dataesr/scientific-paragraphs-categorization huggingface dataset:
If neither LER nor PLER was reported, LER for each genotype in a given cereal-legume combination was calculated from yields in mono-cropping and intercropping. When other treatments were applied (such as different row spacing, and sowing density or proportion), LERs were extracted or calculated from only one treatment. If different levels of N were used, data for each level of fertilizer were considered and averages computed for each genotype combination. In one study, results from two species of cereals or legumes were reported. Thus, data were recorded from each genotype combination from each species and analyzed. Therefore, at least 2 data points from each article (depending on number of genotypes of cereals and legumes) were extracted. In this way, we obtained 262 LER data points. Since only few (10%) LER data points were reported from forage legume species combinations with cereals (2 articles with oats, 1 article with finger millet, and 2 articles with maize) all data from forage and grain legumes were combined and analyzed together.
Data Analysis The main effects of genotype and intercropping and their interaction effects were assessed by counting and calculating the proportion of articles that reported significant or non-significant effects on yields. In addition to the analysis of LER, a fixedeffects ANOVA model was used to test the effect of cereal species, design, and interaction effect on LER across cereal species by categorizing the dataset by cereal species. Because the number of data points of wheat was low (n = 5), and data records from barley and rice were only from replacement design, we excluded these three from the analysis. The number of data records per cereal species varied from 25 (finger millet) to 131 (maize). Similarly, a fixed-effects ANOVA model was used to test the effect of legume species, design, and interaction on LER across legume species by categorizing the dataset by legume species.
However, faba bean, grass pea, guar and hairy vetch, berseem clover, and bitter vetch were excluded because the number of data points (two to four) was low. The mean comparison was done by Tukey's honestly significant difference (HSD) test. To assess the potential of genotype choice for optimizing LER, we calculated three indices using the extracted data from the articles (averages across the site years); to obtain these indices, we first calculated the maximum, median, and minimum LER across different genotype combinations for each article. Then (i) the difference between maximum and median LER was used as a measure for the potential of combined genotype choice to improve LER in comparison to a random choice; similarly, (ii) the difference between minimum and median LER was taken as a measure for the risk to choose an inappropriate genotype combination in comparison to a random choice; and (iii) the range, i.
e. , the difference between maximum and minimum LER from an article was used to characterize the maximum genotype combination effect within a study. The median used to calculate all three statistics were calculated from each individual article. The three statistics are equivalent when only two genotypes were evaluated. Because of sampling effects, it is expected that all three differences would tend to increase (in absolute terms) with increasing number of genotype combinations tested within a study  (Schwarz, 2011) ; therefore, we plotted the indices against the number of genotype combinations. The extracted LER data were subjected to descriptive statistics; all analyses were conducted with R (R CoreTeam, 2020), and figures were produced using the R package ggplot2  (Wickham, 2016).
Geographical Distribution and Characteristics of Studies From about 4,000 search hits using all search terms, only 69 articles fulfilled the inclusion criteria (Table  2 ). The reported research studies were conducted in 28 different countries (Supplementary Table  2 ). The majority of data came from Africa (37%) followed by Europe (24%) and Asia (18%). The included studies considered different contrasting characteristics of genotypes of cereals and legumes evaluated. Overall, 9 cereal crop species and 19 legume species were evaluated in 69 publications with maize as the most frequently evaluated cereal species followed by oat and wheat. Common bean was the most frequently evaluated legume followed by cowpea and soybean. In the considered studies, common bean was only intercropped with maize.
Land Equivalent Ratio as Metric to Gauge Yield Advantage of Genotypes in Intercropping From the 69 studies used for data extraction, 35 studies yielded 36 datasets (one study used two cereal species) and either reported the LERs directly or allowed calculation from the reported yield data. From these 36 datasets, 262 data points (cereal/legume genotype combinations) were extracted, based on a total of 85 cereal and 126 legume genotypes, with a number of cereal/legume combinations (LER) ranging from 2 to 22 per study. The calculated mean and the median LER were 1. 26 and 1. 24, respectively (Figure  2 ), and LER was greater than 1. 0 in 85% of the single cases. Although the number of data points for some cereals, especially wheat, may not be sufficient to compare the median LER with other cereals, the overall outcome was robustly > 1 with the highest median LER of 1.
