dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational Structural Analysis of Mutants Sequence and sequence-related information were retrieved from the Uniprot database  [55]. For structural analysis, crystallographic models for the Bro1 (PDB accession 5crv) and CC domain (PDB accession 5l mL) and the AlphaFold  [56]  theoretical model were used. Models were manually inspected, and mutations were evaluated using the Pymol program (http://pymol. org accessed on 8 January 2022). Linear sequence motifs were identified using the Eukaryotic Linear Motif (ELM) database  [57].
For each area, 12 topsoil samples (the top 15 cm of soil) were taken randomly and then used to form a representative bulk per area, whose a part was used for the chemical analyses and another for the molecular ones. Soil samples were air-dried before being pooled and subsequently analysed. The three highland regions presented different extensions and pedoclimatic conditions according to the FAO soil map (https://www. fao. org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-so il-map-of-the-world/en/) and the Köppen climate classification  (Rubel and Kottek, 2010). The largest area, with the consequent highest number of farms, was the north of the Massif Central, located in the centre of France. This region is characterised by a temperate climate and features andosols. The Italian region is in the eastern Alps and presents an alpine climate and eutric cambisols.
(2020). The undetermined Ct (cycle threshold) was set 40 to equal 0 genes' copies by the transformation. The bioinformatics processing of the sequencing data was based on QIIME  (Caporaso et al. , 2010)  using the similarity threshold of 97 % to cluster the single ASV (Amplicon Sequence Variant -  Callahan et al. , 2017). The ASV profiles were analysed using FAPROTAX 1. 2. 4  (Louca et al. , 2016)  to extract functional profiles, which correspond to lists of ecological functions performed by the taxa identified. The DNA sequences have been deposited in the GenBank repository SRA Archive, under the project code PRJEB56444, https://www. ebi. ac. uk/ena/brow ser/view/PRJEB56444.
Taxonomic and functional profiles of microbial communities Firstly, we used Venn diagrams  (Shade and Handelsman, 2012)  to identify the suites of ASVs, phyla, and functions shared by the microbial communities defined by region, management type, pH, and C org. Venn diagrams were constructed with the 'venn. diagram' function of the 'VennDiagram' package  (Chen and Boutros, 2011). Then, we calculated the dissimilarity matrices, based on the Bray-Curtis distance, of the taxonomic and functional profiles to allow multivariate approaches  (Anderson and Walsh, 2013)  to the analysis of taxonomic compositions and ecological functions within and between microbial communities. We analysed dissimilarity matrices with ANOSIM (Analysis of Similarity -Clarke, 1993) with the 'anosim' function of the 'vegan' library  (Dixon, 2003) , PERMANOVA (Permutational Multivariate Analysis of Variance -Anderson, 2001) with the 'adonis2′ function of the "vegan" library, and the Mantel test  (Mantel, 1967)  with the 'mantel' function of the "vegan" library.
Functional profiles of genes To compare the potentials of specific functions of microbial communities, we analysed the abundances of qPCR gene copies (log-transformed) first with Kendall's correlations between genes and then with an ANOVA based on permutation tests, using the 'lmp' function from the 'lmPerm' library  (Wheeler and Torchiano, 2010). We used two models, one including the main effects of the region (FR, IT, NR), management type (M or P), and their 2-way interaction, and the other including the main effects of pH and C org class. Moreover, to assess possible niche differences between archaeal and bacterial amoA genes (AOA and AOB, respectively), we calculated the ratio between AOB and the total ammonia-oxidation guild (AOA + AOB). The ratio was analysed with a generalised additive model based on beta distribution and log link function, using the function 'gam' of the package 'mgcv'  (Wood, 2017).
Functional diversity The functional profiles presented patterns similar to the taxonomyrelated ones at the phylum level concerning region, management type, and pH (Fig. 3 ). There was also a common and persistent core for the functions (Fig. 3 ), which is not unusual since a large fraction of metabolic genes encoding functions appeared early in Earth's history and propagated into multiple clades  (David and Alm, 2011; Falkowski et al. , 2008). This common functional core may be affected not only by the adaptive loss of functions due to environmental conditions  (Morris et al. , 2012)  but also by horizontal gene transfer  (David and Alm, 2011; Falkowski et al. , 2008). Horizontal gene transfer is independent of the 16Sbased phylogeny on which bacterial taxonomy and metabarcoding assignments are based, suggesting that bacteria may possess actual functional traits that are not detectable through current ribosomal databasedependent annotation methods, which account for the sharing of traits among ASVs.
