dataesr/scientific-paragraphs-categorization huggingface dataset:
Jet response measurements using 𝒁+jet and 𝜸+jet data Measurements of  DB and  MPF using both of the individual  → ℓℓ datasets and the +jet dataset are discussed below. The subsequent combination of the  → ℓℓ and +jet results into the final in situ calibration is detailed in Section 9. 1. The → and → analyses probe jets over the same kinematic space and use exactly the same  ref T binning. Within each bin, the balance distributions  j1 T / ref T and  MPF agree between the channels for both the cores of the distributions (including their means) and their tails (including their standard deviations). The two datasets are combined into a  → ℓℓ channel, which increases the statistical power of the measurement. This combination is done consistently for data and MC simulation, and also for systematic variations.
Impact of background in the 𝜸+jet sample The +jet dataset suffers from non-negligible contamination from dĳet events where one of the jets is misreconstructed as a photon. The purity, i. e. the fraction of actual +jet events, after the nominal selection is estimated using a "sideband" technique based on the event yields in different control regions defined by alternative photon isolation and identification criteria. This technique is described in detail in Refs. [5, 62]. The purity increases with   MPF is inherently more stable against stochastically-oriented effects (pileup, fake photons, etc) compared to  DB due to such contributions cancelling in  MPF when averaged over many events.
Summary of the systematic uncertainties Figure  26  presents the JES uncertainties from various sources, evaluated for both the DB and MPF methods with anti-   = 0. 4 jets calibrated with the EM+JES using the +jet dataset. The total uncertainty is obtained by addition in quadrature of the uncertainties from different sources. Overall, the DB and MPF methods achieve similar levels of precision. The MC generator uncertainties dominate for  ref  Figure  27  shows the uncertainties for the corresponding +jet analyses. Here, the photon purity systematic uncertainty is the dominant uncertainty for the DB method for  ref T < 100 GeV, while it is significantly smaller and subdominant for MPF. The other systematic uncertainties are of similar magnitude for the two methods. For the range 100 GeV ≤  ref T < 400 GeV, the photon energy scale contributes the dominant uncertainty.
7 and  ref PV = 11. 8 are the average values for the full 2012 +jet dataset. As can be seen from Eq. (  21 ), the impact on the jet  T from the two uncertainty components can change sign depending on the amount of pile-up, and become zero for jets produced in events with pile-up conditions matching the 2012 average values. The resulting fractional  T uncertainties are presented for two values of large- jet  T in Figure  33.
The difference between the measured and expected JER is taken as a "non-closure" systematic uncertainty. The truth-particle jet DB width  truth DB , used in the JER measurement (Eq. (  22 )) depends on details of the physics model implemented in the MC generator. A systematic "generator" uncertainty is evaluated to assess this dependence through taking the difference between the extracted JER using different MC generators. Other systematic uncertainty sources considered for the JER measurement are the same as those considered for the JES measurements discussed in Section 7. 3. 4 and are derived for the JER analogously. Figure  36  presents the resulting JER uncertainties for anti-   = 0. 4 jets and also the relative data-to-MC difference of the JER measurement. Results are reported as a function of  ref T , separately for the measurements performed using the +jet and +jet datasets.
Pile-up noise measured using random cones in zero-bias data In the random cone method, a cone of given size is formed at a random values of  and  in zero-bias data, and the energies of all clusters (at either EM or LCW scale) that fall within this cone are combined. The data was collected using a zero-bias trigger that records events occurring one LHC revolution after an event is accepted by a L1 electron/photon trigger. The total  T of a random cone is hence expected to only capture contributions from pile-up interactions. Since jets formed with the anti-  algorithm tend to be circular (Figure  3 (a)), fluctuations of the  T in a random cone can be considered a measure of the expected pile-up fluctuations that are captured by an anti-  jet with a radius parameter equal to the cone size. The  of the cone is randomly sampled within the range for which the noise is being probed, and the random cone method proceeds by forming a second cone at  +  ("back-to-back" in azimuth to the first cone) but at a new random , also restricted to the  range probed.
