dataesr/scientific-paragraphs-categorization huggingface dataset:
None of the pair of loci involved in the dataset were found to be in linkage disequilibrium (not shown). Despite a relatively low level of allelic richness, ranging from 3. 42 ± 1. 03 in socio-spatial unit Sf to 3. 74 ± 0. 91 in socio-spatial unit Cf (Table  2 ), the levels of heterozygosity were moderately high. The average H e ranged from 0. 56 ± 0. 17 in the socio-spatial unit Sf to 0. 63 ± 0. 12 in the socio-spatial unit Cf and the average H o ranged from 0. 57 ± 0. 21 in the socio-spatial units Sf to 0. 65 ± 0. 13 in the socio-spatial unit Cf (Table  2 ). The genetic differentiation among the different socio-spatial units was low but significantly different from zero for females (global F st = 0. 023[0. 011; 0. 036] 95% , p <. 0001) while it was very low and not significantly different from zero for males (global F st = -0.
Data from animals fitted with GPS were randomly subsampled to distribute evenly the number of GPS locations and visual resightings in the dataset. This step was performed to avoid an oversampling of a very local zone over the study area which may artificially bias the cluster analysis. We retrieved an average number of 20 locations for GPS females and males, both being in the order of magnitude of the average number of visual resightings per individual (females: 15. 7 ± 16. 6 (SD), males: 9. 7 ± 8. 9). Visual resightings had the advantage of providing rough spatial behavior from a large number of animals. Conversely, GPS data provided an intense and accurate monitoring (±24. 5 m; Marchand et al. , 2015)  representative of habitats and time periods with very low (or null) visual detection probability (e.
ACKNOWLEDGMENTS We warmly thank all the professionals from the  Office National de la Chasse et de la Faune Sauvage  (Service Départemental  34 ,  Jeanne Duhayer  and  Christian Itty ) and numerous trainees for their technical support in trapping, tagging, sampling, and monitoring VHF and GPScollared mouflons. We thank  J. -M. Cugnasse  for his precious help on the history of the population and  Dominique Dubray  for his involvement in the deployment of GPS collars. We thank all the technical personnel of LBBE for helping with laboratory steps of the study, and the  Antagene laboratory (Limonest, France , www. antagene. com)for DNA extraction and genotyping. We thank  Elodie Bonneau  for the first laboratory and genetic analyzes. We thank  Denise Mirat  for checking the English in the last version of the paper.
We present a study of the low-frequency radio properties of star forming (SF) galaxies and active galactic nuclei (AGN) up to redshift z = 2. 5. The new spectral window probed by the Low Frequency Array (LOFAR) allows us to reconstruct the radio continuum emission from 150 MHz to 1. 4 GHz to an unprecedented depth for a radio-selected sample of 1542 galaxies in ∼ 7 deg 2 of the LOFAR Boötes field. Using the extensive multi-wavelength dataset available in Boötes and detailed modelling of the FIR to UV spectral energy distribution (SED), we are able to separate the star-formation (N=758) and the AGN (N=784) dominated populations. We study the shape of the radio SEDs and their evolution across cosmic time and find significant differences in the spectral curvature between the SF galaxy and AGN populations.
SURVEY DATA The National Optical Astronomy Observatory (NOAO) Deep Wide-field survey (NDWFS; Jannuzi & Dey 1999) targeted the sky seen towards the constellation of Boötes as one of its deep extragalactic fields. Originally the NDWFS covered 9 deg 2 in the optical and near-infrared BW, R, I and K bands. Since then, the Boötes Field has been surveyed across the electromagnetic spectrum, including deep X-ray  (Murray et al. 2005) , mid-infrared  (Jannuzi et al. 2010 ) and far-infrared  (Oliver et al. 2012 ) photometric observations as well as extensive spectroscopic surveys (e. g. Kochanek et al. 2012). The field therefore represents one of the richest multiwavelength datasets among the wide deep extragalactic surveys and is complementary to the deep radio observations provided by LOFAR.
2013). The source extraction for the catalogue was done using the Python Blob Detection and Source Measurement software, (PyBDSM:  Mohan & Rafferty 2015) , which performs Gaussian fitting to decompose radio interferometry images, grouping the Gaussians together into individual sources where appropriate. The LOFAR 150 MHz radio source catalogue contains 6276 sources detected with a peak flux density threshold of 5σrms within the coverage region shown in Fig. 1. The LOFAR 150 MHz luminosity distributions for our total sample classified into SF galaxies and AGN are shown in the lower panel of Figure  2.
