dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, we note that MSD i -based analysis is extremely sensitive to the fitting threshold: if instead of R 2 >0. 8, all trajectories had been used the estimated bound fraction would be 87% instead of 16%. In conclusion, MSD i (R 2 >0. 8) is unreliable for estimating F BOUND when short trajectories are at stake, which is the usual case when performing intracellular SPT of fast-diffusing molecules. MSD i (R 2 >0. 8) most likely fails due to a combination of the following reasons among others. First, it poorly handles localization errors, which dominate the displacements of bound molecules. Second, by only considering trajectories of a certain length (normally at least five frames), it only analyzes a small subsample of the dataset. Third, there is no correction for defocalization bias.
Performance evaluation MSD i (all) analysis performed very poorly both when it comes to estimating D FREE and F BOUND. The mean relative error (bias) for estimating D FREE was À39. 6%, the inter-quartile range (IQR) was 19. 0% and the standard deviation 41. 8%. For estimating F BOUND the bias for MSD i (all) was 22. 0%, the IQR 17. 8% and the standard deviation 15. 8%. Thus, in all but a few edge cases, MSD i (all) cannot reliably estimate D FREE or F BOUND. As for MSD i (R 2 >0. 8), examples of fits are shown in Figure  3 -figure supplement 3 and the Matlab code to perform the fitting is available together with the data (see "Data availability'). In the case of MSD i (all), the main reason for the unreliable estimates is due to defocalization bias. Since fast-diffusing molecules move out-of-focus and thus have shorter trajectories, the 5-frame threshold introduces a large bias against freely-diffusing molecules.
Furthermore, vbSPT does not explicitly model the localization error. It is then expected that the software performs poorly when the localization error is high, as can be expected when imaging intranuclear factors. In practice, the following parameters were used to assess vbSPT performance. The software was run on the full set of 3480 simulations. The priors and optimization parameters were left as default and the scripts to perform the analysis are provided together with the experimental data (please see Data Availability section): dim = 2; trjLmin = 2; runs = 3; maxHidden = 2; bootstrapNum = 10; fullBootstrap = 0; init_D = [0. 001, 16]; init_tD =  [2, 20] *timestep;.
PDF or CDF fitting Although for large datasets PDF-and CDF-fitting perform similarly as shown in Figure  3 figure supplement 9, CDF-fitting tends to provide more reliable estimates of D FREE and F BOUND when the number of trajectories decreases, likely because PDF-fitting is more susceptible to binning noise. Thus, for quantitative analysis we always recommend CDF-fitting, though PDF-fitting can be convenient for making figures since most people find histograms more intuitive.
Choosing allowed ranges for diffusion constants Spot-On comes with default allowed ranges. For example, for the 2-state model, D FREE ¼ 0:5; 25 ½ and D BOUND ¼ 0:0001; 0:08 ½. These ranges are generally reasonable, but may not be appropriate for all datasets. Whenever Spot-On infers a diffusion constant that is equal to the min or max, caution is needed and it may be necessary to change these limits. In particular, unless a molecule is bound to an unusually dynamic scaffold, D BOUND =0. 08 mm 2 /s is almost certainly too high. Thus, we recommend imaging a protein that is overwhelmingly bound, such as histone H2B or H3, fitting the histone data with Spot-On and then use the inferred D BOUND for histone proteins or a slightly larger value as the maximally allowed D BOUND value.
, 2014). Nevertheless, even with paSPT it is still necessary to decide on an optimal density. The key parameters are size of the ROI (ideally the whole nucleus for studies in cells) and D: a large nucleus and a slow D can support a higher density than fast-diffusing molecules in a small nucleus. As a general rule of thumb, we recommend a density of ~1 fluorescent molecule per ROI per frame. This will keep tracking errors at a minimum and still support rapid acquisition of large datasets. All data acquired for this study was acquired at approximately this density. In practice, keeping an optimal density will require some trial-and-error optimization of the 405 nm photo-activation laser intensity. 405 nm excitation does contribute background fluorescence, so we prefer to pulse the 405 nm laser during the camera 'dead-time' (~0.
g. Halo-3xNLS and H2b-Halo in our case). In these cases, some cells will likely exhibit too high a density. To deal with this, Spot-On includes the option to analyze datasets from individual cells first and then excluding a cell with too high a density before analyzing the merged dataset. Which datasets are appropriate for Spot-On? In the sections above, we have discussed how to minimize common experimental biases in SPT experiments and proposed spaSPT as a general solution. However, many 2D SPT datasets recorded under different conditions are also appropriate for Spot-On. For example, SPT experiments without photo-activation or with continuous illumination may also be appropriate for analysis with Spot-On. For example, there may be situations where photo-activation SPT is not possible: in such cases, it will be essential to keep the labeling density sufficiently low that tracking errors are minimized and it might thus be necessary to image substantially more cells to get enough statistics.
