dataesr/scientific-paragraphs-categorization huggingface dataset:
We would like to thank Russell Y. Neches for his criIcal comments on this work. This work was supported by JSPS/KAKENHI (18H02279 and 22H00384, to H. O. ), JST SPRING, Grant Number JPMJSP2110, and the CollaboraIve InternaIonal Joint Research Program of the InsItute for Chemical Research, Kyoto University (No. 2022-26 to M. G. ). ComputaIonal Ime was provided by the SuperComputer System, InsItute for Chemical Research, Kyoto University. We thank J. Iacona, Ph. D. , from Edanz (hnps://jp. edanz. com/ac) for ediIng a draq of this manuscript. Author contribu2ons J. W. and H. O. designed the study. J. W. performed all bioinformaIcs analysis. L. M. contributed to the preparaIon of datasets and helped with the bioinformaIcs analyses. H. O. supervised the work and M. G. , H. H. , Y. O. , and H. E.
Bioinformatic analysis A pipeline based on VSEARCH  (Rognes et al. , 2016)  and available on GitHub (https://github. com/BPerezLamarque/Scripts/) was used for data processing. Briefly, pairedend reads were merged and quality checked. Merged reads were then demultiplexed using cutadapt  (Martin, 2011)  with 0 error accepted in primer or tag sequences. Reads from all samples were dereplicated and clustered as classical 97% sequence similarity Operational Taxonomic Units (OTUs) using VSEARCH as recommended in  Tedersoo et al. (2022). All sequences were checked for the presence of chimeras. The taxonomy of these OTUs was assigned with VSEARCH against the UNITE v9. 0 database  (Nilsson et al. , 2019). Reads were filtered to keep only non-chimeric sequences of > 200 pb and with a total abundance of at least 10 (see Supp.
Methods 2 for details on the functions and parameters used). We used the decontam algorithm (Susana  Rivera et al. , 2011)  to remove potential contaminants in the datasets, using both prevalence and frequency algorithms (Supp. Methods 2). Samples with less than 1,000 fungal reads were discarded. After filtering, we obtained the mycobiota composition of 259 samples (96% of all collected samples; Supp. Table  1 ), with a mean sequencing depth of 21,013 reads per sample (ranging from 1,041 to 130,227; Supp. Fig. 1  &  2). In order to compute UniFrac distances, we reconstructed the fungal phylogenetic trees as in Perez-Lamarque et al. (2022) (Supp. Methods 2).
In order to test whether OTUs are shared preferentially between samples from the same compartments or not, we built one network for each compartment of the in situ dataset and one network by compartment x substrate condition combinations in the in vitro experiment to allow for comparisons of specialization between compartments and substrate conditions. For each sample, we consider an interaction with a fungal OTU as long as the latter represents at least 0. 5% of the reads. We computed the connectance C (i. e. , the proportion of realized links) and the network specialization index H 2 ' (degree of specialization of the entire network) of each network. Connectance ranges from 0 to 1; values close to 1 indicate that a high proportion of all potential links are realized. H 2 ' also ranges from 0 to 1, 0 indicating a strong generalization and 1 that the network is globally specialized.
Potentially transmitted OTUs have similar taxonomy and are mainly affiliated to Penicillium oxalicum (ca. 10% of the root and rhizosphere mycobiota), Eutypella sp. (4%), Pleosporaceae sp. (3. 5%), and Chaetomium sp. (3%). P. oxalicum is commonly found in the rhizosphere and was reported as a plant growth-promoting fungus which could limit the development of some pathogens such as Fusarium spp. (Murali and Amruthesh, 2015) , while the others may be plant pathogens (esp. Eutypella sp. ) or endophytes (such as Chaetomium sp. ; FUNGuild online database,  Nguyen et al. , 2016). Leaf mycobiota of adult individuals in situ display a mixed pattern of colonization. Their composition is similar to seeds which are the main contributor to their mycobiota (48%). This contribution is mainly supported by ubiquitous fungi such as Alternaria consortialis which represent a large share of leaf mycobiota in situ (10%) when using the loose definition of potentially transmitted OTUs.
