dataesr/scientific-paragraphs-categorization huggingface dataset:
Proteins with R 2 unique peptides above a score T1, or with a single peptide above a score T2 were selected as unambiguous identifications. Additional peptides for these validated proteins with score > T3 were also accepted. The thresholds T1, T2, and T3 for each search engine were set by the target-decoy method to control the false discovery rate (FDR) of < 1% for protein identifications and < 1% for peptides (including the ones exported with lower scores). For Mascot, the following thresholds were used: T1 = 14, T2 = 40 and T3 = 10; Phenyx thresholds were set to 4. 2, 4. 75 and 3. 5, respectively (p value < 10 -3 ). The validated proteins retrieved by the two algorithms were merged, any spectral conflicts discarded and grouped according to shared peptides. Non-specific binders were filtered from the drug pull-downs using the SAINT software (version 2.
After removal of organic solvent in a vacuum centrifuge, samples were reconstituted to 10 mL with 5% formic acid  (Bennett et al. , 2011). Individual fractions were analyzed at pH 2. 4 on an Agilent 1200 nano-HPLC system (Agilent Biotechnologies, Palo Alto, CA) coupled to a hybrid linear trap quadrupole (LTQ) Orbitrap Velos mass spectrometer (ThermoFisher Scientific, Waltham, MA) utilizing the Xcalibur software version 2. 1. for data acquisition. Single fractions were loaded onto a trap column (Zorbax 300SB-C18 5 mm, 5 3 0. 3 mm, Agilent Biotechnologies, Palo Alto, CA) with a binary pump at a flow rate of 45 mL/min. Loading and washing solvents were composed of 0. 1% trifluoracetic acid (TFA) in water (solvent A) and 0. 1% TFA in 70% methanol and 20% isopropanol (solvent B). The peptides were eluted by back-flushing from the trap column onto a 16 cm fused silica analytical column with an inner diameter of 50 mm packed with C18 reversed-phase material (ReproSil-Pur 120 C18-AQ, 3 mm, Dr.
, 2005)  with the target loss mass abundance of 0%. Maximal ion accumulation time allowed was 500 ms and overfilling of the C-trap was prevented by automatic gain control set to 10 6 ions for a full FTMS scan and 5 3 10 5 ions for MSn HCD. Intact peptides were detected in the Orbitrap mass analyzer at a resolution of 30,000 with a signal threshold of 2,000 counts for triggering an MSMS event. The maximum ion scan time was set to 200 ms for acquiring 1 microscan at a resolution of 7,500. Peak extraction and conversion of. raw MS files into the. mgf format was performed with msconvert (ProteoWizard Library v2. 1. 2708) and the peak lists searched against the mouse SwissProt database v2014. 03_201403131 (24,830 sequences including isoforms obtained from varsplic. pl (Kersey et al. , 2000)  and appended known contaminants) with the search engines Mascot (v2.
2. 03, MatrixScience, London, UK) and Phenyx (v2. 5. 14, GeneBio, Geneva, Switzerland)  (Colinge et al. , 2003). Submission to the search engines was via a Perl script that performs an initial search with relatively broad mass tolerances (Mascot only) on both the precursor and fragment ions (±10 ppm and ± 0. 6 Da, respectively). High-confidence peptide identifications are used to recalibrate all precursor and fragment ion masses prior to a second search with narrower mass tolerances (±4 ppm and ± 0. 025 Da for HCD). One missed tryptic cleavage site was allowed. Carbamidomethyl cysteine and the TMT 6-plex tag on peptide N-termini and lysine residues were set as fixed modifications. Oxidised methionine was set as a variable modification. To validate the proteins, Mascot and Phenyx output files were processed by internally developed parsers.
Proteins with R 2 unique peptides above a score T1, or with a single peptide above a score T2 were selected as unambiguous identifications. Additional peptides for these validated proteins with score > T3 were also accepted. The thresholds T1, T2, and T3 for each search engine were set by the target-decoy method to control the false discovery rate (FDR) of < 1% for protein identifications and < 1% for peptides (including the ones exported with lower scores). For Mascot, the following thresholds were used: T1 = 12, T2 = 45 and T3 = 10; Phenyx thresholds were set to 5. 5, 9. 5 and 3. 5, respectively (p value < 10 -3 ). The validated proteins retrieved by the two search engines were merged, any spectral conflicts discarded and grouped according to shared peptides. Using the isobar software  (Breitwieser et al.
