dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion The phylogenomic approaches have evolved with the growing of genomic datasets tackling problems of incongruencies and reconstructing well supported molecular revolutionary history of the studied organisms. The era of big data in genomics implies the development of straightforward GUI transformed pipelines and work ow to x bioinformatics bottleneck that hindrances data analysis and interpretation. To our knowledge, BuscoPhylo is the rst web tools developed for both non-specialists and specialists, which provides a complete automated phylogenomic pipeline starting from FASTA les to ready for publication phylogenomic tree. This tool will ll the gap between two major pillar of genomics analysis methods that are genome assembly and phylogenomic analysis providing the researchers much more time to focus on interpretation and downstream analysis.
Simulations Dynamics were simulated by solving ODEs through numerical integration using the R package deSolve. (48)  For each simulation, endemic equilibria for bacterial carriage and colonization were found. Then, two cases of SARS-CoV-2 (one patient, one HCW) were introduced into the facility, assuming complete susceptibility to infection among all other individuals, and using estimates of SARS-CoV-2 transmissibility from early 2020 from a long-term care hospital in Paris, France (see parameter values in Table  S1 ). (  49          qqqqq qqqqqqq q q q q q q q q q q qq qqqq qq q q q q q q q q q SARS-CoV-2 introduced COVID-19 policies instated HCW-to-patient ratio a. Figure Legends qqq q q q q q q q qqqqqq q q q q q q q q q qq qq qq qqq qqqq q b. q qq qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 15 20 qqq q q q q q q q q qqq q q q q q q q q q q q q qq qq qqq qqqq qq qqqq qq q q q q q qqqqq qq qq qq qqq qqqqqqq qqqqqqq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0.
Acknowledgments We thank the members of the  EMAE-MESuRS Working Group on Nosocomial SARS-CoV-2 Modelling for helpful discussion. This study received funding from the  MODCOV  project from the  Fondation de France  (Grant  106059 ) as part of the alliance framework " Tous unis contre le virus ", the  Université Paris-Saclay  ( AAP Covid-19 2020 ) and the  French government  through its  National Research Agency  project  SPHINX-17-CE36-0008-01  and the " Investissement d'Avenir" program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  (Grant  ANR-10-LABX-62-IBEID ). D. R. M. S. is supported by a  Canadian Institutes of   Health Research Doctoral Foreign Study Award  (Funding Reference Number  164263 ). The work was also supported directly by internal resources from the  French National Institute for Health and Medical Research (Inserm) ,  Institut Pasteur ,  le Conservatoire National des Arts et Métiers , and  l'Université Versailles Saint-Quentin-en-Yvelines/Université Paris-Saclay.
Historical and archaeological context, community engagement, and sample collection The first Jewish community of Erfurt (pre-1349) was the oldest in Thuringia, and its cemetery also served nearby towns  (La ¨mmerhirt, 2015; Toch, 1992). During the 1349 pogrom, most Jews of Erfurt and nearby communities were murdered or expelled  (Weigelt, 2016). Jews returned to Erfurt in 1354 to form the second community  (La ¨mmerhirt, 2016) , which was one of the largest in Germany  (Toch, 1992 ) (Data S1, section 2). The individuals we studied were buried in the south-western part of the medieval Jewish cemetery of Erfurt, which underwent salvage excavations in 2013 (STAR Methods and Data S1, section 3). The layout of the cemetery is shown in Figure  1A  (see also Data S1, section 3). Archaeological evidence tentatively suggests that the site was used by the second community (Data S1, section 3).
DNA sequencing We followed existing protocols for DNA extraction, library preparation, and enrichment for about 1. 24 million single nucleotide polymorphisms (SNPs). We then sequenced the enriched and non-enriched libraries and used multiple metrics to demonstrate very low levels of contamination (STAR Methods and Data S2, Table  2 ). We obtained genome-wide data passing quality control for 33 individuals: 19 females and 14 males. The median proportion of human sequences was 0. 03 (range 0. 0003-0. 67; Data S2, Table  2 ), and the median coverage on autosomal targets was 0. 453 (range 0. 01-2. 483). The median number of SNPs covered by at least one sequence was 383k (range 11-842k). Children had significantly lower coverage than adults (p = 6. 7$10 À7 ; Data S1, section 4). We found no traces of Yersinia pestis bacteria in the DNA sequences (STAR Methods, Data S1, section 4, and Data S2, Table  4 ).
