dataesr/scientific-paragraphs-categorization huggingface dataset:
(2006)  or  Stephens et al. (2007)  (see also  Pickett-Heaps et al. , 2011; Basu et al. , 2014; Houweling et al. , 2015; Frankenberg et al. , 2016; Le Quéré et al. , 2018; Crowell et al. , 2019) , but few aircraft measurement programs have been used so far and, as a consequence, their use has rarely been formulated in terms of quality assurance or quality control processes for atmospheric inversions. Compared to previous studies, we benefit from a much larger number of aircraft measurements over the globe in 10 the free troposphere (600,000 for the OCO-2 period, twice as much for the GOSAT period) and from more recent satellite retrievals. We have used data between 2 and 7 km above sea level only, where the age of air varies significantly  (Krol et al. , 2018). Aircraft data in this region of the atmosphere only sample a portion of the carbon cycle.
This has not been explored here. 5). This suggests that LMDz transport errors play a much smaller role in the quality of our inversion results than the choice of 30 assimilated data. In comparison to the GOSAT results, or to previous OCO-2 inversion results  (Crowell et al. , 2019) , OCO-2based annual fluxes are surprisingly close to the surface-based fluxes (usually within 1 σ of the Bayesian uncertainty of the surface-based fluxes). Consequently, the aircraft data used here do not allow us to distinguish between the quality of OCO-2based fluxes and surface-based fluxes. The poor man's inversion still performs worse despite the contribution of a recent dynamic global vegetation model simulation, showing that the OCO-2 performance is not trivial. Following Liu and Bowman.
35 (2016), we attribute the simulation error changes in the free troposphere for the OCO-2 period to flux differences in specific regions of the globe. We find a rather homogeneous geographical distribution of the flux performance with OCO-2-based fluxes and surface-based fluxes alternating as the best ones over continental land masses. This adjoint analysis also illustrates the large footprint of our aircraft data in the free troposphere in terms of flux information, which prevents using them for the evaluation of local fluxes, given our choice of altitude range of 2 -7 km above sea level. 40.
GOSAT in our system: either the data density, the data precision, the data trueness (linked both to the quality of the physical retrieval scheme and to its empirical bias-correction), or a combination of these qualities at once. Further, other GOSAT-based inversions could be more competitive if made differently 50 (e. g. , with a different bias-correction), while other OCO-2-based inversions (e. g. , with a different transport model or with different retrievals), or ours with ACOS v9 retrievals after our study period (e. g. , if the empirical bias-correction is less efficient for later months), could still be found deficient for carbon specialists. As we have shown, aircraft data can help ranking the skill of these alternative inversion configurations between each other and vs. ours (all data used here, apart from the recent INPE data, are publicly available).
(2016)  presented a different strategy in which aircraft profile measurements are assimilated: a 10 compromise has to be found between exploiting valuable data directly (in particular in areas void of surface measurements), or keeping them for validation. Finally, the evidence provided by aircraft measurements in the free troposphere suggests that the quality of OCO-2 retrievals over land is now high enough to provide results that are comparable in credibility to the reference (but sparse) surface air sample network, within the above-discussed limits. For ocean retrievals, this remains unclear as OCO-2 ocean soundings were 15 not tested in this work. The consistency of results from the surface and OCO-2-driven inversions, in stark contrast to the bottom-up fluxes or to the GOSAT-driven inversion, does not seem to be fortuitous.
Data availability: The aircraft measurements are available from https://www. esrl. noaa. gov/gmd/ccgg/obspack/. The OCO-2 data can be obtained from http://co2. jpl. nasa. gov. They were produced by the OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology. The CAMS v18r1 product can be obtained from http://atmosphere. copernicus. eu/ and the GOSAT retrievals from http://climate. copernicus. eu/. All inversion results can be obtained on request to copernicus-support@ecmwf. int.
Bayesian system by a combination of climatologies and other types of measurement-driven flux estimates (e. g. , Emission Database for Global Atmospheric Research version 4. 3. 2, Crippa et al, 2016, scaled globally and annually from Le Quéré et al. , 2018, for the fossil fuel emissions or Landschützer et al. , 2017, for the ocean fluxes). Details can be found in Chevallier.
Acknowledgements: The authors are very grateful to the many people involved in the surface, aircraft and satellite CO2 observations and in the archiving of these data that were kindly made available to them, like  H. Bösch  and  J. Anand  at the  University of Leicester , the  ObsPack team and LaGEE , the  Greenhouse Gases Laboratory  from  INPE. The Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) is an Earth Ventures (EV-1) investigation, under contract with the  National Aeronautics and Space.