ACKNOWLEDGMENTS We thank  Freya Klepsch  and  Patrick Markt (CeMM)  for their input in the design of the drug library. Human islets were provided through the  JDRF  awards  31-2012-783  and 1-RSC-2014- 100-I-X (ECIT Islet for Basic Research Program ) and the  NIDDK-funded Integrated Islet Distribution Program (IIDP) at City of Hope  ( NIH grant  # 1UC4DK098085 ). Next-generation sequencing was performed by the  Biomedical Sequencing Facility at CeMM. This work was funded by  JDRF  grants  3-SRA-2015-20-Q-R, 17-2011-258  (generation of b cells from alternative pancreatic subtypes; P. C. , J. H. -S. , and S. K. ),  17-2011-16 ,  2-2010-567 ,  26-2008-639 ,  17-2013-426  (P. C. ), and  3-PDF-2014-206-A-N (S. S. ); the  European Research Council  (ERC-2015-AdG-695136 Secret-Cells to T. H. ; and StG-2011-281265 to P.
C. ); intramural funds of the  Medical University of Vienna  (to T. H. ); an  EMBO long-term research fellowship  ( ALTF 596-2014  to R. A. R. ); the  NovoNordisk Foundation  (to T. H. );  European Commission   FP7 Marie Curie Actions  ( EMBOCOFUND2012  and  GA-2012-600394  to R. A. R. ); the  Austrian Science Fund  ( FWF P25659-B19  to R. A. K. ); a  DOC Fellowship  of the  Austrian Academy of Sciences  (to J. K. ); the  INSERM AVENIR program  (to P. C. ); the  INSERM  (to P. C. ); the  FMR  ( DRC20091217179  to P. C. ); the  ANR/BMBF  ( 2009 GENO 105 01/01KU0906  to P. C. ); the '' Investments for the Future ''  LABEX SIGNALIFE  ( ANR-11-LABX-0028-01  to P. C. ); the  Max-Planck Society  (to P. C. );  Club Isatis  (to P. C. );Mr. and  Mrs. Dorato  (to P. C. );Mr. and  Mrs. Peter de Marffy-Mantuano  (to P.
Molecular framework For molecular data we relied on a 2014 NCBI GenBank harvest compiled for an earlier study (Grı ´msson,  Grimm & Zetter, 2017). Gene banks now (as of December 1st, 2016) include ∼100 additional accessions (File S2); but the majority of these are either microsatellite marker sequences or sequences of gene regions too variable, or with insufficient taxonomic coverage within the Loranthaceae, to be of any use; thus, we opted against updating the dataset harvested for the preceding study. All analysis files (sorted by steps) are included in the online supporting archive (OSA) in the Supplementary Information (File S1: Steps 1-3 of analysis pipeline). Given the problems with signals in Loranthaceae sequence data (Grı ´msson, Grimm & Zetter, 2017: Files S1, S6), we used the following protocol to prepare data sets for phylogenetic inferences and molecular dating (a detailed description is provided in File S1).
1. 8. 2  (Drummond & Rambaut, 2007; Drummond et al. , 2012) , following the example of an earlier study on Acer  (Renner et al. , 2008). Clock-rooting was performed for five main datasets differing in the gene region coverage (all gene regions, all but excluding the most variable trnL intron, 5' exon (can be incomplete) and trnL-trnF spacer (trnL/LF) region, only plastid regions including or excluding the trnL/LF, only nuclear regions). In addition, the taxon-reduced data set used for the final dating step was analysed (for further details see File S1). For each of the matrices we performed a BEAST run under partition specific substitution models, unconstrained tree topology, a Yule tree prior, and uncorrelated log-normal clock prior [ucld. mean ∼ Gamma (0. 001, 1,000)]. The best fitting substitution models per partition, among the available in BEAST, were selected with JMODELTEST  (Darriba et al.
Use as age constraint The phylogenetic position of Helixanthera within the core Lorantheae is uncertain. Nucleotide data has been produced for three species including Helixanthera kirkii, but the data are partly problematic and too fragmentary. Helixanthera kirkii (only nuclear data available, only species palynologically studied so far) nests deep within the Lorantheae core clade, and H. parasitica Lour. (only plastid data available) is sequentially divergent from all Lorantheae and effectively unplaced (see Fig. 7  and File S1 for further details). The third and best covered species, H. coccinea Danser, groups with species of Dendrophthoe in agreement with the current systematic scheme, but its pollen is yet to be studied. Thus, Helixanthera has not been included in the taxon-reduced speciesconsensus dataset used here for the molecular dating.
