dataesr/scientific-paragraphs-categorization huggingface dataset:
5 mag. pc -1. The figures show that most sightlines cross several dense structures, i. e. a sightline does not probe a unique gas phase, instead series of dense and more diffuse regions are distributed along the sightlines. This probably explains the large dispersion of data points in Fig. 10 , since DIB to extinction ratios for the various phases will be smeared out in different ways depending on the sightlines. However, interestingly, the distance from the Sun to the dense cloud limit increases from Taurus to Orion then to Cepheus (although the increase from Taurus to Orion is much stronger than from Orion to Cepheus), i. e. this distance increases while simultaneously the slope of the DIB to extinction ratio relation with the dust thickness gets shallower. An increase in the limit distances and the number of structures along the sightlines implies that the mixing of diffuse and dense regions must be stronger in Orion than in Taurus and even higher in Cepheus compared to Orion.
For those reasons, we interpret the change of slope among the three clouds shown in Fig. 10  as an indicator of the strength of phase mixing. In case of Taurus, its smaller number of structures and smaller resulting phase mixing, the level off the DIB is more distinctly linked to the dust column and the slope of the decrease is stronger. This is the opposite for Cepheus. If our interpretation is correct, this implies that the actual DIB to extinction ratio decrease from the diffuse to the dense phase is even stronger than suggested by the slopes in Fig. 10. Finally, we have attempted to use the Planck data and all APOGEE sightlines to study the DIB level off in all types of configurations. To do so we removed all conditions on the target distances and kept only the lower limits on A V and EW imposed to avoid too large uncertainties on the DIB to extinction ratio.
We have compared our DIB measurements with their associated visual extinctions extracted from the Starhorse database. We revisited the DIB-A V relationship first reported by  Zasowski et al. (2015)  and found the DIB strength average to be linearly correlated with dust extinction over at least two orders of magnitude, however with a smaller equivalent width-to-extinction ratio than the one derived by these authors for the red giants and later by EMRL16 for the hotter TSS targets: EW DIB = 0. 056 × A 0. 99±0. 03 V (Å). We showed how this variation is explained by the differences between the ASPCAP and Cannon models and by the use of Cannon A V extinctions instead of A V extinctions deduced from the RJCE technique. It has also been found from the global DIB-extinction comparative study that the 15 273 Å DIB tends to weaken with respect to extinction in the spectra of stars situated behind dense regions.
With the goal of going one step further in the analysis of the DIB carrier depletion in dense cores, we investigated the link between the EW/A V ratio and the distribution of dust as traced by Planck. In particularly, the Taurus, Orion and Cepheus cloud complexes were studied using targets distant enough to be located behind the clouds. The depletion effect is spectacularly illustrated for those cloud complexes, thanks to the high quality of the APOGEE data and of the Planck maps. We have fitted an average relationship between the DIB A V ratio and the Planck optical depth τ 353 through a log-log linear formula: EW/A V = 7. 6 × 10 -5 × τ -0. 66±0. 01 353 (Å mag -1 ). We have performed a separate study of the three cloud complexes in a search for potential differences in the DIB depletion effect.
Finally, we have used the entire dataset in an attempt to identify different depletion regimes for the APOGEE sightlines. We found that, not surprisingly, for large dust optical depths exceeding ∼ 10 -4 , i. e. for low latitude and extended sightlines, the DIB to A V ratio tends to the mean value of about 0. 05-0. 06 × A V that is characteristic of a strong mixing of diffuse and dense phases distributed along the extended paths. For intermediate τ 353 values, between ∼ 10 -5 and 10 -4 we clearly see the same depletion effect that is affecting the nearby off-Plane cloud complexes, now extended to mi-plane targets. Finally, for low values of τ 353 below 10 -5 , the DIB to extinction ratio seems to be constant at ∼ 0. 2 × A V , a value significantly above than the strong mixing average. This high value corresponds to the nearby diffuse clouds at the periphery of the local cavity.
