dataesr/scientific-paragraphs-categorization huggingface dataset:
71073 Å) and a Helios optic using the APEX3 software package. [36] Measurements were performed on single crystals coated with perfluorinated ether. The crystals were fixed on top of a kapton micro sampler and frozen under a stream of cold nitrogen. A matrix scan was used to determine the initial lattice parameters. Reflections were corrected for Lorentz and polarisation effects, scan speed, and background using SAINT. [37] bsorption correction, including odd and even ordered spherical harmonics was performed using SADABS. [38] Space group assignments were based upon systematic absences, E statistics, and successful refinement of the structures. The structures were solved using SHELXT with the aid of successive difference Fourier maps, and were refined against all data using SHELXL in conjunction with SHELXLE.
[39] Non-hydrogen atoms were refined with anisotropic displacement parameters. 39a] Neutral atom scattering factors for all atoms and anomalous dispersion corrections for the non-hydrogen atoms were taken from International Tables for Crystallography. [40] Images of the crystal structures were generated with Mercury. [41] position Numbers 2082253 (for R1, second polymorph), 2082254 (for P3a/P3b cocrystal), 2082255 (for R1, first polymorph) and 2096758 (for R3) contain the supplementary crystallographic data for this paper. These data are provided free of charge by the joint Cambridge Crystallographic Data Centre and Fachinformationszentrum Karlsruhe Access Structures service.
Acknowledgements S. T. acknowledges  EPSRC and CDT in Catalysis for funding  under grant agreement n°EP /L016443/1. The  Centre National de la Recherche Scientifique  ( CNRS, International Emerging Action 2020 ), the  Université Paul Sabatier (UPS)  and the  Agence Nationale de la Recherche  ( ANR-19-CE07-0037 ) are gratefully acknowledged for financial support of this work. Nathalie Saffon-Merceron (ICT)  is acknowledged for assistance in the XRD analysis of  R3. Open Access funding enabled and organized by Projekt DEAL.
The airflow within the cavity is generated by means of a fan controlled by a voltage variator and the measurement of the flow rate is estimated by means of a CTV 210 hot wire anemometer from the manufacturer KIMO. the sensors are connected to a Keithley 2700® multimeter allowing a voltage measurement in the order of one-tenth of a microVolt. The multimeter itself is connected to a computer via the RS-232 serial communication channel. Data acquisition and processing are then carried out via the LabVIEW software. An infrared camera is also used to estimate the temperature of the hot wall of the device.
The ingroup includes 20 terminals from the last 20 million years and consensually assigned to Rhinocerotina, i. e. the subtribe including all recent rhinoceroses and their extinct kin (see Supporting Information). Detailed information about the morpho-anatomical characters scored and terminal taxa included in the parsimony analysis are available in Supporting Information. The program PAUP 4. 0a. 158 (Swofford, 2002)  was used for reconstructing the most parsimonious trees. Ages for nodes in the chronologically-constrained phylogenetic tree were obtained using the approach of  Brusatte et al. (2008) , as implemented in the paleotree package  (Bapst et al. , 2012)  in R 4. 0. 3 (R core development team, 2020), with stratigraphical ranges as available in the NOW Database (Table  S2 ; The NOW Community, 2020).
As a prior, the first splitting event in the tree was set to 60 Ma (mean value for Perissodactyla/Ceratomorpha;  Foley et al. , 2016)  and the root of the tree was resolved using the APE package  (Paradis et al. , 2004). Six distinct historical biogeographical models were evaluated in BioGeoBEARS package in R  (Matzke, 2013; Massana et al. , 2015)  using eight basic spatial ranges, coinciding with the geographical areas of terminal taxa included in the phylogenetic and biogeographical analyses (Americas, Afro-Arabia, Europe + Mediterranean, Central Asia, South and Southeast Asia, Indonesia, Taiwan, Philippines). Historical spatial ranges were allowed to cover from 1 to 8 areas, which in turn resulted in 163 potential ancestral states. Body mass was estimated using regressions based either on dental  (Legendre, 1989; Fortelius & Kappelman, 1993)  and postcranial dimensions  (Tsubamoto, 2012).
