dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 9-Quantification of Fusarium graminearum, fungi, and bacteria Fusarium graminearum and overall fungal and bacterial densities were quantified in all the samples using SYBR Green dye-based real-time Q-PCR carried out on an ABI PRISM 7900 HT Sequence Detection System (Applied Biosystems TM, USA). Fusarium graminearum was quantified by using the specific primer pair Fg16NF (ACA GAT GACAAGATTCAGGCACA)/Fg16NR (TTCTTTGACATCTGTTCAACCCA)  (Nicholson et al. , 1998) , which amplifies a 284-bp fragment. One μl of DNA was mixed with 0. 25 µM of each primer, 1 μg of T4 gene 32 protein (MP Biomedicals), 6. 5 µl of Q-PCR SYBR Green ROX MIX (2X) (Thermo Fisher scientific Inc. , USA), and DNAse-RNAse-free water up to a final volume of 13 µl. The real-time Q-PCR conditions consisted of an initial step of 10 min at 95°C for enzyme activation, followed by 45 cycles of 15 s at 95°C (denaturation), 30 s at 64°C (annealing), 30 s at 72°C (elongation), and 30 s at 78°C (data acquisition).
Then a melting curve analysis was performed as follows: 95°C for 30 s, 70°C for 30 s, and then the temperature was increased from 70 to 95°C at a 2 % (°C min -1 ) ramp rate. Fungal quantification was carried out by targeting a fungus-specific 348-bp fragment of 18S rDNA using the primer set FR1 (AICCATTCAATCGGTAIT)/FF390 (CGATAACGAACGAGACCT)  (Prévost-Bouré et al. , 2011; Vainio and Hantula, 2000). For the PCR, 1 μl of DNA was mixed with 0. 25 µM of each primer, 0. 5 μg of T4 gene 32 protein, 6. 5 µl of QPCR SYBR Green ROX MIX (2X), and DNAse-RNAse-free water up to a final volume of 13 µl. The real-time Q-PCR conditions consisted of an initial step of 10 min at 95°C, followed by 40 amplification cycles with 15 s at 95°C (denaturation), 30 s at 50°C (annealing), and 60 s at 72°C (elongation and data acquisition).
2. 11-Statistical analyses DON amounts in the different compartments over time, F. graminearum, fungal, and bacterial molecular biomass, as well as fungal, bacterial, earthworm, and nematode densities were compared between treatments and sampling times by analysis of variance (ANOVA) and Fisher LSD test (p=0. 05) using XLSTAT-Pro version 7. 1 (Addinsoft). The communities characterised based on their TRF size and intensity measured from peak heights were compared by principal component analysis (PCA) using ADE-4 software  (Thioulouse et al. , 1997). PCA results were displayed as variations on a two-dimensional diagram for each community. The resulting significance of the structure was checked using Monte-Carlo tests (p < 0. 05) with 1,000 random permutations of the data.
Acknowledgments The first author was financially supported by  Higher Education Commission (HEC) of Pakistan  under a  Franco-Pakistan  collaborative agreement from 2009 to 2012. This project, FARINE +, was funded under the 4th Call for Projects FUI 2007 and managed by  Pole Vitagora , (the taste, nutrition and health innovative Pole),  Dijon-France. The authors are grateful to  Dr Hanna Friberg  ( SLU, Sweden ) for the fruitful discussions during the meetings of the M. Abid PhD Steering Committee.
It is important to mention that we restrain the analysis to the French economic system. This means that the impact of the scenarios in terms of employment are only estimated for France and do not consider the possible transformations that they may generate in other countries as a consequence of French food system transformations. At the EU level, other member states will have to implement (or are already implementing) similar low carbon strategies in coherence with the FitFor55 package that has just been adopted in the framework of the Green Deal. Non-EU countries might also be affected if a carbon boarder adjustment mechanism is adopted and implemented for the agricultural sector.
• Finally, the quantitative characteristics of the resulting farms are estimated on the basis of innovative farms already present in FADN data and adjusted based on experts interviews and stakeholder workshops. Through this approach, the labour intensity of the production can be approached for each type of farming system, expressed in terms of number of jobs per thousand litres of milk produced. The third step of the analysis aims to construct populations of 2030 farming systems that are coherent with (i) the biophysical targets set by the SNBC, (ii) a set of more general assumptions derived from the broader socio-political storylines (e. g. , average size, specialisation level, demographic framework, regionalisation criteria) and (iii) the plausibility of transitioning from one farm type to another one.
