dataesr/scientific-paragraphs-categorization huggingface dataset:
De novo motif finding was carried using peak motifs  37. The P value for motif enrichment in the peak set compared with the genomic background was calculated empirically. For the TF co-binding matrix, the TFs were clustered based on the Jaccard distance (1 À Jaccard Index) between their target sets using average linkage hierarchical clustering  36. The overlap was validated statistically using the hypergeometric P value, with Bonferroni correction for multiple hypothesis testing. The cutoff for significance was set at 0. 001 (Supplementary Fig. 8c ). Information about total read counts, filtered and mapped reads is presented in Supplementary Table  6. To integrate the FRS12 TChAP-Seq and DE genes with bound genes of PIF4 and the circadian clock, ChIP-Seq data from PIF4, CCA1, TOC1, PRR3, PRR5 and PRR7 were obtained from previously published datasets  29, 61, 62.
RNA samples were processed by first preparing a Trueseq RNA-Seq library (Illumina) and then sequenced at 30 million reads depth at 50 bp single read using Illumina HiSeq 2000 technology at GATC Biotech, Germany. Read quality control, filtering, mapping to the TAIR10 Arabidopsis genome and read counting were carried out using the Galaxy portal running on an internal server (http://galaxyproject. org/). Sequences were filtered and trimmed, respectively, with the Filter FASTQ v1 and FASTQ Quality Trimmer v1 tools with default settings (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Reads were subsequently mapped to the TAIR10 version of the Arabidopsis genome using GSNAPv2 allowing a maximum of five mismatches. The concordantly paired reads that uniquely mapped to the genome were used for quantification on the gene level with htseq-count from the HTSeq python package.
Data was normalized using TMM and common dispersion was then estimated using the conditional maximum-likelihood method implemented in edgeR  64. Differentially expressed genes were defined by a 2-fold difference between samples with corrected P value o0. 05 at a FDR o0. 05. GO-enrichment analysis was carried with Cytoscape 2. 8. 2 software using the BINGO plugin  65  in default mode running with actualized GO and gene annotation definitions for A. thaliana from 26 January, 2016 (http://geneontology. org/).
Acknowledgements We thank  Annick Bleys  and  Blanche Magarinos-Rey  for help in preparing the manuscript and  Veronique Storme  and  Frederik Coppens  for their assistance in the statistical and bioinformatics analyses. We thank  Dr SalomePrat  for providing the Agrobacterium tumefaciens strains containing the ProPIL1:fLUC and 35S:PIF4-HA constructs; and  Prof George Coupland  for providing the ProGI-pDONR207 and the pGWLuc vectors. This study was funded by the  Belgian Science Policy organization  for a postdoctoral fellowship to A. R. and S. I. , by the  Research Foundation Flanders  for a postdoctoral fellowship to P. F. -C. , L. P. and S. D. , and for project  G005312N  and  G005915N , by the  EMBO  for a short-term fellowship to P. F. -C. and by the  Marie Curie Actions programme   FP7-PEOPLE-2013 -IEF  for a postdoctoral fellowship to P.
Data In this study, data from real-time NWP systems were combined to cover forecast ranges from 30 min to 6 h. The models cover the region from 8W to 12E and from 38N to 53N (Figure  1 ). The focus is on probabilistic prediction of modelled 3D radar reflectivities, characterized in each column by the reflectivity maximum and echotop. The echotop parameter is defined as the maximum height at which a certain reflectivity value is present. This information is important for aviation as it says something about the height of the cloud tops. The reflectivity maximum gives information about thunderstorm severity when high values are encountered. The period of study was 6 May 2016 until 6 June 2016, which includes some severe flash flood events over metropolitan France and southwestern Germany. September 2015 data were used for the training of the bias correction method described below, which ensures independence between the training and validation period.
The AROME-NWC forecasts AROME-NWC (in French this system is called AROME-PI for 'prévision immédiate')  (Auger et al. , 2015 ) is an NWP system that targets forecast horizons between 30 min and 6h. It is based on the operational AROME-France deterministic NWP system of Météo-France, as it uses the same 1. 3km resolution and 3D-Var data assimilation method  (Brousseau et al. , 2011). The main differences are that the AROME-NWC assimilation window is 30 min only (vs 1 h in AROME-France), the observation cut-off is 10min (vs 30 min) and the assimilation is not cycled, i. e. AROME-NWC background fields are taken from the AROME-France data assimilation suite. This means that fewer observations are assimilated compared with the deterministic AROME-France runs, but AROME-NWC uses more recent radar data.