, 2018; Martiny et al. , 2015). The presence of distinct ASVs capable of performing shared functions provides an ecosystem buffer against taxonomic diversity variation due to local disturbances, making the entire community performance resistant to impacts of a given extent  (Jurburg and Salles, 2015; Louca et al. , 2018). Thus, it is reasonable to uncouple considerations on ASV diversity from microbial functional diversity thanks to the existence of functional redundancy across taxonomy  (Louca et al. , 2018). It can also be postulated that the current databases used to extract the functional profiles could likely be more influenced by high taxonomy levels, such as phylum, during the assignment. This could also be partly due to the presence of ASVs that lack lineage annotation, as their individuation is based only on a concept of sequence uniqueness, but not on taxonomical recognition, as revealed by the negative correlation between function assignment and total ASVs number (Fig.
The sequencing provides a comprehensive view of the microbial community targeting specific region of the 16S rRNA while the real-time PCR amplifies and detects specific DNA sequences. Both techniques present limitations that can partially explain the different results. Sequencing can be affected by biases due to the difficult distinction of similar ASVs with high-sequence similarity of partial 16S variable regions  (Gao et al. 2017; Jeong et al. 2021)  with consequent limitations to infer the functional profiles using reference database, such as FAPROTAX. Real-time PCR provides quantitative information about a target gene, without distinguishing whether the gene belongs to live microorganisms or is a cell-free relic that has been preserved and accumulated in the soil  (Kralik and Ricchi 2017; Smith and Osborn 2009).
Since its Neolithic domestication in the Fertile Crescent, barley has spread to all continents and represents a major cereal in many modern agrarian systems. Current barley diversity includes thousands of varieties divided into four main categories corresponding to 2-row and 6-row subspecies and naked and hulled types, each of them with winter and spring varieties. This diversity is associated to different uses and allow cultivation in diverse environments. We used a large dataset of 58 varieties of French origin, (1) to assess the taxonomic signal in barley grain measurements comparing 2-row and 6-row subspecies, and naked and hulled types; (2) to test the impact of the sowing period and interannual variation on the grains size and shape; (3) to investigate the existence of morphological differences between winter and spring types; and finally (4) to contrast the relationship between the morphometric and genetic proximity.
Morphometric data A total of 1980 barley seeds corresponding to 58 varieties were studied (S1 Table ). The varieties are divided into four taxonomical groups (Table  1 ): two-row naked (N = 13), two-row hulled (N = 23), six-row naked (N = 10) and six-row hulled (N = 12). The varieties are also divided into three different sowing periods: spring (N = 24), winter (N = 31), and alternative (N = 3) without seasonal preference (Table  1 ). So-called alternative varieties corresponding to varieties that can be sown in spring or winter. An accession includes grains from a single variety grown and sampled the same year in the same field. A total of 66 accessions were analysed. This includes 54 varieties collected a single year and 4 varieties repeatedly harvested on three different years (varieties 10004-CFL33 and 10024-ESV are 6-row-hulledwinter barley; varieties 12510-DLG and 12900-CHI are 2-row-hulled-spring barley).
The lateral view documents the shape of the grains taken in their thickness while the ventral view documents the shape of the grains visualised with the furrow facing upwards. The combination of the two views allows to document the shape of the grains from two different points of view, which somehow allows to study the grains in 2. 5D, approaching a 3D description. A centimetric scale was included in all pictures. The background of each picture was removed, and the grain was converted to a black mask using Adobe Photoshop CS6 software. The 2D outline coordinates was then extracted using R v. 4. 1. 1 and the package Momocs v. 1. 3. 0 (https://github. com/MomX/Momocs/; [39] ). The outline coordinates were scaled using two landmark coordinates manually localized 1 cm apart on the original picture using Image J  [40].
In our case, the lateral view of the barley grain is described by 5 harmonics and the ventral view by 7. The number of harmonics was determined using the harmonic power criterium in the Momocs package  [39]. These harmonic coefficients correspond to shape variables and are analysed first using a Principal Component Analysis (PCA) in order to reduce the dimensionality of the data, assess the overall grain shape variation and detect potential outliers. Then, differences between groups were tested using Multivariate Analyses of Variance (MANOVA). Pairwise differences were assessed using pairwise multilevel comparison (vegan & pairwiseAdonis R packages). Subsequently, discriminant analyses were used to separate groups and to accuracies presented were calculated using leave-one-out cross-validation (CVP) and their confidence intervals (MASS R package,  [42] ).
