dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We are grateful to  Thomas E. Kehl-Fie  ( University of Illinois, USA ) for fruitful discussions. We thank  Anne-Catherine Helfer  for her technical support. FUNDING labEx  NetRNA  [ ANR-10-LABX-0036  to P. R. ,  ANR-17-EURE-0023  to P. R. ] as part of the investments for the future program from the state managed by the  French National Research Agency (ANR) ;  ANR  [ ANR-16-CE11-0007-01  to P. R. and F. V. ]; European Union's  Horizon 2020 research and innovation programme  [ 753137  to D. L. ]; Mass spectrometry instrumentation was funded by  University of Strasbourg ,  IdEx Equipement mi-lourd 2015 and labEx NetRNA. Funding for open access charge:  RIBOSTAPH  [ ANR-16-CE11-0007-01 ].
1%) strains belonged to the order Pleosporales, the rest being distributed in several orders of the phylum Ascomycota. Most of these strains were recovered from superficial tissue. Neoscytalidium dimidiatum, Paraconiothyrium cyclothyrioides and members of the family Didymellaceae were the most prevalent taxa. In addition, those authors demonstrated the usefulness of the LSU as a good molecular marker for a preliminary identification of coelomycetous fungi at genus level. In fact, such locus is easily amplified and many sequences are available in the GenBank database. However, the nucleotide sequences of more phylogenetically informative genes need analysing in order to identify the fungi at species level. Genes such as the RNA polymerase II subunit 2 (rpb2), translation elongation factor 1-alpha (tef1), betatubulin (tub2) and the ribosomal internal transcribed spacer region (ITS), combined in a multi-locus analysis, have all been recommended for this purpose  (25)  Until now, the coelomycetous fungi involved in invasive fungal infections (IFIs) are poorly known in Europe, probably due to the infrequency of these fungi and the complexity of their identification in the absence of characteristic fruiting bodies when grown on culture media used in the clinical lab.
DNA extraction, amplification and sequencing Total genomic DNA was extracted from colonies grown on potato dextrose agar (PDA; 4 g of potato infusion, 20 g dextrose, 15 g of agar-agar, 1 L tap water) after seven days of incubation at 20 ± 1ºC, using the FastDNA kit protocol (Bio101, Vista, CA), with a FastPrep FP120 instrument (Thermo Savant, Holbrook, NY) following the manufacturer's protocol. DNA was quantified using the Nanodrop 2000 (Thermo Scientific, Madrid, Spain). LSU was amplified with the primer pair LR0R and LR5  (67). The amplicons were sequenced in both directions with the same primer pair used for amplification at Macrogen Europe (Macrogen Inc. , Amsterdam, The Netherlands). The consensus sequences were obtained using the SeqMan software version 7. 0. 0 (DNAStar Lasergene, Madison, WI, USA).
Molecular identification and phylogenetic analysis Preliminary molecular identification of the isolates was made using LSU nucleotide sequences in BLAST N searches. Twenty-eight LSU sequences of type or reference strains deposited in the GenBank database by the Westerdijk Fungal Biodiversity Institute (CBS) and the Mae Fah Luang University (MFLUCC) culture collections were used for identification and phylogenetic purposes. DNA sequences generated in this study were deposited in GenBank (accession numbers are given in Table  1 ). For the phylogenetic study, sequences were aligned using the ClustalW application (  68.
Taxonomical identification of the isolates studied, origin and GenBank accession numbers. New sequences generated are indicate in bold. Strains from Westerdijk Fungal Biodiversity Institute, Utrecht, The Netherlands; CNM-CM: Isolates from the National Centre for Microbiology, Instituto Carlos III, Madrid, Spain; CNRMA: Isolates from the National Reference Center for Invasive Mycoses and Antifungals; Institut Pasteur, Paris, France; MFLUCC: Strains from Mae Fah Luang University Culture Collection, Chiang Rai, Thailand. Type strains are indicated by a superscript T b LSU, large subunit ribosomal DNA sequences.
ACKNOWLEDGMENTS We thank  Cécile Gautier  (National Reference Center for invasive Mycoses and Antifungals [NRCMA]) for technical assistance. This work was supported by the  Spanish Ministerio de Economía y Competitividad , grant  CGL2017-88094-P. Olga Rivero-Menendez  holds a fellowship from the  Fondo de Investigaciones Sanitarias  (grant  FI14CIII/00025 ). Ana Alastruey-Izquierdo  is supported by a research project from the  Fondo de Investigación Sanitaria (FIS)  (project  PI16CIII/00035 ).
