dataesr/scientific-paragraphs-categorization huggingface dataset:
From data to farm groups The complete dataset includes 391 individuals and 36 variables (15 indicators for agricultural practices, 8 variables of farm structures, and 13 performance variables). In order to develop the typology of farming systems considering the 15 agricultural practices considered, a first category of farms with no livestock (zero tropical livestock unit (TLU)) was created. A multivariate analysis based on principal component analysis (PCA) and hierarchical ascending classification (HAC) was then used to classify the other farms with TLUs. The 25 farms with no livestock (zero TLU) were treated as a separate group. These farms are geared towards small or very small-scale agriculture (according  Vall et al. 2017 criteria and typology). Since PCA analysis does not tolerate  missing data, indicators 4, 13, and 14 in Table  1  (% of TLUs grazing, % of TLUs receiving feed, % of TLUs receiving fodder) could not be measured as the total number of TLUs was in the denominator to calculate these ratios.
Whenever a significant difference was found (p < 0. 05), it was followed by a Newman Keuls test. Ultimately, we sought to explain the performance of each Ae farming system cluster considering their structural characteristics and considering the intensification of synergies between the agro-sylvo-pastoral components of the system and by the establishment of more intense recycling processes often activated by the presence of livestock. To this end, we have schematized on figures the agroecological functioning of each agroecological farming system clusters (recycling of crops and livestock byproducts, soil protection, spontaneous use of resources, cropping system bio-diversification and on limitation of industrial inputs). These diagrams represent the key elements of the Ae farming system cluster, completed with the data of each agricultural practices (means and standard deviation).
Consequently, for indicators that can vary from 0 to ∞ (trees/ha, kg manure per ha, kg stored fodder per ha, kg mulch/ha), we checked that the maximum values remained at commonly accepted levels, which is the case. For indicators expressed in % (developed fields, Ae equipment ownership rate, share of livestock receiving pasture, fodder or feed, area under no-till, under legumes, treated with mineral fertilizers, organic manure, herbicides), no published data is available. We have, therefore, taken the view that the greater the %, the stronger the Ae nature of the system. On a practical level, the method used to classify a large batch of farms (about 400) in 3 homogeneous Ae clusters with respect to 15 agricultural practices whose intensity increases the agroecological character of the system, by combining a multivariate analysis (ACP+ CAH) to an Ae scoring system, has proven to be effective and simple to implement.
Structural features of the agroecological farming system clusters The structural characteristics of each Ae cluster (Ae+, Ae+/-, and Ae-) are presented in the Table  3. These structural data provide three major pieces of information on the three Ae clusters. First, livestock farming is significantly more prominent in Ae+ cluster compared to Ae-, and Ae+/-(respectively, 36, 26, and 13 TLUs/farm). Second, the level of Ae equipment and buildings (animals and animal power equipment, fodder storage buildings, organic manure production infrastructure) is significantly higher in Ae+ farms, compared to Ae+/-and Ae-ones (respectively, 1006 vs 726 and 675 kFcfa/farm). Thirdly, Ae+ workforce/ha ratio (1. 45) is significantly higher than the ratio of Ae-(1. 12) and Ae+/-(1. 02). This reflects a trend towards a larger labor force in the Ae+ farming system cluster.
1% FA). Peptides were eluted with a multi-step gradient from 2 to 7% of buffer B (ACN 80%, FA 0. 1%) during 5 min, 7 to 23% during 70 mn, 23 to 45% during 30 min and 45 to 95% during 5 min at a flow rate of 250 nL/mn for up to 132 mn. The column temperature was set to 60°C. Mass spectra were acquired using the Xcalibur software using a data-dependent Top 10 method with a survey scan (300-1700 m/z) at a resolution of 120,000 and MS/MS scans (fixed first mass 100 m/z) at a resolution of 60,000. The AGC target and maximum injection time for the survey scans and the MS/MS scans were set to 3x10 6 , 50ms and 10 5 , 100ms respectively. The isolation window was set to 1. 2 m/z and normalized collision energy fixed to 28 for HCD fragmentation. We used a minimum AGC target of 2x10 3 for an intensity threshold of 2x10 4.
Mass spectrometry data analysis. Raw data were analysed using MaxQuant software version 1. 6. 10. 43  (23)  using the Andromeda search engine  (24). The MS/MS spectra were searched against multi databases: a UniProt Homo sapiens database  (20, 415  entries the 19/10/2019), a Influenza A virus database (11 entries the 07/01/2020) and the Bovin pancreatic ribonuclease (UniProt ID: P61823). Usual known mass spectrometry contaminants and reversed sequences of all entries were included. Andromeda search was performed choosing trypsin as specific enzyme with a maximum number of two missed cleavages. Possible modifications included carbamidomethylation (Cys, fixed), oxidation (Met, variable), Nter acetylation (variable). The mass tolerance in MS was set to 20 ppm for the first search then 4. 5 ppm for the main search and 20 ppm for the MS/MS.