Patterns of grain morphological variability in relation to spike anatomy More broadly, the dissimilarity networks calculated on the lateral and ventral grain shapes combined indicates that our dataset is firstly structured by the number of spike rows (2-rows vs. 6 rows), then according to hulled/naked types. The discrimination of 6-row/2-row varieties is mainly determined by the ventral shape while the discrimination of hulled/naked varieties is determined by the lateral outline. This structuration is readily explainable by the constraints imposed by the anatomy of the spike on the morphology of the grains. These constraints are very different for the 6 and 2-row types. In 2-row barley only the central spikelet is fertile at each node of the rachis whereas the 6-row barley has three fertile spikelets at each node.
Another explanation to differences between spring and winter barley can be variation in environmental conditions during barley growth cycle. Several critical growth periods, as tillering, flowering, filling and ripening, are sensitive to rainfall  [63] , drought  [64, 65] , available soil water  [66]. The impact of environmental variability could be increased in our dataset as in the BRC of Clermont-Ferrand varieties were not always sown in the season they were supposed to be, i. e. spring varieties were occasionally sown in December because droughts during the spring sowing period are more frequent and winters are less harsh due to climate change. Unfortunately, this data was not always recorded, it would therefore be important to further investigate this question based on a detailed record of sowing time and environmental conditions along the life-cycle, and to grow the same accession at different periods.
Funding PS is supported by the  Agence Nationale de la Recherche  ( ANR-11-LABX-0021  to  Labex LipSTIC) , the the  Conseil Reǵional de Franche-Comté (" Soutien au LabEX LipSTIC "). G-CF is supported by  NIH  grants  R01 GM-132149 , and  HL-160811 , and  R35 GM-149538.
Production, purification and characterization of recombinant monoand bi-specific antibodies The variable sequences of matuzumab (1Matu), cetuximab (1Cetux), trastuzumab (2Trastu), pertuzumab (2Pertu), patritumab (3Patri; anti-HER3 mAb) and elgemtumab (3Elgem; anti-HER3 mAb) were extracted from the IMGT database (Montpellier, France). The bispecific and tetravalent BiXAb ™ platform was developed by Biomunex Pharmaceuticals (patents WO2018178101 and WO2018127608), based on the initial BsAb format described by Golay et al. (61)  and the accompanying patent (WO2013005194). The BiXAb ™ architecture (Figure  1A ) consists of a tetravalent IgG-like format with two Fabs arranged in tandem and joined by a novel semi-rigid pseudo-hinge linker. This linker is a human sequence that contains the IgG1 hinge, the CH2 domain N-terminus and a human Ig alpha hinge sequence.
8, 10% glycerol, 1% SDS), and heating at 95°C for 5 min, 100 µg of proteins were loaded on 7-12% electrophoresis gels and were separated at 100 V for 2 h. After liquid transfer at cold temperature and constant voltage (100 V) for 2 h, PVDF membranes were saturated in PBS/0. 1% Tween (PBS-T) supplemented with 5% skimmed milk at room temperature for 2 h. Then, membranes were probed with primary antibodies diluted in PBS-T/3% BSA/0. 002% sodium azide at 4°C for 18 h. Membranes were then incubated with a peroxidase-conjugated anti-rabbit antibody (1/2,000 dilution in PBS-T) at room temperature for 1 h. For enzymatic detection, western blots were revealed with the Western Lightning Ultra reagent (Perkin Elmer, Waltham, MA) and images were acquired with G:BOX (Syngene, Cambridge, UK) before quantification with the ImageJ ™ software.
After 4 h of incubation, cells were washed, and incubated with APC-AF750-conjugated anti-CD16 (1/100 dilution) and ECDconjugated anti-CD56 (1/25 dilution) antibodies (membrane CD16/CD56 co-labeling) at 4°C for 30 min to distinguish CD16 + NK92 cells from tumor cells during flow cytometry analysis. After cell fixation and permeabilization according to the supplier's recommendations (#555028, BD Biosciences), intracellular IFNg production by CD16 + NK92 cells was detected by incubation with a phycoerythrin-conjugated anti-IFNg antibody (1/10 dilution) at 4°C for 30 min. After extensive washing, 10,000 events were acquired using a Gallios flow cytometer (Beckman Coulter). The percentages of activated CD16 + NK92 cells that degranulated (CD107a + ) or produced IFNg (IFNg + ) and of cells that exerted both responses (CD107a + IFNg + ) were compared by data analysis with the Kaluza software (version 2.