35 Bayesian system by a combination of climatologies and other types of measurement-driven flux estimates (e. g. , Emission Database for Global Atmospheric Research version 4. 3. 2, Crippa et al, 2016, scaled globally and annually from Le Quéré et al. , 2018, for the fossil fuel emissions or Landschützer et al. , 2017, for the ocean fluxes). Details can be found in Chevallier.
; Dlugokencky, E. J. Network aircraft program) ABOVE Arctic-Boreal Vulnerability Experiment (NASA Airborne Science) https://doi. org/10. 333 4/ORNLDAAC/1658 Sweeney, C. ; McKain, K. ACG Alaska Coast Guard (NOAA/ESRL Global Greenhouse Gas Reference Network aircraft program) Sweeney, C. ; McKain, K. ; Karion, A. ; Dlugokencky, E. J. ACT Atmospheric Carbon and Transport - https://doi. org/10. 333 In situ : Davis, K. J. ; DiGangi, J. P. ; America (NASA Airborne Science) 4/ORNLDAAC/1593 Yang, M. https://daac. ornl. gov/cgi- Flasks: Sweeney, C. ; Baier, B. ; Lang, P. bin/dataset_lister. pl?p=37 ALF Aircraft Observation of Atmospheric GHG at Alta Floresta, Mato Grosso by LaGEE/INPE Gatti, L. V. ; Miller, J. B. ; Gloor, E. ; Peters, W. AOA Aircraft Observation of Atmospheric trace gases by JMA ghg_obs@met.
Microarray and data analysis The quantity and the quality of extracted RNAs were assessed using a Nanodrop 1000 spectrophotometer (Thermo Fisher Scientific, Eclubens, Switzerland) and 2100 Bioanalyzer (Agilent Technologies, Montpellier, France). Microarray experiments used chips containing 45. 000 probes (4x44K Whole Human Genome) and One-Color Microarray-Based Gene Expression Analysis provided by Agilent Technologies. Four hundred ng of extracted RNAs were labeled using Cyanin-3 CTP using a low input Quick Amp Labeling kit, one color (Agilent technologies). This step lead to amplify and label target RNA to generate cRNA (Cy3-) for further oligo microarrays used in gene expression profiling. Labelled RNAs were hybridized for 17 hours at 65˚C using QIAmp labeling kit according to the manufacturer's recommendations.
Slides were washed and scanned with a pixel size of 5 μm using a DNA Microarray scanner G2505C. The raw data were extracted using feature Extraction Software 10. 5. 1. Data were processed using GeneSpring GX 14. 9 software (Agilent Technologies, Montpellier, France). Modulated genes were analyzed using Ingenuity Pathway Analysis (IPA, Qiagen, Courtaboeuf, France) or ClustVis software. The data have been deposited in NCBI's Gene Expression Omnibus and are accessible via GEO series accession number GSE (GSE112086).
This analysis led to a more diffuse association with multiple disease categories, including multiple different types of cancer, not just lymphoma (gastrointestinal disease, organismal injury and   abnormalities, neurological and dermatological disease, S2C and S2D Fig). Four genes overexpressed in Q fever were previously implicated in NHL, namely, BCL2, BCL7A, BCL9 and BCL11 (S2 Table ). Altogether, these results confirm the suspicion that acute Q fever is much less associated with NHL than persistent C. burnetii infection. Finally, to determine whether the correlation found with NHL was specific to C. burnetii infection, we compared the C. burnetii-related dataset to published  [19]  microarray analyses of patients with S. aureus and persistent endocarditis previously performed from whole blood (S3 Fig).
Soraya Mezouar  was supported by a "Fondation pour la Recherche Medicale" (FRM) postdoctoral fellowship  ( SPF20151234951 ). This work was supported by the  French Government  under the  « Investissements d'avenir » (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; the  SIRIC Cancer Research and Personalized Medicine (CARPEM) ; and the  Paris Alliance of Cancer Research Institutes (PACRI).
