dataesr/scientific-paragraphs-categorization huggingface dataset:
The Contribution from ExoClock The overall contribution of the current work can be assessed by examining the distribution of the observations over the years for each planet. To do so, we used the following metrics: 1. mean observation rate: total number of data points divided by years since the first data point. 2. observing coverage: number of years with at least one data point divided by years since the first data point. Figure  6  shows the mean observing rate versus the observing coverage for all 180 planets, while Figure  7  shows the distribution of planets over the two metrics. From both graphs, it is apparent that data collected as part of the ExoClock project have made a significant contribution to the follow-up of known planets, as the mean observing rate and the observing coverage have been doubled (or more) for 75% of the planets.
Model generation, curation and validation Using these completed genomes we created strain-specific GEMs, initially using the curated MGH78578 GEM (iYL1288) as a reference to identify conserved genes and reactions, followed by manual curation (see Methods). The latter was enabled by the availability of matched phenotype data  (Blin et al. 2017)  indicating the ability of each strain to grow in minimal media supplemented with each of 94 distinct sole carbon substrates for which we were able to predict growth in silico using the GEMs (Supplemental Table  2 ). Our phenotypic data included 12 carbon substrates for which growth was demonstrated for at least one strain and for which the corresponding metabolite transport and/or processing reactions were not present in the original iYL1288 model. Literature searches were undertaken to identify the putatively responsible candidate genes and reactions for GEM inclusion.
Five carbon substrates had at least one strain with a false negative call, where the model did not predict growth but we observed a growth phenotype: L-tartaric acid (n=12 strains), Llyxose (n=5), L-sorbose (n=2), propionic acid (n=2) and L-galactonic acid-gamma-lactone (n=1) (Supplemental Table  4 ). In such cases it is assumed that the models are missing information required to optimise for growth on these substrates  (Orth et al. 2012). Despite thorough literature and database searches, we were unable to identify alternate biological pathways that could plausibly fill these gaps in the models. This was particularly notable among the five K. quasipneumoniae subsp. quasipneumoniae strains, which all had false negative predictions for L-lyxose utilisation. These genomes were each missing sgaU (KPN_04590), which was present in all other KpSC genomes and encodes an enzyme that converts L-ribulose-5-phosphate to L-xylulose-5-phosphate.
quasipneumoniae subsp. quasipneumoniae species-specific phenotype. The associated gene, KPN_03428, encodes the enzyme for catalysis of two reactions in the models: CYSDS (cysteine desulfhydrase) and CYSTL (cystathionine b-lyase), the latter of which may also be encoded by KPN_01511 (malY). Notably, malY was present in all other models but absent from all K. quasipneumoniae subsp. quasipneumoniae (closest bi-directional BLASTp hit had 30. 07% identity, well below the threshold required for inclusion as a homolog and considerably lower than the expected divergence between KpSC species  (Holt et al. 2015 )), and no alternate genes encoding putative cystathionine b-lyases could be identified by search of the KEGG database, indicating a lack of genetic redundancy for these reactions. Direct comparison of the K.
2021; Adams et al. 2016). The carbapenem-resistant K. pneumoniae lineage, ST258, has been associated with particularly high IS burden  (Adams et al. 2016 ), and we hypothesise that such insertions contribute to the increased number of gene deletion phenotypes predicted for NJST258-1 compared to other K. pneumoniae strains. Further analyses will be required to assess whether this reduced redundancy is a general feature of ST258 or a trait specific to NJST258-1. These findings indicate that KpSC can differ substantially in terms of metabolic redundancy. While we cannot exclude the possibility that the predicted knockout phenotypes might be rescued by products of non-orthologous genes that are not currently captured in our models, we note that at least for the examples described above, search of the KEGG database did not indicate any additional known redundant metabolic pathways.
7  (Kolmogorov et al. 2019), Raven v1. 1. 10 (Vaser and Šikić 2021)  and Miniasm v0. 3  (Li 2016). The final consensus assembly was then polished with the long reads using Medaka v1. 1. 3 (https://github. com/nanoporetech/medaka) followed by three rounds of polishing using the Illumina reads with Pilon v1. 23  (Walker et al. 2014). All 37 completed genomes were annotated with Prokka v1. 13. 3 (Seemann 2014), using a trained annotation model (created using 10 genomes with Prodigal v2. 6. 3  (Hyatt et al. 2010) ). All genomes were analysed with Kleborate v2. 0. 3  (Lam et al. 2021 ) to obtain ST and other genomic information (see Supplemental Table  1 ).