Acknowledgements. ME acknowledges funding from the  "Region Ile-de-France " through the  DIM-ACAV  project. RL acknowledges support from the  CNRS PCMI national program. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS-IV acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah. The SDSS web site is www. sdss. org. SDSS-IV is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the SDSS Collaboration including the  Brazilian Participation Group , the  Carnegie Institution for Science, Carnegie Mellon University , the  Chilean Participation Group , the  French Participation Group, Harvard-Smithsonian Center for Astrophysics ,  Instituto de Astrofísica de Canarias , The  Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo ,  Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE) ,  National Astronomical Observatory of China, New Mexico State University ,  New York University, University of Notre Dame, Observatário Nacional / MCTI , The  Ohio State University, Pennsylvania State University ,  Shanghai Astronomical Observatory, United Kingdom Participation Group ,  Universidad Nacional Autónoma de México ,  University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University , and  Yale University.
Funding This study was supported by the  National Institute of Health (NIH)  ( CA092824  and  CA209414  to  D. C. Christiani ),  National Natural Science Foundation of China  ( 81530088  and  81473070  to  F. Chen , and  81373102  to  Y. Zhao ),  State's Key Project of Research and Development Program  ( 2016YFE0204900  to  F. Chen ),  Key Project of Natural Science Foundation of Jiangsu, China  ( 14JA31002  to  F. Chen ), A Project Funded by the  Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) , and Outstanding  Young Teachers Training Program  of  Nanjing Medical University  (to Y. Y. Wei). Transdisciplinary Research for Cancer in Lung (TRICL)  and Oncoarray funding sources are detailed following. Sponsors had no role in design of the study, collection and analysis of data, or preparation of the manuscript.
E. Goodman),  NIH   UO1-CA6367307  (PIs: Omen, G. E. Goodman),  NIH   R01 CA111703  (PI:  C. Chen ), and  NIH   5R01 CA151989-01A1  (PI: J. Doherty). The Liverpool Lung project was supported by the  Roy Castle Lung Cancer Foundation. The  Harvard Lung Cancer Study  was supported by the  NIH/NCI  grants  CA092824 ,  CA090578 , and  CA074386. The Multiethnic Cohort Study was partially supported by  NIH  grants  CA164973 ,  CA033619 ,  CA63464 , and  CA148127. Work performed in the MSH-PMH study was supported by the  Canadian Cancer Society Research Institute  ( 020214 ),  Ontario Institute of Cancer and Cancer Care Ontario Chair Award  (to  R. J. Hung  and  G. Liu ), and  Alan Brown Chair  and  Lusi Wong Programs  at  Princess Margaret Hospital Foundation. NJLCS was funded by the  State Key Program of National Natural Science of China  ( 81230067 ),  National Key Basic Research Program  ( 2011CB503805 ), and  Major Program  of the  National Natural Science Foundation of China  ( 81390543 ).
The Norway study was supported by the  Norwegian Cancer Society, Norwegian Research Council. The  Shanghai Cohort Study (SCS)  was supported by  NIH   R01 CA144034  (PI:  J. M. Yuan ) and  UM1 CA182876  (PI:  J. M. Yuan ). The  Singapore Chinese Health Study (SCHS)  was supported by  NIH   R01 CA144034  (PI:  J. M. Yuan ) and  UM1 CA182876  (PI:  J. M. Yuan ). Work in the TLC study has been supported in part the  James & Esther King Biomedical Research Program  ( 09KN-15 ),  NIH Specialized Programs of Research Excellence (SPORE)  ( P50 CA119997 ), and a  Cancer Center Support Grant (CCSG) at the H. Lee Moffitt Cancer Center and Research Institute , an  NCI designated Comprehensive Cancer Center  ( P30-CA76292 ). The  Vanderbilt Lung Cancer Study -BioVU  dataset used for the described analyses was obtained from  Vanderbilt University Medical Center's BioVU , which is supported by institutional funding, the  1S10RR025141-01  instrumentation award, and by the  Vanderbilt Nature Genetics : doi:10.
The Lodz center study was partially funded by  Nofer Institute of Occupational Medicine  under task  NIOM 10. 13 : Predictors of mortality from non-small cell lung cancer -field study. Genetic sharing analysis was funded by  NIH  grant  CA194393. The  ResoLuCENT study (Resource for the Study of Lung Cancer Epidemiology in North Trent ) is funded by the  Sheffield Hospitals Charity ,  Sheffield Experimental Cancer Medicine Centre and Weston Park Hospital Cancer Charity. F. Taylor was supported by a  Cancer Research UK/Yorkshire Cancer Research Clinical Fellowship. B. Zhu 's work was supported by the  Intramural Research Program of the Division of Cancer Epidemiology and Genetics, NCI. The  Environment And Genetics in Lung cancer Etiology (EAGLE)  study (PI:  M. T. Landi ) was supported by the  Intramural Research Program of NIH, NCI, Division of Cancer Epidemiology and Genetics.