Acknowledgments: Access to some confidential data, on which is based this work, has been made possible within a secure environment offered by  CASD-Centre  d'accès sécurisé aux données (Ref. 10. 34724/CASD). The project underpinning this paper has benefited from the support of a scientific committee made up of the following persons to whom the authors would like to express their deep gratitude:  Valentin Bellassen ,  Philippe Boulet ,  Pierre-Alain Jayet ,  Christophe Perrot ,  Jean-Louis Rastoin ,  Bernard Valluis. We also would like to thank several colleagues from the BASIC that have greatly contributed to data collection and first analysis, namely  Delphine McAdams-Marin  and  Théodore Fechner.
Funding: This study has received financial support from: the  French government in the framework of the programme "Investissements d'avenir ", managed by  ANR  (the  French National Research Agency)  under the reference  ANR-10-LABX-01. The European Union's  Horizon 2020 research and innovation programme  under grant agreement No  727243  for the  VALUMICS  project. The  French government  through grants received from the  Ministry of Agriculture  and the  Ministry of the Ecological Transition. The  Daniel & Nina Carasso Foundation  (Grant n  00095500 ), The  Martine & Didier Primat Foundation. The Foundation "Terres Solidaires " (Grant n  FTS-FR-19-15 ). The  French Agency for Environmental Transition (ADEME). The  French Agency for Biodiversity (OFB).
External datasets The following publicly available datasets were used: mouse ESC Hi-C (GEO: GSM2533818) (41); mouse ESC H3K27ac HiChIP (GEO: GSE101498) (42); mouse AML12 Hi-C (GEO: GSE141080) and H3K9me3 HiChIP (GEO: GSE141113) (43); mouse ESC RNA-seq (GEO: GSE122854) (44); MCF7 and 2102Ep RNA-seq (EBI ENA: E-MTAB-3788)  (31). More details about the sequencing datasets used in this study can be found in Supplementary Table  S1.
Sim3C To generate simulated Hi-C data that matched the interaction distance profile of experimental Hi-C data, a modified version of Sim3C (53) was used. In particular, the model was changed to a mix of two geometric cumulative distribution functions with different 'shapes' (shape1 = 8e-5, shape2 = 3e-6). The nbins parameter was set to 200 000 and alpha to 0. 73 (weight of the second geometric CDF). Minor modifications to the output format were also made. Sim3C was then run with -dist uniform, -l 75, -e DpnII, -m hic, -simplereads, -efficiency 0. 9, -anti-rate 0. The position of mapped reads was compared against that of simulated reads using custom scripts (see code repository). Files containing correctly assigned reads were used to generate accuracy tracks, which in turn were used to extract mean accuracy values per element (>1 kb) of selected TE families.
Importantly, high frequency contacts within a few tens of kb are largely due to random collisions, whereas nonrandom interactions can normally only be discerned from the latter at distances over 50 kb  (58) (59) (60). To leverage this alignment principle, we developed PAtChER (Proximity-based Alignment of HiChIP Ends to Repeats; https://github. com/MBrancoLab/PAtChER), a computational pipeline to map the ends of HiChIP data to produce ChIP-seq tracks with increased genomic coverage. When PAtChER encounters Hi-C fragments for which only one end can be uniquely aligned, it uses its location as an 'anchor' to search for possible mapping hits of the nonunique end in its vicinity (Figure  1C ). Instead of searching for all possible hits of the non-unique end in the genome (which would use up prohibitive amounts of processing time), PAtChER restricts the search space by only looking for hits within a user-defined distance from the unique end (Figure  1C ).
If more than one hit is found within this search space, the nearest hit to the unique end is chosen. The search distance parameter (which we will denote here as Sd) therefore affects the stringency of the mapping, as hits beyond this distance will not be considered. We evaluated the gains in mapping coverage by using PAtChER (Sd = 20kb) to process human and mouse Hi-C data -our own Hi-C dataset from 2102Ep cells, and a published dataset from mouse ESCs  (41). We focused on repeats longer than 1kb, and segregated them based on the degree of divergence to the respective family consensus (which also functions as a proxy to their evolutionary age). The fraction of each element that is covered by at least 1 read was measured and, as expected, lowly divergent repeats were poorly covered by uniquely mappable reads (Figure  1D ).
