dataesr/scientific-paragraphs-categorization huggingface dataset:
KEY RESOURCES Acanthamoeba castellanii cells expressing the MV-H2A-H2B-GFP proteins were transfected with 6mg of the MV-H3-H4-mRFP plasmid and selection of the co-transfected cells was performed using 100mg/mL Neomycin and 30 to 50 mg/mL Nourseothricin. Cells were grown in a 96-well glass bottom plate and infected at a MOI of 50. At 4h pi, the cells were washed with PBS containing 10 mg/mL of DAPI and observed for DIC, DAPI, GFP or mRFP fluorescence recording. Both datasets were processed (Motion correction and CTF estimation) by cryoSPARC (v2. 12. 4)  (Zhang, 2016; Zheng et al. , 2017). Images were evaluated by inspecting the CTF fitting resolution. Roughly 1000 particles were then manually picked for generating autopicking templates through 2D classification in CryoSPARC. For MV-NLP with 147bp DNA, this yielded 2,648,493 particles across 6,552 micrographs, and these particles were subjected to four iterative rounds of 2D classification in order to discard bad particles.
Homology modeling Initial homology models of Melbournevirus ORF 369 (H2B-H2A) and ORF 368 (H4-H3) doublets were constructed using SWISSMO-DEL, and the histone structures from the Xenopus laevis nucleosome (PDB 1AOI) were used as a reference  (Bienert et al. , 2017; Guex et al. , 2009; Waterhouse et al. , 2018). Using Modeler (v9. 20), 10,000 loops of each doublet (H2B-H2A Y152 to S192 and H4-H3 F122 to T203, with sequence numbers identified as in Figure  S1A ) were generated  (Sali and Blundell, 1993). To eliminate improbable loops within each doublet, clash identification was performed using CPPTRAJ of the Amber MD package (v18), where a cutoff distance of 0. 8 A ˚between potentially over-lapping atoms was used  (Roe and Cheatham, 2013). Any loops identified by CPPTRAJ without a clash were manually parsed through to eliminate loops that were not sterically overlapping but also not physically relevant, such as those forming ''knots'' in the histone folds.
ACKNOWLEDGMENTS We thank  Garry Morgan  ( CU Boulder ),  Rui Yan , and  Zhiheng Yu  ( Janelia Research Campus cryo-EM facility ) for help with data collection. We also thank  Matthieu Legendre  for performing the expression profile analyses. This study was funded by the  Howard Hughes Medical Institute  (Y. L. , C. M. T. , K. Z. , S. B. , A. W. , G. E. , and K. L. ), an  NIGMS   NSRA fellowship  ( F32GM137496 ) to S. B. , and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  832601 ; N. P. , S. J. , and C. A. ). H. B. is the recipient of an  EMBO Long-Term Fellowship  ( ALTF 979-2019 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Software and algorithms AxioVision rel. 4. 8. 2 Software Carl Zeiss https://www. zeiss. com/corporate/us/ home. html JPK SPM software for AFM image processing V 6. 1. 158 https://customers. jpk. com/ GraphPad Prism version 9. 0. 0 https://www. graphpad. com:443/ CryoSPARC V2. 1 https://cryosparc. com/ UCSF Chimera V1. 14 https://www. cgl. ucsf. edu/chimera/ winCOOT V0. 9. 4. 1 https://www2. mrc-lmb. cam. ac. uk/ personal/pemsley/coot/ (Continued on next page).
Algorithm of the Program We described the implementation of the procedures in detail in the pseudocode (Table  1 ; also see the flowchart in Figure  2 ). All code is available on GitHub https://github. com/cpfdomingues/simulation-code-human-transmission-genes-bacteria (accessed on 4 March 2021). At the beginning of each simulation, we randomly distribute all bacterial pathogens, all virulence gene families, and all resistance gene families by the population-group of people considered in the network during the simulation. We distribute each virulence gene, each resistance gene, and each bacterial pathogen among 1 to 2% of the population. Taking the example of virulence genes, we proceed as follows: (i) take a random number between 0. 01 (1%) and 0. 02 (2%) to obtain the percentage of individuals that should receive a gene of virulence; (ii) convert this percentage into the number of individuals that is going to receive the gene according to the size of the population (% obtained * size of the population); (iii) select randomly as many individuals as those obtained in (ii); (iv) activate this gene in the metagenomes of the selected individuals.
