dataesr/scientific-paragraphs-categorization huggingface dataset:
Apodemini were represented by Apodemus sylvaticus (13; data from  Renaud et al. , 2009). The sampling was completed with wild house mouse (Mus musculus domesticus) samples, documenting the continental and insular variation: Gardouch, France (68), Montpellier, France (13); Eday, Orkney, United Kingdom (18), Fango, Corsica, France (7), Vaitella, Corsica, France (24) and the islet Piana off Corsica, France (7) (data from  Ledevin et al. , 2016; Renaud et al. , 2011). Maximal length and width were automatically extracted, together with 64 points along the outline, using the image analyzing software Optimas 6. 5. The morphological variance in the total sample, including laboratory strains and wild species of murine rodents (200 specimens, with 198 first upper molars (UM1) and 192 first lower molars (LM1)) was summarized using principal component analyses (PCA) (one for the upper and one for the lower molars) on the variance-covariance matrix of the shape coefficients delivered by an outline analysis of the 2D occlusal surface (see  Renaud et al.
In situ hybridization Shh probes were transcribed from a plasmid described by  Echelard et al. (1993) , by means of in vitro transcription with the incorporation of digoxigenin-ddUTP, using a premixed DIG RNA labelling mix (Roche). In situ hybridisation was performed with a conventional protocol. The antibody utilised was an anti-DIG antibody coupled with alkaline phosphatase (Roche); the chromogenic substrate used was BM Purple ready-to-use NBT/BCIP (Roche). Samples were examined with Leica M205 stereomicroscope. Images were taken with the Leica Application Suite 4. 1 software package.
In order to provide a measure of embryonic age, we built a model that could estimate the embryonic age from the body weight and dpc, taking into account intra and inter litter variations and applying a correction effect for the latter. This model and its construction are described in detail in Appendix 1. The code is available at: https://github. com/msemon/cdpc (Semon, 2020a; copy archived at https://github. com/elifesciences-publications/cdpc).
Sample classification and scoring The developmental state of all samples was assessed by combining four separate developmental criteria. Two are related with Shh expression: 1) Shh expression in the R2 (rudimentary premolar) zone, 2) Shh expression in the M1 (first molar) zone, and two are purely morphological criteria: 3) the budcap transition and 4) the appearance of a protrusion, visible in the dissociated epithelium at the site of the R2 signaling center, but only in the latest stages of Shh signaling and after cessation of Shh signaling. For each criterion, the samples were scored as one of 2 or 3 states. This scoring system is summarized in Supplementary file 1. Two tables with all analyzed embryos, with their weight, age in cdpc, information on litter and scored characteristics, and the code used to analyze these data is provided on github at: https://github.
3D reconstruction Dissected tooth germs were fixed overnight in 4% PFA and dehydrated through a methanol series. In toto immunolocalization protocol was adapted from  Ahnfelt-Rønne et al. (2007). Following incubation in methanol added with H202 5% and DMSO 10% for 4 hr at room temperature, they were rehydrated, blocked with serum and incubated successively with an anti-laminin5a antibody (overnight,1/800, kind gift from Jeff  Miner, Miner et al. , 1997)  and a Dylight 549 conjugated Donkey Anti-rabbit antibody (overnight 1/200, Jackson immunoresearch). Following dehydration, they were clarified and mounted in BABB as described in  Yokomizo and Dzierzak (2010). They were imaged with a Zeiss LSM710 confocal microscope at the PLATIM (Lyon, France). The basal membrane labeled by the antibody was delineated semi-manually and reconstructed with the AMIRA software.
Detection of differentially expressed genes with DESeq2 package Reads were then mapped to the mouse genome using Kallisto  (Bray et al. , 2016, version 0. 44. 0, options -l 200, -s 20). The reference cDNA sequences and annotation files for M. musculus are based on C57B6 strain. They were collected from Ensembl 88 (10 5129 cDNAs,  [Zerbino et al. , 2018] , GRCm38). Reads were independently mapped to the FVB/NJ strain cDNAs, collected from Ensembl strains 94, using biomart (10 1520 cDNAs, strain FVB_NJ_v1, accession GCA_001624535. 1). Tximport was used to import and summarize transcript-level estimates at gene level  (version 1. 6, Soneson et al. , 2016). Differentially expressed genes were detected with DESeq2  (Love et al. , 2014) , version 1. 18. 1) with classical one-factor design, and using FDR significance threshold = 0.
