dataesr/scientific-paragraphs-categorization huggingface dataset:
These OTUs belong to the genera Bacillus (16. 6%), Gamella (0. 1%), Lactobacillus (10. 5%), Lactococcus (0. 2%), Mycoplasma (31. 5%), Staphylococcus (0. 7%), Streptococcus (5. 6%) and Weissella (28. 3%). We performed supervised partial least square discriminant analysis (PLS-DA) to understand the dissimilarity of the diet groups in terms of their microbial composition. Only the OTUs, which were present at >0. 1% of the whole dataset, were retained for analysis. Although the clusters of the different dietary groups were not clearly separated by PLS-DA, the group means of ordination were clearly separated (Figure  3a ). The regression coe cients of the discriminatory OTUs on component 1 of PLS-DA are shown (Figure  3b ). Some of the most discriminatory OTUs belonged to Bacillus, Lactobacillus and Weissella.
Data analysis The initial data analysis was performed using the UPARSE pipeline  [67]. First, the forward and reverse reads of each sample were fused. Also, the primer binding sites were removed from the assembled sequences. Then, the sequences were quality ltered using the strategy of ltering by the maximum expected error rate [68]. Sequences with an error rate of > 0. 01 per sequence were removed from the dataset. After quality ltering, sequences from different samples were labelled with the sample name and combined in to a single le. This collection of sequences was dereplicated and the sequences that were observed only once in the dataset (singletons) were removed, as these singletons were most likely sequencing artefacts  [69]. The sequences were further clustered into OTU (Operational Taxonomic Unit) based on a sequence similarity of 97%.
The raw reads were then mapped back to the OTUs to obtain the abundance of each OTU in different samples. The taxonomy was assigned to the OTUs using SINTAX  [70]. Taxonomies with bootstrap con dence value less than 0. 8 were removed from the dataset. Furthermore, OTUs belonging to the phylum Chloro exi were removed from the analysis as it is likely that these OTUs originated from the plant components of the diet. A phylogenetic tree in Newick format was constructed based on the OTU sequences. The OTU table, taxonomy table and phylogenetic tree were imported into the phyloseq package  [71]  in R (version 3. 6. 3) along with the sample metadata. Alpha diversity measures, observed OTUs and Shannon index  [72]  were calculated. Beta diversity was calculated using the Bray-Curtis distance with the phyloseq package.
These calculations were performed using geNorm  [75] , which is implemented in the R [76] package SLqPCR  [77]. The reference genes eef1a and rna18s were used to calculate the normalization factor in liver, while a combination of actb and eef1a was used in muscle, actb and eef1a in adipose tissue, and actb and rna18s in intestine. Normalized relative quantities were analysed for statistical signi cance.
Statistical analysis Zootechnical parameters, including feed e ciency (FE) and speci c growth rate (SGR), are calculated per tank (n=3). HSI, VSI and nal growth data are collected for each sampled individual (n=9). Samples from the same nine sh were also used for gene expression analysis and plasma parameters. For microbiome analysis, three sh were sampled in addition to the 9 already mentioned. Three additional sh per group were sampled for SCFA analysis and for electron microscopy. All statistical analysis was performed using R software (version 3. 6. The data is presented as the mean ± sd, n=3 tanks except for the nal body weight (n=12), hepatosomatic index (n=9) and viscerosomatic index (n=9). Means between the groups were compared using a one-way ANOVA. A p value < 0. 05 was considered as signi cant and are presented in bold.
Acknowledgements Imaging was performed at  Bordeaux Imaging Center , member of the  France Bioimaging national infrastructure  ( ANR-10-INBS-04 ). We thank  Sabrina Lacomme  for her TEM work on this project. We thank  Franck Sandres  and  Anthony Lanuque ,  Fish experimental facility, Donzacq  for feed preparation, experimental setup, monitoring of the experiment and sampling. We thank  Charlotte Andrieux ,  Sandra Biasutti ,  Anne Surget  and  Michael Marchand  for their help in sampling.
Acknowledgments We thank  M. Gabelmann  for useful communications concerning ref. [60]. The work of M. G. and P. S. is supported in part by  French  state funds managed by the  Agence Nationale de la Recherche (ANR) , in the context of the grant " HiggsAutomator " ( ANR-15-CE31-0002 ). M. G. also acknowledges support from the  ANR  grant " DMwithLLPatLHC " ( ANR-21-CE31-0013 ).
