dataesr/scientific-paragraphs-categorization huggingface dataset:
Taxonomy and Phylogeny of Coolia Species Coolia is usually considered a harmful genus of benthic dinoflagellates  [1] , even though marked species-specific differences in toxicity are observed, as reported herein and discussed later in Section 3. 3. Therefore, accurate taxonomy is desirable before any toxicological discussion concerning Coolia spp. For instance, prior to the description of C. malayensis, and without performing genetic analysis, the occurrence of toxic (CAWD77) and non-toxic (CAWD39 and CCMP304) strains of C. monotis was reported in New Zealand  [31]. However, by retrieving genetic sequences of the same strains from GenBank, it is possible to find out that two out of three strains belonged, in fact, to C. malayensis, and that the actual C. monotis strain (CCMP304) was non-toxic.
In the present study, before screening our strains for the presence of toxic compounds and toxic effects, we thus assessed Coolia spp. taxonomy based on morphology and genetic data, with special focus on the two most toxic species, i. e. , C. malayensis and C. tropicalis. From all eight species of Coolia described so far, only one (C. areolata) has no molecular data available to date  [15]. Genetic separation among the species is, in general, well resolved. Despite several studies suggesting that C. malayensis and C. monotis might be the same species  [1, 37] , currently the separation into two different species is more widely accepted  [14, 19]. In the present study, the phylogenetic analysis on Brazilian strains of Coolia spp. revealed similar topology to previous investigations, clearly separating all species based on both ITS (ITS-1, 5.
The species group encompassing C. tropicalis is characterized by a larger cell size compared to the C. monotis group (present study, Table  1 ;  [15, 16, 18, 19] ). Both species groups are also distinguishable phylogenetically, based on the LSU D1-D3 alignment dataset (see Figure  2 ). Furthermore, C. tropicalis is easily separated from the other species within its own group (i. e. , those possessing a rectangular 7" plate), based on the smooth cell surface on both epitheca and hypotheca (present study, Figure  7A-B ;  [15, 23, 28] ). Conversely, species within the C. monotis group are difficult to separate from each other based solely on morphological features. Plate arrangements are quite similar, size ranges overlap, and thecal plates lack any kind of perforation patterns in these smaller-celled species.
palmyrensis present in offshore sites. Coolia santacroce, in turn, had only been recorded, so far, in the Caribbean Sea (GenBank sequences). Thus, a general pattern of Coolia spp. distribution seems to arise from this and previous studies: C. malayensis is globally distributed (see  [14] ); C. monotis is probably broadly distributed in coastal waters of Europe and the East Atlantic  [24] ; while distributions of C. tropicalis, C. canariensis, C. santacroce and C. palmyrensis are more restricted, mainly to warmer waters and/or to offshore sites with lower hydrodynamics (present study,  [22] ).
However, YTX analogues (including cooliatoxin) have never been detected again in other Coolia spp. cultures (present study,  [23] ). Besides YTX analogues, our strains of C. malayensis, C. palmyrensis and C. santacroce also lacked any other toxic compound produced by another genus of benthic dinoflagellates, Gambierdiscus, including maitotoxins, gambierones, gambieroxide, and gambieric acids. A strain of C. tropicalis, however, contained relatively high intracellular levels of 44-methyl gambierone (previously referred to as MTX-3  [35] ) and a novel isomer of the same compound. Spectral data presented by Holmes et al. [30]  suggest that 44-methyl gambierone was not present in that extract (absence of 1037. 5 in the negative ionspray mass spectrum), suggesting either misidentification of species, intra-specific variability of toxin production or divergence of C.
For the second PCR reaction, 1 µL of the first product was added to a new tube containing 2. 5 µL of each primer (ITSfw and 28S364r for ITS region; D1R and D3B for D1-D3; Table  3 ), 12. 5 µL of GoTaq ® G2 Hot Start Green Master Mix (Promega ® , Madison, WI, USA) and 6. 5 µL of nuclease-free water. The second PCR was performed as the first one, changing the MT to 50 • C for ITS, and 56 • C for D1-D3 region. DNA amplifications were controlled by electrophoresis on agarose gel. Positive samples were purified and sequenced, as described in Chomérat et al. [42]  The alignment and phylogenetic analyses were performed as described in Chomérat et al. [42] , with modifications as described below. Both ITS and D1-D3 rDNA region datasets were aligned using MAFFT algorithm, with selection of the q-ins-i strategy  [43].