8 mJy beam -1 at the edge of the image. The source extraction for the catalogue was done using the PyBDSM software, (PyBDSM:  Mohan & Rafferty 2015). The source detection threshold for the construction of the catalogue for this study is 3 σrms. This is a reliable detection limit since our study includes only sources from the catalogue which have LO-FAR detected counterparts. As discussed by  Coppejans et al. (2015) , the VLA-P catalogue was matched to WENSS  (Rengelink et al. 1997)  to check the absolute flux density scale and the primary beam correction. The GMRT image at 608 MHz is a mosaic constructed from four pointings covering 1. 95 deg 2 of the Boötes field (project code 28 064). The mosaic has a resolution of 5 × 5 arcsec and a noise level of σrms ∼ 40 -70 µJy beam -1. Primary flux density calibration was done with 3C286 using the wideband low-frequency flux density standard of  Scaife & Heald (2012).
(2007) , presented above. The innovative method used for this redshift catalogue consists of combining three different z photestimation methods, by using the EAZY photometric redshift software  (Brammer et al. 2008 ) customized with three different template sets: one set of stellar only templates (EAZY default library;  Brammer et al. 2008 ) and two sets including  AGN and QSO contributions (SWIRE; Polletta et al. 2007 ) and (Atlas of Galaxy SEDs;  Brown et al. 2014). This methodology has been motivated by the fact that comparisons of z phot -estimation techniques have shown that for a suite of multiple z-estimates, the ensemble average estimates offer significant statistical improvements in redshift accuracy compared to the estimates of any single set of predictions  (Dahlen et al. 2013; Carrasco Kind & Brunner 2014).
Flux scales The combination or comparison of radio maps at different frequencies requires them to undergo a standard calibration scaling. For this study we adjust the fluxes of the four different bands to the scale that is most accurate at low radio frequencies, that of  Scaife & Heald (2012). Since the main uncertainties of using a standard calibration scale are at frequencies lower that 1 GHz we focus our investigation on the LOFAR and VLA-P data sets. However, we note that the scaling of the high-frequency range covered by the GMRT and WSRT datasets was verified to also be consistent with the scale by  Scaife & Heald (2012). To investigate the reliability of LOFAR fluxes at 150 MHz,  Williams et al. (2016)  have compared high signalto-noise sources to three other data sets with known calibrations in the Boötes Field: NVSS (at 1.
λ[µm] Figure  4. Example SED of dusty SF galaxy from our sample at z=1. 2. The multi-wavelength coverage of our total sample is shown here, from the low-frequency radio till the UV regime. The radio data points represent the radio SED covered in this study: 150, 325, 610 MHz and 1. 4 GHz. The FIR-UV photometry is decomposed into physical components of the host galaxy and the AGN using the code AGNfitter. The integrated luminosities used in our classification scheme were computed by integrating the SED within the areas represented here as the grey-shaded rectangles. The left rectangle covers the integration limits for the main IR components: the galactic cold dust emission (green line) and the hot dust emission from the AGN torus (purple line). The right rectangle is the integration area for the optical main components: the stellar emission (orange line) and the accretion disk emission (blue line).
At nuclear scales, the AGN emission is modelled as a combination of an accretion disk component (Big Blue Bump, BBB) and a hot dust 'torus' component. -3 -2 -1 0 1 2 3 log (L BB /L GA )[0. 1 -1µm] -3 -2 -1 0 1 2 3 4 5 log (L TO /L SB )[1 -40µm] A large set of relevant physical parameters for the galaxy (SFR, M * ) and AGN (NH-torus, L bol ) are calculated, some of which are detailed below. However, for full details we refer the reader to Calistro  Rivera et al. (2016). From the 1955 LOFAR-I-band selected sources within the region of overlap between the multi-frequency radio data (section 3. 2), 1542 (∼ 79 per cent) can be classified as 758 SF galaxies and 784 AGN.
2. 1 applies here as well. Similarly,  Clemens et al. (2010)  studied 20 nearby LIRGS and ULIRGS from 244 to 8. 4 GHz, finding complex curvature in their source as well as claiming not only flattening but a strong turn over at around 1 GHz, which when extrapolated towards lower frequencies, implies a positive spectral index at frequencies < 1 GHz. With the availability of LOFAR data at 150 MHz we can confirm this is not the case for most of the SF galaxies present in our field but the curvature is rather small, presenting a slightly flatter slope but still of negative sign. Spectral flattening in SF galaxies hints towards physical conditions which, integrated over the total galaxy, appear to be a common property in our SF galaxies sample and are expected to remain constant at all redshifts.