The EM-and LCW-scale noise terms can only be fairly compared after applying the jet calibration factor, which is done later in Section 10. 2. 3. Figure  52  shows the average pile-up noise fluctuations expected in different jets in 2012 for the different || regions. The data-MC agreement deteriorates in the more forward regions of the detector. This is likely to arise from poor modelling of the pile-up being exacerbated in this region due to the change in detector granularity and noise thresholds. To extract the pile-up noise term for average 2012 conditions, the noise term in random cones is extracted from the total 2012 zero-bias dataset. To ensure that the  distribution used in other in situ measurements (dĳet, +jet, and +jet) is identical to that in the zero-bias dataset, a reweighting is applied dependent on the  distribution.
The jet energy scale of trimmed anti-  jets with  = 1. 0 is derived using MC simulation in the same way as for  = 0. 4 and  = 0. 6 jets, thus calibrating the jets to the LCW+JES scale. In an additional step, a dedicated calibration of the jet mass for the  = 1. 0 jets is derived. The MC-derived calibration is tested in situ using the direct balance method with +jet events. These studies are used to evaluate uncertainties in the calibration. The total uncertainty for | det | < 0. 8 is found to be around 3% for jets with low  T , falling to about 1% for jets with  T ≥ 150 GeV. At larger | det |, the uncertainty increases to 4%-5% at low jet  T , decreasing to 1%-2% for  T > 150 GeV. The JER is measured in 2012 data using several in situ methods. The JER pile-up noise term is determined using novel techniques that exploit the increased level of pile-up interactions in the 2012 data.
Three measurements of the JER as a function of jet  T and  det are performed using +jet, +jet and dĳet data. A final measurement of the JER is obtained using a statistical combination of these measurements, using a methodology similar to that used for the JES calibration. The different in situ inputs are found to be consistent with each other over the kinematic regions where they overlap. For anti-   = 0. 4 jets in the central calorimeter region || < 0. 8 calibrated with the EM+JES calibration scheme, the JER resolution parameters are measured to be  = 3. 33 ± 0. 63 GeV,  = 0. 71 ± 0. 07 √ GeV, and  = 0. 030 ± 0. 003, which corresponds to a relative JER of   T / T = (23 ± 2)% for  T = 20 GeV and   T / T = (8. 4 ± 0. 6)% for  T = 100 GeV. The jet energy resolution in data is generally well reproduced by the MC simulation.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaĳan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRT, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
For very low count rates (≪1 per ion pulse), the number k corresponds to the number of charged fragments produced in one collision, and hence to the charge of the fragmenting parent cluster ion. Due to the data storing, mass spectra for collision events with a given number k, so-called k-stop mass spectra, can be constructed. For more detailed experimental information, see the experimental section given in the Supplementary Information. Theory. MD calculations were carried out by using TURBOMOLE  38  and the geometries and energies of the critical points in the potential energy surface (PES) by using the Gaussian09 39 program packages. MD simulations were performed in the framework of the Born-Oppenheimer Molecular Dynamics (BOMD) method, assuming classical motion of the nuclei in the electronic potential computed with DFT, in particular with the M062X functional  40.
Acknowledgements The experiments have been performed at the ARIBE facility, part of large-scale infrastructure GANIL. The research was conducted in the framework of the  International Associated Laboratory (LIA) Fragmentation DYNAmics of complex MOlecular systems -DYNAMO , funded by the  Centre National de la Recherche Scientifique (CNRS)  and in the frame of the COST action CA18212 Molecular Dynamics in the GAS phase (MD-GAS). We further acknowledge the technical support by  J. -M. Ramillon ,  C. Feierstein , and  T. Been , and the contributions of  B. Manil ,  V. Bernigaud ,  T. Schlathölter ,  R. Hoekstra , and  S. Bari  in earlier studies. The authors acknowledge the generous allocation of computer time at the  Centro de Computación Científica at the Universidad Autónoma de Madrid (CCC-UAM ).
