dataesr/scientific-paragraphs-categorization huggingface dataset:
Hence, bacteriophages are literally referred to as bacterial parasites or viruses of bacteria  [21, 27]. There are approximately 10 15 bacteriophages occurring in the human gut and the majority of these particles contain the DNA genomes. In fact, of the total DNA viruses that can be matched to an annotated genome database, 99% represent the bacteriophages and the remaining 1% are the eukaryotic animal viruses  [9]. Transmission electron microscopic studies and next-generation sequencing analysis have revealed that the genomes of most of the gut bacteriophages belong either to the dsDNA viruses of the order Caudovirales which encompasses the families Siphoviridae, Podoviridae, and Myoviridae, or the ssDNA viruses which generally belong to the family Microviridae. In addition, the order Caudovirales has been recently expanded to include the families Ackermannviridae and Herelleviridae  [9, 17, 18].
This is followed by extraction steps to isolate the genomes of the VLPs which are then amplified by PCR, utilized for library preparation and sequenced using sequencing technologies such as Illumina, Pacbio, or Oxford nanopore technology. The sequencing generates reads-short, long, or ultra-long-which are quality controlled, annotated, and assembled using different databases and machine learning tools. Finally, the identified VLPs are then subjected to taxonomic and functional profiling to answer key biological questions.
Figure1: Viral metagenomic workflow. A schematic representation of the steps and processes involved in the isolation, identification, and analysis of the virome. Following sample collection, the sample is purified through a series of steps involving ultracentrifugation and filtration to retain the virus-like particles (VLPs). This is followed by extraction steps to isolate the genomes of the VLPs which are then amplified by PCR, utilized for library preparation and sequenced using sequencing technologies such as Illumina, Pacbio, or Oxford nanopore technology. The sequencing generates reads-short, long, or ultra-long-which are quality controlled, annotated, and assembled using different databases and machine learning tools. Finally, the identified VLPs are then subjected to taxonomic and functional profiling to answer key biological questions.
Tubes were centrifuged at 12,000 rpm for 15 min at 4 ° C. The supernatant was removed, and the pellet washed with 500 µL of cold 70% ethanol (-20°C) for 5 min (4°C). The tubes were vortexed, and centrifuged at 12,000 rpm for 5 min at 4 ° C. The supernatant was removed, the pellet was briefly dried, and resuspended in 200 μL of sterile milliQ H2O + 1 mg/mL RNase A (1/10 stock solution at 10 mg/mL). The sample was incubated for 1h at 37 ° C, and DNA concentration was done using NanoDrop. As control, 2 to 5 µL of each sample was run on 0. 8% agarose gel. TCCGTAGGTGAACCTGCGG/ ITS4: TCCTCCGCTTATTGATATTGC). The nucleotide sequences were compared with those deposited in GenBank using the BLAST program on the NCBI website. The phylogenetic tree was constructed using the MEGA-X program  (Kumar et al.
, 2018). The sequences used correspond to a concatemer of the CAL, EF and ITS1 sequences. Supplementary Table (1) shows detailed supplementary information for the strains used in the phylogenetic analyses, with their corresponding geographic origin, substrate, voucher number, and GenBank accession numbers. Briefly, the evolutionary history was inferred by using the Maximum Likelihood method, and Tamura-Nei model  (Tamura & Nei, 1993). The bootstrap consensus tree, inferred from 1000 replicates  (Felsenstein, 1985) , is taken to represent the evolutionary history of the taxa analyzed  (Felsenstein, 1985). Branches corresponding to partitions, reproduced in less than 50% bootstrap replicates were collapsed. The percentage of replicate trees, in which the associated taxa clustered together in the bootstrap test (1000 replicates) were shown next to the branches  (Felsenstein, 1985).
Statistical analysis: Statistical analysis was conducted for the treatments, on a completely randomized design (CRD), and a factorial completely randomized design, with 32 replicates. The R software (R 3. 3. 4; R development core team, 2017) was used to analyze experimental data, calculate means, standard deviations, bar charts, and interaction, using the R package "ggplot2"  (Wickham, 2009). Analysis of variance (ANOVA) was performed using the "lmerTest" R package  (Kuznetsova et al. , 2017). To test the effect of the Trichoderma strains on wheat root development, Trichoderma spores (10 7 spores/10 ml) were incubated with wheat seeds in a tube containing 0. 5X MS medium  (Kapusi & Stoger, 2022)  with phytagel without sugar. 10 days after germination, we measured the primary root growth.