Funding: "This research was funded by The  EUROPEAN UNION  via the contract  Sharco-EU-FP7 n • 204429 (2008 )(2009)(2010)(2011)(2012). Acknowledgments: This work was supported by the  European Union  via the contract  Sharco-EU-FP7 n • 204429  ( 2008-2012 ). We also thank  Dennis Bennett  for the Northern analysis of transformed clones and  Mark Demuth  for plant grafting and plant care.
Viruses A single stock of CpGV-M  [11]  (our laboratory stock 2020-s1) was used in all experiments. The CpGV-R5 isolate has been described previously  [36]. The 2016-r16 stock of this virus isolate was used in all experiments. CpGV-M and CpGV-R5 were adjusted to the same concentration from a primary quantification by qPCR. The AcMNPV clone 1. 2 used for negative control of PCR was provided by CNRS UPS3044 Baculovirus and Therapy. (GenBank accession numbers L22858. 1) [37].
Primer Design The primers were designed using Clone manager V9 (SCI-ED software, Denver, CO, USA). The primers were synthesized by Eurofins MWG (Ebersberg, Germany). The forward primer CpGV-18734F (5 -GCCACCATTAGTGAATCATC-3 ) and the reverse primer CpGV-18855R (5 -TAAGTCAGGACACCCAAACC-3 ) were designed to target the variable region of ORF 24, gene pe38, and to encompass the 24 bps deletions. The resulting amplicons for CpGV-M and CpGV-R5 were 121 and 97 bps in length.
The genotypes have been grouped automatically into HRM profile clusters by the software Precision Melt Analysis TM. Each color represents one cluster. Fluorescence intensity data, shown in Figure  2 , are normalized and reduced (values ranging between zero and one) using values both before and after the melting phase. The software clusters the samples by calculating their probability to belong to the same cluster based on their standard deviation and their mean melting curve  [39]. The cutoff level for clustering was set to α = 0. 05. The 11 samples are aggregated in seven clusters (Figure  3 ). Figure  3a  represents the normalized data, characteristic of a given mix of genotypes. To improve the representation of the results, data was plotted as the difference to the M50-R50 cluster (Figure  3b )  [39].
Analysis of OBs Productions Larvae were allowed to feed on various inoculums, including the pure genotypes, and the resulting OBs [P(Mi-Rj)] were analyzed by HRM. The results obtained for the melting curves of these OBs are shown in Figure  6. The HRM determination and data analysis were done similarly to the previous experiments. The sample values are plotted in solid lines while the standard range curves are represented with dotted lines (Figure  7 ). P(M100-R0), P(M50-R50), and P(M0-R100) infections were associated by HRM software in the same clusters as their corresponding parental inoculum in the standard range (M100-R0), (M50-R50), and (M0-R100), respectively. The other OBs productions did not cluster with their respective parental references. P(M10-R90) was recognized as a new cluster, it was between (M10-R90) and (M25-R75) (closer to (M25-R75) of the standard range).
During measurements the temperature of the sample was recorded every 2 s with a data logger with an accuracy of ±0. 1 °C. For comparison purposes, temperature profiles from the initial temperature 18 °C to -18 °C were considered. The characteristic freezing time and overall freezing time were determined using time-temperature data. The characteristic freezing time was the measure of local freezing rate and it was defined as the time during which the temperature at a particular point changed from the initial freezing point to a temperature at which 80% of the water (at that point) was converted into ice. The temperature range considered for the characteristic freezing time estimation was from -1 to -7 °C, similar temperature range was used by  Li & Sun (2002)  for the determination of characteristic freezing time during ultrasound assisted freezing of potatoes.
Acknowledgements: This work received financial support from the  French National Research Agency (ANR)  and the  Swedish Research Council FORMAS  under the  FREEZEWAVE  project ( SUSFOOD-ERANET, FR :  ANR-14-SUSF-0001 , SE:  2014-1925 ). We would like to thank  Anthony Ogé  ( ONIRIS ) for designing the laser-puff firmness tester. A special thanks to McCain food for supplying potatoes required for the experimentation. The authors would also like to thank  Romain Mallet  ( Univ-Angers -SCIAM)  for their assistance in cryo-SEM imaging respectively.
