dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessment of HB markers for MAS SNP markers covering the QTL confidence interval region were chosen for haplotyping. Phased haplotype information was obtained for each individual from the FlexQTL output and the number of individuals represented by haplotypes and diplotypes by each of the 15 SNP markers was calculated using Excel. Haploview version 4. 2  45  was used to identify recombination hotspots and HBs within the significant QTL regions that were detected in multiple location-years. PediHaplotyper R package  34  was used to haplotype significant QTL regions. Flanking sequences of the SNP markers in the QTL region of FaPFRU were BLAST searched against the NCBI nucleotide database and the scaffolds of the F. vesca v1. 0 genome assembly  46  to confirm marker groupings. SNP markers and SSRs associated with FaPFRU were physically mapped to the identified F.
Pedigree confirmation and correction Of the 23 MSU and ORUS families included in this study, genotypic data of most offspring matched the reported parents in 14 families, matched one of the parents in nine families (MSU 9-18, ORUS 3279, ORUS 3305, ORUS 3315, ORUS 3316, ORUS 3320, ORUS 3324, ORUS 3325 and ORUS 3326), and did not match either parent in one family (ORUS 3324) when using the Excel-based tool (Supplementary Information S1). Two genotypes of 'Fort Laramie' were predicted based on offspring genotypes where 'Fort Laramie' is the parent of MSU 9-8, MSU 9-9, MSU 9-10 and MSU 9-11 and 'Fort Laramie 2' is the parent of ORUS 3315, ORUS 3316 and ORUS 3324. An unknown pollen parent of MSU 9-18, which was supposed to be 'Fort Laramie', was also identified and is being referred to as father (F) of MSU 9-18.
The SSR marker EMFv006 alleles were not able to differentiate between QTL alleles assigned by FlexQTL and was rendered not suitable for MAB applications. To determine if unknown polymorphism existed within the SSR marker EMFv006, analysis by high-resolution melting and amplicon sequencing was performed and no polymorphism associated with SSC level was determined, supporting ineffectiveness of EMFv006 marker for MAS. Interestingly, few QTLs of significance were detected for each of the fruit quality traits and many were not consistent across environments or years. Missing phenotypic data were not  consistent across locations and years and this unbalanced representation of the phenotypic data might have hindered detection of fruit quality QTLs across locations and over years. 3] [14] [15]  Environmental influences may have prevented the detection of some smaller effect QTLs, although QTLs may not have been detected because of two sources of ascertainment bias that are present in this study.
ACKNOWLEDGEMENTS We thank  Dr Eric van de Weg  ( Wageningen University ) for providing access to his 'Holiday' x 'Korona' linkage map and for his valuable technical expertise and advice during data analysis. We also thank  Ted Mackey  ( USDA-ARS; HCRU ) for technical support and  Janet Slovin  and  Luis Osorio  for reviewing the manuscript before submission. This research was funded through the  USDA's National Institute of Food and Agriculture-Specialty Crop Research Initiative  project, ' RosBREED: Enabling Marker-Assisted Breeding in Rosaceae'  ( 2009-51181-05808 ) and ' RosBREED: Combining Disease Resistance and Horticultural Quality in New Rosaceous Cultivars ' ( 2014-51181-22378 ).
High NA water immersion objective (Olympus LUMFL 60 W × 1. 1 NA) (Olympus, Tokyo, Japan) was used for applying 10-20 mW of 740 nm excitation at the sample (PSF is about 0. 4 × 1. 2 μm). SHG signal was collected in forward direction using high NA objective (Olympus, LUMFl 60XW, NA = 1. 1). The SHG signal is detected using high sensitivity single photon GasAsP photomultipliers (H7421-40, Hamamatsu). The photo-pulses are counted by counters of our general purpose acquisition module (NI-USB 6363, National Instruments). The acquisition software was developed with LabView 2010, and in C (Visual Studio 2010, Microsoft). It is important to note that in addition to tissue clearing with 50% glycerol treatment all polarization dependent SHG images were acquired at a depth < 20 μm from the tissue surface to minimize depolarization  16  and birefringence effect  17, 18.