Choosing target-answer pairs Each set consisted of a target image, and four possible response images: one correct answer, and three foils. We included four relatedness-type conditions: motheroffspring pairs, father-offspring pairs, maternal half-siblings, and paternal half-siblings. Each condition had 45 sets, split into three groups of 15 question setseach question set contained 15 unique target-answer pairs of individuals. We excluded full siblings from the dataset. Human parents and offspring typically have a relatedness (R) of 0. 5; and half siblings typically have R of 0. 25 where mothers and fathers are unrelated. However, given that chimpanzee communities in the wild are patrilineal, there is relatedness between males in a chimpanzee community  (Inoue et al. , 2008)  and some females stay and reproduce within their natal communities  (Walker & Pusey, 2020) ; thus, the relatedness of pairs in these categories may be slightly higher than is typically seen in a human datasetas a VISUAL SIGNALS OF KINSHIP IN CHIMPANZEES result, the wild chimpanzee parent-offspring pairs in our dataset will at least have R of 0.
We believe that it is more appropriate to design these experiments to be suitable for participants of all ages. All aspects of the study, including providing access to children under 16 years old, were given ethical approval by the Ethical Committee of the School of Psychology and Neuroscience, University of St Andrews (Approval Code PS13177). Children under the age of 12 completed the questionnaire as an educational game. No data were recorded from them, as we did not feel that it is possible to obtain informed consent for scientific research with this age group online. Older children are capable of giving informed consent when given age-appropriate materials (e. g. ,  Hein et al. , 2015). Participants between the ages of 12 and 15 were presented with an age-appropriate information sheet and consent form, were not asked for any demographic information, and received an age-appropriate debrief message at the end of the experiment.
Procedure Participants were 5315 humans recruited voluntarily online through the Gorilla. sc web platform. The birth year of all participants was collected, and they were directed to an age-appropriate consent form. Participants aged 16 years or older completed an additional demographic questionnaire. The questionnaire asked participants to identify their sex (Female, Male, Other, Prefer not to disclose), age group  (16-20, 21-30, 31-40, 41-50, 51-60 , VISUAL SIGNALS OF KINSHIP IN CHIMPANZEES 60+), whether they had previous experience with primates (No, Yes (0-2 years), Yes (3-5 years), Yes (5+ years)), and whether they were redoing the test. Demographic data were not collected for participants between the ages of twelve and fifteen. Participants under the age of twelve were allowed to complete the experiment as an educational game, but their responses were not saved.
Data analyses Statistical analyses were run in RStudio 1. 2. 5042, running R version 4. 0. 0 (2020-04-24). Data and code are available in a Github repository (https://github. com/Wild-Minds/ChimpFaces). We analysed n=69895 responses from n=5046 participants. We used a G-test of goodness-of-fit to compare the overall percentage of correct answers to a chance level of one in four (25%). We fitted two generalised linear models (GLM) using a binomial error distribution and logarithmic link function, following the procedure suggested by  Bolker and colleagues (2009)  for analysing binomial data. Completion status) and the random effects of Target identity, Answer identity, and Trial number using a likelihood ratio test  (Forstmeier & Schielzeth, 2011; Dobson, 2002 ; using R function anova with argument 'test' set to "Chisq").
43. 17). We assessed model VISUAL SIGNALS OF KINSHIP IN CHIMPANZEES stability by comparing the estimates obtained from the model based on all data with those from the model comprised only the fixed control and random effects excluded one at a time  (Nieuwenhuis et al. , 2011). This revealed the model to be fairly stable with the exception of the effect of the fixed effect of Angle which overlapped zero. Confidence intervals were derived using the function bootMer of the package lme4, using 1,000 parametric bootstraps and bootstrapping over the fixed control and random effects.
Acknowledgments We thank the staff of the  Budongo Conservation Field Station  for all their assistance throughout our research. For permission to carry out research in Uganda, we thank the  Uganda Wildlife Authority  and the  Ugandan National Council for Science and Technology. We also thank the  Royal Zoological Society Scotland  for the core funding they provide to the  Budongo Conservation Field Station. We thank  Roger Mundry  for the generous sharing of his function for assessing model stability and confidence intervals.