From a biogeographical perspective, the current phylogenetic results strongly suggest an island-hopping overwater dispersal for Nesorhinus, from Asian mainland toward Luzon via Taiwan by latest Miocene or Early Pleistocene times. Furthermore, the extinct rhinocerotine Nesorhinus philippinensis from Luzon Island may be the first perissodactyl species positively supporting the "island rule", with a similar height at shoulder, but significantly decreased body weight and limb bone robustness with respect to its kin. Most parsimonious tree (length: 1315 steps; consistency index = 0. 2821; retention index = 0. 4858). Node ages were obtained using the approach of  Brusatte et al. (2008) , as implemented in the paleotree package  (Bapst, 2012).
ACKNOWLEDGEMENTS Jeremy Barns  and  Ana Labrador  kindly provided support for this research as well as  Gisela Concepcion  and  Marcelo de la Cruz Jr. The authors warmly thank  Yuan Wang  ( Institute for Vertebrate Paleontology and Paleoanthropology ) for pictures of Rhinoceros fusuiensis,  Steven Zhang  for discussion on rhinocerotine phylogeny,  Christine Hertler  for providing us access to the G. H. R. von Koenigswald archives curated at the  Senckenberg Museum , and two anonymous reviewers of earlier versions of the manuscript for their expertise and constructive remarks. The Kalinga excavation project was funded by the  French Department for Foreign Affairs  (Project  MARCHE , to TI), a  National Geographic Global Exploration grant  ( GEFNE 129-14  with  Alfred F. Pawlik) , the  French Centre National de la Recherche Scientifique  ( GDRi PalBiodivASE  with  Valéry Zeitoun ), and  Naturalis Biodiversity Center , the  Netherlands.
6a ). In the list of downregulated transcripts, we were struck by the number of annotated genes corresponding to proteins targeted to the IMC and to the apical complex, both structures that are the first to appear when the daughters bud within the mother cell. To better assess the potential localization of the proteins that correspond to downregulated transcripts, we used a HyperLopit proteomic dataset that predicts with high confidence the localization of proteins in the parasite  24. We showed that a high proportion of the downregulated transcripts present in the dataset (331 genes) encode proteins predicted to localize to the IMC or to the apical complex (a total of 30% of the downregulated transcripts present in the HyperLopit dataset; Supplementary Fig. 6b ). Moreover, these two localizations are overrepresented (16% and 14%, respectively) when compared to the whole proteome localization (3% for each; 2487 proteins).
Based on this dataset, the downregulated genes represent 64% of the IMC proteome (52/81) and more than 70% (45/63) of the predicted apical complex proteome. These results suggest that TgAP2IX-5 may act mainly as an activator of genes whose expression shows a cell-cycle-regulated profile with a peak at the late S and M phases. These genes represent a high proportion of the IMC and apical complex proteome. We also examined the upregulated gene list using the same dataset  24. While no upregulated genes are predicted to encode proteins that localize to the IMC or the apical compartment, a majority of the upregulated proteins may localize to the nucleus, ER and rhoptry (Supplementary Fig. 6c ). By RNA-seq, we were able to identify that TgAP2IX-5 either activates directly or indirectly the expression of genes.
We examined the cell cycle expression of the genes whose promoter was targeted by TgAP2IX-5 and found that a majority of these genes were showing an expression peak during the S and M phase, although a cluster of genes showed a strong expression during C and early G1 phase (Supplementary Fig. 7e ). These data confirm the ability of TgAP2IX-5 to act as a bona-fide transcription factor by directly binding to promoters of genes encoding essential proteins for the establishment of the daughter cells. Since RNA-seq does not enable the identification of the genes that are solely directly controlled by TgAP2IX-5, we overlapped the RNA-seq and ChIP-seq dataset. For that, we identified within the differentially regulated genes list whether they were downregulated or upregulated (as initially identified from RNA-seq), and compared this list with the genes identified to be targeted by TgAP2IX-5 from ChIP-seq analysis using the MACS software.
5c ). Since TgAP2IX-5 was shown to directly activate genes that are mainly involved in daughter parasite formation, TgIMC29 was myc-tagged within the iKD TgAP2IX-5 strain and we observed a significant decrease in the TgIMC29 expression in the absence of TgAP2IX-5 as confirmed by Western blot (Supplementary Fig. 8a ). Considering the difference between the RNA-seq and ChIP-seq dataset, we searched the RNA-seq dataset for potential regulators that might be directly regulated by TgAP2IX-5 and therefore may in turn perturb the expression of genes not directly targeted by TgAP2IX-5. Interestingly, eight ApiAP2 TFs were downregulated (TgAP2III-1, TgAP2III-2, TgAP2IV-4, TgAP2VIIa-1, TgAP2X-11, TgAP2XI-4, TgAP2XII-9, and TgAP2XII-2) and four upregulated (AP2IX-5, AP2IX-1, AP2VI-3, and AP2X-9) following the depletion of TgAP2IX-5 as measured by RNA-seq (Supplementary Data 1).