Ancestry estimation and medieval population structure To analyze the ancestry of the EAJ individuals, we represented their genomes as ''pseudo-haploids'' using a single random sequence for each covered SNP. We merged EAJ with modern genomes from the Human Origins dataset (about 593k autosomal SNPs, all also enriched in EAJ), which included seven AJ and 86 other Jews. We projected the EAJ individuals on principal components learned from West Eurasian individuals of the Human Origins dataset (n = 994; STAR Methods). Eight EAJ had (post-merging) coverage of fewer than 50k SNPs, which did not allow reliable projection (Data S1, section 5). We designated these individuals as ''low-coverage'' and excluded them from the principal components analysis (PCA). In the PCA plot, EAJ individuals overlapped with MAJ, as well as with other European Mediterranean populations.
A model with all Levant populations merged together as the ME source fit the EAJ data (p = 0. 07), with ancestry proportions 65% South Italy, 19% Levant, and 16% East-EU. A model with all Mediterranean populations merged as a single source (Middle Eastern, Greek, and Italian, with East-EU as the other source) fit the data (p = 0. 11) with ancestry proportions 89% Mediterranean and 11% East-EU. Models with a Western European source (Germans) instead of Russians were not plausible. There was also no support for a minor ancestry component from East Asians. We next used qpAdm to study the relations between EAJ, MAJ, and other Jewish groups (Data S1, section 7). Erfurt-ME could be modeled with Turkish (Sephardi) Jews (97% admixture proportion) and Germans (3%). Erfurt-EU could be modeled with Erfurt-ME (69%) and Russians (31%).
The levels of Middle Eastern ancestry in Italy were historically variable  (Aneli et al. , 2021; Antonio et al. , 2019; De Angelis et al. , 2021; Posth et al. , 2021; Raveane et al. , 2019) , and Middle Eastern populations have also experienced demographic changes in the past two millennia, particularly African admixture  (Moorjani et al. , 2011)  (Data S1, section 16). Under the extensive set of models we studied, the ME ancestry in EAJ is estimated in the range 19%-43% and the Mediterranean European ancestry in the range 37%-65%. However, the true ancestry proportions could be higher or lower than implied by these ranges (Data S1, section 16). Our results therefore should only be interpreted to suggest that AJ ancestral sources have links to populations living in Mediterranean Europe and the Middle East today.
20; two-tailed Fisher's Exact test); however, a higher prevalence of K1a1b1a in Erfurt-ME is concordant with its higher frequency in AJ of Western European origin  (Behar et al. , 2006; Costa et al. , 2013). Among the other AJ founder lineages, two EAJ individuals carried the K1a9 lineage, one carried N1b1b1 (N1b2), and none carried K2a2a1 (Table  S4 ). Overall, the mtDNA results provide evidence that EAJ were affected by the same bottleneck that affected MAJ. Runs of homozygosity (ROH) appear in individuals whose parents are genetically related and are also expected in founder populations  (Severson et al. , 2019). We detected ROH in EAJ using hapROH  (Ringbauer et al. , 2021)  (STAR Methods and Figure  S4 ). We focused on 16 individuals covered in at least 400k SNPs (Data S1, section 10).
Most of variants are in genes included in pre-conception screening panels (Table  1 ). A caveat is that imputation demonstrates the presence of haplotypes carrying the variants, not the variants themselves. However, the low false positive rate we observed with genotyped founder alleles (Data S1, section 14) argues against this scenario being common. Finally, we assessed variants associated with selected nonmedical or polygenic phenotypes. Variants for lactase persistence, eye and hair pigmentation, and (putatively) plague risk had similar allele frequency between EAJ and MAJ (Data S1, section 15). Osteological stature estimates for 13 adults were correlated with polygenic scores for height, although not significantly (Data S1, section 15).
Radiocarbon dating We performed accelerator mass spectrometry radiocarbon dating on purified collagen extracted from tooth dentin at the Pennsylvania State University Accelerator Mass Spectrometry (AMS) laboratory using previously described methods  (Kennett et al. , 2017; Lohse et al. , 2014). We calibrated dates using OxCal 4. 4 (Bronk Ramsey, 2009) and the IntCal20 48 calibration curve  (Reimer et al. , 2020). We assessed sample quality using stable isotope analysis. We found that carbon-to-nitrogen ratios for all collagen samples fell between 3. 19 and 3. 24, within the range of 2. 9-3. 6 expected for good collagen preservation  (van Klinken, 1999). The estimated dates are reported in Data S2, Table  1  (95. 4% probability intervals) and the full measurements are reported in Data S2, Table  3.
