dataesr/scientific-paragraphs-categorization huggingface dataset:
The SEC column was equilibrated with either buffer B (20 mM Tris/HCl pH 7. 5, 100 mM NaCl, and 5 mM EDTA) or buffer C (50 mM sodium phosphate pH 6. 5 and 5 mM EDTA). The latter was used for samples to be used in circular dichroism (CD), small-angle X-ray scattering (SAXS), TEM, and NMR experiments. In an alternative protocol, the fractions from SEC were pooled, supplemented with 6M GDN, and stored at -20 • C. Prior to each subsequent analysis, the sample was thawed and the buffer exchanged using Sephadex G-25 medium columns (GE Healthcare, Velizy Villacoublay, France). IMAC and SEC were performed at room temperature (RT). Protein concentrations were estimated using the theoretical absorption coefficients at 280 nm as obtained using the program ProtParam from the EXPASY server. The purity of the final purified products was assessed by SDS-PAGE (Figure  2A ).
3% trifluoroacetic acid. Mass analyses of the tryptic fragments were performed on a MALDI-TOF-TOF Bruker Ultraflex III spectrometer (Bruker Daltonics, Wissembourg, France) controlled by the Flexcontrol 3. 0 package (Build 51) (Bruker Daltonics, Wissembourg, France). This instrument was used at a maximum accelerating potential of 25 kV and was operated in reflectron mode and an m/z range from 600 to 4500 (RP_Wide range_Method) or 600 to 3400 (RP_Proteomics_2015_Method). The laser frequency was fixed to 200 Hz and approximately 1000-1500 shots by sample were cumulated. Five external standards (Peptide Calibration Standard, Bruker Daltonics, Wissembourg, France) were used to calibrate each spectrum to a mass accuracy within 50 ppm. Peak-picking was performed with Flexanalysis 3. 0 software (Bruker) with an adapted analysis method.
54 µs. Each FRAP experiment contains 100 images: five pre-bleach and ninety-five post-bleach images. Experiments were conducted on different condensates to determine the standard deviation. Fluorescence recovery after bleaching curves were analyzed with a one phase exponential curve using Zen 2. 3 SP1 FP3 black software (Carl Zeiss Microscopy GmbH, Jena, Germany). On each condensate, four regions were selected: two bleached regions, one region inside the condensate as a reference, and one region outside the condensate as a background reference. Raw data were normalized using Excel and plotted using GraphPad Prism 8. 3. 0 software (GraphPad Prism, San Diego, CA, USA). Fluorescence and bright-field images were formatted using ImageJ 1. 53c software (http://imagej. nih. gov/ij).
Far-UV Circular Dichroism CD spectra were measured using a Jasco 810 dichrograph (Jasco France, Lisses, France) flushed with N 2 and equipped with a Peltier thermoregulation system. Proteins were loaded into a 1-mm quartz cuvette at 0. 06 mg/mL (in 10 mM phosphate buffer at pH 6. 5) and spectra were recorded at 37 • C. The scanning speed was 20 nm min -1 with a data pitch of 0. 2 nm. Each spectrum is the average of five acquisitions. The spectrum of buffer was subtracted from the protein spectrum. Spectra were smoothed using the "meansmovement" smoothing procedure implemented in the Spectra Manager package (Jasco France, Lisses, France). Mean molar ellipticity values per residue (MRE) were calculated as [Θ] = 3300 m ∆A l c n (1) in a final volume of 50 µL. After a 3-weeks incubation at RT, the samples were centrifuged and the sedimented condensate was washed three times with 50 µL of buffer C.
Elution was carried out in the same buffer. Data reduction and frames subtraction were done with the beamline software FOXTROT (available upon request from the SOLEIL staff). Gaussian decomposition was performed using the UltraScan solution modeler (US-SOMO) HPLC-SAXS module (https://somo. aucsolutions. com/) [50]  and the final deconvoluted scattering curves were submitted to the SHANUM program  [51]  to remove noisy, non-informative data at high angles. For both types of experiments, data were analyzed using the ATSAS program package  [51]. The radius of gyration (R g ) and I(0) were estimated at low angles (qR g < 1. 3) according to the Guinier approximation  [52, 53] : Ln[I(q)] = Ln[I(0)] - q 2 R 2 g 3 (2) The pairwise distance distribution functions P(r) were calculated with the program GNOM  [54].
