dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome wide association studies (GWAS) More recent findings have emerged from genome-wide association studies  [28]. The most recent dataset reported included over 20,000 ADHD cases and 35,000 controls. These data were used to estimate that around 30% of the heritability of ADHD is explained by common genetic variation. In total, twelve loci achieved genome-wide significance, including FOXP2; notable because prior work had implicated it in adult ADHD  [29]. These findings place ADHD firmly on the path to detecting very large numbers of associated common genetic variants as more samples are accrued. LD regression analyses that estimate genetic correlations between disorders find strong genetic links between ADHD and a range of outcomes including educational performance, depression, obesity, smoking and lung cancer  [28].
Diagnostic assessment For diagnostic assessment, the use of a semi-structured diagnostic interview is advised, such as the Diagnostic Interview for ADHD in adults, second edition (DIVA 2. 0)  [198] , based on the DSM-IV-TR criteria. DIVA 2. 0 is available online without charge, currently in 19 languages (www. divacenter. eu). The Conner's Adult ADHD Diagnostic Interview for DSM-IV (CAADID) has been validated in English and Spanish  [199]. DIVA 2. 0 has been validated in two European populations  [81, 199] , and is increasingly used in international research. A DIVA 2. 0 app is available in the App and Google Play stores. The update of DIVA 2. 0 for DSM-5 criteria into 'DIVA-5' in all languages is ongoing. DIVA-5-ID, for people with Intellectual Disability (ID) and Young DIVA, for children and adolescents, are new versions of DIVA-5.
Stes, S. : Principal Investigator before 2016 in studies funded by  Janssen-Cilag ,  Lilly  and  Novartis ; speaker fees and advisory board for  Lilly ,  Novartis ,  Janssen-Cilag , and  Shire. Since 2016 no conflicts of interest. Mohr. P. : consultant and speaker for  Eli Lilly ,  Janssen-Cilag , and  Lundbeck. Leppämäki, S. : No conflict of interest. Casas , M. : speaker fees from  Janssen-Cilag ,  Bristol-Mayers Squibb ,  Ferrer-Brainfarma ,  Pfizer ,  Lundbeck ,  Otsuka ,  Servier ,  Lilly ,  Shire ,  GSK ,  Rovi  and  Adamed. Advisory board for  Janssen-Cilag ,  Lilly , and  Shire. Bobes , J. : research grants and/or consultant, advisor or speaker within de last 3 years for: AB-Biotics,  Acadia Pharmaceuticals ,  Casen Recordati ,  D&A Pharma ,  Gilead ,  Ferrer ,  Hoffman La Roche ,  Indivior ,  Janssen-Cilag ,  Indivior ,  Lundbeck ,  Mundipharma ,  Otsuka ,  Pfizer ,  Reckitt-Benckiser ,  Servier  and  Shire , research funding from the  Spanish Ministry of Economy and Competiveness -Centro de Investigación Biomedica en Red area de Salud Mental (CIBERSAM)  and  Instituto de Salud Carlos III ,  Spanish Ministry of Health, Social Services and Equality -Plan Nacional sobre Drogas -and the  7th Framework Program  of the  European Union.
Generation of cDC1 Targeting Cre Constructs and Mice Xcr1 Cre (B6-Xcr1 tm1Ciphe ) and Karma Cre (B6-Gpr141b tm2Ciphe ) mice were made according to a standard gene targeting approach in C57BL/6N-derived ES cells. They were constructed by inserting, through ET homologous recombination, a cassette containing the internal ribosome entry site (IRES) followed by a gene encoding the codon-improved version of Cre recombinase  (34) , into the 3   S1 ) was then performed in PBS, 2% BSA, 2 mM EDTA for 25 min on ice. For exclusion of dead cells 4 ′ ,6-diamidino-2-phenylindole (DAPI, Thermo Fisher Scientific) was added 5 min before acquisition. Data were acquired on a LSRFortessa X-20 flow cytometer (BD Biosciences), and analyzed using FlowJo (Tree Star, Inc. ).
