dataesr/scientific-paragraphs-categorization huggingface dataset:
Model simulations: hindcast and forecast To assess recent historical lake winter temperature trends, each lake model was configured to provide a timeseries of water temperature at 1-m depth intervals from the lake surface down to the maximum depth, at an hourly resolution over the period January 1979 (earliest extent of the ERA5 reanalysis as of August 2020) to December 2019. To assess future temperature dynamics, each lake model was driven using separate sets of meteorological input variables from four different GCM projections: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5. Using data from four individual GCMs allows a more robust estimate of future climate compared to using a single GCM, which would increase uncertainty of projections. Each GCM was bias-corrected to the EWEMBI reference dataset  (Lange 2019) , for the 0.
5°g rid containing each lake and obtained from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b,  Frieler et al. 2017 ). This bias-correction step overcomes many of the limitations of using global-scale circulation models to simulate meteorological conditions on a local scale  (Hempel et al. 2013). Lake model simulations were performed using the following datasets from each of the four GCMs: a historic  reference period and three future climate periods (2020-2099) under different emission scenarios (representative concentration pathway (RCP) 2. 6 and RCP 6. 0 and RCP 8. 5). RCPs relate future concentrations of atmospheric greenhouse gases to radiative forcing in physical models of the future climate system. RCP 2. 6 represents a low-end forcing scenario (where mitigation causes a peak and decline in future radiative forcing by century-end), RCP 6.
Funding This work was carried out with the support of the  Marine Institute. Seán Kelly  was funded under the  Marine Research Programme  by the  Irish Government  ( BEYOND 2020 PBA/FS/16/02 ). Phil McGinnity was also supported by the  Marine Institute  under the  Marine Research Programme 2014-2020  ( RESPI/FS/16/01 ). Tadhg Moore was funded by the  WATExR  project, which is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate , and funded by  MINECO (ES) ,  FORMAS (SE) ,  BMBF (DE) ,  EPA (IE) ,  RCN (NO) , and  IFD (DK) , with co-funding by the  European Union  (Grant Number:  690462 ).
Code availability The General Ocean Turbulence Model source code and instructions for compilation are freely available at https://gotm. net/. The parsac Python code for model calibration is freely available at https://github. com/BoldingBruggeman/parsac. Author contributions Conceptualisation: Seán Kelly; methodology: Seán Kelly, Tadhg Moore, Eleanor Jennings; formal analysis and investigation: Seán Kelly; project administration, supervision, and funding acquisition: Eleanor Jennings and Phil McGinnity; writing-original draft: Seán Kelly; writing-review and editing: all authors. Data availability The ERA5 hourly data on single-level climate reanalysis dataset is available online at https://cds. climate. copernicus. eu/. The ISIMIP2b climate scenarios for each GCM under different RCPs are available online at https://www.
isimip. org/gettingstarted/data-access/. The STOREFISH database can be accessed at http://storefish. org/. The GOTM configuration files for each lake are available at https://github. com/S-O-Ceallaigh/European-LakeCharr. The observed Lough Bunaveela temperature profiles (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) (2019)  are available for download at https://github. com/S-O-Ceallaigh/European-LakeCharr. Observed temperature profiles from Lakes Bourget and Geneva are available from https://si-ola. inra. fr/si_lacs/login. jsf. Observed Windermere temperature profiles are available from https://doi. org/10. 5285/bd37710e-6a53-49f0-9a07-6973408a3342. The 1980-2019 simulated temperature profiles and the future simulated temperature profiles for each lake are available from the corresponding author.
Methods Satellite altimetry. The satellite altimetry product was generated by the Sea Level Thematic Assembly Center (SL-TAC) and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS, http://marine. copernicus. eu/). This is a daily gridded global product in delayed-time (re-analysis product ID: SEALEVEL_GLO_PHY_L4_REP_OBSERVATIONS_008_047) with a spatial resolution of 1/4°available from 1993 to present time. A 1/4°Mercator projection grid roughly corresponds to a resolution of 27. 8 km in latitude × 9. 5 km in longitude at 70°N. This product is generated using Sentinel 3 A, Jason 3, HY2A, Saral/ AltiKa, Cryosat 2, OSTM/Jason 2, Jason-1, Topex/Poseidon, Envisat, GFO, ERS-1/ 2. The SL-TAC produced this dataset with a special care for synchronization, homogenization, cross-calibration, correction of large-scale biases, and crossvalidation.
Before merging missions by optimal interpolation, the SL-TAC filtered the dataset to remove residual noise. The reader is referred to the product documentation for more details. The satellite altimetry product provides Sea Level Anomaly that estimates the variation of the Sea Surface height around a vertical reference (Mean Sea Surface). The product also provides Absolute Dynamic Topography (=Sea Level Anomaly+Mean Dynamic Topography), geostrophic velocity anomalies and absolute geostrophic velocities (derived respectively from Sea Level Anomaly and Absolute Dynamic Topography). The Mean Dynamic Topography employed by the SL-TAC was the MDT-CNES-CLS13 produced by the Collecte Localisation Satellites Space Oceanography division and distributed separately by Aviso (http://www. aviso. altimetry.