The quality of the P(r) was always assessed with the CorMap test. For the SAXS experiments, the R g was calculated for both aggregates and monomeric forms whenever possible. To plot the normalized Kratky plot, the R g and I(0) values of the monomer were used when clearly visible (60-180 min of incubation for the sample at 1 mg mL -1 and 60-90 min of incubation for the sample at 2 mg mL -1 ). The R g and I(0) values of the major aggregated species were used for all the other incubation times. Using the data collected at SOLEIL, we also attempted to describe PNT3 as a conformational ensemble. To this end, we used the program suite EOM 2. 0  [55]  using the sequence of the recombinant protein as input and default parameters (random coil conformers). The quality of the EOM fit was assessed with the CorMap test.
SEC-SAXS data of PNT3 at time-zero have been deposited in the Small Angle Scattering Biological Data Bank (SASBDB)  [56]  under code SASDLF9. The PNT3 ensemble derived using SEC-SAXS data at time-zero have been deposited within the Protein Ensemble Database (PED-DB, https://proteinensemble. org/)  [57]  under accession number PED00203. The theoretical value of R g (in Å) expected for an IDP was calculated using Flory's equation according to  [58] : R g = R 0 N ν (3) where N is the number of amino acid residues, R 0 is 2. 54 ± 0. 01, and ν is 0. 522 ± 0. 01.
Nuclear Magnetic Resonance (NMR) A sample of a freshly purified 15 N-labelled PNT3 at 100 µM in buffer C, also containing D 2 O (10%), was used for the acquisition of 1D 1 H and 2D 1 H, 15 N HSQC spectra with a 22. 3 T Bruker AvanceIII 950 ultra-shielded-plus spectrometer equipped with a triple resonance CryoProbe (TCI) at 310 K (Bruker BioSpin GmbH, Rheinstetten, Germany). The sample was incubated at 37 • C and spectra were recorded at various time-points. Spectra were recorded both at 288 and 310 K. All the spectra were acquired, processed, and analyzed by using the Bruker software TopSpin 3. 6. 2 using standard parameters. Chemical shifts were referenced using DSS for 1 H and indirect referencing for 15 N using the conversion factor derived from the ratio of NMR frequencies  [59].
The final protein concentration was 100 µM. For each sample, 5 replicates were set up and the aggregation index was measured as a function of time. The aggregation index was calculated as follows: Aggregation Index = OD340 (OD280 -OD340) × 100 (4) Measurements were done on 100 µL samples using a PHERAstar FSX Microplate Reader (BMG Labtech, Champigny-sur-Marne, France) in the 220-600 nm wavelength range. The equality of variances between the different data sets was evaluated using Lavene's test and Bartlett's test. Statistical analysis was done using the one-way ANOVA test implemented in the PRISM 8. 3. 0 software (GraphPad Prism, San Diego, CA, USA).
Infection and CR-Staining of Mammalian Cells HEK 293T or HPMEC (human pulmonary microvascular endothelial) cells were seeded in µ-Slide 8 Well IBIDI Chambered Coverslip for Cell Imaging, previously coated for 2 h at 37 • C with Poly-D-Lysin hydrobromide at 50 µg/mL (Sigma-Aldrich) (7 × 10 4 cells/well). The following day, cells were infected with HeV (Hendra virus/Australie/Horse/ Hendra) (accession number: MN062017. 1) at a multiplicity of infection (MOI) of 0. 00025 for 1 h at 37 • C. Then, the medium was replaced with DMEM supplemented with 10% FBS (or endothelial cell growth medium with supplement from Promocell for HPMEC cells) and cells were replaced at 37 • C, 5%CO 2 , in humid atmosphere. After 48 h, cells were fixed with 4% PFA for one week. PFA (4%) was changed and cells were fixed for an additional week.
Note that the possibility that the loss of signal might arise from protein degradation was checked and ruled out by SDS-PAGE (data not shown). After an additional 48-h incubation, only a slight decrease in the overall spectral intensity was observed, suggesting that the events that concurred to reduce the signal mostly took place during the first 24 h. The overall shape of the spectra does not change, which suggests that the species that contributes to the signal maintains its prevalently disordered nature. By contrast, the possibility that structural transitions can occur within the fibrillar species escaping detection by CD cannot be ruled out. The spectra were recorded in 10 mM sodium phosphate pH 6. 5 at 37 °C. Protein concentration was at 0. 06 mg mL -1 (4 μM). The spectra shown in cyan correspond to a freshly purified PNT3 or PNT3 3A sample recorded immediately after elution from the SEC column.
