dataesr/scientific-paragraphs-categorization huggingface dataset:
tauri. Our RNAseq samples describe the transcriptome states of O. tauri associated with three different factors: i) iron deprivation or excess (referred to as "-Fe" and "+Fe" conditions), ii) day or night (referred to as light and dark), and iii) short-or long-term adaptive responses of the cells (referred to as condition 1 and condition 2) (Fig. 1a  and Additional file 1: Figure  S1 ). We first quantified the influence of these factors on the global variability observed in our RNAseq results. We carried out principal component analysis (PCA) on the complete gene expression dataset (all genes and all samples analyzed simultaneously). We found that the percentage of the variance decreased rapidly with increasing principal component (PC) number, with more than 95 % of the total variance accounted for by only five PCs (Fig.
tauri is organized principally around day/night cycles under standard growth conditions  [19, 47]. We show here that the light/dark factor retains its predominant position under conditions of iron stress, suggesting that iron homeostasis is regulated according to the day/night conditions. Transcriptional regulation in response to iron deprivation is a robust cellular process that can be maintained for extended periods Among the genes for which expression data were available, we identified those displaying significantly different levels of expression in conditions of iron deprivation (-Fe) and iron excess (+Fe). We used the DEseq program  [3]  to identify differentially expressed genes and to calculate a risk of error (p-value). The numbers of genes with a p-value < 1 % and a LogFC > 1 (for upregulated genes) or LogFC < -1 (for downregulated genes) in each condition are shown in Additional file 3: Figure  S3A.
Iron metabolism in O. tauri involves many new proteins and pathways that remain to be discovered We defined a set of genes including i) genes differentially expressed in a least two conditions, and ii) genes differentially expressed in only one condition but for which interesting functions were retrieved (Additional file 3: Figure  S3B ). For that, we developed a dedicated process for inferring gene function, combining information from multiple web resources (ORCAE, HHPRED and PFAM databases) and systematic manual inspection of the retrieve predictions (see Methods). We then defined 19 general functional categories (and associated subfunctions) and used them to classify 1,048 genes (Additional file 3: Figure  S3B ). This list of genes, together with detailed information including function, subfunction and database search results, is presented in Additional file 4: Data Sets 1.
The green algae O. tauri and C. reinhardtii display fundamental differences in iron metabolism In a previous study, Urzica et al. [72]  surveyed the iron nutrition-responsive transcriptome of C. reinhardtii with RNAseq technology. We carried out a genome-wide comparison of the transcriptional programs involved in iron metabolism in O. tauri and C. reinhardtii by collecting the RNAseq data from the work of Urzica et al. [72]  and inferring orthology relationships between all coding sequences of O. tauri and C. reinhardtii (with the INPARANOID algorithm  [60] ). Surprisingly, we found that many proteins of functional importance in the metabolism of iron by C. reinhardtii had no orthologs in O. tauri, or that the orthologs of these proteins in O. tauri were not affected by iron nutrition in our dataset (Additional file 6: Figure  S4 ).
3) A lot of genes showing a strong response to iron depletion encode proteins with unknown function in O. tauri, which indicates that several pathways of iron uptake and metabolism remain to be described in this organism. Our work strongly suggests that Ot-Fea1 is directly involved in iron uptake in O. tauri. Homologues of the C. reinhardtii Fea1/Fea2 proteins have been previously considered in the green algal lineage  [7] , but Ot-Fea1 is structurally very different than Cr-Fea1 (it cannot be found by simple BLAST search) and presents unique features that make it a good candidate for nonreductive ferric iron uptake in O. tauri, as we recently showed for ISIP2a in diatoms  [49]. Despite of several attempts, we were not able to generate a ΔOt-Fea1 knockout in O. Fig. 7  Clusters of genes globally induced (a) or repressed (b) by iron starvation and cluster of genes encoding zinc regulatory proteins.
We performed a clustering analysis (using the k-means method) on the complete dataset; very few genes were upregulated in all conditions (experimental conditions 1 and 2, dark/light): these genes are grouped in cluster A. Similarly, genes downregulated in all conditions are grouped in cluster B. One cluster (cluster c) grouped genes encoding zinc regulatory proteins. Results are expressed (according to the color scale) in log base 2 as logFC [-Fe]/[+Fe] tauri, suggesting that the gene is vital for growth. We are currently trying to determine the conditions of Ot-Fea1 iron loading in vitro by overproducing this protein in E. coli and then purifying it and incubating it with extracts of O. tauri cells grown in different conditions. Ot-Irt1 is another protein that is most probably involved in iron uptake in O.