These observations are in good agreement with the results presented by  Whittam et al. (2016). They report spectral flattening for radio-selected galaxies at the high-frequency end (15. 7 GHz) and suggest this may be due to the cores of  Fanaroff & Riley (1974)  class I sources (FRI) becoming dominant at these high frequencies. However, considering high flux uncertainties is especially relevant for AGN due to the different combinations of resolution and source extraction methods used, as they are extended sources. In contrast, this is not a relevant issue for SF galaxies, which are mostly compact sources. These uncertainties might weaken the strength of our conclusions on AGN and better radio data and larger samples are needed to confirm this finding.
THE INFRARED-RADIO CORRELATION (IRC) A remarkable property intrinsic to the radio SED of SF galaxies is the tight correlation with infrared luminosity, which has been observed at a few radio bands across five orders of magnitude in luminosity  (Yun et al. 2001). In this section we will characterize the IRC for the 1. 4 GHz and the LOFAR data at 150 MHz and investigate its evolution with redshift for our sample. For this study we include all detected and undetected sources in both the 1. 4 GHz WSRT and the Herschel -SPIRE data. As explained in detail in section 3. 2. 2, undetected fluxes are estimated using forced photometry measurements on the positions of LOFAR-detected sources. In this section we discard AGN and consider only sources selected as SF galaxies from the total LOFAR-I-band sample, which comprises ∼810 galaxies.
Finally, we test whether the z-evolution of q1. 4 is related to a changing ratio of AGN and SF contribution to the IR emission (LTO/LSB). We find no correlation between these quantities indicating that the redshift evolution is not due to AGN contamination. We discuss possible explanations to the offset observed between the mean q1. 4 of our sample (q1. 4 ∼ 2. 28±0. 02) and the local mean IRC found by  (Bell 2003, q1. 4 ∼ 2. 64±0. 26) ). An important factor to take into account in IRC studies is its sensitivity to selection biases  (Sargent et al. 2010). A common selection bias in radio-selected samples arises from samples being incomplete at IR data for sources with faint radio fluxes  (Ibar et al. 2008; Sargent et al. 2010). This might not affect our sample, since the FIR emission expected for the 150 MHz fluxes in our sample (based on the IRC) is considerably above the flux limit of the XID SPIRE fluxes  (Roseboom et al.
The offset between the mean q1. 4 values can be primarily explained by the redshift evolution and the different redshift ranges probed in the two studies. The picture of decreasing q1. 4 values as a function of redshift is being supported by a number of studies based on FIR and radio data sets of variate properties. Although some theoretical studies predict the opposite trend  (Schober et al. 2016)  and few observational studies claim to find no observable trend  (Pannella et al. 2015) , a long list of recent Herschel -FIR and Spitzer -IR based studies have observed a redshift evolution of q1. 4 (e. g. ,  Ivison et al. 2010b; Bourne et al. 2011; Casey et al. 2012; Jarvis et al. 2010; Smith et al. 2014; Magnelli et al. 2015; Delhaize et al. 2017). However, some of these studies have chosen to favour a no-trend scenario due to the modest evolution compared to the large size of their measurement errors or other observed dependences.
SUMMARY In this paper we have investigated two properties of radio selected SF galaxies and AGN, namely their radio spectral energy distributions and the IR-radio correlation (IR), and how these evolve across the bulk of cosmic history. Our study is based on a radio and I-band selected sample from the Boötes field and comprises ∼ 1500 galaxies at redshifts of 0. 05 < z < 2. 5. Taking advantage of the rich multi-wavelength coverage available for the Boötes field, we constructed panchromatic SEDs for our sample, incorporating UV/optical to far-infrared observations. The sources were then classified into star-formation and AGNdominated galaxies using a multi-wavelength SED-fitting method (AGNfitter code, Calistro  Rivera et al. 2016). We have characterized the statistical radio spectral index and spectral curvature properties for our total samples of SF galaxies and AGN using deep radio data across a wide range in radio frequency  (150, 325, 610 MHz and 1.
ACKNOWLEDGEMENTS The authors thank the anonymous referee for the very careful reading of the manuscript and the many insightful comments and suggestions that improved the paper. We thank  Jacqueline Hodge ,  Frank Israel  and  Michael Brown  for useful discussions and suggestions. GCR, KJD and HJR acknowledge support from the  European Research Council  under the  European Unions Seventh Framework Programme  (FP/2007-2013) / ERC  Advanced Grant  NEW-CLUSTERS-321271. WLW and MJH acknowledge support from the  UK Science and Technology Facilities Council  [ ST/M001008/1 ]. PNB is grateful for support from the  UK STFC  via grant  ST/M001229/1. MJJ acknowledges support from  UK Science and Technology Facilities Council  and the South African SKA Project EKM acknowledges support from the  Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO) , through project number  CE110001020.
