dataesr/scientific-paragraphs-categorization huggingface dataset:
Along with niche overlap test, niche expansion and unfilling were also calculated. The expansion index corresponds to environmental conditions in the invaded area that are absent in the native area. Conversely, unfilling refers to environmental conditions in the native area that are absent in the invaded area. Both indices vary between 0 and 1. All niche conservatism tests were performed using the package ecospat v2. 1. 1  122  implemented in the R software  123  Data preparation and pseudo-absence selection. The preparation of presence-only data for ENMs included two important steps. The first one consisted of filtering presence data to reduce autocorrelation and sampling bias (e. g. , refs  124, 125  ). The second one consisted of selecting pseudo-absence data to calibrate models. Here, we applied two different methods to filter presence data and select pseudoabsences: the first method focused on the geographical space (hereafter Geographical Filtration, GF) whereas the second one focused on the environmental space (hereafter Environmental Filtration, EF).
Geographical filtration (GF) is commonly applied in the ENM literature. We used the R package SPTHIN v. 2. 1-2  125  to remove duplicated occurrences in a radius of 100 km, and therefore reduced auto-correlation biases. Consequently, 682 occurrences were retained. Next, we randomly sampled an identical number of pseudo-absences across the available geographical space, with three repetitions. This approach does not filter out repeated occurrences in similar climatic conditions, and samples pseudo-absences in areas that can fall within areas favourable to the species. Therefore, we expect this method to produce a modelled distribution that fits  more closely the initial distribution of species occurrences, a "lower-bound" estimate of the species distribution as opposed to the Environmental Filtration method described below.
For each set of presence/pseudo-absence, model calibration was realized with 70% of all data. The remaining 30% were used for model evaluations. Models were calibrated and evaluated three times per set of presence/pseudo-absence. Model calibrations were realized with the R package BIOMOD 2 v3. 3-7  4. Because our procedure is a presence/pseudo-absence procedure, we did not calculate discrimination capacity metrics (e. g. the area under the receiver operating characteristic curve or the true skill statistics) because (1) these metrics are designed to be calculated on real absences, and (2) they are dependent on prevalence, which can lead to spurious evaluations of ENMs  138, 139. Rather, we evaluated our models with the Boyce index, specifically developed for such data  109, 140, 141. The Boyce index assess how much model predictions match the observed distribution of species occurrences.
Values of the Boyce index vary between -1 and +1. Positive values indicate a model with predictions that are consistent with the distribution of occurrences in the evaluation dataset whereas negative values indicate a model with predictions that are not consistent with the distribution of occurrences. Boyce index values close to zero mean that the model is not different from a random model. Models with a mean Boyce index higher than 0. 7 were selected (Supplementary Fig. S6 ). Consequently, all models were kept for EF modelling, but MAXENT was removed for GF modelling. These analyses were performed using the R package ecospat v2. 1. 1  122. Forecasts of current and future favourable areas were obtained by ensemble forecasting method. Current and future ensemble forecasts represent consensual projections of the six modelling techniques, obtained through averaged distributions of favourability scores  4, 142, 143.
Acknowledgements We sincerely thank  Jessica Thevenot ,  Jean-Marc Pons ,  Hervé Magnin ,  Guy Van Laere ,  Julien Chalifour ,  Baptiste Angin ,  Arnaud Lenoble  and  François Catzeflis  for sharing occurrence data. We thank  Tarek Hattab  for his valuable advices. This project received funding from the  region Ile de France (ARDoC)  and from the the  Investissement d' Avenir Project Labex BCDiv  ( ANR-10-LABX-0003 ).
Contents This is a citation summary for Dopamine receptors in the Guide to Pharmacology database (GtoPdb). It exists purely as an adjunct to the database to facilitate the recognition of citations to and from the database by citation analyzers. Readers will almost certainly want to visit the relevant sections of the database which are given here under database links. GtoPdb is an expert-driven guide to pharmacological targets and the substances that act on them. GtoPdb is a reference work which is most usefully represented as an on-line database. As in any publication this work should be appropriately cited, and the papers it cites should also be recognized. This document provides a citation for the relevant parts of the database, and also provides a reference list for the research cited by those parts.