Finally, we asked whether mapping accuracy could be affected by non-random long-range interactions, which play important roles in the spatial and functional organisation of genomes  (62). Long-range chromatin loops involving two TEs of the same family could lead to read misassignment. We therefore performed chromatin loop detection in two human Hi-C datasets (MCF7 and 2102Ep cells), and compared the number of reads involved in chromatin loops with those involved in nearby random contacts. Irrespective of the size of the interacting region, proximal contacts outweighed the number of reads involved in chromatin loops by around 12-fold (Supplementary Figure  S3A, B ), suggesting that chromatin loops are unlikely to significantly affect mapping accuracy. To confirm this, we generated a new minigenome wherein duplicated regions were placed at the base of chromatin loops (Supplementary Figure  S3C ).
Accurate protein enrichment detection at repetitive regions using Hi-ChIP and PAtChER Our main objective was to use PAtChER to map Hi-ChIP data in a manner that produces ChIP-seq-like profiles at interspersed repeats. To achieve this, it is critical to include a control Hi-C library (equivalent to the input in ChIP-seq experiments) that is used to control for uneven sequencing depth (Figure  3A ). To test the quality and accuracy of data processed in this manner, we again mapped data to the minigenome containing artificially duplicated regions. We used H3K27ac Hi-ChIP data from mouse ESCs  (42) , and normalised it to Hi-C data from the same cell type  (41). Visual inspection of normalised data showed that PAtChER was able to reconstruct regions of H3K27ac enrichment at the original location, whereas the duplicate region had a much reduced signal (Figure  3B ).
(D) Average mapping accuracy profile across L1HS (>5kb) elements, using Sim3C data. (E) Principle of the minigenome-based approach to assess mapping accuracy. Hi-C data was mapped to a minigenome made up of 960 merged genomic segments, which contained either mm10 reference sequences, or were modified to include 3kb-long duplications at a variable 'inter-repeat' distance from their original locations. Reads (purple lines) that cover the duplicated regions in the mm10-based minigenome become unmappable uniquely in the modified genome. (F) Hi-C data mapped to the minigenomes (Sd = 20 kb) were assessed in terms of read recovery (% of reads mapped at original location compared to a mm10-based minigenome alignment) and mapping accuracy (% of reads mapped to original, correct location), as a function of inter-repeat distance.
Validation of PAtChER outputs using an orthogonal method As a final validation of PAtChER-aligned data at TEs, we compared two orthogonal methods to map DNA methylation at interspersed repeats: (i) a combination of Hi-C with methylated DNA immunoprecipitation (i. e. Hi-MeDIP), followed by processing by PAtChER; and (ii) bisulphite sequencing of the promoter of young human LINE-1 elements, using a modified version of ATLAS-seq (  31 ) that provides locus-specific DNA methylation levels of the most distal 210 bp of full-length LINE-1 elements. We performed Hi-MeDIP and LINE-1 bisulphite sequencing on MCF7 (breast cancer) and 2102Ep (embryonal carcinoma) cell lines. After PAtCHER alignment and normalisation of the Hi-MeDIP data, we used only non-unique reads to measure the enrichment at the most internal part of the LINE-1 region covered by bisulphite sequencing (Figure  4A ).
(B) Correlation between PAtChER-processed Hi-MeDIP enrichment values and bisulphite sequencing data (expressed as % of methylated CpGs) at both LINE-1 regions, in MCF7 and 2102Ep cells. (C) The DNA methylation difference between MCF7 and 2102Ep cells was calculated using data from either Hi-MeDIP or bisulphite sequencing and compared, yielding a strong correlation for both LINE-1 regions (Pearson's R is displayed). (D) Genome browser snapshots displaying bisulphite and Hi-MeDIP data at LINE-1 elements with preferential methylation in either MCF7 (left) or 2102Ep (right) cells. (E) Peak detection was performed on PAtChER-processed or uniquely aligned data. The plots display the percentage of peaks overlapping each LINE-1 element region, depending on their bisulphite methylation status. to gene regulatory activity.
It is also worth noting that, when detecting areas of enrichment, any mis-aligned reads are more likely to decrease sensitivity than yield false positives because, unlike areas of true enrichment, alignment errors will be randomly distributed. This prediction is supported by the low rate of Hi-MeDIP peak detection at hypomethylated human LINE-1 elements (Figure  4D ). Peak detection using the minigenome approach also displays a low false positive rate (Figure  3E ), and we believe this to be a conservative estimate, because here the random spread of mis-aligned reads is limited by the fact that there are only two copies of each repeat. We propose that the use of similar minigenomes (i. e. pseudo reference genomes) could be used in a flexible and bespoke manner to estimate false positive rates from any HiChIP dataset, taking into account the size and spacing of TEs of interest.