Given the lack of daughter parasite formation observed after TgAP2IX-5 depletion, it is surprising that the outer core centrosome (as represented by TgCentrin1) is duplicated but remains inactivated by the parasite's kinases, such as TgMAPK-L1  10. In addition, TgAP2IX-5 may participate in the regulation of the expression of TgFBOX1 because it is found present at its promoter. However, TgFBOX1 transcript (which has a transient expression during the cell cycle) is not present in the RNA-seq dataset. TgFBOX1 localizes early at the daughter cell bud and may organize the daughter cell scaffold  30. Depletion of FBOX1 does not lead to such a dramatic effect on budding like those observed in the iKD TgAP2IX-5 strain. Other components of the outer core, such as the SFA fibers that were shown to be involved in the emergence of daughter cells  5  , but were present at the centrosome after TgAP2IX-5 depletion.
Apical cap component, such as TgAC2 and TgAC7 may also be loaded early on the forming buds. Similarly, TgGAPM3 expression is directly dependent on TgAP2IX-5 and its invalidation provokes IMC collapse  36. However, TgIMC15, a protein known to be loaded early on the centrosome before reaching the forming buds is not affected by TgAP2IX-5 depletion. TgIMC15 may have a limited role in daughter cell formation or centrosome activation as suggested by the mild phenotype exerted by its mutant  37. This suggests that TgAP2IX-5 may activate the centrosome but also directly regulates the expression of key components of the daughter cell scaffold that will be needed in a short timeframe to proceed with daughter cell formation (Fig. 7 ). We observed that RNA-seq and ChIP-seq datasets only partially overlapped.
Primary antibodies used for IFAs include: anti-TgEno2, anti-TgISP1 34 , anti-TgIMC1 (a gift from Pr. Ward, U. Vermont), anti-TgCentrin1 (a gift from Pr. Gubbels, Boston College), anti-TgChromo1  22  , anti-TgSortilin (Golgi), anti-TgACP (Plastid), anti-TgTom40 41 (mitochondrion), anti-HA (Sigma-Aldrich) and anti-myc (abcam) antibodies were used at the following dilutions: 1:1000, 1:500, 1:500, 1:500, 1:500, 1:500, 1:500, 1:500, 1:200, respectively. Confocal imaging was performed with a ZEISS LSM880 Confocal Microscope or Apotome Microscope at 63 magnification. All images were processed using Carl Zeiss ZEN software. Quantification of immunofluorescence assays was carried out manually by counting the concerned signal corresponding to the organelle by visual observation. Signal corresponding to 100 parasites was counted for each replicate.
Purified DNA (1 ng) was used as a template for library preparation using the Nugen Ovation ® Ultralow System V2 kit according to the manufacturer's instructions. Libraries were validated using a Fragment Analyzer and quantified by qPCR (ROCHE LightCycler 480). Sequencing, preprocessing, and dataset cleaning. Clusters were generated on a flow cell with within a cBot using the Cluster Generation Kit (Illumina). Libraries were sequenced as 50 bp-reads on a HiSeq 2500 using the sequence by synthesis technique (Illumina). HiSeq control software and real-time analysis component were used for image analysis. Illumina's conversion software (bcl2fastq 2. 17) was used for demultiplexing. Raw sequencing datasets for RNA-seq (110 M total reads) and ChIP-seq (28 M total reads) were quality-checked with FastQC v0.
11. 8-0. Sequencing adapters were trimmed with Cutadapt v1. 18  42. Finally, low-quality bases (LEADING:20, TRAILING:20, SLIDINGWINDOW:4:25) were trimmed and reads shorter than 30 bp were filtered out using Trimmomatic v0. 38. 1  43. FastQC was used to quality-check each intermediary dataset and the final cleaned datasets (105 M cleaned RNA-seq reads, 23 M cleaned ChIP-seq reads). RNA-seq analysis. Cleaned datasets (2 conditions * 3 biological replicates * 2 technical replicates = 12 datasets) were aligned with HiSAT2 v2. 1. 0 44 against the T. gondii ME49 genome from ToxoDB-39  45  (93 M aligned reads), and expression for annotated genes was quantified using htseq-count (union mode) from the HTSeq suite v0. 9. 1. Raw counts from technical replicates were added before performing differential expression analysis between iKD TgAP2IX-5 samples treated with or without auxin for 6 h with DESeq2 v1.