05. 19202 genes are in common between FVB and reference strain C57B6 and have a MGI annotation. Out of these genes, 2234 genes (11. 6%) presented a significant difference of expression between the mapping on the reference strain C57B6 and the mapping on FVB strain (DESeq2, adjusted p-value<0. 05, considering the mapping effect that is with 12 replicates). This is presumably a mapping artifact, due to the sequence divergence between mouse strains. These genes were removed, and the remaining 16,968 genes were retained for further analysis. 3619 genes were found to be differentially expressed between the two strains taking into account the jaw of origin (lower/upper) (~jaw + Strain). Processed data with statistics are provided in Supplementary file 2. Raw and processed data were deposited in NCBI Gene Expression Omnibus (GEO, accession number GSE135432; https://www.
ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE135432). The mapping data (by Kallisto, on each reference strain as discussed in the text), R source code and parameters are available on github at: https://github. com/msemon/trDUHi_FVB (Semon, 2020b; copy archived at https://github. com/elifesciences-publications/trDUHi_FVB).
The embryos were harvested at 17. 5 dpc, 72, 96 or 120 hr after tamoxifen application and beta-galactosidase activity was detected on whole embryos or dissociated epithelia of upper and lower cheek region. The X-gal (Sigma) concentration in the staining buffer was 3 mM. Samples with positive staining were postfixed in PFA (4%) overnight. After post-fixation, the samples were washed in PBS and photographed using a Leica MZ6 stereomicroscope equipped with a Leica EC3 digital camera (Leica Microsystems GmbH, Wetzlar, Germany). Data are summarized in Supplementary file 3.
25 c dpc difference). For each pair of samples, we can then compute a pairwise developmental distance: the distance between the two samples in terms of developmental state, computed as the sum of the score difference obtained for each four developmental criteria (Supplementary file 1). For each strain for both upper and lower molars, we could then plot the distribution of these pairwise developmental distances. Finally, in order to compare total developmental variation between strains, we subjected these pairwise developmental state differences (between pairs of samples at c dpc) to a Wilcoxon rank-sum test. Using this method, we can measure the degree of developmental variation found in a set of samples and compare between strains. The code is provided on github at: https://github. com/luke-hayden/dvpap/tree/master/devstate  (Hayden, 2020 ; copy archived at https://github.
Measurement of developmental variation over time We first calculated pairwise developmental state distances for each sample in relation to all nearby samples, over computed embryonic time. Then to obtain developmental variation over time, we used locally estimated scatterplot smoothing (LOESS), a non-parametric regression method to plot a complex curve through many data points, weighting the contribution of data points according to their proximity to the point of estimation. The code is provided on github at: https://github. com/ luke-hayden/dvpap/tree/master/devstate  (Hayden, 2020 ; copy archived at https://github. com/elifesciences-publications/dvpap).
Other statistical analyses The statistical significance of differences in the timing of developmental events was tested using logistic regression (embryo weight as a predictor of state, with strain as an additional preictive factor), examining changes in the scoring of a developmental criterion (four criteria scored, see previously) in relation to computed embryonic age. The statistical significance of differences in the sizes of various morphological features was tested using Student's t-test. Fisher's test was used to test differences in the relative rarity of a developmental state within a given window. The code is provided on github: https://github. com/luke-hayden/dvpap/tree/master/devstate (Hayden, 2020 ; copy archived at https://github. com/elifesciences-publications/dvpap). See also Supplementary file 4.
This induces a variability on weight. in. log in model Equation 1 which is described in the model Equation 2 by the addition of a stochastic term, eps. litter. dev, following a Gaussian distribution centered on 0 with a standard deviation, sd. litter. dev, that will be estimated from data. weight:in:log i ¼ a þ b Ã age:in:dpc i þ eps:litter:dev i (2) However, different litters at the same age in dpc, and at the same embryonic age, may have different mean weights because each pregnancy will provide a specific environment to Monte Carlo Markov-Chain (MCMC) techniques were used to estimate the joint posterior distribution of parameters from prior distributions and data. Computations were performed using the JAGS software via the R package rjags  (Plummer, 2016 , a runnable R script is provided in Supplementary material with data corresponding to strain FVB).