Grey nodes represent OTUs and colored ones represent samples. Node diameter is proportional to the betweenness centrality. Width of the edges is proportional to the relative abundance of an OTU in a sample. See Supp. Methods 3 for connectance (C) and specialization (H 2 ') calculation and significance tests. (c) Total dataset: round colored nodes represent samples in vitro, colored rhombuses represent samples in situ. (d) Samples in vitro with potential sources of mycobiota (bulk soil and seeds used for the experiment). Colored rhombuses correspond to in situ samples, squares to sample from the autoclaved condition and triangles to non-autoclaved condition.
General descriptors Data on donor animals were obtained from the FNC database. We focused on six mammalian species of farm animals that represent the majority of FNC collections: cattle, sheep, goat, pig, horse, and donkey. For each donor, we retrieved its national identifier, breed, year of birth, the amount of material (either straws or pellets) available in the collection, the main motivation for entry into collection, and the year the donor was first collected, if available. The motivation for entry into the collection was defined at the time of the creation of the FNC as one of three main categories (types): type I for endangered breeds, type II for individuals with one or more exceptional characteristics, and type III for individuals who were representative of their breed at a particular time.
Regression trees We built regression trees to identify the criteria that best explained the data structure in terms of number of donors per breed and number of doses per donor. The trees were created and visualized using the rpart package v4. 1-15  (Therneau & Atkinson, 2019)  and the partykit package v1. 2-15  (Hothorn & Zeileis, 2015). They were built using a CART (classification and regression tree) algorithm  (Breiman et al. , 1984)  via recursive divisions of the dataset that aimed to reduce the heterogeneity of the dependent variable at each node. Thus, the partitioning of the dataset was based on minimizing the within-branch variance while maximizing the variance between branches. In the first model, the number of donors per breed was the dependent variable, while the species, the use (Use), and the diffusion (Diff) of the breed were the explanatory variables.
Genetic contribution For the subset of 17 breeds described above, pedigrees of individuals-either from the breeding scheme for the breed, or from the entire population for breeds in a conservation program-were extracted from herd books or species-specific French national databases. Then, pedigree data were analyzed using PEDIG software  (Boichard, 2002). All pedigrees were formatted using the ped_util module tracking 25 generations back. The studied populations included the individuals born between 2011 and 2020 and all donors registered in the FNC. Genetic contributions of the FNC donors were calculated from pedigree data using the contribution module of PEDIG for each year from 2011 to 2020. The number of direct (i. e. , first generation) offspring of the cryoconserved sires was calculated for each year over the same period.
Acknowledgments The authors would like to acknowledge all the managers of the studied breeds who sent us useful information for this study. We would like to thank the  Centre Départemental de l'Elevage Ovin des Pyrénées-Atlantiques (CDEO) , the organization of approved selection of the Lacaune ( UPRA Lacaune) ,  Alliance R&D , and  LIGERAL , who permitted us to have access to the data and provided comments concerning their breeds. The authors would like to acknowledge  Diane Buisson  and  Jean-Baptiste Tertrain  for their help in extracting the pedigree data. The authors also thank the staff of the  French National Cryobank  and the partners of the  CRB-Anim infrastructure  project,  ANR-11-INBS-0003 , funded by the  French National Research Agency  through the ' Investing for the Future' program , for all the information made available for this study.
Funding This study was partially funded by  INRAE  and the project  GenResBridge. GenResBridge has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  817580. The PhD of AJ was funded by  IDELE (Institut de l'Élevage) ,  IFIP (Institut français du porc) ,  SCC (Société Centrale Canine) , and  GenResBridge.
PRJNA923860), was reconstituted in water at a concentration of 2 g/L, following the producer's instructions. Before each trial, the viability of the conidia in the commercial product was assessed and con rmed to be at least 85%, as per the method described by  Pertot et al. 2016. To determine the colony forming units, a serial dilution of the conidia suspension was plated on potato dextrose agar, and after 24-48 hours of incubation at room temperature, the colonies were counted. Bacillus subtilis PTA-271. Bacillus subtilis PTA-271 (GenBank Nucleotide EMBL Accession No. AM293677 for 16S rRNA and DDBJ/ENA/GenBank Accession No. JACERQ000000000) was isolated from the rhizosphere of healthy Chardonnay grapevines in Champagne, Marne, France  (Trotel-Aziz et al. 2008 ). To initiate bacterial growth, 1 mL glycerol stock was added to sterile Luria Bertani (LB) medium and then incubated between 25-28°C.
