dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Spike protein sequences have been extracted from the GISAID repository on a country-specific basis and the date-stamp associated to each sequence has been used to obtain a temporal dimension of viral variants appearance. Note that each genome sequence in the GISAID data collection corresponds to the most frequent Spike variant occurring in a single infected individual. The pruned dataset (see Supplementary material) has been clustered per month to obtain groups of distinct variants. Firstly, we computed the LM between each pair of sequences, thus counting by an unweighted approach the minimal number of amino acid substitutions, deletions and insertions needed to transform one sequence into the other, and vice-versa. Secondly, the algorithm constructed a tree of proximity by pairing sequences that are the closest to each other into a branch.
To combine branches that contain more than one sequence, we used Ward's method, after having checked that other choices do not significantly affect the results (more details in the Supplementary material). The tree is completed when all sequences are grouped into a single branch. To define the clusters, we considered a cutoff in the distance so that branches whose Ward distance is larger than the cutoff are considered as separate clusters. We applied the same cutoff to all branches. The clustering procedure is applied to sequencing data binned in time, where the duration of each bin depends on the available data per day during the period of interest. The procedure depends on two parameters, the cutoff and a threshold in the size of each cluster, which can be tuned by optimising the dataset coverage and number of clusters, without any prior knowledge of the variants.
Emerging variants as time-ordered cluster chains. The time evolution and emergence of SARS- CoV-2 variants can be studied by applying our ML algorithm to the Spike sequence data binned in time, by calendar month. Hence, we have divided the sequence dataset for England following the date tag in the GISAID repository. For each month, we run the ML algorithm on the pruned data to define clusters, retaining only the ones comprising at least 1% of the monthly dataset. The cutoff on the Ward distance r W between branches, as well as the 1% threshold above, were chosen to optimise the coverage of the dataset (i. e. we required that the defined clusters cover at least 90% of the data) while keeping the number of clusters below 10. The optimisation analysis, presented in the Supplementary material, showed that the optimal range for the branch cutoff is r W ∈ [50, 200].
In this respect, the results for r W = 200 in Fig. 2B  can be directly compared to the VoCs identified by the WHO: Comparing the frequencies of occurrence in Fig. 2D , we see that v2 can be associated to the Alpha VoC, while v3 matches the epidemiological data for the Delta VoC. We also checked that the dominant Spike variant for the two chains presents the mutations characteristic of the two VoCs: N501Y, D614Y and P681H for the Alpha VoC; L452R, T478K, D614G and P681R for the Delta VoC. These results have been corroborated by a cluster analysis of the global dataset, without time binning, and by a similar analysis for the data of Wales and Scotland, as shown in the Supplementary material. The chain analysis, however, allowed us to better probe the time evolution and emergence of the persisting variants.
The inverse of the number of clusters defined above quantifies a "confidence indicator" (C. I. ) for the early warning, and it is plotted in Fig. 3B  as a function of the Alpha VoC percentage in each week dataset. The C. I. can be interpreted as the probability of identification of the correct emerging variant via the first cluster. The result shows that probabilities above 50% require the presence of the new variant in at least 1% of the sequences. In Fig. 3C , we show the Alpha VoC percentage as function of time in weeks. An early warning can be issued as soon as the new variant surpasses 1% of the data, leading to an early warning for the Alpha VoC in week 44, i. e. 6 weeks after the first detection. If the chain persists for 3 more weeks, an emerging variant is identified in week 47, hence 6 weeks before the official classification as a VoC by WHO.
4. 2 Pango lineage). Our monthly analysis already identified this lineage as a dominant Spike variant in cluster 43 (Fig. 2 ), branching off from the main Delta chain in August 2021 (date at which data acquisition was stopped for the purposes of this work). To determine whether Table  1. Early warning for the Alpha VoC. Results of the ML analysis applied to weekly binned data after the first detection of the Alpha VoC in England. The columns contain the 2020 calendar week number, with the initial date (Monday), the total number of sequences in the dataset for each week and the percentage of Alpha VoC sequences identified a posteriori in the data. The fifth column contains the minimal number of clusters in the ML output that allows to isolate the Alpha VoC cases. When this indicator is equal to 1, an early warning can be issued (week 44).