Samples were reacted for about 4 h, then rinsed to neutrality with UltraPure water and microcentrifuged for 1 min at 3000 rpm between each rinse. After the final rinse samples were freeze dried overnight (about 12 h). Next, 7-8 mg of sample was weighed into 12 mL glass exetainers for analysis. An additional 1 mg aliquot of sample was separated and placed on the ThermoFisher Nicollet Summit FTIR diamond ATR for QC/QA analysis. Samples were analyzed on a Thermo Scientific Delta V IRMS with a GasBench carbonate device for d 13 C enamel and d 18 O enamel. The full results of the isotope analysis are reported in Data S2, Table  5 , and the final values of d 13 C enamel and d 18 O enamel appear in Data S2, Table  1.
We initially obtained DNA data for 34 individuals. However, one individual was represented at only 52 SNPs, and was omitted from all analyses. We determined mitochondrial haplogroups using HaploGrep2  (Weissensteiner et al. , 2016). The procedure for Y-chromosome haplogroup determination is described in Supplementary Text S5 of  (Lazaridis et al. , 2022) , using the YFull YTree v. 8. 09 phylogeny (https://github. com/YFullTeam/YTree/blob/master/ytree/tree_8. 09. 0. json), obtaining information about SNPs from ISOGG YBrowse (https://ybrowse. org/gbrowse2/gff/snps_hg38. csv; accessed Oct 18, 2020), lifting coordinates from hg38 to hg19 using liftOver and intersecting with the SNPs present in the v. 8. 09 tree. The haplogroup calls were converted into letter-number haplogroup designations (e.
Pathogen DNA scan We screened the Erfurt individuals for the presence of pathogens using MALT  (Herbig et al. , 2016; Vagene et al. , 2018). We used a custom RefSeq genomic dataset containing bacteria, viruses, eukaryotes, and the human reference sequence GRCh38 to construct the MALT database using default parameters  (Giffin et al. , 2020) , with an index step size of 6. For each Erfurt individual, we screened all merged, de-duplicated sequences, applying a minimum complexity filter with a threshold of 0. 3. We ran MALT (version 0. 3. 8), using the parameters -mode BlastN, -alignmentType SemiGlobal, -minPercentIdentity 0. 85, -topPercent 1, -minSupport 1, -maxA-lignmentsPerQuery 100. We then screened the results using the HOPS workflow  (Hubler et al. , 2019)  to determine whether there was evidence of authentic DNA from a set of 348 pathogens of interest (Data S2, Table  4 ).
Detecting genetic relatives We used READ  (Monroy Kuhn et al. , 2018)  to identify 1 st -and 2 nd -degree relatives. We identified five individuals with 1 st -degree relationships (Figure  S1A ). Three individuals -I14850 (female), I14853 (male), and I14898 (female) -were all identified as 1 st -degree relatives of one another, and we denoted them as Family A (Figure  1B ). To determine their relationships, we first used information on age at death and mitochondrial DNA haplogroup. Individuals I14853 and I14898's estimated ages were 14-17 and 10-13 years old, respectively (Data S2, Table  1 ), and hence they must have been siblings. I14850 was estimated to die in her 40s, and all three individuals shared the same mtDNA haplogroup. These constraints are consistent with I14850 being either the mother or a sibling who has survived to adulthood.
Investigating genetic ancestry using PCA We used smartPCA  (Patterson et al. , 2006) , with the option "lsqproject", which enables projection of individuals with high missingness when the PCs were learned from modern individuals. The following populations from the Human Origins dataset were used: Armenian, Iranian, Turkish, Albanian, Bergamo, Bulgarian, Cypriot, Greek, Italian_South, Maltese, Sicilian, Italian_North, English, French, Icelandic, Norwegian, Orcadian, Scottish, BedouinA, BedouinB, Jordanian, Palestinian, Saudi, Syrian, Abkhasian, Adygei, Balkar, Chechen, Georgian, Kumyk, Lezgin, Ossetian, Jew_Ashkenazi, Jew_Georgian, Jew_Iranian, Jew_Iraqi, Jew_Moroccan, Jew_Tunisian, Jew_Libyan, Jew_Turkish, Jew_Yemenite, Basque, Spanish, Spanish_North, Druze, Lebanese, Belarusian, Croatian, Czech, Estonian, Hungarian, Lithuanian, Ukrainian, Canary_Islander, Sardinian, Finnish, Mordovian, Russian, and Polish.
We projected the Erfurt individuals on the PC space learned by the West-Eurasian populations. Down-sampling experiments showed that 50k or more SNPs provide reproducible assignment of individuals to Erfurt subgroups (Data S1, section 5). We consequently excluded eight EAJ individuals with <50k SNPs from all PC analyses. We also ran PCA with a larger sample size of MAJ, as follows. We merged the Human Origins dataset with whole-genomes of n = 544 modern AJ sampled in the USA and Israel  (Lencz et al. , 2018). These genomes were generated in Phase 2 of The Ashkenazi Genome Consortium (TAGC), and relatives were removed  (Fridman et al. , 2020). The merged dataset had about 470k SNPs. We used the same Human Origins populations as above to learn the PC space, except that here we removed the Human Origins MAJ population.
