dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This research was funded by the  Gordon and Betty Moore Foundation  through Grant  GBMF2807  awarded to  Paul Falkowski  and by a grant from  Region Ile-de-France  to  Slim Karkar. Eric Bapteste  was funded by the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013 , Grant Agreement Number  615274 ). We thank  Arik Harel  and  Debashish Bhattacharya  lab members for helpful discussions. We thank  Philippe Lopez ,  Pierre-Alain Jachiet ,  James McInerney , and  Michel Habib  for critical discussions about network methods. We are grateful to the reviewers of this manuscript for their constructive comments.
We have used the GGG (version 2012) software suite  (Wunch et al. , 2011)  to retrieve columnar concentrations of H 16  2 O, H 18 2 O and HDO from the selected spectral windows from spectra recorded at the Bremen TCCON site during the period 2010-2012. We then analysed retrieval results from measurements taken under different conditions: various humidity levels, wide atmospheric temperature range (summer and winter measurements), and different solar zenith angles. At first, the refinement of the windows was done empirically depending on fitting residuals. At the end, only spectral windows with a correlation between retrieved columnar concentrations of at least 0. 9 were preserved. The refined HDO  and H 16  2 O spectral windows were then combined with those used in the TCCON community. Usage of additional windows in our retrievals allowed us to improve the accuracy of the a posteriori calculated δD values by 25 % (comparing to the model).
Measurement noise The FTIR instrument, which is used here, has a signal-tonoise ratio (SNR) of about 200 and 900 in the selected HDO and H 18 2 O windows, respectively. We have estimated how the measurement noise affects the precision of a posteriori calculated δD and δ 18 O. For such an estimation we have simulated atmospheric transmittances using the FIRE-ARMS software in the selected spectral windows with the constant vertical profiles of -15 ‰ for δ 18 O and -200 ‰ for δD, which are in a range of natural atmospheric abundances of H 18 2 O and HDO. A normally distributed noise of different magnitudes representing different noise levels was added to the simulated spectra to imitate real measurements. Then the retrieval of δ 18 O and δD was performed with only one fitting parameter: the scaling factor of the a priori profile of the isotopologue.
Line list In the present study, we use a modified GGG line list  (Wunch et al. , 2010) , which is based on HITRAN 2008  (Rothman et al. , 2009) ,  Toth (2005)  and  Jenouvrier et al. (2007)  line lists, with all H 2 O lines substituted by the UCL08 (University College London database) water line list  (Shillings et al. , 2011). UCL08 is a compilation of experimental data  (Jenouvrier et al. , 2007; Mikhailenko et al. , 2007 Mikhailenko et al. , , 2008;; Coudert et al. , 2008; Tolchenov and Tennyson, 2008) , HITRAN2008 and additional theoretical lines  (Barber et al. , 2006). Strong lines of the H 16 2 O, H 18 2 O and HDO are identical to those in HITRAN2008. The modified line list allowed us to improve the agreement with ECHAM5-wiso for δ 18 O from r 2 = 0. 64 to r 2 = 0. 82. However, the agreement between δD values does not change.
As expected, a posteriori calculated columnar values of δ 18 O and δD are shifted in comparison to the model. The most likely reason for the shifts is spectroscopic uncertainties (see Sect 4. 3). We have calculated and corrected the offsets of 58 and -23 ‰ between retrieved and modelled δ 18 O and δD correspondingly. HITRAN2012  (Rothman et al. , 2013) , which became available recently, declares more accurate spectroscopic line intensities and half widths. Retrieval results obtained with this new version of HITRAN database have smaller systematic shifts from the model (+7 and -8 ‰ for δ 18 O and δD, respectively) than those obtained with our GGG+UCL08 line list. Conversely, the correlation is smaller and the slope between retrieved and simulated isotopic ratios is further from the expected.