5. 2 below, except for the porosity of the Kinetex C18 column used (1. 7 µm, instead of 2. 6 µm). Mass spectra were acquired from 100 to 1700 m/z, with an acquisition rate of 2 spectra s -1. The targeted MS/MS mode was applied over m/z 50-1700, with an MS scan rate of 10 spectra s -1 and an MS/MS scan rate of 3 spectra s -1. Three fixed collision energies were applied by ionization mode (15, 30 and 45 eV in ESI + ; 30, 45 and 90 eV in ESI -), to obtain an overview of the fragmentation pathways. Instrument control, data processing and analysis were conducted using Mass Hunter software v. 8. 0 (Agilent Technologies, CA, USA).
Funding: This research was funded by  International Atomic Energy Agency (IAEA)  through the  Bentox  Project, RC#  18827 , and the  IAEA Core Research  Project  K41014. Further funding was obtained from  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) :  PVEX , n. 88881. 172853/2018-01 ;and PDSE, n. 88881. 190128/2018-01. P. H. and N. C. acknowledge the  European H2020 program  for funding the  EMERTOX  project (grant number  778069 ). The APC was funded by  H2020 778069-EMERTOX.
The aim of this project is to investigate the transition from the Neolithic to the Bronze Age in Switzerland in detail, with a specific focus on the timing of the arrival, source and mixture process of steppe-related ancestry, and the social and demographic structure before and after this transition. Using relatively dense temporal sampling, we generate genome-wide data from 96 individuals dating to the Neolithic and Early Bronze Age period from Switzerland, Southern Germany, and the Alsace region in France. We also generate data from one individual from the Early Iron Age and the Roman period, respectively. We find the expected large genetic turnover at the beginning of the third millennium BC and a highly genetically structured population in the region of present day Switzerland at that time period.
Results Ancient DNA authentication and uniparental markers. The ancient individuals from this study originate from 13 Neolithic and Early Bronze Age sites in Switzerland (Fig. 1b ), Southern Germany, and the Alsace region in France. All samples taken from the individuals were radiocarbon dated (Supplementary Note 2 and Supplementary Data 1). In a preliminary screening, 263 samples were enriched for mtDNA. We reconstructed complete mitochondrial genomes, used them to estimate DNA library contamination (Supplementary Data 1), and identified 96 samples that had less than 5% of contamination for further analyses. We determined mtDNA haplogroups using the software haplogrep (ref. 16 , Supplementary Note 3, Supplementary Fig. 1 , and Supplementary Data 1) and found the macrohaplogroups N1a, W, X, H, T2, J, U2, U3, U4, U5a, U5b, K, and U8 in our samples.
For genome-wide analysis, we genotyped all selected individuals on ~1. 2 million genomic SNPs 2 , also containing 49,704 SNPs on the X chromosome and 32,670 SNPs on the Y chromosome. SNPs on the X chromosome were used to estimate nuclear contamination in male individuals (Supplementary Data 1), and we again used a threshold of 5% to select clean libraries for further analysis (96 individuals). We also determined Y chromosomal haplogroups (Supplementary Note 4, Supplementary Fig. 2 , Supplementary Data 1, and Supplementary Table  1 ). Population turnover at the transition to the Bronze Age. We combined the genotype data of the new 96 individuals from this study that passed our contamination tests with 399 published ancient genomes from the same time period from Central and Western Europe as well as Neolithic individuals from Anatolia and the Pontic steppe (individuals annotated as Yamnaya Samara in ref.
6  ) and genotype data of modern individuals from the POPRES  17  and the Human origins (HO) 1 datasets for various analyses. We projected our 96 new ancient genomes from Switzerland and surrounding regions and 52 published ancient genomes selected to reflect the genetic landscape of Europe at different time points onto the first two principal components constructed from 1960 individuals of 38 European populations from the POPRES dataset (Fig. 1c  and "Methods"). Two distinct clusters can be identified and were also confirmed by ADMIXTURE analysis (Supplementary Note 5), one consisting of individuals dating to 4770-2500 calBCE, and one comprising individuals dating to 2900-1750 calBCE. The oldest individuals from the sites of Niederried (CH) and Lingolsheim (F) fall close to ancient individuals from Anatolia associated with early agricultural contexts.