Alternative clock-based roots For four of the five comprehensive datasets (all taxa, different sets of gene samples), the clock-inferred root was placed between the predominately Old World Lorantheae and a mostly southern hemispheric, American-Australasian clade collecting all three root parasitic genera and the members of the other two aerial parasitic tribes, the (probably paraphyletic) Psittacantheae and (putatively monophyletic) Elytrantheae (Table  2 ). In the case of the most-inclusive data set (all taxa, all gene regions), the root was shifted by two nodes and placed within the Lorantheae subtree, splitting the genetically divergent subtribes Loranthinae and Ileostylinae from the remainder of the Lorantheae (= Clade J according to  Vidal-Russell & Nickrent, 2008a). The subsequent evolutionary scenario would imply that root parasites and other southern hemispheric lineages share an ancestor with only the Loranthinae and Ileostylinae.
This would mean a paraphyletic Lorantheae tribe, which is highly unlikely  (Nickrent et al. , 2010; Su et al. , 2015; Grı ´msson, Grimm & Zetter, 2017). Thus, this alternative root was not further considered. In contrast to these roots, the taxon-reduced, less 'gappy' dataset (42 species covering, at least partly, all included gene regions) recovered the outgroup-inferred root, with Nuytsia as sister to all other loranths.
Timing of evolution of main Loranthaceae lineages The main, currently aerial parasitic lineages, of Loranthaceae evolved about 20 myrs earlier (Table  3 ) than estimated by Vidal-Russell & Nickrent (2008b); a discrepancy easily explained. In contrast to the earlier study, we can exclusively rely on ingroup fossils as age constraints, which provide direct evidence for the occurrence of several Loranthaceae lineages in the middle Eocene. Vidal-Russell & Nickrent (2008b) used two sets of fossil constraints for their dating of an all-Santalales dataset. The first set used a single fossil (Anacolosidites Cookson & K. Pike) to constrain the root age of an Olacaceae s. l. subclade, the former Anacolosideae (= Aptandraceae), to 70 Ma, providing generally older estimates than the second, preferred set.
Savolainen & Chase (2001)t study, i. e. Wikstro ¨m,Savolainen & Chase (2001), to constrain the (ingroup) root age. Using secondary dating constraints and age priors based on outgroup fossils typically leads to overly young age estimates (e. g. Grimm & Renner, 2013, for Betulaceae; Garzon-Ordun ˜a et al. , 2015, for Solanaceae and Ithomiini; Schenk, 2016, for simulated data). For example, in the two families of Canellales, namely Canellaceae and Winteraceae, crown group estimates using ingroup fossils as age priors are about double the age of those inferred based on a large magnoliid dataset including only root age constraints for the Winteraceae and the order.
ACKNOWLEDGEMENTS Ignacio Escapas  and  Benjamin Bomfleur  are thanked for supplying South American literature,  Kanchi Natarajan Gandhi  from the  IPNI  team for clarifying the standard author form for the Indian palynologist  Satish Srivastava ,  Marco Simeone  for providing a compilation of plastid substitution rates reported in literature, and two anonymous reviewers and the editor for their constructive comments on the manuscript. Alastair Potts and  Maxine Smit  are acknowledged for proof-reading the original submission and final manuscript.
Funding This study was funded by the  Austrian Science Fund (FWF)  with grants to FG, project number  P24427-B25 , and  GWG , project number  M1751-B16 , and by Synthesis FP7-the  European Union -funded Integrated Activities Grants  DK-TAF 1971 ,  SE-TAF 1918 , and GB-TAF 3740 to FG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Furthermore, it enables the development of molecular-based diagnostic tests for population screening. Here, we report the characterization of a reciprocal translocation t(3;4) (p1. 3;q1. 5) that segregates in a Large-White pig line, which has been selected since six generations for residual feed intake (RFI)  [18]. The translocation breakpoints for both chromosomes were identified at the base-pair level using a positional cloning strategy in somatic cell hybrids that were generated from a t(3;4) (p1. 3;q1. 5) carrier and their location was further confirmed by using data from whole-genome next-generation mate-pair sequencing. This translocation is located within the ADAMTSL4 gene and thus modifies its expression in translocation carriers. The flanking sequences were used to develop a PCR-based diagnostic test to genotype all the animals of the selected line.
