dataesr/scientific-paragraphs-categorization huggingface dataset:
Illumina HiSeq sequencing and data analysis Amplicons obtained from each whitefly sample were pooled in equimolar concentrations for Illumina sequencing (FASTERIS SA, Switzerland). A composite sample with this pool of combined equimolar ratios was subjected to a spin column purification using the same kit, and then the pool was quantified using Nanodrop. Another pool prepared with the same samples was also sequenced in a different lane but in the same flow cell to increase the depth of sequencing and the coverage of samples. Illumina sequencing results generated paired-end (PE) sequence data that were merged into a single sequence based on their location in the V4-V5 region of the 16 s rDNA gene. Unique barcodes were attached to primer sequences at both ends to separate the samples. Raw fastq files were then filtered and merged by Trimmomatic using the following parameters: (i) the read tail was truncated when four consecutive bases had a low quality < 15; (ii) sequences with reads longer than 60 bp were kept and merged according to their overlap, with no more than 2 bp mismatches; and (iii) the sequences of each sample were separated according to barcodes (exact match) and primers (allowing 2 nucleotide mismatches in the seed) and reads below 60 bp bases were removed.
The DADA2 plugin  (Callahan et al. , 2016) , which was implemented in the QIIME2 program version 2018, was adopted to perform a second filtering operation on the sequences  (Bolyen et al. , 2018). Filtering operations included the "Denoise and dereplicate paired-end sequences" to enable additional read quality filtering and trimming, chimera filtering, denoising, and connecting paired reads as previously described  (El Hamss et al. , 2022 , 2021). DADA2-generated amplicon sequence variants (ASVs) were clustered using the VSEARCH consensus taxonomy classifier  (Rognes et al. , 2016). Taxonomic assignment of ASVs was analysed using the SILVA database. ASVs with their generated taxonomic groups were merged to form operational taxonomic units (OTUs) and were clustered together with a 97% similarity cutoff.
Sequences ranging from accession number OR184995 to OR185264 have been deposited in the GENEBANK database. These sequences were analysed at two distinct levels: the endosymbiont level and the meta microbiome level. At the meta microbiome level, we included all bacteria that have not yet been localized within the whitefly body. Venn diagram at different taxonomic levels was constructed in the microbiome package  (Lahti et al. , 2017; Shetty and Lahti, 2019)  to identify core microbiome membership associated with each life stage of the SSA1-SG1 whitefly. The Venn diagram method was used based on Abundance-Occurrence  (Neu et al. , 2021)  in each taxonomic level, starting from phylum down to the genus level. Bacterial taxa occurring in at least 50% of the samples of each group (i. e. , egg, nymph, or pupae) with a minimum relative abundance threshold of 0.
Statistical analyses and comparison of meta microbial communities First, samples rank abundance curve, and rarefaction curve were measured using the function "specaccum" of the vegan package  (Team, 2009)  in R  (Team, 2020)  to see if the number of samples used accurately detected all taxa. Different tests were adopted to determine life stage-associated microbiome, including diversity, correlation, co-occurrence, and compositional analysis. The diversity parameters of the microbiome of whiteflies at various life stages were tested using one-way analysis of variance (ANOVA). The parameters included the richness measured by the number of observed ASVs and species evenness measured by Shannon and Simpson indices. The differences between the composition of microbiome in each life stage were visualised using the non-metric multidimensional scaling (NMDS) distance algorithm based on the Bray-Curtis distance.
For NMDS analysis and mapping, the beta diversity distance matrix was calculated using the vegan package. Both the Capscale and permutational multivariate analysis of variance (Adonis) functions of the vegan package were used to test the structure of microbial communities according to life stage. We conducted a bootstrapping analysis with equal weight to life stage and with 10,000 resampled datasets to address potential sample size bias between eggs, nymphs and pupae. This analysis, performed using R software, aimed to assess whether observed distributions were related to life stage or discrepancies in sample size. We used Adonis and Capscale for this evaluation. Another hierarchical clustering method by unweighted pair group method with arithmetic mean (UPGMA) was performed on a matrix of presence/absence to capture clusters of microbiome per life stage.
