dataesr/scientific-paragraphs-categorization huggingface dataset:
Ethics and dissemination The study was approved by the Ethics Committee of Geneva Canton, Switzerland (reference number: CCER2019-02257). All participants will give an informed consent to participate in the study. Results will be published in relevant scientific journals and be disseminated in international conferences. Anonymity of the participants will be guaranteed when presenting the data at scientific meetings or publishing them in scientific journals. Individual participant information collected during the study is considered confidential and disclosure to third parties is prohibited. Data sharing and reuse: Datasets and metadata from this trial will be deposited in ZENODO (a generic and free repository based at CERN, Geneva), and made public at the time of publication. Data in the repository will be stored in accordance with funder and university data policies.
Second, this CBM intervention is anchored within the dual-process models of behaviour, arguing that automatic reactions towards PA represent additional targets for interventions. Accordingly, this trial will examine for the first time the efficacy of these new types of interventions, which directly targets the automatic precursors of PA behaviour. Third, we relied on an accelerometer-based measure of PA, which guarantee the validity and reliability of our primary outcome. Finally, in addition to PA behaviour, we will collect data on physical and mental health at multiple time points over 1 year. However, potential limitations should be noted. The first limitation is related to the fact that the trial is based on a single centre, which will limit the generalisation of the results to other centres.
Terrestrial biosphere models typically use the biochemical model of Farquhar, von Caemmerer, and  Berry (1980)  to simulate photosynthesis, which requires accurate values of photosynthetic capacity of different biomes. However, data on tropical forests are sparse and highly variable due to the high species diversity, and it is still highly uncertain how these tropical forests respond to nutrient limitation in terms of C uptake. Tropical forests often grow on soils low in phosphorus (P) and are, in general, assumed to be P rather than nitrogen (N) limited. However, the relevance of P as a control of photosynthetic capacity is still debated. Here, we provide a comprehensive dataset of vertical profiles of photosynthetic capacity and important leaf traits, including leaf N and P concentrations, from two 3-year, large-scale nutrient addition experiments conducted in two tropical rainforests in French Guiana.
These data present a unique source of information to further improve model representations of the roles of N, P, and other leaf nutrients in photosynthesis in tropical forests. To further facilitate the use of our data in syntheses and model studies, we provide an elaborate list of ancillary data, including important soil properties and nutrients, along with the leaf data. As environmental drivers are key to improve our understanding of carbon (C) and nutrient cycle interactions, this comprehensive dataset will aid to further enhance our understanding of how nutrient availability interacts with C uptake in tropical forests.
Photosynthesis in C 3 species is typically represented in nearly every major large-scale TBM by the  Farquhar, von Caemmerer, and Berry (FvCB)  model of photosynthesis  (Farquhar et al. , 1980; von Caemmerer and Farquhar, 1981). The FvCB model determines photosynthesis (A) by the most limiting of two processes: Rubisco activity and electron transport. Empirical studies to determine the key parameters of these two processes (i. e. , the maximum rate of carboxylation, V cmax , and the maximum electron transport rate, J max ) and to test empirically their limitations on the leaf and canopy scale are necessary for obtaining the data required for parameterizing the FvCB model  (Medlyn et al. , 2015). Plant trait databases, now widely available, offer an excellent opportunity for parameterizing models.
, 2014; Mo et al. , 2019; Norby et al. , 2017; Bahar et al. , 2016). Consequently, vertical variation in leaf traits within the canopy of tropical forests is often not accounted for in a proper way by TBMs. Environmental drivers are key to improve our understanding of C and nutrient cycle interactions and our ability to model them. Climate data are often directly available at high spatial resolution and at the global scale from databases such as WorldClim  (Ruiz-Benito et al. , 2020) , while observations of soil properties and soil nutrient availability are often missing  (Vicca et al. , 2018). Soil variables have been shown to be strong predictors of leaf traits in higher plants  (Maire et al. , 2015). Comprehensive soil data, including soil properties such as texture and pH, as well as important nutrients, are needed to further enhance our understanding of how and why nutrient availability interacts with C uptake in tropical ecosystems and their responses to global environmental change  (Vicca et al.
, 2017). At both forest sites, an instrumentation tower is in place at which meteorological measurements and measurements of ecosystem net CO 2 exchange with the eddy covariance technique have been carried out on a continuous basis since 2003 in Paracou  (Bonal et al. , 2008)  and since 2014 at Nouragues. The monthly rainfall data measured at the instrumentation towers of Paracou and Nouragues are shown in the dataset.