This procedure yields a reliable estimate of the diffusion of each variant. For this purpose, we used the full dataset from GISAID for the whole UK, using the VoC classification embedded in the GISAID data. As shown before, this classification is equivalent to the result of our ML approach. The result is shown in the top row of Fig. 6 , where we show the number of sequences (left plot), the new number of infections per variant and the result of the MeRG fit (middle) and the frequency of the VoCs (right). Note that the total numbers are plotted in blue, while the VoCs (1) I c (t) = A e γ (t-t 0 ) 1 + e γ (t-t 0 ) , Figure Patterns of spike protein diversification of the emerging variants. Heatmaps indicate for each cluster in a chain (A) the location and number of amino acid substitutions counted per all variants in a cluster when compared to the previous cluster and normalised accordingly (see Supplementary material).
Subdominant variants in each cluster are retained if their frequency is above 1% in the cluster. (B) recapitulates the sum of all the values from all chains in A, while (C) shows the S protein structure for reference. in colours. We considered the epidemiological data from the most recent waves, which developed between September 2020 and February 2021. The green curve in the middle plot shows that, after the first peak at the beginning of November, a second smaller peak developed. We describe the two with two independent sigmoids. The second sigmoid is subtracted from the data when fitting for the Alpha VoC data. The parameters from the fit are reported in Table  2. As the social conditions during this period did not change substantially, it is meaningful to compare the γ parameters for the Alpha and Delta VoC with the other ones (in green).
The results, shown in Fig. 6  and Table  2 , demonstrate that the MeRG framework provides an excellent modelling of the data. Figure  6. MeRG model for epidemiological data of variants. Results of the MeRG fitting of the number of infected associated to each relevant variant. Each row corresponds to a geographical region. In the left column we show the total number of sequencing available on GISAID (in colour the ones associated to the relevant VoC or VoI); the middle column shows the number of new daily infected (per 100. 000 inhabitants); the right column shows the percentage of each VoC or VoI in the sequencing data. All plots show daily rates, with data smoothened over a period of 7 days. In the middle plots, the data are shown by dots, where blue corresponds to the total and the colours show the number of infected associated to each variant.
Limitations. This study was performed on the sequencing data from a single region, England. This is justified by the fact that the sequencing dataset associated to England on the GISAID open-source genome repository is by far the largest and most uniform compared to other countries/regions. Biases relative to specific data-taking practices un England may induce biases in the analysis. To validate the results, however, we have also analysed the data for Wales and Scotland, as presented in the Supplementary material. We chose the other two nations of Table  2. MeRG fit parameters. Parameters from the fit of the VoC/VoI for the UK, South Africa, California and India, also shown in Fig. 6. The fit follows the MeRG model, according to which each variant can be fitted by an independent logistic function.
Data availability All raw data used in this work are obtained from open-source repositories: GISAID (https:// www. gisaid. org/) for the sequencing; Ourworldindata. org (https:// ourwo rldin data. org/) and the UK Coronavirus Dashboard (https:// coron avirus. data. gov. uk/ detai ls/ cases) for the epidemiological data. The Machine Learning code is available at https:// github. com/ Adele deHoff er/ ML-Covid.
Sixty of the hybrids were genotyped with a 600k Axiom Affymetrix array  62. Data can be found and downloaded at https://doi. org/10. 15454/KLD0GH 'genotypic dataset'. After quality control, 465,621 polymorphic SNPs were retained for analysis (excluding SNPs with minor allele frequency lower than 0. 05 and/or missing value for more than 20% of hybrids). Missing values were otherwise imputed  63. All physical positions referred to hereafter are based on the B73 reference genome  64. We tested the representativeness of the above panel of varieties (named 'genetic progress panel' below) by comparing it with other two panels. First, we considered a series of 96 lines with expired Plant Variety Protection, known as ex-PVPs. These represent the founding material and germplasm pool for maize breeding from 1970 to 2000 in America and Europe  65, 66  , for which genotypic information is available based on a set of 29,296 public SNP markers  67.