5 at 37 • C. Protein concentration was at 0. 06 mg mL -1 (4 µM). The spectra shown in cyan correspond to a freshly purified PNT3 or PNT3 3A sample recorded immediately after elution from the SEC column. The spectra shown in light and dark blue were recorded from the same samples incubated at 37 • C for 24 h and 72 h, respectively. Note the much more pronounced decrease in the signal for the spectrum at 24 h of PNT3 compared to PNT3 3A. MRE (Θ) is expressed in deg cm 2 dmol -1. Negative staining transmission electron microscopy (TEM) unequivocally confirmed the presence of amyloid-like fibrils (Figure  4D ). Altogether, these data provide strong evidence for the presence of amyloid-like polymers as the structural basis of phase-separated solid-like PNT3 condensates. Notably, fibril formation does not require a liquid-to-hydrogel transition, indicating that phase-separated condensates nucleate amyloid-like fibers in the liquid state.
Rg range (Figure  6D ), consistent with the formation of extended particles with a rod-like shape  [79]. This observation is supported by the shape of the pairwise distance distribution function after 4. 5 h of incubation that exhibited features typical of rod-like particles (Figure  6E ). This behavior is consistent with the formation of fibrillar species  [80]. As expected, the formation of elongated structures was delayed at 1 mg mL -1 and appeared only after 4. 5 h of incubation (Supplementary Figure  S2C ). Finally, in order to obtain an ensemble description of the monomeric species, we sought to use the program suite EOM 2. 0  [55]. To this end, we used data collected by SEC-SAXS so as to ensure the maximal monodispersity of the sample. In addition, data were collected both without any prior incubation at 37 °C, so as to enable analyzing the sample at time zero, and after 1 h of incubation.
5 h of incubation that exhibited features typical of rod-like particles (Figure  6E ). This behavior is consistent with the formation of fibrillar species  [80]. As expected, the formation of elongated structures was delayed at 1 mg mL -1 and appeared only after 4. 5 h of incubation (Supplementary Figure  S2C ). Finally, in order to obtain an ensemble description of the monomeric species, we sought to use the program suite EOM 2. 0  [55]. To this end, we used data collected by SEC-SAXS so as to ensure the maximal monodispersity of the sample. In addition, data were collected both without any prior incubation at 37 • C, so as to enable analyzing the sample at time zero, and after 1 h of incubation. In both cases, the SEC elution profile features a major peak corresponding to the monomeric species and an additional peak corresponding to aggregates (Supplementary Figure  S3A ).
Acknowledgments: We thank  Patrick Fourquet  for the mass spectrometry analyses done using the mass spectrometry facility of  Marseille Proteomics  (marseille-proteomique. univ-amu. fr),supported by  IBISA  ( Infrastructures Biologie Santé et Agronomie );  Plateforme Technologique Aix-Marseille ; the  Cancéropôle PACA; Région Sud-Alpes-Côte  d'Azur; the  Institut Paoli-Calmettes ; the  Centre de Recherche en Cancérologie de Marseille (CRCM) ;  Fonds Européen de Développement Régional and Plan Cancer. We thank the  imaging core facility (ImagImm) of the Centre d'Immunologie de Marseille-Luminy (CIML) , supported by the  French National Research Agency program  ( France-BioImaging   ANR-10-INBS-04 ). The support and use of the resources of the CERM/CIRMMP center of Instruct-ERIC is also gratefully acknowledged.
Funding: This work was carried out with the financial support of the CNRS. It was also partly supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)  ( ANR-10-INSB-05-01 ) and by the  Aviesan Sino-French  agreement on the Nipah virus study. E. S. was supported by a  joint doctoral fellowship  from the  Direction Générale de l'Armement (DGA)  and  Aix-Marseille University. G. P. was supported by a joint doctoral fellowship from the  AID (Agence Innovation Défense)  and  Aix-Marseille University. J. N. was supported by a post-doctoral fellowship from the  Infectiopôle Sud. F. G. was supported by a post-doctoral fellowship by the  Fondation por la Recherche Médicale (FRM, France).
