dataesr/scientific-paragraphs-categorization huggingface dataset:
We used the ESM2 version with 3B parameters, trained on the UniRef50 dataset. SENSE-PPI is designed as a Siamese architecture (Figure  4 ) consisting of two identical modules with shared weights that are merged together to provide a single output value p 2 [0, 1] describing the confidence in the interaction, where 0 and 1 stand for low and high confidence respectively. The Siamese design is needed to ensure the commutativity of the model: the output score has to be the same for pairs A-B and B-A. The model takes two amino acid sequences of variable size (L, M ) as input. Sequences longer than 800 amino acids are trimmed accordingly to this maximum size. The 36 layers of the PLM ESM2 are used to define two embeddings, of size L ⇥ 2560 and M ⇥ 2560, for the two amino acid sequences, respectively.
Acknowledgments i-Bio initiative at  Sorbonne University  for the PhD fellowship and travel grants to KV;  Sorbonne Center for Artificial Intelligence (SCAI)  and LIP6 laboratory ( UMR 7606 ,  Sorbonne University-CNRS)  for access to their clusters;  Agence Nationale de Recherches sur le Sida et les Hepatites Virales  [ ANRS-AAP-2021-CSS-12 ] (AC).
The high performance of SENSE-PPI in single model species predictions has been demonstrated here to open up new possibilities in PPI network reconstruction for non-model organisms, which are often characterized by very sparse or absent biological information. Our analysis shows that training on a model species such as H. sapiens and testing on a phylogenetically distant species such as C. elegans, yet provides excellent inference. Moreover, we show that if we construct a training dataset out of model organisms that are evolutionary close to a non-model species, we are able to improve the performance: we succeeded in improving the test scores on A. pisum by switching the training data from H. sapiens to a combination of C. elegans and D. melanogaster. As a general PPI reconstruction strategy for a non-model organism, the user must first identify the best model organism(s) close to the non-model species, train on it, and infer PPI interactions in the non-model species.
4. 1. 4 14 Ten SENSE-PPI datasets from model and non-model organisms: training on a species and testing on others We used the same methodology employed for the construction of the SENSE-PPI human dataset to build test sets on four non-model organisms, E. caballus, B. taurus, N. scutatus and A. pisum, and six model organisms, M. musculus, D. melanogaster, C. elegans, Gallus gallus, S. cerevisiae and E. coli. All these test sets contain 15,000 positive and 150,000 negative entries that were extracted from the STRING v12. 0 [57]. In order to conduct additional testing on the non-model organism A. pisum, we have also constructed a separate training dataset by merging data from D. melanogaster and C. elegans. This dataset contains 23,324 positive and 233,240 negative interactions coming from D.
sapiens trained on H. sapiens is 0. 836. C and D: Plots of MCC scores versus mean pair sequence identity for model (C) and non-model (D) organisms. E and F: Comparison of two versions of SENSE-PPI di↵ering by the size of the ESM2 embedding module. The trend of SENSE-PPI MCC scores versus the mean pair sequence identity is shown for 4 di↵erent testing datasets coming from [53] for the "light" version (E) and the regular version (F). Figure 1: Performance of SENSE-PPI on six model and four non-model organisms. SENSE-PPI is trained on the SENSE-PPI human dataset. For each experiment, the testing dataset comprises 5,000 positive and 50,000 negative interactions. A: Matthews correlation coe cient (MCC) versus mean pair sequence identity (see Methods) for the ten experiments. Dotted line: least squares polynomial fit with degree 4.
Sociodemographic data collection 24638 samples were collected between May 2020 to April 2023. All patients attending the clinical centers with COVID-19 symptoms were asked to participate in the study. After acceptance, age, sex, collection date, the patient status (ex. have a symptom and did not have contact), control (has already been tested) or contact (know a close recent case) and region of origin were collected as sociodemographic information. Clinical data like febrile statute and symptoms were also collected before sampling. For children, the acceptance of the parent or tutor were asked.
Data analysis The consensus genome was generated using Illumina GeVarLi (GEnome assembly, VARiants calling, and Lineages assignment) reads analysis pipeline proposed by TransVIHMI, IRD for the AFROSCREEN project (https://forge. ird. fr/transvihmi/nfernandez/GeVarLi). NextClade v2. 5. 1 web tool (https://clades. nextstrain. org/) was used to generate the sequence quality assessment report. Pangolin web v4. 1. 1 (https://pangolin. cog-uk. io/) was used to determine the lineage of each sequence. The evolution of different variants according to their sampling date was presented using the ggplot2 package of the statistical analysis software Rstudio version 4. 2. 2. Phylogenetic analysis of SARS-CoV-2 was performed using Iqtree script (http://www. iqtree. org/ ). The Wuhan-Hu-1 reference genome (LR757998.