The AROME-EPS forecasts AROME-EPS (in French this system is called PE-AROME for 'prévision d'ensemble')  (Bouttier et al. , 2012 (Bouttier et al. , , 2016) )  is an ensemble prediction system, also based on the AROME-France NWP system except for a lower horizontal resolution of 2. 5km. The ensemble comprises 12 members. It is scheduled for operational production for April 2017. In the experimental dataset used here, forecasts are initialized at 2100UTC covering a forecast hori-zon of 45h. Initial and lateral boundary conditions originate from the PEARP global ensemble model  (Nuissier et al. , 2012; Bouttier et al. , 2016) , and model uncertainties are represented by stochastic physics  (Bouttier et al. , 2012)  and surface perturbations  (Bouttier et al. , 2016).
The radar observations Radar observations were taken from the ARAMIS dataset of Météo-France  (Tabary et al. , 2013; Bousquet and Tabary, 2014). ARAMIS is a national radar network of 30 ground-based scanning radars over mainland France and the island of Corsica, including S-, C-and X-band instruments. Radar scans are subject to quality control and local raingauge-based corrections, and then are interpolated in space and time to a grid of 2. 5 km horizontal resolution. In the vertical, the observations are presented on a regular grid from altitudes of 500 m to 12 km above sea level. The reflectivity maximum and echotop are calculated based on the 3D radar data. The network covers about 1000×1000 km so that there are approximately (1000/2. 5) 2 = 160000 2D observation points for a single field of the reflectivity maximum and echotop per hour, which are used for training of the bias correction and running the model verification described next.
Point probabilities seem ill-suited to users' requirements for convection warnings; for instance aircraft will tend to stay at a certain security distance from areas with high radar reflectivities, because intense convection can have hazardous consequences (such as lightning) in the vicinity of convective towers. In practice, recommended safety distances from high reflectivity areas are between 10 and 20 NM (i. e. 18. 52 and 37. 04 km) in the horizontal and 5000ft (i. e. 1524m) in the vertical (NATS, 2010). To get an idea of the spatial uncertainty of the forecasts in our dataset to use it as a criterion for the upscaling, the model domain of AROME-EPS was shifted several grid cells in all directions with respect to observations, and verification was conducted for each of these displacements.
The 15min shifted fields were corrected with the relationship estimated for the closest full hour. This was done because the training of the bias correction method is computationally expensive. The fact that location errors increase with increasing lead time makes a correction for the AROME-France and AROME-EPS difficult for long lead times, which for AROME-EPS in this study reach +29 h, especially if the error is not systematic. In the case of a systematic location error, e. g. a model drift, one could try to displace the forecast field about this error before calculating the statistical relations. The SAL method also shows a diurnal cycle in the structure, amplitude and location components. Thus, a daytime dependent correction could be beneficial, but was not applied. It would divide the dataset, which is already sparse, into daytime dependent subsets.
Blending with deterministic and probabilistic forecasts The ensemble size of the LAF ensemble based only on nowcasts is limited and may be too small to estimate the forecast probability density function accurately, especially for longer forecast horizons. Furthermore, the spread of the LAF ensemble cannot easily be optimized, as is generally possible in a perturbed ensemble by adapting the perturbations. One idea is to combine the LAF ensemble with deterministic and ensemble forecast members, resulting in a larger ensemble. These forecast products are (or will soon be) operationally available. The demonstrated procedure to produce probabilistic short-range forecasts is for this reason computationally cheap, as no additional forecasts have to be produced. A blending of nowcasts based on extrapolated radar data with probabilistic high resolution forecasts for precipitation forecasts has already been used by, for example,  Kober (2010) ,  Kober et al.
