dataesr/scientific-paragraphs-categorization huggingface dataset:
Design of Primers and Probes Nucleotide sequences of vanA and vanB variant genes were collected from the GenBank database and aligned. As shown in Figure  1 , identified sequences discriminated between both genes using different internal bases, which were used to pick the specific probe for each gene. A conserved sequence of 160-bp between vanA and vanB genes was identified, then conserved, and common bases of both ends were then used as the annealing sites of common primers (Figure  1 ). The designed primers and probes were checked in silico to avoid misleading amplification of vanC gene variants.
Sequencing the hair sample and assessing authenticity First, we extracted DNA from Sitting Bull's hair, built indexed Illumina sequencing libraries, and sequenced them on the Illumina HiSeq 2000/2500 platforms. After mapping, we removed duplicates and filtered away bases with base quality below 20 and reads with mapping quality below 25, which left us with sequencing data covering 0. 8% of the genome with a depth of coverage of 2. 7, or equivalently a mean sequencing coverage of 0. 02×. Next, to assess the authenticity of the sample, we estimated base specific error rates of the quality-filtered sequencing data and found markedly higher error rates at SNP loci with transitions (fig. S1 ), which is typical of ancient DNA due to postmortem degradation  (22). The remaining error rates were all below 0.
001. Thus, the results of this analysis both corroborate the authenticity of the hair sample and suggest that most of the errors in the data can be avoided by removing transitions, which we did before performing all subsequent analyses. The authenticity of the sample is also supported by results from multidimensional scaling (MDS) analyses based on the sequencing data from Sitting Bull combined with SNP chip data from worldwide populations included in the Human Genome Diversity Panel (HGDP)  (23). The MDS plot obtained for joint dataset clearly shows that the sample from the hair lock clusters with Native American individuals (Fig. 2 ) as would be expected since the hair sample is from Sitting Bull.
Assessing the familial relationship between Sitting Bull and Ernie LaPointe using a new method To be able to assess the familial relationship between Sitting Bull and Ernie LaPointe, we genotyped 13 unrelated Lakota Sioux individuals, including Ernie LaPointe, using the extensive Illumina Omni 5M chip. Unfortunately, the sequencing data obtained from the hair sample were so limited in amount that the overlap between this and the chip data from Ernie LaPointe and the other Lakota Sioux individuals is restricted to 2259 polymorphic loci after excluding data from loci with transitions. This is not sufficient to perform reliable relatedness estimation for distantly related individuals using existing methods. Therefore, to investigate whether Ernie LaPointe can be genetically identified as the great-grandson of Sitting Bull, we developed a new probabilistic method tailored to the situation where one  has limited sequencing data from a historical individual and SNP chip genotype data from a present-day individual.
Investigating what can be concluded from the obtained estimate To more properly assess what can be concluded from the F rel estimate obtained for Sitting Bull and Ernie LaPointe, we performed simulations of data similar to the real data. More specifically, we simulated 100,000 datasets each consisting of low-depth sequencing data from a historical individual, SNP chip genotype data from four reference individuals, and two individuals for which we later estimated F rel : one, E unrelated , that is unrelated to the historical individual and one, E great-grandchild , that is the great-grandchild of the historical individual. Each of these dataset were simulated to have the same low number of SNP loci that are polymorphic among the four reference individuals and E unrelated or E great-grandchild (n = 2259) (Fig.
3 ). The results of applying the new method to these simulated datasets suggest that with the limited data at hand, an estimate of 0. 1143 or higher can, in principle, occur even if Ernie LaPointe and Sitting Bull were not related but that this is unlikely to occur (P < 0. 00389). Thus, the results from the real data combined with the simulation results show strong support for Ernie LaPointe being Sitting Bull's great-grandson. Notably, assuming the simulations are sufficiently realistic, the simulation results also show that the method has 97. 0% power to detect a great-grandchild if one uses a null model of the individuals being unrelated and a 0. 05 significance threshold, making it a fairly powerful method for such distant relatives given the low amount of data available. Last, the simulation results show that the estimates provided by the method are biased when applied to the simulated data; it does not, on average, provide estimates that are equal to the expected value of F rel (Fig.
