dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion This study provides a comprehensive assessment of the abundance of foodborne pathogens and the presence of AMR genes in Egypt and the UK. With the goal of establishing a link between the prevalence of AMR and various bacteria, data published over the last ten years (2013 to 2022) were examined. 56 papers were selected and met Fig. 3  Diagram showing the flow of the study selection process according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  [84]  the inclusion criterion out of 190 papers. Beef, chicken, retail chicken, raw meat, ready to eat meat, fish, milk, other dairy products (cheese, ice cream, yoghurt, cream), water, vegetables, pigs, animals (sheep, dog, badger, fox), environmental samples (water, drainages) and other food sources were all subjected to a planned follow-up AMR risk group evaluation.
Simulations For simulations, we use a Wright-Fisher approach, drawing each new individual from the previous generation. First, we determine the patch of the parents (according to the migration kernel). Each parent is then sampled with replacement from this patch, weighted by its fitness. One gamete is sampled from each parent (with possible recombination and mutation events) to form the new individual and this is repeated to reach 𝑁 individuals in each patch. The corresponding code is written in Java programming language and source code is available online  (Laroche & Lenormand, 2022a). Table  1  gives the parameter values used in the simulations. We consider a landscape made of 𝑛 = 400 patches, which allows browsing a wide range of proportions of habitat 2 from 0. 25% (i. e. 𝑙 = 1) to 21% (i.
Construction of the human PDZ resource for Y2H assays To build the human PDZome 2. 0 resource allowing to test for PDZ interactions by Y2H, the 266 known human PDZ domain sequences (Table  S1 ), bearing boundaries optimized based on available structural data  (28) , were introduced in the prey vector by Gateway ® approach (Fig. 1A ). We also included 39 PDZ tandems (Table  S2 ). The PDZ tandems were designed using the online UniProt resource (https://www. uniprot. org/). First, all PDZ proteins with more than one PDZ domain were included in the list (multi-PDZ proteins). Then, within these multi-PDZ proteins, those in which 2 PDZ domains were connected by a linker region of up to 36 amino acid residues acids were included. The final list of 39 tandems, belonging to 28 PDZ proteins, represent around 20% of the human PDZ proteome (Table  S2 ).
Acknowledgements This work was supported by grants from the  French National Research Agency  ( ANR-18-CE13-0017 , Project SynTEV), the  Fund for Scientific Research-Flanders  ( Fonds Wetenschappelijk Onderzoek-Vlaanderen  Grants  G. 0846. 15  and  G0C5718N ), and the  Institut National du Cancer (Projets Libres de Recherche "Biologie et Sciences du Cancer" INCa   9474 ). J. -P. B. is a scholar of  Institut Universitaire de France. M. C. benefited of a  CONACYT  fellowship.
Cohort Information We assessed parental factors affecting neurodevelopmental disease in two North American autism cohorts: the Simons Simplex Collection (SSC)  21  and the Simons Foundation Powering Autism Research (SPARK)  22. We analyzed genetic and/or phenotypic data from 2,517 trios from SSC and 7,175 trios from SPARK. We also analyzed genetic and/or phenotypic data from an additional 67 parent pairs in SSC and 1,570 parent pairs in SPARK for families where the relevant proband data were not available. We also collected genomic and/or phenotypic data for another neurodevelopmental disease cohort of 203 samples from 43 trios, 4 larger families, and 21 spouse pairs carrying the 16p12. 1 deletion. Probands in these families were ascertained for developmental delays or other neurodevelopmental phenotypes and were identified as 16p12.
1 deletion carriers through clinical diagnostic tests. Families provided informed consent to give blood samples and phenotypic data under Pennsylvania State University Institutional Review Board approved protocol #STUDY00000278, and deidentified data from medical reports were collected under approved protocol #STUDY00017269. We further assessed assortative mating and spousal relatedness in 26,610 partner pairs from the UK Biobank  23  , identified using Data Return 1872  24  (Application ID: 45023). UK Biobank is a database containing genetic, lifestyle, and health information from 500,000 volunteer participants from the UK. Additionally, we assessed profiles of genetic relatedness in parent pairs of children ascertained for neurodevelopmental disorders carrying rare copy number variants (CNVs) from another clinical cohort evaluated at GeneDx.