Likewise, as we show in Figure  4JK , motion-blurring is a major concern for fast-diffusing molecules, but for a slowly diffusing molecule like Halo-CTCF it makes only a small difference. Thus SPT datasets recorded with continuous illumination may also be appropriate provided that the protein of interest is known to diffuse sufficiently slowly. We also note that since Spot-On uses the loss of fast-diffusing molecules over time to correct for bias and to estimate the free population, it is essential that all trajectories are included in Spot-On for analysis. For example, some tracking and localization algorithms ignore all trajectories below a certain length (e. g. five frames), but this will cause Spot-On to misestimate the loss of molecules moving out-of-focus and thus it is imperative that trajectories of all lengths be included when analyzing data using Spot-On.
Acknowledgements ASH and MW contributed equally to this work and are alphabetically listed. We are very grateful to  Davide Mazza  who inspired this work and provided invaluable comments on Spot-On, to  Florian Mueller  for suggestions on the web-application,  Christophe Zimmer  for insightful discussions,  David McSwiggen  and  Sheila Teves  for kindly providing cell lines,  Carolyn Elya  and  Chiahao Tsui  for the name ' Spot-On ', and to members of the  Tjian/Darzacq labs  and  Maxime Dahan  for discussions. We also thank  Astou Tangara  and  Anatalia Robles  for microscope maintenance. ASH is a postdoctoral fellow of the  Siebel Stem Cell Institute. This work was supported by  NIH  grants  UO1-EB021236  and  U54-DK107980  (XD), the  California Institute of Regenerative Medicine  grant  LA1-08013  (XD), by the  Howard Hughes Medical Institute  ( 003061 , RT) and used the computational and storage services (TARS cluster) provided by the  IT department at Institut Pasteur, Paris.
Data availability All raw 1064 spaSPT experiments (Figure  4 ) as well as the 3480 simulations (Figure  3 ) are freely available in Spot-On readable Matlab and CSV file formats in the form of SPT trajectories at Zenodo. The experimental data is available at: https://zenodo. org/record/834781; The simulations are available in Matlab format at: https://zenodo. org/record/835541; The simulations are available in CSV format at: https://zenodo. org/record/834787; And supplementary software used for MSD i and vbSPT analysis as well as for generating the simulated data at: https://zenodo. org/record/835171.
Computer code Spot-On is fully open-source. The web-interface can be found at: https://SpotOn. berkeley. edu. All raw code is available at GitLab: https://gitlab. com/tjian-darzacq-lab. The web-interface code can be found at https://gitlab. com/tjian-darzacq-lab/Spot-On; the Matlab command-line version of Spot-On can be found at: https://gitlab. com/tjian-darzacq-lab/spot-on-matlab; the Python command-line version of Spot-On can be found at https://gitlab. com/tjian-darzacq-lab/Spot-On-cli; the SPT simulation code (simSPT) can be found at: https://gitlab. com/tjian-darzacq-lab/simSPT; finally, the 'TrackMate to Spot-On connector' plugin, which adds an extra menu to TrackMate which allows one-click upload of datasets to Spot-On can be found at: https://gitlab. com/tjian-darzacq-lab/Spot-On-TrackMate.
The inter-continental admixture history of Latin America makes it an ideal setting in which to examine patterns of sub-continental genetic structure, the historical correlates of this structure and its impact on physical appearance. We examined data for over 500,000 autosomal SNPs typed in more than 6,500 individuals born in Brazil, Chile, Colombia, Mexico and Peru (denoted the CANDELA sample, Supplementary Fig. 1 ). To infer ancestry in this sample, we collated data for 2,359 individuals from 117 reference populations (including 430 newly genotyped individuals from 42 populations) representing five major bio-geographic regions: Native Americans; Europeans; East/South Mediterraneans; Sub-Saharan Africans and East Asians (Fig. 1A , Supplementary Table  1 , Supplementary Fig. 2 ). We grouped the reference population individuals into 56 homogeneous clusters based on patterns of haplotype sharing, using the program fineSTRUCTURE 2 (Supplementary Tables  2  and 3 ).