The OD 450 to OD 620 absorbance values were used for calculations. The concentrations of FSH in plasma samples were calculated from the standard curve plotted for known bovine FSH concentrations in the range of 0. 1 to 3. 2 ng/ml by using Magellan software (Tecan) with a four-parameter logistic regression. The sensitivity of the FSH EIA was assessed for FSH standard samples by determining the lowest FSH concentration giving absorbance values significantly different from the absorbance values of the zero FSH concentration in six independent experiments (comparisons done using paired t tests). The accuracy of the FSH EIA was assessed by overloading bovine plasma samples with known amounts of purified bovine FSH (bFSH-I-2, USDA). For this purpose, the FSH concentrations of plasma samples recovered from 13 different cows were measured before and after the addition of bovine FSH to obtain expected increases in plasma FSH concentrations ranging from 0.
Data analysis The Dynpeak Software previously validated to analyze the pulsatile secretion of LH in jugular blood  (Vidal et al. , 2012)  was used for the detection of LH and FSH pulses on the eight time series of plasma samples collected on cows at 10-min intervals for 5 h. Pulse detection was used to calculate the time interval between two successive pulses and determine the percentage of co-occurrence of LH and FSH pulses. This percentage was estimated from the number of LH and FSH pulses occurring simultaneously, divided by the total number of detected pulses. The amount of AMH present in the follicular fluid for each size class of follicles was estimated from the formula: Q = C × V × N, where C is the AMH average intra-follicular concentration of the class (expressed in ng/ml), V the average follicular volume (expressed in milliliters) and N the number of follicles of the class.
For the class of follicles larger than 5 mm in diameter, the individual values of AMH concentrations and follicular volumes were used for the estimation AMH intra-follicular amount. GraphPad Prism 6 Software (GraphPad Software, La Jolla, CA, USA) was used for the statistical analyses of data. Data are expressed as mean ± SEM. Data of the FSH standard curves were fitted to a four-parameter logistic regression, or to a linear regression after Log transformation. For analysis of hormonal pulsatility in the plasma of cows, means were compared by t test with Welch's correction when variances were heterogeneous, and correlations were assessed using Pearson correlation coefficients (r). For the analysis of follicular and hormonal data in the six studied calves, non-parametric analyses were performed.
For instance, the high FSH levels found at days 1 and 11 of the cycle preceded the respective emergence of the first and the second follicular waves of the cycle, 1 day later (Figure  2b ). In Cow 7913 with an abnormally long luteal phase, two follicular waves also occurred, preceded by increases in FSH concentrations, but the second follicular wave led to the development of a persistent follicle with an ovulary size from day 15 up to the next estrus at day 32. The pattern of LH and FSH secretion was studied during the peri-ovulatory period on plasma samples recovered at 10-min intervals for 5 h on Rouge-des-Prés cows. For seven among the eight time series of data which were analyzed, plasma samples were found to be recovered before the occurrence of the LH/FSH pre-ovulatory peak. For these time series, the average time interval between two successive pulses was not different for  LH and FSH (50.
Acknowledgements This work was supported by the  INRA/DGER PREDICTOV development project. The authors acknowledge the staff of the  UEPAO experimental unit in Nouzilly  who took care of all experimental animals during the experiment, and participated in blood sampling and ovarian ultrasonography scanning, and  Dominique Gennetay  for her technical participation in hormonal assays. The authors thank also  Dr Yves Combarnous  who provided the hormones for cross-reaction studies,  Dr AF Parlow  of the National Hormone and Pituitary Program of the  NIDDK  who supplied FSH standard preparations for assays and FSH antibodies for FSH RIA,  Dr Stéphane Fabre  who participated in the design and management of the experiment dedicated to the study of FSH and LH pulses on cows, and  Dr Frédérique Clément  who helped to analyze hormonal pulses with Dynpeak Software.
RNA-Seq analysis Adapter clipping and quality trimming of the raw reads were performed using Trimmomatic-0. 35. The sequenced raw reads were processed to obtain high quality clean reads. Ambiguous reads (reads with unknown nucleotides "N" larger than 5%) and low-quality sequences (more than 10% quality threshold QV<20 phred score) were removed. A minimum length of 100 nts after trimming was applied. Finally, high-quality (QV>20), paired-end reads were used for reference based read mapping. The genome of Triticum aestivum L. was taken as reference for analysis. The Gene Feature Format files were downloaded from Ensembl Plants (TGACv1. 37). The reads were mapped to the reference genome using TopHat v2. 1. 1 with default parameters. Cufflinks v2. 2. 1 program assembled the transcriptome data from RNAseq data and quantified their expression.