DNA Extraction and Sequencing DNA extraction and sequencing of samples within the SPARK, SSC, and UK Biobank cohorts were performed by the relevant biobank. For directly recruited participants from the 16p12. 1 deletion cohort, DNA was extracted from peripheral blood using the QIAamp DNA Blood Maxi extraction kit (Cat. No: 51104, Qiagen), while clinical collaborators sent DNA samples directly to the Girirajan laboratory. SNP arrays for 36 samples were generated as previously described  14  , while 92 additional samples were run on Illumina OmniExpress 24 v 1. 1 microarrays at the Northwest Genomics Center at the University of Washington (Seattle, WA, USA). Illumina HiSeq X was used to generate 150-bp paired-end whole genome sequence (WGS) data at ≥30X coverage for 118 individuals by Macrogen Labs (Rockville, MD, USA).
1%. To achieve a similar frequency threshold with a smaller sample size, variants in the 16p12. 1 deletion cohort were filtered for those that were private within related individuals. ANNOVAR  31  was used to assign variants to genes from GENCODE v19 (16p12. 1 deletion cohort) or GENCODE v38 (SSC, SPARK, and UK Biobank)  32  , and variants in all cohorts were filtered for loss-of-function (LOF), missense, or splice variants in protein-coding genes. LOF variants were those annotated as 'stopgain', 'stoploss', 'frameshift_deletion', 'frameshift_insertion', or 'frameshift_substitution' by ANNOVAR, and missense variants were those annotated as 'nonsynonymous_SNV', 'nonframeshift_deletion', 'nonframeshift_insertion', or 'nonframeshift_substitution'. CADD Phred-like scores  33  were annotated using vcfanno as a deleteriousness metric and missense and splice variants were filtered for those with a CADD Phred score ≥25.
sph. umich. edu/wiki/VcfCooker) to convert PLINK files to VCF files. SNPs were imputed using the TOPMed v. r2 imputation server using Eagle v2. 4 for phasing  37. After imputation, VCF files were converted back to PLINK format, and SNPs were again filtered using the criteria from the initial quality control. SNPs were additionally filtered to remove samples with >±3SD of the mean heterozygosity and strand-flipped to match the GWAS summary statistics. We filtered the GWAS summary statistics for SNPs present in the HapMap3 dataset  38  , and used 1000 Genomes datasets to calculate linkage disequilibrium matrices for the SNPs. After regressing betas of GWAS SNPs according to linkage disequilibrium, we used the LDPred2-auto model  39  to calculate autism PRS for probands in the SSC cohort.
Phenotype Assessment We assessed self-reported psychiatric phenotypes in probands and parents in the SPARK cohort and 23 quantitative autism and cognitive metrics in probands (Supp. Table  2 ) and two quantitative autism metrics in parents (Social Responsiveness Scale (SRS)  40  and Broad Autism Phenotype Questionnaire (BAPQ)  41  ) from the SSC cohort downloaded from SFARI base (https://base. sfari. org). We further assessed neurological and psychiatric phenotypes in 57 parent pairs from 16p12. 1 deletion families. Information was collected on each adult family member either through self-reports using a standardized clinical questionnaire or clinician reports. Curated data was generated using a union of clinical diagnoses, self-reported phenotypes, and interpreted responses to clinical questionnaires.
We also assessed phenotypic correlations in the UK Biobank using self-reported diagnoses of psychiatric disorders from Data Field 20544. Linear regression was used to assess the predictive power of parental SRS and BAPQ scores and various genetic factors, including autism PRS and rare CNVs and SNVs, towards child quantitative phenotypes, while tetrachoric correlations were used to assess phenotypic correlations between psychiatric diagnoses in parents and children and between spouse pairs. To minimize the effects of comorbidities and isolate the specific correlations of interest, pairs were removed from cross-disorder tetrachoric correlations if both members of the pair had either diagnosis of interest. For example, when examining the relationship between the female member of the spouse pair having anxiety and their partner having depression, pairs where the male partner had anxiety or the female partner had depression were excluded from the analysis.
Kinship Calculations In the 16p12. 1 deletion, SSC, SPARK, and UK Biobank cohorts, we followed the same initial SNP quality control steps outlined above for PRS calculations. Additionally, SNPs within long-range linkage disequilibrium regions  42  were removed, as were samples with ±2 standard deviations of the mean cohort heterozygosity rate. For SPARK, SSC, and the 16p12. 1 cohort samples, we next assessed ancestry using fastStructure  43  to cluster samples (k=5-6) against individuals with known ancestry from the HapMap3 project  38. Kinship analysis in these cohorts was restricted to pairs with self-reported or calculated European ancestry. UK Biobank samples were filtered for pairs of European ancestry using Data Field 22006. KING  44  was used to calculate kinship coefficients for 44 parent pairs from the 16p12.