The Potential of Genotype Choice for Intercropping The distribution of the LERs within the studies around the median (Figure  5 ) indicates that genotype-specific effects play a role in the performance of mixtures in comparison to sole crops. Overall, the range (i. e. , difference between maximum LER and minimum LER within a study) varied between 0 and 1. 98, showing the potential of large genotype effects in intercropping. Conversely, there was a risk to obtain low LERs by nonappropriate genotype choice (i. e. , as indicated by the difference of minimum LER and median LER, red points in Figure  5 ); the One article evaluated two cereal species resulting in a total of 70 datasets (out of one publication, two datasets were extracted). difference between minimum and median ranged from -0. 55 to 0.
Plant Material and sampling method Experiments were performed outdoors at the INRAE Pech Rouge experimental unit (Gruissan, France, 43. 14'/3. 14''W) under a semi-arid Mediterranean climate in 2017 (temperature, rainfall and evapotranspiration data described in  Alem et al. , 2021). Experimental plots were managed through drip irrigation keeping the predawn leaf water potential (ΨPD) higher than -0. 5 MPa  (Giorgi & Lionello, 2008)  to correspond to a moderate water stress. The set of the genotypes comprised 3 traditional varieties, Grenache N, Merlot N and Morrastel N (https://plantgrape. plantnet-project. org/ fr/), and 3 new disease-resistant varieties derived from 4 (3197-81B, 3197-373N) or 5 (3184-1-9N) backcrosses of Muscadinia rotundifolia with V. vinifera varieties  (Bouquet al. , 1980).
Data normalisation and presentations R® software (version 4. 1. 2) was used to build graphical representations and to analyse the data (R Core Team, 2017). Tests as shapiro. test ("stats", for normality) and bartlett. test (for variance equality) were always performed before the selection of the appropriate statistical test. Software packages were "ggplot2" (Version 3. 3. 5), "car" (Version 3. 0-12) and "rcompanion" (Version 2. 4. 6).
The six prophages previously characterized by Zünd et al  (Zünd et al. , 2021)  were found among the induced prophages. Of the 16 prophages regions defined by Hi-C (15 in vitro and / or in vivo + 1 in vivo only), 11 were found to be among the 13 induced prophages. For the remaining 5 functional Hi-C-defined candidate prophages, we were unable to detect any significant signal in the virome data suggesting that those prophages are not active enough, or at all, in the test conditions, or are not functional or produce abortive cycles. Two induced prophages were also not found to be associated with a specific 3D pattern, possibly due to a lack of structuration of the corresponding phages in their insertion site or less likely to differences between fecal samples recovered for Hi-C and virome. We then compared the prophages coordinates defined by Hi-C data with variations of virome sequences and found that the boundaries of the prophages often agree between the datasets (9/11) (Table  2 ).
In-depth analysis of induced prophages The above 13 induced prophages were named after French castles and their genomes were analyzed in detail (Fig. 5b  and Table  2 ). First, we looked for a circularization signal, in the virome sample reads (Methods). We detected such a signal for 12 out of 13 prophages, which allowed us to precisely map the prophage coordinates. Second, using those exact coordinates, we annotated the genomes using the PHROG database (Terzian et al. , 2021) (Methods). Between 25% to 60% of the genes were annotated, and we identified terminase/portal proteins, two hallmarks of phage proteins, in all genomes (Fig. 5b ). Integrases were also frequently found, in agreement to the temperate lifestyle of these phages. Third, PhageTermVirome  (Garneau et al. , 2021)  was successfully used for 10 phages to confirm the viral nature of these prophages as well as to identify their packaging method and termini.
(Table  2 ). Finally, we searched for homologs using BLAST, but only distant homologs were identified, showing that these prophages are relatively new. Thus, we used vContact2  (Bin Jang et al. , 2019)  to generate shared protein networks and identify the groups of phages that most closely relate to these 13 phages. While only few high-quality RefSeq genomes clustered closely to the OMM 12 prophages, many metagenomic phage sequences belonging to the Cenote Human Virome Database (CHVD) (Tisza and Buck, 2021) closely clustered to them (Supplementary Figure  8 ). This clustering allowed us to predict their possible morphology (Table  2 ). Overall, these 13 induced OMM 12 phages represent newly characterized viruses from representative of intestinal bacteria, from which the assignation of a number of viral reads from metagenomics studies will contribute to lower the "viral dark matter".
