dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Phantom is the result of interactions over the years with numerous talented and interesting people. This work was supported by resources awarded under Astronomy Australia Ltd's merit allocation scheme on the gSTAR and swinSTAR national facilities at Swinburne University of Technology and the Pawsey National Supercomputing Centre. gSTAR and swinSTAR are funded by Swinburne and the Australian Government's Education Investment Fund. We thank Charlene Yang from the Pawsey Supercomputing Centre for particular help and assistance as part of a Pawsey uptake project. We used splash for many of the Figures  (Price, 2007). We thank Max Tegmark for his excellent icosahedron module used in various analysis routines. We thank the three referees of this paper for extensive comments on the manuscript.
A. 1 Smoothing kernels A Python script distributed with Phantom can be used to generate the code module for alternative smoothing kernels, including symbolic manipulation of piecewise functions using sympy to obtain the relevant functions needed for gravitational force softening (see below). Pre-output modules for the six kernels described in Section 2. 1. 6 are included in the source code, and the code can be recompiled with any of these replacing the default cubic spline on the command line, e. g. make KERNEL=quintic. The double hump kernel functions used in the twofluid dust algorithm (Section 2. 13. 4) can be generated automatically from the corresponding density kernel by the kernels. py script distributed with Phantom, as described in Section 2. 12. 2. The pre-generated modules implementing To the user what appears is a sequence of output files written at discrete time intervals.
PASA (2018) doi:10. 1017/pas. 2018. xxxtiallyfundedTerrence Tricco). CN is supported by the Science and Technology Facilities Council (grant number ST/M005917/1). CF gratefully acknowledges funding provided by ARC Discovery Projects (grants DP150104329 and DP170100603). SCOG acknowledges support from the Deutsche Forschungsgemeinschaft via SFB 881 (sub-projects B1, B2, B8) and from the European Research Council via the ERC Advanced Grant 'STARLIGHT' (project number 339177).
4E , middle), while super-enhancers active in other cell types, like the cortex (Fig. 4E  right), are not overrepresented in ESC HRS. Therefore, super-enhancers active in the ESCs are strongly associated with ESC HRS. Finally, using data available in the literature (ENCODE Project), we showed that, in ESCs, HRSs are not correlated with trimethylation of lysine 9 on histone 3 (H3K9me3), which marks constitutive heterochromatin. In contrast, they overlap with trimethylation of lysine 36 on histone 3 (H3K36me3) (Fig. 5A, B ), which marks transcriptionally active exon regions  (Hon et al. 2009 ). This latter result was confirmed on each chromosome using appropriate randomizations (Supplemental Fig. S6C ). We conclude that HRSs are associated with TSSs of highly expressed genes and active super-enhancers.
Raw data filtering Sequencing tags were trimmed and aligned on the mouse reference genome of e14Tg2a mouse ESCs (129P2 built from mm9 assembly), and read positions were determined. Reads mapping to multiple positions and reads with more than two mismatches were removed. Two bioinformatic filters were then applied to exclude potentially aberrant reads. Indeed, according to our protocol, all relevant reads should have a size of 18 to 20 nt due to MmeI digestion (filter 1), and they should have one of their extremities next to a StyI site (filter 2). Table  1  summarizes the number of reads obtained at each step of data filtering. Note that tag alignments to the mm10 assembly would not be expected to improve raw data processing since 97% of StyI sites are identical between the two assemblies and that the missing 3% corresponds to additional sites that are essentially lying in telomeric regions of chromosomes and not in gene-rich regions where most HRSs are located.
For HRS distribution with respect to A/B compartments (Fig. 3B, C ), all HRSs were uniformly randomized on the whole genome. To test the significance of the overlap between HRSs and H3K36me3 (ENCFF001KDY), exons or introns (UCSC mm9 assembly), the HRSs present in gene bodies were randomized only to gene body sequences. The distributions corresponding to 1000 random realizations were represented by their mean and the 95% confidence interval around this mean. A/B compartments were computed as described by  Lieberman-Aiden et al. (2009)  using Hi-C data sets from mouse ESCs (see Supplemental Methods;  Dixon et al. 2012). All tracks were plotted with the WashU EpiGenome Browser (https://epigenomegateway. wustl. edu) (mm9 assembly). LAD analyses were performed on data available from   HRS_ES Rep1 179, 252, 738 141, 324, 269 100, 805, 055 86, 937, 331 Loop_ES Rep1 159, 698, 048 128, 239, 638 103, 759, 365 99, 481, 107 HRS_ES Rep2 171, 281, 165 133, 948, 011 106, 454, 375 101, 747, 116 Loop_ES Rep2 157, 653, 173 130, 379, 866 97, 704, 507 85, 914, 663 HRS_ES Rep3 178, 797, 813 142, 705, 723 99, 653, 241 77, 299, 053 Loop_ES Rep3 167, 059, 132 130, 341, 772 106, 625, 626 102, 814, 442 gDNA Rep1 187, 711, 184 143, 521, 739 108, 219, 978 97, 427, 221 gDNA Rep2 176, 570, 100 141, 149, 872 104, 771, 787 90, 338, 854 gDNA Rep3 170, 893, 178 139, 667, 463 101, 734, 372 87, 578, 132  DamID maps of lamin B1 in mouse ESCs (NimbleGen microarray probes)  (Peric-Hupkes et al.
