dataesr/scientific-paragraphs-categorization huggingface dataset:
Collected data The following data were collected from each patient's electronic records: Sex and age at the time of locoregional treatment; histology of the primary cancer; metastatic status; Target BM location, size, radiographic features, status of the overlying cortical bone, presence of synchronous primary tumor, concomitant treatments other than ablation, proximity to nearby non-target critical structures; Type of ablation and anesthesia, the need for protective measures and consolidation, complications encountered. The metastatic status was defined by the burden of systemic disease including bone and visceral organs. BM location was categorized into four different groups including the spine, pelvic ring, long bone, and others. The size of the target BM was selected according to the maximum diameter observed on the most recent multiplanar cross-sectional imaging available before ablation.
Data analysis Basic descriptive statistics were used for continuous variables. An univariate analysis was conducted to identify factors predicting local progression at the treated site; factors reporting a p values <. 1 entered a multivariate model. The Kaplan-Meier method was used to outline LPFS, BDFS, DFS and OS; and the log-rank test to stratify these results according to tumor histology. All significance levels were set at a 0. 05 value. Statistical analysis was performed by using SAS 9. 4.
This statistical analysis with the GLM was done at two different time points (70 and 120 min after the beginning of the incubation period), representative of the most common times for digesta passage rate in broilers. For this, SPSS (v22. 0) was used with a univariate ANOVA, with enzyme as fixed factor. Tukey was used as a post-hoc test, and statistical significance was accepted at P < 0. 05.
The first one, expression quantitative trait locus (eQTL) mapping, takes advantages of methodologies developed for mapping quantitative trait loci (QTL), either in the linkage mapping or statistical association framework, considering transcript expression levels as quantitative traits  (Nica and Dermitzakis, 2013). With the availability of genome sequences, this approach allows inference of the location of genomic regions responsible for the variation in gene expression levels and distinction between distant and local eQTLs, according to their distance to the regulated gene. Distant eQTLs are steadily inferable to trans-acting regulatory elements, i. e. genes that modify or regulate the expression of distant genes. However, eQTL mapping often lacks resolution to determine whether a local eQTL corresponds to a polymorphism in a trans-acting regulatory element adjacent to the gene it regulates or in a cis-acting element in the regulatory region of a gene that affects its own expression  (Rockman and Kruglyak, 2006).
RNA sequencing data processing and read count generation Raw sequencing data quality was assessed using FASTQC v. 0. 11. 5 software  (Andrews, 2011). Sequencing adapters were trimmed out using CUTADAPT 1. 9. 1  (Martin, 2011). Then, data were cleaned and filtered as described in  Causse et al. (2013). On average, cleaning steps removed 7. 38% of the data (min = 2. 70%, max = 9. 37%). Remaining data were aligned to the tomato reference genome (Heinz 1706, v2. 5) using TOPHAT2 v2. 1. 1  (Kim et al. , 2013)  with default parameters and providing the tomato gene model (annotation v2. 4) to support the mapping process. Alignments were filtered to keep only concordantly mapped reads using SAMtOOLS 1. 3. 1  (Li et al. , 2009)  and read counts per gene were generated for each library using HTSEQ-COUNT 0.
Differential gene expression analysis Counts from mapped reads of leaf and fruit samples were analyzed separately using the Bioconductor package DESEQ2 version 1. 6. 3 (Love et al. , 2014). Count data were first normalized using the relative log expression method  (Anders and Huber, 2010)  to correct for differences among library sizes. Then, weakly expressed genes were filtered out using the HTSFilter function as recommended in  Rau et al. (2013)  to avoid the negative effect of background expression noise. In total, of 18 843 genes among leaf samples and 19 097 genes among fruit samples were retained in the analysis, corresponding to genes with maximum normalized read count across samples above 25. Leaf and fruit normalized read counts are available in Tables  S2  and S3 , respectively. A regularized log transformation was applied to normalize gene expression levels prior to perform a principal component analysis (PCA).