Density Functional Theory studies To gain deeper insights into the structural and electronic properties of the Azo-bistpy and its Co(II)-metallopolymer, theoretical investigations were performed at their electronic ground state geometry using density functional theory (DFT) as implemented in the CP2K software package. [17] The geometry optimization of Azo-bistpy and its complexes, namely trans-Azobistpy-Co, cis-Azo-bistpy-Co, were performed in gas phase and on a graphene surface. All the structures are displayed in Figure  S1 -S5 of the Electronic Supplementary Information (ESI). Figure  S2  of the ESI displays the frontier molecular orbitals (MO) as the highest occupied and lowest unoccupied MOs, namely HOMO and LUMO, respectively, computed for Azo-bistpy at its electronic ground state. As far as the trans-Azo-bistpy conformer is concerned, the HOMO is localized on the azoaryl group whereas the LUMO is delocalized on the azobenzene and on the two central pyridyl moieties of the terpyridil groups.
Acknowledgements E. B. , S. B. -L. and M. M. gratefully acknowledge the  University of Strasbourg Institute for Advanced Study (USIAS)  for funding this work with an  USIAS Fellowship. We gratefully acknowledge financial support from the  European Commission  through the  Marie Sklodowska- Curie  project  ITN  project  iSwitch  ( GA-642196 ), the  Agence Nationale de la Recherche  through the  Labex projects CSC  ( ANR-10-LABX-0026 CSC ) within the  Investissement d'Avenir program  ( ANR-10-120 IDEX-0002-02 ) and the  International Center for Frontier Research in Chemistry  (icFRC). Computational resources were provided by the  Center for Information Services and High Performance Computing (ZIH) of the Technische Universität Dresden. Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
Homozygous Deletion in FveGA20ox4 Is Strictly Linked to the [r] Phenotype The R (RUNNERING) locus carrying the r mutation was previously mapped onto LGII within a 989-kb range  (Sargent et al. , 2004)  (Figure  2A ). Using microsatellites in the 'Ilaria_F 2 ' population enlarged with 1350 additional individuals, we mapped the r mutation and reduced the region to 87. 5 kb (Figure  2A ). Wholegenome sequencing (WGS) (Supplemental Table  1 ) of pools of [r] recombinants (homozygous for runnerless) allowed the identification of 23 SNPs/InDels linked to the r mutation in a 63. 28-kb region. We further checked the status of these SNPs/InDels in two genotypes, i. e. , F. vesca displaying either [R] ('Pawtuckaway') or [r] ('Baron Solemacher') phenotypes, using genome sequence data available in a public database  (Jung et al.
, 2014) , hypothesizing a single origin for the r mutation in the available F. vesca germplasm. We made this assumption because [r] has been used for decades in F. vesca breeding, like [PF]  (Iwata et al. , 2012). Only one deletion (DEL) and 10 SNPs are linked to the r mutation, encompassing a 59. 11-kb region containing eight genes (Figures  2B  and 2C ; Supplemental Table  2 ). To further reduce the R locus, we used [R] heterozygous recombinants. We investigated the heterozygosity of the R locus through phenotyping of seedlings issued from selfing. Among the [R] recombinants, one named alpha5-58 was found to be informative because it was heterozygous for runnering, according to the phenotype of its progeny, and it recombined just after the DEL in FveGA20ox4 (Figure  2D ). By combining phenotyping and genotyping data from the 26 F 3 lines issued from selfing, we unambiguously identified the DEL at position 25,536,553 on chromosome 2 as the r mutation; it is located in a GA20ox gene (cited as FveGA20ox4;  Mouhu et al.
ACKNOWLEDGMENTS This research was funded by the  French Ministry of Research. We thank  Nahla Bassil  ( USDA-ARS, NCGR, Corvallis, OR ) for diversity resources and  Peter Hedden  ( Rothamsted Research, UK ) for Arabidopsis mutant seeds. We thank  Johann Petit  for photos,  Frédéric Delmas  for plant transformation,  Norbert Bollier  for recombinant protein production,  Jean-Philippe Mauxion  for genotyping, and  Alain Bonnet  for help with phenotyping. CNRGV Toulouse confirmed the sequence of Fragaria diploid in the R locus by BAC sequencing. WGS data were produced by  GeT-PlaGe Toulouse, France. This work was supported by  Région Aquitaine  ( REGAL  Project) and by the  French Academy of Agriculture  ( Dufrenoy Grant  to T. T. , 2015) and was carried out under the auspices of the  EU-FP7-KBBE-2010-4 Project (Grant 265942 ).