Maximum peptide charge was set to seven and seven amino acids were required as minimum peptide length. The "match between runs" feature was applied with a maximal retention time window of 0. 7 min. One unique peptide to the protein group was required for the protein identification. A false discovery rate (FDR) cutoff of 1 % was applied at the peptide and protein levels. With the TMT labeling, reporter ion MS2 signals were corrected for isotope impurities and used for the statistical analysis. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  (25)  with the dataset identifier PXD040783. To find the proteins more abundant in one condition than in another, the intensities quantified using Maxquant were compared. Reverse hits, potential contaminants, and proteins not well identified with a 1% FDR ("Only identified by site") were first removed from the analysis.
Only proteins identified with at least one peptide that is not common to other proteins in the FASTA file used for the identification (at least one "unique" peptide) were kept. Additionally, only proteins with at least six quantified intensity values in one of the two compared conditions were kept for further statistics. After this filtering, intensities of the remaining proteins were first log-transformed (log2). Next, intensity values were normalized by median centering within conditions (section 3. 5 in  (26) ). Missing values were imputed using the impute. mle function of the R package imp4p  (27). Statistical testing was conducted using a limma t-test thanks to the R package limma  (28). An adaptive Benjamini-Hochberg procedure was applied on the resulting p-values thanks to the function adjust.
p of the cp4p R package (29) using the robust method described in  (30)  to estimate the proportion of true null hypotheses among the set of statistical tests. The proteins significantly more abundant in the infected crosslinked condition than in both controls (non infected-crosslinked and infected-non crosslinked) were defined as representing the "Core interactome" (adjusted p-value lower than a false discovery rate level (FDR) of 1% and log2(fold-change) higher than 2) and the "Expanded interactome" (adjusted p-value lower than a FDR of 10% and log2(fold-change) higher than 1. 5). Protein-protein interaction networks have been determined using the app stringApp  (31)  of Cytoscape  (32). Widths of the edges are function of the "experiments" scores provided by STRING  (33). This score reflects the confidence we can have in an interaction from known interactions listed in several experimental databases.
Antibodies were diluted in a staining solution (PBS, 5% Normal donkey serum, 3% Normal goat serum, 0. 125% fish gelatin, 0. 2% Triton X-100). Coverslips were mounted on glass slide with Fluoromount-G (Invitrogen, 00-4958-02) and examined using a Leica TCS SP8 confocal microscope with HC PL APO CS2 40X (1. 3) or 63X (1. 4) oil objectives. The fluorescence signals were acquired with the LAS X software and analysed with Fiji (ImageJ). Metadata can be provided upon request.
Alternatively, cells were infected with the WSN-PB2-2A-mCherry virus at a MOI of 10 -2 PFU/cell. The fluorescence signals were acquired using the Incucyte S3 (Essen Bioscience) every 6 h (10x objective, 5 fields per well) and were analysed using the Incucyte software. To assess the production of infectious viral particles, cells were infected with the WSN virus (3x10 -4 PFU/cell) or the seasonal H1N1pdm09 and H3N2 viruses (3x10 -3 PFU/cell) in OptiMEM supplemented with 0. 8 µg/mL TPCK and 1% BSA. Plaque assays were performed on MDCK cells (WSN and H3N2) or MDCK-SIAT cells (H1N1pdm09) as described in  (42).
Using a fold-change (FC) threshold of 2 and a false discovery rate (FDR) threshold of 1%, we found 18 cellular proteins enriched in the infected over the non-infected samples, and 41 cellular proteins enriched in the crosslinked over the non crosslinked samples (Figure  1B  and Supp File 1). We defined the 16 proteins enriched in both data sets as the high-confidence core interactome (Figure  1B  and Figure  2A ). We also defined an expanded NP-mRNA interactome with relaxed thresholds (FC > 1. 5 and FDR < 10%), which includes 35 additional cellular proteins (Figure  2B  and Supp File 1). Among these, the GRSF1  (44, 45) , hnRNPK  (46, 47) , hnRNPA2B1  (48, 49) , NUDT21 (50) and PABPC1  (51)  proteins have already been documented as being involved in the IAV life cycle, supporting the robustness and relevance of our dataset and analysis.