Bioinformatics analyses Analysis of raw sequencing data The general protocol used for the analysis of raw sequencing data (FASTQ files) is presented in Additional file 2: Figure  S2. FASTQ files comprise the sequencing results (reads). Step 1 consisted in controlling the quality of sequencing results using FASTQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and CUTA-DAPT programs (https://cutadapt. readthedocs. org/en/ stable/). Step 2 consisting in mapping the reads on the O. tauri CDS sequences available in the ORCAE database  [66] , using the BOWTIE program  [37]. Step 3 Mapping outputs were next converted in SAM and BAM files with SAMTOOLS programs (http://samtools. sourceforge. net/) for visual inspection with IGV program  [61]. Step 3 consists in calculating the number of reads mapped on each CDS sequence in each condition, using the BEDTOOLS program (http://bedtools.
Functional annotation of genes A systematic process combining information from multiple web resources was applied for inferring gene function. This process consisted in searching the ORCAE database (http://bioinformatics. psb. ugent. be/ orcae/), looking for predicted domains for each gene product (homology detection and structure prediction by HMM-HMM comparison (http://toolkit. tuebingen. mpg. de/hhpred) and Pfam database (http://pfam. xfam. org/)). To assess whether any of the functions were observed in gene clusters at a frequency greater than that expected by chance, p-values were calculated using hypergeometric distribution as described in  [11].
Phylogenetic analysis of Ot-Fea1 We first collected a dataset of known Fea and Isip homologs (C. reinhardtii Fea1 and Fea2 and P. tricornutum Isip2a and Isip2b), aligned them with Mafft (--maxiterate 1000 -localpair options)  [32]  and trimmed the alignment by hand. The resulting alignment was then used for iterative searches of the nr protein database with Jackhmmer (e-value cutoff 1e-5) until the searches converged. Sequences displaying similarity to the original alignment were extracted and, using Mafft (--maxiterate 1000 -localpair options). The alignment was trimmed with BMGE (matrix BLOSUM30 and block size 1) and a phylogeny was constructed with Phyml (WAG substitution model)  [27].
Elution was achieved with a linear gradient from 60/40 % (vol/ vol) buffer A/methanol to 100 % methanol at a flow rate of 1. 0 ml/min for 25 min. Buffer A consisted of 56 mM NH 4 H 2 PO 4 dissolved in 44 % (vol/vol) HPLC-grade methanol with the pH adjusted to 3. 5 with concentrated orthophosphoric acid. The absorbance of the eluate was assessed at 400 nm. A 1 mg/ml standard solution of heme b (hemin, Sigma) was diluted in dimethyl sulfoxide. HPLC was carried out and the data were processed with Chromeleon v7. 1. 0. 898 (Dionex).
Acknowledgments Funding is acknowledged from the  ANR " PhytoIron " ( ANR 11 BSV7 018 02 ; to C. B. , E. L. and F. -Y. B. ) projects, as well as the  EU   MicroB3  project (to C. B. ). R. S. acknowledges the  Czech Science Foundation  ( 13-25349S ), project  BIOCEV  ( CZ. 1. 05/1. 1. 00/02. 0109 )and  National Programme for Sustainability II (LQ1604)  from the  Ministry of Education , Youth and Sports. J. P. -Y. was supported with a post-doctoral fellowship by the 7th research program of the European Union FP7/2007-2013 under the grant agreement n°PIOF-GA-2011-301466.
Except for phagocytosis, no adequate immune response is mounted in these mutant flies  31. We used this infection model to test two mutants of C. glabrata generated by Cas9 (Supplementary Methods and Supplementary Figs  S2  and S5A,B ). The first is the yps11 strain in which the YPS11 gene was disrupted. This gene encodes an aspartyl protease that was shown to be upregulated during macrophage internalization  12. The second strain is mutant for the CAGL0K04301g gene, which codes for a putative serine/threonine kinase in C. glabrata. The latter is homologous to the mitochondrial protein FMP48 in S. cerevisiae  43  and to a putative kinase implicated in virulence and mouse kidney colonization HSL1 in C. albicans  33, 44. Since the Candida Genome Database describes this gene as an "Uncharacterized ORF"  45  , we decided to rename it VPK1 for Validated Protein by Knock-out 1.