2010). Gene content analyses (Fig. 4B ; Supplemental Fig. S4 ) were performed using UCSC annotation data (reFlat. txt file, mm9 built). RNA-seq data from ESCs (used in Fig. 4C ) were downloaded from the Gene Expression Omnibus repository (GSE47948)  (Wamstad et al. 2012 ) and GRO-seq data from ESCs (used in Fig. 4D ) from GSE27037  (Min et al. 2011). ChIP-seq data used in Figure  5B  and Supplemental Figure  S6C  were downloaded from the ENCODE Project (ENCFF001KEV, ENCFF001KFB, ENCFF001 KFH, ENCFF001KFN, ENCFF001KFT, ENCFF001ZHE, ENCFF001 ZID; http://genome. cse. ucsc. edu/encode). Super-enhancer data (Fig. 4E ) were downloaded from the dbSUPER database (Khan and Zhang 2016; http://asntech. org/dbsuper/). GO analyses (Fig. 6 ; Supplemental Fig. S8A ) were performed using the functional annotation tool on the DAVID 6.
Acknowledgments We thank  Isabelle Degors ,  Sébastien This ,  Emeric Dubois ,  Françoise Carbonell ,  Marie-Noëlle Lelay-Taha , and  Marjorie Drac  (DNA combing platform) for technical assistance. We thank  Florence Rage ,  Rémy Bordonné , and  Patrick Lomonte  for providing antibodies;  Cyril Esnault  and  Jean-Christophe Andrau  for help with bioinformatics; and  Eric Soler  and  Mounia Lagha  for critical reading of the manuscript. This work was supported by grants from the  Institut National du Cancer  ( PLBIO 2012-129 ,  INCa_5960  to T. F. ), the  AFM-Téléthon  (no. 21024  to T. F. ), the  Ligue Contre le Cancer, comité de l'Hérault , the  Agence Nationale de la Recherche  ( CHRODYT ,  ANR-16-CE15-0018-04 ), and the  CNRS. M. -O. B. was supported by the  University of Montpellier  and Z.
Retrotransposition of SINE B2_Mm1 elements drove a species-specific expansion of CTCF occupancy in Mus caroli Previous studies have shown that the SINE B2 element carries a CTCF binding motif and can thus drive the expansion of CTCF binding in rodents  (Bourque et al. 2008; Schmidt et al. 2012). We took advantage of the closely related Muridae genomes to investigate the molecular mechanisms behind this expansion. We determined the genome-wide binding for CTCF in livers of the four Muridae by performing ChIP-seq experiments (Fig. 4A ; Supplemental Methods SM1. 11). In addition, we used a previously published data set to identify CTCF genome-wide binding in immortalized lymphoblast cells from four primate species  (Schwalie et al. 2013). We found between ∼24,000 and 48,000 CTCF binding sites across the four Muridae species and between ∼21,000 and 57,000 across the four Hominidae species (Supplemental Fig.
Methods Sequencing and assembly of Mus caroli and Mus pahari genomes Genomic DNA was extracted from one Mus caroli CAROLI/EiJ and one Mus pahari/EiJ female using Invitrogen's Easy-DNA kit (K1800-01). Following  Gnerre et al. (2011) , 180-bp overlapping paired-end libraries were prepared, and following  Park et al. (2013) , 3-kb matepair libraries were prepared. These libraries were sequenced using the Illumina HiSeq 2000 platform. The reads were assembled into contigs and scaffolds using the ALLPATHS-LG assembler  (Gnerre et al. 2011). High molecular weight DNA was extracted from Mus caroli/EiJ and Mus pahari/EiJ following the protocol in Supplemental Methods SM1. 2 to construct an optical map using the OpGen platform. The OpGen Genome-Builder software was used to assemble the NGS scaffolds into super scaffolds based on the optical map.