Bootstrap values were 100 for every branch  (Blom et al. 2009 ). The scale bar denotes 0. 01 substitutions. The host plant orders (red for Cucurbitales, blue for Rosales) and geographic origins (black for Eurasia, green for North America, purple for the Southern hemisphere) of the original cluster-2 inocula are color-coded. Outgroups were two actinobacterial genomes, Acidothermus cellulolyticus 11B  (Barabote et al. 2009 ) and Jatrophihabitans endophyticus DSM45617 (GenBank accession nr. FQVU00000000. 1). References for the published Frankia genomes are  Normand et al. (2007)  for ACN14a, CcI3, and EAN1pec,  Sen et al. (2013)  for QA3,  Swanson et al. (2015)  for ACN1ag,  Nouioui et al. (2019)  for CpI1-S,  Normand et al. (2018)  for ARgP5,  Mansour et al. (2014)  for CcI6,  Oshone et al. (2016)  for Allo2, Ghodhbane-  Gtari et al.
8 (Roche) with default settings. Completeness, contamination, and strain heterogeneity were estimated with BUSCO (v2. 0; Simão et al. 2015) , using the bacterial-specific single-copy marker genes database (odb9). Data were plotted by BUSCO plot (v2). For Cj1_Dg_nod and Cm1_Dg_nod, to estimate the relationship between the detected strains and the corresponding vcstrains, all binned contigs of were compared with the final draft genome of their vc-variants by applying BLASTN (threshold >1Â10 À20 ;  Altschul et al. 1997). Read mapping and SNP calling were performed as recently described  (Rupp et al. 2015). Briefly, reads were mapped to the final draft genome sequences of Dg1_Cn_nod and Dg1_Dg_nod with Bowtie2  (Langmead and Salzberg 2012). The Genome Analysis Toolkit (GATK) IndelRealigner algorithm  (McKenna et al.
2010 ) was applied for indel realignment, whereas SNPs were called using the GATK HaplotypeCaller algorithm  (McKenna et al. 2010). Identified SNPs were exemplarily checked with ReadXplorer 2. 2. 3. (Hilker et al. 2014 (Hilker et al. , 2016)). For the annotation of the genomes, Prokka  (Seemann 2014)  and GenDB  (Meyer et al. 2003)  were applied. Draft genome sequences were deposited at the EMBL/GenBank/ DDBJ databases in BioProjects PRJEB19438-49 (for details, see supplementary table  S2 , Supplementary Material online). Completeness of the reconstructed draft genomes and bins were estimated by calculating the content of bacterial BUSCOs (e-value: 0. 001, data set v. 3. 0. 2)  (Waterhouse et al. 2018). The results are shown in supplementary table  S3 , Supplementary Material online. The reconstructed and annotated Frankia (meta-)genomes were used for comparative genome analyses.
Identifying Transposases and Inverted Repeats To identify transposases in the Frankia genomes, the draft genomes (see table  1 ) were subjected to multiple BLASTX searches  (Ye et al. 2006 ) against a database of transposase amino acid sequences. Each (meta-)genome was searched against 5,180 transposase ORF amino acid sequences, mostly from the ISfinder web site  (Siguier et al. 2006)  as of May 2015. To parse and analyze search results with accuracy and repeatability, a multistep process was employed: First, all areas of a (meta-)genome that contained one or more transposase hits (e-value cutoff 10 À4 ) were designated as "footprints. " Then, the footprints were subjected to BLASTX searches (e-value cutoff 10 À4 ) to identify the transposase occupying it. In the case of several BLASTX hits, the transposase hit with the highest score was chosen.
If this hit did not cover the entire footprint sequence, the remainders were searched until the footprint had transposases designated to its entire sequence. Then, since transposases were occasionally identified as several fragments, an in-house script was used to join these fragments into single units. The criteria for joining two fragments were 1) that the fragments should be within 1 kb of each other; 2) that the fragments should be hits to transposases on the same strand; and 3) that the fragments should be hits to consecutive parts (650 amino acids) of transposases. A fraction of transposase value was computed for each transposase hit to evaluate its completeness. This value was calculated as the number of nucleotides covered by the transposase hit divided by the full nucleotide length of that transposase.