Statistical Analysis Tetrachoric correlations were performed using the correlation package  45  in R. Linear regression was performed using statsmodels in Python, with proband age at the time the Autism Diagnostic Observation Schedule (ADOS) was administered included as a covariate in the models. Pearson correlations and ttests were performed using the pearsonr and ttest_ind functions in the scipy stats (v. 1. 7. 1) package  46  in Python (v. 3. 9. 7), respectively.
ACKNOWLEDGEMENTS This work was supported by grants  T32GM102057  to  Miss Corrine Smolen  and  NS122398  and  GM121907  to  Dr. Santhosh Girirajan  from the  National Institutes of Health. Dr. Charles Schwartz  was supported, in part, by a grant from the  South Carolina Department of Disabilities and Special Needs. Dr. Corrado Romano  was supported by the  Italian Ministry of Health-Ricerca Corrente 2022. We are grateful to the families in the 16p12. 1 deletion cohort who participated in this study. We are grateful to all the families in SPARK, the SPARK clinical sites and SPARK staff. We are also grateful to all of the families at the participating Simons Simplex Collection (SSC) sites, as well as the principal investigators ( A. Beaudet ,  R. Bernier ,  J. Constantino ,  E. Cook ,  E. Fombonne ,  D.
DATA AND CODE AVAILABILITY Microarray and genome sequencing data from participants in the 16p12. 1 deletion cohort who consented to having their information shared anonymously will be made available at NCBI dbGaP study accession phs00245. Phenotype and genetic data from the SPARK and SSC autism and UK Biobank cohorts through the cohorts' data portals: SFARI base (https://base. sfari. org) and UK Biobank database (https://www. ukbiobank. ac. uk), respectively. All code for analysis will be available at GitHub (https://github. com/girirajanlab/Assortative_mating_project).
Measure of the generation time Strains were destocked from -80°C on YPD agar and allowed to grow at 30°C. After 48 hours, growth curves were performed by inoculating 10 5 cells in 200 μL in YPD broth on a 96 wells plate. The plate is then incubated at 28°C without agitation in a TECAN sunrise. Measurements of the optical density at 600 nm were taken each 10 minutes. The generation time was established using the R package GETITshiny 3. 4 available on GitHub (DOI. 10. 5281/zenodo. 10227629).
Long-read sequencing, de novo assembly and genome analyses. Genomic DNA was extracted using QIAGEN genomic TIP 100/G, following the manufacturer's protocol. Size selection was done using Circulomics SRE kit. DNA from 10 strains was then prepared using the ONT LSK109 kit coupled with the EXP-NBD104 barcoding kit and sequenced on a single R9. 1 flowcell using a MK1C Minion sequencer. Standard High Accuracy base calling was performed on the Mk1C with the 22. 05. 8 version of minknow, using Guppy (V6. 1. 5, model=high accuracy). For data processing, barcodes were removed from sequences using porechop. Then sequences were downsample using Filtlong to have a coverage of 40X enriched for reads with high quality and longer length. Genome assembly was performed using 3 different assemblers : Canu (v2.
1), SMARTdenovo (https://github. com/ruanjue/smartdenovo/ v1. 12) and NextDenovo (https://github. com/Nextomics/NextDenovo). Contigs were polished using 1 to 3 rounds of Racon (v1. 4. 21) and 2 rounds of Medaka (https://github. com/nanoporetech/medaka v1. 2. 3). Finally reference Scaffolding was performed with Ragout (v2. 3). SVs were called using MUM&Co (v2. 4. 2)  28  , on all 3 assemblies. As a criteria of validation, we only validated SVs found in at least 2 assemblies. We also checked that we detected the expected deletions corresponding to the auxotrophic markers and the insertions of resistance cassettes. For chromosomal rearrangements, manual scaffolding was performed and validated by karyotyping with pulsed-field gel electrophoresis. SNPs were called using medaka_variant. We only considered haploid SNPs with a minimum quality score of 45 which corresponded to the lower quality score of selected CAN1 mutations validated by Sanger sequencing.