Odds ratio for selection in the simulated and 1000G populations For each whole-genome sequence dataset, either simulated or empirical, and each of the K neutrality statistic used, the OR was computed using all SNPs genome-wide and the logistic region model set as follows  (Kudaravalli et al. 2009 ): (equation 2) with being the indicator function equal to 1 for PSV SNPs or equal to 0 otherwise, being the indicator function defined above, and being the coefficient of the logistic regression (the constant term was omitted). We used the 'glm' R package (family=binomial) to estimate and to compute the OR for the effect of selection according to equation (1). Indeed, if , this implies an enrichment effect for PSVs among SNPs with selection signals due to the enrichment of SNPs with selection signals in the PSV SNP class  (Kudaravalli et al.
2008; Fagny et al. 2014). In simulated WGS data, the simulated regions containing PSVs, namely the PSV regions, are randomly defined and contain X selective sweeps of various intensity, age and frequency of the selected allele at the onset of selection (the remaining mutations are ENVs, see the section "Simulating WGSs"). It must be emphasized that PSV regions in simulated WGS data do not necessarily include a selected variant. Indeed, in the real data, PSV regions contain functional mutations, but only a fraction of these regions exhibit selection signals and may thus contain functional selected mutations. The other PSV regions behave as neutral regions as they only contain functional mutations with no detectable advantageous effects on fitness. For each analyzed population, the PSVs and ENVs are determined using the 1000G VEP (Ensembl Variant Effect Predictor) annotations.
ABC, acceptance rules and accuracy evaluation We used the 'abc' R package and the standard ABC method  (Beaumont et al. 2002) , in which posteriors are built from accepted simulated parameters subsequently adjusted by local linear regression (method="Loclinear" in the 'abc' package). The accepted simulated parameters are those which provide the best fit with empirical data (the 'abc' parameter 'tol' was set to be equal to 0. 005). We used the mean of the posterior distribution as point estimate and computed the 95% credible interval from these accepted parameters. To test the accuracy of.
Odds Ratios for selection capture the number of occurring sweeps We estimated the number of selective sweeps using ABC and summary statistics that measure the enrichment of selection signals in PSVs regions, relative to ENV regions; a graphical illustration summarizing the rationale behind our approach is shown in Figures  1  and S1. Because ABC inferences are based on summary statistics that exhibit a monotonic relationship with the parameter to estimate, we first present the relationships between the odds ratio for selection (OR) and the number of sweeps in African, European and East Asian simulated populations. We explored two different scenarios (Figure  2 , Supplemental Material, Figures  S2  and S3 ): in the first, we simulated X incomplete sweeps per whole-genome sequence (WGS) dataset (Materials and Methods) by randomly drawing s and t from flat distributions, and , and excluding complete sweeps using a rejection algorithm (Figure  2 ).
The underlying distributions of s and also t are therefore enriched in low values (Figure  2A ), since (all other things being equal) complete sweeps tend to be stronger or older than incomplete sweeps. In a second, more realistic scenario, we simulated X sweeps per WGS data while keeping the complete sweeps (Figure  S3 ). The age of selection was randomly drawn from a flat distribution, , whereas we aimed to reproduce the excess of mutations with low or moderate effect on fitness  (Boyko et al. 2008 ) with a distribution enriched in low values (a mixture between a Gamma distribution with 60% of and a L-shape distribution with 90% of , Figure  S3A ). For each scenario, we simulated WGS data with fixed numbers of sweeps, , using human recombination maps and a demographic model previously inferred for these populations (Materials and Methods).
The estimations are increased because the East Asian ORs simulated with higher X values provided a better fit with the European 1000G ORs (see the in the Figure  S23 ). The low discrepancies obtained were confirmed using pseudo-empirical datasets simulated with bottlenecks four times stronger (or weaker) than in the ABC simulations used for the estimations (Figure  S24 ). Overall, we did not obtain point estimates that largely exceeded the estimations shown in Tables  1  and S2. We acknowledge that new ABC estimations based on different demographic models may differ. Nevertheless, our results suggest that they should be of the same order of magnitude. Lastly, we performed estimations of X in the studied 1000G populations using various distributions of s. In all cases, we found low numbers of selective sweeps, although we noticed a moderate influence of the shape of the distributions of s on the estimations of X (Table  S2F ).