4A, B ). CFU assays showed a constant increase of the fungal burden during yps11 cells infection and a reduced virulence in survival assays (Fig. 5D, E ). This suggests that YPS11 might not be involved in the regulation of cell wall composition, but more likely hinders the maturation or incorporation of proteases or other proteins at the cell wall that would be important for pathogenicity. We named the CAGL0K04301g gene Validated Protein by Knock-out 1 (VPK1), because the Candida Genome Database describes it as uncharacterized ORF, and because our results validate it as a gene whose product impacts the infection process in D. melanogaster (Fig. 5D-F ). Having in mind that the understanding of the role of VPK1 needs further investigation, we would like to emphasize several observations that would suggest a direct or indirect role in the virulence of C.
Identification of CRISPR-Cas9 targets in the C. glabrata genome. To characterize the best CRISPR-Cas9 targets for a given C. glabrata gene, we have developed a dedicated bioinformatics tool. Named CASTING (for « CAS9 Targets IN Genomes »), it has been implemented in Python 2. 7 and is mainly based on the library PyRNA provided by the RNA-Science-Toolbox project (https://github. com/fjossinet/RNA-Science-Toolbox). Using the complete C. glabrata CBS138 genome (NCBI BioProject PRJNA12376), extended with the new genes identified by Linde and colleagues  59  , CASTING takes as input a gene name, a genomic location, or a user-defined sequence. In a first step, CASTING searches for all PAM motifs of the form NGG or NAG. In a second step, each motif is extended by 20 nt on its 5′ -end. Among all these potential 23nt-long sequences, CASTING filters out suboptimal sequences containing k-mers as described by Xie and colleagues  60.
In a third step, CASTING restricts the candidates to the sequences with a GC content between 35% and 75%. For each remaining on-target sequence, CASTING delegates to the tool SeqMap the identification of its off-target sites  61. All the on-target sites are then ranked according to the sgRNA activity predictive model proposed by Doench and colleagues  62. CASTING has been made available online at http://charn-ibmc. u-strasbg. fr:8080/casting. html. The website is based on the Python microframework Flask (http://flask. pocoo. org) and on the JavaScript libraries jQuery (https://jquery. com) and Bootstrap (http://getbootstrap. com).
Vials containing infected flies were put in an incubator for 240 hours at 29 °C. After 96 h of incubation, flies were moved to new vials. Results are expressed as percentage of surviving flies at different time points post-infection. Each survival experiment is representative of at least two replicates done with three independent yeast colonies obtained by two independent disruption experiments. Statistical significance during survival experiments was calculated with the method of Mantel and Cox using the log-rank test (GraphPad PRISM6 software), and p < 0. 05 was considered significant.
Acknowledgements The research in this paper has been published under the framework of the  LABEX :  ANR-10-LABX-0036_  NETRNA. It was supported by the  University of Strasbourg Institute for Advanced Study (USIAS)  as part of a  USIAS Fellowship  (to F. J. ), the  CNRS  and the  Investissement d' Avenir Program  Grant  ANR-11-EQPX-0022. We would also like to thank  Dr. Adrien Franchet ,  Miriam Yamba ,  Catherine Socha  and  Dr. Samuel Liégeois  for their technical help with Drosophila.
Site description We tested ORCHIDEE-CROP using winter wheat and maize at seven eddy-covariance sites, which are part of the CarboEurope-IP project (http://www. carboeurope. org/). These sites span different climatic conditions (Table  1  and Fig. S1  in the Supplement). All the sites recorded the meteorological half-hourly variables necessary to run ORCHIDEE-CROP as well as CO 2 fluxes (NEE), and latent and sensible heat fluxes. The NEE half-hourly data were gap filled and partitioned into gross primary productivity (GPP) and total ecosystem respiration (TER) using the online eddy covariance processing tool  (Moffat et al. , 2007; Papale et al. , 2006; Reichstein et al. , 2005). Management information (e. g. , sowing and harvest date, irrigation and fertilization) and crop development monitoring data (e.
Climate forcing data and atmospheric CO 2 At each site, meteorological forcing on a half-hour time step was used as a model input. This included air temperature, precipitation, wind speed, atmospheric water vapor pressure, shortwave and longwave incoming radiation, and mean near-surface atmospheric pressure. Annual CO 2 atmospheric concentration was derived from background atmospheric measurements. There were gaps in the meteorological data, mainly caused by instrumentation malfunction. Therefore, we reprocessed the data using standardized proce-dures for gap-filling and quality control  (Moffat et al. , 2007; Papale et al. , 2006). A significant source of systematic errors in comparisons between modeled and eddy covariance fluxes were attributed to the lack of energy balance closure in the eddy covariance measurements  (Foken, 2008).