Results Data were obtained in service mode using the FORS2 instrument  21  attached at the Cassegrain focus of the Unit 1 (Antu) of the ESO VLT. FORS2 was used in imaging polarimetric mode with the V_HIGH filter (centred at 557 nm with a 123-nm full-width half-maximum, or FWHM), R_SPECIAL (centred at 655 nm with a 165-nm FWHM) and I_BESS (centred at 768 nm with a 138-nm FWHM). Reflectance spectra of comet 2I/Borisov show no prominent emission lines in the range covered by the R_SPECIAL and I_BESS filters  2, 22  (hereafter referred to as R F and I F filters, respectively), but the V_HIGH filter (hereafter referred to as V F filter) covers exactly the second brightest emission after the CN(0-0) at 387 nmthe C 2 Swan band at 512 nm. However, comet 2I/Borisov appears to be C 2 -depleted  23  , so the V F filter is also not severely contaminated by molecular emissions.
In addition to the aperture polarimetry described above, we also employed a slit polarimetry method  25  to measure polarisation along the comet tails, and we obtained also photometric measurements of the coma. Aperture polarimetry. The results of our aperture polarimetry measurements are given in the observing log of Table  1 , and data obtained in the R F and I F filters are shown in Fig. 1 , together with the polarisation measurements of comet Hale-Bopp, of comet 1P/ Halley, and of other comets, selected with the help of an online database  26. Polarimetric observations of comets are obtained in filters with different effective wavelength and bandwidth, and using different apertures. In Fig. 1  we show literature data obtained with filters with different bandwidths, all centred at wavelengths between 620 and 695 nm for comparison with data taken with the R F filter (panel a), and between 715 and 810 nm for comparison with data taken with the I F filter (panel b).
Because of the small number of data points, none of these functions allow us to make a reliable extrapolation of the behaviour of the polarisation of comet 2I/Borisov in the negative branch, nor at large phase angles. However, Eq. (  1 ) may be conveniently used to describe the polarimetric curve of comet Hale-Bopp. Figure  1  shows also the best-fits to the Hale-Bopp data obtained with filters that have effective wavelengths close to those of the R F and I F FORS2 filters (note, however, that some outliers have not been considered for the best-fit to the data). We finally note that polarimetric measurements of comet 2I/ Borisov taken in the phase angle range 12. 5-28 ∘ , using the Advanced Camera for Survey/Wide Field Channel (ACS/WFC) of the Hubble Space Telescope (HST), have been recently presented in another study  37.
Discussion The photometric data obtained in our and other investigations have large uncertainties, and are compatible with very different classes of small bodies of our Solar System, including short and long-period comets, Centaurs, Jupiter Trojans and some main belt asteroids  4  , and the same is true for the reflectance spectra. In summary, photometric data do not point to 2I/Borisov as an object with distinctive characteristics. Instead, the polarimetric characteristics of 2I/Borisov are more suggestive of its interstellar origin. Several studies  46, 47  have divided comets into two polarimetric classes: low-and high-polarisation comets. This different behaviour is usually ascribed to a very different dust-to-gas ratio in the coma  8. Since gas contamination dilutes the polarisation produced by the dust scattering, the higher dust-to-gas ratio, the higher the comet polarisation.
(  4 ), and their deviations from zero would flag the possible presence of systematics effects. Null parameters were found consistent with zero for all datasets except those obtained on 2020-02-06, where they deviate from zero by ≃5σ for aperture radius > 1″. The presence of systematic effects, also suggested by the deviation from zero of U/I in the I F filter, is likely due to the bad seeing conditions under which the observations were carried out (the observations were actually repeated at a later epoch), joined to a particularly crowded background. For this epoch, polarimetry was measured within a 1″ wide aperture. In addition to aperture polarimetry described above, we also employed a slit polarimetry method  25  which allows us to measure polarisation along the tail, or any other chosen direction.
As a drawback for using such a large aperture size, most of the frames had to be discarded due to the crowded stellar background which contaminated the photometry of the comet. In the end, we obtained reliable photometry only from data obtained during the night of 2019-12-25 for each of the FORS2 filters used (V F , R F and I F ), and during the night of 2020-02-06 for the images gathered with R F and I F filters. In order to calculate the dust optical colours in the Johnson-Cousins UBVRI system, we calculated an empirical transformation between FORS2 filters and the former system (private communications with C. Jordi). We integrated the spectra for Johnson-Cousins R, V and I filters, as well as for V F , R F and I F filters using a virtual star with G = 15 without adding any interstellar extinction.