Bulk density We sampled soil bulk density in the wet season of 2015. In each plot, we took five cores at two depths, and these samples were sieved through a 2 mm sieve. We collected the soil fraction, the roots, and the stones, which were dried and weighed separately at 105 • C for 24 h. In our database, we report two measures of bulk density: inclusive bulk density is the weight of the dried soil core divided by its volume (i. e. , the volume of the auger, 754 cm 3 ), whereas exclusive bulk density is calculated by dividing the total weight of the soil fraction (excluding roots and stones) by the volume of the entire core (i. e. , 754 cm 3 ).
Soil particle size distribution The particle size distribution at plot level was analyzed only in the wet season of 2015, assuming it would not change with seasonality. Therefore, we mixed by hand the five samples per plot that were sieved (<2 mm) after extraction using a gouge auger and analyzed these mixed samples as one composite sample per depth and per plot. We determined the soil particle size distribution using sedimentation with the hydrometer method  (Gee and Bauder, 1986)  after soil organic matter oxidation with H 2 O 2 , as described in protocol 1. 3. 5 "Soil texture" in the Supporting information S1 "Site characteristics and data management" in  Halbritter et al. (2020). Soil particles were dispersed with sodium hexametaphosphate, and the quantity of sand, silt, and clay was determined using a hydrometer.
These curves were fitted with the FvCB model using the fitaci https://doi. org/10. 5194/essd-14-5-2022 Earth Syst. Sci. Data, 14, 5-18, 2022 function from the "Plantecophys" package  (Duursma, 2015)  in R 3. 3. 3 (R Core Team, 2019) to obtain the biochemical parameters V cmax , J max , and, when possible, TPU.
Ancillary data The vertical structure of a tropical rainforest is complex and multi-layered, resulting in great variation in light availability within the canopy  (Yoda, 1974). We assessed the light environment of each studied tree by visually estimating the canopy light exposure or Dawkins' crown illumination index  (Dawkins, 1958). This index describes a tree's light environment based on a five-point scale ranging from (1) no direct light for suppressed trees to (5) crown fully illuminated for emergent trees (Fig. 5 ). Sampling height of the mature trees was measured with a Forestry Pro rangefinder (Nikon, Tokyo, Japan) by tree climbers situated at sampling height pointing towards the soil. For saplings, we measured the total height of the tree using a measuring tape, and additionally we measured the diameter at 10 and at 50 cm above surface level.
Data availability This photosynthesis-soil database is provided as an excel workbook and is freely available at https://doi. org/10. 5281/zenodo. 5638236 (Verryckt, 2021). Photosynthetic CO 2 response curves are presented as raw A n -C i files which can be analyzed by any user of the database, but V cmax and J max values, fitted using the "Plantecophys" package  (Duursma, 2015)  in R, can also be found in the database in addition to the leaf traits. The soil database of 2015, including bulk density, soil particle size distribution, soil moisture, and nutrients, and the fertilizer composition are shown in separate sheets.
Summary Publicly accessible and usable datasets from experimental sites are needed to greatly enhance the power of data synthesis, as well as model development and evaluation  (Vicca et al. , 2018; Halbritter et al. , 2020). We provide vertical profiles of photosynthetic capacity data and important leaf traits from two 3-year large-scale nutrient addition experiments conducted in two tropical rainforests in French Guiana. Our dataset is extremely valuable to the modeling and tropical ecology community as we present a valuable source of information to further improve model representations of the roles of leaf nutrients in photosynthesis in tropical forests. We present raw A n -C i curves, which allows the curves to be fit under the same assumptions as curves collected at other sites, avoiding bias in the method of analysis.
In addition, we provide V cmax and J max values making these values immediately available to the modeling community. We provide leaf-level photosynthesis data at several heights within the canopy from mature trees and saplings allowing us to study differences in sunlit and shaded leaves. Ancillary data such as herbivory and leaf phenol concentration can be of great value as additional data to other studies on these topics. A large set of soil properties and nutrient availabilities in the soils underlying the studied trees were made available as these data are highly relevant to understand how and why nutrient availability interacts with C uptake in tropical forests. Author contributions. LTV, LVL, CS, DA, IU, RO, JL, OG, GP, AGG, EAC, OM, PC, MO, LF, LFL, PRFG, HV, MV, CR, EV, LB, and IAJ contributed to the field work and collected the data.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. jamin Leudet, and Stefan van Beveren for climbing the trees to collect the selected branches. Financial support. Lore T. Verryckt is funded by a PhD fellowship from the Research Foundation Flanders (FWO). This project was funded by the European Research Council Synergy Grant ERC-2013-SyG-610028 IMBALANCE-P. The research stations of Paracou and Nouragues received support from "Investissement d'Avenir" grants managed by Agence Nationale de la Recherche (CEBA: ANR-10-LABX-25-01, AnaEE-France: ANR-11-INBS-0001).