Mediterranee Infection 10-IAHU-03). -This work was also supported by Region Provence Alpes Cote d'Azur and European funding FEDER PRIMMI (Fonds Europeen de Developpement Regional - Plateformes de Recherche et d'Innovation Mutualisees Mediterranee Infection) -GK is supported by -GK is supported by the Ligue contre le Cancer Comitede Charente-Maritime (equipe labellisee); AgenceNational de la Recherche (ANR) -Projets blancs; ANR under the frame of E- Rare-2, the ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC); Canceropole Ile-de-France; Chancelerie des universites de Paris (Legs Poix), Fondation pour la Recherche Medicale (FRM); a donation by Elior; the European Commission (ArtForce); European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR); the European Research Council (ERC); Fondation Carrefour; Institut National du Cancer (INCa); Inserm (HTE); Institut Universitaire de France; LeDucq Foundation; the LabEx Immuno-Oncology; the RHU Torino Lumière; the Seerave Foundation;.
Then the specific conditions of a given individual should be evaluated. Thus, analyses of gene variants related to nutrition and metabolism, and of gut microbiota composition and function should be completed. Evaluation of the nutritional status of the individual as well as disease story, anthropometric data and a range of other individual characteristics (e. g. , gender, age, ethnicity) have to be considered as well. With these data, the potential health effects could be envisaged, and the more efficient dietary interventions dietary habits recommendations could be established. The dose of the bioactive phytochemical to be administered could also be identified, although this is not an easy task, as many related phytochemicals can also exert effects and lead to synergies that are difficult to estimate with the current knowledge.
When this information is available and validated, mathematical models to show the best nutritional interventions for a given volunteer or population sub-groups to respond to the treatment with PFB will be developed. Once the active phytochemicals are identified, and as their content in different food products is available in free online databases [Phenol explorer (http://pheno l-explo rer. eu/); Phytohub (http://phyto hub. eu/)], various food products containing the specific phytochemical bioactives that produce the desired effects for a given subject or particular population group will be selected and specifically recommended. Finally, there will be a need to develop consumeraffordable (simple, reliable, cost-effective) applications for self-classification and self-monitoring. These applications could also suggest some food associations and culinary preparations leading to optimal nutrition with a correct administration of the bioactive phytochemicals.
Acknowledgements This article is based upon work from  COST Action FA1403 POSITIVe  (Interindividual variation in response to consumption of plant food bioactives and determinants involved) supported by  COST (European Cooperation in Science and Technology ; https ://www. cost. eu). The authors also acknowledge all the partners of the  COST Action POSITIVe  for their involvement in the network and their key contribution to the advancement in the field covered by the  Action.
Finally, the average ISIC3 industry has 69 workers in the WBES, while the average industry has 53 workers in the 2001 US Census. Two points worth highlighting concern sample size and survey weights. First, while the core tax inspection variable is always available, data on tax compliance are only available for earlier surveys. 15 Second, when possible we apply survey weights. However weights are missing from some early surveys. The core tax inspection results are drawn from the sample with survey weights, but to preserve sample size, we report results on tax compliance without dropping observations with missing weights. We show in the appendix that results remain unchanged in the smaller sample with full survey weights.
In Section 4. 4, we show that the results hold when constructing the exogenous size-ranking with British and German firms in the Amadeus dataset, which are subject to stringent information reporting requirements. 18 Finally, in section 4. 3 we show that for the richest countries in the WBES (countries with per capita GDP above $21,000) 19 the estimated coefficients of size on tax inspection is zero. This evidence supports the first stage identifying assumption that in high fiscal capacity countries, firm size is not driven by size-based inspection. The IV strategy provides a causal estimate of the size-gradient under the first stage validity, discussed above, and the exclusion restriction. The exclusion restriction assumes that the US size-ranking of WBES industries only impacts tax inspection in the WBES country through the WBES size-ranking.
Using different instruments The core results use the industry-size rank in the 2001 US Census. This section shows that results are robust to the choice of the exogenous size-distribution used as an instrument. Table  A1  presents the first stage and IV coefficients on tax inspection, for six different definitions of the instrumental variable. Columns 1-3 report results using each of the last three waves of the US firm Census (1991, 2001, 2015). We obtain the same ranking of WBES size and similar IV coefficients. 26 Since the validity of the instrument rests on the assumption that the firm-size distribution in the US is undistorted by tax enforcement, using size rankings from other high fiscal capacity OECD countries should yield similar results. We test this by constructing the IV with British and German firms in the Amadeus database.