Results Circulation of samples from qPCR test to the depot in GISAID database: Among 24638 samples received in CERFIG, 5697 was positives. From these positives', depending on the Ct value (Ct < 30) with one sample by patient, 627 samples were good for sequencing. The whole genome sequencing was undertaken for 365 (59,2% of sequenceables) samples collected from May 2020 to March 2023. Retrospectives analysis was done in 2022 for 21 samples collected in 2021. From them 282 samples could have their lineage assigned and 78. 3% (246/282) of these were submitted to the GISAID platform (Table  1 ). Thus, a total of 246 nasopharyngeal COVID-19 samples con rmed by RT-PCR and collected between May 2020 to March 2023 were successfully sequenced and were considered for all analyses. These samples were from 5 Guinean administrative regions with 82,5% from Conakry region (Fig.
Study Participants Women in this study were enrolled in the Environment and Reproductive Health (EARTH) study, a prospective cohort designed to study environmental and dietary determinants of fertility among couples seeking fertility care at the Massachusetts General Hospital (MGH) Fertility Center  [27]. Women aged 18-45 were eligible to participate. Among the N = 956 women enrolled in EARTH, this cross-sectional analysis included 339 women enrolled between 2009 and 2015 who provided a spot urine and a blood sample. We excluded 219 women without phenol data and 398 women lacking serum thyroid and autoimmunity biomarker data; as previously described, this included 133 women using thyroid-interfering medication (predominantly levothyroxine, methimazole, propylthiouracil, amiodarone, antipsychotics, anticonvulsants, or high-dose steroids) at study entry  [28].
Limits of detection (LOD) ranged from 0. 1 to 1. 0 µg/L, depending on the biomarker, and changed over the course of study. We excluded urinary butylparaben from the analysis because of the relatively low detection rate (57%), and we also excluded urinary propylparaben because it was highly correlated with methylparaben (r = 0. 87) and had a lower detection rate. To accommodate concentrations below the LOD, we used a left-censored normal multiple imputation strategy (with m = 10 imputations), as described by Lapidus et al. [33] , and implemented it via the mice doMIsaul "https://github. com/LilithF/doMIsaul (accessed on 1 April 2022) and multiLODmice (https://github. com/glenmcgee/multiLODmice (accessed on 1 April 2022)) packages in R. Each analytical run included the following, in addition to study samples: a set of calibrators (prepared in methanol with commercially available analytical standards of the target analytes), reagent blanks (prepared in HPLC grade water), and high-and low-concentration quality control (QC) materials (made from urine spiked with known concentrations of the target analytes).
e. , no interactions and no non-linearity). By contrast, BKMR allows for non-linearity as well as high-order interactions among mixture components, but this flexibility decreases power. As a compromise, we also fit GAMs, which allow the estimation of flexible, nonlinear relationships without committing to a fully non-parametric approach that allows for high-order interactions, which are difficult to estimate in small samples. Another strength is the multiple imputation strategy, which uses information about all observed data to impute non-detectable biomarker concentrations. Potential confounding bias was limited because all the participants attended a single medical center and were enrolled in an established cohort study with homogenous demographics. All serum and urine samples were collected and processed under a common protocol before determining thyroid function and thyroid autoimmunity biomarkers, and urinary phenol biomarker concentrations were quantified at the CDC using the analytical approach used in other studies including NHANES.
Genetics of seed size Despite impressive variation and critical agronomic importance, the genetics underlying faba bean seed size have remained obscure, with only a few large seed weight QTL regions detected  38. We collected seed size data for the 197 accessions at two locations for 3 years and combined these with the SPET marker data to carry out a high-resolution genome-wide association study (GWAS). This identified 15 markertrait associations, which were stable across trials and GWAS methods (Fig. 3d , Extended Data Fig. 6 , Supplementary Figs. 8-11 and Supplementary Tables  13  and 14 ). The most prominent signal was located on chromosome 4 within the Vfaba. Hedin2. R1. 4g051440 gene (Fig. 3d, e  and Supplementary Fig. 12 ), which is highly expressed in faba bean seeds, resides within a previously identified seed weight QTL region  38  (Supplementary Note 1) and is homologous to the Arabidopsis CYP78A genes known to regulate seed size  39  (Fig.