Image analysis and simulations were performed using MATLAB (MathWorks, Natick, MA, USA). All experimental P-SHG polarization stacks were obtained for input polarization angles α uniformly distributed between 0° and 180° with 20° increments. This incremental step is a good sampling compromise for accurate acquisition of SHG parameters and quality of the recording, avoiding motion artifact. Data were obtained from at least 3 samples in each experimental condition and at least 4 ROIs per sample. Automatic thresholding was performed on the polarization stacks before fitting with LLS method. The total image processing time for a 512 × 512 pixels image is about 3 s, that includes stack reading, image thresholding, LLS computing time and image display. The LLS computing time is about 20% of the total.
Class 2 (Fig. 3c-e ) includes rat aorta media, rat aorta adventitia and rat extensor digitorum lungus perimysium. Mean ρ exp values are 1. 6-1. 7 for the indicated ROI in the SHG image and r k > 0. 85 indicates that the three-levels of hierarchical organization are θ = °53 ,   3 found in straight fibrils. Note that for rat tracheal perichondrium, constant tilt θ Tc has been added in the simulation to better-fit experimental data (see Fig. 1e ). This is justified by the fact that undulations are clearly visible on the SHG image. The second class includes (c) rat aorta media, (d) rat aorta adventitia and (e) rat EDL perimysium. It is characterized by three helical levels of organization θ θ θ , , H θ = °12 H H SC 3. The third class encompasses (f) rat aorta intima, (g) liver vessel, (h) mousse heart epimysium, (i) rat femoral cortex bone (longitudinal section) and (j) mouse femoral cortex bone (transverse section).
Acknowledgements We thank  Nathalie Théret  for critical reading of the manuscript and  Régis Morel  for technical assistance. This work was supported by  Région Bretagne ,  Rennes Métropole ,  Conseil Général d'Ille-et-Villaine, Valorial Rennes, Ministère de l'Enseignement Supérieur et de la Recherche  and the  European Union Federal Funds FEDER.
Gene space completeness In order to investigate the completeness of the gene space in the five assemblies we determined rates of capture for conserved single-copy eukaryotic genes (BUSCO)  [47]  and an R108 transcriptome assembly, and assessed MAKER-P annotations. Because completeness results for all 5 assemblies were quite similar we discuss only results for the Pb base assembly and present results for the other assemblies in the supplement (Additional file 1: Table  S2 ). The BUSCO analysis indicates that the base assembly (Pb) captured nearly all of the genes (878 of the 956 genes in the dataset; 91. 8%). Nearly 16% (151) of the putative single-copy genes in the BUSCO database were duplicated within the assemblies. These putative duplicates might be due to true duplications in the R108 genome or they might be due to artificial redundancy in the assembly.
Given that the scaffold N50 was 3. 7 Mb in the Pb assembly to which these technologies was added, the discrepancy between the two technologies in joining scaffolds less than 100 kb did not have as great an effect on our assemblies. However, if a much more fragmented assembly were used, we would expect Dovetail to perform much better than BioNano if only one scaffolding or mapping technology were used. If both technologies are used, applying Dovetail first to incorporate the smaller scaffolds and create a more contiguous substrate for BioNano to use makes sense and would be especially critical for highly fragmented assemblies. A second difference in the two technologies also supports applying Dovetail prior to BioNano for combined strategies. Dovetail breaks sequences it identifies as chimeric as it runs the software.
BioNano Five grams of young leaf tissue was obtained from greenhouse-grown plants dark-treated for 24 h before harvest. High molecular weight DNA was extracted and a de novo whole genome map assembly was generated using the BioNano Genomics (BNG) (BioNano Genomics, San Diego, CA) platform at the Bioinformatics Center at Kansas State University. High Molecular Weight (HMW) DNA was nicked and labeled according to the IrysPrep protocol. In brief, HMW was double digested by a cocktail of single-stranded nicking endonucleases, Nt. BspQI (GCTCTTC) and Nt. BbvCI (CCTC AGC), and then labeled with a fluorescent-dUTP nucleotide analog using Taq polymerase. Nicks were ligated with Taq DNA ligase and the backbone of the labeled DNA was stained using the intercalating dye, YOYO-1. The nicked and labeled DNA was then loaded onto an IrysChip for imaging automatically on the Irys system (BioNano Genomics).