Acknowledgements We acknowledge the contribution of the  Plateau de Biologie Experimentale de la Souris (PBES) , more especially  Marie Teixeira ,  Farida Henry and Celine Angleraux  for DUHi mice recovery and breeding, and the  Plateau Technique Imagerie/Microscopie (PLATIM)  of  SFR Biosciences Gerland-Lyon Sud  ( UMS344/US8 ), more especially  Claire Lionnet  and  Christophe Chamot  for their support and help with 3D reconstructions. We are grateful to  Jeff Miner  for the anti-laminin5 antibody and  Pascale Chevret  for her expertise in the phylogeny of murine rodents. This work was supported by  Agence Nationale de la Recherche  ( ANR-11-BSV7-008  ' Bigtooth ') and  Fondation pour la recherche medicale (FRM ;  SPF20140129165 ). Salaries were supported by  CNRS for SP and SR, Ecole Normale Superieure de Lyon for MS.
Data availability Sequencing data have been deposited in GEO under accession codes GSE135432. -All data generated or analyzed during this study are included in the manuscript and supporting files. Sources and codes are available on GitHub https://github. com/msemon/cdpc; https://github. com/luke-hayden/ dvpap/tree/master/devstate; https://github. com/luke-hayden/dvpap/tree/master/devmorph; https:// github. com/msemon/trDUHi_FVB. The following dataset was generated:.
Subjects The study was approved by the ethics committee (CCP16163/C16-07) and preregistered prior to the research being conducted on ClinicalTrial (clinicaltrials. gov/ct2/ show/NCT02960698). All subjects gave informed consent. We recruited adult TD patients and sex-, educational level and age-matched healthy controls (HC). For all participants, the exclusion criteria were: incapacity or unwillingness to give consent for the study; substance addiction (excluding nicotine and recreational use of cannabis, i. e. , less than once per week); history of psychosis and presence of neurological (including childhood tics) or psychiatric conditions for HC. All participants were screened for the presence of psychiatric disorders (Mini International Neuropsychiatric Interview, MINI  [17] ), ICD (Minnesota Impulse Disorders Interview, MIDI  [18] ), impulsivity (Barratt Impulsivity Scale, BIS-11  [19] ) and anxiety (State-Trait Anxiety Inventory, STAI  [20] ).
Neuroimaging data acquisition and pre-processing All subjects were enrolled in a multi-modal MRI protocol on a 3 T Siemens Scanner (PRISMA) with a 64-channel head coil. The MRI sequences parameters were as follows: (i) a sagittal T1-weighted magnetization prepared rapid gradient echo (MP2RAGE) sequence (TR = 5 s, TI = 700/2500 msec, fov = 256, 1 mm isotropic, Ipat acceleration of 3), (ii) a multi-echo echo-planar imaging (EPI) sequence of 11 min duration for the resting-state, with a multi-slice, multi-echo acquisition scheme (TR = 1. 9 s, TE = 17/36/56 msec, Ipat acceleration factor 2, Multi-band 2, isotropic voxel size 3 mm, dimensions = 66 × 66 in plane × 46 slices), acquired with eyes opened and fixed on the cross which was monitored by an eye tracker; (iii) a multi-shell diffusion tensor imaging (DTI) acquisition (TR = 3.
5 s, TE = 75 msec, Multi-band of 3, isotropic voxel size = 1. 75 mm, 60 direction with b = 2000 s/mm 2 , 32 direction with b = 1000 s/ mm 2 , 8 direction with b = 300 s/mm 2 , one b = 0 was each 10 directions). For each EPI sequence (resting-state fMRI and DTI) one extra volume of opposite phase direction was acquired. T1-weighted images (MP2RAGE) were first denoised using a Matlab implementation of the algorithm (www. github. com/JosePMarques/MP2RAGE-related-scripts [22] ) and pre-processed (segmentation into gray and white matter and cerebrospinal fluid, normalization to MNI space, smoothing 10 mm) using the Computational Anatomy Toolbox (www. neuro. uni-jena. de/cat/) extension from SPM12 (www. fil. ion. ucl. ac. uk/spm/). Functional MRI multi-echo data were processed with the open-source MEICA toolbox (www.