All Final Neolithic and Early Bronze Age individuals fall within the range of modern-day Europeans, but none of the newly sequenced individuals of this study overlap with the present-day Swiss populations in this analysis, suggesting additional population changes in the region after the Middle Bronze Age. Our individuals sequenced in this study fall in PC space between WHG individuals, Western Anatolian Neolithic Farmers (ANF) and steppe pastoralists from Samara (YAM), similar to other Late Neolithic individuals such as the Tyrolian Iceman  18  and Bronze Age populations such as individuals of the Bell Beaker complex  6  from Europe. Therefore, we modeled them as a threeway mixture between these three populations using qpAdm from the ADMIXTOOLS package (ref. 19 , "Methods" and Supplementary Tables 2 and Supplementary Data 4).
We analyzed pairwise genetic differences across all analyzed genomic positions between individuals before and after the genetic turnover and found that the mismatch rates increase, on average, by around 0. 009 after 2700 BCE for all populations (Fig. 2c , see also "Methods"). This is more than twice the increase that can be attributed to the between-population variability in rates and indicates a significant increase in genetic diversity after the arrival of the YAM-related ancestry component in Central Europe. Modern populations would be expected to have higher levels on average but are not compared in this analysis since the modern individuals from published datasets usually do not originate from groups with the same background (e. g. , being related distantly) as could be expected in multiple burials.
Comparing our newly analyzed individuals from Switzerland with ancient genomes from Great Britain, Iberia, and Germany 2,9,21 we modeled the arrival time of the YAM-related ancestry in the different broadly defined European regions (Fig. 2b , see "Methods"). While our models indicate that the proportions of the YAM-related ancestry peaks earlier in the Swiss dataset (around 2750 BCE) compared with the comparative datasets from refs. 2, 9, 21 (around 2600 BCE), these differences fall within the uncertainty of the analysis (Supplementary Fig. 6 ), so may be considered suggestive of an earlier arrival of stepperelated ancestry, but not conclusive. We also caution that differences are likely affected by uneven sampling through time in the three different datasets, and so expect the precision of this analysis to improve with denser temporal sampling in the future.
Timing and duration of the genetic turnover. We used the software DATES (ref. 22 , https://github. com/priyamoorjani/ DATES) to estimate the admixture time between YAM-related and Late Neolithic ancestries in all Final Neolithic and Early Bronze Age individuals from Switzerland with substantial admixture proportion. Our estimates range between 3 and 60 generations ago, with substantial uncertainty. If the mixture occurred as one single event in the history of all individuals, we would expect more recent individuals to have a higher admixture time estimate (i. e. , more generations ago) than individuals of older dating. However, we observe only a slight trend towards more generations in individuals with younger C 14 dates (Fig. 3a ), which suggests that the process of admixture with steppe-related ancestry occurred over several hundred years rather than as a single pulse.
19 , Supplementary Data 3 and 4). The model remains fitting for Iberia Middle Neolithic and France Neolithic populations (p ≥ 0. 01 and p ≥ 0. 3, respectively) but it fails when we add Late Neolithic individuals from Switzerland or Globular Amphora as additional right population (p ≤ 2e-7 and p ≤ 2e-6, respectively). This suggests that both the local Swiss Late Neolithic population as well as people associated with the Late Neolithic Globular Amphora culture, located further east, are better proxies of the genetic sources for Final Neolithic and Bronze Age populations from Switzerland than Western ANF and steppe pastoralists. Kinship before and after the genetic transition. In five burial sites, we identified first-degree relatives using the software lcmlkin  23  and READ  24  , and by calculating pairwise mismatch rates across all analyzed genomic sites between individuals (see "Methods").
Population movement in Switzerland after the Bronze Age. We compared present-day Swiss people to regional Final Neolithic populations (Spreitenbach, Bad Zurzach, Wartau) to test whether there are additional ancestry components in present-day Swiss people. For that analysis, we made use of information available in the present-day dataset (POPRES) about the self-reported language group and split the individuals into three linguistic regions, as they were shown to be distinguishable genetically in previous studies  27    Italian-speaking Switzerland. To test for continuity between the ancient and present-day population we used the method qpWave from the ADMIXTOOLS package  19  and found that a simple continuity can be rejected (p = 0. 0003) for all three linguistic regions separately and the entire present-day Swiss population combined, consistent also with the PCA (Fig.