22. 1 using the SARTools framework v1. 6. 6  46  with the default parameters (fitType = "parametric", alpha = 0. 05, pAdjustMethod = "BH", cooksCutoff = TRUE, independentFiltering = TRUE). P values for multiple testing were adjusted using the Benjamini-Hochberg method. Differentially expressed genes with adjusted p-value below 0. 05 and log2 fold change above 2 were kept. The dataset is deposited in the GEO database under the accession number GSE150406. ChIP-seq analysis. Cleaned datasets (Input & IP * 2 biological replicates * 2 technical replicates = 8 datasets) were aligned with Bowtie2 v2. 3. 4 against the T. gondii ME49 genome from ToxoDB-39  45  (14 M aligned reads). Alignments from biological and technical replicates were merged using SAMtools v1. 9 and duplicates were identified with Picard MarkDuplicates v2.
18. 20. ChIP quality and dataset consistency were checked using the deepTools suite v3. 1. 3  47. Fragment size was estimated and peaks were called with MACS2 v2. 1. 2  48. Additional bigwig tracks comparing ChIP with IP were generated with MACS2 bdgcmp. The dataset is deposited in the GEO database under the accession number GSE150406. Statistics and reproducibility. All data were analyzed with Graph Pad Prism software version 8 (San Diego, California, USA). Differences in the means were assessed by Student's t-test. In all cases, p values are two-sided and P < 0. 05 was considered as significant. All experiments were repeated at least three times using biologically independent replicates. A minimum of 100 parasites was scored in each independent experiment. Open Access This article is licensed under a Creative Commons Attribution 4.
Acknowledgements We wish to thank the  BioImaging Center Lille  for access to instruments and  Lille University  for access to the high-performance computing resources and the bilille cloud. This work was supported by  Centre National de la Recherche Scientifique (CNRS) ,  Institut National de la Santé et de la Recherche Médicale (INSERM) , grants from the  French National Research Agency (ANR)  [grant number  ANR-13-JSV3-0006-01  to M. G. ] and the  CPER CTRL Longévité  (to Bilille).
e. , 3 years of education). We preregistered hypotheses (based on the cognitive literature and prior studies evaluating Montessori education), measures, and analytic strategy for both experiments. Thus, this study reflects a primarily confirmatory effort. Data were analyzed using both frequentist and Bayesian statistics. Children from conventional classrooms followed the French preschool curriculum, administered by teachers with the same academic training. This ensured homogeneity in the control group (see Appendix S3). Children from Montessori classrooms followed the Montessori preschool curriculum, which was adapted to the constraints of public education in France. Such adaptations can be unavoidable because public education will often impose pedagogical and material constraints on programs.
Only 8% of individuals living in this area have an income that is below the poverty threshold. This private preschool received an official accreditation from the Association Montessori de France, which is the French affiliate of the AMI. As such, it followed a rigorous and high-fidelity Montessori curriculum. The school does not receive any public funding and entirely relies on tuitions from parents (€5,100/year). Data were collected from three cohorts of children in these two schools at the end of their kindergarten year, that is, June 2017, June 2018, and June 2019. The experiment was approved by the local school board and was performed in accordance with the ethical standards established by the Declaration of Helsinki. Parents gave their written informed consent and children gave their assent to participate in the experiment.
Procedure Children were tested individually in a quiet room of their preschool. The tests were administered by different experimenters (graduate students, research assistants, and undergraduate students) in five sessions of approximately 15-20 min. The order of sessions was randomized on a child-to-child basis. No feedback was given to children during testing. Children, teachers, and parents were also not informed about the results during the 3 years of the study. Finally, teachers and parents were blind to the tests until the end of data collection.
Materials The tests used at each time point of Experiment 2 are identical to those described for Experiment 1 (see Tables  1 2 3 ). The only exceptions are (a) the test measuring planning skills and (b) the scale measuring FAS. We introduced those at a later stage in the study and could only collect longitudinal data on a small subset of participants. Therefore, these two tests were not analyzed in Experiment 2. In addition, only nine subtests of the test measuring quantitative knowledge are analyzed in Experiment 2 (compared to 10 subtests in Experiment 1) because the last subtest was also added at a later stage during the study.