Mapped reads were subjected to Cufflinks, followed by Cuffmerge and Cuffdiff. Log2 Fold Change (FC) values greater than one was considered up-regulated whereas less than 1 were considered as down-regulated. These genes were further categorized on the basis of statistical significance (p<0. 05) and the False Discovery Rate (FDR 0. 05) after Benjamin-Hochberg corrections for multiple testing for their significant expression. Comprehensive gene annotation of wheat sequences was done using KOBAS 3. 0  (Xie et al. , 2011)  annotate module by alignment with Rice sequences (BLASTP, cutoff 1e-5). MapMan was used to visualize the pathways involving wheat differentially expressed genes (DEGs). Pathway enrichment analysis was performed using KOBAS standalone tool. MeV was used to construct heatmaps for selected DEGs using the normalized expression values of genes.
Homoeolog specific expression analysis for genome bias To identify wheat homoeologous triplets; ensembl biomart TGACv1 was used to extract all possible homoeologous relations. This led to 86830 pairwise homoeologous relations. An inhouse script was used to select only the triplets where contribution from each genome was 1:1:1 (A:B:D), thus generating 16850 triplets. Further, triplets resulting from potential translocation events were not considered, i. e. , only homoeolog triplets from same chromosome (eg. , 1A, 1B, 1D triplet is accepted, whereas, 2A, 3B, 3D is rejected) were taken for analysis. Finally, 15604 triplets (15604*3 = 46812 genes) were used for studying genome induction biasness in response to iron stress. Paired end reads were aligned to the reference (selected scaffolds from genome that harbour 15604 triplets) using TopHat v2.
Dried polar phase were shaken for 1. 5 hr at 30°C in 40 µl of MeOX (40 mg ml -1 methoxyaminhydrochloride in pyridine) followed by 30 min shaking at 37°C in 80 µl BSTFA mixture (70 µl N,O-bis(trimethylsilyl)trifluoroacetamide + 10 µl alkane mix). The derivatized metabolites were subjected to GC-MS analysis (Agilent technologies 7890, USA) coupled with mass spectrometry. Measurement from an injection volume of 1 µl was taken in split-less mode in DB-5 column (30 m × 0. 25 mm, 0. 25 µm film thickness, Agilent) using helium as carrier gas. Metabolites were separated as described by  Wagner et al. (2013). Qualitative analysis of chromatograms was performed in MassHunter Qualitative analysis Sp1 workstation (Agilent, USA). Identification and annotation of each compound was supervised manually using AMDIS software and NIST08 database (http://www.
Differential expression analysis, homoeolog (A, B and D) induction and expression bias during -Fe response The effect of Fe starvation on the wheat root transcriptome has not been investigated till date. To perform this study, RNAseq technology was used to identify the changes in the transcripts of wheat roots, where plants were grown in presence or absence of optimal Fe (20 DAS). Our analysis resulted in 87 million quality filtered reads, with an average of nearly 22 million reads from each sample (more than 87% reads had a quality score greater than Q30). Filtered reads from the four libraries had a mapping rate ranging from 81. 7% to 85. 4% when mapped against release-37 of the wheat genome using TopHat (Supplementary Table  S2 ). As quality check, a strong correlation within the two biological replicates from each condition was observed, while a clear variation was seen between the two conditions (Fig.
2A ). We thereafter analysed the expression values as FPKMs (Fragment Per Kilobase of transcript per Million mapped reads), calculated by using Cufflinks software. Differentially expressed genes (DEGs) were then identified by calculating logFC (log2 fold change) and performing statistical tests between FPKM values from control and stressed samples using CuffDiff. 50610 genes with an FPKM of greater than or equal to 1 in at least one of the two conditions were considered to be "expressed transcripts". In all, 7221 genes had logFC > 0 and 8010 had logFC< 0 (Fig. 2B ). On setting up a criterion of logFC of more than 1 for up-regulated genes, and that of less than -1 for down-regulated genes and an FDR < 0. 05, a total of 3478 genes were highly expressed, whereas, 2376 were down-regulated under -Fe condition in wheat roots (Fig.