Identification of the strains A PCR targeting 16S rRNA gene with the primers 27F/1492R  [20]  was performed for each strain isolated from the evolved communities. A digestion of the PCR products was then performed with the AluI restriction enzyme and followed by an electrophoresis for the identification of the strains at the genus level. For the genera that were represented by several strains in our experiment (i. e. Pseudomonas and Escherichia), we performed further analyses for an identification at the strain level. We used data from gyrB sequencing at the community level to determine which Pseudomonas strain was present in the community and coupled it with analyses at the strain level for formal identification. The different strains were identified based on the presence or not of atzD gene (assessed by PCR) and the resistance or not to nalidixic acid and amoxicillin (assessed by growing the strains on agar plates containing a mix of the two antibiotics at a final concentration of 100 µg.
5, 1. 3, and 2. 0% for sectors 2, 3, and 29 respectively, mainly due to one contaminant at 28 that is ∼4 magnitudes fainter than the target in the Gaia G band (Fig. A. 1). We extracted the data spanning three transit durations around the centre of each transit. Finally, we normalised each transit with a straight line fitted to the outof-transit parts of the light curve. To account for the differences in stellar flux levels at the times of each transit  (Czesla et al. 2009) , we corrected the normalised transit curves using the GP model described above. To properly correct for the stellar flux variability, it would be necessary to have observed the purely photospheric stellar surface without any activity regions. This does not seem to be the case here, as there is no global flat flux maximum.
Seeing-limited photometry We conducted ground-based photometric follow-up observations of GJ 3090 as part of the TESS follow-up observing program 2 (TFOP; Collins 2019) to attempt to rule out or identify nearby eclipsing binaries (NEBs) as potential sources of the TESS 2 https://tess. mit. edu/followup detection, measure the transit-like event on target to confirm the depth and thus the TESS photometric deblending factor, and refine the TESS ephemeris. We used the TESS Transit Finder, which is a customised version of the Tapir software package  (Jensen 2013) , to schedule our transit observations. The observations are summarised in Table  1. Three observations searched for and found no NEBs within 2. 5 of the target star. One observation was inconclusive, and two observations detected the event on-target, as indicated in Table  1.
We searched each season of data for modulations  due to stellar rotation using the methods from  Maxted et al. (2011). We show the results in Fig. 2. A clear rotational modulation is seen at a period of 18. 2 ± 0. 4 days. The error is estimated from the dispersion between different datasets and is dominated by phase shifts of the modulation. In most years, the modulation has an amplitude of 12-18 mmag. In 2011, the modulation is weaker and the dominant power is at the first harmonic, and in 2014, the modulation is below our detection threshold.
HARPS We obtained differential RVs of GJ 3090 with HARPS  (Mayor et al. 2003) , the optical velocimeter installed at the ESO 3. 6 m telescope at La Silla Observatory. Under the ESO program 1102. C-0339(A), we collected 55 spectra over a time span of 326 days, with an exposure time of 900 s each (one spectrum taken on 2019 January 24 has a 1800-s exposure time), slow readout mode (104 kHz), and without simultaneous wavelength calibration. The signal-to-noise ratio at 650 nm ranges between 20 and 71, with a mean value of 42. The spectra were reduced with the HARPS Data Reduction Software 4. We used the template-matching algorithm described in  Astudillo-Defru et al. (2017b)  to compute the RVs, which have a median RV precision of 2. 0 m s -1 (dominated by photon noise) and a dispersion of 8.
3 and non-informative priors for the remaining parameters. We sampled from the posterior with dynesty (Speagle 2020). In Table  3 , we list the prior, the median, and the 68% CI of the marginal distributions of the inferred system parameters. We used the stellar mass and radius derived in Sect. 3 to compute the planetary masses and the radius of planet b. Figure A. 3  shows the data sets and the model from this analysis. In the next section, we use a self-consistent Newtonian model that takes the gravitational interactions between the planets into account. Even though we detect no interactions, accounting for them allows us to exclude combinations of system parameters that would result in detectable interactions and constrain the true mass of planet c.