Yield and environmental conditions in Europe. We tested the representativeness of the experiments presented here based on the information 5 collected over 35 years in 59 locations representative of the European maize growing area and of typical soil types of these regions, representing the ten European countries with the highest maize growing. Briefly, soil data were obtained from the JRC European Soil Commission database and from the Crop Growth Monitoring System. Meteorological data represent 35 years of daily weather (1975-2010) obtained from (i) the AGRI4CAST database of the JRC or the INRAE CLIMATIK databases. Sowing dates were simulated in each field as the first day from February to May in which the frequency of frost was <5% in the following ten days. We showed that these rules captured the variability of sowing dates in European databases  5.
Two watering regimes were simulated in each site, either rainfed or watered every seventh day with a water volume equaling the difference between reference evapotranspiration and rainfall. Plant density was adjusted in each country based on the JRC database. Nitrogen supply at sowing was calculated as 70% of nitrogen needed to reach the maximum yield of the optimum genotype with the optimum sowing date. Yields presented in Fig. 1  were simulated based on the information on climate and management practices presented above, over the 59 × 35 combinations of location and year  5. For that, we used a modified version of the APSIM model 72 parametrized for the B73×UH007 hybrid  73. Platform experiments. The seven platform experiments presented here are synthesized in Supplementary Table  2 , and the hybrids involved in each of them are presented in Supplementary Table  1.
The phenotypic variables considered in each experiment are presented in a spreadsheet (Supplementary data 11) in which each sheet corresponds to one experiment and presents in detail the traits measured in this experiment with their entity (e. g. , ear or plant), characteristic (e. g. , area), methods, unit, and phenological stages at which each measurement was taken. This file also presents the mapping of these terms onto public ontologies. Each of these sheets is presented again as a csv file in an experiment-specific directory of the dataset https://doi. org/10. 15454/KLD0GH, together with complements of methods and the datafile from which all values can be downloaded. Three experiments were carried out at the Phenoarch platform 26 https://www6. montpellier. inrae. fr/lepse/Plateformes-de-phenotypage/Montpellier-Plant-Phenotyping-Platforms-M3P/PhenoArch.
c and d  30. Radiation use efficiency (RUE) was calculated as the ratio of biomass to intercepted light, with the latter calculated from 3D plant reconstructions  44. Reproductive development was studied in Exp. a, c and d. Four plants per hybrid were sampled on the first day of pollen shedding, individually recorded for each plant. Ears were dissected to measure (i) silk length (from ovary insertion to tip) and the number of emerged silks  42  (silks are modified styles of each ovary that collect pollen) and (ii) the number of ovary cohorts per ear  38  (rings of ovaries or grains initiated at a common date, localized at a common distance from the ear base). Raw trait values were corrected for spatial disturbances by fitting a mixed model to daily measurements (R package SpATS  74, 75  ) with a fixed term for genotype and random effects for rows and columns as well as a smooth surface defined on row and column coordinates.
Generalized heritabilities were calculated daily with the same R package, using the same model but with genotypes included as a random term  74, 75. Genotypic predictions for traits at individual time points, t, were obtained from a generalized additive model fitted to the spatially adjusted daily measurements, e y i;k t ð Þ, for each plant k of genotype i e y i;k t ð Þ ¼ α i þ f i t ð Þ þ ϵ i;k t ð Þ; ϵ i;k t ð Þ N 0; σ 2 À Á ð1Þ where α i is a genotype-specific intercept, f i t ð Þ is a genotype-specific thin plate regression spline function on time, and ϵ i;k t ð Þ is a random error term. Model (1) was fitted with the gam function of the mgcv R package by REML. Variance components were obtained by treating smooth terms as random effects Three series of experiments were performed in the Phenodyn platform  76  , https://www6.
be/en/research-institutes/eli/elia/rootphair. html), with a completely randomized design involving 30 hybrids and 30 replicates per hybrid (Supplementary Table  2 ). The greenhouse temperature was 23 ± 3 °C and 16 ± 2 °C during day and night. Supplementary LED lighting was used to obtain a 16:8 photoperiod. Seeds were pre-germinated on vertical filter paper and transferred to the aeroponics platform. The latter allows roots to grow at a rate only constrained by assimilating availability and temperature. Shoot and root dry and fresh weights were measured after 2 weeks. Genotypic means were corrected for spatial effects as above. Field experiments. The 30 field experiments presented here are synthesized in Supplementary Table  2. The phenotypic and environmental variables considered in each experiment are presented in a spreadsheet (Supplementary data 1) in which each sheet corresponds to one experiment, as presented above for experiments in controlled conditions.