This differentiation matches the colonial history, Portuguese migration having concentrated in Eastern South America while the Spanish settled mainly in Central America and Western South America  1. The relatively small contribution inferred for the Basque and Catalan agrees with historical information documenting that Spanish migrants to America originated mainly in Southern and Central Spain 8. In addition, the Brazilian sample shows substantial Italian and German ancestry, and these components concentrate in the South of the country. This pattern is consistent with the documented migration to Southern Brazil of large numbers of Germans and Italians in the late 19th century  9. To assess the time-frame of admixture between the ancestry components described above we used the program GLOBETROTTER  5.
Genotype datasets The CANDELA dataset (http://www. ucl. ac. uk/candela) consists of genotypes from 6,852 individuals ascertained in five Latin American countries (Brazil N=676, Chile N=1,891, Colombia N=1,713, Mexico N=1,288 and Peru N=1,284) (Supplementary Fig. 1 ). This study sample and ethical approval has been described in detail in Ruiz-Linares et al. 2014  4. Briefly, adult individuals of both sexes were ascertained at one main recruitment site per country (Porto Alegre in Brazil, Arica in Chile, Medellín in Colombia, Mexico City in Mexico and Lima in Peru). A structured interview recorded the birthplace of volunteers and their ancestors (up to grandparents), as well as information on the language(s) spoken by them. We have previously reported genome-wide association studies based on Illumina OmniExpress chip data obtained in these individuals  22, 24, 25.
To perform ancestry analyses in the CANDELA individuals we collated a reference population dataset from regions having potentially contributed to admixture in Latin America. We combined publicly available data with data from newly genotyped samples obtained here (Fig. 1 , Supplementary Table  1 , Supplementary Fig. 2 ). Altogether we collated data for 2,359 individuals from 117 reference populations (38 Native American, 42 European, 15 East/South Mediterranean, 15 Sub-Saharan African and 7 East Asian). Of these, 42 were newly genotyped population samples (comprising 27 Native American, 7 European and 8 East/South Mediterranean), including a total of 430 individuals. These individuals were genotyped on the Illumina HumanOmniExpress chip which includes 730,525 SNPs. PLINK v1. 9  26, 27  was used to exclude SNPs and individuals with more than 5% missing data, markers with minor allele frequency <1%, related individuals, and those who failed the Xchromosome sex concordance check.
The same QC filters had been applied to the CANDELA dataset  22, 24, 25. Individuals born outside the country were relocated when coming from one of the five countries included in this study or otherwise removed. Similar quality controls were applied to the public reference population datasets. In addition, unsupervised ADMIXTURE  28  analyses of reference population samples were used to identify and exclude Sub-Saharan Africans, East Asians and Europeans with less than 95% of their own continental ancestry. In the case of Native Americans, all individuals were initially retained (regardless of admixture levels), but reference individuals with less than 95% Native American ancestry were only used for haplotype phase inference. In the case of East/South Mediterranean individuals, ADMIXTURE consistently inferred Sub-Saharan African ancestry.
The estimated Sub-Saharan African ancestry proportions were found to be quite homogeneous across individuals, possibly indicating relatively old shared ancestry. Based on this assumption, we excluded 4 individuals with admixture proportions deviating markedly from those observed in the population sample, suggestive of recent admixture (three Moroccans with Sub-Saharan African ancestry >40% and one Libyan with Sub-Saharan African ancestry of 79%; both of these populations have an estimated average Sub-Saharan African ancestry of ~20% +/-3%). After QC, the merged CANDELA + reference population dataset comprised genotypes for 546,780 autosomal SNPs in 8,647 individuals (including 6,589 Latin Americans and 2,058 individuals from the reference population samples).