Before mapping, poly N read tails were trimmed, reads of ≤ 40 bases were removed, and reads with quality mean of ≤ 30 bases were discarded. Reads were then aligned against the Mus musculus genome from Ensembl version 84 using STAR  [22]. Alignments from reads matching more than once on the reference genome were removed using the Java version of SamTools  [39]. All overlapping regions between alignments and referenced gene were counted using HTSeq-count 0. 5. 3  [4]. The sample counts were normalized using DESeq 1. 8. 3  [3]. Statistical treatments and differential analyses were also performed using DESeq 1. 8. 3. Data availability The RNASeq gene expression data and raw fastq files are available on the GEO repository (www. ncbi. nlm. nih. gov/geo/) under accession number: GSE107401 (accession password: mlkfkwoezxujvsn).
Adhesion and spreading assays Adhesion and spreading in vitro assays were performed in 24-well dishes. Silicon strips on coverslips were used to separate two coated areas of each coverslip  [8]. Surfaces were coated overnight at 37 °C with recombinant EphrinB3-Fc fusion at 10 µg/ mL and Fc equimolar (as control) on each half, or myelin extract (100 µg/mL) and PBS buffer (as control). Before cell seeding, strips were removed and coverslips were washed carefully with PBS. 10 5 SC were seeded in serum-free Sato medium to avoid proliferation, and allowed to adhere for 3 h. Data were always expressed as ratio in respect to the intra-coverslip control  [12]. Survival assay GFP + SC were seeded on uncoated glass coverslips in normal medium. After overnight adhesion, medium was changed, adding Sato serum-free medium supplemented with clustered EphrinB3 at 10 µg/mL or Fc equimolar (as control), or with myelin extract (100 µg/mL) or PBS (as control).
GFP + /Hoechst + SC, with the whole or more than half of the cell area in contact with Glut1 + endothelial cells, were counted as "closely associated cells" while those with only "tip" contacts or no contacts were considered as "not associated cells". Data are expressed as the percentage of total counted cells in both groups. SC-axon alignment in the lesion. GFP + /Hoechst + SC, with the whole or more than half of the cell in parallel orientation and aligned closely to NF200 + axons (excluding alignment to BV), were counted as "closely associated cells" while those with only "tip" contacts or no contacts were considered as "not associated cells". Data are expressed as the percentage of total counted cells in both groups.
Statistics The sample size calculation was performed by the resource equation method to minimize the sample size, following the ARRIVE guidelines for reporting animal research. Each n represents one animal or SC sample in the experiment. The grafting experiments were repeated at least three times with a different set of animals each. For the in vitro analysis, experiments were performed at least three times with SC obtained from different dissections and dissociations. Statistical analysis was carried out using GraphPad Prism 6 software. All values were expressed as mean ± SD. Normality in the variable distributions was assessed by the D'Agostino-Pearson omnibus test and Grubbs' test was used to detect and exclude possible outliers. When normality test was passed, means were compared by two-tailed Student's t test.
4 ± 0. 2; EphrinB3-pre-incubated SC on myelin: 0. 8 ± 0. 2. Ratio of round cells myelin/PBS: Fc: 2. 9 ± 1; EphrinB3: 1. 5 ± 0. 5). (Fig. 4a, b ). To determine if the reduction in the number of SC adhering to myelin was due to cell death, we assessed cell viability by comparing the percentage of caspase3 + nuclei or Hoechst + pycnotic nuclei in SC incubated with PBS or myelin for 3 h. SC exhibited a typical bipolar morphology and no difference was observed in the percentage of caspase3 + nuclei or Hoechst + pycnotic nuclei between control and myelin-treated groups (PBS: 4. 4 ± 1. 6‰, myelin: 4. 0 ± 1. 7‰, n = 6). These data corroborate our previous results demonstrating that short incubation time with myelin does not induce apoptosis  [17]. SC adhesion to surfaces coated with EphrinB3-Fc was reduced compared to Fc as control in a dose-dependent manner (ratio of adhered cells EphrinB3-Fc: 2.
4g ). Intersection of the RNA-Seq datasets from Isl1 KO and Baf60c knockdown ESC-derived CPCs showed that 42. 6% of the genes deregulated by Isl1 loss-offunction are also deregulated upon Baf60c knockdown (Fig. 4h ) and revealed that genes activated by both Isl1 and Baf60c are involved in cardiac muscle contraction and sarcomere organization (Fig. 4i ). These results indicate that Baf60c may work in an axis with Isl1 to promote chromatin reorganization at cardiomyocyte structural and contractile protein encoding genes in CPCs well before these genes are highly expressed in differentiating cardiomyocytes. Isl1 works in concert with the Brg1-based SWI/SNF complex to regulate its target gene expression Baf60c is a cardiac-specific subunit of the Brg1-based SWI/SNF complex, which plays an important role in heart development.