F. Y. Gu  was also supported by  Roswell Park Cancer Institute and Cancer Center Supporting  Grant  P30CA016056. L. L. Marchand was supported by a program project grant from the  NCI, NIH :  P01 CA168530  and grant  U01 CA164973. The Toronto study (PI:  J. McLaughlin ) was supported by  Canadian Cancer Society Research Institute  ( 020214 ). The  Canadian Urban Environmental Health Research Consortium  is funded by the  Canadian Institutes for Health Research  ( J. McLaughlin ). S. J. Chanock was supported by the  Intramural Research Program  of the  National Institutes of Health NCI's Division of Cancer Epidemiology , and the  American Cancer Society. This work was also supported by  Cancer Research UK  ( C1298/A8362  to  R. S. Houlston ,  C1298/A8780  and  C1298/A8362  to  J. McLaughlin  and  C18281/A19169  to  R.
The following simulations (Section 4) take into account neutral recirculation below the dome with explicit pumping of neutral gas ( pump ), as well as re-fueling from a gas-puff at the top of the outboard side ( gas ) for a self-consistent steady state particle balance = + pump gas core in the converged state (i. e. no implicit sink is necessary in here). Furthermore, previous simulations were based on the default reaction model in EIRENE (see sections 2. 4. 1. and 2. 4. 2. in reference  [30]  and Fig. 2 ) which includes only a minimal set of molecular reactions for ionization and dissociation from the HYDHEL database. The simulations discussed in Section 4, on the other hand, include additional reactions such as elastic collisions of molecules with the background plasma and the molecular charge exchange (ion conversion) process  1  in reference  [31]  for the full list of reactions used in both SOLPS and EMC3-EIRENE simulations below).
+ + + + D D D D 2 2 (see table Only volumetric recombination ( + + D e D) is still neglected in EMC3-EIRENE), however, so-called 'molecular assisted recombination' (MAR) is accounted for by the molecular charge exchange process mentioned above followed by recombination of molecular ions. The rate coefficients for molecular processes are taken from the AMJUEL database  [32] , which includes a density dependence of rate coefficients 〈σv〉and 〈ΔE σv〉that is neglected in the default model. In addition to these processes, neutral-neutral collisions (in BGK approximation  [33] ) are included.
Comparison between EMC3-EIRENE and SOLPS-ITER Previously, simulation results from EMC3-EIRENE and SOLPS-4. 3 were compared for attached conditions in ITER  [35]. Since the present advancement has significantly extended the operation range of EMC3-EIRENE, we now present a comparison (now with SOLPS-ITER  [36] ) which shows good agreement also for the transition to detached conditions. A gas fueling scan was performed with EMC3-EIRENE for an axisymmetric ITER configuration for the initial non-active phase based on the existing SOLPS cases 2292, 2294, 2296, 2297, 2298 and 2299 from the ITER database. To allow for a realistic particle balance in the new EMC3-EIRENE simulations, we have implemented the volume under the divertor dome to match the machine pumping configuration and its representation in SOLPS.
The implementation in EMC3-EIRENE is shown in Fig. 4 : Gas puffing is introduced at the top of the machine, and pumping is accounted for by a surface below the dome with an albedo of 0. 9928 (i. e. a pumping fraction of 7. 2 • 10 3 ). The dome support is taken into account by axisymmetric, semi-transparent surfaces with 50 % transmission (as they are in SOLPS) rather than their discrete toroidal setup. Model parameters for anomalous cross-field diffusion are set to = D 0. 3 m s 2 1 and = = 1 m s e i 2 1. The edge input power of 20 MW is split equally between electrons and ions during the gas fuelling scan. Impurity radiation is not as important for the low power cases as it is for the high power cases, and is neglected in both SOLPS and EMC3-EIRENE simulations here. The TRIM database is used in EIRENE for reflection on plasma facing components which are assumed to be all Beryllium in all 2D and 3D simulations.