Gene annotation Mus caroli and Mus pahari genes were annotated using a combination of three annotation pipelines: TransMap  (Stanke et al. 2008) , AUGUSTUS  (Stanke et al. 2006) , and a new mode of AUGUSTUS called Comparative AUGUSTUS (AUGUSTUS-CGP)  (Konig et al. 2016). The GENCODE set of Mus musculus transcripts (M8 release)  (Harrow et al. 2012)  was used with the TransMap pipeline. In addition, RNA-seq data was used with the AUGUSTUS and AUGUSTUS-CGP pipelines. To prepare the RNA-seq data, RNA was extracted from multiple tissues (brain, liver, heart, kidney) from Mus caroli and Mus pahari using Qiagen's RNeasy kit following the manufacturer's instructions. RNA-seq libraries were generated with Illumina's TruSeq Ribo-Zero strand-specific kit and then sequenced on the Illumina HiSeq 2000 platform with 100-bp paired-end reads.
Divergence time estimation The divergence times of Mus musculus from Mus caroli and Mus pahari was estimated based on a set of fourfold degenerate sites from amino acids conserved across all mammals. Three different subsets of fourfold degenerate sites with similar size were created based on (1) random selection; (2) tissues-specific genes; and (3) housekeeping genes. BEAST 2  (Bouckaert et al. 2014 ) was used to infer the divergence time independently with the three data sets of fourfold degenerate sites and different evolutionary models (calibrated Yule model, Birth-Death Model, GTR, HKY85, strict clock, uncorrelated relaxed clock). Fossil record information of the mouse-rat divergence  (Jacobs and Flynn 2005)  was used to calibrate the molecular clock in all our analyses. See Supplemental Methods SM1.
Retrocopy analysis Retrocopies in the Muridae and Hominidae genomes were detected as previously described  (Navarro and Galante 2013). In order to comprehensively annotate retrocopies in Mus musculus and Homo sapiens, we used a combination of manual and automatic curation workflows. We considered the manually annotated processed pseudogenes from GENCODE M13 and v24, respectively  (Pei et al. 2012) , and processed pseudogenes from pseudopipe  (Zhang et al. 2006; Sisu et al. 2014). Mature transcript sequences were derived from Ensembl v86 and aligned to the corresponding reference genome using BLAT (mask=lower; -tileSize=12; -minIdentity=75; -minScore=100). The age of each retrocopy was estimated as t = 2d/(r parent + r retrocopy ) where d is the sequence identity between a retrocopy and its parental gene; r parent is the nucleotide evolutionary rate of the parental gene defined from the set of one-to-one gene orthologs shared between the four Muridae or four Hominidae; and r retrocopy is the nucleotide evolutionary rate of the retrocopies calculated from the retrocopies shared between the four Muridae or the four Hominidae.
CTCF binding site analysis We profiled the binding of CTCF in livers of Mus musculus C57BL/ 6J, Mus caroli CAROLI/EiJ, Mus pahari/EiJ, and Rattus norvegicus using the ChIP-seq protocol described in  Schmidt et al. (2009). The paired-end libraries were sequenced at 100 bp on the HiSeq2000 platform. In addition, the data set from Schwalie et al (  2013 ) was used to identify the CTCF binding sites in primates. Sequencing reads were aligned to the appropriate reference genome using Bowtie 2 version 2. 2. 6  (Langmead and Salzberg 2012). MACS version 1. 4. 2 (Zhang et al. 2008)  was used with a P-value threshold of 0. 001 to call read enrichment representing CTCF binding sites. Peaks present in at least two biological replicates were used for the analysis. The binding motif in each CTCF binding region was identified with the FIMO program from the MEME suite version 4.
Acknowledgments This project was supported by the  Wellcome Trust  (grant numbers  WT108749/Z/15/Z ,  WT098051 ,  WT202878/Z/16/Z , and  WT202878/B/16/Z ), the  National Human Genome Research Institute  ( U41HG007234 ),  Cancer Research UK  ( 20412 ), the  European Research Council  ( 615584 ), the  Biotechnology and Biological Sciences Research Council  ( BB/N02317X/a ), and the  European Molecular Biology Laboratory. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2010-2014 ) under grant agreement  244356  (NextGen) and from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  HEALTH-F4-2010-241504  (EURATRANS). We thank the genomics, bioinformatics, and BRU cores at the  CRUK Cambridge Institute  for technical support, the sequencing facilities at the  Wellcome Sanger Institute , and computational support from  EMBL-EBI  and WSI as well as the  Conservatoire Génétique de la Souris Sauvage (ISEM, France) and Plateforme Cytogénomique évolutive of the LabEx CeMEB.