Second, measurements above 66°N can rely on several other satellites: ERS-1 and -2, Envisat, SARAL/AltiKa, GFO (<72°N), HY-2A, ICESat, Cryosat-2, Sentinel-3. A comprehensive validation of the gridded product of geostrophic velocities was recently provided for ERS-1/2 and Envisat satellites using surface drifters and tide gauges in the whole EAC, with a particular focus on the Barents Sea  70, 71. The authors demonstrated that RMS differences between the drifter (corrected for Ekman currents) and altimetry velocities ranged within 7-15 cm s -1 , which was comparable to previous estimates at lower latitudes. More importantly, they also concluded that the dataset provided sufficient spatial and temporal coverage to consistently resolve the synoptic and large mesoscale variability. Moreover, the product was recently found appropriate to monitor the long-term northward Atlantic inflow in the Norwegian Sea  40  with even lower RMS differences between drifters and satellites velocities (0.
7-8 cm s -1 ). The dataset used in this study was enhanced from additional satellites (i. e. , CryoSat-2 and Sentinel-3A) that offered a better data coverage at high latitudes for recent years (since 2011). Surface currents inferred from altimetry were found to reflect the variability of the deeper layers in the slope current in the Nordic Seas  40. For this study, the derived surface geostrophic velocities were evaluated again toward five long-term in situ current meters in the southwestern Barents Sea and showed reasonably good Pearson's correlation coefficients (r = 0. 43-0. 71, Supplementary Note 2. 1). In addition, by evaluating the effect of steric and mass related effect on the SLA, we demonstrated that the increasing long-term trend was driven by change in currents (Supplementary Note 2.
2). Satellite sea surface temperature (SST). We used the NOAA 1/4°degree and daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2, provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, https://www. esrl. noaa. gov/psd/data/gridded/data. noaa. oisst. v2. highres. html  72 ) to extract the 4 °C isotherms during the month of March between 1998 and 2016. This dataset was constructed by combining satellites observations from the Advanced Very High-Resolution Radiometer (AVHRR) and the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) with in situ platforms (ships, buoys) on a regular global spatial grid. Missing observations in SST were optimally interpolated. The dataset was delivered with error fields providing a measure of confidence. This allowed us to build monthly composites maps excluding values with estimated errors higher than 50%, following the same method used for the altimetry dataset.
Satellite particulate inorganic carbon (PIC). Ocean colour satellites detect coccolithophore blooms because they produce calcite plates (coccoliths) that shed light and produce 'turquoise' colored waters. Remotely sensed ocean-color offers a 20-year time-series from 1998 to present. In this study, we used the GlobColour database (http://hermes. acri. fr), which provides a continuous dataset of ocean-color satellite data products derived from the merging of five ocean-color sensors MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (Sea-WiFS), Visible Infrared Imaging Radiometer Suite (VIIRS) and Ocean and Land Colour Imager (OLCI-A). These sensors measure visible and near-infrared solar radiation reflected back from the ocean surface layer.
Monthly composite maps were derived excluding pixels with error estimates (percentage of the SLA signal variance) higher than 50% or sea-ice concentrations higher than 15%. The seasonal cycle, known to be highly variable for SLA and the velocity fields, was removed because the analysis focused on the interannual and decadal time scale. The maximum amplitude in SLA generally occurs in fall  83  , whereas the velocity fields are maximum during the winter  84. More details on the EOF analysis are available in Supplementary Note 2. 3. Trends in surface absolute geostrophic velocities were only derived for pixels associated with more than 50% of data points. Lagrangian calculation of particles trajectories. The Lagrangian experiments needed daily temporal resolution and directly employed daily absolute surface geostrophic velocities delivered from CMEMS.
Acknowledgements L. O. was funded by an  NSERC/CRSNG   "Visiting Fellowship" in Canadian laboratories  in the framework of the  DFO-International Governance Strategy (IGS). L. O. was also supported by the  joint international laboratory Takuvik (ULaval/CNRS) , the  Canada Excellence Research Chair in remote sensing of Canada's new Arctic frontier. This research was supported by the  Sentinel North program of Université Laval , made possible, in part, thanks to funding from the  Canada First Research Excellence Fund. M. A. was supported by a  European Union 's  Horizon 2020 Marie Sklodowska-Curie grant  (no. 746748 ). The authors would like to thank the international agencies and programs from the  Copernicus Marine Environment Monitoring Service (CMEMS) , the  National Oceanic and Atmospheric Administration (NOAA) , and the  ACRI-ST company  for freely providing access to their satellite altimetry,  Sea Surface Temperature  and Ocean-color data.
This work heavily relies on the following toolbox for mapping: Pawlowicz, R. , 2018. "M_Map: A mapping package for MATLAB", version 1. 4j, [Computer software], available online at www. eoas. ubc. ca/~rich/map. html as well as environmental toolbox developed by  Chad Greene  ( 2019 ): The Climate Data Toolbox for Matlab (https://www. github. com/chadagreene/CDT), GitHub. Retrieved March  11, 2019. The authors thank  Francesco D'Ovidio  for the Lagrangian tools,  Martine Lizotte  for language support and  Marie-Isabelle Pujol  for her advices on satellite velocity uncertainties.