MATERIALS AND METHODS To explore the preferences of urban forest practitioners, we conducted semi-structured interviews  (Schensul et al. , 1999; Creswell, 2018)  with urban forestry professionals in two cities in the United States (U. S. ): New York City, New York (New York), and Phoenix, Arizona. Respondents were asked a series of questions intended to ascertain the qualities of areas of the urban forest that they most and least preferred, and were given maps to help guide their answers. Interviews were audio recorded and transcribed verbatim prior to coding in NVivo 12. We relied on a system of thematic classification  (Braun and Clarke, 2006)  to code and analyze our data. Following a line-by-line coding process  (Creswell, 2018) , we sorted respondents' stated preferences for urban forests into themes  (Braun and Clarke, 2006).
Data Collection This study was approved by the Behavioral Research Ethics Board of the University of British Columbia (Approval Certificate Number H16-02583-A002). Our data were collected as part of a larger research project investigating urban green equity in U. S. cities. The interviews lasted between 50 and 100 min, during which participants responded to a series of questions designed to investigate patterns in urban forest decision making and practitioner perspectives on the nature of urban green equity and its role in governance. The interview instrument (Appendix A) was developed collaboratively with the research team following a review of relevant literature. Once completed, it was reviewed by experts in semi-structured interviews and piloted with an expert in urban forest governance.
Exploring phenotypic polymorphism in C. mauritiana For the different morphological leaf traits assessed in situ, a weak grouping of trees according to their population of origin was generally observed, except with LA and LDMC, which showed a significant population effect in ANOVA (Analysis of Variance) test (Supplementary Fig. S2 ). However, the inter-population variability remained difficult to interpret even in LA and LDMC since it was not significantly correlated with the climatic parameters in the population sites (data not shown). To evaluate genetically based variation, inter-population variability was then assessed in the accessions grown in the collection. Substantial variability of morphological traits was found within populations (Supplementary Fig. S3 ). Multiple and single linear regression tests were performed between both morphological and biochemical traits, and the climatic parameters in the sites where the populations originate, to identify putatively adaptive  traits.
For the morphological traits, the most significant results in multiple regression (Table  1 ) were obtained for SLA and LTE (Pvalue ¼ 4. 1 Â 10 À5 and P-value ¼ 1. 2 Â 10 À6 ). In particular, variations in temperature (e. g. correlations with Tmin) explained a large part of the variability of both SLA and LTE (54. 4% and 52. 1%, respectively). Single regression tests (Supplementary Table  S2 ) were also highly significant between SLA and parameters related to temperature (Tmean, Tmax and Tmin) as well as between LTE and Tmin, Tmean or Pmean_JAS. However, no regression tests were yet significant when considering truncated datasets removing all data points from population A, suggesting that the observed correlations were for the most part the result of a single population effect (Supplementary Table  S3 ).
Measurement of leaf morphological traits Digital images of leaves collected in both in situ and ex situ populations were produced by scanning. The length (L), maximum width (W) and LA were then determined using ImageJ 1. x software. Leaf fresh mass (FM) was measured directly using a precision balance. After drying in a heat chamber at 50 C for 48 hours, the leaves were weighed to obtain their dry mass (DM). SLA, LDMC and LTE were calculated according to  Vile et al. (2005). Correlations and collinearity between the phenotypic traits (i. e. L, W, LA, FM, DM, SLA, LDMC and LTE) were assessed by computing Pearson's correlation coefficients and a PCA in R 3. 5. 2. Non-redundant phenotypic traits were selected among traits that showed correlations with jrj > 0. 80 and collinearity in PCA.
Climatic data For each site of origin considered in this study (Fig. 6 ), a three-decade set of Climate Normals was retrieved from the M et eo-France R eunion database that is fed by a dense network of meteorological stations. The following annual climatic parameters were obtained from climate normals dataset: Tmin, Tmax, Tmean, Trange, Pmean, Pmean_JAS and PET. Pearson's correlation coefficients were calculated and a PCA was performed in R 3. 5. 2 to examine possible correlations and to reveal redundant factors (i. e. factors that were correlated at jrj > 0. 8 and which showed collinearity in PCA).
Analysis of leaf phenotypic traits Mean values per tree were used for both leaf morphology and leaf wax composition. Boxplots were performed using the ggplot2 R package  (Wickham 2016). Multiple linear regressions were performed between the mean leaf phenotypic traits and the climatic parameters at the site of origin in R 3. 5. 2 using the step function to optimize the choice of climatic parameters to be included in the model. Simple linear regressions were also performed between population-average foliar phenotypic traits and each climatic parameter individually. To evaluate the robustness of the observed correlations and to rule out the possibility of artifacts due to a single population, each regression model showing P-value <0. 01 was reanalyzed, considering truncated datasets removing all data points from one of the eight populations.