We have also shown that long-range chromatin loops have no discernible effect on mapping accuracy, due to their much lower contact frequency when compared to proximal random interactions (Supplementary Figure  S3 ). Nonetheless, the potential bias generated by cases of particularly strong interactions between repeats should not be overlooked. These specific cases can be assessed by extracting and analysing longrange interactions from the Hi-C or HiChIP data used for PAtChER-based mapping. A number of changes can be employed to the workflow presented. Namely, all the HiChIP datasets analysed here relied on restriction enzymes for the initial chromatin digestion, which can lead to uneven coverage, as seen for L1HS elements (Figure  1E ). This limitation can be easily overcome by the use of existing restriction-independent digestion strategies  (74, 75) , which result in more even coverage of the genome.
Hi-C data was mapped to a minigenome made up of 960 merged genomic segments, which contained either mm10 reference sequences, or were modified to include 3kb-long duplications at a variable 'inter-repeat' distance from their original locations. Reads (purple lines) that cover the duplicated regions in the mm10-based minigenome become unmappable uniquely in the modified genome. (F) Hi-C data mapped to the minigenomes (Sd = 20 kb) were assessed in terms of read recovery (% of reads mapped at original location compared to a mm10-based minigenome alignment) and mapping accuracy (% of reads mapped to original, correct location), as a function of inter-repeat distance.
ACKNOWLEDGEMENTS We thank  Stefan Schoenfelder  for help with setting up Hi-C, and  Alex de Mendoza  for critical reading of the manuscript; GenoMed, the  Genomics Core Facility  at  IRCAN , for sequencing of the BS-ATLAS-seq libraries; and  Stuart Newman  for supporting the use of the  HPC at University of Essex  for the analysis of chromatin loops. FUNDING  BBSRC  [ BB/T000031/1 , industrial  CASE  training grant  BB/R505997/1  awarded via the  London Interdisciplinary Doctoral Programme) ;  Fondation pour la Recherche Médicale (FRM)  [ DEQ20180339170  to G. C. ];  Agence Nationale de la Recherche  ( LABEX SIGNALIFE ) [ ANR-11-LABX-0028-01]; RetroMet [ANR-16-CE12-0020 ];  Im-pacTE  [ ANR-19-CE12-0032 ]; CNRS [GDR 3546]; equipment acquisition for the GenoMed facility was supported by  FEDER ,  Région Provence Alpes C ôte d'Azur, Conseil Départemental 06, ITMO Cancer Aviesan  (plan cancer) and  Inserm ;  Queen Mary's Apocrita HPC facility , supported by  QMUL Research-IT  ( 80 ).
Elastic Recovery The elastic recovery (ER) of each material was calculated using the Analis ® software (Medelpharm, Beynost, France). It corresponds to the evolution of the tablet thickness between the compression peak and the measurement of the thickness outside the matrix, immediately after compression. This evolution is characterized by percentage (%). The measurement was performed according to Equation (  7 ) ER = Th -D D •100, ( 7 ) where D is the distance between the punches during the compression peak and Th is the measured thickness of the tablet outside of the matrix.
Lubricant Sensitivity Materials' lubricant sensitivity was investigated by mixing them, for 5 min, with two different concentrations of magnesium stearate (0. 5% and 2. 5%), using a 3D mixer (Turbula ® ), in order to evaluate the effect of the internal lubrication on tablet tensile strength and disintegration time compared to tablets obtained without lubricant. External lubrication was evenly carried out using an external lubrication system (Medel'Pharm, France). The lubricant is conveyed by compressed air and filled into a narrow tube in the supply part. The «puff» is generated by the Venturi inside the air blow cabinet (5-6 bars). The microdosing unit can contain a maximum of 4 g of lubricant. The amount of the lubricant pulverized can be adjusted by the setting of the position (high or low) of the container and the lubrication time can be fixed by the Analis software.
Statistical Analyses Data were analyzed using a two-way repeated-measures analysis of variance (ANOVA) followed by a Bonferroni test. The statistical analyses were performed using GraphPad Prism software 7. 01 (GraphPad Software, Inc. , San Diego, CA, USA). Results were expressed as means ± SEM. Differences were considered statistically significant when p < 0. 05. Survival kinetics of the R-IVET library in the TIM-1 system were compared to that of a theoretical transit marker provided by the computer. This marker evolves according to the volume of each compartment, the rate of dilution by digestive secretions, and the chyme flow between two successive compartments, and it corresponds to a 100% survival rate. The comparison was independently performed in each compartment and at each time of sampling.