Acknowledgments This work was supported by the  U. S. Department of Energy  under grants  DE-SC0012315  and  DE-SC0013911 , and by the  College of Engineering  at the  University of Wisconsin -Madison. This work was done in the framework of the  ITER Science Fellow Network. The views and opinions expressed herein do not necessarily reflect those of the  ITER Organization. Computer simulations have been performed at the  UW-Madison Center for High Throughput Computing (CHTC)  and at the ITER HPC cluster.
X-ray imaging High-speed X-ray imaging was carried out at beamline ID19 at The European Synchrotron (ESRF). The cells were imaged under a polychromatic beam with a pixel size of 11. 35 μm and varying fields-of-view (FOV) and frame rates, from 2000 fps with a FOV of 1822 × 1140 pixels (horizontal × vertical), to 9141 fps with a FOV of 1616 × 316 pixels (horizontal × vertical). A PCO. Dimax (PCO AG, Germany) detector and LuAG(Ce) (Lu 3 Al 5 O 12 :Ce) scintillator were used for all highspeed imaging experiments. The radiographs were flat-field corrected and enhanced using MATLAB's adaptive histogram equalization (adapthisteq) function. Imaging properties and post-processing were similar to previous studies  [9, 10, 12].
S2  of Supplementary Material. The current model does not account for mass loss during the process of thermal runaway. When the temporal surface temperatures were generated for the three simulations, an additional step of applying temperature-dependent tensile strength properties of the casing was applied to estimate the bursting pressure of the casing. The tensile strength as a function of temperature of a common structural steel (S350GD)  [29]  was used, the data and functions for which are provided in Supplementary Information. The burst pressures were estimated using Barlow's formula (Eq. (  2 )), which calculates the bursting pressure (P) from the ultimate tensile strength of the casing material (S), the thickness of the casing (t), and the outer diameter (D). = P St D 2 (2).
, 2017). To date, no nuclear phylogenetic Annonaceae tree has been generated, mainly because of difficulties in amplifying nuclear genes using standard Sanger approaches  (Pirie et al. , 2005). In this study, we aimed to generate for the first time a nuclear phylogenetic tree of Annonaceae at both generic and species level using target sequence capture and next generation sequencing. We designed and tested a baiting kit useful at several taxonomic levels with the ultimate aim of reconstructing the Annonaceae Tree of Life, including all species in a well-resolved phylogenetic tree based on a wide coverage of the nuclear genome. Specifically, we wanted to answer several questions: Can we generate a baiting kit useful for Annonaceae wide phylogenetic analyses? Are plastid and nuclear phylogenetic relationships concordant in Annonaceae? Do we detect differences in branch lengths based on nuclear data between Annonoideae and Malmeoideae?.
Bait Design Reads generated from the transcriptome of each species were assembled de novo using Trinity (v2. 2. 0)  (Grabherr et al. , 2011)  using default values. Using the resulting contigs, orthologous loci were selected following these steps. Candidate Open Reading Frames (ORF) were extracted and translated using TransDecoder (https://github. com/TransDecoder) resulting in a number of full protein sequences (start and stop codons present). These were then clustered together based on protein similarity using CD-HIT (v4. 6. 6) (Li and Godzik, 2006; Fu et al. , 2012) , to reduce redundancy due to allelic diversity or alternatively spliced isoforms. This step was repeated four times, with percentage identity cutoffs of 0. 99, 0. 97, 0. 95, and 0. 9, leading to four sets of protein sequences with different levels of reduced redundancy.
Taxon Sampling In order to validate our nuclear-baiting kit for family-wide Annonaceae phylogenetics, we undertook a two-level analysis. First, we aimed to reconstruct, for the first time, a generic level nuclear phylogenetic tree of Annonaceae (referred to as "Annonaceae" analyses). We sampled 65 genera (out of 110) from all four subfamilies (see Supplementary Data Sheet) representing a total of 11 tribes out of the 15 currently recognized tribes  (Chatrou et al. , 2012b; Guo et al. , 2017b). From one to 12 species were sampled per tribe. All genera were represented by one species, except for the pantropical genus Xylopia (two species included: one from South America, one from Africa) and the paraphyletic genus Friesodielsia  (Guo et al. , 2017a)  for which we sampled one African species and one South-East Asian species.