For the asymptotic case n = ∞ we have t(0. 05) = 1. 645 and t(0. 025) = 1. 960. To quantify the disagreement between 2I/Borisov and the other comets, we fitted the ensemble cometary data to Eq. (1) using nonlinear least squares minimisation, subtracted the fit from the data and compared the predictive confidence limits (12) on the observations to the Borisov measurements. The result for the R F -filter and I F -filter measurements separately is shown in Fig. 6. Here we considered only data with available observational uncertainties and 0 < α < 60 ∘ , yielding sets with N = 393 (R F filter) and N = 113 (I F filter) respectively. We then discarded the 5% of each dataset with the highest uncertainties. Our two R F -filter measurements of 2I/Borisov at α = 27. 5 ∘ and α = 28. 6 ∘ (red points, panel a) lie between PL 05 and PL 10 , therefore the probability P that both R F -filter measurements follow the same phase-polarisation behaviour as the other comets is 2.
Acknowledgements This work is based on observations made with  ESO Telescopes at the La Silla Paranal Observatory  thanks to Director Discretionary Time under programme  ID 2104. C-5003. The work of T. S. -R. was carried out through grant  APOSTD/2019/046  by  Generalitat Valenciana (Spain). This work was supported by the  MINECO (Spanish Ministry of Economy ) through grant  RTI2018-095076-B-C21  ( MINECO/FEDER, UE). We would like to thank  Carme Jordi  ( ICCUB-IEEC ) for her kindly contribution on the calculation of the empirical colour transformation between photometric systems.
Code availability IRAF is the Image Reduction and Analysis Facility, a general-purpose software system for the reduction and analysis of astronomical data. It is written and supported by the National Optical Astronomy Observatories (NOAO) in Tucson, Arizona, USA. NOAO is operated by the Association of Universities for Research in Astronomy (AURA) under cooperative agreement with the National Science Foundation. The code is available from http://iraf. noao. edu/. A simple FORTRAN code was used to read the standard IRAF output and combine the fluxes to obtain aperture polarimetry as explained in Methods subsection Polarimetry. Three custom-made IDL (Interactive Data Language, version 8. 7, by Harris Geospatial Solutions, Boulder, Colorado, USA) routines were also utilised for the computation of the polarimetric profiles.
Samples and Data Preparation Sampling, Molecular, and Sequencing Methods The whole genomes of 96 individual barn owls (T. alba) were used in this study (supplementary table  S1 ): 94 individuals were sampled in 11 Western Palearctic localities: Canary Islands (Tenerife Island-WC), Portugal (PT), France (FR), Switzerland (CH), Denmark (DK), Serbia (SB), Greece (GR), Italy (IT), Aegean islands (AE), Cyprus (CY), and Israel (IS). In addition, one Eastern (Tyto javanica from Singapore) and one American barn owl (Tyto furcata from California) were used as outgroups. Illumina whole-genome sequences of individuals from PT, FR, CH, DK, and the outgroups were obtained from the GenBank repository (BioProject PRJNA700797). For the remaining 61 individuals, we followed a similar library preparation and sequencing protocol as outlined in  Machado et al.
To assess the quality of the phasing, we examined phase accuracy by using the switch-error-rate metric  (Choi et al. 2018). When comparing two phasing for an individual's variants, a switch error occurs when a heterozygous site has its phase switched relative to that of the previous heterozygous site. Thus, for each individual, we compared the true local phasing inferred form the read-based approach (WhatsHap) and the statistical phasing of this individual's variants statistically phased by Shape-It, with read-based phase information ignored only for the individual considered (same version and parameters than in the paragraph above). The final estimation of the switch error rate was done using the switchError code to compare both phasing sets (available at https:// github. com/SPG-group/switchError) (supplementary fig.