The assembled contigs were taxonomically classified using Kraken2 v2. 1. 1 (ref. 31 ) with a database including sequences of plants, insects and bacteria, and with Blob-Tools v1. 1 (ref. 49 ). The genome completeness and consensus accuracy were evaluated by Merqury v1. 3 (ref. 50 ). The levels of homozygosity, heterozygosity and duplication were assessed by various tools such as Merqury, FindGSE v1. 94 (ref. 21 ) and GenomeScope v1. 0 (ref. 51 ). The centromere regions were identified in each chromosome using chromatin immunoprecipitation followed by sequencing (ChIP-seq) with the CENH3 (a centromere-specific histone H3 variant) antibody as previously reported  52. In brief, the raw reads from ChIP-seq were trimmed by cutadapt v. 1. 15 (ref. 53 ) and mapped to the preliminary pseudomolecules using minimap2.
Genome size estimation and quality assessment The distribution of the k-mer (K = 101) frequency was estimated from PacBio HiFi reads using Jellyfish v2. 2. 10 (ref. 58 ). The output histograms were used to estimate the genome size and heterozygosity using findGSE v1. 94 (ref. 52 ). The completeness of the assembly was assessed by two independent approaches: (1) self-alignment of HiFi reads to the assembly by minimap2 v2. 20 followed by single variant (SV) calling using Sniffles v1. 0. 11 (ref. 59 ); and (2) BUSCO v3. 0. 2b  60  analysis with Embryophyta database 9.
0 database using DANTE v0. 1. 1 (https://github. com/ kavonrtep/dante). Satellite repeats were annotated using similarity searches to a custom database of satellite DNA families described in our previous studies  18, 31, 74, 75. The distribution of satellite repeats on metaphase chromosomes of V. faba was examined using fluorescence in situ hybridization (FISH). Chromosome preparation, probe labelling and FISH were performed as previously described  31  , with hybridization and washing temperatures adjusted to account for the probe AT/CG content to allow for 10-20% mismatches. Chromosomes were counterstained with 4′,6-diamidino-2-phenylindole (DAPI), mounted in Vectashield medium (Vector Laboratories) and examined using a Zeiss AxioImager. Z2 microscope with an Axiocam 506 mono camera. Images were captured and processed using ZEN 3.
Gene model annotation The repeat sequences were masked using RepeatMasker v4. 1. 1 (http:// www. repeatmasker. org) with a custom repeat library generated by RepeatModeler v2. 0. 1 (using the Hedin/2 assembly). The gene annotation was conducted using BRAKER v2. 1. 6 (ref. 76 ) (etpmode, min_contig 10000). The RNA sequencing libraries (Supplementary Table  2 ) were aligned using STAR 2. 7. 8a  77, 78. The protein database Viridiplantae OrthoDB v10. 1 (ref. 79 ) (https://onlinelibrary. wiley. com/doi/10. 1111/ tpj. 14546merged), with the translated sequences of the previously published V. faba transcriptome assembly  16  , was used as input for BRAKER, together with alignments generated by mapping the faba transcriptome assembly using GMAP v2020-10-14 (ref. 80 ). In addition, M. truncatula genes ('Mt4.
0v2_Genes') and P. sativum genes ('pissa. Cameor. gnm1. ann1. 7SZR') were aligned using GMAP v2020-10-14. The generated alignments were used to polish the BRAKER gene models. To account for any gene models missed by BRAKER prediction but present in the Hedin/2 transcriptome assembly, the gene models from GMAP faba transcriptome alignments and BRAKER were compared using bedtools v2. 30. 0, retaining only the GMAP genes that did not have an intersection with the BRAKER gene models. For these genes, a further filtration was done to eliminate any short (less than 50 amino acids) translated proteins, in-frame stop codons or low (less than 200 reads) expression featureCounts, subread v2. 0. 1 (ref. 81 ). Completeness of the annotation was assessed for Hedin/2 and Tiffany by aligning one Iso-Seq dataset  82  and assembled transcriptomes produced for faba bean cultivars Hiverna, Dozah and Farah.
Symbiotic gene discovery Total RNA sequencing was carried out for three biological replicates per condition. Eighteen libraries were prepared, and paired-end Illumina HiSeq mRNA sequencing (2 × 100 bp RNA sequencing) was performed by GeneWiz, which produced around 2 × 70 million reads per library on average. Adaptor sequences were removed using CLC Genomics Workbench 11 (CLC Bio Workbench, Qiagen). Only inserts of at least 30 nt were conserved for further analysis. Reads were mapped to the Hedin/2 genome using CLC Genomics Workbench 11 according to the manufacturer's recommendations. The mapped reads for each transcript were normalized as total counts and used for calculating gene expression. Intact and broken pairs were counted as one. The total counts of each transcript under different conditions were compared using proportion-based test statistics  86  implemented in the CLC Genomic Workbench suite.