2013). For each GEM, on every core carbon substrate we simulated growth in M9 media with that substrate as the sole carbon source using FBA (as described above), but with one gene knocked out using the single_gene_deletion function. Each gene was knocked out in turn, and optimised biomass values ≥0. 001 were considered positive for growth. Four gene-substrate combinations were selected for further investigation by interrogation of the model gene-protein-reaction rules and search of the KEGG database  (Kanehisa et al. 2002)  using KofamKOALA  (Aramaki et al. 2019)  for redundant genes/pathways. Where relevant, pairwise chromosomal comparisons were performed using BLASTn  (Camacho et al. 2009 ) and visualised using the Artemis Comparison Tool  (Carver et al. 2005). The putative insertion sequence was identified by BLASTn search of the ISFinder database  (Siguier et al.
This work was funded by an  Australian Research Council Discovery Project  ( DP200103364 , awarded to KLW, KEH, JM and SB), a  2019 Endeavour Fellowship  (awarded to JH), the  Bill & Melinda Gates Foundation  ( OPP1175797 , awarded to KEH) and a  National Health and Medical Research Council of Australia Investigator Grant  ( APP1176192 , awarded to KLW). CR was supported by a  Roux-Cantarini grant  from  Institut Pasteur. SB and JSLF were supported by the  SpARK  project "The rates and routes of transmission of multidrug resistant Klebsiella clones and genes into the clinic from environmental sources," which has received funding under the 2016  JPI-AMR call "Transmission Dynamics " (MRC reference  MR/R00241X/1 ). Under the grant conditions of the  Bill & Melinda Gates Foundation , a Creative Commons Attribution 4.
In this paper we report on spatial intensity interferometry measurements within the Hα line on two stars: the Luminous Blue Variable supergiant P Cygni and the late-type B supergiant Rigel. The experimental setup was upgraded to allow simultaneous measurement of two polarization channels, instead of one in our previous setup, and the zero baseline correlation function on-sky to validate independent estimates obtained from the stellar spectrum and the instrumental spectral throughput. Combined with simultaneous spectra measurements and based on radiative transfer models calculated with the code CMFGEN, we were able to fit our measured visibility curves to extract the stellar distances. Our distance determinations for both P Cygni (1. 61 ± 0. 18 kpc) and Rigel (0. 26 ± 0. 02 kpc) agree very well with the values provided by astrometry with the Gaia and Hipparcos missions, respectively.
Based on the measured spectra and the radiative transfer code CMFGEN, we determine the distance of P Cygni and Rigel from modeling their measured visibilities. This paper is organized as follows. We first describe our upgraded experimental setup in the next section (Sec. 2), which allows in particular measuring the polarization-resolved intensity correlation functions, and then we present our observations and the spatial intensity correlation functions measured on P Cygni and Rigel (Sec. 3). Sect 4 describes the radiative transfer code CMFGEN and our modeling approach to determine the distances of P Cygni and Rigel. Finally, our results are compared to the ones found in literature and then summarized in Sect. 5.
Intensity correlations on P Cygni P Cygni was observed at C2PU, within the Hα line, between 3 August 2020 and 9 August 2020 as reported in Table  1. The total integration time was 40. 3 hours. We detected in average 320×10 3 cps (counts per second) per detector on telescope 1 (T1, where the signal from each polarization channel is split into two) and 715 × 10 3 cps per detector on telescope 2 (T2). Those new data will be compared to the ones obtained during our first observations in 2018, also within the Hα line, but with only one polarization channel. The results have been published in  Rivet et al. (2020) , where we estimated the distance of P Cygni by comparing the measured visibilities to simulations computed with the code CMFGEN, with the physical parameters of P Cygni constrained by contemporaneous observed spectra.
They are compatible with each other within the error bars. To calculate the expected area of the bunching peak, we need to numerically compute the g (2) (τ ) function from the spectrum using Eqs. (  2 ) and (  3 ), as explained in  Rivet et al. (2020). Fig. 4  presents one spectrum reported on the AAVSO database (AAVSO 2020) and measured on August 8 th 2020 using an eShel spectrometer (from Shelyak) with a resolving power R = 11650. We can observe a strong emission line, slightly weaker than the one reported in 2018  (Rivet et al. 2020). For a point-like source, we get Aexp = 2. 35 ps (2. 55 ps in 2018). This value is compatible with AV within 1σ and with AH within 2σ. In the rest of the paper, we will thus consider that we can use the value measured with one telescope as the zero baseline value A = A(rB = 0).