Flow cytometry analysis of CDC initiation by expression of the C4c complement fraction After trypsin detachment, suspended BxPC3 cells (1x10 5 ) were incubated at 37°C with a 10 mg/ml-saturating concentration of BiXAb ™ , parental 1MAbs or 2MAbs in culture medium/1% complete human serum (#ICSER-25ml; Serum Innovative Research, Novi, MI) for 30 min. C4c complement fraction binding was revealed by labeling cells with a FITC-conjugated anti-C4c antibody (1/100 dilution) at 4°C for 30 min. After washing, 10,000 events were acquired using a Gallios flow cytometer. The mean fluorescence intensity (MFI) was quantified with the Kaluza software.
Flow cytometry analysis of apoptosis Cell apoptosis was assessed using the Annexin V/7-AAD kit (#IM3614; Beckman Coulter) according to the manufacturer's protocol. 100,000 BxPC3, Sw1990, and C-PDX P4604 cells were grown in 1 ml of medium/10% FCS in 12-well plates (Sarstedt) at 37°C for 24 h, before serum starvation in culture medium/2% FCS for 24 h. After medium removal, cells were incubated with various concentrations of BiXAb ™ or 2MAbs in medium/2% FCS at 37°C for 48 h. Some cells were incubated with staurosporine (200 nM; positive control for apoptosis) at 37°C for 3 h. After centrifugation, the cell pellet of each well was labeled with 10 µl of annexin-FITC and 20 µl of 7-AAD. After 15 min incubation at 4°C in the dark, Annexin V/7-AAD labeling was measured with a Gallios flow cytometer and analyzed with the Kaluza software.
Data processing and network modeling Heatmaps were obtained with R and the ComplexHeatmap library (62). The HER/AKT/ERK signaling models were computed using the aiMeRA R library  (63). To cluster the antibodies on the basis of the phenotypic response, we assigned each antibody to a feature vector that included AKT and ERK activation (i. e. phosphorylation) rates in the six cell lines (BxPC3, AsPC-1, CFPAC, Sw1990, P4604, SKBR3; 12 values in total), the sum of ADCC values measured in BxPC3, Sw1990, CFPAC, and AsPC-1 cells (this total was multiplied by 6 to balance with the other features), and the sum of the viability percentages in BxPC3, BxPC3-GR, and P4604 cells (this total was multiplied by 3 to balance with the other features). Based on these feature vectors, we used hierarchical clustering with Euclidean distance (dist function in R) and Ward D method (hclust function in R).
(A) Immunohistochemistry analysis of the penetration of the BiXAb ™ 3Patri-1Cetu-Fc, the 2MAbs 3Patri+1Cetu, and 3Patri and 1Cetu in tumor xenograft sections (x20 magnification; left panels). Antibodies were detected with a peroxidase-conjugated anti-human Fc. Human Fc-positive cells (right panel) were quantified in the whole tumor section using the QuPath software. Immunohistochemistry analysis (x20 magnification; left panel) of CD31 angiogenesis (B) and Ki67 proliferation (C) in Sw1990 cell xenografts. CD31+ microvessel density was estimated as the mean value in ten histological fields using the Image J software. The percentage of Ki-67+ cells in tumors was quantified in the whole tumor section using the Qupath software. IRR BiXAb™, negative control BiXAb™. Data are the mean ± SEM (n=5 mice/group).
Acknowledgments The authors greatly appreciated  Dr. S. Plyte  ( Biomunex Pharmaceuticals ) for helpful scientific discussion and corrections during the manuscript processing. The IRCM animal facility, PDX and MRI platform staff are greatly acknowledged. We also acknowledged the  "Reśeau d'Histologie Expeŕimentale de Montpellier" -RHEM  facility supported by  SIRIC Montpellier Cancer Grant INCa_Inserm_DGOS_12553 , the european regional development foundation and the occitanian region ( FEDER-FSE 2014-2020 Languedoc Roussillon ),  REACT-EU (Recovery Assistance for Cohesion  and the  Territories of Europe) ,  IBiSA  and Ligue contre le cancer for processing our animal tissues, with histology technics and expertise. We also thank  Drs. H. Viéand B. Clemenceau  ( Nantes-Angers Cancer and Immunology Research Center ) for providing the CD16/FcgRIII (158V allotype)transfected human NK92 cell line.