Hi-C analysis Forward and reverse reads were aligned separately with bowtie2 v2. 3. 5. 1  (Langmead and Salzberg, 2012). From these alignments, Hi-C matrices and genomic distance law were generated using hicstuff v3. 0. 3 (https://github. com/koszullab/hicstuff). Matrices were balanced using ICE algorithm  (Imakaev et al. , 2012). For comparative analysis, matrices were binned at 10 kbp resolution and were downsampled to the same number of contacts. Reproducibility between replicates was assessed using the hicreppy v0. 0. 6 implementation (https://github. com/cmdoret/hicreppy) of the HiCrep algorithm  (Yang et al. , 2017). Comparison between matrices were done using log2 ratio and serpentine v0. 1. 3 60 for flexible binning  (Baudry et al. , 2020).
Bacterial genomes annotations and genomic features Genomes were annotated using the last version of the NCBI Prokaryotic Genome Annotation Pipeline (PGAP)  (Li et al. , 2021). The coverage was computed using tinycov (https://github. com/cmdoret/tinycov), v0. 3. 0. The GC content and the GC skew were computed using dnaglider (https://github. com/cmdoret/dnaglider), v0. 0. 4. The parS sites have been detected using a degenerated parS consensus sequence TGTTTCACGTGAAACA  (Livny et al. , 2007)  and allowing 2 mismatches. The ori and ter position were approximated based on the GC skew inversion. The shift closer to the parS cluster were annotated as the ori and the shift as the opposite of the genome as the ter. Figures were generated using pyGenometracks v3. 6  (Lopez-Delisle et al. , 2021).
Prophage annotations Prophage annotation was performed using the OMM 12 genomes  (Lamy-Besnier et al. , 2021)  except for B. caecimuris, B. longum and F. plautii for which the version re-assembled in this article was used. Both Vibrant  (Kieft et al. , 2020), v1. 2. 1 and Virsorter2 (Guo et al. , 2021) , v2. 2. 3 with their respective databases, were used to annotate the bacterial genomes. The data from mitomycin C-induced prophages was obtained from Zünd et al. (Zünd et al. , 2021).
Shared-proteins network analysis OMM 12 induced prophages were clustered using vConTACT2 (Bin Jang et al. , 2019) v0. 9. 19 with the following parameters: --rel-mode 'Diamond' --db 'ProkaryoticViralRefSeq201-Merged' --pcs-mode MCL --vcs-mode ClusterONE. Publicly available phage genomes from ViralRef Seq V. 201  (Brister et al. , 2015)  and Cenote Human Virome Database (CHVD) (Tisza and Buck, 2021) were used as reference collections for the analysis. The resulting network was visualized and annotated using Cytoscape v3. 9. 1  (Kohl et al. , 2011). The edge-weighted springembedded model was applied to position genomes sharing most protein clusters.
Detection of ssDNA phage in virome samples Cleaned reads from virome 5 and virome 6 (Supplementary Table  1 ) were merged into one single dataset. Reads were assembled using SKESA  (Souvorov et al. , 2018)   (Terzian et al. , 2021) (v3, PHROG 514 and 713 respectively). No significant hits were obtained on the database using a e-value threshold of 1x10 -3. • Lourenço, M. , Chaffringeon, L. , Lamy-Besnier, Q. , Pédron, T. , Campagne, P. ,  Eberl, C. , Bérard, M. , Stecher, B. , Debarbieux, L. , and De Sordi, L. (2020). The Spatial Heterogeneity of the Gut Limits Predation and Fosters Coexistence of Bacteria and Bacteriophages. Cell Host Microbe 28, 390-401. e5. https://doi. org/10. 1016/j. chom. 2020. 06. 002. • Mäkelä, J. , and Sherratt, D. (2020). SMC complexes organize the bacterial chromosome by lengthwise compaction.
, 2020), with the "phage" recipe. Then, each predicted protein was compared to the PHROG(Terzian et al. , 2021)     database v3 using the HHsuite v3. 3. 0 (hhsearch_omp function). For hits with a probability above 88%, the corresponding PHROG annotation was manually added to the corresponding protein. The annotated genomes were then visualized using Clinker(Gilchrist and Chooi, 2021)     v1. 1. 0.
default option to predict ssDNA sequences. Except for the M13 spiked control, no ssDNA was identified in the sequenced samples. All contigs from the SKESA and Metaviral SPAdes assemblies were joined in a single multifasta file and proteins were predicted and annotated using PROKKA(Seemann, 2014). The whole set of predicted proteins was then screened with BlastP for Microviridae characteristic MCP and Rep proteins contained in the PHROG database.