1c ). To assess whether ancient Swiss individuals from the Final Neolithic are symmetrically related to different linguistic presentday groups, or share an excess of alleles with any of them, we calculated D-statistics of the form D(Mbuti, Test, Swiss-French, Swiss-Italian), D(Mbuti, Test, Swiss-German, Swiss-Italian), and D(Mbuti, Test, Swiss-French, Swiss-German) where "Test" are the different Neolithic groups. The first two D-statistics were, with few exceptions, all negative with a |Z| ≥ 1. 099 (maximum values to be found in D(Mbuti, Test, Swiss-French, Swiss-Italian) for Singen with -3. 431 and Bad Zurzach -3. 068) indicating the least genetic affinity of the Final Neolithic and Early Bronze Age individuals of this study to the Italian-speaking group in our present-day Swiss dataset. For D(Mbuti, Test, Swiss-French, Swiss-German) some variation between the sites can be found (Fig.
g. , Italy are not genetically described so far for this particular time span. However, individuals without any steppe-related ancestry can be found up until 2479-1945 BCE for example in Iberia or until 2900-1700 BCE in the Minoan population of Crete  21, 36  and even later on Sardinia where steppe-related ancestry arrives around 300 CE  37  and where studies of present-day Sardinians found indications of continuity in mountainous regions since Neolithic times  38. Also noteworthy is the remarkably early arrival of the stepperelated ancestry component in Switzerland, at least as early as or even earlier than in regions of Germany and Great Britain. However, further investigations are needed, especially since datasets from the regions in Great Britain and the MES region in Germany show gaps in the sampling between the Late/Final Neolithic and the Early Bronze Age potentially biasing the results, to draw any conclusions about the exact route in which the steppe-related ancestry spread through Central Europe.
06 U/µl USER enzyme (NEB). After a 30 min incubation at 37 °C, 0. 12 U/μl UGI was added and the reaction was incubated for another 30 min at 37 °C. This was followed by the procedure described above  42. The non-UDG treated library and the two UDG-half libraries were enriched for 1. 2 Mio nuclear SNPs using an in-solution hybridization protocol  2, 43. Bioinformatic processing. After sequencing all data were processed using the software package EAGER  44. Adapters were removed and paired-end data were merged using Clip&Merge  44. Mapping was performed with BWA with the mismatch parameter set to 0. 01 and a seed length of 1000 against the human genome reference GRCh37/hg19. If necessary, PCR duplicates were removed using DeDup  44. BAM files from different libraries of the same extract were merged after mapping and quality control.
Reads mapping to the mitochondrial genome were extracted from the BAM files and the mitochondrial genome was reconstructed and the amount of mitochondrial contamination was estimated using the software schmutzi  45. The web-based tool HaploGrep  46  was used to determine mitochondrial haplogroups. Genetic sex was determined by comparing X chromosomal reads to Y chromosomal reads  47. For male samples, nuclear contamination was estimated using ANGSD  48. Samples with mtDNA or nucDNA contamination >5% as well as samples with <10,000 SNPs of the HO dataset were excluded from further analysis. Population genetic analysis. Pseudo-haploid genotypes for the 1. 2 M SNPs were retrieved using pileupcaller (https://github. com/stschiff/sequenceTools). A reference dataset of 399 published ancient genomes, the HO dataset or the POPRES dataset were compiled.
The overlap between the 1. 2 M targeted SNPs and the HO data are 593,054 SNPs, between the POPRES and the target SNPs 133,682. For the principal component analysis on the newly sequenced individuals, published ancient individuals and the European population from the POPRES smartpca from the EIGENSOFT package (version: 16000) was used with the parameters lsqproject: YES and shrinkmode: YES. Relative proportions of ancestry components in the newly sequenced individuals, published ancient individuals from Germany, Great Britain, and Iberia were estimated using qpAdm (version: 632) from ADMIXTOOLS (ref. 19 , https:// github. com/DReichLab) using a threshold of 100k SNPs for analysis on an individual level (Supplementary Note 3) and modern reference individuals (Mbuti, Papuan, Onge, Han, and Karitiana) from the HO dataset and published ancient individuals (Ust Ishim, Ethiopia 4500BP, Villabruna, MA1).