The code CMFGEN To provide a robust interpretation of our interferometric data of P Cygni and Rigel, we used unified photosphere-wind models calculated with the non-LTE (local thermodynamic equilibrium) radiative transfer code CMFGEN  (Hillier & Miller 1998). For a set of stellar and wind parameters, CMFGEN solves in an iterative way the radiative transfer, statistical, and equilibrium equations in the comoving frame. This code has been successfully used in the literature to model observables of different types of hot stars and then to determine their stellar and wind parameters (e. g. , see Hillier 2012, 2020, and references therein). It is well-understood that radiative line-driven winds of hot stars show density fluctuations due to local agglomerations of matter, called wind clumps (e. g.
,  Eversberg et al. 1998). This feature must be taken into account in the modeling of hot stars in order to well reproduce their observables, and then to obtain more accurate estimates of the wind mass-loss rates  (Bouret et al. 2005; Fullerton et al. 2006; Davies et al. 2007 ). The code CMFGEN allows us to implement the effect of wind clumping, using the so-called microclumping approximation  (Hillier et al. 2001). This assumes a void interclump medium and wind clumps' sizes smaller than the photon mean-free path for any value of wavelength. In CMFGEN, the wind clumping is parameterized by the volume filling factor, f (r), as follows: f (r) = f∞ + (1 -f∞)e - v(r) v initial , ( 8 ) where r is the distance from the center of the star, f∞ is the filling factor value at r → ∞, v(r) is the wind velocity, and v initial is the onset velocity of clumping in the wind.
Model parameters of P Cygni and Rigel Both P Cygni and Rigel were previously studied using the code CMFGEN to model different types of observables (e. g. ,  Najarro et al. 1997; Najarro 2001; Chesneau et al. 2010; Richardson et al. 2013; Chesneau et al. 2014; Rivet et al. 2020). In Table  5 , we summarize the main stellar and wind parameters of our reference models for P Cygni and Rigel: stellar luminosity (L ), effective temperature (T eff ), gravity surface acceleration (log g), radius (R ), mass (M ), mass-loss rate ( Ṁ ), wind clumping factor (f∞), terminal velocity (v∞), and the wind velocity law exponent (β). Following  Rivet et al. (2020) , we adopted the stellar and wind parameters for P Cygni based on the study of  Najarro (2001)  that used CMFGEN to model the ultraviolet, visible, and infrared spectrum of P Cygni.
As described in  Rivet et al. (2020) , the chemical composition of our CMFGEN models for P Cygni also follows  Najarro (2001). For Rigel, the model parameters are based on  Chesneau et al. (2010)  and  Chesneau et al. (2014)  that used CMFGEN to model interferometric data of Rigel. 1 Solar chemical composition is assumed in our models for Rigel, as in  Chesneau et al. (2010)  and  Chesneau et al. (2014). In turn, these interferometric studies based their 1 See Table  1  of  Chesneau et al. (2014). analysis on the stellar and wind parameters derived for Rigel by  Przybilla et al. (2006)  and  Markova et al. (2008). The adopted values for the photospheric parameters T eff and log g are in good agreement with other spectroscopic studies of Rigel in the visible region. For instance, using models calculated with the non-LTE radiative transfer code FASTWIND  (Santolaya-Rey et al.
Comparison to the observed spectrum of P Cygni and Rigel We compared our reference models to public spectroscopic data of P Cygni from the AAVSO database (AAVSO 2020) and Rigel from the A. R. A. S. Spectral Data Base (ARAS 2020) observed in 2020. Due to the high Hα variability of P Cygni  (Markova et al. 2001 ) and Rigel  (Kaufer et al. 1996) , we analysed observed Hα line profiles that were recorded close in time to our interferometric measurements: August 8th (P Cygni) and February 5th (Rigel). Our reference CMFGEN models for P Cygni and Rigel are compared to their visible spectra around the Hα line in Figs. 11  and 12 , respectively. For the comparison to the spectrum of P Cygni observed in 2018, we used the same CMFGEN model from  Rivet et al. (2020). 2 As pointed by  Chesneau et al. (2014) , the mass-loss rate of Rigel derived from Brγ is up to about one order of magnitude higher than the one derived from Hα.