Data access The genome assemblies of Mus caroli and Mus pahari from this study have been submitted to the European Nucleotide Archive (ENA; https://www. ebi. ac. uk/ena) under accession numbers GCA_900094665 (Mus caroli) and GCA_900095145 (Mus pahari). All reads from the ChIP-seq and RNA-seq experiments in this study have been submitted to ArrayExpress (https://www. ebi. ac. uk/ arrayexpress) under accession numbers E-MTAB-5768 (RNA-seq) and E-MTAB-5769 (ChIP-seq). A supplemental web page with links to raw data and other information is available at http://www. ebi. ac. uk/research/flicek/publications/FOG21.
Fatigue tests The tests were performed on an INSTRON servohydraulic testing machine (capacity of 100kN). The tests were force controlled, the load ratio was set to R=0 and the frequency was 2 Hz. All the tests were performed at room temperature (25°C). The number of cycles between the initiation and the final failure was less than 3% of the total lifetime. To simplify the analysis, the number of cycles to initiation was therefore considered equal to that leading to the final failure. For each test, an evaluation of the dissipated energy according to the protocol described in Section 2. 4. 2 was achieved. For confidentiality issues, all the energetic and mechanical data were divided by arbitrary values, kept the same for all materials to allow comparison.
Fatigue curves In order to keep a reasonable amount of figures, the fatigue results are plotted in the Figures  7  and 8  comparing the criterion prediction to the experimental data. At this stage, only the experimental data are of interest. First of all, it is important to underline that these curves can obviously not be used for fatigue design as the amount of samples is too limited to afford a statistical approach. The goal of the fatigue test was only to provide a few failure data to check the tendencies predicted by the heat build-up predictions. The curve obtained for the material A with the sample geometry of type 1 nevertheless provides a richer database and it seems that the scattering remained limited. This also seems to be confirmed by the reasonable alignment obtained for the other materials and sample types.
A very recent paper  [45]  presents a comparison of the experimental curves to numerical simulations for one of the material investigated here. The model includes elasticity, plasticity and non-linear viscoelasticity coupled to plasticity and strain rate. The identification is achieved on a extensive database and validated throughout the heat buildup curve prediction. The agreement is good and it appears that the first regime can be related to pure visco-elastic effects, that the second one is correlated to the onset of plasticity even if the contribution remains limited to the overall dissipation and that the third regime is induced both by plastic effects and by the coupling of plasticity to non-linear visco-elasticity. In this study, the focus is on the application of the existing protocol and checking its relevancy for the materials tested.
Conclusions This paper focused on thermostable matrices, reinforced with short carbon fibers and aimed at three main objectives. The first was to highlight and investigate if a heat build-up approach can be applied to these materials. This was checked both from a measurement point of view (adiabaticity, spatial distribution of the dissipation sources) and from a criterion point of view (evaluation possible over a limited amount of cycles). Compared to previous papers on other materials published by the authors, the adiabatic analysis was here performed over each pixel, throughout the analysis of the temporal variation. A second objective was to check the ability of the technique to predict the mean fatigue curve of these materials. This was assessed on a wide database for one material and one sample geometry.
They can help quantify the potential for rejuvenation episodes, such as the accretion of cold gas and the growth of a young stellar disc in a spheroid-dominated galaxy. And H i in particular often exists at large radii, where it can provide evidence of past interactions for long times (e. g. van de Voort et al. 2015). Here we present archival and new data on H i emission in nine slow rotator early-type galaxies; with these additions, virtually all of the slow rotators in the volume-limited Atlas 3D survey now have sensitive H i data. In Sections 2 and 3 we describe the selection of targets, the observations, and the data reduction. In Section 4 we provide updated statistics on H i in slow rotators, compare the properties of their H i to fast rotators, and consider some implications for the origin of the gas.
DATA AND IMAGING NGC 4191, NGC 4636, NGC 5576, and NGC 5813 were observed with the VLA in October 2015, and NGC 4690 was observed in March 2017. These data were obtained in the D configuration, giving baselines from 0. 2 to 5 kλ and resolutions ∼ 55 ; more detailed observing parameters are given in Table  1. The array configuration and time on source were chosen to give beam sizes and sensitivities close to those obtained at WSRT for the other Atlas 3D galaxies. The flux, bandpass, and time-dependent gain calibrations were made following standard procedures, using 3C286 once per observing session as the primary flux and bandpass calibrator, and secondary gain calibrators were observed every 20 minutes. Calibration was performed using the eVLA scripted pipeline versions 1. 3. 8 and 1. 3. 9 in their respective versions of the CASA software package 4.