Dynamical modelling We modelled the TESS photometry and HARPS RV measurements and accounted for the gravitational interactions between the adopted components of the system using a photodynamical model. The TESS transits are the most numerous of the transit observations and dominate the determination of the planet-tostar radius ratio. For simplicity, we did not model the transit observations with other instruments. The planet positions and velocities in time were obtained through an n-body integration. The sky-projected positions were used to compute the light curve  (Mandel & Agol 2002)  using a quadratic limb-darkening law  (Manduca et al. 1977 ) that we parametrised following  Kipping (2013). The light-time effect  (Irwin 1952)  was included in the model. To account for the integration time, the model was oversampled by a factor 3 and was then binned back to match the cadence of the data points  (Kipping 2010).
2017)  for the model of the error terms of the transit light curves, and the QPC kernel  (Perger et al. 2021; Ambikasaran et al. 2015)  for the RVs. For the photometric dataset, we added a transit normalisation factor and a jitter parameter. For the RV, we added a systemic RV and a jitter parameter. In total, the model has   10. Posterior distribution comparison for the planet eccentricities, the mutual inclination between the planet orbits, and the mass of planet c before (dynamical) and after (stable) the stability constraint. For the eccentricities, the posteriors for the RV analysis (Sect. 4. 1) and Keplerian modelling (Sect. 4. 2) are also shown. 28 free parameters. We used normal priors for the stellar mass and radius from Sect. 3, non-informative sinusoidal priors for the orbital inclinations (uniform in cos i), and non-informative uniform prior distributions for the rest of the parameters.
This work has been supported by a grant from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ). Based on data collected under the  ExTrA  project at the  ESO La Silla Paranal Observatory. ExTrA is a project of  Institut de Planétologie et d'Astrophysique de Grenoble (IPAG/CNRS/UGA ), funded by the  European Research Council  under the  ERC Grant  Agreement n. 337591-ExTrA. O. A. , V. B. and M. T. 's work has been carried out in the frame of the  National Centre for Competence in Research PlanetS  supported by the  Swiss National Science Foundation (SNSF). O. A. and V. B. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  SPICE DUNE ; grant agreement no. 947634 ). M. T. thanks the  Gruber Foundation  for its support to this research.
We thank the referee for constructive criticism that clarified a few important issues in the original manuscript. S. K. Y. acknowledges the support from the  Korean National Research Foundation  ( 2020R1A2C3003769 ). A. C. acknowledges the support from the  Korean National Research Foundation  ( 2022R1A2C100298211 ). T. K. was supported by the  National Research Foundation of Korea  ( 2020R1C1C1007079 ). R. A. J. was supported by the  Yonsei University Research Fund  ( Yonsei Frontier Lab ,  Young Researcher Supporting Program ) of 2021. This study was partially supported by the  Spin(e)  grant  ANR-13-BS05-0005  of the  French Agence Nationale de la Recherche  and the  Center for Galaxy Evolution Research  ( 2022R1A6A1A03053472 ). It also relied on the HPC resources of the Horizon Cluster hosted by the  Institut d'Astrophysique de Paris.
2 µg/mL (95% confidence interval (95%CI): 0. 7-1. 7) and 27. 0 µg/mL (95%CI: 0-54. 7) for the G614 strain and the Omicron variant, respectively, indicating a ~22 fold change between the two strains. Overall, our results indicate that AZD7442, when used as pre-exposure prophylaxis, has inhibitory activity in vivo against the Omicron BA. 1 variant, although this activity is less than against a B. 1 G614 virus. This is in line with previous results obtained in vitro  [5] [6] [7] [8] [9] [10]  and a recent in vivo study in which a curative treatment with 5mg/kg of antibody cocktail, synthesized without any modification in their Fc region, efficiently inhibited the Omicron variant replication in lungs of hamsters  13. The U. S. Food Drug Administration (FDA) has published pharmacokinetic data obtained after intramuscular administration of 300mg of AZD7442  14  , the dose used successfully in the PROVENT clinical trial (NCT04625725)  15.