The AROME ensemble and deterministic systems tested here are not designed to be used for nowcasting for two reasons. One reason is that the cut-off, the waiting time for the observations to be available at the forecast centre, in the data assimilation for both systems is too long to use in the very short range. The other reason is that the AROME-EPS system currently has a lack of spread at short ranges and up to 6 h. The lack of spread will be improved in the future by the application of an ensemble data assimilation technique. For longer lead times, the spread of the AROME-EPS system is better. The forecasted time instances between +6 and +29 h are therefore added from the 2100 UTC initialized AROME-EPS runs to the LAF ensemble to add a forecast at each hour of the day. In a similar way, from the deterministic runs, lead times from +6 to +11h, initialized every 6h, are used as additional ensemble members.
The bias correction successfully reduces this conditional forecast bias. After the correction, the FBI is of the order of 1 for a broad range, meaning that the frequencies of the forecast and observed values are nearly identical. High values are still underrepresented. Based on the available data, the statistical relationships between forecasts and observations were calculated for a period in autumn and applied on a dataset in spring. If longer time series are available, the training should be tested for the same season using independent years. The Talagrand diagram shows an underdispersion of the ensemble, represented by the U-shape, as well as a bias, represented by the asymmetry. The bias correction reduces both. As mentioned in Section 3. 4, a daily cycle in the bias of the reflectivity can be seen using the SAL method.
| Simulation environment and land-use information The impact evaluation was set up as a simulation experiment running the model described below for 20 10 km 2 agricultural landscapes from Scania, southern Sweden, selected to obtain uncorrelated gradients of landscape heterogeneity and oilseed rape cover. Using land-use data combined with yearly field-level crop-cover data from 2007 to 2013, we generated two rasters with 25 × 25 m resolution for each landscape and year, one containing land-use and crop-cover codes, the other containing the length of agricultural field block edges per cell. A field block is a field or a group of fields that is surrounded by uncultivated field borders. Nesting and floral resource raster layers were derived from the land use/crop-cover and edge rasters using a lookup table converting this information to nesting quality, floral cover, and attractiveness values.
2011 ) and singular spectrum analysis (SSA;  Mahecha et al. 2007; Mahecha et al. 2010; Wang et al. 2012)  effectively provide insight into the model fit at different time scales. In this contribution, we used two DGVMs and one forest stand-scale model, to simulate NEE. We evaluated model results by comparing model NEE outputs with observed NEE data over a period of at least 16 years for three beech forest sites in Soroe (Denmark), Vielsalm (Belgium) and Collelongo (Italy). The SEMs currently in use, i. e. SSMs, RA, WA and SSA, were combined across multiple sites. The objectives of the study were to define environmental conditions under which the models perform poorly and to identify the related model processes that should be revised for the adequate reproduction of NEE dynamics of forest sites.
1997; Suckow et al. 2001; Lasch et al. 2005). For the evaluation period, the three models, selected to represent different types of process-based models used for the simulation of the carbon balance of forests, were forced by the meteorological data measured at the sites. At the point of initialisation, models were fed with the site specific soil properties that remained constant over the simulations. For CARAIB and 4C, EC and meteorological data were obtained from the daily aggregated FLUXNET2015 data (FULLSET, http://fluxnet. fluxdata. org/data/ fluxnet2015-dataset/fullset-data-product/). For ISBA CC , the hourly FLUXNET2015 data were used for meteorological forcing. In order to set the model's carbon pools to steady state and to obtain a mature forest at the beginning of the evaluation period, model spin-ups were run for both DGVMs, CARAIB (using ERA-Interim reanalysis;  Uppala et al.
Site selection We used data from three FLUXNET sites: Soroe (Denmark), Vielsalm (Belgium) and Collelongo (Italy). At all sites the dominating vegetation type consisted of mature beech (Fagus sylvatica L. ) forest (from 76 years to 106   (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  for Soroe and for an 18-year period  (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014)  for Vielsalm and Collelongo. A detailed description of the sites including stand and soil characteristics is provided in Table  2.
The water uptake per cohort is calculated from the soil water availability, itself depending of the daily precipitation and the relative share of fine roots, and is limited by the transpiration demand of the cohort (D tr ). The DRI of a cohort is defined as the average of the ratios of uptake and demand over the time period of interest (number of days). In addition, the residuals of the centered and normalized observed and simulated time series (Res cn ) were calculated. The centering and the normalization eliminated the effect of consistent model biases and differences (package dplR in R version 3. 1. 2). Based on the Lomb-Scargle Fourier Transform 1000 surrogate time series were generated within the borders of a first order autoregressive (AR(1)) spectrum. Assuming that the background noise could be approximated by an AR(1) model, the hypothesis of a spectrum being purely appointed to noise could be rejected at the chosen confidence levels (95% and 99%).