Overview of whitefly meta microbiome composition A total of 4,533,906 high-quality (>Q30) reads were filtered and recovered from 65 whitefly samples. At a sequence variant level, 188 ASVs were obtained and deposited in GeneBank with the accession numbers ranging from OR184995 to OR185264, and their relative frequency per sample was calculated (Fig. 3 ). Hierarchical clustering dendrogram and bar chart showed the composition of whitefly according to their life stage. The taxa were classified into three phyla and were obtained for each whitefly life stage (Table  S1 , Fig. 3 ), and the rank abundance curves of each sample progressively stabilised, showing consistent species distribution (Fig. S3A ). The rarefaction curve also reached a plateau, indicating that the tested samples accurately detected all bacteria (Fig.
Results The E9-A20-D4 holoenzyme could be expressed with a good yield in insect cells using a single recombinant baculovirus. The double tag, a 6His-tag on E9 and a Strep-tag on D4, allowed an efficient purification and led to a stoichiometric complex (Fig  1AB ). SEC-MALS analysis of the purified complex yielded a single peak of the complex with a mass of 185 kDa compared to a theoretical mass of 197 kDa (Fig  1C ). The efficiency of the purification step on the streptactin column allowed to simplify the purification protocol for cryo-EM sample preparation by the omission of the TEV cleavage and second Ni-column purification step. The analysis of the E9-A20-D4 complex by cryoelectron microscopy was hampered by strong preferential orientations of the particles at the ice-air interface. One dataset showed less prominent preferential orientations, which allowed the 3D reconstruction of the complex (S1 Fig) and refinement to a resolution of 3.
58 Å rms differences for Cα positions when A20 is compared to the MPXV counterpart (S3B Fig) and still 2. 28 Å rms when only the ligase domains are superposed. The electron density of Li and co-workers for this domain is far below the overall resolution of 3. 1 Å and is not easily interpretable explaining why 57 residues (res. 46-101) from the linker with the N-terminal domain and the N-terminal part of the ligase domain have not been modelled. As our structure of the VACV ligase domain is close to the MPXV one from Peng and coworkers and also the Alphafold model (Fig 2C ), we assume that is closer to the true structure. It fits the electron density deposited in EMD-34887 evenly well as the model in pdb entry 8hm0 of Li and co-workers  [20]. The residues of the E9-D4 contact are located in well-defined parts of the structures and are the same for the apo form of MPXV and VACV holoenzymes.
The dimer of trimers observed by Li and coworkers  [20]  in pdb entry 8hlz is intriguing. The assembly of this MPXV hexamer if formed by two extended F8-A22-E4 complexes interacting around a quasi-2-fold axis using in trans the E9-D4 interface observed in the heterotrimer (S3C Fig,  top ) creating additional contacts between thumb domain and A22. In order to explain the transition to a compact trimer with an E9-D4 contact in cis, the author proposed a dissociation of A22 from D4 in trans position and rebinding of A22 to D4 in cis. This model can be refuted as we showed that the E9-D4 interaction is transient with less important buried surface (380 Å 2 analyzed with PDBePISA  [28] ) than the A20-D4 interaction (830 Å 2 ) or for comparison the E9-A20 interface (670 Å 2 ). In addition, the interface appears to be extremely important as it is not possible to produce A20 without bound D4  [15].
We rather propose a transition of the hexamer to the trimer involving a dissociation of E4 from F8 in the hexamer, a change of the conformation of A22 and rebinding to F8 in cis leading to two compact trimeric complexes (pdb entry 8hm0, S3C Fig,  bottom ). The DNA-free cryo-EM structure of VACV E9-A20-D4 clearly exhibited discrepancies when compared to the SAXS data from the holoenzyme complex in solution (as shown in Fig  3B ). This suggested a distinct conformation with a greater radius of gyration (4. 97 nm vs. 4. 18 nm) and a larger maximal dimension (18. 0 nm vs. 12. 6 nm, as outlined in Table  1 ). For A20, Alphafold 2 predicted 3 domains and allowed to identify two hinge regions ( The conformational variability of the holoenzyme has been explored further by Alphafold 2 predictions of the complex structure.