Retrieval results and comparison with simulations About 6000 spectra recorded from January 2010 to May 2012 at the Bremen TCCON site were processed in order to retrieve columnar values of H 16 2 O, H 18 2 O and HDO. Each spectral window of the species of interest (see Table  1 ) was processed independently, and the results were filtered and averaged with respect to uncertainty based on spectral residuals. Since the GGG suite uses NCEP/NCAR reanalysis data for vertical profiles of atmospheric temperatures interpolated to local noon for a whole day of measurements, we have used spectra recorded in a time range of local noon time ±3 h only. We removed the days with measurements that cover less than 2 h from the comparison. An offset correction was applied to the retrieval results in order to remove the shifts due to spectroscopic uncertainties (as described in Sect.
Acknowledgements. This research at  Ural Federal University  was supported by the grant from the  Russian Government 11. G34. 31. 0064  ( WSIBISO  Project) and by the  RFBR  grant  12-01-00801-a. Financial support by the  European Commission  within the research project  NORS  is acknowledged. The project MUSICA is funded by the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013)/ ERC , grant agreement no. 256961. Edited by:  H. Worden.
Photon, electron and hadron candidates are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers  [37]. The trigger  [38]  consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, which applies a full event reconstruction. The data sample used for this analysis was acquired during the pPb run in early 2013 and corresponds to an integrated luminosity of 1. 1 nb -1 (0. 5 nb -1 ) for forward (backward) collisions. The hardware trigger was employed as an interaction trigger that rejected empty events. The software trigger required one well-reconstructed charged particle with hits in the muon system and a transverse momentum greater than 600 MeV/c.
The largest difference in yields of each resonance with respect to the nominal result is considered as the systematic uncertainty. The luminosity is determined with an uncertainty of 1. 9% (2. 1%) for the pPb forward (backward) sample from the rate of interactions that yield at least one reconstructed track in the VELO. The absolute calibration is determined with van der Meer scans, as described in Ref. [28]. The trigger efficiency in the forward sample is determined directly from the data using a sample unbiased by the trigger decision. The corresponding uncertainty is 2. 1%. Due to the limited sample size, the trigger efficiency in the backward sample is estimated using the forward sample, since it has been observed that the dependence of trigger efficiencies on the charged-particle multiplicity is small  [28].
Acknowledgements We thank  F. Arleo ,  J. P. Lansberg  and  R. Vogt  for providing us with the theoretical predictions and for the stimulating and helpful discussions. We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  and  Region Auvergne (France) ;  BMBF ,  DFG ,  HGF  and  MPG (Germany) ;  SFI (Ireland) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands); SCSR (Poland);  MEN/IFA (Romania) ;  MinES, Rosatom ,  RFBR  and  NRC "Kurchatov Institute" (Russia) ;  MinECo, XuntaGal and GENCAT (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  and the  Royal Society  ( United Kingdom );  NSF (USA).
We also acknowledge the support received from  EPLANET ,  Marie Curie Actions  and the  ERC  under  FP7. The Tier1 computing centres are supported by  IN2P3 (France) ,  KIT  and  BMBF (Germany) ,  INFN (Italy) ,  NWO  and SURF (The Netherlands),  PIC (Spain) ,  GridPP  ( United Kingdom ). We are indebted to the communities behind the multiple open source software packages on which we depend. We are also thankful for the computing resources and the access to software R&D tools provided by  Yandex LLC (Russia).
ChIP was performed with anti-59methyl-cytosine. Immunoprecipitated complexes were sequentially eluted and cross-link reversed. DNA fragments were purified using a PCR purification kit (Qiagen). The ChIPed DNA was amplified and hybridized to the Affymetrix GeneChip Human Tiling 2. 0R Array Set as previously described  [25]. DNA without immunoprecipitation was used as the input control. Microarray data was normalized by quantile normalization using Bioconductor  [26]. The normalized data was then analyzed using the model-based analysis of tiling (MAT) array algorithm to identify genomic regions with the highest mean histone methylation scores  [27]. Default parameters were used for each algorithm to find ChIP regions from all samples. The MAT library and mapping files were annotated with the nearest reference gene (1 kb distance) using the refseq annotation file based on Human Genome Assembly version 18 (hg18) which was downloaded from UCSC genome browser.