The screenings have been done as described in  Makhloufi et al. (2014)   (Budak et al. , 2013; Cifarelli et al. , 2013). Multiple alignments of the DNA sequences were performed by ClustalW software  (Larkin et al. , 2007). In order to avoid an amplification of the exon-intron junction, prediction of the exon boundaries within Triticeae expressed sequence tags (ESTs) were performed based on rice and Arabidopsis genomic sequences. The second has been deduced due to the very strong homology between different species of Triticeae. We tried to identify nonspecific T. turgidum primers from genes whose function was studied. For this, some WRKY genes that had been specifically studied in the context of abiotic stress, were chosen for primer design: HvWRKY38 (AY541586) (group IIa)  (Marè et al. , 2004)  and TaWRKY2 (EU665425) (group I); TaWRKY19 (EU665430) (group I)  (Niu et al.
BAC Sequencing, Assembly, and Annotation BAC DNA was extracted using a NucleoSpin R 96 Flash kit (Macherey-Nagel) and the insert size was estimated with NotI digestion (Fast Digest NotI; fermentas). Positive BAC clones were outsourced for 454 Life Sciences pyrosequencing technology using the GS Junior Roche system (Kit 454 Titanium; Roche). Sequence data assembly was performed with Newbler 2. 8 software sold by 454 Life Sciences/Roche for 454 data  (Veras et al. , 2013). The assembly was performed on data previously processed by the software Pyrocleaner after clearing reads from contamination by the host E. coli. Sequenced BAC DNA was analyzed using TriAnnot Pipeline v. 3. 8 improved for wheat species (http://wheat-urgi. versailles. inra. fr/Tools/TriAnnot-Pipeline) enabling annotation, masking of transposable elements, and gene structure organization (Text S1)  (Leroy et al.
Alignment, Phylogenetic Tree, and Sequence Analysis of TdWRKY Genes The TdWRKY protein sequences were submitted to the CDD (Conserved Domains Database) from NCBI, and to Motif Scan detection in MyHits (http://myhits. isb-sib. ch/) with Prosite databases selected. Homologous proteins from whole genome sequenced monocot plants (Wheat, Barley, Brachypodium, Maize, Sorghum and Rice) and Arabidopsis were selected for sequence alignments using DNAMAN software (http://www. lynnon. com/). A neighbor joining phylogenetic tree was derived from a MUSCLE alignment 3. 8  (Edgar, 2004)  of TdWRKY proteins, their homologous WRKY proteins from cereals and their closest members from A. thaliana. The tree was then produced by MEGA 6 software  (Tamura et al. , 2013)  using the Neighbor-Joining method with 1000 bootstrap replicates.
Gene Expression Analysis Total RNA from at least 30 salt-treated and untreated leaves and roots from OR and GR genotypes were extracted using a Pure Link Plant RNA Reagent kit (Invitrogen). Total RNA was DNase treated (Promega), and first-strand cDNA was reverse transcribed from 2 µg of total RNA using an M-MLV Reverse Transcriptase kit (Promega) according to the manufacturer's instructions. First-strand cDNA generated from total RNA including salt-treated and untreated samples from either the OR or GR genotype was subjected to real time quantitative expression analysis. The latter was performed in a fluometric thermal cycler (DNA Engine Opticon 2; MJ Research, Walthan, MA, USA) using SYBR Green fluorescent dye following the manufacturer's instructions. Results were shown using SDS2. 2 software on an Applied Biosystem 7900 HT Fast Real-Time PCR System.
Identification, Sequencing and General Features of the Sequence Composition of T. durum BAC Clones 516,000 clones from the Durum wheat bacterial artificial chromosome (BAC) library, were screened for individual clones harboring WRKY genes. Six T. durum BAC clones were selected and fully sequenced by 454 technologies. BAC clone annotation revealed 12 non-TE genic features that were classified into two categories: 9 protein coding genes, one hypothetical and 2 gene fragments (Table  S4 ). The gene assignments were all supported by at least one full-length cDNA, an EST and/or homolog in another monocot plant such as T. aestivum, O. sativa, H. vulgare, Z. mays, and B. distachyon. One gene per insert was predicted for TD14H23 (GenBank accession no. KY091673), TD473J01(GenBank accession no. KY091677), TD493B21 (GenBank accession no.