For each realization of the model, the generation at which the mutator subpopulation appears is chosen randomly. Both null and refined versions of the model are freely available in the SIMPLE_GUI_Neutral. R package (https://github. com/Nico-LCQB/SIMPLE_GUI, DOI 10. 5281/zenodo. 8325449). For both models, each realization outputs the number of mutants and mutations of each type and the number of the generation at which the single and double mutations occurred. For each condition tested, one realization corresponds to the same number of colonies as was experimentally tested to measure the observed double mutation rate. A minimum of 100 realizations were performed per condition. 7.
ABSTRACT Microbial rhodopsins are often found more than once in a single genome (paralogs) that often have different functions. We screened a large dataset of open ocean singleamplified genomes (SAGs) for co-occurrences of multiple rhodopsin genes. Many such cases were found among Pelagibacterales (SAR11), HIMB59 and the Gammaproteobacteria Pseudothioglobus SAGs. These genomes always had a bona fide proteorhodopsin and a separate cluster of genes containing a second rhodopsin associated with a predicted flotillin coding gene and have thus been named flotillinassociated rhodopsins (FArhodopsins). They are quite divergent from the other proteorhodopsin paralog and contain either DTT, DTL or DNI motives in their key functional amino acids. FArhodopsins are mainly associated with the lower layers of the epipelagic zone.
Paralog rhodopsin recovery and diversity To identify rhodopsin paralogs, we first tried to locate them in the RefSeq database (April 2022) which includes cultivated marine microbes, but none was found, with the known exceptions mentioned above. This was not surprising considering the narrow range of diversity covered by cultivated marine bacteria, particularly of streamlined and abundant microbes. Fortunately, an alternative to monoclonal genomes of marine bacteria is single-amplified genomes (SAGs). Although many of them are incomplete they offer a significant guarantee that they start from a single cell and that the redundant genes found in them are real paralogs. Thus, we used a large collection of marine SAGs, composed of 12,715 partial genomes, which were produced using a random cell selection strategy from the epipelagic seawater samples from tropical and subtropical latitudes of the Atlantic and Pacific Oceans  (15).
The taxonomic composition of marine prokaryoplankton in this database is consistent with previous 16S rRNA amplicon and shotgun metagenomic studies of marine off-shore waters, indicating that this dataset is representative of the in situ microbial communities  (15). We removed SAG assemblies with an estimated completeness lower than 50 %, which is the threshold for classifying them as medium quality  (17). Finally, a total of 4,751 SAGs were screened for the presence of type-1 rhodopsin genes in their genomes (see methods). Among them, 63 % coded for type-I rhodopsins (Figure  1A ). As previously described from metagenomic assemblies, most of these sequences were found in microbes from the order Pelagibacterales (#1,619), SAR86 (#386), Flavobacteriales (#258), HIMB59 clade (#191), and Ca. Actinomarinales (#139), as well as other less abundant taxa (Table  S1 ).
g. Fonsibacter), we included in the analysis samples taken from lakes in North America  (33) , Europe, and Asia  (34) (35) (36) , including Lake Baikal  (37, 38) , where representatives of the Pelagibacter Ia. 5  (18, 37)  clade were recently found. Table  S3  summarizes the number of rhodopsins type-I, type-III, FArhodopsins, and flotillins recovered in each metagenomic sample. In agreement with what we found in the dataset of marine SAGs, microbial type-I rhodopsins represented the majority of rhodopsin sequences in marine and freshwater assembled contigs, followed by heliorhodopsins. We could recover hundreds of FArhodopsin sequences, including from freshwater datasets (Table  S3 ). This analysis showed the uneven recovery of FArhodopsin and flotillin sequences, which could call into question the strong association described previously between FArhodopsin and flotillin.
Recruitment of metagenomic and metatranscriptomic fragments on the FAR gene cluster To assess the abundance and distribution of this novel group of rhodopsins, we performed metagenomic fragment recruitment analysis by recruiting all the FAR gene clusters in the SAGs of Pelagibacterales (#24), HIMB59 (#69) and Pseudothioglobus (#10) taxa to 140 metagenomes from a depth profile in the Mediterranean Sea  (29)  and the Tara Oceans datasets  (31). As expected, the three taxa showed differential depth recruitment in the Mediterranean Sea (Figure  4A ) and the same was found for the Tara Oceans datasets containing deeper samples (Figure  S3 ). Pelagibacterales are the most abundant microbes in surface layers of the water column, although it is known that this diverse order has an eurybathic distribution, with some clades being detected in the deep chlorophyll maximum (DCM), lower photic (LP) metagenomes, and in meso-and bathypelagic samples  (29).