A more precise molecular description of these two Trichoderma fungi was done by characterizing the partial sequences of their Elongation factor (EF-1) and Calmodulin (Cal) gene sequences. The three partial sequences (ITS-EF-1 and Cal) were then concatemerized (550, 350, and 489 bp, respectively) and compared to the same concatemerized sequences of 14 Trichoderma species (Supplementary Table  1 ) from the public database and representing the Trichoderma diversity. The result of this analysis (Figure  1 ) showed that the two isolates probably belong to the two species T. afroharzianum and T. guizhouense belonging to the Harzianum species complex.
Funding This work was funded by the ' AUF-CNRSL-UL ',  Lebanon Francophonie University Agency (AUF) , the  National Council for Scientific Research in Lebanon (CNRS-L)  and the  Lebanese University  (UL). This work was also supported by the  CNRS  and has benefited from the support of the  LabEx Saclay Plant Sciences  ( ANR-10-LABX-0040-SPS ,  LabEx SPS  and  ANR-17-EUR-0007 ,  EUR SPS-GSR ) which is managed by the  French National Research Agency  under the  program 'Investissements d'avenir'  ( ANR-11-IDEX-0003-02 ).
In the present study, we investigated the combined effect of HPP and biopreservation on the dynamics of bacterial communities of cooked ham with a reduced level of nitrite salts. We had previously selected a nisin-producing Lactococcus lactis strain, which, although sensitive to HPP treatment, was able to recover and regrow after a 500 MPa HPP treatment  [25]. Here, we first determined and collected bacterial communities present on commercial diced cooked ham. Then, the dynamics of these bacterial communities were monitored, following their inoculation in sterile diced cooked ham with a reduced nitrite level, in the presence of the bioprotective strain, and after the application of the HPP treatment. Our study provides data in line with the following questions: does HPP display a generic effect on microbial inactivation, or is there a microbiota signature in pressurized ham? Is the use of nisin-producing L.
The MiSeq Reagent Kit v. 2 (2 × 250 paired-end reads, 15 Gb output) was used according to the manufacturer's instructions for library preparation and sequencing on a MiSeq 2 (Illumina, San Diego, CA, USA). The quality of the obtained sequences were checked with FastQ files generated at the end of the run (MiSeq Reporter Software v. 2. 4, Illumina, San Diego, CA, USA) and additional PhiX Control (v. 3, Illumina, San Diego, CA, USA). The corresponding pairs of sequences were then attributed to their respective samples using the individual multiplexing barcodes. Quality controls indicated a PHRED quality score of at least 30 for 94% of the reads and a median number of 65,520 ± 14,180 reads per sample and 95,860 ± 11,300 reads per sample for 16S rDNA and gyrB amplicons, respectively.
Operational Taxonomic Unit (OTU) Analysis and Accession Numbers For each sample, paired-end sequences were then loaded in the FROGS (Find Rapidly OTUs with Galaxy Solutions, v. 2. 0) pipeline  [29] , checked for quality, and assembled. We retained merged sequences with a size of 280 ± 50 bp for gyrB and 450 ± 50 bp for the 16S rRNA gene. SWARM clustering  [30]  was applied on the assembled sequences using a maximal aggregation distance of three nucleotides for 16S rRNA gene sequences; for gyrB sequences, clustering was more stringent, with a maximal aggregation distance of two nucleotides in order to potentially assign OTUs to the subspecies level. After chimeras and spurious OTUs (low-abundance and low-frequency OTUs arising from sequencing artifacts) removal as described previously  [28] , the dataset comprised 69 OTUs for the 16S rDNA dataset and 252 OTUs for the gyrB dataset.
Taxonomic assignment of 16S rDNA OTUs was performed with the Blastn+ algorithm v. 2. 10 [31]  on the SILVA 128 SSU database  [32] , using a threshold of 97% identity for species assignment. For gyrB OTUs, it has been previously demonstrated that PCR amplification of gyrB can also recover the paralogous parE gene, which encodes the β subunit of DNA topoisomerase IV. In order to determine which of the two genes had been amplified for each species, the OTU sequences were blasted against the gyrB/parE databases established by Poirier and colleagues  [28]. Both gyrB and parE OTUs were retained in our diversity analysis. We assigned taxonomy to the species level when 95% of a sequence matched over 90% of length coverage found in the database. The last step consisted of comparing the taxonomic assignments obtained from the 16S rDNA and the gyrB amplicon sequencing.