Simulation experiments A set of simulations were performed for each crop-site (Table  1 ) using STICS (JavaStics-v11, http://www6. paca. inra. fr/stics/), ORCHIDEE, and ORCHIDEE-CROP to evaluate the performance of ORCHIDEE-CROP and the impacts of the parameterizations of the nitrogen limitation factor and soil hydrology schemes (Table  3 ). Observed climate data and crop type at each site were used to drive the models (in ORCHIDEE, winter wheat is described by the C3 crop standard parameters and maize by the standard C4 crop ones). The same mean soil depth and soil water holding capacity were prescribed for the seven sites, and were averaged from the Harmonized World Soil Database (HWSD; http://webarchive. iiasa. ac. at/ Research/LUC/External-World-soil-database/HTML/). For each site, we selected 1 year of observation during which winter wheat or maize was cultivated.
The sowing date was inputted into the model for each crop-site according to the management data (Table  2 ). However, the harvest date in ORCHIDEE-CROP was determined by crop development processes. The observed nitrogen fertilization and irrigation information for each crop-site were used in STICS experiment STI-WN (Tables  2  and 3 ). In STICS, the real date and quantity of applied irrigation and nitrogen fertilization can be introduced into the model, which affects the water balance and nitrogen transformation modules, respectively  (Brisson et al. , 2008). All simulations based on ORCHIDEE and ORCHIDEE-CROP started from an equilibrium state of carbon pools where the climate was obtained using a model spin-up. For this spin-up, site-specific meteorological half-hourly data were repeatedly cycled for 300 years to force ORCHIDEE and ORCHIDEE-CROP until the soil water reached a steady state (data not shown).
Then, simulations were conducted for the period of evaluation, starting from the initial conditions at the end of model spin-up. Notably, C input from manure applications was not taken into account in this study due to a lack of data for historical manure applications. The same cultivar choice (represented by the "Soissons" and "DK250" variety parameters in STICS for winter wheat and maize, respectively), rather than site-year-specific varieties, was made at all sites for winter wheat and maize (see Table  3 ). This may lead to some discrepancies between simulated and observed values, but our main purpose was to evaluate the improvements achieved by ORCHIDEE-CROP in a generic way, without having to calibrate the model for each site. Sensitivity tests were conducted to evaluate the effects of nitrogen limitation and water stress on crop development, carbon, and energy budgets.
Metrics for evaluating model performance Three metrics were used to evaluate the model-data agreements at a daily resolution for NEE, H, and LE fluxes, and the LAI, AGB, and grain yield biometric variables. First, we calculated the index of agreement (IOA)  (Willmott et al. , 1985) , given by   Note: a DY, with dynamic nitrogen processes; NO, without nitrogen processes; ND, without dynamic nitrogen processes but with a simplified additive nitrogen response of crop productivity to fertilization. For ORCHIDEE-CROP, we introduced a fixed nitrogen limitation factor for leaf growth (innlai, ranging 0. 0-1. 0) during the whole crop growing season. b IOA =1. 0 - n i=1 (O i -P i ) 2 n i=1 P i -Ō + O i -Ō 2 , (4) Two soil hydrological schemes (the two-layer soil scheme, referred as 2LAY, and the 11-layer soil diffusion scheme, referred as 11LAY; in detail see  Guimberteau et al.
S11 ). The episodes of LE with low biases (during LE peaks) coincided with high H biases, even though net radiation appeared to be realistic, except for the maize site IT-Bci in Italy (Fig. S12 ). ORCHIDEE-CROP could also capture the spatial gradients of carbon and energy fluxes across different crop sites in Europe. There were significant correlation coefficients between the observed and modeled GPP, NEE, H, and LE data, with r ranging from 0. 75 to 0. 90. Evaluation of IOA revealed a generally good agreement between the observed and modeled GPP, NEE, H, and LE data with IOA ranging from 0. 70 to 0. 90 (Figs. 10, S14-S16).
General performance of ORCHIDEE-CROP ORCHIDEE-CROP has been developed as an Agro-LSM and adopts a generic framework to integrate the crop processes from STICS into the ORCHIDEE LSM. Given its generic structure, ORCHIDEE-CROP, tested using wheat and maize in this study, can simulate other crop types. Crop phenology, development, carbon allocation, and grain filling can be calculated from climate forcing data and is mediated by limiting factors (e. g. , nitrogen, extreme temperatures, and low soil moisture). A significant improvement was obtained using ORCHIDEE-CROP compared to ORCHIDEE for the simulated crop phenology and development at different winter wheat and maize sites. It showed 65-95 % (IOA) for biometric data and 78-98 % (IOA) agreement with the observed data for all turbulent fluxes, despite the lack of detailed crop management (e.