Leaf samples were collected from all plants at 24 hours (24 hpt) and four days after the application of the BCAs (4 dpt), to analyze the expression of plant defense genes. Furthermore, the rhizosphere of each plant was collected at two additional time points: 30 days after BCA application (T2) and 90 days after BCA application (T3) for microbiome analysis. DNA extraction, sequencing and data analysis of the high-throughput sequencing assay. Genomic DNA was extracted from a 0. 5g rhizosphere sample using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany), following the manufacturer's indications. The DNA yields were measured using the Invitrogen Qubit 4 Fluorometer with the Qubit dsDNA HS (High Sensitivity) (Thermo Fisher Scienti c, Waltham, MA, USA), and the extracts were adjusted to a concentration of 10-15 ng/μL.
Additionally, a positive control containing DNA from a grapevine rhizosphere sample  (Berlanas et al. 2019 ) was incorporated to validate the experimental procedures. To verify the size of the libraries, they were subjected to gel electrophoresis on 2% agarose gels stained with GreenSafe (NZYTech) and visualized under UV light. In cases where the 16S and ITS2 libraries exhibited weak or no amplicon bands, a second PCR round was performed using the same conditions as described earlier, with the addition of 6. 25 μL of Supreme NZYTaq 2x Green Master Mix and 1X of a PCR enhancer mix  (Ralser et al. 2006). Following PCR, the libraries were puri ed using Mag-Bind RXNPure Plus magnetic beads (Omega Biotek, Norcross, GE, USA), following the manufacturer's instructions. After puri cation, the libraries were pooled in equimolar amounts based on quanti cation data obtained from the Qubit dsDNA HS Assay (Thermo Fisher Scienti c, Waltham, MA, USA).
Droplets were created using the QX200TM droplet generator (Bio-Rad Laboratories) and carefully transferred into PCR plates using a multichannel pipette. The PCR plate was then sealed using PCR plate heat seal foil and the PX1TM PCR plate sealer (Bio-Rad Laboratories). PCR ampli cation was carried out in a C1000 touch thermal cycler (Bio-Rad Laboratories) with the following thermal cycling conditions: an initial denaturation stage at 95°C for 10 minutes, followed by 40 cycles of denaturation at 94°C for 30 seconds and annealing at an optimized temperature (59°C) for 60 seconds. A nal extension step of 10 minutes at 98°C was performed. The PCR plates were transferred to a QX200TM droplet reader (Bio-Rad Laboratories) for analysis, and the data were analyzed using QuantaSoftTM software (Bio-Rad Laboratories).
The sequences were then grouped based on barcode motifs and labeled with their corresponding sample names. Fungal ITS sequences were extracted using ITSx 1. 0. 11  (Bengtsson-Palme et al. 2013) , while this extraction step was not applied to the 16S sequences. Following this, the sequences were clustered into operational taxonomic units (OTUs) and any chimeric sequences were eliminated using Usearch-UPARSE 8. 1. 1861  (Edgar 2013) , employing a pairwise identity threshold of 97% against the UNITE fungal dynamic database  (Abarenkov et al. 2010 ) and SILVA database (https://www. arb-silva. de/). The representative consensus sequences were derived from the clusters using MAFFT 7. 222  (Katoh et al. 2009). Finally, the identi cation of OTUs was performed using blastn, tblastx, and makeblastdb 2.
5. 0+ (https://blast. ncbi. nlm. nih. gov/Blast. cgi, accessed on 28 May 2023). To ensure comparability between samples, the dataset was normalized using the Total Sum Scaling standard approach. Alpha-diversity metrics, including Observed richness, Shannon, and Simpson diversity, were calculated using the Phyloseq package. To assess differences in fungal and bacterial alpha-diversity among soils and biocontrol agent treatments, multiple mean comparisons were conducted using Tukey's signi cant difference range test (p ≤ 0. 05). PERMANOVA was utilized to identify OTUs (Operational Taxonomic Units) that signi cantly differed in relative abundance among experimental factors, and Bonferroni corrections were applied to control for false positives. Good's coverage values and rarefaction curves were also computed to assess the adequacy of sampling depth.