6. 0 and 4. 7. 0. The bootstrapped flux densities of the secondary gain calibrators are in good agreement with previous measurements in the VLA calibrator database and the NRAO VLA Sky Survey  (Condon et al. 1998 ). An initial round of imaging revealed any bright line emission in the data, and the velocities of that emission combined with the known target velocities were used to select frequency ranges for estimating continuum levels. Continuum emission was subtracted using order 0 or 1 fits to the individual visibilities. The final data cubes were produced with a Briggs robustness parameter of 0. 5 and cleaned (where necessary) to a residual level of 1 to 1. 3 times the rms noise level. NGC 1222, NGC 1289, NGC 5831, and NGC 5846 were observed in several older VLA projects, as described in Table 1.
Observing strategies, data reduction, and imaging are similar to those used for the newer data, except as noted here. The NGC 5846 group, which also includes NGC 5845, NGC 5838, and NGC 5831 from the Atlas 3D sample, plus many late-type galaxies, was observed in a square 36-point mosaic spaced at 15 (the half-power radius of the primary beam). The final image has a gain above 0. 90 in a square region 76 on a side, centered on 15h05m47 +01d34m30 (J2000). We note that NGC 5839, also a member of the Atlas 3D sample, is nominally in this field but it falls outside the velocity range covered. To the best of our knowledge, the archival data on NGC 1289 and the NGC 5846 group have not been previously published. In contrast to all the other data used here, NGC 1222 was observed in the C configuration, giving baselines from 0.
S12 also make a careful analysis of the typical angular sizes and velocity widths of detected H i features in the Atlas 3D survey. Based on this analysis, they adopt M(H i) upper limits for the H i nondetections by calculating the statistical uncertainty in a sum over a data volume of 50 km s -1 and six synthesized beam areas (typically 1. 2×10 4 square arcseconds or 110 kpc 2 to 450 kpc 2 at the distances of this sample). The mass limit corresponds to three times this formal uncertainty. That procedure is also adopted here. In Section 4. 2 we also bring in some upper limits from the Arecibo telescope, and for those data we adopt a mass limit defined by three times the uncertainty in a sum over 50 km s -1 and one Arecibo beam (3. 7×10 4 square arcseconds).
Summary of H i detections and limits H i emission is detected in NGC 1222 at high signal-to-noise ratio, and more detailed descriptions of the distribution and kinematics are provided in section 6 below. We measure the total line flux from an integrated spectrum (Figure  1 ) to be 11. 2 ± 0. 7 ±1 Jy km s -1 , where the 0. 7 Jy km s -1 represents the statistical uncertainty due to thermal noise in the data and the 1 Jy km s -1 is a rough estimate of the uncertainty associated with the spatial region of integration. It is notable that the flux recovered in these data is 60% larger than the 6. 9 ± 0. 8 Jy km s -1 quoted by  Thomas et al. (2004a)  using the same data. The discrepancy may be due to the choice of data volume to integrate, as the recovered flux density of the gain calibrator B0320+053 is consistent with the NRAO calibrator manual and the 1.
Fitting the full three-dimensional H i data cube with the 3DBarolo software (Di  Teodoro & Fraternali 2015)  reproduces all these results, with a slightly larger inclination 49 • ± 4 • and correspondingly lower rotation velocity 184 ± 12 km s -1. The inner kinematic PA of 200 • matches the photometric major axis of the H i ridge to within a few degrees and the location of the kinematic twist matches the end of the ridge. This twist is apparently not associated with a significant change in the inclination.
SUMMARY We present new VLA H i observations of 5 Atlas 3D slow rotator early-type galaxies, plus newly reduced archival data on 4 others. Three other Atlas 3D fast rotators are also present in the observed fields along with numerous spirals and dwarf galaxies, and we find one H i cloud without an apparent optical counterpart. With these new data, 34 of the 36 Atlas 3D slow rotators now have sensitive H i observations at arcminute-scale resolution (4 to 10 kpc) from either WSRT, the VLA, or Arecibo. Typical column density limits are about 2. 5×10 19 cm -2 , and typical mass limits are a few 10 7 M of HI. The Atlas 3D survey is the first large, volume-limited survey of stellar kinematics in early-type galaxies. Thus it provides the best opportunity to make the connections between the galaxies' histories, as recorded in their stellar kinematics, with their histories as recorded in their cold gas.