Predicted structure of the retinal binding pocket The most notable characteristic of microbial rhodopsins is their capability to respond to light through the use of the retinal cofactor. However, when  (16)   We used AlphaFold  (41)  to predict the structure of marine FArhodopsin to examine its retinal binding pocket. The predicted structure showed high similarity to that of the closest homolog with an experimentally determined high-resolution structure (PDB: 4jq6, blue proteorhodopsin from Med12, a predicted protein from a metagenomic sample from the Mediterranean Sea at 12 m deep (  42 )), which allowed us to adapt the retinal position to the predicted structure. However, when analyzing amino acids that are highly conserved among Pelagibacterales FArhodopsins (present in more than 99% of cases), we observed that four of these residues would intersect the retinal in its alltrans conformation (Figure  5A ).
SYNTHESIS The discovery, two decades ago (2), of rhodopsin sequences in marine prokaryoplankton communities resulted in a significant leap forward in the understanding of the biology of these microorganisms and their interaction with the ecosystem. In the photic zone, the proton-pumping activity of rhodopsins may provide "cheap" solar energy  (46). However, rhodopsins can perform a wide variety of other functions  (47, 48). Furthermore, the presence of paralogs with very divergent activities and even sequences has been known since the first microbial rhodopsins were described in halophilic archaea, and they are intensively looked after since they reveal novel physio-ecological roles and can be of potential application for optogenetics. In this work, we have analyzed the distribution and the presence of rhodopsin paralogs in a large dataset of marine SAGs (15) that provide strong evidence that the detected genes come from the same genome  (15).
coli suggests that these rhodopsins might be retinal-less or even blind rhodopsins (not using light). Furthermore, most freshwater FArhodopsins do not have the lysine residue needed for retinal binding. However, a more in-depth screening of several metagenomic datasets indicates that they tend to associate with the photic (or twilight) zone, but not with bathypelagic waters. In addition, we have always detected a standard retinal-binding proton pumping proteorhodopsin present elsewhere in the genome, which indicates a clear lightinteractive lifestyle (at least periodically). A possible explanation would be that flotillins enhance the formation of a heterocomplex of DTE and FArhodopsins in lipid rafts. This kind of rhodopsin heterocomplexes has been described in fungi  (59, 60) , on which a classic blue or green-absorbing rhodopsin cyclase (a kind of rhodopsin linked to a type III guanylyl cyclase) interacts with another rhodopsin cyclase, but with an absorption in the near-infrared spectrum.
METHODS Screening of rhodopsin proteins from single-amplified genomes and metagenomic datasets. To evaluate the presence and the number of rhodopsin sequences in the euphotic marine microbiome, a compendium of 12,715 singleamplified genomes (SAGs) were downloaded from NCBI under BioProject PRJEB33281  (15)  and subjected to study. Prior to the analysis, the degree of completeness and contamination of SAGs were estimated using CheckM v1. 1. 2 (61), and SAGs with > 50 % completeness and < 5 % contamination were kept. Taxonomic classification of SAGs was performed using the GTDB-Tk v 2. 1. 0 tool (62) using the Genome Taxonomy Database (GTDB) release R207  (63). Metagenomes from different marine and freshwater datasets were used to retrieve similar protein sequences to FArhodopsins, as well as the contigs containing them.
Marine datasets included metagenomic assemblies and PacBio CCS reads from a local time series in the Mediterranean Sea  (28, 29) , Hawaii Ocean Time-series (HOTs) and Bermuda Atlantic Time-series Study (BATS)  (30) , as well as from large oceanic expeditions, such as Tara Oceans  (31)  and GEOTRACES  (30). Freshwater datasets represented metagenomic assemblies from Lake Baikal  (37, 38) , Spanish reservoirs  (34) , and a compendium of 17 different freshwater lakes located in Europe and Asia  (36). Assembled contigs from marine and freshwater metagenomes longer or equal to 5Kb were assigned family-or genus-level classification if at least 50 % of the genes shared the same best-hit taxonomy. Contigs failing this threshold were classified at the level of phylum. Recovery of rhodopsin proteins was achieved using a custom and curated database of rhodopsin sequences, comprising all the families described so far from both type-I and type-III rhodopsins.