Inference of haplotype similarity patterns CHROMOPAINTER 2 was used to infer haplotype similarity (informally, "chromosome painting") across individuals. We set-up the software to provide estimates of the proportion of DNA in every CANDELA and reference population individual (denoted recipients) that is most closely related to each reference population individual (denoted donors), allowing us to reconstruct haplotype similarity profiles for all individuals in terms of the reference samples. The recombination scaling constant ܰ and the mutation parameter θ used by CHROMOPAINTER were jointly estimated for every individual in a subset of chromosomes (1, 6, 13 and 22) with 10 Expectation-Maximization steps, starting from default values defined by the software. The average ܰ and θ values across chromosomes (weighted by chromosome size) were then used for subsequent CHROMOPAINTER runs on all autosomes (ܰ = 290.
83 and θ = 0. 00038). Genetic distances from the HapMap Phase II genetic map build GRCh37 were used in the CHROMOPAINTER runs. CANDELA individuals with >99% European ancestry (52 Brazilians, of which 37 reported German and 15 Italian ancestors) or with >95% Native American ancestry (1 Colombian, 22 Mexicans, 65 Chileans and 17 Peruvians) were included amongst the donors as they may harbour ancestry components not present in our reference dataset. In Supplementary Note 5 we show how that our conclusions about ancestry are similar if these individuals are excluded from the reference dataset. In total, 157 CANDELA individuals and 1,942 reference individuals were added to the panel of donors, for a total of 2,099 samples. The remaining 116 individuals from the initial reference dataset were excluded.
Definition of homogeneous clusters of reference population individuals To evaluate genetic structure in the reference data, independent of population sample labels, we used fineSTRUCTURE  2  , a program that defines homogeneous clusters of individuals based on the similarity of the haplotype copying profiles obtained by CHROMOPAINTER. To run fineSTRUCTURE, a likelihood adjustment factor (c) is initially calculated in order to account for the inaccurate assumption that the amount of DNA matching among individuals is independent. Using default CHROMOPAINTER settings to infer the adjustment factor, this was estimated as c=0. 236. Two MCMC runs were performed using 2,000,000 iterations (sampling every 10,000). Following Leslie et al. 2015  3  , for each run the sample with maximum posterior probability was selected and an additional 100,000 hill-climbing moves were then performed to search for merges or splits that further improve the overall model likelihood 2.
Estimation of the number of generations since admixture The times and sources of major admixture events were inferred using the program GLOBETROTTER  5. GLOBETROTTER tests for evidence of one or more pulses of admixture between two or more ancestral groups, and dates these admixture events and infers the genetic make-up of the admixing groups involved. Due to the recent nature of intermixing in the Americas, admixture times and proportions may vary substantially across CANDELA individuals. Therefore we tested each individual separately, restricting this analysis to the 6,352 individuals inferred by SOURCEFIND to have ancestry from more than one surrogate cluster. For each haploid genome of each individual, we used 10 random samples of genomewide local matching to donor clusters per haplotype as provided by the CHROMOPAINTER analysis described above.
Differences in allele frequencies of GWAS hits in Mapuche and Central Andean populations To test whether allele frequencies differed between individuals with Mapuche versus Central Andean ancestry at loci previously identified as being associated with facial features  22  , we first inferred the allele frequencies at these loci in each of the Mapuche and Central Andean populations. As we have relatively few reference individuals with Mapuche and Central Andean ancestry, we inferred allele frequencies by combining these reference samples with admixed Candela individuals that were inferred to carry the appropriate Native ancestry at these loci. To do so, we used the software RFMix  37  to infer local continental ancestry in the subset of phased Candela individuals described earlier. Three continental reference panels peer-reviewed) is the author/funder.
available DNA samples. We thank Chris Tyler-Smith and Caroline Costedoat for comments on the manuscript. This work was funded by grants from the Leverhulme Trust (F/07 134/DF to A. R. -L. ), BBSRC (BB/I021213/1 to A. R. -L. ), Wellcome Trust/Royal Society (098386/Z/12/Z to G. H. ), Universidad de Antioquia (CODI sostenibilidad de grupos 2013-2014 and MASO 2013-2014), Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Apoio a Núcleos de Excelência Program) and Fundação de Aperfeiçoamento de Pessoal de Nível Superior. V. G. is supported by Fundação para a Ciência e Tecnologia (FCT) and Programa Operacional Potencial Humano (POCH), through the grant SFRH/BPD/76207/2011. IPATIMUP integrates the i3S Research Unit, which is partially supported by FCT.