Acknowledgements. We thank the staff of the  Nouragues Ecological Research Station , managed by USR mixte LEEISA (CNRS; Cayenne), and the Paracou station, managed by  UMR EcoFoG (CIRAD, INRA ; Kourou). We are grateful to  Valentine Alt ,  Samuel Counil ,  Jocelyn Cazal ,  Gonzalo Carrillo ,  Jean-Loup Touchard ,  Anthony Percevaux , Ben-.
Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. jamin Leudet, and Stefan van Beveren for climbing the trees to collect the selected branches. Financial support. Lore T. Verryckt is funded by a PhD fellowship from the Research Foundation Flanders (FWO). This project was funded by the European Research Council Synergy Grant ERC-2013-SyG-610028 IMBALANCE-P. The research stations of Paracou and Nouragues received support from "Investissement d'Avenir" grants managed by Agence Nationale de la Recherche (CEBA: ANR-10-LABX-25-01, AnaEE-France: ANR-11-INBS-0001).
Microarray Analysis CD73+ and CD73-subsets of memory CD4 T cells and of CD8 T cells were isolated by cell sorting as described above. RNA was purified using an RNeasy Micro Kit (Qiagen, Courtaboeuf Cedex, France) and quantified using a ND-8000 spectrophotometer (Nan-oDrop Technologies, Fisher Scientific, Illkirch Cedex, France) before being checked for integrity on the 2100 BioAnalyzer (Agilent Technologies, Massy Cedex, France). Labeled cRNA was hybridized on Illumina Human HT-12v4 bead chips following the instructions of the Illumina protocol. Quality controls were checked using Illumina GenomeStudio software. Raw data were filtered both on detection average signal p-values (p ≤ 0. 05) and the proportion per group threshold of 0. 5. The lumi bioconductor package algorithm included in the Flexarray software was used to preprocess and normalize Illumina microar-ray data.
The background was corrected using RMA background adjustment followed by log2 to stabilize variance and quantile normalization. Differentially expressed genes were analyzed using local-pooled-error test (LPE)  [69]. Genes with a fold change ≥ 1. 5 and false discovery rate (FDR) ≤ 0. 05 were considered statistically significant. The microarray data have been deposited in the MIAME compliant database Gene Expression Omnibus ( http://www. ncbi. nlm. nih. gov/geo/, GEO Series accession number GSE160805).
The following are available online at https://www. mdpi. com/1422-006 7/22/2/912/s1. Author Contributions: N. S. , J. Z. , C. M. L. M. , H. M. M. , B. F. d. S. G. , A. D. K. conceived experiments; N. S. , J. Z. , C. M. L. M. , C. P. , V. B. , Y. X. , M. B. , L. C. , K. M. , W. H. -C. , H. M. M. , performed experiments; A. D. K. , S. K. , A. L. , B. C. , K. K. , M. D. , Y. L. recruited subjects; N. S. , J. Z. , C. M. L. M. , C. P. , V. B. , K. M. , W. H. -C. , H. M. M. , H. H. analyzed the data; N. S. , J. Z. wrote the first drafts of the paper. All authors have read and agreed to the published version of the manuscript. Funding: This work has been supported by the Investissement d'Avenir program managed by the ANR under reference ANR-10-LABX-77 and by the Agence Nationale pour la Recherche sur le SIDA et les hepatites virales (ANRS), the Vaccine Research Institute (VRI), as well as an NHMRC Fellowship to J.