SNP chip data from Lakota Sioux individuals Saliva samples from 14 Lakota Sioux individuals, including Ernie LaPointe, were collected using the Oragene OG-300 DNA Self-Collection Kit (Genotek). We extracted DNA following the prepIT L2P protocol (Genotek). DNA extracts were processed for genotyping on the extensive Illumina Omni5-Quad-Exome BeadChip at AROS Applied Biotechnology (Aarhus, Denmark) to maximize the overlap with the Sitting Bull sequencing data. This led to data from 4,334,816 SNP loci. Among the 14 individuals, two were self-reported to be half-siblings, and one of these was removed from the dataset before all analyses. Estimates from simulated data (f estimated from 5 samples).
Dataset used for error rate estimation We used the quality-filtered sequencing data from the Sitting Bull hair sample and used high-quality sequencing data for the genome of individual NA12778 from the 2013 release of 1000 Genomes Project as the high-quality genome  (30). To determine the ancestral alleles and, thus, the derived alleles, we used the outgroup species chimpanzee as is standard in human studies. In particular, we used the multiway alignment that includes both chimpanzee and human (pantro2 from the hg19 multiz46).
Dataset used for MDS analysis We used the HGDP data provided with the bammds software tool  (23, 31) , which contain data from 644,074 SNP loci for a range of populations from across the world. From these data, we removed all transition SNP loci, since these are much more error prone (fig. S1 ). This left us with genotype data from 120,643 SNP loci. The qualityfiltered sequencing data from the Sitting Bull hair sample overlapped 654 of these loci, and these were the reads that were included in the analysis along with the genotype data. The extraction of these reads was performed by the bammds software tool that we used for the MDS analysis.
Dataset used for ADMIXTURE analyses We merged the Lakota Sioux SNP chip data with genomic data for 40 individuals from the CEU 1000 Genomes population keeping only the SNPs that overlap and are consistent between the two datasets. After merging, we removed SNPs with minor allele frequency below 5%, SNPs with any missingness and SNPs in high linkage disequilibrium (using the PLINK option --indep-pairwise 100 10 0. 5) leaving us with data from 363,519 SNP loci.
Dataset used for estimation of F rel We combined the quality-filtered sequencing read data from the Sitting Bull hair sample with the SNP chip data from the 13 not closely related Lakota Sioux individuals. When doing so, we first discarded all SNPs that did not overlap with any read data from Sitting Bull. This left us with data from 49,347 SNP loci. Subsequently, we removed all SNP loci with transitions, since these are much more error prone (fig. S1 ), which left us with 10,142 loci. Then, for each of the remaining loci, we sampled a single read from Sitting Bull, and if the read had one of the two alleles present in the SNP chip data at that locus, we included the locus in the analyses; if not, we discarded the locus, leaving us with 10,041 SNP loci. Last, we removed 8 of the 13 Lakota Sioux individuals because they were admixed (fig.
Data simulations Each of the 100,000 was simulated so it mimicked the data from the analyses of the real data from of Sitting Bull and Ernie LaPointe. Specifically, each dataset consisted of low-depth sequencing data from one historical individual, SNP chip genotype data from four reference individuals, and SNP chip genotype data from two individuals for which we are interested in estimating F rel : one, E unrelated , that is unrelated to the historical individual and one, E great-grandchild , that is the great-grandchild of the historical individual. Further, the number of simulated loci was chosen so the number of SNP that are polymorphic among the four reference individuals and the individual of interest (i. e. , the five individuals that were used for allele frequency estimates when applying our method to the dataset) was, on average, approximately equal to the number of SNP loci used in our real data analyses, namely, 2259.
The data for each dataset were simulated by first sampling population allele frequencies from a uniform distribution between 0. 1 and 0. 9 (mimicking that we have data from loci on a SNP chip) for a preset number of SNP loci. Then, using these population allele frequencies, we simulated a pool of 15 haplotypes. Assuming that our loci are few and far between and thus independent, we simulated each of these haplotypes by independently for each locus sampling an allele from a Bernoulli distribution using the population allele frequency for that locus. Last, based on these haplotypes, we simulated 1) The low-depth sequencing from the historical individual by sampling two haplotypes from the pool of haplotypes without replacement and then randomly sampling an allele from one of these two haplotypes at each locus 2) The SNP chip genotype from four reference individuals by sampling four pairs of haplotypes from the pool of haplotype without replacement 3) The SNP chip data from E unrelated by sampling two haplotypes from the haplotype pool without replacement 4) The SNP chip data from E great-grandchild , by simulating first a child of the historical individual based on the two haplotypes of the historical individual, then a grandchild based on the two haplotypes of the child, and lastly a great-grandchild based on the two haplotypes of the grandchild.