This indicates that both the phys-ical conditions of Rigel's photosphere and wind are well described by our adopted CMFGEN model for this star. We are aware that values of β much larger than 1. 0 (up to ≈3. 0) can be required to reproduce the Hα line of OB supergiants (e. g. , see  Puls et al. 2008, and references therein). For instance, based on models calculated with the code FASTWIND,  Markova et al. (2008)  derived β up to 1. 5 for their sample of late-type B supergiant (which included Rigel), but without specifying a value for Rigel, while  Haucke et al. (2018)  derived β = 2. 6 for Rigel also based on spectroscopic modeling using FASTWIND. As our physical model for Rigel is based on Chesneau et al. (  2010 ) and  Chesneau et al. (2014) , and the wind velocity law exponent is not specified in these studies, we initially adopted β = 1.
ACKNOWLEDGEMENTS We thank the referee  Chris Haniff  for helping us to improve this paper. We acknowledge the financial support of the  UCA-JEDI  project  ANR-15-IDEX-01 , the  Doeblin Federation , the OPTIMAL platform, the  Région PACA  (project  I2C ) and the  French ANR  (project  I2C ,  ANR-20-CE31-0003 ).
Sample and Data Information Saliva samples were collected in 2001 with the approval of the Medical Board of PNG and with support from the Diocese of Alotau, PNG (Missionaries of the Sacred Heart, M. S. C. ), particularly Fr Joe Ensing (M. S. C. ) and then Bishop Desmond Charles Moore (M. S. C. ). Written informed consent was obtained from each donor, after the project was explained and all questions answered to the satisfaction of the donor. Genetic work within this study was additionally approved by the Ethics Commission of the University of Leipzig Medical Faculty. MtDNA and Y-chromosome data were published in a previous study  (van Oven et al. 2014). Here, we generated genome-wide data (∼1. 6 million SNPs) for 255 (192 from Massim, 33 from Gulf Province, and 30 from Central Province) individuals on the Illumina Infinium Multi-Ethnic Global Array (MEGA); genotyping was carried out by the Laboratorio de Servicios Genómicos at the Laboratorio Nacional de Genómica para la Biodiversidad, Irapuato, Mexico, on our request.
9  (Purcell et al. 2007). For quality control, we first excluded sites with more than 5% missing data in the entire data set and sites with more than 50% missing data and/or Hardy-Weinberg equilibrium P values below 0. 00005 within a population group (except for groups with only one individual). Then, we removed individuals with more than 5% missing data or with parents speaking different languages or coming from different locations. We also filtered out individuals to exclude up to first-degree kinship pairs. Data missingness and Hardy-Weinberg equilibrium were calculated using PLINK v1. 9 while the individuals to be removed to avoid first-degree relatedness (kinship coefficient ≥0. 177) were inferred using KING  (Manichaikul et al. 2010) , as implemented in PLINK v2  (Chang et al. 2015).
MBE We therefore ran SHAPEIT v2 with options -burn 10,-prune 10, and -main 30 for iteration number with 500 conditioning states, leaving other parameters as default; BEAGLE v5. 1 with options burnin = 12, iterations = 24, phase-states = 24, and ne = 800,000 (this is smaller than the default value, but is recommended by the authors for populations with a smaller effective population size, as expected for island populations in Oceania); EAGLE v2 with -Kpbwt 40,000 (which determines the number of conditioning haplotypes). We did not use a reference panel for phasing as 1,000 Genomes or other available WGS data sets lack representative cohorts for Oceanian populations.
IBD Analyses We identified shared IBD blocks between each pair of individuals and homozygous-by-descent (HBD; same as ROH) blocks within each individual using refinedIBD  (Browning and Browning 2013). Both identified IBD and HBD blocks are considered as IBD blocks in our analyses, which is analogous to pairwise shared coalescence (PSC) segments in a previous study  (Al-Asadi et al. 2019). The IBD blocks within a 0. 6 cM gap were merged using the program merge-ibd-segments from BEAGLE utilities (https:// faculty. washington. edu/browning/refined-ibd), allowing only one inconsistent genotype between the gap and block regions. We used IBD blocks at least 2 cM in length shared by individuals within a population to investigate the demography of each population group. Then, we used IBD blocks in 1-5, 5-10 cM, and over 10 cM to investigate the sharing between individuals from different populations for different time periods  (Ralph and Coop 2013; Al-Asadi et al.