Magnetic resonance imaging (MRI) Embryos were analyzed by MRI, as previously described. 54 Briefly, paraformaldehyde-fixed embryos were embedded in a MRI contrast agent and imaged using an 11. 7 Tesla magnet. The 3D MRI dataset obtained was reconstructed into axial TIFF slices. Image datasets were analyzed and 3D reconstructions created using Amira 3. 0 software.
The PCA plots in each RNA-Seq dataset were obtained using rowVars and prcomp into a custom R-script. Chromatin immunoprecipitation (ChIP) and ChIP sequencing Mouse embryos at E8. 25-E9 were collected into cold PBS. Cardiogenic mesoderm and heart region was crosslinked with 1% formaldehyde at room temperature for 15 min, whereas mESCderived CPCs for 10 min. Crosslinking was terminated by adding 125 mM glycine followed by three washes with cold PBS. Single cells were obtained by disruption of the tissue using pestle and mortar and nuclei were isolated by incubation in lysis buffer (50 mM Tris pH 8. 0, 2 mM EDTA pH 8. 0, 0. 1% NP-40, 10% glycerol) along with protease and phosphatase inhibitors. Nuclei were frozen at -80 °C until further processing. Around 60-80 E8. 25 to E9. 0 embryos were pooled and lysed in nuclear lysis buffer supplemented with protease and phosphatase inhibitors (50 mM Tris pH 8.
ChIP-qPCR For ChIP-qPCR analysis pools of 20 cardiogenic regions of E8. 25-E9 embryos or 10 6 CPCs obtained by directed differentiation of ES cells into cardiomyocytes were used. The following antibodies were utilized: anti-Isl1 (39. 4D5, DSHB), anti-Brg1 (Millipore, 07-478), anti-Gata4 (Santa Cruz, sc-1237) and anti-H3 (Abcam, ab1791) antibodies. Average threshold cycle (Ct) values were used to calculate % enrichment compared to 1% input and negative control region Ct value was used to calculate fold enrichment. ChIP-Seq data analysis ChIP-Seq reads were mapped to the mouse reference genome mm9 (UCSC assembly) using the default settings of bowtie2. The MarkDuplicates. jar from Picard 1. 136 was used to remove PCR artefacts. Peaks were called using MACS14  60  with the parameter (p1e8 -nomodel -shiftsize 80) for Isl1-ChIP-Seq and Gata4-ChIPexo (GSE72223) and (p1e3 -nomodel -shiftsize 80) for Brg1-ChIP-Seq (GSE116281).
Peaks overlapping blacklist defined by ENCODE were removed. Bedtools v2. 15 (intersect -wa)  61  was used to find the common binding areas in experimental replicates and between the different Isl1-ChIP-Seq, Brg1-ChIP-Seq (GSE116281), Gata4-ChIP-exo (GSE72223) and ATAC-Seq datasets. Genome wide distribution of the reads was performed using ngs. plot with mm9 Ensembl (version 67) as reference annotation. ChIP-Seq peak annotation was performed using annotatePeaks. pl (default settings), which assigns each peak to the closest gene transcriptional start site (TSS). For data visualization bam files were converted to BigWig files using BamCoverage from deepTools2  62  (-b 20 -smooth 40 -e 150 -normalizeTo1x 2150570000). Venn diagrams were generated with BioVenn online tools  63  and protein coding genes were used for all comparisons.
39 Libraries were mixed in equimolar ratios and sequenced on NextSeq500 platform using V2 chemistry. ATAC-Seq reads were mapped with bowtie2 to mm9 mouse genome. Samtools view (-F 1804 -f2) was used to remove unmapped, mate unmapped, not primary aligned reads. The MarkDuplicates. jar from Picard 1. 136 was use to remove PCR artefacts. Peaks calling was performed with MACS14 60 (p1e5nomodel and -shiftsize 100), and peaks overlapping blacklist defined by ENCODE with the help of Bedtools v2. 15 (intersect -wa). 61 Similar to ChIP-Seq analysis, Bedtools v2. 15 (intersect -wa) was used to obtain open chromatin regions overlapping by at least 1 bp in the replicates. BamCoverage function of deepTools2  62  (-b 20 -smooth 40 -e 150) was used to create normalized bigwig files (reads per genome coverage, RPGC).