0, 50 mM NaCl and 2 mM DTNB). Graph-Pad Prism 6 software was used to obtain the catalytic parameters by fitting the data to the Michaelis-Menten (MM) equation. For coumaric OPNA surrogates, measurements were taken at 10 µM, and hydrolysis was followed by fluorescence (360/40, 460/40 nm). We assumed that K M [S], allowing us to estimate kinetic parameters with the following equation k cat K M = k [E] and using one-phase decay non-linear regression in Graph-Pad Prism 6 software. For CM Soman hydrolysis, degradation of both enantiomers was discernible. In this case, data were treated with two-phase decay non-linear regression, enabling us to determine a fast and slow k cat /K M  [50]. For OPNAs, only k cat estimations were determined considering that [S] K M (here, 25 mM for all tested agents) since v = v max and then k cat = v max [E T ].
HCl (1 M) was added in 1 of each pair of samples until the pH reached 5-6 (quenching enzyme activity). These samples were filtered, D 2 O was added to give a 10% v/v D 2 0 in the resulting mixture, and the samples were immediately analysed with 31 P NMR. The decontaminated panels were briefly rinsed with water and dried with adsorbent paper without rubbing. Next, all panels were extracted twice with 10 mL of diethyl succinate for 90 min. The amount of agent in each extract was quantified by GC-FPD (Agilent 6890, employing a Wax column (15 m, 530 µm diameter and 1 µm film thickness). Temperature programmes-GD: isothermal at 160 • C; VX: 0. 4 min at 170 • C, followed by a temperature gradient to 195 • C (50 • C•min -1 ). Quantitative data were calculated using a calibration curve. The amount of agent found on a panel was calculated by the sum of the amounts of agent found in the two consecutive extractions.
rHAChE Inhibition Assay Inhibition assays were carried out as previously described  [50, 51]. First, PTEs (0. 13 nM) were incubated with ethyl-paraoxon (5 µM) in PTE activity buffer (200 mM Tris pH 9. 0, 50 mM NaCl) and samples were collected for 40 min. Secondly, these samples (PTE 0. 13 nM and ethyl-paraoxon 0. 5 µM) were incubated for 15 min with rHAChE (activity around 2. 2 U•mL -1 ). Finally, rHAChE activity was measured using Ellman's reagent (DTNB, 4 mM) and acetylthiocholine (2. 5 mM). The reaction was monitored at 412 nm for 10 min with a microplate reader (Synergy HT, BioTek, Winooski, VT, USA)  [52]. Curves were fitted using one-phase decay equation on GraphPad Prism 6 software, and k cat /K M values were determined.
Funding: This work was financially supported by the  Programme Investissements d'Avenir (PIA3)  ( DOS0096690  and  DOS0096734  by  BPI France and Région Provence-Alpes-Côtes d'Azur). The study also benefitted from the  Méditerranée Infection   10-IAHU-03  programme of the  French Agence Nationale de la Recherche (ANR). Part of the work has been financially supported by the  Netherlands Ministry of Defence.
sonneratii (1 male, 1 female), 3 G. gallus murghi (males) and 2 G. gallus spadiceus (males). Finally, we added an African village chicken (Cameroun) to the dataset to represent a population which had not been submitted to organized management or selection for industrial purposes. To validate our first batch of 54 samples, we performed a preliminary analysis by genotyping all our samples with the 60 K SNP Illumina iSelect chicken array  (Groenen et al. , 2011). The results confirmed the species assignment of the tested samples, since there were clearly groups, one for each species (Fig. S1 ). A subset of 18 individuals was then selected for resequencing according to the following criteria: their position in the Neighbor-Joining tree for the 54 wild animals, the quality and quantity of DNA available for sequencing, and the sex, with a priority given to females in order to collect data on the W chromosome and to facilitate the phasing of haplotypes on Z chromosome.
Additional mitochondrial sequences Sixty-five additional mitochondrial sequences (from G. gallus, G. sonneratii and B. thoracicus) were extracted from  Nishibori et al. (2005) ,  Miao et al. (2013)  and  Meiklejohn et al. (2014). These studies performed sequence assembly to reconstruct full length mitochondrial sequences whereas we used a genotyping by sequencing approach and kept only SNPs with very high quality score. Mitochondrial sequence of B. thoracicus was produced by  Shen et al. (2009). Accession numbers are shown in Table  S2. Mitochondrial sequences for our 26 Gallus samples were obtained by projecting the mitochondrial SNPs found in the previous step to the mitochondrial sequence of the G. gallus reference genome (galGal5 assembly). To make sequences comparable between studies and avoid bias in genetic distances, we aligned all sequences against the G.