Construction of the baculovirus expressing E9-A20-D4 The MultiBac baculovirus/insect cell expression system  [32]  was used in order to express the VACV DNA polymerase holoenzyme using sequences of E9L, A20R, and D4R of VACV strain Copenhagen (GenBank accession number M35027. 1). Gene synthesis and cloning was performed by GenScript. E9L was inserted into the pUCDM vector. Recombinant E9 contains a 6His-tag and the Tobacco Etch Virus (TEV) protease cleavage site sequence fused to its N-terminus. A20R and D4R (D4 carrying an N-terminal Strep-tag) were both cloned into the pFL plasmid. In consequence, E9 carries an N-terminal TEV-cleavable His-Tag with the sequence MSYYHHHHHH DYDIPTTENL YFQ↓GAMDP, which replaces the N-terminal methionine. D4 carries an N-terminal Strep-tag with the sequence MASWSHPQFE KSGGGGGLVP RGSA before the N-terminal methionine residue.
Growth kinetics of mutant VACV in vitro Twenty-four well plates containing 8×10 5 Vero cells were infected with VACV at a MOI of 0. 05 in DMEM at 37 °C in a 5 % CO2 atmosphere. At 1 h post infection (hpi), the viral inoculum is removed and fresh media containing 2. 5 % v/v FCS was added and the plates incubated at 37 °C. Cells were harvested at 8, 24, 48, and 72 hpi. Virus yield was determined by titration of the virus on Vero cells as described  [50]. Funding: This work is funded by the French Agence Nationale de la Recherche (ANR-22-CE11-0007-01 and ANR-13-BSV8-0014), the University Grenoble Alpes (G7H-IRS20A71) and the ANR framework of the "Investissements d'avenir" program (ANR-15-IDEX-0002). It used the platforms of the Grenoble Instruct-ERIC center (ISBG; UMS 3518 CNRS-CEA-UGA-EMBL) within the Grenoble Partnership for Structural Biology (PSB), supported by French Infrastructure for Integrated Structural Biology (FRISBI, ANR-10-INBS-0005-02) and GRAL, a project of the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003) within the Grenoble Partnership for Structural Biology.
Acknowledgments We thank  Guy Schoehn  for data collections on the Glacios electron microscope and his operation of the IBS EM facility. We are grateful to  Aymeric Puech  and  Rémi Pinck  for assistance and operation of the  IBS-EMBL cryo-EM computing facility. WPB acknowledges the support of the  CNRS  for a sabbatical in  Utz Fischer 's group at the  Biozentrum, University of Würzburg. WPB is grateful to  Emilia Gärtner  for excellent support. We thank  Fred Garzoni  and  Imre Berger  for help in setting up the Multibac system.
Acknowledgements YRZ, MB, DWS and ML were supported by the  TULIP Laboratory of Excellence  ( ANR-10-LABX-41 ), and by the  BIOSTASES Advanced Grant , funded by the  European Research Council  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement no. 666971 ). We thank all members of the  BEF Scale  working group for valuable discussions and feedback.
Dimension Two -Operational activities Regarding Data collection and methods sharing, some joint data warehouses between sectors were set up (2 [2-3]), in particular for sequencing or notification data. These are accessible to relevant stakeholders to upload or download data, or only to access data. Examples of joint aggregated surveillance databases are presented in the literature  [42] [43] [44]. Regarding Data sharing, our study revealed a lack of adherence to FAIR data principles (2 [1-2. 5]). Despite the recognized importance of FAIR data, practical application remains challenging  [36] [37] [38]. Numerous obstacles, including ethical, organizational, legal, technical, political, and economic issues, hindered timely and effective data sharing between health authorities and stakeholders  [2, 19, 38, 39].
Furthermore, given the simplicity and limited time required, we recommend conducting such OH-EpiCap evaluations regularly to monitor the evolution of OH practices over time and their impact on health at the human-animal-environment interface. This work was supported by funding from the European Union's Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme. Funding sources did not affect the design of this study, data collection, data analysis, decisions on publication, or preparation of the manuscript.
These limitations considerably hinder the comparability of soil erosion assessments and their practical implications between European countries. Understanding how connectivity elements are currently implemented in models and how connectivity affects the applications can be considered to be a prerequisite for conducting more reliable and harmonised erosion assessments in agricultural landscapes. Therefore, our research aims to address the above limitations through the following research questions: • Which soil erosion models are used in Europe to assess the risk of soil erosion and the effect of mitigation measures? • Which environmental parameters are included in the models and which data sets are being used to estimate these parameters? • How are connectivity elements and mitigation measures considered or parameterised in the models used? To answer these questions, we conducted and evaluated a Europeanwide survey, where we sent a questionnaire to soil erosion modellers who produce policy-relevant soil erosion risk maps for supporting local, regional, or national authorities or decision-makers.