A trend towards higher numbers of sweeps occurred in non-African populations We found more sweep signals in Eurasian populations (Table  1 ), an observation that has been attributed to a greater extent of genetic adaptation outside Africa due to drastic changes of environmental pressures  (Coop et al. 2009; Granka et al. 2012; Pybus et al. 2015; Schrider and Kern 2017). For example, we detected a recent European-specific selection signal likely due to the late Pleistocene warming at the end of the last ice age in Northern hemisphere  (Cooper et al. 2015) , a cold climatic period which could favor both light skin pigmentation and increased sensitivity to UV-induced melanoma  (Key et al. 2016; Lopez et al. 2014). Regulatory mutations, protective against melanoma as they downregulate the PLEK2 gene in skin cells exposed to the sun (GTEx database)  (Ardlie et al.
Applying our methodology to Drosophila data may help to understand if lower are due to lower or lower driving lower rates of detectable beneficial mutations. From a methodological standpoint, our ABC framework appears to provide unbiased estimations of X when the distribution on s is known. Although here we leveraged widely used neutrality statistics, the OR can be computed for any other kind of statistics, keeping in mind that background selection should be simulated  (Johri et al. 2020 ) when using statistics sensitive to this selection regime. Our study presents however some limitations. We noticed a dependency on the s distributions used, which suggests that a careful evaluation of their influence on the estimation of X is needed. We also observed a lack of accuracy in the estimation of S or T, indicating that other summary statistics that better describe the genomic extent of selection signals should be incorporated in selection analysis.
The observed enrichments of candidate SNPs in and near genes were found to be lower than those obtained with computer simulations assuming high numbers of sweeps (Figure  S25A-C ). Furthermore, the adaptation rates predicted from our estimations were found compatible with those estimated using different sources of data and methods. Our estimated numbers of recent sweeps in humans are helpful to better understand the outcomes of classic scans of selection, keeping in mind that genomic regions evolving under complex selection regimes can only be detected with powerful methods such as those recently implemented  (Schrider and Kern 2017). The methodology proposed in this study also provides an alternative to methods implemented in Drosophila, and can also be applied to great apes  (Nam et al. 2017; Prado-Martinez et al.
Specifically, the OR computed for each WGS dataset and for each neutrality statistic separately is defined as , (equation 1) (Kimura 1977; Mcdonald and considered as candidate SNPs of selection those with the most extreme values for neutrality statistics known to detect recent selection and previously found to be insensitive to background selection (BGS). The OR is based on the classic comparison between putatively neutral mutations and mutations potentially targeted or influenced by selection(Kimura 1977;Mcdonald and.
Hence, we set up a new plugin on ImageJ called "Detect, Track and Colocalize" (DTC) to track individual objects in two colors on 2D time lapse movies and check for co-localization or proximity/associations between both channels over a tracked period of time (https://github. com/fabricecordelieres/IJ-Plugin_DTC, accessed on 27 July 2021). Our results indicate that the TGN and PI3Ppositive compartments can remain associated for an average time of 6 s (Figure  3c ). In comparison, PI3P-positive compartments that are not associated with the TGN could be followed unassociated for an average time of 19 s (Figure  3c ). However, our quantifications showed that metazachlor does not alter the dynamics of association between the TGN and PI3P-positive compartments as the number of associations or the time of association per track is not different on metazachlor-treated roots as compared to untreated controls (Figure  3d ).
For the analysis of association between intracellular dots in time-lapse movies (Figure  3c, d ), a newly developed ImageJ plugin "Detect, Track and Colocalize" (DTC) (https://github. com/fabricecordelieres/IJ-Plugin_DTC, accessed on 27 July 2021) was used. Before the analysis, Gaussian blur filter and subtracting background was applied similarly to the fixed image analysis. The PI3P dots that appear in the movie for 3 frames (4 s) in minimum were tracked with 10 pixels (approximately 1. 3 µm) maximum displacement per 2 s. Within the tracks, the number of frames that have the closest TGN within the range from 1 pixel (approximately 125 nm) to 5 pixels (approximately 625 nm) and the number of continuous sequences of those frames were quantified.
1c ), excluding all trials the animals aborted or did not make a target selection  31. Because there were almost always fewer error trials than correct trials in one session (99% of cases), and most of the errors occurred during the early acquisition stage of learning, we used the same number of correct trials as error trials from the beginning of the session to calculate the mean behavioral performance immediately after either correct or error trials. We termed this period of learning the "memory acquisition stage" (see below). We included 282 behavioral data sets from five monkeys where they learned at least one location-scene association and made at least 20 error trials in one session. Across all sessions, the averaged performance immediately after error trials was 74. 46% (SD = 12. 26%), which was significantly better (paired t-test, t(281) = 4.