Beta-Diversity and Statistical Analysis Bacterial diversity analyses were performed using the phyloseq package (v. 1. 24. 2) of R  [33]. For each of the samples, analysis of diversity was either performed at the OTU level or at the species level. In studies at the species level, OTUs with similar taxonomic assignments (both gyrB and 16S rDNA) were merged using the TAX_GLOM function of phyloseq package, and their abundance (number of reads) was averaged. This resulted in a dataset of 56 species with different taxonomic assignments. Similarly, comparative analysis between ham samples with different experimental conditions was performed after technical (16S rDNA/gyrB) and biological (biological repetition) data were merged with the phyloseq function MERGE_SAMPLES. In this case, species abundance was averaged between the four replicates.
Samples from Ham_B were revealed to contain the most original microbiota among the four types of ham, with the three most abundant 16S rDNA or gyrB OTUs being assigned to unknown species, as the identity percentage of these OTUs to gyrB genes in databases was lower than the ANI (Average Nucleotide Index) threshold of 95% classically used for bacterial species boundary  [38]. The most abundant Firmicutes gyrB OTU was assigned to a Carnobacterium sp. , showing only 89. 0% identity (as best match) to gyrB gene of Carnobacterium funditum strain DSM 5970. Among Proteobacteria, one OTU was assigned to a Psychrobacter sp. with the closest match being 89. 9% to the gyrB gene of Psychrobacter cryohalolentis FDAARGOS_308, a strain isolated from Siberian permafrost (GenBank: GCF_002208775. 2). For the second Proteobacteria OTU, the best identity score found was 91.
6 % with Vibrio sp. SM1977 gyrB gene, a species isolated from a coralline algae surface (GenBank: CP045699. 1). It should be noted that the abundance of these two uncharacterized species (Psychrobacter sp. and Vibrio sp. ) varied significantly according to the packaging type used for storage, which may correlate with their 10 times higher counts on air stored samples (Table  1 ), although we have no evidence for their cultivability in our growth conditions. species boundary  [38]. The most abundant Firmicutes gyrB OTU was assigned to a Carnobacterium sp. , showing only 89. 0% identity (as best match) to gyrB gene of Carnobacterium funditum strain DSM 5970. Among Proteobacteria, one OTU was assigned to a Psychrobacter sp. with the closest match being 89. 9% to the gyrB gene of Psychrobacter cryohalolentis FDAARGOS_308, a strain isolated from Siberian permafrost (GenBank: GCF_002208775.
2). For the second Proteobacteria OTU, the best identity score found was 91. 6 % with Vibrio sp. SM1977 gyrB gene, a species isolated from a coralline algae surface (GenBank: CP045699. 1). It should be noted that the abundance of these two uncharacterized species (Psychrobacter sp. and Vibrio sp. ) varied significantly according to the packaging type used for storage, which may correlate with their 10 times higher counts on air stored samples (Table  1 ), although we have no evidence for their cultivability in our growth conditions. (B, C ) Barplot composition of the top 10 species identified among Firmicutes and Proteobacteria phyla, respectively. Species are plotted according to their relative abundance in the percentage of the whole microbiota. Novel genus nomenclature was applied for Latilactobacillus species (L.
Acknowledgments: The authors express their gratitude to the  INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility , doi:  10. 15454/1. 5572390655343293E12 )for providing computational resources and data storage. We also thank the  INRA @BRIDGe platform  for carrying out the MiSeq sequencing runs.