Transcriptome assembly To evaluate how the transcriptome was represented in the genome assemblies, the transcriptome of 14 day old R108 roots was sequenced using Illumina's RNA-Seq protocol. The transcriptome was assembled using the Transcriptome Assembly Pipeline (BPA2. 1. 0) [61]. The BPA pipeline includes a kmer sweep assembly strategy with ABySS (using the kmer values of 50, 60, 70, 80 and 90)  [62] , followed by an OLC (overlap layout consensus) assembly with CAP3  [63]  to find overlaps between contigs (unitigs). Scaffolding with ABySS and gap closure were performed to obtain the final assembled transcriptome sequences  (Simpson et al. 2009 ). The transcripts were clustered at 98% sequence identity using the CD-HIT-EST software  [64]. Finally, the set of transcript sequences were filtered by length (minimum length of 100 bp).
BUSCO Benchmarking Universal Single Copy Orthologs (BUSCO) provides a quantitative assessment of genome assemblies based on orthologs selected from OrthoDB  [47]. Assembly assessments were performed using the plant early release of BUSCO v1. 1b1, which contains 956 genes that are present in at least 90% of the plant species used to assemble the database  [47]. tBLASTn searches were used to identify BUSCOs followed by Augustus gene predictions and classified into lineage specific matches using HMMER within the BUSCO package.
Structural annotation To understand how gene sequences were affected by the assembly strategies, the MAKER-P genome annotation pipeline was used to annotate the five genome assemblies  [68] [69] [70]. All available truncatula R108 transcripts were assembled using the Trinity Assembler. All transcripts were from a single tissue, root, which is not ideal. Nevertheless, GMAP alignments to A17 indicate that the transcript assembly contains the majority of genes. Further, within the five assemblies, relative capture rates of these transcripts should not be biased by the lack of evidence transcripts from multiple tissues. The resulting assembly was used as input for expressed sequence tag (EST) evidence for MAKER-P annotations  [71, 72]. The MAKER-P pipeline aligns the provided ESTs to the genome and creates ab initio gene predictions with SNAP  [73]  and Augustus  [74, 75]  using evidence-based quality values.
Lowcomplexity sequences and short tandem repeats were scanned and removed using Dustmasker  [77]  and Tandem Repeat Finder  [78]. Potential contaminant sequences (best hit in non-plant species) were filtered by BLASTing  [79]  against NCBI Nucleotide (nr/nt) database. Genes with more than 50% CDS in these regions comprised the accession-specific gene set. Pfam analysis and functional enrichment were then performed on this novel gene list  [80].
Availability of data and materials The R108 v1. 0 assembly, sample information and the raw PacBio reads are available in Genbank (BioProject: PRJNA368719, Biosample: SAMN04571790, PacBio reads: SRS1353205, assembly MWMB00000000. 1). The gene annotation (GFF3) and BioNano (BNX) files are available under DOI numbers DOI: 10. 13140/ RG. 2. 2. 28253. 51686 and DOI: 10. 13140/RG. 2. 2. 32950. 80964, respectively. The R108 RNA-Seq data are available in the NCBI sequence read archive (SRA), under BioProject accession number SRP077692.
2010; Daddi et al. 2010; Geach et al. 2011; Magdis et al. 2012; Magnelli et al. 2012; Bauermeister et al. 2013). The COLD GASS survey (CO Legacy Database for GASS,  Saintonge et al. 2011a ) was designed to provide a cohesive picture of molecular gas across the local galaxy population with M * > 10 10 M. Unlike all the studies mentioned above, the sample was selected purely by redshift and stellar mass rather than targeting specific classes of galaxies and, with 366 galaxies observed as part of a cohesive observing campaign, is homogenous and large enough to statistically characterise scaling relations and their scatter. Significant results from COLD GASS include the demonstration that star formation efficiency varies systematically across the galaxy population  (Saintonge et al. 2011b (Saintonge et al.
g. Lagos et al. 2011; Genel et al. 2014; Popping et al. 2015; Lagos et al. 2015; Davé et al. 2017). Until now, COLD GASS could only provide information for the relatively massive galaxy population (M * > 10 10 M ). Recognising the need to understand the link between gas, star formation, and global galaxy properties in lower mass galaxies, we launched a second IRAM-30m large programme to extend the sample down to stellar masses of 10 9 M. We present here the combination of the two surveys, now collectively referred to as xCOLD GASS for "extended COLD GASS"; it contains IRAM-30m CO(1-0) measurements as well as a wide range of measured global properties for 532 galaxies spanning the entire SFR-M * plane at M * > 10 9 M. This paper presents the full catalog of IRAM-30m measurements, including both CO(1-0) and CO(2-1) line fluxes, complementary APEX CO(2-1) observations, and a catalog of global galaxy measurements derived from GALEX, SDSS, and WISE data products.