Acknowledgements This study was financially supported by the  Belgian Science Policy Office [BELSPO BRAIN-be program , contract number  BR/121/A2/MASC ] as well as by the Methusalem funding to the  PLECO Research Centre of Excellence. The first author has been supported by a  Short Term Scientific Mission of the European COST Action FP1304 PROFOUND. CPOR  acknowledges funding from the  German Federal Ministry of Education and Research [BMBF , grant number  01LS1201A1 ]. We thank  Giorgio Matteucci ,  Tanguy Manise  and  Andreas Ibrom  for having provided additional site specific information. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks:  AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia , and  USCCC.
The  ERA-Interim  reanalysis data were provided by  ECMWF  and processed by  LSCE. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and  Fluxdata  project of FLUXNET, with the support of  CDIAC , the  ICOS Ecosystem Thematic Center , and the  OzFlux, ChinaFlux  and AsiaFlux offices.
,  [25] [26] [27] ). DSD climatological studies, over several years, and at several locations in a given region, are quite rare. To our knowledge, the most complete study according to these criteria was led by  [28] , based on a dataset from five sites in Italy, over nine months to three years, depending on the particular site. This study used two types of disdrometer (Joss-Waldvogel and Pludix) and pointed out strong differences in DSD parameters and Z-R relationships when passing from convective to stratiform episodes. The identification of stratiform/convective areas in precipitation systems was previously studied by  [29]  with radar data and one optical disdrometer in Barcelona in one case study. Later, it was shown in Alès (Cévennes, France), during an event with about 100 mm in about 16 h, that rainfall was organised into several phases, each one presenting a very stable DSD over several hours, but with abrupt transitions from one phase to the next  [30].
Data Availability, Checking, Filtering and Correction Data are available on the OHMCV  [38]  and HyMeX  [39]  databases in NetCDF format. The time step for data acquisition is one minute. Data were aggregated to five minutes, in order to ensure a sufficient amount of drops each time step. Only time steps for which the rainfall intensity was above 1 mm/h (considered to be enough intense for this climate) have been kept in this study. Several DSD data filtering or correction methods were compared, by computing rainfall totals from DSD spectra and using as a reference, the daily totals of the available rain gauges closest to the disdrometers. The rain gauge corresponding to StEF's disdrometer is located in Aubenas, at 2 km; the rain gauge corresponding to La Souche station is located 100 m from the disdrometer.
3. The last one (S3) was the "correction" method, proposed by  [42]. This method consisted of applying pre-determined correction factors, depending on the intensities and the diameters that were estimated during inter-comparison measurement campaigns, using a 2DVD (used as a reference) and Parsivels of both generations. These campaigns took place between September and November, in both 2012 and 2013, in the Cévennes-Vivarais region, and between April and June 2014 in Payerne (Switzerland)  [42]. In addition, two more filters were applied to the S1, S2, and S3 methods, in order to exclude solid and mixed particles and/or data recorded when dirt was present on the protective glass of the laser disdrometer. This information about hydrometeor types and laser quality is given by the Parsivel itself.
73 vs. 8. 80) to be obtained. The "filtering" method (S2) was, therefore, been retained. Some disdrometers had data gaps, due to problems with the motherboard of the device or power failures. Periods with dirty laser glass also caused gaps. However, after application of the filters, each disdrometer recorded at least more than 5600 five-minute rainy time steps during the five-year period, 2012-2016.
Daily Weather Pattern Influence Figure  7  represents the influence of weather patterns (WP) on DSD characteristics and the Z-R relationship using the whole DSD dataset. Two groups can be identified: (1) WP3, 5, 6, and 8 are characterized by both lower concentrations, stronger characteristic diameters, and lower shape parameters; (2) WP1, 2, 4, and 7 that are characterized by higher concentrations, lower characteristic diameters (except for WP7 and slightly WP4 due to high dispersion) and more important shape parameters. In these rather coarse groups, however, particular WPs stand out: • WP8 (Anticyclonic) is the most particular of the first group, especially in terms of concentration (very low) and characteristic diameter (very large). This explains the shifted position towards the high Z values of the 80% confidence ellipse of this WP on Z-R plot.