Chromosome walking and STS selection Microsatellite markers were chosen on the genetic maps of Sus scrofa (SSC) chromosomes SSC3 and SSC4. In a second step, the SNP sequences of the Illumina pig 60K SNP chip were used as STSs and pairs of primers were defined based on the sequences on either side of each SNP (S1 Table ). In a third step, new SSC3 and SSC4 STSs were defined in the selected intervals using the pig Sscrofa 10. 2 draft sequence as reference (http://www. ensembl. org/Sus_scrofa/ Info/Index). Primers were selected using Primer3 software, and PCR conditions were determined for each STS (S1 Table ).
Mate-paired sequencing The pooled genomic DNA of five Large White animals (50 ng in total), one of which was heterozygous for the t(3;4)(p1. 3;q1. 5) translocation, was used to generate a mate-pair library following the Nextera protocol from Illumina. The target insert size of 6 kb was selected and the library was sequenced with read lengths of 100 bp for each mate. A second pool of five normal Duroc animals, which are assumed to harbor only normal chromosomes with respect to the translocation, was used as a reference normal sample. Sequencing data of the Duroc and Large-White pooled samples are publicly available in the Sequence Read Archive (SRA) database under accession numbers SRX2996560 and SRX2996558, respectively. Sequences were trimmed for adapters using cutadapt  [23]. The reverse complement sequences were aligned to the Sscrofa10.
2 genome assembly using bwa  [24]  and alignments with a mapping quality above 10 were kept for further analysis. We used three different software to identify structural variants (SV) from this mate-pair sequencing data: delly (doi: 10. 1093/bioinformatics/bts378), SVDetect  [25]  and GASVPro  [26]. All three software were used with default parameters. Only translocations that were supported by more than 10 mate-pairs were considered as reliable candidate translocations. For SVDetect, the set of candidate translocations was limited to balanced translocations.
Comparative mapping and fine mapping of the breakpoint In order to control the quality of the pig draft sequences in the two intervals of interest, the porcine sequences (pig Sscrofa10. 2 assembly) were compared to the human draft genome sequence (human GRCh38/hg38 assembly). On SSC3, we observed a collinear organization between the 23. 803-23. 876 Mb interval on SSC3 and the 22. 764-22. 831 Mb interval on HSA16. Seven new STS markers were designed from the pig draft sequence and PCR were performed with Hb1. 8 DNA as template (Fig  3B ). The minimum interval was defined between the SSC3_Tsloq_frag29 and SSC3_Tsloq_frg30 loci, which mapped to the same BAC clone CH242-117J1 (accession number CU928801). Five additional STSs were defined from the BAC sequence and a short interval of 968 bp containing the breakpoint was defined on SSC3 between the SSC3_Tsloq_frag29 and SSC3_Tsloq_frag32 loci (Fig  3B ).
Characterization of the breakpoint By applying this chromosome walking approach, we identified the two most proximal positive loci obtained with the Hb1. 8 clone, on both sides of the breakpoint, i. e. SSC3_Tsloq_frag29 on SSC3 and ADAMTSL4 on SSC4. In order to bridge the breakpoint region, the four possible crossed-combinations of oligonucleotides (combining one primer for the SSC3_Tsloq_frag29 locus and one primer for ADAMTSL4) were tested on Hb1. 8 DNA. One primer combination yielded an amplification product of approximately 1 kb. This product was purified, directly sequenced, and the sequences were compared to identify precisely the junction between the two chromosomal segments. The der(4) breakpoint was precisely mapped on SSC3 to position 48,007 Mb within the sequence of clone CH242-117J1 (GenBank accession number CU928801) and on SSC4, to position 27,558 Mb within the sequence of clone CH242-160A10 (GenBank accession number CU655899).
Translocation breakpoint mapping by next-generation sequencing We used data from a parallel study on the detection of structural variations (SVs) in pigs to confirm the translocation breakpoint. In that study, samples from different animals were pooled to detect breed-specific SV. A heterozygous carrier of t(3;4)(p1. 3;q1. 5) was included in a pool of five Large White animals as a test case for the detection of SVs. Three SV-finding computational tools were tested on the generated sequencing data (see Materials and Methods). The translocation was identified with the three tested software but among a very large number of putative translocations (more than 500 for SVDetect and delly). The translocation was readily detected only if the analysis was limited to SSC3 and 4, with 34 supporting matepairs, among a few (4 or 5) translocation candidates (SVDetect and delly, respectively).