1 dex in width the ratio between the number of galaxies expected from the stellar mass function and the number of objects in the xCOLD GASS sample (i. e. the weight as a function of log M * is the ratio between the orange dashed histogram and the filled gray histogram of Fig. 3a ). To illustrate the impact of this weighting on other key parameters, Fig. 3  shows the difference between the observed distribution of stellar mass surface density, NUV-r color and metallicity for the xCOLD GASS sample before and after the weights are taken into consideration (filled gray and black solid line histograms, respectively). Any scaling relation or mean quantity presented in this paper takes these weights into account, as they make the xCOLD GASS sample volume-limited. To derive accurate star formation rates for all xCOLD GASS galaxies, photometry is extracted from the WISE and GALEX survey databases.
The IR and UV components of the total SFR are calculated and combined following exactly the method described in  Janowiecki et al. (2017). We also extract information from the SDSS DR7 database to provide us with information about the structural properties and chemical composition of the galaxies. For all the xCOLD GASS objects, the following key parameters are given in  Table 1:   1  , where r 50,z is the radius encompassing 50% of the z-band flux, in kpc. Galaxies with log µ * > 8. 7 are considered to have a significant stellar bulge. 7. concentration index: Ratio of the r-band Petrosian radii encompassing 90% and 50% of the flux, C = r 90,r /r 50,r. The concentration index can be regarded as a proxy for the bulge-to-disc ratio  (Weinmann et al. 2009) , with C > 2. 5 being bulgedominated galaxies.
5-2. 0 mK per 20 km s -1 channel to achieve a detection of CO(1-0) with S/N> 5. When the precipitable water level was particularly low, we favoured the redder galaxies (predicted to be CO-faint) in order to achieve rms sensitivities of 0. 8-1. 0 mK in a reasonable amount of time (average of ∼ 2 hour per target). The overall strategy was to observe a galaxy until either the CO(1-0) line was detected with S/N> 5, or the rms noise was low enough to allow us to put a stringent upper limit on the gas fraction of M H2 /M * = 1. 5% for COLD GASS and 2. 5% for COLD GASS-low. The IRAM data were all reduced with the CLASS software. Individual scans were baseline-subtracted (first order fit, excluding the spectral region within ±300km s -1 of the expected line center based on the optical redshift), visually examined to reject those with distorted baselines or anomalous features, and combined into a final averaged spectrum binned to a resolution of 20 km s -1.
To help disentangle these effects, we have obtained additional CO(2-1) data with the APEX telescope. With a beam size of 27 at this frequency, these observations are a good match for the CO(1-0) fluxes measured with the IRAM 22 beam. Observations were performed through the allocation of a total of 78 hours via both the ESO (proposal 091. B-0593) and Max-Planck channels. The APEX-1 receiver was used, tuned to the redshifted frequency of the CO(2-1) of each xCOLD GASS object, with a simple on-off observing mode. Integration times were set based on average observing conditions, and rms noise requirements predicted from the measured IRAM CO(1-0), a standard value of r 21 = 0. 7 and a Galactic conversion factor. The detection rate (S/N > 4) is 85%. The APEX data were reduced in CLASS using a procedure identical to that described above for the IRAM data.
The CO LF for this sub-sample of N galaxies is then constructed, and this procedure is repeated N times. The final uncertainty on each data point in the LF determined from the full xCOLD GASS sample is the 1σ distribution at the given luminosity within the N sub-sampled LFs produced by the bootstrap method. These are the points and errors shown in Fig. 5 , for both the methods of treating the non-detections. Figure  5  illustrates that even in the L CO interval where the data is not affected by the stellar mass limit of the sample, the xCOLD GASS CO LF differs from the observed and empirically derived LFs from  Keres et al. (2003)  and  Vallini et al. (2016) , respectively. The knee of the xCOLD GASS CO LF is at larger L CO , shifted downwards in number density and has a steeper slope. When the non-detections are assigned their 3σ upper limits (red symbols), the faint-end slope appears to be yet steeper.