A noteworthy gradation was found in the concentration, size, and shape parameters, according to the level of convection and organisation. Otherwise, the orographic precipitations present similar properties as showers for the characteristic diameter and the Z-R relationship. They are close to those of scattered rainfalls for concentration and shape parameter. This study analysed mainly the trends on the relationship coefficients for each external factor. A study will be led on the same dataset to obtain Z-R relationships, suited for radar data processing, as well as to assess errors in quantitative precipitation estimates, due to DSD variability, according to the same external factors, on a large and medium scale, independently of other radar sources of errors (calibration, VPR, sampling issues).
The future study will also illustrate the large amount of spatial and temporal variability that remains within the precipitation systems. Finally, another study is expected to extend this climatology to other Mediterranean locations (South Alps, Corsica, Spain, Italy, and Tunisia) in the framework of the HyMeX project. Midi-Pyrénées) and the team of the OSUG data centre helped in accessing the data and attributing DOIs to the individual datasets. Sahar Hachani's thesis was funded by IRD and Université Tunis El Manar.
Acknowledgments: The authors thank the providers of operational data:  Météo-France  for rain gauge data and radar data, and EDF for weather patterns. They warmly thank the colleagues of  IGE's technical  staff who helped in installing and maintaining the observation system over several years:  Romain Biron ,  Martin Calianno ,  Simon Gérard ,  Matthieu Le Gall ,  Coralie Aubert ,  Jean-Paul Laurent ,  Catherine Coulaud ,  Hélène Guyard ,  Frederic Cazenave , and  Stéphane Boubkraoui. In addition, the authors acknowledge people who hosted the experiments:  Jean Mazel  ( La Souche ), the municipality of  Saint-Etienne-de-Fontbellon (StEF) , the secondary school (collège) of  Villeneuve-de-Berg, Pierre-Alain Ayral and Rosario Spinelli at Ecole des Mines d'Alès , as well as  Jean-François Didon-Lescot ,  Jean-Marc Domergue , and  Nadine Grard  at  UMR ESPACE  for their valuable contribution for the maintenance of the disdrometers of Tourgueille and Valescure.
The authors acknowledge Hervé Andrieu (IFSTTAR) and  Victor Le Boulch  ( ENTPE ) for their valuable contribution to rainfall typing. Disdrometers were funded by  OHMCV, ANR FloodScale, HyMeX , and  Région Rhône-Alpes. The publication costs were covered by HyMeX. OHMCV is supported by the  "Institut National des Sciences de l'Univers" (INSU/CNRS) , the  French Ministry for Education and Research, the "Observatoire des Sciences de l'Univers de Grenoble" (OSUG) , and the  "SOERE Réseau des Bassins Versants" (Alliance Allenvi). The  FloodScale  project is funded by the  French National Research Agency (ANR)  under contract no. ANR 2011 BS56 027 , which contributes to the  MISTRALS/HyMeX programme  (http://www. mistrals-home. org). IGE is part of  Labex OSUG@2020  ( ANR10 LABX56 ), which funded the contract of  Martin Calianno.
CD genes differentially expressed in normal BMPC and MMC A list of 266 probesets representative of genes encoding CD molecules was defined using the Human Cell Differentiation Molecules database (http://www. hcdm. org) (Supplementary Table  1 ). Comparison of CD gene expression in BMPC from healthy controls and MMC samples from patients with MM (HM cohort) using Significance Analysis of Microarrays (SAM) showed that nine CD genes were significantly overexpressed in MMC compared with BMPC and 16 were significantly downregulated [ratio ≥2; False Discovery Rate (FDR) <0. 05; 1000 permutations] (Tables  1  and 2 ) (Figure  1 ). Among the CD genes significantly overexpressed in MMC samples, the biological function of CD200, CD1d, CD47, CD59 and CD32 (Fc γRII) in MM has been already investigated  [11] [12] [13] [14] [15] [16] [17] [18] [19] [20].