For cDNA synthesis, rst-strand cDNA was generated from 150 ng of total RNA using the Verso cDNA synthesis kit (Thermo Fisher Scienti c, Inc. , Waltham, MA, United States). PCR conditions were based on those described by  Gruau et al. (2015). Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was conducted with Absolute Blue qPCR SYBR Green ROX Mix following the manufacturer's indications (Thermo Fisher Scienti c, Inc. , Waltham, MA, United States). The qRT-PCR was performed using a BioRad C1000 thermocycler with the Bio-Rad manager software CFX96 Real-Time PCR (BioRad, Hercules, CA, United States). Six defense-related genes, chosen for their sensibility to pathogens or bene cial microorganisms  (Trotel-Aziz et al. 2019) , were monitored through qRT-PCR using speci c primers (Table  S2 ).
Results High-throughput amplicon sequencing. After paired-end alignments, quality ltering and deletion of chimeras and singletons, a total of 6,063,168 fungal ITS2 sequences and 6,209,021 bacterial 16S sequences were generated from 93 and 96 samples respectively (three samples were removed from fungal analysis due to the low number of sequences read), and assigned to 644 fungal operational taxonomic units (OTUs) and 1,078 bacterial OTUs (Table  S3 ). Good's coverage values ranged from 98. 1% to 100% in all samples, capturing nearly all the diversity with an adequate sequencing depth (Table  S4 ). Sequencing data are deposited under BioProject Acc. No. PRJNA989550, and the Sequence Read Archive (SRA) experiments are available by Acc. Nos. SRR25169931 and SRR25177381. Alpha and beta diversities of fungal and bacterial rhizosphere microbiota are signi cantly affected by the type of soil and the time of sampling.
Acknowledgments We are grateful to the French Polar Institute (IPEV) and the Terres Australes et Antarctiques Françaises for providing financial and logistical support for this study through the polar program #119 (ECONERGY). We wish to thank the Zone Atelier Antarctique et Terres Australes AS was supported by a 'Turku Collegium for Science and Medicine' Fellowship and a Marie Sklodowska-Curie Postdoctoral Fellowships (#894963).
Confocal microscopy As described before in  Miklaszewska et al. (2023) , samples (embryo and endosperm) were incubated with 1 mM BODIPY™ 505/515 for 30 min and washed with phosphate-buffered saline (PBS, pH 7. 2). Samples were observed under a confocal laser scanning microscope (CLSM) Olympus F3000 (Olympus, Tokyo, Japan) with excitation at 505 nm and emission at 515 nm for BODIPY. All images were analysed and processed using Image J software. The analysis was performed in three biological replicates.
Accumulation of ppGpp The content of alarmone ppGpp was determined in developing rape seeds. To verify whether the higher ppGpp level in the last developmental stage results from the lower water content, the results were analysed on both a fresh (Fig. 2B ) and an adjusted dry (Fig. 2C ) weight basis. The direct analysis of alarmone content in dry biomass was impossible because of the great instability of ppGpp at high temperature, which is necessary during the drying procedure. Thus, the data for the FW was recalculated to DW using means of water content (Fig. 1B ). The analysis showed that ppGpp level increased during seed development (Fig. 2B, C ). The highest ppGpp content was recorded for seeds collected   at 80th DAF, 5 or 3-fold higher than at earlier stages (35 and 56 DAF) on a fresh (Fig.
, 2006). Although cow persistence, including culling, mortality rates, and longevity, is one of the main concerns in dairy production  (Schuster et al. , 2020) , these rates have mostly been analysed in different settings and studies. However, revealing the importance and contribution of each farming area in explaining cow persistence is valuable for developing improved herd health programs. Common statistical analyses, such as generalised linear models, do not allow the explanation of a multivariate outcome or the aggregation of several explanatory variables into thematic blocks  (Bougeard et al. , 2012). An innovative statistical method called multiblock partial least squares (mbPLS) regression  (Wold, 1984; Wangen and Kowalski, 1989)  was recently introduced in veterinary epidemiology to describe datasets with a large number of variables organised into thematic blocks and to allow the use of composite outcomes  (Lupo et al.