Immunoblotting Sodium dodecyl sulfate-polyacrylamidegel electrophoresis (SDS-PAGE) and western blotting were performed as previously described  [23]. In brief, whole-cell lysates were separated using 10-12% gels and transferred to polyvinyldenefluoride (PVDF) membranes (Bio-Rad Laboratories, Hercules, CA). The antibodies used for immunoblotting included anti-PARP, -caspase 9, caspase 3 and a-tubulin (Cell Signaling Technology, Danvers, MA). Expression profiling and GSEA of available datasets miR-152 and miR-10b-5p expression was analyzed using publicly available dataset where primary bone marrow selected CD138+ cells were obtained from either MM patients or HDs (GSE16558). Similarly, the expression levels of DNMT1, E2F3, BTRC and MYCBP were analyzed using publicly available datasets where primary bone marrow selected CD138+ cells obtained from either MM patients or healthy individuals were studied (GSE5900; GSE 2658)  [31].
Statistical analysis miRNA expression data were normalized according to manufacturer's instructions (Nanostring technologies, Seattle, WA). To further define those miRNAs differentially expressed between groups (with vs without 5-aza-CdR treatment), the expression patterns of normalized data were analyzed and hierarchical clustering was performed using dChip (http://www. hsph. harvard. edu/cli/complab/dchip/)  [34]. The enrichment analysis of targeted mRNAs of miR-152, -10b-5p and miR-34c-3p in GEO datasets was performed using GSEA, and considered significant with false discovery rate (FDR) ,0. 25, as previously reported  (31). Mann-Whitney U rank ranksum test with GraphPad software was applied to describe the distribution of miRNAs and gene levels in MM patients compared with HDs. Statistical tests were unpaired, 2-sided t-tests comparing two conditions.
1C. We next analyzed the expression levels of these miRNAs in MM using GSE16558, and found that both miR-152 and miR-10b-5p were significantly down-regulated in MM patients compared to HD (Fig. 2B ). Table  1. Methylation frequency of miR-152, -10b-5p and miR-34c-3p in MM cell lines. Methylation analyses of miR-152, -10b-5p and miR-34c-3p in MM cells miR-152, -10b-5p and miR-34c-3p present with 2, 3 and 3 distinct CpG islands in their upstream chromosomal regions, respectively (1000 bp upstream and 500 bp upstream) as shown in Fig. 3A. We further examined the methylation status of these miRNAs using MSP analysis and showed that the miR-152 upstream promoter region was methylated in RPMI 8266, OPM2, IM9 and H929 cells; while partial/no methylation was detected in MM. 1S and U266 cells. Moreover, miR-10b-5p methylation was observed in all MM cell lines tested.
MiR-152 and miR-10b-5p mediate the activation of oncogenic target genes Given that miRNAs inhibit mRNA expression, we hypothesized that the predicted target genes of these specific miRNAs are highly expressed in CD138 + bone marrow-derived MM cells. We therefore screened publically available mRNA datasets (GSE5900; GSE2658) and found that MM patients present with a significant enrichment of miR-152-and miR-10b-target genes, as shown by GSEA (FDR,0. 25; Fig. 5A ), whereas no significant differences were observed for miR-34c-predicted targeted genes. We next focused on the predicted genes of miR-152-and miR-10b-5p. DNA-methyltransferase 1 (DNMT1), a predicted target of miR-152; beta-transducin repeat containing E3 ubiquitin protein ligase (BTRC) and Myc binding protein (MYCBP), predicted targets of miR-10b-5p and E2F transcription factor 3 (E2F3), predicted target of both miR-152 and miR-10b-5p were examined in MM cells (Figure  S2 ).
Enhanced expression of miR-152 induced a significant reduction of DNMT1 and E2F3 expression. In addition, E2F3, BTRC and MYCBP were significantly increased after miR-10b-5p mimic transfection (Fig. 5B ). Moreover, we confirmed that the expression of these genes is significantly up-regulated in MM patients compared to healthy donors using GSE5900 and GSE2658 as shown in Fig. 5C. These results suggest that methylation of miR-152 and miR-10b-5p enhances MM progression through increased expression of specific oncogenes, such as DNMT1, BTRC, MYCBP and E2F3.