Microarray Data Generation and Analysis DCs were generated in vitro from mouse BM FLT3-L cultures and sorting by flow cytometry to over 98% purity, as live, singlet, CD11c + cells that were SiglecH + for eq-pDCs, SIRPα -CD24 high for eq-cDC1 and SIRPα + CD24 -/low for eq-cDC2. Total RNA (50 ng) was used as starting material for each sample to synthesize biotinylated probes, using the NuGen protocol as described previously  (39). Affymetrix Mouse Gene 1. 0 ST raw. CEL files were analyzed in the R statistical environment (version 3. 4. 1). Data were RMA normalized using the oligo package and processed as described previously  (40). Heatmaps of Log2normalized expression values of selected genes were performed using the Morpheus website from the Broad Institute (https:// software. broadinstitute.
As expected on the basis of previous report  (21) , other cells than cDC1, in particular cDC2 and to some extent pDCs, represented the major fraction of the cells targeted in Clec9a Cre mice (Figure  3 ). In contrast, in all examined organs, except for the CLNs, cDC1 represented the major fraction of the cells targeted in Xcr1 Cre mice, CD4 + T cells constituting the second most frequent cell types expressing tdRFP in these organs, and the most frequent in the CLNs. This reflects the higher numbers of CD4 + T cells as compared to cDC1s in all these organs. Finally, cDC1 represented the major fraction of targeted cells in Karma Cre mice in all examined organs, except for the skin where 64. 4 ± 5. 8% of the tdRFP + cells were mast cells (Figure  3 ). We assessed the expression pattern of the Karma gene in mast cells and compared it to a variety of other immune cell types, using the database from the Immgen consortium.
Although, we did not examine tdRFP expression in other non-hematopoietic cells, it is unlikely that some of these cell types would be targeted in Xcr1 Cre or Karma Cre mice considering that the Xcr1 and Gp141b genes were not expressed outside of the hematopoietic system in all of the transcriptomic databases we queried. To inactivate specifically a candidate gene in cDC1, both alleles of this candidate gene should be excised. This requires a breeding strategy in which one Cre allele and one floxed allele of the gene to be inactivated are brought by the same germ cells, where unexpected recombination could occur. We have tested the frequency of germline recombination for both the Xcr1 Cre and Karma Cre models. Only the Xcr1 Cre model showed adventitious Cre activity in germ cells resulting in progeny with recombined Rosa26 tdRFP locus in all their cells.
ACKNOWLEDGMENTS We thank  Frédéric Fiore  and members of the  Centre d'Immunophénomique (CIPHE) (Aix Marseille Univ ,  INSERM US012 ,  CNRS UMS3367 ,  Marseille, France ) for generating the Xcr1 Cre and Karma Cre mice, and for taking care of mouse breeding;  Marilyn Boyron , the cytometry core, and animal house facilities (CIML,  UMR7280 ,  France ) for critical technical assistance. The authors also thank  Rebecca Gentek  ( Aix Marseille Univ, CNRS, INSERM, CIML, France ) for helpful discussion and protocol sharing. This work benefited from data assembled by the  ImmGen consortium.
FUNDING This work was in part carried out in the frame of the Innate Immunocytes in Health and Disease (I2HD) collaborative project between CIML, AVIESAN, and SANOFI. It was supported by grants from the  European Research Council  under the European Community's  Seventh Framework Program  [FP7/2007-2013 grant agreement number  281225  to  M. Dalod ; FP7/2007-2013 grant no. 322465  (Integrate) to BM], from the  Agence Nationale de la Recherche  ( ANR ;  XCR1-DirectingCells  to KC), from the  Fondation pour la Recherche Médicale  (label Equipe FRM 2011, project number  DEQ20110421284  to MD), which supported the Karma Cre mouse generation, from the  Program Hubert Curien (PHC)  Maïmonide-Israel 2017 project number  38155SJ  (to MD) supported by the  French Ministries of Foreign Affairs (MAEDI) and of Research and Higher Education (MENESR)  and from the  Fondation ARC pour la recherche sur le cancer  (to KC).
Finally, the coupling last step of phenothiazine on fluorinated triazine 2 was carried out using the synthetic method reported previously for pyrrole and fluorobenzene derivatives to lead to the TRZ 3(Ph-PTZ) TADF molecule. 40, 41 Chemical characterization of TRZ 3(Ph-PTZ), including NMR and HRMS, is provided in Supporting Information. To gain insights into the electronic states of TRZ 3(Ph-PTZ), we performed density functional theory (DFT) and time-dependent density functional theory (TDDFT) calculations. The ground-state geometries were optimized with Gaussian09 software at the B3LYP/6-31G* level. Phenothiazine has been described in previous studies to be a nonplanar, central six-membered ring because of the presence of sulfur. In such a moiety, the length of the (C-S) and (C-N) bonds was estimated to be 1.