Each of these sheets is presented again as a csv file in an experiment-specific directory of the dataset https://doi. org/10. 15454/KLD0GH, together with complements of methods and the datafile from which all values can be downloaded. One intense field experiment (Exp. k) tested the effect of plant density in the Phenofield platorm  79. Plants were sown at densities of either 7 or 9 plants m -2 , either irrigated or with a water deficit imposed by rain-out shelters. The experiment followed an alpha-lattice design with three replicates of two-rows plots, 6 m long. Light, air temperature, relative humidity (RH), and wind speed were measured every hour with an on-field weather station. Soil water potential was measured every day with tensiometers at 30, 60, and 90 cm depths with three replicates per treatment.
The number of grain cohorts was calculated in each experiment by dividing grain number by the mean ear number per plant (as measured in each experiment) and by the number of grains per cohort measured in platform and intensive field experiments, very stable for each hybrid (Supplementary Fig. 5 ). The two central lines of each plot were mechanically harvested, and grain yield was scaled to 15% moisture content. Individual grain weight was measured and used to calculate grain number per m 2 from grain yield. BLUEs were calculated, for grain yield components and flowering traits, by fitting a mixed model that included a fixed term for genotype, row and column as random terms and a smooth surface, using the R package SpATS  74, 75. Generalized heritabilities were then calculated with genotype included as a random term  74, 75.
1f ), namely well-watered (mean soil water potential during the vegetative and flowering phases above -0. 1 MPa) and water deficit (mean soil water deficit below -0. 1 MPa, see time courses in Fig. 1f ). Evapotranspiration was simulated with the APSIM crop growth model  72  , which simulates plant growth, transpiration and soil evaporation every day, based on environmental data and parametrized for the B73×UH007 hybrid but with final leaf number, phyllochron and ligulochron set to the mean genotypic estimate over four generations of hybrids  73. Calculations at the base of Supplementary Fig. 13  follow those in ref. 52. Briefly, yields measured in each experiment were plotted against evapotranspiration simulated as above in the same field for each studied hybrid. Hybrids were then clustered by year of release.
Genetic gain, Bayesian networks, and genotype × environment interaction. Genetic gain for a trait was defined by the slope of the linear regression of the trait value on the year of release in the genetic progress panel. Genotypic means for traits were obtained for each environmental scenario or experimental treatment with a linear mixed model including a fixed term for hybrids and a random term for experiments (R package lme4). A Bayesian network was constructed (R package bnlearn) to evaluate the conditional dependencies between year of release, measured phenotypic traits and grain number. For each environmental scenario, acyclic-directed Bayesian networks were fitted to scenario-specific genotypic means of grain number and phenology, together with estimates for traits measured in phenotyping platforms, centered and scaled prior to analysis.
37p 0. 05% Bayenv XtX37based on the differentiation of the 22 most recent and 22 most ancient hybrids. c RUS identified by combining methods. d Percentage of resulting RUS that colocalize with considered QTLs, and p-value of enrichment: comparison with 100,000 random genomic regions of same size at QTL regions. N = 60. Source data in https://doi. org/10. 15454/ KLD0GH/ genotypic dataset for the physical map and in corresponding papers for QTLs.
Acknowledgements This work was supported by the  EU  project  FP7-244374  (DROPS), the  Ministry of Agriculture  and  PROMAÏS  project  MAP-C2008-P227  (DivZea), the projects  ANR-10-BTBR-01  ( Amaizing ) and  ANR-11-INBS-0012  ( Phenome ), and the  EU  project  H2020 731013 (EPPN 2020  ). We acknowledge the scientific advisory board,  B Aizac ,  J Beigbeder ,  J Betran ,  JF Bizar ,  A Gallais ,  Ph Moreau ,  C Tabel , and  A Vincent. We are grateful to key partners for field experiments:  JL Olivier JM Groisset  and  C Deluc  (Limagrain),  G Brosse  (Caussade),  G Lene  and  H Magnan  (RAGT),  P Schopp  (KWS),  M Fourneau  (Mas seeds),  F Tanzi  and  E Basilico  (Syngenta),  A Fournier  and  B De Solan (Arvalis) ,  P Bataillon ,  S Malavieille  and  B Largardere  ( INRAE ),  C Gelot  and  C Grizeau  (GEVES),  F Daveau  and  E Voinot  (Euralis),  J Lopez  and  MH Tixier (Biogemma/Limagrain).