Wizard of Oz (WOZ) prototyping employs a human wizard to simulate anticipated functions of a future system. In Natural Language Processing this method is usually used to obtain early feedback on dialogue designs, to collect language corpora, or to explore interaction strategies. Yet, existing tools often require complex client-server configurations and setup routines, or suffer from compatibility problems with different platforms. Integrated solutions, which may also be used by designers and researchers without technical background, are missing. In this paper we present a framework for multi-lingual dialog research, which combines speech recognition and synthesis with WOZ. All components are open source and adaptable to different application scenarios.
These low-fidelity prototyping techniques do not, however, work well with speech and natural language. The Wizard of Oz (WOZ) method can be employed to address this shortcoming. By using a human 'wizard' to mimic the functionality of a system, either completely or in part, WOZ supports the evaluation of potential user experiences and interaction strategies without the need for building a fully functional product first  (Gould et al. , 1983). It furthermore supports the collection of domain specific language corpora and the easy exploration of varying dialog designs  (Wirén et al. , 2007). WOZ tools, however, are often application dependent and built for very specific experimental setups. Rarely, are they re-used or adapted to other application scenarios. Also, when used in combination with existing technology components such as ASR or TTS, they usually require complex software installations and server-client configurations.
Thus, we see a need for an easy 'out-of-the-box' type solution. A tool that does not require great technical experience and therefore may be used by researchers and designers outside the typical NLP research and development community. This demo is the result of our recent efforts aimed at building such an integrated prototyping tool. We present a fully installed and configured server image that offers multi-lingual (i. e. English, German, French, Italian) ASR and TTS integrated with a web-based WOZ platform. All components are open-source (i. e. adaptable and extendable) and connected via a messaging server and a number of Java programs. When started the framework requires only one single script to be executed (i. e. there is a separate script for each language so that the components are started using the right parameters) in order to launch a WOZ driven system environment.
Integrated Prototyping Framework In order to offer a flexible and easy to use prototyping framework for language technology applications we have integrated a number of existing technology components using an Apache AC-TIVEMQ messaging server 2  and several Java programs. Our framework consists of the JULIUS Large Vocabulary Continuous Speech Recognition engine 3  , an implementation of the GOOGLE SPEECH API 4  , the WEBWOZ Wizard of Oz Prototyping Platform 5  and the MARY Text-to-Speech Synthesis Platform 6. All components are fully installed and connected running on a VIR-TUAL BOX server image 7  (i. e. Ubuntu 12. 04 LTS Linux Server). Using this configuration we offer a platform that supports real-time speech recognition as well as speech synthesis in English, French, German and Italian.
Automatic Speech Recognition The JULIUS open-source Large Vocabulary Continuous Speech Recognition engine (LVCSR) uses n-grams and context-dependent Hidden Markov Models (HMM) to transform acoustic input into text output  (Lee et al. , 2008). Its recognition performance depends on the availability of language dependent resources i. e. acoustic models, language models, and language dictionaries. Our framework includes basic language resources for English, German, Italian and French. As those resources are still very limited we have also integrated online speech recognition for these four languages using the Google Speech API. This allows for conducting experiments with users while at the same time collecting the necessary data for augmenting and filling in JULIUS language resources.
Messaging Server and Gluing Programs In order to achieve the above presented integration of ASR, WOZ and TTS we use an Apache AC-TIVEMQ messaging server and a number of Java programs. One of these programs takes the output from our ASR component and inserts it into the WebWOZ input stream. In addition it publishes this output to a specific ASR ActiveMQ queue so that other components (e. g. potentially an NLU component) may also be able to process it. Once an ASR result is available within WebWOZ, it is up to the human wizard to respond. WebWOZ was slightly modified so that wizard responses are not only sent to the internal WebWOZ log, but also to a WIZARD ActiveMQ queue. A second Java program then takes the wizard responses from the WIZARD queue and pushes them to a separate MARY queue. While it may seem unnecessary to first take responses from one queue just to publish them to another queue, it allows for the easy integration of additional components.
Demo Setup The optimal setup for the demo uses two computer stations, one for a wizard and one for a test user. The stations need to be connected via a LAN connection. The test user station runs the prototyping framework, which is a fully installed and configured Virtual Box software image (Note: any computer capable of running Virtual Box can serve as a test user station). The wizard station only requires a modern web browser to interact with the test user station. A big screen size (e. g. 17 inch) for the wizard is recommended as such eases his/her task. Both stations will be provided by the authors.