S6 ). Population expected and observed heterozygosity, population-specific private alleles, population-specific rare alleles (mac 5), population-specific total number of polymorphic sites (table  1 ), population-level genetic diversity (pi and Watterson estimator-supplementary table  S2 ) were estimated on the 5,151,169 variants data set using the package hierfstat v. 0. 5-10 (Goudet 2005). To account for differences in sample sizes, which ranges from 4 to 10, population-specific statistics were calculated by randomly sampling five individuals from the larger populations (all except FR and SB) ten times in a bootstrap-fashion and estimating the mean and SD. Individual-based relatedness (b) (Weir and Goudet 2017) and inbreeding coefficient for SNP data were calculated with the R package SNPRelate  (Zheng et al.
2012). Overall F ST , population pairwise F ST and population-specific F ST (Weir and Goudet 2017) were computed with the package hierfstat. CIs for population-specific F ST were computed by dividing the SNPs into 100 blocs, and bootstrapping 100 times 100 blocks with replacement. Finally, PCA were also performed with the R package SNPRelate, first with all individuals and second only with the 66 European ones (excluding WC, CY, and IS).
Haplotype Sharing To measure shared ancestry in the recent past between individuals, we ran fineSTRUCTURE  (Lawson et al. 2012 ) on the phased data set including all individuals. For this analysis, we initially modeled haplotype sharing between individuals using ChromoPainter to generate a co-ancestry matrix, which records the expected number of haplotypes chunks each individual donates to another. For this ChromoPainter step, we converted phased haps files to ChromoPainter phase files using the impute2chromopainter. pl script provided at http:// www. paintmychromosomes. com and generated a uniform recombination map with the makeuniformrecfile. pl script. Using the version of ChromoPainter built into fineSTRUCTURE v. 2. 0. 8, we performed ten EM iterations to estimate the Ne and Mu parameters (switch rate and mutation rate).
Niche Modeling In order to identify the regions of high habitat suitability for barn owls at the LGM (20,000 years BP) and to support the demographic scenarios tested in the previous section, we modeled the past spatial distribution of the species in the Western Palearctic. We built species distribution model (SDM) using Maximum Entropy Modeling (MaxEnt), a presence-only based tool  (Elith et al. 2011). Current climatic variables for the Western Palearctic (supplementary fig. S10 ) were extracted from the WorldClim database at 5 arc min resolution using the R package rbioclim (Exposito-Alonso 2017), and filtered to remove variables with a correlation of 0. 8 or higher (supplementary fig. S9 ). The variables retained were: Mean Diurnal Range (Bio2), Min Temperature of Coldest Month (Bio6), Temperature Annual Range (Bio7), Mean Temperature of Wettest Quarter (Bio8), Precipitation Seasonality (Bio15), Precipitation of Driest Quarter (Bio17), and Precipitation of Coldest Quarter (Bio19).
Barriers and Corridors Migration Surface Estimation in the Western Palearctic The Estimated Effective Migration Surface (EEMS) v. 0. 0. 9 software  (Petkova et al. 2016 ) was used to visualize geographic regions with higher or lower than average levels of gene flow between barn owl populations of the Western Palearctic. Using the SNP data set pruned for LD produced above, we calculated the matrix of genetic dissimilarities with the tool bed2diff. The free Google Maps api v. 3 application available at http://www. birdtheme. org/useful/v3tool. html (last accessd December 08, 2021) was used to draw the polygon outlining the study area in the Western Palearctic. EEMS was run with 1,000 demes in five independent chains of 5 million MCMC iterations with a burn-in of 1 million iterations. Results were visually checked for MCMC chain convergence (supplementary fig.
Isolation by Distance in Continental Europe To investigate how population structure correlated with spatial distances between European populations and to detail the role of the Alps as a barrier to gene flow, we performed Mantel tests as implemented in the ade4 package v. 1. 7-15 (Dray and Dufour 2007, p. 4)  for R. We compared the genetic distances (pairwise F ST between populations, see Population Structure and Genetic Diversity for details) with different measures of geographical distance between populations: the shortest distance over land via direct flight and the distance constrained by the presence of the Alps, forcing the connection of the Italian population to the other populations via the Greek Peninsula (supplementary fig. S13 ). We also tested the linear regression between both variables in R.
Acknowledgments We thank the following institutions and individuals for providing samples or aiding in sampling: The  European Barn Owl Network ,  Sylvain Antoniazza  and  Reto Burri , the  Natural History Museum of Tenerife , the  Wildlife Rehabilitation Centre La Tahonilla  ( Cabildo Insular de Tenerife ),  Kristijan Ovari , the  Pali c Zoo (Serbia) , and the Natural History Cumer et al. doi:10. 1093/molbev/msab343MBE.