ACKNOWLEDGEMENTS We thank  Cyril Matthey Doret  and  Agnès Thierry  for assistance during computational and experimental work, respectively. We also thank  Marie-Agnès Petit  for her precious advices and help in prophages annotation. We thank  Lorenzo Chaffringeon  for fruitful discussions. This research was supported by funding to BS from  DFG-STE-1971/11-1  ( PhaStGut  project); to BS from the  European Research Council  under the  Horizon 2020 Program  ( ERC grant  agreement  865615 ), to LD and  MMa  from  PRCI   ANR-20-CE92-0048  ( PhaStGut  project); to RK from the  European Research Council  under the  Horizon 2020 Program  ( ERC grant  agreement  771813 ) and from  JPI-EC-AMR   STARCS ANR-16-JPEC-0003-05. QLB. received funding by  École Doctorale FIRE-Program Bettencourt. AB is supported by an  ENS  fellowship by the  French Ministry of Higher Education, Research and Innovation.
org/10. 1128/JB. 01239-07. • Lopez-Delisle, L. , Rabbani, L. , Wolff, J. , Bhardwaj, V. , Backofen, R. , Grüning, B. , Ramírez, F. , and Manke, T. (2021). pyGenomeTracks: reproducible plots for multivariate genomic datasets. Bioinformatics 37, 422-423. https://doi. org/10. 1093/bioinformatics/btaa692.
default option to predict ssDNA sequences. Except for the M13 spiked control, no ssDNA was identified in the sequenced samples. All contigs from the SKESA and Metaviral SPAdes assemblies were joined in a single multifasta file and proteins were predicted and annotated using PROKKA(Seemann, 2014). The whole set of predicted proteins was then screened with BlastP for Microviridae characteristic MCP and Rep proteins contained in the PHROG database v2. 4. 0 and Metaviral SPAdes (Antipov et al. , 2020) v3. 15. 2 using k-mer size 21 and resulting contigs (≤ 15 kb ≥ 1 kb) were analyzed with VIBRANT (Kieft et al. , 2020) v1. 2. 1 and VirSorter2 (Guo et al. , 2021) v2. 2. 3 with.
Databases of Natural Products for Dereplication The database (DB) of natural products (NPs) (CH-NMR-NP DB), containing the experimental 1 H (δH) and 13 C (δC) chemical shifts of 32854 NPs, was prepared as previously described, from the data available on the dedicated JEOL RESONANCE Inc. website  (Bruguière et al. 2020 ), available at https://www. j-resonance. com/en/nmrdb/. Additionally, DBs containing the predicted δC values of NPs isolated from species belonging to the Clusia (ClDB) and Calophyllum (CaDB) genera were prepared by including the 166 and 307 metabolites described from Clusia and Calophyllum genera, respectively, in the SciFinder ® website  (Bruguière et al. 2021). The C-typeGen program included in the MixONat software created then a DB, in the format required by MixONat  (Bruguière et al.
C NMR Dereplication Assisted Isolation Dereplication analyses were carried out using the MixONat software available on https://sourceforge. net/projects/mixonat/. A small amount (15 mg) of fractions B1-23 was dissolved in deuterated chloroform or methanol-d4 (600 µL). A WALTZ-16 decoupling sequence was used to obtain 13 C NMR (100 MHz) spectra, with an acquisition time of 1. 04 s (32768 data points) and a relaxation delay of 2 s. Twelve thousand scans were collected to obtain a satisfactory S/N ratio. The spectral width was 250 ppm. A 1 Hz exponential line broadening filter was applied to each FID prior to Fourier transformation. Spectra were manually phased and baseline corrected using the MestReNova software (Mestrelab Research, Santiago de Compostela, Spain) and referenced on the central resonance of the deuterated solvent.
For DEPT-135 and DEPT-90 experiments, 6000 scans were required for 15 mg of extract and alignment of the 13 C spectrum was carried out using a selected δC. A minimum intensity threshold was then used to collect positive 13 C NMR and DEPT-90 signals and positive and negative DEPT-135 signals, while avoiding potential noise artifacts. The peak list and intensity data obtained from each spectrum were exported as a. csv file using Excel Microsoft Office (Microsoft, Redmond, WA) software and used as an input file in MixONat software. The software sorted data from metabolites contained in the DB (CH-NMR-NP DB, ClDB or CaDB) and assigned a score between 1 and 0, depending on the number of matching experimental/predicted δC values for each metabolite contained in the DB. The presence of the predicted NPs was confirmed by comparing the experimental 13 C NMR chemical shift values with those reported in the literature for the predicted NPs when analyzed in the same deuterated solvent, with a displacement tolerance range of ± 0.