Acknowledgments We appreciate the financial support from the  IMBALANCE-P  project of the  European Research Council  ( ERC-2013-SyG-610028 ). The demonstration matlab code that tests the sensitivity of long-term carbon stocks to clay content using the matrix version of the ORCHIDEE-MICT model is archived in an external database available at https:// doi. pangaea. de/10. 1594/PANGAEA. 891344 ( Huang, Zhu , et al. , 2018). Other relevant data are available in the supporting information.
Molecular structures of damaged nucleosome We obtained the nucleosome molecular configuration from the RSCB Protein Database, entry 1kx5  [20]. This is an x-ray structure of the entire histone octamer with 147 DNA bp resolved at an average RMS of 1. 94 Å, reconstituted from human nuclear extract expressed in E. coli; only 6 histone residues were unidentified in this experimental structure, with respect to the known histone sequences, therefore the model can be considered nearly complete. The 147 bp DNA is a palindromic sequence, chosen to maximize the degree of ordering and increase the x-ray spatial resolution. To obtain a model structure useful for our computer simulations, we removed all the crystallization water molecules and ions from the published structure, and added two DNA extensions of length 20 bp at each end of the nucleosomal DNA, with repeated sequence d(AGTC)  [18].
(The-symbol indicates the break site along each backbone, the Á Á Á indicate the central interacting base pair. ) In this way, the two backbone cuts of each DSB are spaced by 1 bp always comprising an AÁ Á ÁT pair (Fig  1b ), which remains initially bonded by only its two hydrogen bonds, plus the stacking interactions on each intact side of the chain, while the other half of stacking is readily reduced, as soon as the MD relaxation starts. The CHARMM-27 force field database  [22, 23]  and its extension to treat nucleic acids  [24, 25]  were used for the molecular bonding and non-bonding force parameters. Strict comparisons between CHARMM-27 and AMBER force fields  [26, 27]  ensure that the results of longtime, finite-temperature MD trajectories of nucleic acid fragments with largely different conformations are consistent, and able to correctly reproduce the key structural quantities (bond angles, hydrogen-bond structure, base tilt, twist, shuffle, etc.
Molecular dynamics simulations For the molecular dynamics (MD) simulations we used the GROMACS 5. 1 computer code  [28, 29]. Nucleosome models O and M1-M4 were solvated in water box of size 14. 5 or 18×19×10 nm 3 with periodic boundary conditions in the three directions, containing about 82,600 or 110,500 TIP3P water molecules, plus 480 Na + and 250 Cl -ions to ensure neutralization of the phosphate backbone charge, and a physiological salt concentration around 0. 15 M. All the MD simulations were carried out at the temperature of 310 K and pressure of 1 atm, or 350 K and 50 atm for the thermal stability study (at constant-{NVT}, hence the small overpressure within the typical numerical fluctuation for a system of this size). Because of the requirements of stress calculations (see below), we could not use standard Ewald-sum electrostatics but plain cut-off Coulomb forces.
Steered MD and umbrella sampling Steered molecular dynamics (SMD) was performed on the fragments with the constant-force pull code available in GROMACS, only on the M1 model. In this case, we enlarged the water box to 18 nm in the x-direction, to allow possible outward extension of the broken DNA end, resulting in a system of 107,000 water molecules. Since the objective was to promote the detachment of one of the broken DSB ends from the nucleosome core, we applied a constant force parallel to the direction x and perpendicular to the superhelical axis, by means of a harmonic-spring fictitious potential attached to the C4 0 and P atoms of the last two base pairs at one DSB end. After some tests, the spring constants were set at 100 and 75 kJ mol -1 nm -2 , respectively for the two DNA strand ends farther and closer to the nucleosome surface.
To provide a reaction force keeping the system in place, all the atoms of the H3 opposite to the DSB were retained by soft harmonic restraints, with a spring constant of 250 kJ mol -1 nm -2. Pulling speeds of 1 to 5 m/s were used for most SMD simulations. Forces and displacements were recorded at intervals of 5-10 time steps. Umbrella sampling was performed by extracting 100 configurations spaced by 50 ps during the first 5 ns of the force pulling simulation; force bias was progressively reduced from 100 down to 10 kJ mol -1 nm -2 , to extract the zero-bias limit of the free-energy profile; the weighted-histogram analysis was used to interpolate and connect the data from discrete configurations.