Allele-specific quantification and ASE test Allele-specific gene expression (ASE) levels were estimated using 48 909 high quality exonic SNPs between both parental genotypes identified from high depth genome resequencing (Cervil: 13. 39, Levovil: 6. 79 completed to 509,  Causse et al. , 2013). The exonic SNPs were distributed over 12 391 tomato genes (36% of the known tomato CDS). Individual RNA-seq reads tended to span multiple SNPs. Thus, to avoid double-counting, allele-specific counts were created on the basis of individual reads summed across the gene, ensuring consistency of the reads originating from either parental haplotype, using a homemade Perl script (available upon request). The allele-specific detection was applied on the parental and F 1 libraries. Reads that could not be consistently assigned to either haplotype were discarded (in average 4.
Gene ontology enrichment analyses Enrichments in GO associated to biological process were achieved using the Wallenius noncentral hypergeometric distribution in the Bioconductor package GOseq, an extension of the standard Fisher's exact test able to account for selection bias due to gene length  (Young et al. , 2010). Gene lengths [based on the open reading frame (ORF), in bp] and GO associated to each gene were retrieved from the ITAG v2. 4 (Slim GO terms, International Tomato Annotation Genome). The gene space was composed of the 24 968 annotated tomato genes (72% of the known genes). A FDR threshold of 5% called significantly enriched GO.
Primer design and quantitative gene expression analysis in the RILs Gene expression variation of 274 target genes (183 in fruit and 91 in leaves; Tables  S6  and S7 ) were quantified under both watering regimes in Cervil, Levovil, their F 1 hybrid, and the 124 RILs by quantitative real-time microfluidigm PCR (performed on the Gentyane platform, INRA Clermont-Ferrand, France). Primer3  (Untergasser et al. , 2012)  designed primers pairs for each targeted transcript. Gene-specific primer lists are available in Tables  S6  and S7. First strand cDNAs were synthesized from the RIL RNA samples using oligo(dT) and Superscript III enzyme, followed by a Ribonuclease H treatment to remove any RNA in the samples. PCR pre-amplification was achieved pooling all primers in order to increase the amount of the initial cDNA molecules several fold, while preserving the relationships between the transcripts.
Thermal cycling conditions consisted of 10 min at 95°C and 14 cycles of 15 sec at 95°C, and 4 min at 60°C. In each sample, remaining primer pairs were removed using an exonuclease I treatment avoiding any interference in the qPCR reaction. Preamplicons were loaded into the BioMark HD system (Fluidigm, San francisco, CA, USA). On each of the BioMark HD chip, a negative control, four dilution points, and three reference genes were included. RT-Q-PCR results were captured and analyzed using the Bio-Mark HD software (Fluidigm). The EvaGreen fluorescent signal was standardized to a passive reference dye (ROX) included in the EvaGreen PCR master mix. The BioMark HD software allowed computing the cycle number at which the fluorescence passed the cycle threshold (CT) for each reaction. Relative expression levels were obtained by normalization from two reference genes for leaf samples and three reference genes for fruit samples using the 2 ÀΔCT method (Table  S8 ).
Leaf and fruit normalized expression data are available in Tables  S6  and S7 , respectively. Normalized expression levels under both watering regimes and ratio of normalized expression under drought relative to normalized expression under control for each genotype were used in the subsequent eQTL mapping analysis. Pearson coefficients were computed to estimate correlations between gene expression measured through RNA sequencing and expression measured though microfluidigm qPCR in leaf and fruit samples of Cervil, Levovil, and the F1 hybrid, under control and drought conditions. Pearson coefficients were computed as well to estimate correlation between the expression levels of 16 genes related to sugar metabolism measured in fruit and phenotypic traits related to sugar metabolism measured in the RILs.
(e)QTL and (e)QTL-by-watering regime interaction mapping Plant vigour, fruit quality, and gene expression levels recorded in the RILs were used in mapping analysis. Log 10 transformations were applied when distribution were skewed. Analysis were performed using a genetic map constituted of 501 SNP markers  (Albert et al. , 2016a). (e)QTLs were identified by simple interval using the Expectation-Maximization (EM) algorithm method implemented in R/QTL package  (Broman et al. , 2003). A 1000-permutation test was computed to identify the LOD threshold at a genome-wide significance level a = 0. 05 (LOD = 3. 13). For each detected QTL, position, LOD score, marker at the peak, confidence interval (LOD decrease one unit, genetic CI in cM), average values of the two parental alleles and PVE were reported.