37p 0. 05% Bayenv XtX37based on the differentiation of the 22 most recent and 22 most ancient hybrids. c RUS identified by combining methods. d Percentage of resulting RUS that colocalize with considered QTLs, and p-value of enrichment: comparison with 100,000 random genomic regions of same size at QTL regions. N = 60. Source data in https://doi. org/10. 15454/ KLD0GH/ genotypic dataset for the physical map and in corresponding papers for QTLs. RUS Flowering time Architecture Adaptive Regression Days to anthesis rh PAD Response grain # Bayenv Florigen transcripts Light ear layer Yield in stress rowth/stomata Regression a Bayesian XtX b Combined c % RUS % RUS % RUS coloc. p-value coloc. p-value coloc. p-value with enrich. e with enrich. e with enrich e QTLs d QTLs d QTLs d Phenology Days to anthesis 6.
Protein spots found to be reproducibly altered were further evaluated with Pro-teomweaver™ imaging software version 2. 1 (Definiens). Following automatic spot detection and gel normalization, spot matching between all gels was monitored and edited where appropriate. The automatic normalization was applied on spot intensities to adjust them in a way generating a comparable pattern between spot pairs/different gels. The relative intensity of individual spots in 2-DE gels of an/an and +/+ mice was quantified using spot volumes of 16-bit gray scale images. The relative intensities of selected protein spots were measured via densitometric measurements. The student's t-test was utilized to display whether there is a significant difference between sample pairs (p < 0. 05).
00 m/z. LC-MS raw data out of MS instrument were extracted by the TurboSEQUEST algorithm as DTA files and converted to MASCOT generic format files (MGF). Mass spectra analysis was done utilizing our MASCOT in-house license with automatic searches in NCBI databases. MS/MS ion search was performed using the following parameters: (i) taxonomy: mammalia, (ii) proteolytic enzyme: trypsin, (iii) one missed cleavage was allowed, (iv) mass value of monoisotopic peak, (v) peptide 'precursor' mass tolerance was set to 1 Da, fragment mass tolerance was set to 1 Da and (vi) methionine oxidation as well as acrylamide adducts (propionamide) on cysteine residues as variable modifications, and a carbamidomethylation on cysteine residues as a fixed modification. Proteins were accepted to be correctly identified with scores of p < 0.
18S sequences Available 18S gene sequences from Haplosporidian organisms and the Paramyxida Marteilia refringens were downloaded from GenBank and included in the phylogenetic analyses together with the sequences longer than 1000 bp obtained in the present study. Alignments were performed using ClustalW  (Thompson et al. 1994 ) in MEGA 7  (Kumar et al. 2016 ) with default parameters. The analysis involved 46 nucleotide sequences and a total of 2071 characters. Prior to the phylogenetic analysis of sequences, the program jModelTest 0. 1. 1  (Posada, 2008 ) was used to select the best fitting substitution model according to the corrected Akaike information criterion (AIC)  (Hurvich & Tsai, 1993). A total of 88 candidate models, including models with equal/unequal base frequencies, with/without a proportion of invariable sites (+I), and with/without rate variation among sites (+G) were tested.
The best-fit model of nucleotide substitution was the GTR+I+G model. Tree topology was therefore inferred based on a Bayesian approach using MrBayes v 3. 1. 2 (Huelsenbeck & Ronquist, 2001)  and implementing the GTR + I + G model of nucleotide substitution. Internal transcribed spacer (ITS) regions, including the ITS 1, 5. 8S rRNA gene, and ITS 2 Available ITS1-5. 8S -ITS2 sequences from  Carnegie et al. 2014  were downloaded from GenBank and aligned with sequences obtained in this study using ClustalW  (Thompson et al. , 1994)  in MEGA 7  (Kumar et al. , 2016)  with default parameters. Evolutionary divergence was estimated between sequences obtained in this study and closest sequences found in Genbank using the Maximum Composite Likelihood model  (Tamura et al. , 2004). The rate variation among sites was modelled with a gamma distribution (shape parameter = 1).