The xCOLD GASS value of r 21 ∼ 0. 8 corresponds to an excitation temperature of 10K, assuming that the gas is optically thick  (Leroy et al. 2013). This value of r 21 is slightly larger than the values of 0. 5- * L * CO α [Mpc -3 ] [K km s -1 pc 2 ] Detections only (1. 30 ± 0. 71) × 10 -3 (7. 00 ± 1. 88) × 10 9 -1. 13 ± 0. 05 Detections + non-detections (9. 84 ± 5. 41) × 10 -4 (7. 77 ± 2. 11) × 10 9 -1. 19 ± 0. 05 0. 7 reported from resolved observations of the discs of nearby star-forming spiral galaxies (e. g. Leroy et al. 2013; Rosolowsky et al. 2015). Higher values still of r 21 ∼ 1 are usually reported for the nuclei of galaxies (e. g. Braine & Combes 1992; Sakamoto et al. 1995; Leroy et al. 2009 ). In Fig. 6  we highlight the position of merging systems found within the xCOLD GASS dataset.
2017). The dependence of both f H2 and the molecular ratio (R mol ≡ M H2 /M HI ) on M * in the xCOLD GASS sample is qualitatively similar to those observed previously in the 10 9 -10 11. 5 M stellar mass range by combining the COLD GASS data with CO observations from the Herschel Reference Survey  (Boselli Figure 6. Relation between observed CO(1-0) and CO(2-1) line luminosities, with the former having been observed through the 27 beam of the APEX telescope (top), or the 11 beam of the IRAM telescope (bottom). In the top panel, the CO(1-0) luminosities have been corrected by a small aperture correction to account for the beam size difference, and the solid line shows the best fitting value for r 21. In the bottom panel, the dotted line is the 1:1 relation while the dashed line is a 4:1 relation.
Furthermore, the flat stellar mass distribution (see Fig. 3 ) ensures a large dynamic range for all the physical properties under consideration (e. g. M * , SFR, metallicity) while preserving the statistical relevance of the sample. III. Homogeneous and accurate measurements: all the physical properties provided as part of the survey have been measured across the sample using the same quality data products, and the same methods. This was done both for the molecular gas measurements themselves, all derived from consistent IRAM-30m observations, and the quantities measured from the ancillary multi-wavelength datasets (SDSS, WISE, GALEX,. ) IV. Consistent observing strategy: by using a rigorous observing strategy, we can provide some meaningful and constraining upper limits on CO line fluxes for all the galaxies with line emission is not detected.
Massive black holes (BHs) inhabit local galaxies, including the Milky Way and some dwarf galaxies. BH formation, occurring at early cosmic times, must account for the properties of BHs in today's galaxies, notably why some galaxies host a BH, and others do not. We investigate the formation, distribution and growth of BH 'seeds' by using the adaptive mesh refinement code RAMSES. We develop an implementation of BH formation in dense, lowmetallicity environments, as advocated by models invoking the collapse of the first generation of stars, or of dense nuclear star clusters. The seed masses are computed one-by-one on-thefly, based on the star formation rate and the stellar initial mass function. This self-consistent method to seed BHs allows us to study the distribution of BHs in a cosmological context and their evolution over cosmic time.
Contact Graph Routing. Instead of a centralized route calculation, CGR, proposed by S. Burleigh (NASA JPL), follows a distributed approach: the next hop is determined by each DTN node on the path by recomputing the best route to destination, as soon as a bundle (i. e. , bundle protocol data unit) is received. This routing procedure assumes that a global contact plan, comprising all forthcoming contacts, is timely distributed in advance in order to enable each node to have an accurate understanding of the network  [47]. Table  2  shows the contact plan for the sample network from Figure  1. Routes can thus be calculated by each node on demand, based on extensive topological knowledge of the network combined with the assumed traffic status. This workflow is illustrated in Figure  2  where a contact plan is initially determined by means of orbital propagators and communication models, then distributed to the network, and finally used by the DTN nodes to calculate efficient routes to the required destinations.
introduced an opportunistic extension as a means of enlarging CGR applicability from deterministic space networks to opportunistic terrestrial networks  [48]. The latter is not a trivial contribution since it could, if successful, pave the way towards implementing space DTN advances on ground-based networks. At the time of this writing, CGR procedure is being formally standardized as part of the Schedule-Aware Bundle Routing (SABR) procedure in a CCSDS Blue Book  [47]. All these modifications have been implemented in ION software. As a result, this software becomes an important point of reference for the latest routing and forwarding mechanisms for space DTNs. The current version 3. 5. 0 of ION was released in September 2016 and is available as free software  [33]. Even though the latest version of the CGR algorithm is implemented as part of the ION 3.