Biological functions of the viral mRNA binding proteins We performed a Gene Ontology (GO) enrichment analysis of the core and expanded NP-mRNA interactomes, which revealed a strong enrichment for GO terms related to RNA/mRNA metabolic processes and RNA/mRNA splicing (Figure  2C  We compared the expanded NP-mRNA interactome with two previously published datasets on the global interactome of human poly(A) mRNAs, in total about 800 proteins with an overlap of 658 common proteins  (9, 10). Most of the proteins of the NP-mRNA interactome (42 out of 51) are found in both datasets while eight proteins are found in one or the other, and only one, AKR1C1, was not previously identified as a poly(A) mRNA-binding protein (Figure  2E  and Supp File 2), indicating that the NP-mRNA binders correspond to a specific subset of the cellular poly(A) mRNA binders.
Overall, our data show that the interaction of TDP-43 with the viral polymerase is RNA-independent. The association between FluPol and TDP-43 was further assessed by co-immunoprecipitation, where HEK-293T cells were transfected with a plasmid to overexpress HA-tagged TDP-43 or mCherry 48 h prior to being infected at a high MOI with a recombinant influenza virus expressing a 3xFlag-tagged polymerase (WSN-PA-3xFlag). Cells were crosslinked at 4 hpi by PFA, then lysed in the presence of RNase A, and PA was immunoprecipitated using an anti-Flag antibody. Samples following immunoprecipitation, and corresponding input lysates, were analysed by Western blotting and the presence of HA-tagged TDP-43 or mCherry was determined (Figure  4E ). TDP-43 was present in the immunoprecipitated samples while mCherry, which was expressed to a much greater level in the infected cells, was absent.
DISCUSSION The RBPome of influenza virus mRNAs has so far been unexplored. We provide here the first characterisation of host proteins interacting with a specific influenza virus mRNA, namely NP-mRNA, during infection. Our workflow was designed to maximise the signal-to-noise ratio, by applying i) the low background eRIC method (  14 ) a single optimized LNA-modified capture probe; ii) differential analyses with respect to both mock-infected and non-crosslinked control samples in order to eliminate proteins bound non-covalently to beads or RNA  (14) ; and iii) stringent statistical and fold-change cut-off values to define a core and an expanded interactome of 16 and 51 proteins, respectively. The high validation rate by CLIP-qPCR, with four out of five proteins of the core interactome being confirmed to bind viral positive-stranded mRNAs, but not viral negative-and positive-stranded genomic and antigenomic RNAs, supports the accuracy and specificity of our dataset.
Our own focused RNAi screen targeting the 16 components of the NP-mRNA core interactome revealed five proteins with proviral activity (hnRNPA2B1, hnRNPH1, TDP-43, SF3B4 and SF3A3). The relatively low number of proteins identified as having pro-or antiviral function might be due to functional redundancy among the RBPs and/or to the fact that the knock-down was not effective enough in some instances. Five proteins from the expanded NP-mRNA interactome, GRSF1, hnRNPK, hnRNPA2B1, NUDT21 and PABPC1 were previously documented as being involved in the IAV life cycle. GRSF1 and PABPC1 were found to bind the 5' UTR of viral mRNAs including the NP-mRNA and to stimulate their translation  (44, 45, 51, 54, 77) , further supporting the robustness of our dataset. The hnRNPK protein was previously shown to bind the viral M1-mRNA and to regulate its splicing jointly with the NS1-BP protein  (46, 47).
Interestingly, the viral RPBome represents a strongly interconnected network of protein according to the STRING database. Indeed, each protein interacts on average with ~40% of other proteins within the viral RBPome, with hnRNP proteins showing the highest degree of connectivity. In contrast, each protein interacts on average with only ~4% of other proteins within the group of 658 RPBs found to bind cellular mRNAs in two independent studies (9, 10), with ribosomal proteins being mostly involved (Supp File 2). The 51 host proteins interacting with influenza virus NP-mRNA could possibly be representative of a particular subset of cellular mRNPs that share a similar protein composition and function. Indeed, some evidence suggest that mRNA processing is a highly controlled, RNP-driven process  (78).