We then projected both EAJ and the TAGC MAJ on the PC space. Due to the large MAJ sample size, we plotted the positions of these individuals as a 2-dimensional kernel density plot (Figure  S2A ) using the function stat_density_2days() from the package ggplot2 in R. The density was scaled to 1 using the argument contour_var = "ndensity". To evaluate the effect of coverage on placement in PC space, we down-sampled a randomly selected subset of n = 525 MAJ individuals to match the set of SNPs covered by the EAJ genomes. Each of the 25 (non-low-coverage) EAJ genomes was used to match 525/25 = 21 MAJ individuals. We then selected a single allele at random from each down-sampled MAJ individual and projected the MAJ and EAJ individuals as above (Figure  S2B ). See Data S1, section 5 for details on the PCA that included MAJ of Western and Eastern European origin.
Admixture modeling with ADMIXTOOLS For the f 4 and qpWave analyses, we used the Human Origins dataset merged with whole-genomes of n = 544 modern AJ  (Lencz et al. , 2018). We used ADMIXTOOLS  (Patterson et al. , 2012)  version 5. 1 for running the analyses. We computed f 4 -statistics with default parameters. In the qpWave and qpAdm analyses, we only used transversion SNPs (about 110k) to avoid bias due to ancient DNA damage. In all population-level analyses, we (1) omitted one of each pair of first-degree relatives, keeping the individual with the higher coverage; and (2) included the low-coverage individuals (<50k SNPs), except in analyses that required the Erfurt-EU and Erfurt-ME group affiliation.
Detecting admixture with qpWave We ran qpWave with the option "allsnps:YES", which means that in each f 4 test, the program uses all SNPs that were non-missing in all four populations, but a different set of SNPs can be analyzed for each underlying f 4 -statistic. We ran qpWave tests separately against reference (''right'') European and reference Middle Eastern populations. In our qpWave analyses with European reference populations, we chose the right populations as modern populations that represent main European ancestries: Russian, Norwegian, French, Spanish, Bulgarian, and Italian_North, with Primate_Chimp as an outgroup (first right population). Middle Eastern populations are closely related, and we therefore chose the right populations as follows. We ran f 4 tests of the form f 4 (EAJ, MAJ; ME1, ME2) where ME1 and ME2 represent all possible pairs of populations from: BedouinA, BedouinB, Palestinian, Lebanese, Syrian, Jordanian, Egyptian, Saudi, and Druze.
, 2020)  were from Bronze-Age Megiddo (Megiddo_MLBA) and the ancient Rome individuals  (Antonio et al. , 2019)  were from Late Antiquity (Italy_LA. SG) and Imperial Rome (Italy_Imperial. SG). For the analyses at the individual level, we used all SNPs, as the coverage of many individuals was already low. To guarantee that using all SNPs did not bias the results, we repeated the analyses at the population level with all SNPs instead of just transversions and verified that the results remained qualitatively unchanged (Data S1, section 7). We included first-degree relatives in the individuallevel analyses, but omitted the low-coverage individuals (<50k SNPs). For individuals for which the Eastern-EU ancestry proportion was inferred to be negative (Figure  3 ), we re-ran qpAdm with only Southern EU and Middle Eastern sources.
The AJ source was 30 MAJ individuals that were sequenced in Phase 1 of The Ashkenazi Genome Consortium  (Carmi et al. , 2014a)  and were not included in the MAJ dataset that was used for the original qpWave analyses. For each admixture scenario, we simulated 30 individuals using the procedure described in Data S1, section 6. We used admixture times of G = 5; 10; 15; 20 generations. The qpWave p values reported in Table  S2  show that the maximal proportion of the AJ gene pool that could have been replaced by East-EU admixture and still remain consistent with being a clade with MAJ is about 2%-4%. In our simulations, all gene flow was assumed to occur over a single generation. With continuous gene flow, a replacement of a proportion m of the gene pool per generation over 20 generations would lead to a total replacement of 1 À ð1 À mÞ 20 of the total ancestry.