Qualitative chemical screening was expanded by mass spectrometry analysis of OE. MS/MS data were used to perform tentative compounds dereplication by matching experimental spectra to spectral libraries by use of the Global Natural Product Social Molecular Networking (GNPS) online platform. The best spectral matches (Figures  S6-S20 ) and estimated compounds are presented in Table  3. Such molecular analysis allowed for identification of 15 tentative compounds classified either as terpenoids, steroids, alkaloids or compounds containing phenolic moiety as observed qualitatively. Additional matching of spectra with other public databases might be useful for more robust compounds identifications.
MS/MS Molecular Networking A molecular network was created using the online workflow (https://ccms-ucsd. github. io/GNPSDocumentation/) on the GNPS website (http://gnps. ucsd. edu) [98]  and MS/MS data which experimental setup is provided in Supplementary. The data was filtered by removing all MS/MS fragment ions within +/-17 Da of the precursor m/z. MS/MS spectra were window filtered by choosing only the top 6 fragment ions in the +/-50Da window throughout the spectrum. The precursor ion mass tolerance was set to 2. 0 Da and a MS/MS fragment ion tolerance of 0. 9 Da. A network was then created where edges were filtered to have a cosine score above 0. 8 and more than 6 matched peaks. Further, edges between two nodes were kept in the network if and only if each of the nodes appeared in each other's respective top 10 most similar nodes.
Dose response curve analyses were performed in RStudio v. 1. 1. 463 environment for R software (R Core Team (2018). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www. R-project. org/. ) using drc package  [104]. Effective concentration 50% (EC 50 ), a concentration resulting in 50% inhibition of bacterial growth, estimated from log-logistic three parameter dose response models, was used as a comparative feature of relative potency of the tested samples.
Conclusions The presented research aimed to analyze chemical properties of extracts and semipurified fractions of Eunicella cavolini collected in the Adriatic Sea, as well as their corresponding potential to exhibit antiproliferative, antioxidant, anti-inflammatory and antibacterial activities. Obtained in vitro data showed that the purification of organic extract resulted in fractions with higher biological activities. In particular, F2 and F3 were found to be more active against tested cancer cell lines (MCF-7, CFPAC-1, HepG2 and SW620) and Staphylococcus aureus, which might be correlated with observed potential of radicalscavenging activity and chemicals detected in these samples. Furthermore, the F1 fraction was observed to be a possible inhibitor of lipoxygenase. Our results indicate a diversity of extract/fractions in the type of compounds that may have a synergistic or antagonistic effect.
Supplementary Materials: The HRMS and MS/MS were performed with different samples which spectra are available online. Figures  S1-S5 : HRMS spectra of OE, F1-F4, respectively. Figures  S6-S20 : MS/MS match between GNPS database (green) and Eunicella cavolini sample (black) for compounds 1-14 from Table  3 , respectively.
5 mM NH 2 OH/NH 3 mixtures. The IC data were analysed by using the Chromeleon 6. 8 program. Computational methods. DFT calculations of reaction energetics were carried out with a periodic plane-wave implementation and ultra-soft pseudo-potentials using the QUANTUM ESPRESSO version 6. 1 on a single Fe-N 4 moiety embedded into a graphene unit cell  64. The self-consistent continuum solvation implicit solvation model as implemented in the Environ QUANTUM ESPRESSO module was used to model the presence of implicit water  65. The surface charge density was modulated by changing the total charge of the system and a planar counter charge was introduced above the slab to compensate the charge  48, 65, 66. The relaxed surface states were also re-calculated using the Vienna Abinito Simulation Package as spinpolarised single point calculations using the RPBE+U and HSE06 functionals  42.
This paper presents the Colony database and middleware designed to address these issues. A top requirement is an edge-first approach  [24]  that locates data on device, to provide availability, fast and seamless response independently of network connectivity and of location, and so that users have ownership of their data. However, this makes it challenging to satisfy consistency and freshness expectations. To answer this challenge, Colony takes a hybrid approach, and provides the highest consistency guarantees compatible with availability (TCC+), strengthened further (to SI) in well-connected zones. CRDT data types ensure convergence without rollbacks. A related challenge is the overhead of concurrency metadata (fat vector clocks), which we limit thanks to a flexible forest topology and to SI zones.