This advantageous method is used for the first time to synthesize TADF emitters and can easily be extended to other synthetic routes. ■ EXPERIMENTAL SECTION Materials and Methods. All chemicals are reagent/ analytical grade and used without further purification. 1 H, and  13  C NMR spectra were recorded on Brucker AC-300 (300 and 75 MHz, respectively) and are internally referenced to residual solvent signals (CDCl 3 ). Data for 1 H and  13  C NMR are reported as follows: chemical shift (δ, ppm), multiplicity (s = singlet, d = doublet, m = multiplet), coupling constant (J in Hz), integration, and assignment. High-resolution mass spectra were obtained from Ecole Polytechnique Mass Spectral facility. Infrared spectrum was performed with JASCO FT/IR-6100 spectrometer. All of the starting materials were purchased from the usual suppliers (Sigma-Aldrich, Alfa Aesar, and Acros Organics).
Using the Gaia-DR1 TGAS catalogue, we independently derive the intrinsic velocity dispersion of the clusters from the astrometric parameters of cluster members. Results. The intrinsic radial velocity dispersions derived by the spectroscopic data are larger than those derived from the TGAS data, possibly due to the different masses of the considered stars. Using M tot and r hm we derive the virial velocity dispersion σ vir and we find that three out of four clusters are supervirial. This result is in agreement with the hypothesis that these clusters are dispersing, as predicted by the "residual gas expulsion" scenario. However, recent simulations show that the virial ratio of young star clusters may be overestimated if it is determined using the global velocity dispersion, since the clusters are not fully relaxed.
Gaia-ESO observations and data GES is obtaining medium and high resolution optical spectra of ∼ 10 5 stars selected in the Galactic field and in star clusters in order to provide a homogeneous overview of the distributions of kinematics and chemical element abundances in the Galaxy. Specifically, GES is collecting a big dataset of radial velocities (RVs), stellar parameters (i. e. , effective temperature, surface gravity, metallicity), and elemental abundances for large numbers of representative stars in clusters, covering a wide range of ages and stellar masses. Gaia-ESO observations are performed with the FLAMES instrument  (Pasquini et al. 2002) , using both GIRAFFE and UVES spectrographs, that permit the simultaneous allocation of 132 and 8 fibres, respectively. In the observations of young nearby open clusters, GIRAFFE is used for late-type stars with a V magnitude between 11 and 19 with the HR15N setup, that obtains medium resolution spectra (R ∼ 17000) in the wavelength range 6470 Å < λ < 6790 Å.
Target selection One of the main goals of GES is the study of cluster kinematics and dynamics based on large, unbiased samples of members. Known members from the literature do not provide suitable samples, because they are often biased by the selection method. For this reason GES adopts an inclusive selection strategy: all candidate members observed with GIRAFFE have been selected in an unbiased way, down to 19th magnitude (V band), and covering a relatively large area on the sky, from a strip around the cluster sequence. This is defined as the sequence drawn by the known members reported in the literature in the different color- A&A proofs: manuscript no. Bravi_2018 magnitude diagrams (CMDs). When the optical photometry catalogues are either inhomogeneous or incomplete, the selection is based mainly on the photometry of the Two Micron All Sky Survey (2MASS,  Skrutskie et al.
In the case of IC 2602 ESO archival data have also been retrieved and analyzed. In order to be consistent with the Gaia-ESO selection method, we considered only the archive data for stars that are in the strip of the CMD used for the GES selection. Table  2  summarizes the number of targets observed in each cluster. We list the number of stars observed with the different GIRAFFE and UVES setups, as well as the number of targets for which stellar parameters were derived.