GENOME SEQUENCING AND MAPPING The whole genome of the 47 S1 individuals of M. laricipopulina, plus the parental strain 98AG31, was resequenced using the Illumina HiSeq 2000 technology. A total of over 40 billions bp were sequenced, with a relatively uneven repartition of sequencing depth per individual (Table  S1 ). Over three quarters of reads (268 millions) mapped to the reference genome, leading to an average genome sequencing depth of 265X when considering all individuals, but ranging from 1. 3X to 9. 9X with most individuals presenting a final genome sequencing depth of about 5-7X. Genome sequences were deposited in GenBank under the BioProject ID PRJNA255081 and the SRA accession number SRP044324.
ACKNOWLEDGMENTS We are grateful to  Mathilde Chertier  for her help in genotyping the offspring and DNA extractions, to  Bénédicte Fabre  for her help in the production of poplar plants, and to  Katherine J. Hayden  and the two reviewers for their helpful comments. This work was supported by grants from the  French National Research Agency  ( ANR-12-ADAP-0009 ,  GANDALF  project;  ANR-13-BSV7-0011 ,  FunFit  project) and from  INRA  (EFPA innovative project). The UMR IAM is supported by a grant from the  French National Research Agency  ( ANR-11-LABX-0002-01 ,  Laboratory of Excellence ARBRE). Michaël Pernaci  was supported by a PhD fellowship from the  French Ministry of Education and Research (MESR).
Genetic markers The populations were genotyped with 3,299 DArT markers generated by Triticarte Pty, Ltd. (Canberra, Australia; http://www. triticarte. com. au), including 2,545, 1,572 and 2,236 polymorphic markers for DH1, DH2 and RIL, respectively (1,156 markers were common to the three populations). The genetic map was built from the data of Triticarte for about 60 % of the markers; the others were placed close to the DArT markers where the LD was the highest. LD was calculated as r 2 with a R application (R Development Core Team 2011). Out of the 3,299 markers, 2,772 have been successfully mapped  (Bordes et al. 2013 ) on the whole genome. Markers were not selected based on minor allele frequency.
GEBV estimation Five statistical methods were used to estimate GEBV using DArT markers: • GBLUP assumes pedigree relationships in the training population, based on marker genotypes, and then estimates breeding values using a BLUP animal model  (Henderson 1975). Computations were carried out using the pedigree package in R  (Coster 2010 ). • Bayesian ridge regression (BRR) uses a Gaussian prior distribution with a variance common to each marker effect (de los Campos and Perez 2010; Perez et al. 2010). The prior residual variance and degree of freedom were S e = 4. 5 and df e = 3, respectively, and the prior variance and degree of freedom of marker effects were S bR = 0. 009 and df bR = 3, respectively. Estimates of lambda were based on a heritability, h 2 = 0. 37. The number of iterations used as burn-in was 20,000, and the number of iterations made in the Gibbs sampler was 60,000.
Acknowledgments This project was funded by the  FSOV (Funds de Soutien al'Obtention Vegetale)  under grant  FSOV2008A. The excellent work in experimental units by  INRA (Clermont-Ferrand , Estrees-Mons and Rennes) and Bioplante is also gratefully acknowledged. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
29 Fewer crashes may occur as cyclists become more experienced and skilled. 28 To explore whether non-linearity of risk also applies to SBCs, we examined the frequency of SBCs in a sample of countries with varying amounts of cycling. Using a literature search, we examined (1) the frequency of SBCs resulting in fatal injuries; (2) the frequency of SBCs resulting in severe non-fatal injuries (here defined as casualties who are admitted to the hospital (hospitalised) or treated at an emergency department (ED)); (3) how bicycle modal share is associated with the proportion of cycling injuries occurring in SBCs; and (4) the hypothesis that an increase in bicycle modal share is related to a less than proportional increase in the share of SBCs among all road crash victims. We aimed to include data from countries with varying bicycle modal shares to allow for hypothesis testing.