Funding This work was supported by the  French National Research Agency (ANR)  under the  Program Investissement d'Avenir  (grant agreement:  Labex MabImprove ,  ANR-10-LABX-53-01 ) and the  ANR contract DUALPancHER  (grant agreement:  ANR-16-CE17-002 ). ER and GJ were recipients of PhD studentships from  ANR , and CD was a recipient of an engineer fellowship from  ANR.
Material The German Federal ex situ genebank at IPK Gatersleben harbors one of the largest wheat collections worldwide (https:// www. ipk-gatersleben. de/en/infrastructure/gene-bank). Recently,  Schulthess et al. (2022a)  conducted (i) GBS for 7,651 genebank accession numbers and 325 elite wheat cultivars; and (ii) WGS for 444 genebank accessions and 322 elite cultivars and advanced breeding lines. In some cases, different phenotypes were observed for a specific accessions. Hence, those accessions were represented by more than one SSD line. In total, 94 heterogeneous genebank accessions represented by two SSD lines and 171 cultivars represented by three independent samples were genotyped with GBS. For WGS, only two genebank accessions were genotyped using two independent SSD lines. All the remaining samples were genotyped once.
In total, GBS and WGS experiments were run for 8,412 and 768 samples, respectively. GBS and WGS data of the same genebank accessions can be identified by the SSD-PGR, while for cultivars the cultivar name can be used. Samples genotyped with WGS were assigned to three groups by  (Schulthess et al 2022a; Schulthess et al 2022b): (i)  the "PRE-GREEN REVOLUTION", consisting of landraces and cultivars released before 1970; (ii) the "OLD CULTIVAR PANEL", consisting of cultivars released between  1971 and 2000; and (iii)  the "NEW CULTIVAR PANEL", consisting of cultivars released after 2000. Here, these data were reanalyzed with an aim to identify large introgressions. All analyzed data are publicly available at the European Nucleotide Archive (https://www. ebi. ac. uk/ena/) under the project IDs PRJEB41976, PRJEB4873, and PRJEB48988.
Methods Coverage analysis was adapted from  Keilwagen et al. (2022)  and used for the GBS and WGS data. For GBS, the complete dataset was used, while down-sampled data were used for WGS. Raw sequencing data were adapter-and quality-trimmed with Trim Galore (version 0. 4. 0; non-default parameters: quality ≥ 30, read length ≥ 50; https://github. com/FelixKrueger/TrimGalore). Trimmed reads were individually mapped against the wheat reference genome of Chinese Spring v2. 1  (Zhu et al. , 2021)  using  BWA-mem (v0. 7. 15-r1140)   (Li, 2013). A concatenated reference sequence of Chinese Spring version 2. 1 and rye  (Rabanus-Wallace et al. , 2021)  was used to infer the potential origin of introgressions. Unmapped reads, supplementary reads, and non-primary alignments were removed from mapped reads using SAMtools (version 1.
Still, introgressions on chromosome 4A were often identified in the collection genotyped with WGS, indicating that these introgressions could be identified more easily with WGS than with GBS. Fewer samples with introgressions on chromosome 6A were present in the NEW CULTIVAR PANEL than in the OLD CULTIVAR PANEL and the PRE-GREEN REVOLUTION PANEL. Despite being very conservative, focusing only on introgressions of at least 25 Mb, 130 samples were identified to have introgressions in the WGS dataset in this analysis, but were determined to have no introgressions in the original study by  Schulthess et al. (2022a). Most of these introgressions had a moderate size (slightly larger than 25 Mb). However, some samples with large introgressions were also identified, e. g. , TRI 1005 with an introgression on chromosome 7A and TRI 7716 with an introgression on chromosome 2D (Figure  2 ; Supplementary Table  2 ).
Duplicates and heterogeneity Genebank accessions might not consist of a single genotype, especially if they are landraces or CWR  (Keilwagen et al. , 2014; Singh et al. , 2019; Mihelich et al. , 2020; Badaeva et al. , 2021). Some genebanks, including IPK, have established protocols for splitting accessions based on phenotypic differences in characterization and regeneration trials  (Hintum et al. , 2002). However, accessions may still be mixtures of different genotypes after line splitting. Based on phenotypic differences detected in specific genebank accessions,  Schulthess et al. (2022a)  genotyped more than one SSD line per accessions. Hence, some accession or cultivar names occurred more than once in the datasets for various reasons. Some of these samples belonged to the same genebank accessions, but two SSD lines were produced and analyzed per accessions (SSD-PGR), e.