Search for cuticle biosynthesis-related genes in C. mauritiana and regression analysis A list of genes involved in the biosynthesis of leaf cuticular wax and cutin in Arabidopsis thaliana was obtained from  Li-Beisson et al. (2013). The sequences of these genes were retrieved from the Arabidopsis Information Resource (TAIR) database (www. arabidopsis. org). The X-blast algorithm was used against the C. canephora gene database (www. coffee-genome. fr; Denoeud et al. 2014 ) to find the best blast mutual hit between Arabidopsis-and C. canephora-predicted peptides. In this way, a total of 176 orthologous genes potentially involved in the biosynthesis of leaf cuticular wax and cutin in coffee were obtained. The expression level of these orthologous genes was retrieved in C. mauritiana. Genes with total read counts <460 (corresponding to a mean count of 20 reads in 23 samples) were filtered out, leaving a total of 100 orthologous genes significantly expressed in C.
SNP filtering SNP calling was performed according to  Garot et al. (2019b)  using the Genome Analysis Toolkit pipeline  (De Pristo et al. 2011). The following filters were applied to the SNP dataset: SNPs with <10 reads (DP < 10) per individual and Genotype Quality <30 (GQ < 30) per individual were filtered out using BCFtools v. 1. 3 in the SAMtools package; all SNPs with missing values (not available, NAs), low allelic frequencies (minor allele frequency, MAF < 0. 06) and that applied to only one population were removed using VCFtools v. 0. 1. 13 (Danecek et al. 2011). Using an in-house R script, genotype data were filtered for each gene to only retain one SNP per haplotype, therefore limiting linkage disequilibrium among SNPs. A total of 18,321 SNPs were finally retained.
Acknowledgments The authors wish to thank the  International Coffea collection (Saint-Pierre, Reunion Island) , which is maintained by  CIRAD  and  IRD , for providing sample plant leaves. The authors also thank the  Reunion Island National Park  for providing permits ( DIR-I-2015-154 ;  DIR-I-2016-069 ;  DIR-I-2018-157 ) and access to C. mauritiana in situ populations ( Certificate of compliance  to the  Access and Benefit-sharing Clearing-House ,  ABSCH-IRCC-FR-248728-1 ). The authors acknowledge  Meteo France Reunion and Francois Bonnardot  for kindly providing climatic layer data. The authors also acknowledge CIRAD for hosting visiting researchers at the  Pôle de Protection des Plantes  ( 3P ,  Unit PVBMT), Saint-Pierre (La R eunion ). The authors would like to thank the  CGFB Bordeaux Metabolome Facility-MetaboHUB  ( ANR-11-INBS-0010 ) where GC-based analyses were performed.
Comparison between targets of AtFD, OsFD1, OsFD4 and OsHBF1 In order to compare the FD-bound regions in Arabidopsis and rice, we retrieved ChIP-seq data for FD from  Collani et al. (2019). We next identified putative rice orthologs using the EnsemblPlants (https://plants. ensembl. org/index. html) database (Table  S2 ). The overlap between corresponding genes bound by AtFD, OsFD1, OsFD4 and HBF1 was illustrated using a Venn diagram  (Oliveros, 2007).
Flowering time experiments under inductive conditions demonstrated that no additive effect could be detected in the osfd4-1 osfd1-2 double mutant relative to osfd1-2 (Fig. 1d ), i. e. the phenotypic effect observed was that of osfd1-2 single (Fig. 1b-d ). These data indicate that OsFD4 encodes a promotor of flowering in rice and that the effect of osfd4 and osfd1 mutants is not additive.