Supplementary Materials: The following are available online at https://www. mdpi. com/1422-006 7/22/2/912/s1. Author Contributions: N. S. , J. Z. , C. M. L. M. , H. M. M. , B. F. d. S. G. , A. D. K. conceived experiments; N. S. , J. Z. , C. M. L. M. , C. P. , V. B. , Y. X. , M. B. , L. C. , K. M. , W. H. -C. , H. M. M. , performed experiments; A. D. K. , S. K. , A. L. , B. C. , K. K. , M. D. , Y. L. recruited subjects; N. S. , J. Z. , C. M. L. M. , C. P. , V. B. , K. M. , W. H. -C. , H. M. M. , H. H. analyzed the data; N. S. , J. Z. wrote the first drafts of the paper. All authors have read and agreed to the published version of the manuscript. Funding: This work has been supported by the Investissement d'Avenir program managed by the ANR under reference ANR-10-LABX-77 and by the Agence Nationale pour la Recherche sur le SIDA et les hepatites virales (ANRS), the Vaccine Research Institute (VRI), as well as an NHMRC Fellowship to J.
02% of the viral contigs identified in the microbial dataset. However, the corresponding phages were not detected in the viromes, and their genetic maps suggest they might be defective. We conclude that among pig fecal samples, phages and prophages rarely carry ARG. Furthermore, our dataset allows for the first time a comprehensive view of the interplay between prophages and viral particles, and uncovers two large clades, inoviruses and Oengus-like phages.
Sample preparation sequencing and assembly Starting from 14 pig or piglet fecal samples, parallel DNA extractions were conducted for the purified viral fraction, named VP, and the total microbiota, named P, using standard procedures. All details are available in the Supplemental Information. Raw sequencing data and SPAdes assembly outputs (>2 kb, contigs for VP samples, scaffolds for P,134 before the clustering step at 95% identity) are accessible through the NCBI, Bioproject PRJNA741980.
Analysis and labeling of the bona fide viral contigs within the VP dataset The analysis of all VP contigs of a size above 2 kb was performed in two steps. From the 7755 VP contigs, the 2480 with the highest RPKM (above 100) were first analyzed. Several programs and methods were applied to identify non viral contigs (bacteria and plasmids, see Supplementary Fig. 1 ). Virsorter  [21]  recognized 23% (568 contigs) of these contigs as viral (all categories were accepted, cat1 and cat2 were vastly dominant). The Inovirus detector  [22]  was then run on the remaining contigs and 143 inoviruses (putative or confirmed, most were circular). To add contigs corresponding to phages recently added into public databases, Mega-BLAST was run against the nt database of NCBI (April 2020, E-val below 10 -16 ).
As bacterial hits could also correspond to prophages, all BLASTn hits, as well as the remaining unclassified contigs were taken for a search centered on proteins, making use of PHROG, a database of phage proteins  [23]  (see https://phrogs. lmge. uca. fr). For this, an HMMER hmmscan  [24]  of proteins from all remaining contigs was run against PHROG profiles, and all contigs with at least 5 orfs and 35% of them homologous to a PHROG family (E-value below 10 -12 ) were counted as phages. In total, this BLASTn + PHROG step allowed for the recognition of 966 additional contigs from the following origins: 654 phage, 232 eukaryotic viral (collectively designated below as "viruses" for short, while "phages" is kept for bacterial viruses), 72 bacterial and 8 plasmid fragments. Next, the VIBRANT classifier  [25]  was used on the remaining uncharacterized contigs, adding 325 phages and 29 viruses.
In a second step, we therefore used VIBRANT and the Inovirus detector on the remaining 5275 low-abundance contigs and obtained an additional 3654 viral and 201 inovirus contigs. In total, 5806 VP contigs (75%) were recognized as viral (and 77. 3% of the contigs treated by VIBRANT, which filters for contigs with ≥2 kb and 4 ORFs). Of these 5806 viral contigs, 44% were considered complete or high quality by CheckV  [27]. Other bio-informatic methods ARG search, VP viral contig classification, host prediction for the viral contigs, as well as the procedure to partition active phage versus inactive prophage contigs within the microbial P dataset, are all described in the Supplementary Information.
We think this laboratory vector comes from the control DNA of the Genomiphi kit used to prepare DNA prior sequencing, suggesting that the sequencing depth was sufficient to detect even minor contaminants. Homologs for all remaining, non phage contigs were searched with BLASTn against NCBI nt database (May 2019) and most of them had a taxonomic association that is typical of intestinal bacterial species (Table  1 ). We noted that the six ARG-positive contigs of chromosomal origin came from the viromes with less viral enrichment (Supplementary Table  2 ). Given the lower level of purity, it seems likely that these fragments came from remaining free bacterial DNA rather than viral particles. In contrast, the plasmid contig homologous to pCd13 from Clostridioides difficile came from a highly purified virome, making bacterial contamination unlikely.