Joined hits were considered as one transposase for these purposes, and the fraction of transposase value was computed as the sum of their coverages divided by the shortest of the originating transposases. For statistical analysis, Wilcoxson rank sum test was performed with the wilcox. test function of the R program for statistical computing (R Core Team 2019). Scripts and data used, as well as GenBank files with annotated transposase hits for the investigated Frankia (meta-) genomes, are available in supplementary file S1, Supplementary Material online. Inverted repeats were identified with the "Find repeats" function in Unipro UGENE v. 1. 18. 0 using the following settings: Window size ¼ 12, Minimum identity per window ¼ 92%, min and max distance between repeats ¼ 0, 11,000 bp and the "Search for inverted repeats" option checked  (Okonechnikov et al.
Protein Phylogeny To investigate the evolutionary history of Frankia sp. Cppng1 putative nodU gene (cmcH), a phylogenetic analysis was conducted in combination with three sets of sequences. First, known NodU sequences of 11 rhizobial genera were collected from GenBank. Up to three sequences were collected from each genus from different species or strains. Species with their genome sequences available in Integrated Microbial Genomes (IMG)  (Markowitz et al. 2014)  were preferred over species without. Next, for each species with their genome available in IMG, by using its NodU sequence as a query for BLASTP  (Altschul et al. 1990 ) against its genome, all NodU-like (but not NodU) carbamoyl transferases with similarity score better than 1e À20 were collected. Finally, by using the putative NodU sequence from Frankia sp.
Cppng1 as a query for BLASTP against NCBI nonredundant database, carbamoyl transferases highly similar to Frankia sp. Cppng1 putative NodU (1e À150 or better, 45% amino acid identity or better) were collected from a phylogenetically diverse group of Actinobacteria  (Sen et al. 2014 ). These sequences were first aligned using MAFFT v7. 272  (Katoh and Standley 2013)  with accuracy-oriented alignment parameters (-localpair -retree 2 -maxiterate 1,000). Then the best substitution model for this multiple sequence alignment was calculated using ProtTest3  (Darriba et al. 2011 ). PROTTEST3 predicted the best substitution model to be LG with invariable sites, with gamma distribution, and with empirical base frequencies (LGþI þ GþF) based on Bayesian information criterion (BIC), Akaike information criterion (AIC), and corrected Akaike information criterion (cAIC).
2015, with  Dg1_Dg_nod1 and Dg1_Dg_nod2 from this study). Thus, the data obtained in this study imply that cluster-2 Frankia strains often appear in groups, a fact that in combination with our inability to culture most of them may be responsible for their wide apparent host range in that 1) an inoculum can be made up from strains with different host specificity and 2) strains that do not nodulate the host plant used to prepare the inoculum might be carried over because they might grow on the nodule surface. (Meta-)genomes sequenced by  Nguyen et al. (2016)  and in this study contained maximally three different strains; however Frankia cluster-2 inocula are very likely to contain more than two to three strains since the (meta-) genomes shown here can only encompass strains that actually colonize nodules of a particular host plant.
Previous attempts with Purshia sp. and Ceanothus sp. as host plants were unsuccessful  (Silvester 1977; Benson and Silvester 1993) , and nodulation of C. arborea by the Northern Hemisphere inoculum Dg1 failed as well (data not shown). So while the fact that Frankia cluster-2 inocula represent strain assemblages is complicating the interpretation of cross-infection studies, it is worth noting that Coriaria is the only host plant genus distributed in both hemispheres, and so far, the only successful cross-hemisphere inoculation involved a Coriaria species. The fact that most inocula used in this study were propagated in nodules of D. glomerata might have biased the outcome, both with regard to the members of the strain assemblages that were sequenced, and with regard to the results of cross-inoculation studies.
cannabina. When Datisca sp. arrived in North America-allopatric speciation between D. cannabina and D. glomerata has been dated to 25 Ma  (Zhang et al. 2006 )-Frankia cluster-2 strains from Eurasia encountered the microsymbionts of the North American host plants, Dryadoideae and Ceanothus sp. , all from the Rosales order. The phylogenetic position of the metagenomes from the North American inocula (fig. 1 ), one of which was isolated from Cea. thyrsiflorus, suggests that the Eurasian lineage of Frankia cluster-2 outcompeted the endogenous North American cluster-2 strains. Again, the results presented here may be biased based on the use of D. glomerata to propagate inocula. Nevertheless, the fact that the Cv1 metagenome isolated from nodules of Cea. thyrsiflorus maps in the same lineage as the three North American metagenomes that were propagated in D.