github. com/ME-ICA/ me-ica/), in version v3. 2 beta1. This toolbox implements specific pre-processing steps for multi-echo data, then uses the different echos to perform decomposition in spatial and temporal maps in order to separate blood oxygen level dependent (BOLD) components from non-BOLD components in the signal. All processing steps were detailed in  [23, 24]. Briefly, all echos were slice-time corrected and realigned to the first TR using rigid-body motion corrected, where registration was driven by the first echo and applied to all other echos. Echos were then aligned to the anatomical volume using affine registration, and a single warp was performed to combine all pre-processing steps. The final step included in MEICA was the decomposition and denoising of the weighted average of all echo-times using a principal component analysis to reduce dimensionality of the dataset by removing thermal noise followed by an independent component analysis to separate BOLD (bloodoxygen-level dependent) from non-BOLD components.
The main output of MEICA was the denoised time-series that only contained BOLD signal and low variance components. The residual movements were then assessed (reported in Supplementary materials) and compared between the groups of patients as a part of the quality control. DTI data (fractional anisotropy [FA] and mean diffusivity [MD]) pre-processing was performed using the FMRIB software library pipeline, including correction for motion and eddy currents, removal of nonbrain tissue of each volumes, tensor reconstruction, nonlinear registration, alignment to the MNI space and thresholding data at 0. 2.
Neuroimaging data analysis fMRI Functional connectivity analysis was performed using DPSARF toolbox (http://rfmri. org/DPARSF) and 122 regions from the AAL atlas were used for the construction of whole brain correlation matrix. We performed onesample t tests to select resting-state BOLD signal correlations significantly different from 0, followed by a regression analysis with z-transformed premature responses. For both tests, a significance level was set at p ≤ 0. 01 following permutation adjustments (n = 5000).
MP2RAGE and DTI We performed whole-brain multivariate voxel-based regression analyses using the Permutation Analysis of Linear Models implemented in Matlab  [25] , on gray matter signal and the DTI metrics (FA and MD) including the total intracranial volume as covariable of noninterest and the zscore of premature response as covariable of interest. The threshold for significance was set at p ≤ 0. 05 following familywise error correction corrected within modality and within contrast, following probabilistic threshold-free cluster enhancement  [26]  and 5000 permutations. The signal from statistically significant clusters was extracted using the Marsbar toolbox  [27]. We performed bootstrapped correlations of the signal with clinical measures as well as groups comparisons using Anova with Tukey correction for multiple comparisons post-hoc.
Subjects' clinical and demographic characteristics We recruited 64 TD patients and 34 HC. After quality inspection, noncomplete data on 9 TD and 3 HC (one or more modalities of MRI missing or low quality, all subjects performed the task) were excluded from the final analysis. As shown in Table  1 , there were no significant demographic differences between HC and TD groups. TD patients showed a higher level of anxiety (STAI), impulsivity (BIS-11), and number of expressed impulsive behaviors (MIDI). Sub-groups of TD patients either medicated with antipsychotics (n = 19) or medication free (unmedicated TD group, n = 36) had similar demographics and clinical scores, except for anxiety which was higher in unmedicated TD (F (1;53) = 9. 27, p = 0. 004).
Therefore, within this context, the European Space Agency (ESA) funded a series of Fiducial Reference Measurements (FRM) projects targeting the validation of satellite data products of the atmosphere, land, and ocean, and setting the framework, standards, and protocols for future satellite validation efforts. The FRM4SOC project was structured to provide this support for evaluating and improving the state of the art in ocean colour radiometry (OCR) and satellite ocean colour validation through a series of comparisons under the auspices of the Committee on Earth Observation Satellites (CEOS). This followed the recommendations from the International Ocean Colour Coordinating Group's white paper and supports the CEOS ocean colour virtual constellation. The main objective was to establish and maintain SI traceable ground-based FRM for satellite ocean colour and thus make a fundamental contribution to the European system for monitoring the Earth (Copernicus).