Phylogenetic analysis and divergence time estimation The single-copy genes identified from 19 legume species (Supplementary Table  6 ) by OrthoMCL v2. 0. 9 were selected for the phylogenetic analysis. The fourfold degenerate synonymous site (4d locus) was extracted to build the evolutionary tree by PhyML v3. 0 (ref. 90 ) and TreeBest v1. 9. 2 (https://github. com/Ensembl/treebest). Molecular clocks and divergence times were estimated using MCMCTREE v4. 4 in the PAML v4. 5 package 91 using the phylogenetic tree and the divergence time of known species (from published literature or using Timetree (http://www. timetree. org/)).
Tandem duplicate gene discovery Tandem duplicated genes were also discovered using the CRBHits v0. 0. 4 package  95  function tandemdups. To confirm the results, genes were also classified using DupGen_finder v25Apr2019 (ref. 96 ), with A. thaliana serving as the outgroup. V. sativa was excluded from TD analysis owing to suspected fragmentation of its structural annotation, which could result in inflation in the number of genes annotated as tandem duplications (TDs) (Supplementary Table  17 ). The age of duplications was estimated using T = Ks/2r, r = 1. 5 × 10 -8. Ks was calculated using CRBHits using method 'Li'. Synteny between Hedin/2 and Tiffany genes was analysed using CRBHits v0. 0. 4 package function rbh2dagchainer (type = 'idx', gap_length = 1, max_dist_allowed = 20), which internally uses the DAGchainer algorithm  97.
Syntenic tandem duplicated gene (TDG) clusters were discovered by connecting TDG clusters in individual genomes using the syntenic gene pairs found between Hedin/2 and Tiffany. To minimize the effect of unplaced genes on copy number variation analysis, as unplaced genes can result in spurious copy number variation calls, we corroborated the synteny-based results with Orthofinder v2. 5. 4 (ref. 98 ) analysis. Only clusters that had the same or higher copy number in the same genotype, based on both synteny and Orthofinder results (for Orthofinder, only genes on the matching chromosomes and unplaced contigs were considered), were retained for further analysis. Syntenic clusters were functionally annotated with human readable descriptions using prot-scriber v0. 1. 0 (https://github. com/usadellab/prot-scriber).
All effects except the mean were random effects. The significance of each random effect except G were then tested one at a time using lmertest package in R. Only effects with a P value larger than 0. 05 were included in the final model. The final model had G and µ as a fixed effect and all others as random. The BLUEs were then extracted from G. Prediction accuracies of seed-size-related traits were investigated by fivefold cross-validation using the genomic best linear unbiased prediction (gBLUP) method. The fitted model in matrix notation is of the form y = 1µ + Zu + e, where y is a vector of observed phenotypic records (BLUEs), µ is the intercept, 1 is a vector of ones, Z is a design matrix linking records to accessions, u is a vector of (genomic) breeding values of the accessions assumed u∼N(0, Gσ g 2 ), where σ g 2 is the additive genetic variance and G is the genomic relationship matrix (GRM).
Acknowledgements This research was supported by grants from the  Innovation Fund Denmark  ('NORFAB',  5158-00004B ) to J. S. ;  FACCE-JPI ERA-NET SusCrop Profaba  to S. U. A. , D. M. O. , F. L. S. and A. H. S. ;  German Leibniz Association  in the frame of the  Leibniz Junior Research groups  ( J118/2021/REPLACE ) to M. J. ; the  German Federal Ministry of Education and Research (de. NBI ,  031A536B ) to H. G. ;  Novo Nordisk Fund  (# NNF20OC0065157 ) to A. H. S. ; the  Jane and Aatos Erkko Foundation grant (PanFaba)  to A. H. S. ; the  Alexander von Humboldt Foundation  in the framework of  Sofja Kovalevskaja Award  to A. A. G. ; the  Biotechnology and Biological Sciences Research Council  award  BB/P023509/1  to D. M. O. ; the  German Research Foundation (DFG)  project number  497667402  to A.