Second, to assess its usefulness for reconstructing specieslevel relationships, we undertook a species-level sampling of the African tribe Piptostigmateae (referred to as "Piptostigmateae" analyses). This tribe comprises seven genera  (Ghogue et al. , 2017)  most of them recently revised  (Versteegh and Sosef, 2007; Couvreur et al. , 2009 Couvreur et al. , , 2015;; Marshall et al. , 2016; Ghogue et al. , 2017). We sampled a total of 29 out of 39 species covering all seven genera (see Supplementary Data Sheet). One to five individuals were sampled per species, plus a number of outgroup taxa, leading to a total of 83 individuals included in our analyses. All DNA was extracted from silicagel dried leaves, except for two samples, where DNA was extracted from air dried herbarium samples collected in 2000.
DNA Extraction, Library Preparation, In-solution Hybridization, and Sequencing DNA was extracted from silica gel dried leaves following the MATAB and chloroform separation methods of  Mariac et al. (2014)  and  Scarcelli et al. (2006). The full protocol is provided in Supplementary Data Sheet Protocol 1. Illumina libraries were constructed following a modified protocol of Rohland and Reich (2012) using 6-bp barcodes and Illumina indexes to allow for multiplexing at different levels. Extra steps were added to the  Rohland and Reich (2012)  protocol to allow for amplification and in-solution hybridization as follows: total DNA for each individual was sheared using a Bioruptor Pico (Diagenode, Liége, Belgium) to a mean target size of 500 bp. DNA was then repaired, ligated and nick filled-in before an 8-11 cycle prehybridization PCR was performed.
After clean-up and quantification, libraries were bulked, mixed with biotinlabeled baits and hybridized to the targeted regions using the bait kit designed above. The hybridized biotin-labeled baits were then immobilized using streptavidin-coated magnetic beads. A magnetic field was applied and supernatant containing unbounded DNA was discarded. Enriched DNA fragments were then eluted from the beads and amplified in a 14-16 cycle real-time PCR to complete adapters and generate final libraries. Libraries were sequenced on an Illumina HiSeq v3 platform pair end and length of 150 bp (Illumina, SAn Diego CA, USA) at CIRAD facilities (Montpellier, France) with around 18 pmol of the capture-amplified DNA libraries deposited on the flowcell. The full step-by-step protocol is provided in Supplementary Data Sheet Protocol 2.
Bioinformatics Demultiplexing with a 0-mistmatch threshold was undertaken using the demultadapt script (https://github. com/Maillol/ demultadapt). Adapters were removed using cutadapt (v1. 2. 1)  (Martin, 2011)  with the default parameters. Reads were filtered according to their length (>35 bp) and quality mean values (Q > 30) using a custom script (https://github. com/ SouthGreenPlatform/arcad-hts/blob/master/scripts/arcad_hts_ 2_Filter_Fastq_On_Mean_Quality. pl). Forward and reverse sequences were paired according to their name in the fastq files using a comparison script, adapted from TOGGLe  (Tranchant-Dubreuil et al. , 2018). A terminal trimming of 6 bp was performed on reverse sequences to ensure removal of barcodes in case of sequences shorter than 150 bp using the fastx trimmer script which is part of the fastx toolkit (https://github.
Paralog Identification HybPiper flags potential paralogs when multiple contigs are discovered mapping well to a single reference sequence. The program uses coverage and identity to a reference to choose a "main" sequence and denotes the others as potential paralogs. We took flagged loci and constructed gene trees using RAxML (v8. 2. 9)  (Stamatakis, 2014). We examined each tree to determine whether putative paralogs formed a species clade, in which case these were unlikely true paralogs and we could continue with the main sequence selected by HybPiper. If the "main" and alternative sequences formed separate clades they were likely true paralogs so we removed the entire locus from the downstream analyses.
Phylogenetic Inference We inferred trees using a generic-level dataset of Annonaceae and a species-level dataset of the tribe Piptostigmateae (see Supplementary Data Sheet). For each dataset we identified those exons that had 75% of their length reconstructed in 75% of individuals. We used only those loci in which at least 75% of the exon length was recovered because the use of fragmented sequences is known to increase gene tree error  (Sayyari et al. , 2017). We chose loci that were found in 75% of taxa to limit the amount of missing data but still recover loci that were not shared among distantly related clades. We then took the corresponding supercontigs (i. e. , targeted exon regions plus surrounding off-target captured sequence data) for phylogenetic inference. We initially tried tree inference using exons only but support was generally lower than trees based on supercontigs and the topology was similar so we decided to proceed using supercontigs (results not shown).