5c ), consistent with these sites being highly represented in the core promoter sequences. We next examined the overlap between genes bound by OsFD1, OsFD4 and HBF1 and observed that 698 putative target genes were shared by all tested bZIPs; 220 genes were shared by OsFD4 and HBF1, and 991 were shared by OsFD1 and HBF1. All genes common to OsFD1 and OsFD4 were also common to HBF1, thus no gene was found at the intersection between OsFD1 and OsFD4 datasets only (Fig. 5d  and Table  S3 ). Finally, 7, 28 and 14,028 genes were uniquely targeted by OsFD4, OsFD1 or HBF1 (Fig. 5d ). The DAP-seq method uses the entire naked genome to identify transcription factor binding sites. Thus, many bound regions might not be functionally relevant for regulation of gene expression in vivo  (O'Malley et al. , 2016; Bartlett et al.
5f ). Finally, we compared putative targets of OsFD1, OsFD4 and HBF1 with targets of AtFD  (Collani et al. , 2019). To this end, genes identified by ChIP-seq data were retrieved from  Collani et al. (2019)  and the corresponding putative rice orthologs were assigned based on the EnsemblPlants database (Table  S2 ). The overlap between corresponding genes indicates the existence of targets possibly being part of common regulatory modules, evolutionary conserved (Fig. S4  and Table  S4 ).
DAP-Seq identifies genome-wide OsFD1, OsFD4 and HBF1 binding sites Application of DAP-seq using purified OsFD1, OsFD4 and HBF1 proteins resulted in the identification of hundreds of putative direct binding sites for these bZIPs. Consistent with a function in core promoters, binding sites were enriched in the proximity of the TSS. It should be noted that the in vitro nature of DAP-seq allows for the identification of direct binding events by a single TF, specifically capturing those mediated by individual homodimers. Given that OsFD1, OsFD4 and HBF1 formed heterodimers, these events may represent only a fraction of all in vivo binding events resulting from additional protein interactions. Integrating DAP-seq data with expression data allowed us to focus specifically on those genes that were expressed in relevant tissues and on those differentially expressed in the osfd4-1 and osfd1-2 mutants.
Our data provide a first examination of the direct genome-wide binding landscape of bZIP flowering regulators in rice. This approach needs now to be refined for several reasons. First, we used a single purified bZIP transcription factor in DAP-seq reactions. Being disengaged from its florigen-containing complex, its DNA binding capacity might be biased. Arabidopsis FD can bind functional sites without assembling into a FAC and with no need for FT or its closest homologue, TWIN SISTER OF FT (TSF)  (Collani et al. , 2019). Also purified OsFD1 alone can bind a fragment of the AP1 promoter containing a C-box element (GACGTC) in gel shift assays  (Taoka et al. , 2011). We previously showed that HBF1 can bind in vitro an ABRE/G-box element in the Ehd1 promoter region, a finding that we further corroborated in this study  (Brambilla et al.
OsMADS15 has been proposed as direct target of an OsFD1-containing FAC  (Taoka et al. , 2011). Other proteins could possibly stabilize OsFD4 and OsFD1 in vivo and allow binding to these loci. Alternatively, the rice OsFD1-OsMADS connection might be indirect. Data in support of a direct connection have not been as thoroughly repeated and validated in rice as they have been in Arabidopsis, in which direct contacts between FD and the C-box element in the AP1 promoter were disproved  (Benlloch et al. , 2011; Jung et al. , 2016; Collani et al. , 2019; Li et al. , 2019). Further assessment of in vivo binding will be necessary. Genome scanning by DAP-seq identified identical core bZIP-binding motifs that were highly enriched in peaks of all three bZIP datasets. The same 5'-CACGT-3' core motif was identified as bound by OsFD1, OsFD4 and HBF1.
De novo pathogenic variants in the GATAD2B gene have been associated with a syndromic neurodevelopmental disorder (GAND) characterized by severe intellectual disability (ID), impaired speech, childhood hypotonia, and dysmorphic features. Since its first description in 2013, nine patients have been reported in case reports and a series of 50 patients was recently published, which is consistent with the relative frequency of GATAD2B pathogenic variants in public databases. We report the detailed phenotype of 19 patients from various ethnic backgrounds with confirmed pathogenic GATAD2B variants including intragenic deletions. All individuals presented developmental delay with a median age of 2. 5 years for independent walking and of 3 years for first spoken words. GATAD2B variant carriers showed very little subsequent speech progress, two patients over 30 years of age remaining non-verbal.