GES versus TGAS velocity dispersion In this section we compare the velocity dispersion obtained from the GES (see Sect. 4. 2) and TGAS data (see Sect. 4. 3). We decided to use the values of σ ⊥ estimated from the sample of G17 within the radius R GES (last row of Table  5 ), with an error given by the sum of statistical error and systematic error (∼ 0. 1 km s -1 ). In Fig. 6 , we show, for the four clusters, the spatial distribution of the members selected by G17 from the TGAS catalogue and the spectroscopic candidates selected by GES. The G17 members of IC 2602 and IC 2391 within R GES are uniformly distributed in the whole area of GES observations. Instead, for IC 4665 and NGC 2547, the TGAS members seem to cover only a section of the cluster area. This may be related to the larger distance of these clusters, indeed, the membership selection in G17 is based on position, parallaxes and proper motions, so it is strongly affected by distance.
The velocity dispersion derived from the GES data are higher than those derived from the TGAS data but consistent within 2σ. Furthermore, we stress that the typical error uncertainty in proper motions in DR1  (Gaia Collaboration et al. 2016a ) is ∼ 1 mas yr -1 , which corresponds to about 0. 7 -1. 7 km s -1 at the distance of the four clusters studied in this paper. Therefore, we are pushing the Gaia data to their precision limit and their systematic errors need to be better investigated. However, all four cluster analyzed in this work show the same trend, i. e. TGAS dispersions are smaller than GES ones. The origin of this discrepancy could be due to two main explanations. The first is the presence of asymmetries of the system  (Baumgardt & Kroupa 2007). In fact, to derive the velocity dispersions, in GES we use the radial velocity of stars while in TGAS we use the velocity perpendicular to the plane containing the line of sight.
On the basis of this analysis we obtained the following main results: • The velocity dispersion measured from the RVs is higher than that measured from TGAS data. Given the masses of the stars in the GES and in the TGAS sample, this discrepancy would suggest that the system is relaxed and in a state of energy equipartition. However, given the limited numbers of cluster members in the TGAS sample and the error on astrometric parameters, we are not able to draw a firm conclusion. Important progresses will be possible very soon with the second Gaia data release. • The velocity dispersion measured with GES data is higher by about a factor two than what calculated by assuming virial equlibrium, given the masses of the clusters and the spatial distribution of their members. This result indicates that clusters are in supervirial state and two explanations are given to interpret it.
The first is the "residual gas expulsion" scenario (e. g. ,  Kroupa et al. 2001; Goodwin & Bastian 2006) , which suggests that clusters became unbound after the "feedback" from massive stars swept out the gas which did not form stars. The second is that the observed velocity dispersion could be higher than the virial one because most stellar systems do not fully relax, even after 20 -30 crossing times, as shown in N-body simulations of  Parker & Wright (2016). • In each cluster we found many new high probability members and confirmed many of those known in the literature. New high probability members are extended across the whole area covered by GES observations, suggesting that these clusters could be more extended than previously thought. When the observations of the Gaia-ESO survey are completed and data from the second Gaia data release will be available, we will able to study the kinematic of a larger sample of young clusters in a six dimensional space and solve the many open issues in this area of star formation.
Acknowledgements. The authors thank the referee,  R. J. Parker , for his review. Based on data products from observations made with  ESO Telescopes at the La Silla Paranal Observatory  under programme ID 188. B-3002. These data products have been processed by the  Cambridge Astronomy Survey Unit (CASU)  at the Article number, page 12 of 14  L. Bravi  et al. : The  Gaia-ESO Survey: kinematical and dynamical study of four young open clusters Institute of Astronomy ,  University of Cambridge , and by the FLAMES/UVES reduction team at  INAF/Osservatorio Astrofisico di Arcetri. These data have been obtained from the  Gaia-ESO Survey Data Archive , prepared and hosted by the  Wide Field Astronomy Unit, Institute for Astronomy, University of Edinburgh , which is funded by the  UK Science and Technology Facilities Council.
This work was partly supported by the  European Union   FP7 programme  through  ERC  grant number  320360  and by the  Leverhulme Trust  through grant  RPG-2012-541. We acknowledge the support from  INAF and Ministero dell' Istruzione, dell' Università' e della Ricerca (MIUR)  in the form of the grant " Premiale VLT 2012 " and by  PRIN-INAF 2014. The results presented here benefit from discussions held during the Gaia-ESO workshops and conferences supported by the  ESF  ( European Science Foundation ) through the  GREAT Research Network Programme. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
L. B. wishes to thank  J. G. Fernández-Trincado  and  B. Tang  for the useful comments and  G. Conte  who worked at the production of Fig. 3. E. Z. wishes to thank  A. Brown ,  T. Marchetti , and  C. F. Manara  for useful discussions. J. L. -S. acknowledges the  Office of Naval Research Global  (award no. N62909-15-1-2011 ) for partial support. F. J. E. acknowledges financial support from  ASTERICS  project (ID: 653477 ,  H2020-EU. 1. 4. 1. 1. -Developing new world-class research infrastructures).