ACKNOWLEDGMENTS We acknowledge the experimental teams of UR GAFL for their help in phenotyping plants. We thank especially  Yolande Carretero ,  Gis ele Riqueau , and  Margaux Deberos  for their involvement in the experimentations. Thanks to  Marie-Laure Martin-Magnette ,  Christopher Sauvage , and  Joel Chadoeuf  for scripts sharing and for their advices on RNA sequencing data and statistical analyses. The  ANR  project  ADAPTOM ANR-13-BSV7-012  supported this work. E. A. was supported by an  INRA   PhD fellowship.
DATA AVAILABILITY Illumina sequencing data can be found in the NCBI SRA repository under project number PRJNA472702. The allelic value and location of SNP markers can be found in supplemental data of Albert et al. (2016a). Resequencing data of the parental lines can be found in the European Nucleotide Archive (ENA) with the project number PRJEB4395 (http:// www. ebi. ac. uk/ena/data/view/PRJEB4395). BAM files and SNP characteristics of the genomic sequences are available on the SolGenomics ftp site ftp. solgenomics. net/projects/ causse_tomato_snp8lines. Other data (read counts, expression data from Fluidigm) are available as supplemental data.
This depends on the fact that different systems or properties require in general different amounts of non-local correlation to correct the description provided by the semilocal functional. This is shown, for example, in Figure  1 , where we report the mean absolute errors, obtained from SG4 + rVV10m as functions of the value of the parameter b in the rVV10m non-local term, for the lattice constants of the two training sets we have employed, i. e. , NOB4 and ALK5. It can be seen that the two sets display an opposite behavior. Therefore, ALK5 systems are slightly overbound when b = 8. 5 while NOB4 materials are underbound using b = 12. Hence, no absolute best value can be identified for the b parameter. This shows that, in general, obtaining a uniformly good performance is a very hard task. To test the functional, we have considered a set of layered materials (Graphite, h-BN, MoS 2 , MoTe 2 , VS 2 , VSe 2 , PdTe 2 , WS 2 , NbTe 2 , and NbSe 2 ; reference data were obtained from References  [20, 21] ), and a set of molecular crystals (ammonia and urea [hydrogen-bond], CO 2 , naphthalene, and pyrazine [van der Waals]; reference data were obtained from Refences  [67] [68] [69] [70] [71] ).
In addition, we have considered a set of iono-covalent bulk materials (named SET27) including subsets of different characters. In more detail: Al, Ca, K, Li, Na, Sr, Ba (simple metals); Ag, Cu, Pd, Rh , Pt (transition metals); GaAs, GaP, GaN, Si, SiC, Ge (semiconductors); LiCl, LiF, MgO, NaCl, NaF (ionic solids); AlN, BN, BP, C (insulators). Reference data were taken from References  [64, [72] [73] [74] [75]. Finally, we have performed some calculations on the S22 test set  [76, 77] , in order to verify the performance on non-covalent molecular complexes. The S22 calculations have been performed using a local version of the TURBOMOLE program package (V7. 0, TURBOMOLE GmbH, Karlsruhe, Germany)  [78, 79]  and a def2-TZVPP basis set  [80, 81]. All solid-state calculations have been carried out with a locally modified version of the Quantum Espresso program package (V6.
Cohesive energies are defined as the opposite of the interaction energies. The energies of the unit elements have been obtained placing them into large boxes, so that interactions between periodic replicas were negligible. In more detail, for SET27 and ALK5, atoms have been placed in a box with dimensions 13 × 14 × 15 Å; for layered materials, the monolayer calculations have been performed at fixed relaxed bulk geometry with vacuum space of about 33 Å for Graphite and h-BN, and 37 Å for all other materials; for the molecular crystals, the minimum distance between molecular unit elements has been set to about 14 Å. The atomic data sets required by projected augmented wave (PAW) calculations  [84]  have been generated by the ATOMPAW code  [85] , using the input files provided on the ABINIT program web site  [86].