Materials and Methods The genetic diversity of the M1 gene and segment 7 was analyzed from 624 influenza A/H3N2 genomes (GenBank accession no. OP546682-OP547305  [6, 7] ) that had been obtained from respiratory samples collected between September 2021 and April 2022 and were sent for diagnosis of respiratory virus infections to our clinical virology laboratory at university hospitals of Marseille, southeastern France. Influenza A virus infections were diagnosed using the FTD Respiratory pathogens 21 (FTD) (Fast Track Diagnosis, Luxembourg), the GeneXpert Xpert Flu/RSV (GeneXpert) (Cepheid, Sunnyvale, CA, USA), or the BioFire FilmArray Respiratory panel 2 plus (BioFire) (Biomérieux, Marcy-l'Etoile, France) assays, which all target the M1 gene (plus two other genes for the GeneXpert assay). For influenza A virus genome sequencing, viral RNA was extracted from respiratory samples using the EZ1 Virus Mini Kit v2.
0 on an EZ1 Advanced XL instrument (Qiagen, Courtaboeuf, France) or the KingFisher Flex system (Thermo Fisher Scientific, Waltham, MA, United States), following manufacturer's instructions. Then, viral RNA segments were amplified by a procedure adapted from that described by Zhou et al. [8] , and nextgeneration sequencing (NGS) of the amplicons was performed using Illumina technology on a NovaSeq 6000 instrument (Illumina Inc. , San Diego, CA, USA), as previously described  [9]. Viral genomes were assembled by mapping NGS reads on the genome of the influenza A/H3N2 virus strain A/New York/392/2004 (GenBank Accession no. NC_007366. 1-NC_007373. 1) with the CLC Genomics workbench v. 7 software [10].
To our best knowledge, no country other than France has yet emitted an alert for impaired qPCR performance to detect recently or currently circulating influenza A/H3N2 viruses. Finally, it is worth noting that the sequencing of the genomes of influenza viruses is currently dramatically underperformed compared to SARS-CoV-2 genomes. According to the GISAID database  [12, 13] , as of 5 October 2022, 141 influenza A/H3N2 virus sequences have been obtained in France from human samples collected since January 2021 and this number is one according to the NCBI Influenza Virus Database sequences  [14]. Hence, influenza virus genomic surveillance deserves to be carried out more extensively, in partic-ular, to ensure the performance of commercialized qPCR tests, but more generally in order to draw from it the information that has shown great interest in the context of the genomic surveillance of SARS-CoV-2 infections  [1, 2].
All the plants sampled were at the same physiological stage with a bunch approaching harvest. For each microenvironment and treatment, two replicate composite samples consisting of subsamples from five plants were taken. Additional information about the region, the banana cropping system, and the use of trees and shrubs to improve banana productivity are given by  (Mpiira et al. , 2013; Kongkijthavorn, 2017). Banana yield data were previously reported by  (Staver et al. , 2015).
Data Processing Data analysis was performed by employing QIIME version 1. 9. 1  (Caporaso et al. , 2010b). Joined paired-end reads with more than three consecutive low-quality base calls (Phred quality score ≤ 25) were truncated at the position where their quality began to drop, and only reads with > 75% consecutive high-quality base calls, without any ambiguous characters, and longer than 200 nucleotides in length were retained for further analyses. Demultiplexed high-quality reads were de novo clustered into operational taxonomic units (OTUs) with uclust  (Edgar, 2010) , using a 97% similarity threshold. From each OTU, the most abundant sequence was selected as representative, and the taxonomy was assigned with the uclust-based consensus taxonomy assigner against the Greengenes database (version 13.
Statistical Analysis The datasets were normalized by rarefying to the lowest number of reads (3,351 high-quality reads) and MetagenomeSeq's cumulative sum scaling  (Paulson et al. , 2013)  was used for alpha and beta diversity analysis, respectively. Phyloseq, MicrobiomeAnalyst, and vegan R packages implemented in RStudio v1. 3. 1093 were used to analyze bacterial community diversity and composition  (Oksanen et al. , 2007; Allaire, 2012; Core Team, 2013; McMurdie and Holmes, 2013; Dhariwal et al. , 2017; Chong et al. , 2020). Significant differences in alpha diversity based on Shannon index (H') were determined using ANOVA and Tukey-HSD post hoc test. The normalized weighted Unifrac dissimilarity matrix  (Lozupone et al. , 2011)  was subjected to permutational ANOVA (PERMANOVA) to test for significant effects of factors on microbial community structure.