However, more data are needed to confirm the hypothesis that cluster-2 Frankia nod genes are involved in host specificity. The first metagenome of an inoculum from the Southern hemisphere was obtained, Cppng1_Ca_nod. The strain represents a novel cluster-2 Frankia species and does not contain the canonical nod genes nodAB, but still contains nodC. It could nodulate a northern hemisphere Coriaria species, C. terminalis.
Acknowledgments We are very grateful to  Susanne Renner  ( LMU Munich, Germany ) for helpful discussions about the evolution and systematics of Cucurbitales, to  Michael Lovave  and  Tiberius Jimbo  ( Papua New Guinea Forest Research Institute, PNG ) and  Axel Dalberg Poulsen  ( Royal Botanic Garden Edinburgh, Scotland ) for providing the Cppng1 inoculum and to  Matthew L. Carlson  ( University of Alaska at Anchorage, AK ) for providing the Dd1 inoculum. We thank  Warwick Silvester.
I is focused on AGN and multiplets with kpc-to-pc scale separation, while Sect. II on gravitationally bound SMBHs with sub-pc separations. Within each section we describe the datasets already available, the observational results and the predictions resulting from models and state-of-the art numerical simulations. We then discuss the most effective ways to define, observe, analyse and interpret the wealth of data collected in different wavebands. Finally, in Sect. III, we present first the techniques and strategy developments in this field, describing the future electromagnetic observing facilities and numerical set-up. Then, we explore the low-frequency GW Universe, the new window which will provide a complementary view of SMBHs by detecting the GW signal from those black holes coalescing in binaries.
These candidates should then be confirmed by pointed follow-up observations. We also show the investigations carried out through optical, X-ray, mid-infrared, and radio techniques used to detect and characterize spatially resolved and unresolved dual AGN systems with kpcto pc-scale separation. 1. 1. Optical surveys of dual AGN at sub-kpc/kpc separation: the search for candidates In the optical, the photometric and spectroscopic SDSS and BOSS surveys are vast databases that have been explored to search for pair candidates (e. g. ,  Hennawi et al. 2006 Hennawi et al. , 2010;; Myers et al. 2008; Shen and Loeb 2010; Sandrinelli et al. 2014 Sandrinelli et al. , 2018b;; Lusso et al. 2018; Lena et al. 2018 ). Yet, one of the main limitations of such surveys in the search for pair candidates is mostly due to the fiber collision limit: fibers cannot be placed closer than 55 for SDSS and 62 for BOSS.
4. 2. 2. SDSS has provided the largest database of extra-galactic spectra and has been commonly used for the identification of double-peaked narrow-line emitters. In particular, the [OIII] λλ4960, 5007 emission lines have been investigated for signatures of double peaks, since these are expected to be the brightest rest-frame optical emission lines in case of AGN photoionization  (Rosario et al. , 2010). Other high-ionization lines, such as  [NeV]  λ3426 or  [NeIII]  λ3869, can be be used in a similar way in the higherredshift sources. High-ionization double-peaked emission lines have been identified serendipitously in individual sources  (Gerke et al. , 2007; Xu and Komossa, 2009; Barrows et al. , 2012; Benítez et al. , 2013)  or through systematic studies of large spectroscopic databases such as SDSS  (Wang et al.
It brings with it the strengths inherent to the optical and NIR wavelength range (that is medium/high spatial and spectral resolution); however, it also bears its limitations (mostly the effects of dust obscuration, and the fact that not every AGN displays optical signatures). Moreover, the drastic reduction in the number of candidates that this technique allows to achieve comes at the cost of using a wealth of data which often are not publicly available and that can be obtained only through the access to highly competitive facilities.
Besides using X-ray imaging spectroscopy for the discovery of AGN pairs, one method of detecting/observing dual AGN in X-rays consists of follow-up observations of previously identified multiple AGN systems (e. g. , pre-selected in optical band, see previous Sect. 1. 1). This technique is presented in Sect. 1. 2. 1. Another method relies on using survey fields (e. g. , COSMOS, the Chandra Deep Field North and South), taking advantage of the plethora of multiwavelength data typically available in such fields, needed to select dual AGN candidates and define their properties; this technique is further discussed in Sect. 1. 2. 2 and Sect. 1. 2. 3. The advantage of the first method is clearly the possibility of targeting the system with a "proper" instrumental set-up, chosen to maximize the possibility of detecting/separating the AGN on the one side and defining their properties on the other side.