To avoid downward-biased estimates of p e , we set individuals with steppe ancestry proportion zero, which are observed after t e , to have zero residual value. To incorporate uncertainty in dating estimates, we did not use the mean date for each individual, and instead randomly sampled a date for each individual from a normal distribution truncated such that the upper and lower bounds of the date estimates form a 95% confidence interval around the sampling mean. We repeated this sampling process 100,000 times and found an optimal value for t e for each sample, for each dataset. Optimal values of t e were only retained if they produced stable solutions with a positive-definite Hessian matrix. For the uncertainty in the proportion of steppe ancestry it is assumed it is identically distributed, with mean zero and unbiased for all points, as it is standard in least-squares regression.
To account for correlations in the data due to the same samples being used to calculate multiple pairwise distances, we included the ID of the samples as random effects. We found that both the time period (before and after 2700 BCE) and the population of origin for samples were significant predictors for the pairwise mismatch rate. Analyses were performed using R version 3. 4. 3 (R Core Team 2017) via the robustlmm package  50. Admixture date estimates. Admixture dates were estimated using DATES, which was extensively tested in ref. 22  , based on the 1240k SNP panel for single individuals and for groups of individuals (ref. 22 , https://github. com/priyamoorjani/ DATES). Kinship analysis. Kinship between individuals was assessed using the software READ  23  , lcmlkin  24  , and calculating pairwise mismatch rate on autosomal markers and confirmed by mtDNA haplotype and Y chromosomal haplogroups based on the 1240k SNP panel (Table  1 ).
qpWave analysis. Population continuity since the Bronze Age was tested with qpWave (version: 410) from ADMIXTOOLS (https://github. com/DReichLab) between newly sequenced Swiss Bronze Age individuals and modern Swiss individuals from POPRES using Mbuti, Karitiana, Hakka Taiwan, Papuan, Onge, Han, Hungarian, MA1, EHG, and WHG as outgroups and differences in the genetic affinity between the Bronze Age individuals and the four linguistic regions of Switzerland were assed with D-statistics of the form f4(X,Y; Test, Outgroup). Functional SNP analysis. Phenotypic SNPs were genotyped using GATK version 3. 8  51. Low coverage positions (e. g. , 1×) with reference or alternative alleles carrying A or T were inspected manually to exclude the influence of ancient DNA damage.
Acknowledgements This study was funded by the  German Research Foundation   KR 4015/4-1  and the  Swiss National Foundation   CR31I3L_157024. This project has also received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  under grant agreement no. 771234-PALEoRIDER (W. H. , A. B. R. , and L. P. ) and the  Heidelberg Academy of Sciences  ( WIN project  " Times of Upheaval: Changes of Society and Landscape at the Beginning of the Bronze Age ") (P. W. S. and J. K. ). We thank  Anna Sapfo Malaspinas ,  Ivan P. Levkivskyi ,  Sven Bergman ,  Michal Feldman , and  Choongwon Jeong  for helpful discussions and comments. Special thank goes to the  Archaeological Service of the Canton of Bern (Switzerland). The authors also would like to thank the  Archaeological Services of the Cantons of Aargau (Switzerland) ,  Basel-Landschaft (Switzerland) ,  Solothurn (Switzerland), St.
Data availability Genome-wide data are accessible in the Sequence Read Archive under the accession numbers SAMN14206592-SAMN14206687 (https://www. ncbi. nlm. nih. gov/sra/ PRJNA608699) and mitochondrial consensus sequences on GenBank under the accession numbers MT079018-MT079111. The POPRES dataset contains sensitive patient information and is therefore not publicly available but access can be requested via https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs000145. v4. p2.
, 2011). A matrix of squared deviations from species means was calculated for each year for each species, so that if the abundance of a particular species was the same in all years, the values for that species were zero in each year. The total of these squared deviations were then summed across the species-by-year abundance data (SSTotal). BDTotal was then calculated as (SSTotal/(n-1)), where n refers to the number of years. For this study, the mean monthly abundances of each taxa were log10 (x+1) transformed, with any gaps in the monthly mean time-series filled through linear interpolation, before being further aggregated to annual means  (Richardson et al. , 2006). These annual means were then chord transformed prior to analysis to make the data suitable for Beta Diversity analysis and to avoid putting a large emphasis on rare species  (Legendre and Borcard, 2018; Legendre and Gallagher, 2001).