A. G. and B. U. ; the  Czech Science Foundation  project number  GACR 20-24252S  to J. M. ; the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 834221 ) to J. S. ; the  BMBF-funded de. NBI Cloud within the German Network for Bioinformatics Infrastructure (de. NBI)  ( 031A532B ,  031A533A ,  031A533B ,  031A534A ,  031A535A ,  031A537A ,  031A537B ,  031A537C ,  031A537D  and  031A538A ); and  Grains Research Development Corporation as well as Agriculture Victoria  for PacBio sequencing, parts of the public dataset curation for pulses. plantinformatics. io(DAV  1905-003RTX ) and RNA sequencing data (BioProject ID  PRJNA395480 ). We acknowledge  CSC-IT Center for Science, Finland  and  ELIXIR CZ Research Infrastructure (Czech Ministry of Education, Youth and Sports  grant no.
1981; Jenkin et al. 1988 ) there is a potential for O 3 formation, so using an alkene as the reference compound (hence removal by OH and O 3 ) would not be a good choice. Use a photolysis database (e. g. Mainz Photolysis Database) to ensure that neither the reference nor substrate is predicted to be lost by photolysis (this should always be checked too). It is good practice to use more than one reference compound. (2) Experimental method • Introduce RH and SH into the chamber to test for wall-loss rates  (Chap. 4). Leave for a reasonable period of time, (certainly much longer than the mixing time) sufficient to ensure an accurate estimate of the wall loss. Checks on wall-loss rates should be done on a regular basis as the conditions of the walls may change and wall-loss rates can vary with temperature and pressure.
-http://www. kintecus. com/. • Chemistry with Aerosol Microphysics in Python (PyCHAM) box model • AtChem2 (Sommariva et al. 2020)-https://github. com/AtChem/AtChem2. • DSMACC (Emmerson and Evans 2009)-http://wiki. seas. harvard. edu/geoschem/index. php/DSMACC_chemical_box_model. • Kintecus(O'Meara et al. 2021)-https://github. com/simonom/PyCHAM. The AtChem online website contains tutorial material and a number of examples.
Human immune cells integrate into transplanted HIOs To bioengineer an HIO with immune cells, we transplanted PSC-derived HIOs under the kidney capsule of humanized NSGS mice (see workflow in Extended Data Fig. 1a ). We collected and analyzed HIOs at 12, 16 and 20 weeks post-transplant. We confirmed the presence of human immune cells in the blood of humanized mice at each time point (Extended Data Fig. 1b-d ) and found that HIOs successfully engrafted and grew to a similar size compared with those grown in nonhumanized control mice (Fig. 1a, b ). However, we noticed that HIOs transplanted for periods of 16 and 20 weeks were more heterogeneous in size and this phenomenon may be due to an accumulation of mucus within the HIO lumen which cannot be drained out. Regardless, the effect is independent of the presence of immune cells as we observed the same heterogeneity in size at 16 and 20 weeks in control mice.
Mass cytometry analysis reveals a GALT-like immune signature profile in HIO By mass cytometry, we next determined the immune signature in transplanted HIOs, and this was compared with the humanized mouse SI as an internal control. Using unsupervised clustering analysis, human CD45 + immune cells were classified and organized into 13 clusters as visualized in a heatmap and uniform manifold approximation and projection (UMAP) graph (Fig. 2a, b ). Based on the combination and level of expression of markers on the heatmap and the expression patterns on individual UMAP graphs (Fig. 2a  and Extended Data Figs. 3  and 4 ), we were able to define 12 immune cell types corresponding to the major immune cell lineages found in the intestine, for instance, T cells, B cells and innate lymphoid cells, as well as tissue-specific immune cells such as intraepithelial lymphocytes, mucosal-associated invariant T-like cells and LTi-like cells (Fig.
Mass cytometry staining and data analysis Mass cytometry staining. Antibodies used in this panel were purchased from Fluidigm except anti-human CD45RO antibody which was purchased from BioLegend and was labeled with Maxpar X8 Antibody Labeling Kit (Fluidigm) according to the manufacturer's instructions. All the reagents used in the following protocol were purchased from Fluidigm and all incubations were done at room temperature. Samples were first stained for 5 min with Cell-ID Cisplatin at a final concentration of 5 μM in Maxpar PBS and then washed in 5 volume of Maxpar Cell Staining Buffer. Before being stained for cell surface markers, cells were fixed in Fix I Buffer for 10 min, washed twice with Maxpar Barcode Perm Buffer and barcoded with Cell-ID 20-Plex Pd Barcoding Kit for 30 min. Samples were pooled and then stained for 30 min with a cocktail of 25 antibodies in a final volume of 100 μl of Maxpar Cell Staining Buffer (antibody dilution 1:200) (Supplementary Table ).