If the IBD sharing among the Kula/ non-Kula groups and among all Oceanians were equal then R will be zero, and the more shared migration the Kula/non-Kula groups have, the larger R will be. We calculate the standard error of the R-statistic by jackknife resampling the chromosomes. The significance of the R-statistic between Kula-versus non-Kula groups was estimated by comparing the observed value to the distribution of simulated values generated by randomly assigning Massim groups to either Kula-or non-Kula groups and calculating the difference in relative IBD similarity. Scripts made for analyzing IBD results are available from https://github. com/dangliu/Massim_project.
Local Ancestry Inference and Ancestry-Specific Analyses We ran the PopPhased mode of RFMix v1. 5. 4  (Maples et al. 2013)  to infer local ancestry across the genome for each individual in our data set with options -e 3 (three EM iterations), -G 85 (85 generations since the admixture event; as suggested in a previous study  (Bergström et al. 2017 )), -n 5 (minimum five reference haplotypes per tree node; as suggested by the authors of RFMix), -use-reference-panels-in-EM (the reference samples were used in EM iterations), and the East Asians (including both Austronesian and non-Austronesians to increase power) and Papuan highlanders (who showed no more than 0. 0001 East Asian-related ancestry in the ADMIXTURE results for K = 2) as reference panels. The Papuan highlanders were randomly down-sampled to 50 individuals, to be the same as the sample size of East Asians.
We used 0. 95 as the cutoff of forward-backward probability (i. e. , the posterior probability of an inferred ancestry at an SNP in a haplotype). We interpreted the inferred East Asian-related segments as Austronesian-related segments, as indicated in f4 results (supplementary fig. S10 , Supplementary Material online). Global ancestry for each Massim group was calculated from the local ancestry profiles of the corresponding individuals. Papuan-/ Austronesian-related ancestry-specific PCA was performed with PCAmask  (Moreno-Estrada et al. 2013). Analyses of local ancestry inference, global ancestry calculation, and ancestry-specific PCA were done using a pipeline from a previous study  (Martin et al. 2017) ; scripts for preparing input files for these analyses were modified from the pipeline (available from https://github.
com/dangliu/Massim_project). We generated Papuan-/Austronesian-related ancestry masked genomes using our own script (available from https:// github. com/dangliu/Massim_project) for ancestry-specific ADMIXTURE and TreeMix analyses. We ran ADMIXTURE v1. 3. 0 for K = 2 to K = 7 and for K = 2 to K = 6 with 20 replicates for each K on Austronesian and Papuan ancestry masked data, respectively. We ran TreeMix v1. 12  (Pickrell and Pritchard 2012)  only for Austronesian ancestry masked data (as there were too few SNPs in the Papuan ancestry masked data for this analysis) with 0-2 migration events and ten independent runs, and selected the topology with the highest likelihood for further investigation. Papuan/Austronesian ancestry-specific PCA, ADMIXTURE, and TreeMix analyses were all performed on East Asian and Oceanian individuals with at least 33% Papuan-/ Austronesian-related ancestry based on the ADMIXTURE results for K = 2, respectively.
Acknowledgments We thank all sample donors for making this work possible;  Joe Ensing  (M. S. C. ) for valuable assistance in sample collection;  Roland Schröder  for technical assistance;  Benjamin Vernot  and  Lluis Quintana-Murci  for data access;  Janet Kelso ,  Sandra Oliveira , and  Irina Pugach  for helpful advice concerning computational analyses; and  Ben Shaw  for helpful discussion of the archeological information concerning the Massim and the Kula tradition. The field work received valuable support by the  Missionaries of the Sacred Heart (M. S. C. )  and the  Diocese of Alotau-Sideia  via several Fathers serving on the different islands, the  St Paul's Pastoral Centre Hagita , and the then  Bishop Desmond Charles Moore (M. S. C. ). This study was supported by the  Max Planck Society.
Transcripts of interviews held in Creole were subsequently translated into French. The 44 interviews were studied using content analysis of recurrent themes  [29]. Categories and signi ers were developed using the constant comparison method  [30]. The themes which emerged were tested and modi ed during additional cycles of data collection. The saturation of data, with a wealth of social con gurations and relations to health, hospital and home environment, could be obtained despite the practical constraints of the recruitment of participants immediately after the inclusion in the ERMIES study. The work was done on the N-Vivo 10 QSR International qualitative analysis software, which allowed the qualitative data collected to be processed and several themes constituting the management of diabetes in the ordinary context to be determined.