More specifically, we simulated each of these offspring by sampling one new haplotype from the pool of haplotypes and combining this with a haplotype that was a recombination of the two haplotypes of the parent (e. g. , the historical individual). The recombination was simulated by randomly sampling one allele at each locus from the two haplotypes. When simulating the datasets, we assumed that the genotypes from the present-day individuals can be obtained reliably without any errors. In contrast, we assumed a fixed error rate of 0. 001 for the historical individual and added errors by simply switching the simulated sampled allele to the other allele segregating at this locus randomly with probability equal to the error rate. We used an error rate of 0. 001 to mimic the error rate estimated for nontransition loci that is below 0.
Estimating Frel from the simulated datasets For each of the simulated datasets, our F rel estimation method was applied to (i) the simulated data from the historical individual, (ii) the simulated data from the individual, E unrelated or E great-grandchild , for which we want to estimate F rel, and (iii) allele frequencies estimated from four reference individuals and E unrelated or E great-grandchild (i. e. , the same approach as we have used for Sitting Bull and Ernie LaPointe). The estimation method was implemented in R using the optim function with the optimization algorithm set to L-BFGS-B and lower and upper bound for F rel set to 1 × 10 -12 and to 1 -(1 × 10 -12 ), respectively. Estimating F rel from the real data As described above the estimation method was implemented in R using the optim function with the optimization algorithm set to L-BFGS-B and lower and upper bound for F rel set to 1 × 10 -12 and to 1 -(1 × 10 -12 ), respectively.
Acknowledgments: We thank  E. L. 's wife,  S. LaPointe , his sisters  M. Little Spotted Horse-Anderson  and  E. Little Spotted Horse-Bates , and the anonymous participants for providing consent and endorsing this research project. Furthermore, we thank  R. Nielsen  for valuable input regarding the new computational method. Funding: This work was funded by the  Danish National Research Foundation, Denmark. T. S. K. was funded by a  Carlsberg Foundation   Young Researcher Fellowship  awarded by the  Carlsberg Foundation  in 2019 ( CF19-0712 ). Author contributions: E. W. initiated and led the study. E. L. helped collect the saliva samples from the  Lakota Sioux individuals  and provided access to  Sitting Bull 's hair repatriated by the  Smithsonian. A. S. -O. performed the DNA work. J. V. M.
, 2018)  in order to test the ability of CBEs to induce cancer-related mutations in zebrafish. We first developed an automated script to rapidly detect codons allowing to generate nonsense mutations after a C-to-T conversion within the restricted PAM [À19, -13] bp editing window (Source code 1). Using this script, we designed a series of sgRNAs targeting in a selection of tumor suppressor genes. We induced the Q94* mutation in Tek in five of eight randomly chosen embryos, Q273* mutation in Bap1 in four of eight randomly chosen embryos and Q21* mutation in p53 in six of eight randomly chosen embryos as well as Q170* in p53 in seven of eight randomly chosen embryos by C-to-T conversions (Figure  1D-G , Table  1, Table 2 ). Among the different targeted mutations, the highest efficiency was achieved with the tp53 tumor suppressor gene, for which we reached up to 86% of C-to-T conversion for the introduction of the Q170* mutation (Figure  1G , Table  1, Table 2 ).
