derive training corpora for syntactically constrained generation from this dataset as follows. We enrich the WEBNLG texts with labels indicating syntactic structures that are realised by these texts by first, parsing 1  these texts and then using syntactic templates to identify the target structures occurring in those texts. We use the following list of syntactic labels: subject relative, object relative, sentence coordination, VP coordination, passive voice, apposition, possessive relative, pied piping, transitive clause, prepositional object, ditransitive clause, predicative clause. Based on the resulting, syntactically enriched, WEBNLG corpus, we then build four training corpora (T2T syn , TX syn , D2T syn , TR syn ) using the sets of RDF triples as pivots to relate paraphrases.
For data-to-text generation (D2T syn ), the input is a linearised and delexicalised version of the set of RDF triples representing the meaning of the output text, for text-to-text generation (T2T syn ), the input is a text and for hybrid data-and-text-to-text generation (TX syn and TR syn ), the input is a text and a linearised RDF triple. For the text-to-text datasets, we additionally require that, for any corpus instance k, T i , T o , T o differs from T i on exactly one syntactic label 2. Test data. For any input k, I occuring in the test data, we ensure that k, I does not occur in the training data. (where I is either a set of RDF triples, a text or a text and an RDF triple).
, 2002). In addition, we compute the proportion of output satisfying the input syntactic constraint (Synt) and the BLEU score for these output which satisfy the input syntactic constraint (BLEU syn ). The number of output satisfying the input syntactic constraint is computed by first parsing the generated output and then applying the templates used for the automatic annotation of the training data. Diversity (Sim, #Txt/Mg). To measure the level of paraphrasing obtained, we group together inputs which share the same meaning (i. e. , inputs that are linked in the WEBNLG dataset to the same set of RDF triples) and we compute the number of distinct texts generated per meaning (# Txt/Mg). We further analyse these sets by computing the average pairwise similarity (Sim) of the texts present in these sets.
The comparison with T2T generation is particularly striking as the training data is 3 to 5 times larger for the T2T syn model than for the TX syn and the TR syn model respectively. Similarly, it is noticable that although the T2T syn training corpus is 3 times larger than the D2T syn corpus, the T2T syn and the D2T syn models show similar results. This is in line with results from  (Aharoni and Goldberg, 2018)  which shows that rephrasing is a difficult task. Linguistic Adequacy. Overall the linguistic adequacy of the syntactically constrained models is high with a BLEU score with respect to a single reference ranging from 46. 20 (D2T syn ) to 83. 87 (TX syn ). Moreover, the generated sentences show close similarity with the reference sentence realising the input constraint (BLEU syn : from 48.
16 to 89. 32). formula sim(S1, S2) = 2 * match(S1,S2) len(S1)+len(S2) where a match is defined as the sum of the length of the matching segments (match(S1, S2) = m∈overlap(S1,S2) len(m). While the baseline models underperform in terms of BLEU scores, the manual evaluation (# SPar/Mg) indicates that they, in fact, produce acceptable output. The low BLEU scores for these models are probably due to the fact that each output is evaluated against a single reference while the dataset is constructed to maximise the number of paraphrases available for a given input.
Microscopy and data analysis Samples were imaged on Leica SP2 or TCS SP8 microscopes (CAIC, University of Cambridge) at 20X or 40X magnification and 1024/1024 pixel resolution. Images were processed with Image J and assembled with Adobe Illustrator. Quantification of fluorescence signal intensities was performed with Image J software. In each case the n refers to the number of individual specimens analyzed, which were from two or more independent experiments. For experiments to compare and measure expression levels, samples were prepared and analyzed in parallel, with identical conditions and the same laser parameters used for image acquisition. For each confocal stack a Sum slices projections was generated. Signal intensities were obtained by manually outlining the regions of interest, based on expression of markers, and measuring the average within each region.