Since huge phages are often found in pig microbiota, we searched for them in our dataset, and found one distant homolog in the P set, and three additional among the virome VP contigs (Section 2 of Supplementary Information). Of the 220 000 contigs, 16 940    We reasoned however, that some of these contigs might correspond to uninduced prophages and some may even be defective prophages. We therefore used the collection of virome reads to determine which of the viral-P contigs were forming viral particles (see Supplementary Information). Among these 16 940 viral-P contigs, 23. 7% were covered by a significant amount of virome reads and classified as "active phages" (Fig. 4  and Supplementary Fig. 2 ). Figure  5  reports a synthetic representation of the main phyla detected in the pig microbiota, among OTU with abundance above 10 RPKM across the 14 samples (see details in Supplementary Table  5 ).
The last sample (piglet P7) had a distinct profile, as virus reads represented 51% of the total. More precisely, a bin of two contigs (12. 6 kb total) was responsible for 52% of all viral signal. Upon closer inspection, these contigs had a typical Salasmaviridae/Sarlesviridae organization (Supplementary Fig. 3 ), but its host could not be predicted. Interestingly, the P7 sample also contained a very large 14 Mb OTU (bin of 401 contigs). A BLASTn search against the nt database of the NCBI indicated that its closest (77% identity) and most frequent (18% of contigs from this bin) parent was Blastocystis hominis (Eukaryote, Stramenopiles). We also noted that in this sample, richness and abundance in Bacteroides and Prevotella species was low. This suggests that there is intense predation on bacteria in the microbiota from sample P7.
ACKNOWLEDGEMENTS We would like to acknowledge the Migale bio-informatics facility at  INRAE  for professional support, and  Michi Waygood  and  Amanda Toperoff  from  Crayon-bleu  for editing service. MBD is recipient of graduate scholarships from the  FRQNT and Sentinel North. SM holds a T1 Canada Research Chair in Bacteriophages. QL-B and FT were recipient of a financial support from an  INRAE   MEM program.
More recently, the evolving field of omics has generated molecular biomarkers for CF. Panels of genes or proteins were used to predict exacerbations  [9, 10] , whereas circulating miRNAs predicted liver disease  [11]. DNA methylation is a very stable epigenetic modification that can be easily measured in a small amount of genomic DNA, using suitable techniques for routine clinical testing  [12]. DNA methylation profiling is a recent area of investigation in cystic fibrosis and few genome-wide datasets have been generated so far  [13, 14]. In a previous study, we showed that DNA methylation levels correlated with FEV 1 %, in nasal epithelial cell samples from CF patients  [13]. Specifically, DNA methylation levels at 187 CpG dinucleotides were differentially methylated between CF patients with mild versus severe lung disease and explained 70% of FEV 1 % predicted variations  [13].
The FEV 1 % predicted, adjusted for age, was used to stratify CF patients into three groups (mild, intermediate, and severe) depending on the severity of their lung disease. Patients with FEV 1 % predicted values corresponding to the top and bottom quartiles were classified as mild and severe, respectively  [18, 19]. A pulmonary exacerbation was defined as an increase in respiratory and systemic symptoms requiring an oral or intravenous antibiotic treatment, as validated by experienced physicians participating in the study. The study was approved by the local institutional review board (CPP Sud Méditerranée III, #2016. 09. 02bis) and registered at clinicaltrial. gov under reference #NCT02976714. Informed written consent was obtained from all participants. In case of pediatric patients, the minor and both parents signed the written consent.
Enhancers Prediction To assess the chromatin organization in the regions of interest, we retrieved H3K27 acetylation (GSM906395), H3K4 mono-methylation (GSM910572), and H3K4 tri-methylation (GSM915336) ChIP-seq data for a lung sample from the Roadmap Epigenomics Project (http://www. roadmapepigenomics. org) (accessed on 7 March 2021). Reads were aligned to the reference human genome build hg19 using Bowtie2  [20]. For peak calling we used MACS2  [21]  and default parameters with the exception of no-lambda option because no input was available. Moreover, to detect H3K4 mono-methylation peaks, we used the broad option. Finally, by assessing the histone profile in the regions of interest, we annotated the active enhancers. A CpG site associated with H3K27 acetylation and H3K4 mono-methylation, but not with H3K4 tri-methylation, was considered associated with an active enhancer  [22].