We study an ensemble of six multi-year global Bayesian carbon dioxide (CO 2 ) atmospheric inversions that vary in terms of assimilated observations (either column retrievals from one of two satellites or surface air sample measurements) and transport model. The time series of inferred annual fluxes are first compared with each other at various spatial scales. We then objectively evaluate the small inversion ensemble based on a large dataset of accurate aircraft measurements in the free troposphere over the globe, which are independent of all assimilated data. The measured variables are connected with the inferred fluxes through massconserving transport in the global atmosphere and are part of the inversion results. Large-scale annual fluxes estimated from the bias-corrected land retrievals of the second Orbiting Carbon Observatory (OCO-2) differ greatly from the prior fluxes, but are similar to the fluxes estimated from the surface network within the uncertainty of these surface-based estimates.
The OCO-2-based and surface-based inversions have similar performance when projected in the space of the aircraft data, but the relative strengths and weaknesses of the two flux estimates vary within the northern and tropical parts of the continents. The verification data also suggest that the more complex and more recent transport model does not improve the inversion skill. In contrast, the inversion using biascorrected retrievals from the Greenhouse Gases Observing Satellite (GOSAT) or, to a larger extent, a non-Bayesian inversion that simply adjusts a recent bottom-up flux estimate with the annual growth rate diagnosed from marine surface measurements both estimate much different fluxes and fit the aircraft data less. Our study highlights a way to rate global atmospheric inversions. Without any general claim regarding the usefulness of all OCO-2 retrieval datasets vs.
Introduction Carbon dioxide (CO 2 ) is increasingly monitored in the global atmosphere due to its important role in climate change. For example, NOAA's GLOBALVIEWplus Observation Package (ObsPack, Cooperative Global Atmospheric Data Integration Project, 2018) archives high-quality measurements made at the surface or from aircraft by various institutes. Despite occasional budget difficulties  (Houweling et al. , 2012) , the number of collected data points has exponentially increased over the years, with, in reference to 1980, 6 times more measurements in 2000 and 100 times more measurements in 2017. In addition, the ground-based Total Carbon Column Observing Network of column retrievals (TCCON,  Wunch et al. , 2011)  is less than 15 years old but already operates about 30 sites over the globe.
These inversion configurations differ in the assimilated data and in the transport model. The assimilated data are either surface measurements in ObsPack and related databases, retrievals from the Greenhouse Gases Observing Satellite (GOSAT) or the second Orbiting Carbon Observatory (OCO-2). The transport models are two versions of the atmospheric general circulation model of the Laboratoire de Météorologie Dynamique  (LMDz, Hourdin et al. , 2013)  nudged towards analysed meteorological variables. The Bayesian inversion system from the Copernicus Atmosphere Monitoring Service (CAMS, https://atmosphere. copernicus. eu/, last access: 21 November 2019,  Chevallier et al. , 2005)  is used in all six inversions. We use a "poor man's inversion"  (Chevallier et al. , 2009)  based on recent bottom-up fluxes and on the global annual atmospheric growth rate estimated from the average of marine surface measurements  (Conway et al.
, 2014)  to define a baseline for the skill of each Bayesian inversion result. Our use of airborne measurements in the free troposphere as verification data is motivated by their frequent, WMOtraceable calibration, their independence from all data assimilated here (including the measurements in the boundary layer) and their spatial distribution that samples all oceans and continents. Arguably they are the only CO 2 dataset that possesses all three of these qualities. In the following, data and models are described in Sect. 2, while Sect. 3 presents the various results. The results are discussed in Sect. 4, and Section 5 concludes the study. 2 Model, system and data.
, 2011)  or ERA5 (https://www. ecmwf. int/en/forecasts/ datasets/archive-datasets/reanalysis-datasets/era5, last access: 31 January 2019) with a relaxation time of 3 h. Online and offline models are consistently run at the same spatial resolution in order to avoid any challenging interpolation of the air mass fluxes for the sub-grid processes (see e. g. Yu et al. , 2018) : here 39 eta-pressure layers between the surface and around 80 km above sea level (km a. s. l. ), and 96 grid points × 96 grid points, i. e. a horizontal resolution of 1. 89 in latitude × 3. 75 • in longitude. This configuration discretises the 2-7 km a. s. l. region of the atmosphere, which will be a major focus in the following, into 6 to 10 layers, depending on local orography. We use two physical formulations of LMDz, called 5A (in code identification number 1649) and 6A (in code identification number 3353), as described by  Remaud et al.