, 2007). Sediment accumulation in Frb in WaTEM/ SEDEM  (Krasa et al. , 2019). from field measurements  (Petan, 2010). The majority used nationally or regionally available rainfall station data or spatially distributed rainfall erosivity grids based on empirically derived functions of measured rainfall station data. Two model applications (Finland and Luxembourg) used the European R-factor map produced by  Panagos et al. (2015a)  for their specific area, while two model applications used a fixed R-factor for their entire modelled area (Czechia and Flanders). Regional or national soil databases (16 responses/59 %) are the most frequently used data sets, followed by "Other" (8 responses/30 %), which contained mostly field measurements and local soil surveys (Fig. 4a ). EU data sets, such as LUCAS or the Soil Erodibility Data set  (Orgiazzi et al.
, 2018; Panagos et al. , 2014a)  are scarcely used. Especially silt, sand, clay and organic matter content are widely used input soil data (Fig. 4b ). Soil parameters were stated as derived from pedotransfer functions, expert-based methods, literature values, and directly assessed from measured field data or from soil (polygon) maps. These methods were also sometimes combined within one model application. While eight K-Factor maps are based on the approach of  Wischmeier and Smith (1978)  and four on the USLE nomograph, the majority (11) use various approaches to compute the K-Factor (Fig. 4d ). The latter are mostly based on national adaptions, such as the  DIN 19708 (2017)  standard in Germany that uses empirical computation methods based on local soil data or experiments. The resolution of the used DEM for modelling varied from 1 m to 25 m, with most applications using 5 m and 10 m resolutions.
Hydrological corrections of the DEM (depression and sink filling) was used by 43 % of model applications via various methods (39 % No, 17 % no answer). A variety of single and multiple flow direction algorithms were used. None of the model applications considered the influence of tillage direction and oriented roughness on flow direction. The number of answers concerning the computation of the L-Factor divides into the two methods proposed by  McCool et al. (1987)  and  Desmet & Govers (1996) , with different combinations for using the slope-dependent exponent in both approaches. Similarly, S-Factors are computed using the methods of  McCool et al. (1987) ,  Nearing (1997) ,  Govers (1991)  and  Wischmeier & Smith (1978). The main land use data sources were regional and national databases (15).
Field mapping (5) and Remote Sensing and GIS Mapping (6) were also applied in several model applications. The Corine Land Cover Database (2), LUCAS Land Cover Information (none) and Integrated Administration and Control System (IACS, 2) are scarcely used (Figs. 5-C4 ). The land use parameters used in the model applications (Figs. 5-C4 ) predominantly consist of canopy cover (8) and surface cover (9). Some model applications use information about prior land use (5) and rather specific parameters, such as rockiness or rock fraction of the surfaces (mentioned in 'Others', where detailed or special answers are possible). Only a few include surface roughness (2) and soil moisture (2). This is also reflected by the use of different methodological approaches to estimate C-Factors. C-Factors are mostly taken from a general or regional specific literature, as well as from laboratory analyses or field experiments.
C. L. ), while soybean has been grown only in recent years (Gobierno de Aragón, 2020). Each feeding strategy was formulated by accounting for nutrient requirements restrictions by using linear programming with WinFeed® software (WinFeed Ltd. , Cambridge, UK), considering the ideal AA balance based on lysine requirements (as the first limiting AA), and the  ingredient limits to avoid anti-nutritional factors effects suggested by  Feedipedia (2016). To this end, the production performance and nutrient requirements of the pigs were based on patterns commonly observed during the fattening period for high-performance pigs  (NRC, 2012; FEDNA, 2013)  or moderate-performance pigs  (Millet et al. , 2006; ITAB, 2014). The BAU feeding strategy included the same ingredients as CircHigh, but they were outsourced instead of being produced at farm level.