Thus, the detection of such translocation signals from pooled DNA samples requires dedicated bioinformatics approaches (currently under investigation). We would like to stress that the identification of structural variantshere a reciprocal translocation-using paired-end or mate-pair sequencing data is a feasible approach at the local level regardless of the size of the segments involved. The ability to detect such an event relies only on the existence of read pairs, here mate-pairs, which span the translocation breakpoints. This contrasts with the cytogenetic approach for which the ability to detect such events depends strongly on the size of the segments involved in the rearrangement. Here, starting from the initial characterization of the translocation, by combining somatic hybrid clone-based maps and sequencing, we obtained the precise sequences of the two implicated genomic regions, and defined accurately the breakpoints within the ADAMTSL4 gene.
Acknowledgments This project was partly supported by the  French National Research Agency (ANR) , program " PIG_FEED " ( ANR-08-GENM-038 ), and by  INRA. We are grateful to the  INRA experimental farm (Rouille-GenESI, Vienne, France)  and especially to the technicians who took care of animals. We thank  Helen Hayes  for English proofreading.
Aura MLS The MLS measurement covers latitudes between 82 • N and 82 • S since August 2004  (Waters et al. , 2006). It is onboard the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Aura satellite. MLS measures millimeter-and submillimeter-wavelength thermal emission from the limb of the Earth's atmosphere every 25 s, from which vertical profiles of more than 15 chemical species are retrieved. We used the standard O 3 data product (240 GHz radiances) retrieved with the version 4. 2 data processing algorithm, which is publicly available from http: //mls. jpl. nasa. gov/. The quality of the O 3 data is as follows from  Livesey et al. (2017). The vertical × horizontal resolutions are 3 km × 300 km at 100 hPa and 3 km × 500 km at 10 hPa. The precision is 0. 03 ppmv at 100 hPa and 0.
, 2016). MLS has covered the location of OAPA (51. 6 • S) on a daily basis since measurements began in 2005. The long-term stability of the MLS ozone dataset has been shown to be very good (e. g. ,  Nair et al. , 2012; Hubert et al. , 2016). For comparison between DIAL and MLS, the DIAL profile is convolved using the following equation  (Livesey et al. , 2017) : X comv = X a + A [X DIAL -X a ] , (1) where X a is the a priori profile for each retrieval and A is the averaging kernel functions (matrix) of MLS. X DIAL is the DIAL ozone profile, and X comv is the convolved DIAL ozone profile, which is converted to each MLS grid for comparison. We used A for the polar winter condition from two A's that have been provided in the MLS dataset; the other is for the tropical condition. We used 500 km in distance, in the great circle distance (between 47.
The DIAL profiles were linearly interpolated onto the pressure grids for the MLS data; the vertical increments of the DIAL profile are as small as 150 m. The MIROC-CTM profiles on the day of each DIAL measurement were interpolated onto the pressure grids for the MLS data using a cubic spline. Both interpolated values were used to compute differences (MIROC-CTM -DIAL) (see Figs. 2, 5,  and 6 ). For converting the original DIAL geometric altitude and O 3 number density to pressure and O 3 mixing ratio, the NCEP reanalysis data  (Kalnay et al. , 1996)  are used. These data are registered in the NDACC database. Possible deviations could be expected if we were to use other meteorological data for the conversion process in DIAL. However, in this study, we used the DIAL data that registered in the NDACC database.
Example of vertical profile comparison Figure  2a  shows vertical profiles of O 3 measured with DIAL compared with those of MLS on the same day (14 November 2009) as an example. The asterisks and dotted line show the converted DIAL profile using Eq. (  1 ) and the original high vertical resolution DIAL profile, respectively. Each MLS profile was color coded with its measurement latitude to observe the latitudinal difference between DIAL and MLS. The bar in MLS O 3 profiles shows the precision reported for individual profiles. The bar in the DIAL O 3 profile shows the total uncertainty. The combined uncertainty (root sum squared) is shown in the right panel. In addition to the DIAL and MLS profiles, we also compared the 24 h average O 3 profiles from MIROC-CTM at 12:00 UTC. We have extracted data from six locations between 48.
Time series comparison All 23 DIAL profiles obtained in September-November 2009 were evaluated for their variability with time. The PV values at the location and time of all O 3 profiles from DIAL, MLS, and MIROC-CTM were investigated to place the measurements inside or outside the polar vortex. The degree of PV values at each measurement or model grid is a robust indicator of the location relative to the polar vortex. Here, we used meteorological data from the NASA Global Modeling and Assimilation Office (GMAO) Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2) reanalysis  (Molod et al. , 2015; Gelaro et al. , 2017)  (http: //gmao. gsfc. nasa. gov/reanalysis/MERRA-2/). We calculated the scaled PV (sPV) for pressures between 100 and 6 hPa from the PV values from MERRA-2 and PV / sPV ratios as a function of PT.