S1 ) in wheat roots. Metallo-reductases are important components of Strategy-I represented by ferricchelate reductase (FRO). Most of the transcripts encoding for wheat FROs do not show significant changes in starved roots as compared to control except for one transcript. Interestingly, iron regulated transporters (IRT; TRIAE_CS42_7DS_TGACv1_622068_AA2032200; TRIAE_CS42_4AL_TGACv1_289466_AA0971640) were significantly expressed in Fe starved wheat roots, suggesting their involvement in wheat under metal stress. The high expression of TaIRT1was also confirmed by qRT-PCR (Supplementary Fig. S1 ). This suggest that IRT function might be conserved among the plant species. In addition to this, forty-five genes showed exclusive transcript abundance under Fe starvation (Supplementary Table  S3 ). No transcript for any of these genes was detected in the control root samples, suggesting their high specificity for Fe response.
5A ). Overall, catalytic activity and binding activities were most significantly enriched GO terms in -Fe condition (Fig. 5B ). Further mapping of upregulated genes to databases such as, Kyoto encyclopedia of genes and genomes (KEGG) pathway  (Xie et al. , 2011)  and  MapMan (Thimm et al. , 2004)  revealed enrichment related to phenyl-propanoid biosynthesis, amino acid biosynthesis and carbon metabolism, and glutathione metabolism (Fig. 5C  and Supplementary Fig. S2 ). The role of glutathione in response to Fe starvation is intriguing and deserves further investigations. GO category "integral to membrane" was found to be associated with pathways clustered into "metal ion and trans-membrane transport" and "photosynthesis" related GO categories (Fig. 6A ). Clustered pathways involved in "response to nutrient levels" and "Sulphur amino acid metabolism" was also enriched (Fig.
Glutathione-S-transferases are involved in the response to Fe starvation in wheat Glutathione mediated conjugation of multiple metabolites plays important role during the metal stress  (Zhang et al. , 2013). Our transcriptome data revealed the enhanced expression of multiple glutathione-S-transferases (GST) in Fe-starved wheat root as compared to control (Supplementary Table  S9 ). To correlate the expression response with its enzymatic activity, temporal response of GST was measured in wheat roots under -Fe conditions. Using the GST functional assay, the activity was determined in wheat roots of plants grown for 10, 15 and 20 days under Fe starvation. Our activity assays showed significant increase in the GST activity under -Fe condition, which peaked at 10 and 20 days after the beginning of Fe starvation compared to control plants (Fig.
Fe starvation causes an accumulation of organic acids and polyhydroxy acids in wheat To obtain a comparative insight of the metabolite profile of wheat roots of plants grown in absence or presence or Fe; GC-MS profiling analysis was performed. Metabolites were extracted from the roots of three replicate pools of plants each containing eight seedlings. Qualitative processing of each chromatogram for peak area and identification was performed in MassHunter version B. 05. 00 software coupled with NIST11 compound library. The compound annotation was determined by comparing individual resolved peaks to library searches based on mass spectra and compounds chromatographic retention indices. Interestingly, analysis resulted in the identification of 54 metabolites and further 39 annotated metabolites were analysed for their response ratio (Supplementary Table  S10 ).
As represented in Figure  9 , the transport of these Fe-complexes occurs via membrane transporters via ZIFL genes encoding for TOM proteins  (Nozoye et al. , 2011; Nozoye et al. , 2015). Multiple wheat ZIFL show high transcript accumulation and are closest homolog of OsTOM1 from rice, thereby speculating its role in Fe acquisition. Utilizing gain-and loss of function approaches OsTOM1 has been demonstrated to be a DMA effluxer for its role in enhancing mobilization and thereby improving Fe uptake  (Nozoye et al. , 2011). Altogether, based upon the high similarity and response under Fe starvation, it is tempting to propose wheat ZIFL (ZIFL4) as a functional transporter of DMA. Although, other ZIFLs were also significantly expressed but functional wheat transporter for DMA needs to be deciphered.
PS secretion was also observed during our analysis for the roots of C306 exposed to Fe starvation. In this study a high PS release of ~40-45 nmolg -1 root biomass (3 hrs) -1 was observed, reinforcing our RNAseq data that DMA biosynthesis genes and its transporters were highly upregulated. Control plants show accumulation of ~1-2 nmolg -1 suggesting that wheat releases a basal level of PS to maintain the constant flux of Fe in roots. Upon careful analysis of the RNAseq data, it was observed that multiple such efflux transporters are represented in the list that are highly abundant under Fe starvation. During our study, we identified TaDMAS1 encoding for deoxymugineic acid synthase that was highly responsive for Fe starvation (Supplementary TableS5). Earlier, TaDMAS1 was reported as a gene that was broadly expressed across the tissue and was regulated during Fe-starved condition.