The mean serum concentrations peak at ~30µg/mL two weeks after dosing and remain above 12 µg/mL for more than 150 days, with limited inter-individual variability (coefficient of variation was 25-39% even beyond 200 days after dosing). In our study, the plasma EC90 value estimated from hamsters treated preventively and infected with the G614 strain are largely below these concentrations. In contrast, the EC90 value estimated in hamsters infected by Omicron is close to the abovementioned exposures observed in humans after the dose of 300mg. However, the linear and dose-dependent pharmacokinetics of AZD7442 between the 300mg and 3000mg doses suggest that the 600mg dose, currently being tested in the TACKLE, ACTIV-2/3 and DisCoVeRy clinical trials (D8851C00001, NCT04723394, NCT04518410, NCT04501978 and NCT04315948), may be a recourse to compensate for the diminished efficacy of this monoclonal antibody cocktail against the Omicron variant.
Viruses The B. 1 BavPat1 SARS-CoV-2 strain (G614 strain) was obtained from Pr. C. Drosten through EVA GLOBAL (https://www. european-virus-archive. com/) and contains the D614G mutation. Virus stocks of this strain was produced using VeroE6 cells (passage history: 2 for in vivo studies). The clinical strain of the SARS-CoV-2 BA. 1 Omicron variant used here is named 2021/FR/1514 and is available through EVA GLOBAL (www. european-virus-archive. com, ref: 001V-04436, GISAID: EPI_ISL_7899754). Virus stocks of this strain was produced using VeroE6/TMPRSS2 cells (passage history: 1 for in vivo studies). All virus stocks were characterized by whole-genome sequencing (Ion Torrent) in order to verify the absence of additional mutations, especially in the spike-coding region. All experiments with infectious viruses were performed in a biosafety level 3 laboratory.
Graphical representations and statistical analysis Experimental timelines were generated on biorender. com. Graphical representations and statistical analyses) were realized using Graphpad Prism 7 software, except nonlinear regressions and their corresponding graphical representations that were performed using the package nlreg, implemented in R statistical software (http://www. R-project. org). Statistical details for each experiment are provided in the figure legends. P values less than 0. 05 were considered statistically significant.
CP registers/surveillance systems CP was confirmed at a minimum age of 4 years. 13 For the study, the definition of CP  14  included the five criteria agreed on by SCPE and the ACPR: (1) is an umbrella term for a group of disorders; (2) is a condition that is permanent but not unchanging; (3) involves a disorder of movement and/or posture and of motor function; (4) is due to a nonprogressive interference, lesion, or abnormality, and (5) the interference, lesion, or abnormality originates in the immature brain. 15, 16 Registers/surveillance systems providing data included children with a diagnosis of CP at the age of 2 years, but who died before age 4 or 5 years, but excluded children with a diagnosis of CP who died before the age of 2 years. We requested that registers provide descriptive data about the geographical region represented, size of region, continuity of data collection, numerator and denominator definitions, definition of CP, data sources and methods of data acquisition, and consent requirements to confirm inclusion.
Published literature on prevalence of CP A broad systematic literature search strategy was designed with an academic librarian, on the basis of the search originally used by Oskoui et al. 3  Searches were conducted in MEDLINE and EMBASE in November 2020, along with handsearching. There were no limits on language of publication, but the search was limited to papers published from 2011, to include papers published since the systematic review by Oskoui et al. 3  Abstracts and titles were exported into referencing software, and automatic and manual de-duplication was performed (using Covidence systematic review software, Veritas Health Innovation, Melbourne, Australia; available at www. covid ence. org). Titles and abstracts were screened for possible inclusion by one investigator (SG), then the full text of potential articles was retrieved and reviewed by two (SG and SM).
Original research articles were included if they reported population-based prevalence of CP from birth year 1995 with an internationally agreed definition of CP (denominators defined as live births or children aged between 0 and 18 years in the region). The following studies were excluded: (1) abstract available only; (2) studies describing a subgroup of CP only (e. g. severe motor involvement); (3) studies including people with CP outside the target age range (e. g. born before 1995; 50% of children younger than 4 years); and (4) studies from a region already represented in the current study with newer/equivalent data from a participating CP register/surveillance system or literature. Sets of two investigators independently reviewed each article meeting all eligibility criteria (SG, SM, HSS, SJH, GH, KH, KM).