Procedure Testing procedures and test sessions were the same as in Experiment 1. Data were collected over three time points from two cohorts of children. The first time point was at the beginning of their first year of preschool (i. e. , in October 2015 for cohort #1 and in October 2016 for cohort #2). The second time point was halfway during their second year of preschool (i. e. , January 2017 for cohort #1 and January 2018 for cohort #2), The third and final time point was at the end of their kindergarten year (third year of preschool; i. e. , June 2018 for cohort #1 and June 2019 for cohort #2). At each time point, five to seven experimenters collected the data.
Context. Gaia's Early Third Data Release (EDR3) does not contain new radial velocities because these will be published in Gaia's full third data release (DR3), expected in the first half of 2022. To maximise the usefulness of EDR3, Gaia's second data release (DR2) sources (with radial velocities) are matched to EDR3 sources to allow their DR2 radial velocities to also be included in EDR3. This presents two considerations: (i)  Boubert et al. (2019) , hereafter B19, published a list of 70 365 sources with potentially contaminated DR2 radial velocities; and (ii) EDR3 is based on a new astrometric solution and a new source list, which means sources in DR2 may not be in EDR3. Aims. The two aims of this work are: (i) investigate the B19 list in order to improve the DR2 radial velocities being included in EDR3 and to avoid false-positive hyper-velocity candidates; and (ii) match the DR2 sources (with radial velocities) to EDR3 sources.
Introduction The second Gaia data release (DR2, Gaia  Collaboration et al. 2018 ) included line-of-sight radial velocities from Gaia's Radial Velocity Spectrometer (RVS,  Cropper et al. 2018)  for the first time. The radial velocities are for 7 224 631 stars brighter than G RVS = 12 mag  (Sartoretti et al. 2018; Katz et al. 2019) , based on 22 months of data. The third Gaia data release (DR3) will include new radial velocities for stars brighter than G RVS = 14 mag, based on 34 months of data, expected in the first half of 2022 1. DR3 has been split into two releases in order to release data products as soon as they are ready. Gaia's Early Third Data Release (EDR3) includes improved astrometry and integrated photometry (Gaia  Collaboration et al. 2021). EDR3 does not include the new radial velocities for two reasons.
Firstly, their derivation is based on an internal release of EDR3 astrometry and so there is a time lag between these Data Processing and Analysis Consortium (DPAC) products being 1 https://www. cosmos. esa. int/web/gaia/release ready. Secondly, once validated by DPAC-Co-ordination Unit (CU) 6 (Spectroscopic Processing), the new radial velocities are further validated by CU4 (Object Processing) to derive binarystar orbital parameters. The magnitude derived from RVS spectra (G RVS ) and the RVS spectra themselves are also being validated by CU7 (Variability Processing) and CU8 (Astrophysical Parameters), deriving variability and astrophysical parameters, respectively. CU9 (Catalogue Access) validates DPAC data products holistically before they are publicly released in DR3. To maximise the usefulness of EDR3, radial velocities from DR2 have been transferred and improved in EDR3.
Various filters were applied to DR2 radial velocities at different stages prior to publication  (Sartoretti et al. 2018; Katz et al. 2019; Arenou et al. 2018). It was decided not to revisit any DR2 filters when transferring and improving the DR2 radial velocities to EDR3 sources i. e. the starting point for this work are the 7 224 631 sources with radial velocities in DR2. By design, the number of sources with radial velocities in EDR3 cannot exceed the number in DR2 but it can be fewer. Firstly, in propagating radial velocities from DR2 sources to EDR3 sources, there is the opportunity to filter out DR2 radial velocities identified as potentially erroneous after DR2 was published. Filtering is a choice consistent with DPAC policy for the early data releases. Adding trustworthy quality flags would take much more validation time so the filtering is a compromise DPAC chooses.
Cross-matching sources with radial velocities in DR2 and sources in EDR3 As explained in  Torra et al. (2021) , the identifier of a specific object can change between subsequent data releases. As a consequence, the assignment of DR2 radial velocities to EDR3 objects cannot be based exclusively on their DR2 source identifier, as this could lead to erroneous associations between the radial velocity and the source. The general recommendation from DPAC in that respect is to use a positional cross-match between the two catalogues and a global DR2 to EDR3 cone search neighbourhood is provided together with EDR3  (Gaia Collaboration et al. 2021). This global cross-match uses a 2 arcsec search radius, which provides several possible matches to a given DR2 source within this radius. For the specific purpose of the DR2 radial velocity propagation to EDR3, a similar cross-match (limited to the sources holding radial velocities) was performed in order to select one unique and best possible source match to each relevant source.