We also introduced a stop codon by a C-to-T conversion in the tp53 gene in 1 out of 4 randomly analyzed embryo with 16% efficiency (Figure  2F , Table  1, Table 2 ). These results demonstrate that in addition to the classical NGG PAM it is now also possible to target NAA PAMs in zebrafish, thereby significantly expanding the range of cytidine bases that can be converted. For these new CBEs, we added a function in our script to choose the PAM recognized by the Cas9-D10A of the chosen CBE to generate the desired base editing (Source code 1). Together with the use of the ancBE4max and ancBE4max-SpymacCas9 CBE variants, we were now able to target mutations that could not be generated with the BE4-gam base editor and reproduce a wider range of human cancer mutations in zebrafish. Genetic alterations that lead to oncogene activation and/or tumor suppressor inactivation are responsible for tumorigenesis.
sgRNA design A sequenceParser. py python script was developed and used to design sgRNAs for the creation of a stop codon. The first function of the script is to ask which PAM will be used to then execute the rapid detection of codons that are in the right editing windows from this predefined PAM to generate a STOP in frame after C-to-T conversion. The ORF sequence file extension is. txt and the letters in lower cases. The script can be executed from the command line interface (such as the terminal or PowerShell console) using Python version 3. Efficiencies of sgRNAs were validated using CRISPOR online tool  (Haeussler et al. , 2016). All the synthetic sgRNAs were synthesized by IDT as Alt-R CRISPR-Cas9 crRNA and Alt-R CRISPR-Cas9 tracrRNA. List of the crRNAs used in this study and the targeted C bases for each targeted locus.
50, Macherey-Nagel), and Sanger sequencing was performed by Eurofins. Sequence analyses were achieved using ApE software and quantifications of the mutation rate done using editR online software  (Kluesner et al. , 2018). For the verification of cbl mutant F1 embryos, tek, bap1 and tp53 mutations, PCR fragments were subsequently cloned into the pCR-bluntII-TOPO vector (Invitrogen). Plasmid DNA was isolated from single colonies and sent for sequencing. Mutant alleles were identified by comparison with the wild-type sequence using ApE and Geneious softwares. Primer sequences used to amplify the targeted loci:.
Imaging Embryos were oriented in low-melting agarose 0. 6% with an anesthetic (Tricaine 0. 013%) diluted in egg solution. The inverted laser scanning confocal microscope Zeiss CLSM-LSM780 was used for high-resolution microscopy, employing a 40Â water immersion objective. Z-stacks were acquired every 1-2 mm. Leica MZ10F was used to image the whole embryos the cbl mutant adult fish. Image analyses were performed with ImageJ software.
Acknowledgements We thank  Sophie Vriz  for sharing the Tg(7xTCF-Xla. Siam:GFP) transgenic line and the members of the fish-facility in Institut Curie. We also thank  Celine Revenu  and  Viviana Anelli  for early contribution. MR was supported by the  Fondation pour la Recherche Medicale  (FRM grant number  ECO20170637481 ) and la  Ligue Nationale Contre le Cancer. Work in the  Del Bene laboratory  was supported by  ANR-18-CE16  'iReelAx',  UNADEV  in partnership with  ITMO NNP/AVIESAN (National Alliance for Life Sciences and Health)  in the framework of research on vision and  IHU FOReSIGHT  [ ANR-18-IAHU-0001 ] supported by  French  state funds managed by the  Agence Nationale de la Recherche  within the  Investissements d'Avenir program. MCM was supported by  World Wide Cancer Research , grant no.
PSF shaping or multiplane detection diverts photons for 3D encoding, resulting in a loss of lateral localization precision relative to basic 2D set-ups. The theoretically achievable localization precision and axial range depend on the PSF shape and can be calculated. Some PSFs have even been engineered to achieve theoretically optimal precisions over a given axial range  99, 277. Many software packages are now available for 3D SMLM with PSF engineering  99  (see TABLe  1 ). The iterative nature of MLE generally makes these algorithms slower than less precise, non-iterative algorithms  79  such as intensity-weighted centres of mass. However, thanks to the availability of graphics processing units, state-of-the-art MLE algorithms can now analyse SMLM image sequences in real time  99.
Software More than a hundred independent software packages have been developed for analysing SMLM data. Web resources are available that objectively compare the results of localization software on simulated images and a small set of experimental data  99, 103. Although not without caveats (analysis results are typically provided by the authors of software themselves, require expert fine-tuning and are contingent on simulation assumptions and the choice of experimental data), these benchmarks provide a good starting point for choosing state-of-the-art SMLM software. See also  TABLe 1  for a selection of recommended software packages.