An important area for further research could be a more mechanistic parameterization of the root profile in the model. Notably, the simple function of additive nitrogen fertilization on crop productivity can lead to better agreement between the observed and modeled crop yields in ORCHIDEE-CROP, which showed a 50 % decrease in the NRMSE (Fig. 6 ). The remaining discrepancies in simulated crop yields and energy fluxes are generally within the observed uncertainties for measurement and energy balance closure. More importantly, ORCHIDEE-CROP has the ability to capture the spatial gradients of crop-related flux variables, such as GPP, NEE, H, and LE, across the studied sites in the different European climate zones (Figs. 10, S14-S16). This is important for further applications of this model using gridded data over Europe, or even the globe, when attempting to investigate regional/global yield variations and the interactions between croplands and the climate system.
Acknowledgements. We thank  Jinfeng Chang , and  Chao Yue from Laboratoire des Sciences du Climat et de l'Environnement (LSCE) of France  for their valuable help for Fortran coding. This work was supported by the  EU   FP7  project  GHG-Europe (Greenhouse gas management in European land use systems ; grant no. 244122 ). Edited by:  A. B. Guenther.
Methodology for tracking corotating density structures Along with a comprehensive complement of in situ instrumentation, each STEREO spacecraft carries a suite of imagers -the SECCHI package  (Howard et al. , 2008). SECCHI comprises an Extreme Ultraviolet Imager (EUVI), two coronagraphs (COR-1 and COR-2), and two Heliospheric Imagers (HI-1 and HI-2). The HI instruments are described in detail by  Eyles et al. (2009) ,  Harrison et al. (2009)  and  Brown, Bewsher, and Eyles (2009). The detectors have image-pixel size of 70 arcsec (HI-1) and 4 arcmin (HI-2), observing in white-light with a pass band of 630-730 nm and 400-1000 nm, respectively. The HI images are recorded at 2048 × 2048 pixels (images are binned onboard to 1024 × 1024 pixels). The images are taken every 40 min for HI-1 and every 2 hours for HI-2.
CDS propagation and validation versus in situ measurements To determine the nature of a sample of the identified CDSs, we estimated their arrival times at a number of probes making in situ measurements of the interplanetary plasma, namely ACE, Wind and the STEREO spacecreft themselves. The cadence of in situ plasma measurements we used was of 1 hour, being comparable to HI-2 images cadence (2 hours). This analysis was carried out for a subset of 61 events that occurred during 2007 and 2008. Focus was placed on the solar minimum period, when both the white-light images and in situ data are easily interpretable due to the low occurrence rate of CMEs. The impact time at each in situ probe of every CDS observed during that period was predicted and a comparison with the in situ measurements undertaken.
Comparison with in situ catalogues of SIR/CIRs In situ catalogues of SIR/CIRs have been produced using ACE and Wind datasets for the interval spanning the years 1995 to 2009  (Jian et al. , 2006; Jian, Russell, and Luhmann, 2011)  and using STEREO datasets for the years 2007 to 2014  (Jian et al. , 2013, catalogue updated online). These catalogues provide a comprehensive list of SIR/CIRs and their physical properties (including the solar wind speed before and after the stream interface, as well as the maximum values of the density, pressure and magnetic field). The information contained within the STEREO SIR/CIR catalogue (which includes 248 events from ST-A and 231 from ST-B) is compared here with our predictions. The first point to note is that we see fewer events in total (190) than are detected in situ over the same period.
All measurements correspond to magneto-plasma parameters (described below) either directly measured in (left-hand columns) or extrapolated to (right-hand column) the ecliptic plane. The red diamonds overlaid in each panel show the estimated Carrington rotation number versus longitude of each CDS in our catalogue. Panel (a) shows the polarity of the interplanetary magnetic field derived from the OMNI dataset (King and Papitashvili, 2005), converted from RTN coordinates to azimuth angle, φ IM. We consider that all field orientations that are within 90 • of the average Parker spiral orientation azimuths of 45 • and 315 • have negative and positive polarity, respectively. We have shifted the times of in situ measurements backward by 5 days to roughly account for propagation time to 1 AU. In this way we have associated to the resulting 'solar date' of the measurement, a Carrington rotation number and a longitude.