Discovery and follow-up observations of dual AGN and candidates The first spatially resolved SMBH pair was identified in X-rays, and was based on a dedicated Chandra observation which targeted the nearby ultraluminous IR galaxy (ULIRG) NGC 6240. Chandra imaging spectroscopy has revealed that both galaxy cores emit luminous point-like X-rays (Fig. 2 ), and show similar X-ray spectra which are flat, heavily absorbed, both exhibiting a strong neutral iron line  (Komossa et al. , 2003; Nardini, 2017). These are the tell-tale signs of heavily obscured AGN. The nuclei are at ∼ 1 kpc separation and are likely both Compton thick (i. e. , with absorption column density along the line of sight N H > ∼ 10 24 cm -2 ). Recently,  Kollatschny et al. (2019) , using VLT/MUSE data at a resolution of 75 milliarcsec, have reported the discovery of a third nucleus in this galaxy: the Southern component appears to host two distinct nuclei, separated by 198 pc only.
e. , a strongly obscured AGN with a two-sided jet). This result shows that even X-ray observations alone are sometimes not conclusive (see, e. g. , the recent results by  Hou et al. 2019  on an [OIII]-selected AGN candidate pair sample, but see also  Comerford et al. 2015 , where the invaluable strength of X-rays to pinpoint and characterize the AGN emission in [OIII]-selected dual AGN is fully exploited) or can be misinterpreted if multi-wavelength data of comparable/higher resolution and quality are not available. We also note that sophisticated analysis techniques may sometimes help in identifying dual  AGN (Foord et al. , 2019)  at relatively close separations. One peculiarity of many of the dual systems mentioned so far is that some members of these systems show no (or very weak) explicit AGN evidence in their optical/nearinfrared spectra.
AGN variability Time-domain surveys in the 2020s will provide powerful datasets for selecting SMBHB candidates whose nature can be tested with follow-up observations. The Large Synoptic Survey Telescope (LSST), scheduled for first light in 2020, will be the flagship time-domain machine. The well sampled, long-duration, high signal-tonoise light curves from LSST, in combination with the unprecedented sample size of about a million quasars, will be the best database for identifying candidate SMBHBs, based on (semi)periodic variability. LSST is currently under construction on Cerro Pachón in the Chilean Andes. The telescope features an effective aperture of 6. 7 m and will be equipped with a 9. 2 deg 2 field-of-view survey camera and ugrizy filter set, spanning the range from the atmospheric cutoff in the UV to the limit of CCD sensitivity in the near-infrared.
The BHM program encompasses three scientific areas: eROSITA spectroscopic follow-up, reverberation mapping, and an all-sky multiepoch spectroscopic survey. eROSITA will be a key player in searches for dual AGN via sensitive X-ray imaging surveys (Sect. 5. 2) and BHM will provide spectroscopic identification and redshifts for ∼400,000 eROSITA X-ray sources (primarily AGN at high Galactic latitude) in the first ∼ 1. 5 years of the survey. BHM will also build on the SDSS-IV reverberation-mapping and time-domain spectroscopic survey (TDSS) programs to deliver new black hole mass measurements for ∼ 1000 -1500 0. 1 < z < 4. 5 quasars/AGN and a few to a dozen epochs of spectroscopy per target for 25,000 quasars spanning temporal baselines of months to a decade. For most of the sky, this dataset will not yield enough spectroscopic epochs to fully populate radial velocity curves for SMBHB candidates.
Such systems would be detectable as single/dual AGN, if fed individually (on the larger scale) or if surrounded by a circumbinary disk which feeds the two SMBHs through mini-disks. A quantity of interest for observations is the characteristic residency time which, on these scales, is of order of ∼ 10 Myrs or less, yet sufficiently extended to enable detection. At the time of writing, there are few hundred pc and sub-pc scale SMBHB candidates described in the literature but their nature as true binaries is inconclusive and remains to be tested through time-domain and continued multi-wavelength monitoring. The new photometric (e. g. , ZTF, LSST and others) and spectroscopic searches (e. g. , SDSS-V) promise to provide even longer baselines and larger datasets of AGN which can be used to search for binary signatures -a crucial advancement given the low incidence of sub-pc SMBHBs expected from theory.