This paper outlines the FRM4SOC project structure, objectives and methodology and highlights the main results and achievements of the project: (1) An international SI-traceable comparison of irradiance and radiance sources used for OCR calibration that set measurement, calibration and uncertainty estimation protocols and indicated good agreement between the participating calibration laboratories from around the world; (2) An international SI-traceable laboratory and outdoor comparison of radiometers used for satellite ocean colour validation that set OCR calibration and comparison protocols; (3) A major review and update to the protocols for taking irradiance and radiance field measurements for satellite ocean colour validation, with particular focus on aspects of data acquisition and processing that must be considered in the estimation of measurement uncertainty and guidelines for good practice; (4) A technical comparison of the main radiometers used globally for satellite ocean colour validation bringing radiometer manufacturers together around the same table for the first time to discuss instrument characterisation and its documentation, as needed for measurement uncertainty estimation; (5) Two major international side-by-side field intercomparisons of multiple ocean colour radiometers, one on the Atlantic Meridional Transect (AMT) oceanographic cruise, and the other on the Acqua Alta oceanographic tower in the Gulf of Venice; (6) Impact and promotion of FRM within the ocean colour community, including a scientific road map for the FRM-based future of satellite ocean colour validation and vicarious calibration (based on the findings of the FRM4SOC project, the consensus from two major international FRM4SOC workshops and previous literature, including the IOCCG white paper on in situ ocean colour radiometry).
Introduction Copernicus  [1]  is the European system for monitoring the Earth. It includes earth observation satellites, notably the Sentinel series developed by ESA  [2] , ground-based measurements and data processing to provide users with reliable and up-to-date information delivered through a set of Copernicus Services related to environmental and security issues. The Copernicus Marine Environmental Monitoring Service (CMEMS, [3]) provides critical marine information in near-real time to the various levels of the user community. Copernicus satellite missions are designed to serve CMEMS by providing systematic measurements of the Earth's oceans to monitor and understand large-scale global dynamics as well as providing data for coastal and inland water applications including eutrophication monitoring, sediment transport and environmental impact assessments  [4].
The results from the AMT cruises have enabled the intercomparison of simultaneous measurements of water leaving radiance and reflectance. The differences observed between these measurements form a key component of estimating errors and uncertainties resulting from environmental variability, as well as instrument deployment methodology, instrument specifications and calibration. The main AMT comparison for FRM4SOC was conducted from 23rd September to 4th November 2017 from Southampton, UK to South Georgia and the Falkland Islands on AMT-27, to compare along track measurements of Lw and Rrs(λ) between PML and Tartu Observatory (TO) radiometers. Measurements were carried out in various solar zenith angle, water and weather conditions. The ambient temperature varied from 1 °C to 28 °C. Altogether, data was collected from ~30 stations.
The FRM4SOC Field Intercomparison Database of OCR During the course of the project PML designed and built a database for FRM4SOC. Essentially this is a PostgreSQL database with a GIS web portal interface. It provides a web interface to remotely sensed, modelled and in situ data. Its functionality includes the ability to carry out simple analysis and plotting, as well as at all stages of analysis the ability to download data for local processing if preferred. Figure  23  shows the overall design. The portal uses the Open Geospatial Consortium (OGC) Web Map Service for displaying imagery data and the OGC Web Feature Service (WFS) and Sensor Observation Service (SOS) interface standards for interacting with in situ data. The analysis and plotting capabilities include: time series; latitude or longitude Hovmöller plots; scatter/regression; compositing; animations; and match-ups from CSV files.
IMPLEMENTING FRM C1 Measurement results collected for EO data validation shall have metrological traceability to the units of SI with related uncertainty evaluation. C2 Space agencies should: i. in the medium term, encourage and stimulate the adoption of FRM requirements, and ii. in the long term, when sufficient progress and consensus is achieved, use only FRM for the routine validation of satellite ocean colour data. In the near term, use of non-FRM quality data for satellite calibration or validation should only be done with great care. C3 Space agencies and National Metrology Institutes should consider forming a symbiotic relationship in order to harmonise approaches, methodologies and implement the principles of FRM worldwide. C4 Financial support from ESA and other space agencies or entities shall be ensured for implementing the principles of FRM.