Economic performance Economic performance was assessed using three indicators. Economic benefits of feeding pigs in the three alternative scenarios were assessed to determine the opportunity cost of feed circularity. The decrease in cost of each alternative feeding strategy compared to that of the BAU feeding strategy, assuming on-farm crop production and feed manufacturing costs, was estimated using the mean of the most recent available data before and after the beginning of the Ukrainian conflict, which increased agricultural costs worldwide (Supplementary Tables  S6  and S7 ). In addition, to assess the benefit of growing temporary pastures for managing sheep at a high carrying capacity, the economic benefit of producing a vetch-oat cover crop was estimated by difference between the sowing costs and the surplus lamb incomes due to the increased carrying capacity allowed by temporary pastures (Supplementary Table  S8 ).
Thus, balanced complementary livestock species such as those analyzed in this study would support production of protein while minimizing feed-food competition. A third limitation was the uncertainty in estimates of certain crop yields, their harvest index and the corresponding nutritional composition of the feeding strategies. Nevertheless, these data were collected mainly from regional references in order to estimate the actual values as accurately as possible. In the study region, each ha of arable land could feed ca. 5-6 places (or 10-12 fattening pigs, assuming 2. 0 batches per year), depending of the feeding strategy. To close feed-manure cycles in the Netherlands and Denmark,  Willems et al. (2016)  estimated that the maximum number of pig places per ha per year would range from 8 to 9 if all feed was produced on-farm (i.
Acknowledgments This study was conducted thanks to a  Senior Researcher Mobility Grant  from the  Ministry of Universities of Spain  ( 2022 ,  PRX21/00146 ). The authors acknowledge funding from the  ERA-NET  project  Mi Bicycle (Mitigation and adaptation through better biomass cycling in crop livestock systems of North and Western Europe)  funded under the joint call  ERA-NET Cofund SusAn ,  FACCE ERA-GAS ,  ICT-AGRI-FOOD  and  SusCrop.
2. 6. Comparison with other screening works For the four main metabolites considered (ethanol, glycerol, acetate and succinate), we compared the results of the present work with significant datasets previously obtained. Data were acquired online or directly from the authors. These data compared different sets of strains in similar conditions but with different analysis timing (resumed in Table  1 ). To overcome these differences, a normalised yield value was calculated as follows: 𝑌 𝑛𝑜𝑟𝑚 = (𝑌 -𝜇) 𝜇 With: Y the yield of a metabolite for a designated strain, μ the mean of all strains and Ynom the normalised yield. For the data set produced in the present work, normalisation was performed with and without optimised strains.
If most of the correlations are significant with or without evolved and genetically modified strains, slight differences appear between these two data sets. Moreover, this methodology affords a greater precision in metabolic yield assessment, corroborating existing data obtained in the last years and generating a robust and standardised dataset that can be reused in other studies on yeast metabolism. It enables yields to be precisely defined in a wine-like context, using a synthetic grape must with metabolite assessment at fermentation final stage. Lastly, it provides for the first time a screening of S. cerevisiae strains on lactate production in a wine-like context. To simplify the discussion on metabolic yield results and their connections, a map of carbon central metabolism in oenological conditions is available in Figure  6.
Acknowledgements The authors would like to thank  Jean-Luc Legras  and  Carole Camarasa  for helpful discussions. They also thank  Philippe Chatelet  for useful suggestions on the text. The authors thank the  University of Azores ,  Ricardo Franco-Duarte ,  Marie-José Ayoub , as well as the  National Research Institute of Brewing (Hiroshima, Japan)  and all the international yeast collections for providing strains. Finally, the authors thank  Anne Ortiz-Julien  from  Lallemand SAS  for her support of this work.
Life cycle inventory Inventory data for giant gourami fingerling production and brooder management were compiled mainly by collecting the data from fish farms and production centres producing giant gourami fry located in the Bogor and Tasikmalaya regencies (n = 37,  Kristanto et al. , 2020). We also used experimental data from the experiments we performed on giant gourami egg production in the BPPSIGN Center, Tasikmalaya  (Arifin et al. , 2020; Slembrouck et al. , 2019 Slembrouck et al. , , 2020) )  and larval rearing in the RIFAFE, Cijeruk  (Arifin et al. , 2019; Prakoso et al. , 2021). The inventory of the experimental phase was recorded during daily on-site monitoring of one entire production cycle from November to April 2018. The descriptive data concerning the studied sub-system are summarized in Table  1.