However, the biochemical characterisation of individual endogenous cellular mRNPs using in vivo RNA-centric approaches remains challenging and very few datasets are available  (11). To our knowledge, the only human endogenous mRNP characterised so far is the c-myc mRNP  (79). Only five proteins of the influenza NP-mRNA interactome (hnRNPAB, PABPC1, ELAVL1, SRSF2, RPS6) are found among the list of 229 proteins reported to bind the c-myc mRNA. Conversely, none of the hnRNPA2B1, hnRNPH1, TDP-43, SF3B4 or SF3A3 NP-mRNA binders was found to bind the cellular actin mRNA in the present study. Much broader comparisons between the interactomes of individual mRNAs will be needed in the future to uncover common and distinct regulatory mechanisms among cellular and viral mRNAs. To our knowledge, so far only one study has investigated viral RNA-host protein interactions in influenza virus-infected cells on a proteome-wide scale, using the viral cross-linking and solid-phase purification or VIR-CLASP approach  (80).
Whether some of these motifs are actually bound by TDP-43 warrants further investigations. Our data reveal that the TDP-43 protein promotes influenza virus replication. Within our proteomics dataset, TDP-43 was among the strongest hits as it showed a ~2. 5-and 4-fold enrichment in infected versus mock and in crosslinked versus non-crosslinked samples, respectively. It also showed strong binding to the viral NP-and NA-mRNAs in subsequent CLIP-qPCR experiments, with an observed ~1000 fold-enrichment compared to the mCherry control. CRISPR-mediated KO of TDP-43 in A549 cells resulted in a moderate but reproducible reduction in the accumulation of viral RNAs and proteins, which was reversed upon TDP-43 rescue. Moreover, we demonstrate that the interaction between TDP-43 and the viral polymerase is RNA-independent, does not involve the N-terminal domain of TDP-43 and could be mediated primarily by its disordered C-terminal domain.
The model we propose for TDP-43 recruitment to viral mRNAs may be applicable to hnRNPH1, hnRNPA2B1, and SF3B4, which would confer a central role to the viral polymerase for the recruitment of mRNA processing factors. A common feature of these four proteins is that they have long C-terminal disordered domains, known to serve as scaffolds for dynamic protein-protein interactions and the formation of biomolecular condensates, and to be a common feature of RNAPIIrelated transcription factors  (88, 89). Our finding that these proteins get assembled into influenza mRNPs might serve as a starting point to explore to what extent phase-separation plays a role in transcription and post-transcriptional processing of viral mRNAs. A-B. Interaction network among the 16 proteins of the core interactome (A) and the 51 proteins of the expanded interactome (B), according to the STRING database  (33).
Cells were co-transfected with an NP expression plasmid, where NP was transcribed from a RNPAII promoter. (E) To directly compare the association between a viral mRNA transcribed by the RNAPII or from a vRNP complex, HEK-293T cells were transfected with a mCherry-3xFlag or TDP-43-3xFlag expression plasmid, alongside either a RNAPII promoter-driven NA expression plasmid (ochre bars), or a PolI-driven NA vRNA expression plasmid together with expression plasmids for PB2, PB1, PA & NP, to reconstitute a vRNP (blue bars). At 48 hpt cells underwent CLIP-qPCR, and the levels of NA-mRNA and actin-mRNA were determined, and their individual enrichments over the mCherry negative control were plotted. The data shown are the mean ± SD of three independent experiments in triplicates. The significance was tested with a one-way ANOVA after log10 transformation of the data, with Dunnett's multiple comparisons (reference: mCherry) (C-D) or with Tukey's multiple comparisons test (E) using GraphPad Prism software (**p < 0.
The data shown are the mean ± SD of three (A) or four (B) independent experiments performed in triplicates. The chicken ANP32A (chANP32A) and RPB1 controls were included in two of the three experiments shown in (A). The dotted line represents the NLR measured for the TOP3B protein, which was selected as a negative control based on previously published data  (17). C. Schematic representation of TDP-43 subdomains and deletion mutants. Nter: N-terminal domain; RBD: RNAbinding domain; Cter: C-terminal domain; NLS: Nuclear Localization Domain; RRM1 and RRM2: RNA Recognition Motifs 1 and 2; Q/N: Glutamine/Asparagine-rich region; Gly-rich: Glycine-rich region. Amino-acid substitutions are indicated. D. RNase sensitivity of the interaction between TDP-43 and FluPol. Split-luciferase-based iPCA was performed as in (A), with the indicated cellular proteins fused to Gluc2.
A. Lysates (Input) and pull-down samples (IPaFlag) were analysed by western blot with an anti-HA antibody. Both the mCherry-HA control and TDP-43-HA are well expressed in the transfected cells, but only TDP-43-HA is detectable in the pull-down. The star denotes the heavy chain of the antibody used for the immunoprecipitation. Closed and open arrowhead : TDP-43-HA and mCherry-HA, respectively. F. Mapping of TDP-43 binding domain to the FluPol. Split-luciferase-based PCA was performed as in (B). The wildtype (wt) or indicated TDP-43 variants tagged with Gluc2 were transiently co-expressed with PB2-Gluc1, PB1 and PA expression plasmids derived from the WSN virus. The data shown are the mean ± SD of nine independent experiments performed in triplicates. The significance was tested with a one-way ANOVA with Dunnett's multiple comparisons test (reference: wt or RBD-Cter) using GraphPad Prism software (* p< 0.