SMLM has also helped to clarify how bacteria interact with host cells, for example by visualizing how septin cages entrap Shigella as part of a cellular defence mechanism with dSTORM  185. For more applications of super-resolution imaging in studying bacterial pathogens, see reF. 186. Reproducibility and data deposition SMLM involves a much larger number of experimental and analytical parameters than conventional microscopy studies, and the intrinsic stochasticity of single-molecule blinking makes SMLM images particularly noisy. These factors pose a challenge when reproducing SMLM experiments and it is therefore especially important to carefully document experimental protocols and computational analyses and to make data and software available. Many free and open-source software tools for SMLM are now available and have been benchmarked  99  , but there is still a need to record and share the analysis parameters used, such as adjustable thresholds.
Competitions and platforms that benchmark downstream analyses such as cluster identification or single-particle reconstruction will be useful to complement the existing challenges on localization, and to increase the reproducibility of quantitative analyses. Image deposition is increasingly recognized as important, leading to data-sharing initiatives in the microscopy community, such as archiving data on Zenodo, FigShare, BioStudies and the Image Data Resource  187. However, the large volume of images generated by SMLM experiments is an impediment to raw data sharing. Therefore, we propose that a good first step is to share localization data together with metadata detailing the target molecule or molecules and/or epitopes; the fluorescent labelling technique used, with details of antibody concentrations and the functionality of fluorescent protein tagging; the fixation protocol, if relevant; microscope parameters such as the laser wavelength, laser power density, camera type, exposure time, pixel size, objective magnification, numerical aperture and immersion medium; the reconstruction soft ware and parameters used; and a localization table including x and y coordinates, frame numbers and, preferably, photon counts.
Axial drift can also deteriorate 2D SMLM images; therefore, 3D drift correction methods should be used whenever possible  102, 188, 191. Many SMLM software packages provide drift correction tools (TABLe  1 ). Unlike drift, high-frequency vibrations that affect localization precision by blurring individual, diffraction-limited frames cannot be corrected computationally. Therefore, it is important to isolate the microscope from sources of vibration. The best way to avoid residual vibrations and drift artefacts is to eliminate them using active, real-time adjustment of the stage position  190, 192, 193  ; for example, fiducial markers added to the coverslip can be tracked with a second camera and a closed loop system to drive piezoelectrical actuators that precisely reposition the stage  194.
High-content screening SMLM approaches using an automated microscope and automated data analysis workflow enabled 3D imaging of an entire 96-well plate  220. Issues with fluid exchange have been tackled using a fluidics system composed of low-cost Lego® hardware controlled by ImageJ software  221. Further, maS 3 TORM uses a fully automated and coordinated 3D SMLM microscope with a pipetting robot to perform staining experiments in situ  214. Faster SMLM. The speed of SMLM is typically low. Nevertheless, tremendous progress in optimizing speed has been made since the first PALM study, which required ~2-12 h to image a single FOV. Using sCMOS cameras with high (>1 kHz) frame rates can dramatically increase the rate of localization, although high laser powers (>50 kW cm -2 ) are required to maintain good SNR and localization precision  26, 222.
In both implementations, localization precision is improved ~2-fold over standard SMLM methods. MINFLUX can theoretically achieve arbitrarily high localization precision, although SIMFLUX and repetitive optical selective exposure currently offer faster acquisition speeds as image acquisition times do not scale with the FOV in these methods. With further advances in 3D, multicolour and live-cell imaging  249  and hardware and software optimizations, these methods are poised to set a new standard for molecular-resolution SMLM.
Acknowledgements The authors apologize to the authors of numerous papers that could not be cited owing to limited space. M. Le. and C. Z. thank  B. Lelandais  for excellent comments on the manuscript and  M. Singh  for acquiring the image shown in Fig. 3b. C. Z. acknowledges funding by  Institut Pasteur ,  Fondation pour la Recherche Médicale  (grant DEQ  20150331762 ),  Région Ile de France ,  Agence Nationale de la Recherche  and  Investissement d'Avenir grant   ANR-16-CONV-0005. M. La. acknowledges funding from the  National Institutes of Health/ National Institutes of General Medical Sciences (NIH/NIGMS)  under grant  RO1 GM133842-01. G. B. and M. S. acknowledge funding by the  German Research Foundation (DFG)  ( SA829/19-1 ) and the  European Regional Development Fund  ( EFRE project  ' Center for Personalized Molecular Immunotherapy ').