Detecting runs of homozygosity in the ancient genomes To identify runs of homozygosity (ROH) in the ancient genomes, we used the Python package hapROH version 0. 1a8  (Ringbauer et al. , 2021) , using 5,008 global haplotypes from the 1000 Genomes Project as the reference panel and the pseudo-haploid genotypes of the ancient genomes as input. As recommended for datasets with genotype data for 1240k SNPs  (Ringbauer et al. , 2021) , we applied our method to 16 Erfurt individuals covered in at least 400k SNPs and called ROHs longer than 4 cM. These individuals include two parent-child pairs. We used the default parameters and post-processing of hapROH, which are optimized for this data type (described in detail in  (Ringbauer et al. , 2021) ). We report the sum of the lengths of all ROHs longer than 4 cM in each individual in Data S2, Table  1.
To contextualize the ROH results, we repeated the ROH analysis in other ancient populations. We used the Allen Ancient DNA resource (V50. 0, Oct 10 2021; https://reich. hms. harvard. edu) and extracted previously published genomes from individuals who lived in the past z2000 years. We downloaded the data in eigenstrat format. As above, we used hapROH with the recommended settings  (Ringbauer et al. , 2021)  and applied it to pseudo-haploid genotypes. The populations we studied included Hungary Langobard  (Amorim et al. , 2018) ; Germany Early Medieval  (Veeramah et al. , 2018) ; Italy Imperial, Late Antiquity, and Medieval  (Antonio et al. , 2019) ; and Denmark Viking  (Margaryan et al. , 2020). The sum of the lengths of ROH segments in individuals belonging to these populations is shown in Figure  S4.
To estimate demographic parameters using both modern IBD and ancient ROH, we computed their expected counts based on the same demographic model, accounting for the sampling of the ROH segments 26 generations ago. We then computed the log-(composite) likelihood per haplotype pair separately for IBD and ROH, and computed the joint log-likelihood as their sum. This guarantees that each data type contributes roughly equally to the likelihood. Finally, to estimate the parameters of the two-population model, we used the same approach as above, except that we computed the coalescence probabilities taking into account that two lineages can coalesce during the period of population split only if both descended from the same sub-population. We used parametric bootstrap for model selection, using simulations to generate the empirical null distribution of the increase in log-likelihood when fitting a two-population model to data simulated under a a single-population model (Data S1, section 13).
For each high-confidence variant detected in EAJ, we determined whether the corresponding gene is present in pre-conception carrier screening (PCS) panels. We considered four PCS sequencing-based panels aimed at the AJ population: Genpath (https://www. genpathdiagnostics. com/hcp/womens-health/carrier-screening/ashkenazi-jewish-cancer-screening/), SEMA4 (https://sema4. com/ elements/expandedcarrierscreen/), fulgent (https://www. fulgentgenetics. com/beacon-ashkenazi-jewish-female-carrier-screening) and Baylor genetics (https://www. baylorgenetics. com/geneaware/). All data were downloaded in November 2020. For each variant, we indicate in Table  1  the number of panels in which the gene is included. expected for a parent-child relationship. (C) The output of OxCal for ten samples that underwent radiocarbon dating.
For each individual, the underlying bars denote intervals that have a cumulative probability of 95. 4%. The estimated dates are almost equally likely to pre-date or post-date the 1349 pogrom. One individual -I13869 -was inferred to be much more likely to date to the end of the 13 th century. However, there was a small peak in the late 14 th century, and this individual is the daughter of I14904 (Figure  1B ), who is more likely to date to the 14 th century. This implies that I13869 might also date to the 14 th century, despite this event having a smaller probability. 3  (A) Tests of the form f 4 (MAJ, EAJ; X, chimp), where X represents any non-Jewish West-Eurasian population. Each country on the map was colored based on the Z score for deviation from zero of the f 4 -statistic when replacing X with the local population in the Human Origins dataset.
ACKNOWLEDGMENTS We are thankful to  Ephraim Shoham-Steiner  for consultations when planning this study and to  Rabbi Ze'ev Litke , who undertook a ruling on contexts in which ancient DNA studies could be permissible. We thank the  Jewish community of Thuringia  for approving the study. We thank  Douglas Kennett ,  Nick Patterson ,  Lily Agranat-Tamir , and  Maria Stu  ¨rzebecher for discussions;  Shaul Stampfer ,  Sergio DellaPergola , and  Itsik Pe 'er for commenting on the manuscript;  Kim Callan ,  Fatma Zalzala ,  Kristin Stewardson ,  Nicole Adamski , and  Ann Marie Lawson  for wet laboratory work;  Rebecca Bernardos  for sample management; and  Iosif Lazaridis ,  Matthew Mah ,  Adam Micco , and  Zhao Zhang  for bioinformatics support. The study was funded by the  Israel Science Foundation  grant  407/17  and the  United States-Israel Binational Science Foundation  grant  2017024  to S.