ACKNOWLEDGEMENTS We would like to thank  Dr. Feng Zhang  ( Broad Institute, Cambridge, USA ),  Dr. Mikhael Matrosovich  ( Philipps Universität, Marburg, Germany ),  Dr. Pierre-Olivier Vidalain  ( CIRI, Lyon ),  Dr. Daniel Marc  ( INRAE, Nouzilly, France ),  Dr. Caroline Demeret ,  Dr. Sandie Munier  and the National Reference Center for  Respiratory Viruses  (all at  Institut Pasteur, Paris, France ) and for sharing reagents used in this work. We would like to thank  Yves Jacob ,  Anastasia Komarova  and  Valérie Najburg  ( Institut Pasteur, Paris, France ) for helpful discussions. Some figures were created with BioRender. com.
FUNDING This work was funded by the  Agence Nationale de la Recherche  [ ANR-18-CE18-0028  to SC and  NN ,  ANR-10-LABX-62IBEID  to MM and  NN ,  ANR-21-CE35-0007  to MM]; the  Marie-Skłodowska Curie Global Fellowship  [MSCA-IF-GF: 747810  to DGC]; and the  European Research Council Fellowship  [ PTFLU 949506  to DGC and SB]. Funding for open access charges [ Agence Nationale de la Recherche / ANR-10-LABX-62IBEID ].
Antigenic site III is the only region bound in this conformation and the inter-protein contacts are predicted to be largely different. While interactions between CDRs and residues 334-336 are predicted to be conserved, the Glu52-Arg333 salt bridge appears to be lost (Supplementary Figure  S3D ). Overall, such pose suggests a lower affinity of RVC58 for RABV-G in the post-fusion conformation compared with the prefusion state. To test this hypothesis, we performed umbrella sampling simulations to infer DG bind between RVC58 and RABV-G in both pre-and post-fusion conformations. To validate our approach and compare predicted binding affinity (K D ) values with those determined experimentally, we also ran simulations on the available crystal structure of the RVC20 bound to RABV-G domain III (PDB ID 6TOU) in the pre-fusion conformation (Figure  3E ).
3 3 FIGURE 3 Analysis of the RVC58 binding to RABV-G. Binding poses of RVC58 (magenta) to RABV-G (teal) in pre-fusion (A) and post-fusion (B) conformations. RABV-G glycoprotein has been colored as follows, to distinguish the three domains: FD domain in light orange, PHD domain in smudge and CD domain in light blue, respectively. (C) Superposition of RVC58 model (magenta) in complex with RABV-G (gray) and the CryoEM structure of RVA122 (sand) in complex with the pre-fusion trimer of RABV-G (palecyan surface; PDB:7U9G). Superposition of RVA122/RVC58:RABV-G complexes has been limited to a single chain for clarity. (D, F, G) Zoomed view of the contact region of RVC58 in complex with RABV-G in the pre-fusion state. The RABV-G epitopes directly involved in the interaction have been highlighted in yellow.
A search for a W Z resonance, in the fully leptonic final state (electrons or muons), is performed using 139 fb -1 of data collected at a centre-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. The results are interpreted in terms of a singly charged Higgs boson of the Georgi-Machacek model, produced by W Z fusion, and of a Heavy Vector Triplet, with the resonance produced by W Z fusion or the Drell-Yan process. No significant excess over the Standard Model prediction is observed and limits are set on the production cross-section times branching ratio as a function of the resonance mass for these processes.
The distribution of the number of pile-up events reproduces the bunch structure and the average number of interactions per bunch crossing in the various run periods. A pileup weight is defined, which is applied to Monte Carlo to correct for the difference between the distribution of average number of interactions used to produce the sample and that measured for the recorded data. For all samples, except those generated with Sherpa  [48] , the EVTGEN 1. 2. 0 program  [49]  was used to simulate the properties of the b-and c-hadron decays. The GM VBF benchmark signal samples, pp → H ± 5 j j → W ± Z j j → ± ν + -j j, vetoing W or Z bosons in the s-channel, were produced with MadGraph 2. 7. 2  [50]  at next-to-leading order (NLO) in QCD  [1, 51] ; the generator is referred to as MADGRAPH hereafter.