g. , due to colinearity or nonsignificance), or a nonconvertible statistical analysis only (e. g. , PCA, AIC). Only parametric and nonparametric statistics that calculate effect sizes can be used as data in meta-analyses  (Rosenberg, Adams, & Gurevich, 2000). This left us with 46 papers. We recorded multiple results from the same publication in cases where the papers reported results separately and could have published each result as a separate experiment, unless it was clear that the results were embedded spatially with a small scale inside a larger scale, in which case only one result was included in our database. In other words, we used our professional judgment to assess whether results that happened to be published in the same document were actually methodologically nonindependent, by reading the methods sections.
| Data coding We distinguished between richness and diversity by interpreting richness as a measure of number of species, and diversity as Shannon's diversity index. Shannon's diversity index is essentially richness weighted by species abundances (evenness). These two measures can vary independently, so we do not consider them equivalent  (Purvis & Hector, 2000). Path types were coded as trampling (off-trail), trail (unpaved), road (paved), trail and road, highway (multi-lane paved and highspeed), railway, and road and railway. Combinations were used where the effects of the two path types could not be separated into independent analyses. Traffic type was coded as walking, equestrian, vehicle (standard cars and other normal vehicular traffic), heavy machinery (such as logging trucks and logging machines), walking and vehicle, walking and equestrian, and mountain biking.
Not all statistical values are signed (e. g. , F values are always positive) so we checked the appropriate sign against the reported results. We further chose to reverse the signs for studies with the N-F (near-far) design since in these studies, a large impact of paths on plant biodiversity would be registered as negative (decreasing values of richness/diversity observed for increasing distances). Thus, in our dataset, positive effect sizes indicate that paths increase plant biodiversity, and negative effect sizes indicate that paths decrease plant biodiversity. We did not use a prepackaged program for meta-analysis. Neither the richness nor the diversity data were normally distributed, and we were unable to transform them to normality. We thus assessed whether the mean Fisher Z transforms (effect sizes) were different from 0 using the Kolmogorov-Smirnov test.
, 2004). Longer times since onset of disturbance (e. g. , trails vs. trampling) may allow more species to establish. Differences between the models for richness and diversity may reflect the smaller sample size for diversity and the more uniform F I G U R E 2 Distribution of the Fisher's Z transformations for plant richness effects dataset for that sample, but also may reflect functional differences between richness and diversity as measures. A relatively small number of unpublished papers with null diversity effect sizes could shift the obtained results. It seems, however, that rather than not reporting null outcomes most researchers are simply not interested in calculating Shannon's diversity index despite having the possibility to do so. In addition, although there were significant differences between effect sizes across habitat types for the richness data (data not shown), habitat type did not contribute to the best models of the richness data.
This may reflect both the large number of habitat  The comparison factor describing the experimental design was included in the models for both richness and diversity. There were no presence-absence studies in the diversity dataset, while this was the significant positive variable for richness. Near-far had a significant positive effect for diversity and a positive nonsignificant effect for richness. Both near-far and presence-absence designs compare areas with and without (far from) path effects, while high-low comparisons (also significant for diversity) include paths at differing densities. "Low density paths" and "far from paths" may effectively be the same thing and may be more influenced by paths than a "path-absent" condition: The real differences between the experimental designs often come down to context-specific details.
Second, more studies across various habitats that examine the community ecology, functional ecology, and mechanisms of plant loss, establishment and species pool filtering would help to clarify why species richness increases in one case and decreases in another, when the disturbance is nearly identical. In particular, studies that focus on species composition would be valuable. However, while many papers already provide species lists or analyses to identify community types, this kind of data is not easily comparable across habitat types. While we understand the value of habitat-specific data, care should be taken to also discuss species composition in terms that are comparable, for example, in terms of establishment F I G U R E 7 The categories in the plant type variable were not included as explanatory factors in the best models for plant diversity or plant richness.