RT-PCR, sequencing and phylogenetic analysis The viral RNA was extracted from blood samples using NucleoSpin R RNA kit (Macherey Nagel, Germany), following manufacturer's instructions. A 288-bp region of the region 5 ′ UTR was amplified by end-point RT-PCR using the Pan-Pestivirus primers Forward 324: 5 ′ -ATG CCC WTA GTA GGA CTA GCA-3 ′ and Reverse 326: 5 ′ -TCA ACT CCA TGT GCC ATG TAC-3 ′ (14) and Luna Universal One-Step RT-qPCR Kit (New England BioLabs, USA). The results were visualized on a 1% agarose gel, amplicons of the expected size were purified using NucleoSpin Gel and PCR Clean-up kit R (Macherey Nagel, Germany) and then sequenced by the Sanger method (Eurofins Genomics R ). The sequences obtained were aligned using ClustalW, implemented in the Geneious 10. 2. 3 software (Biomatters, New Zealand).
The phylogenetic tree of the 5 ′ UTR region was built using the Maximum Likelihood method with the Kimura 2-parameter model, Gamma distribution with Invariant Sites, as determined by a model prediction analysis, and 1,000 bootstrap replicates using the MEGA X software  (16). Reference sequences of Pestivirus A, Band Hspecies and genotypes were retrieved from NCBI GenBank database and included in the phylogenetic analysis. Nucleotide similarity percentages were calculated using the distance matrix algorithm of the MEGA X software  (16).
Results Using a Pan-Pestivirus end-point RT-PCR targeting the 5 ′ UTR it was possible to obtain an amplicon of the expected size from 146 of the analyzed samples, from 31 departments (Figure  1 ). The amplicons were purified and subsequently sequenced. These samples were compared with sequences from known genotypes of Pestivirus A, B and H species obtained from GenBank to build a phylogenetic tree. No sequence belonging to Pestivirus B and H were found. Except for sample ON693854, all the field sequences analyzed belonged toPestivirus A. Most of them corresponded to genotypes 1e (57. 9%) (N = 84) and to 1b (32. 4%) (N = 47), being identified in 26 and 20 of the sampled departments, respectively. To a lesser extent, samples of the genotypes 1d (3. 4%) (N = 5) and 1l (2. 1%) (N = 3) were detected, each in 2 different departments.
In Europe 1e genotype is distributed throughout the entire continent, mainly in France, where it is the dominant strain (13) and in Italy  (21)  and Switzerland (22), where it is the second most abundant strain. The 1e genotype is estimated to have originated in northern Italy in the Lombardy region between 1957 and 1988, where a large number of isolates are concentrated  (23). From this region it using the Maximum Likelihood method and the two-parameter Kimura model ( ). Reference strains were used for each known species/genotype. The GenBank accession number is indicated. Field strains described in this study are identified by the GenBank accession number (Table ). The asterisk identifies the samples that correspond to the new x genotype. spread to the rest of the country and to neighboring countries between 1990 and 2000 following the movements of cattle  (21).
Building on two recently published global datasets including historical soybean yield  18, 19  and retrospective meteorological forcing  20  , we developed data-driven relationships between climate and soybean yield to estimate soybean suitable areas over Europe. Several machine learning algorithms were trained and tested at the global scale (Random Forest, Artificial Neural Networks, Generalized Additive Model, and Multiple Linear Regression) to predict soybean yield as a function of monthly climate inputs (solar radiation, minimum and maximum temperature, rainfall, and vapour pressure) calculated over the growing season (April to October). A large share of the training data was taken from major soybean-producing countries (Argentina, Brazil, Canada, China, India, Italy and the United States), and zero-yield data points were randomly sampled in climate zones known to be unsuitable for soybean production (e.
g. deserts and arctic areas) and added to the dataset so that they represented about 20% of the final dataset. The most accurate algorithm was selected after running a cross-validation procedure assessing model transferability in time and space  21. The selected algorithm (Random Forest) was then run for the entire Europe to assess potential distribution of soybean suitable area in rainfed conditions under current and future climate. Projections of soybean suitability in Europe were performed using 16 climate change scenarios consisting of bias-corrected data produced by eight Global Climate Models of the Coupled Model Intercomparison phase 5 (CMIP5)  22  and two Representative Concentration Pathways (RCPs; 4. 5 and 8. 5 W m -2 )  23  in the 2050s and the 2090s. The projections assume a growing season from April to October and no irrigation, although soybean is often irrigated in Europe  14.