Study design and herd recruitment This historical single-cohort study aimed to include 120 dairy herds according to the following inclusion criteria: (1) having at least dairy cows, (2) a loose-housed keeping system for cows, and (3) no intention to cease production in the coming years. A list of dairy herds with at least 100 cows was acquired from Estonian Livestock Performance Recording Ltd. (ELPR) in January 2019, including 182 holdings. Due to the lack of data on the last two inclusion criteria, herds were selected randomly from the sample frame using a random number generator in Stata® MP14. 2 (StataCorp, Texas). The aims and methodology of the study were introduced to the farm manager over a phone call, specifying whether the herd met all inclusion criteria. In cases of non-fulfilment of criteria or no interest in participating in the study, a new herd was randomly selected from the sample frame.
Data collection For each herd, the data for the year preceding the farm visit were collected from the ELPR. This included the number of cows in the herd exactly a year before the farm visit and at the date of the visit, and the number and age of culled cows (including cows slaughtered for meat production, died, or euthanised on farm, and excluding cows that were sold to another farm for dairy purposes) during the year preceding the farm visit. The average number of cows in the herd over the year before the farm visit was obtained by calculating the average of the number of cows in the herd during the farm visit and one year prior. Herd culling rate (CR) was calculated as the proportion of culled cows among the average number of cows in the herd. The mean value was calculated from the ages of all culled cows (MAofCC) within the same period, excluding cows that were sold to continue the production in another herd and is referred to as 'longevity' hereafter.
The Estonian Agricultural Registers and Information Board (EARIB) collects national cattle data, and animal owners are obliged to report all cattle births, movements, and deaths to the registry. For each herd, the cow on-farm mortality rate (MR) was calculated based on the EARIB data. The numerator included the number of cows that died (unassisted or euthanised) within the months preceding the farm visit, and the denominator was the average number of cows present in the herd over the same period. The theoretical framework of the questionnaire and data collection protocol was to capture the data of the possible risk factors from different factor groups that affect dairy cow health, performance and persistency. After a thorough literature review and drafting the preliminary version of the questionnaire and data collection protocol, a group of herd health experts were consulted for the review of the questions and for possible additions.
Animal-based measurements (ABMs) included body condition scoring in the close-up group and 2-3 months after calving, animal cleanliness scoring in the close-up group, among calving animals, and 2-3 months after calving, rumen fill scoring 2-3 months after calving, presence of visible injuries in lactating cows, and visible damage by mites/lice and ringworm among lactating cows (Supplementary Table  1  and 2 ). ABMs were recorded with the intention to assess 25% of the animals in the respective group with a minimum of 10 records. In case of more than one lactating cow group on the farm, the one with the highest number of animals was chosen for animal assessments. Two veterinarians involved in data collection were pre-trained in three joint farm visits to harmonize the questioning and performing farm measurements and ABMs.
Data editing The questionnaire data were digitalised using the electronic survey tool LimeSurvey (LimeSurvey GmbH) and farm data were exported in the Excel format. Data on farm variables were interpreted and answer categories were composed by aggregating identical answers. Rarely occurring answers were merged into one category (called 'Other') to avoid having variable categories with a low number of observations. The proportion of cows with visible lesions and empty rumen filling were calculated; cows with cleanliness scores of 3 and 4 were grouped together, and the proportion of those out of all assessed cows was used to calculate the proportion of dirty cows in the respective area. The average score was calculated for the body condition of the cows in each group. Out of all 172 explanatory variables 136 were obtained by using farm employee questioning, 24 were ABMs including 6 variables established based on animal testing for infectious disease antibodies, and 12 variables were based on on-farm measurements.
According to the discussion with herd health experts, explanatory variables were aggregated into thematic (X 1 , …, X 14 ) blocks, with several variables per block  (11, 20, 4, 14, 13, 9, 8, 13, 27, 10, 7, 7, 21, and 8, respectively)  collected from dairy herds (Table  1 , Supplementary Table  1  and 2 ). Continuous data were categorised when not normally distributed at roughly the median and quartile values to ensure balanced groups or at accepted threshold values when available. Out of 172 explanatory variables, 168 were categorical variables and four were continuous variables (Supplementary Table  1  and 2 ). The dataset including the recorded herd data was merged with one including herd MR, CR, and MAofCC data.