Inversion system LMDz is embedded within the CAMS CO 2 inversion system. This system minimises a Bayesian cost function to optimise the grid cell 8-day surface fluxes (with a distinction between local night-time fluxes and daytime fluxes, but without fossil fuel emissions, which are prescribed) and the initial state of CO 2. To do so, it assimilates a series of CO 2 observations over a given time window within the LMDz model. The minimisation approach is called "variational" because it explicitly computes the gradient of the cost function using the adjoint code of LMDz. Prior information about the surface fluxes is provided to the Bayesian system by a combination of climatologies and other types of measurementdriven flux estimates (e. g. Emission Database for Global  Atmospheric Research version 4.
Assimilated observations All assimilated observations are the dry air mole fraction of CO 2. Assimilated surface air sample measurements have been selected from four large ongoing databases of atmospheric CO 2 measurements: (i) NOAA's ObsPack  (Cooperative Global Atmospheric Data Integration Project, 2018, and Car-bonTracker Team, 2018) , (ii) the World Data Centre for Greenhouse Gases archive (WDCGG, https://gaw. kishou. go. jp/, last access: 21 November 2019), (iii) the Réseau Atmosphérique de Mesure des Composés à Effet de Serre database (RAMCES, http://www. lsce. ipsl. fr/, last access: 21 November 2019) and (iv) the Integrated Carbon Observation System Atmospheric Thematic Centre (ICOS-ATC, https://icos-atc. lsce. ipsl. fr/, last access: 21 November 2019). The list of se-lected sites and maps of their location are given by  Chevallier (2018a).
Each dataset provides at least 5 years of measurements. The error variances assigned to these measurements in the inversion system correspond to transport modelling uncertainty (analytical measurement uncertainty of in situ CO 2 data is a negligible component) and are computed as the variance of the high-frequency variability of the deseasonalised and de-trended CO 2 time series of the daily mean measurements at each site. These variances are then inflated in order to give the same weight to each measurement day at a given location. GOSAT was launched in January 2009, as a joint project of the Japan Aerospace Exploration Agency (JAXA), the National Institute of Environmental Studies (NIES) and Japan's Ministry of the Environment (MOE)  (Kuze et al. , 2009). OCO-2 is a NASA satellite that was launched in July 2014  (Eldering et al.
s. l. We avoid data below 2 km because (i) local anthropogenic emissions affect many aircraft measurements there, and (ii) some of the aircraft flew in the vicinity of measurement sites that have been used in the surface-based inversions. We avoid data above 7 km because the measurement variations (and the flux regional signal) are much reduced there. A few outliers for which the difference between model and observation is larger than 40 ppm are rejected: they likely represent very local pollution plumes. We define two periods for the following statistical computations. They are based on the availability of the satellite retrievals and of the aircraft data in the databases used here: a "GOSAT period" from July 2009 to September 2016 and an "OCO-2 period" from December 2014 to December 2017.
Principle We build an ensemble of six Bayesian inversions using the inversion system from Sect. 2. 2, the two transport model versions from Sect. 2. 1 and the three observation datasets from Sect. 2. 3. The assimilation periods differ (Sect. 2. 2), but the prior fluxes and the prior error model are the same. For each inversion, the posterior model simulation statistically fits its own assimilated data well within their 1σ uncertainty. Note that the surface-based inversion with LMDz5A is exactly the same as the CO 2 inversion product version 18r1 of CAMS that was released in November 2018 (http://atmosphere. copernicus. eu/, last access: 21 November 2019). In the figures, we will refer to the surface-based inversions by the generic name "SURF" for simplicity. We first present the carbon budget estimates.
Differences with aircraft data Figure  7  presents the statistics of model-minus-measurement differences per measurement programme for the GOSAT period. Note that the data number varies by several orders of magnitude among the programmes: there are a few hundred samples for most of the 37 programmes, but a few thousand for CALNEX2010, KORUS-AQ, ORCAS, SGP and ATom, a few tens of thousands for ACT, DC3, DISCOVER-AQ, GSFC, HIPPO, SEAC4RS and SONGNEX2015, and 900 000 for CONTRAIL. Obviously, many measurements may fit into a single time-space block of the global transport model. We will only discuss bias differences larger than 0. 15 ppm (i. e. above the calibration uncertainty of the aircraft data, see Sect. 2. 4) and those that are statistically significant at the 0. 05 level, as reported in the figure.