Model fitting and selection Among algorithms tested in this study, Random Forest appears to be the most accurate in terms of root-mean-squared error of prediction (RMSEP) and Nash-Sutcliffe model efficiency coefficient (MEF) (Table  1 , Figure  S2 ). It achieves the lowest prediction error (RMSEP = 0. 35 t ha - 1 ) and the highest efficiency (MEF = 0. 93), as estimated with an unstratified cross validation procedure. It also displays the best transferability in time (RMSEP = 0. 45 t ha -1 when applied to years different from those used for training) and space (RMSEP = 0. 43 t ha -1 when applied in locations distant by 500 km -or 5 grid-cells -from those used for training). Our results reveal that transferability in space decreases with increasing distance between training and test datasets for all models, with a threshold of 1000 km above which the performance of the selected algorithm deteriorates markedly (Table  1 ).
However, despite the limited number of grid-cells located in Europe in the training dataset, most European cropping areas are within 1000 km of these grid-cells (Figure  S3 ). This provides a good level of confidence in yield projections from the selected algorithm, except in the north-east (Ukraine, Belarus, Russia, Latvia, Estonia, Lithuania), west (Ireland), the south-west and south-east (south-western Spain, Portugal, Turkey), where projections should be interpreted with more caution.
The assessment of transferability in space revealed that our model predictive ability decreased markedly when the distance between training and test grid-cells was higher than 1000 km (Table  1 ). The global soybean yield dataset we used in this study  20  contains only a few grid-cells in Europe, which are located in the north of Italy (Figure  S21 ). This low amount of grid-cells located in Europe is consistent with the limited (although expanding) extent of soybean production area in Europe, which was especially true around the year 2000 that corresponds to the time period depicted by the dataset we used  18. These few grid-cells located in Europe are of great value as they allow capturing local features of soybean yield -climate relationships in our model. However, increasing the number of observed soybean yield data in Europe appears as a great opportunity to improve yield predictions from climate inputs.
However, day length has not been included in the predictors of our model because it is a function of latitude, and thus there is a risk of confounding effects associated with environmental or socio-economic variables correlated with latitude (e. g. GDP per capita, access to fertilizer and modern varieties  52  ). Likewise, maturity group was not taken into account directly because, to our knowledge, no global dataset of soybean maturity groups is available. We believe this approach is likely to have a small impact on our results for three reasons. First, model residuals show no association with latitude (Figure  S27 A ). Importantly, residuals were not larger and did not reveal any bias at high latitudes where day length might be an issue. Second, although short day lengths may be an issue at high latitudes, there is already some evidence from farmer's fields and field experiments that soybean could be grown at high latitude (up to 55-57 °N) in Europe (Figure  S5-A ).
Third, our model doesn't project high yields at latitudes higher than 55-57 °N. Nevertheless, we acknowledge that further research will benefit from exploring soybean cultivation at high latitudes with process-based crop models including the effect of day length on soybean physiology  53. Soil type is known to have impacts on crop growth and yield in multiple ways. However, no reliable historical soil dataset is currently available at the global scale for key soil characteristic relevant to crop growth  54, 55. We therefore decided not to include soil type in our model. Nevertheless, we note that some analyses based on global soil datasets suggest no important limitation to root growth in European soils  56  , thus making our yield projections more likely to be pessimistic than optimistic.
The fixed growing season assumed in this study may be a reason for relatively optimistic soybean yield projections. However, in Northern and Eastern Europe, the positive impact of climate change on yields can be interpreted as the result of a decrease in cold stress, which would compensate for the negative impact of reduced crop growth duration. Here again, further research will benefit from the use of process-based crop models able to capture these processes, to make comparisons with the results presented in this paper. spatial resolution of the yield data (1. 125 degree) using the projectRaster() function of the raster R package with argument method set to "bilinear".
To avoid any confusion with technological progress, soybean yield data are detrended in order to remove the increasing trends of soybean yield time series due to improved cultivars and technological progress  71. For all grid-cells, yield time series are detrended using a cubic smoothing spline f(t) and each yield data is then expressed relatively to the expected yield value in 2010 as Yd(t) = f(2010) + A(t), where f(  2010 ) is the smoothing spline yield estimate for the year 2010 (the most recent year available in the yield dataset) and A(t) is the yield anomaly A(t) = yield(t)-f(t). Histograms of soybean yields before and after detrending are shown in Figure  S22. The soybean growing season is defined country-by-country according to the crop calendars provided by the Agricultural Market Information System (available at: http://www.