Code availability The code for Lagrangian experiments is based on Lamta 0. 2 Copyright (C) 2007/12/5 Francesco d'Ovidio (francesco. dovidio@locean-ipsl. upmc. fr). This is a free Octave software under the terms of the GNU General Public License (http://www. gnu. org/ licenses/) and can be distributed and modified. Outputs were analyzed using ©Matlab custom scripts that are accessible at https://gitlab. com/loloziel/oziel_et_al_nc_2020.
Sequence analyses Whole exome data were generated from individuals III:2 and III:3 of family 1 and from the 6 other SS cases. Exomes were enriched with the SureSelect Human All Exon v6 kit (Agilent Technologies, Santa Clara, USA) and DNA libraries were sequenced on a HiSeq 4000 instrument (2 × 100 cycles, Illumina, San Diego, USA). The average exome coverages ranged from 115 × to 197 × and 100% of the NOTCH3 region was covered with at least 25 ×. Variants were filtered on the minor allele frequency (MAF < 0. 001), which was estimated using the in-house database of the Helmholtzzentrum (> 20. 000 exomes) and confirmed by the Genome Aggregation Database (gnomAD). NOTCH3, PALLD and ANGPTL4 sequence variants were confirmed by Sanger sequencing using standard protocols.
Differential gene expression of GSE58368 and GSE55203 To find out differentially expressed genes in Notch3 knockout (KO) mouse models we analyzed 2 microarray datasets (GSE58368 and GSE55203) derived from the Gene Expression Omnibus (GEO) database (https ://www. ncbi. nlm. nih. gov/gds/). Using the GEO2R web tool(https ://www. ncbi. nlm. nih. gov/geo/geo2r /) samples from the same cell type were analyzed comparing either heterozygous vs. homozygous (GSE58368) or homozygous vs. wildtype (GSE55203) mice.
1a ). Exome-data analysis revealed in both siblings a large homozygous region of 9 Megabases on chr. 19p13. , indicating consanguinity. This region harbored only two homozygous variants shared by both siblings. First, a missense variant was detected in the KANK2 gene p. (Met278Lys), which is not present in any publicly available database. This variant was discarded since mutations in this gene have been associated with a distinct recessive disease characterized by a nephrotic syndrome and palmoplantar keratoderma with woolly hair (OMIM*614610). Second, a homozygous nonsense variant was identified in exon 14 of the NOTCH3 gene, p. (Arg735Ter) (Fig. 1b ), which lies in the Epidermal Growth Factor repeat (EGFr) 19 of NOTCH3. This variant is predicted to result in a premature stop codon and elicit nonsense-mediated mRNA decay  [16].
The variant is present heterozygously in 2 out of 61. 000 individuals in the gnomAD database. (https ://gnoma d. broad insti tute. org/). The healthy mother was tested to be heterozygous for the p. (Arg735Ter) variant (Fig. 1c , II:3). No other family member was available for genetic testing either because they lived in another region of the country and were not able to transfer to the clinic for testing or were not interested in participating in the study. Based on the severity of the mutation and the known role of NOTCH3 in cerebrovascular disease, we believe that this mutation is most probably the cause of the disease in this family. To analyze whether NOTCH3 loss-of-function (lof) mutations are a more frequent cause for SS, we performed WES in 6 additional unrelated patients (Table  2 ).
We found none of the patients to carry a rare variant (MAF < 0. 01) in the NOTCH3 or in the ADA2 gene. Moreover, high coverage sequencing allowed us to also exclude copy number variations in both genes. We next hypothesized that genetic variations in genes involved in the NOTCH3 pathway might be plausible disease candidates. To explore this possibility, we made use of two microarray datasets of Notch3 KO mouse models from which RNA expression data of brain microvascular fragments (GSE55203) or brain-derived smooth muscle cells (GSE58368) were deposited. Using the GEO2R tool we searched for genes which were significantly downregulated in KO cells compared to wildtype or heterozygous cells (p value < 0. 01), assuming that these genes are likely downstream in the NOTCH3 signaling pathway. We then intersected these 445 genes with the 85 genes carrying lof variants in our 6 patients.
We found two patients with lof variants in putative NOTCH3 downstream genes. Patient 895 carried a heterozygous nonsense variant in the Palladin (PALLD) gene, p. (Arg287Ter) and patient 898 carried a heterozygous frameshift variant in the Angiopoietin-like 4 gene (ANGPTL4), p. (Gly313AlafsTer49). Both variants are present heterozygously in the gno-mAD database (PALLD: 10/277212 alleles, ANGPTL4: 57/280350 alleles) (Fig. 2 , Table  2 ).
Discrepancies between reports, such as the anatomical and spatial extent of a rodent homologue of the human default-mode network (DMN)  (Becerra et al. , 2011; Gozzi and Schwarz, 2016; Guilfoyle et al. , 2013; Hübner et al. , 2017; Liska et al. , 2015; Lu et al. , 2012; Sforazzini et al. , 2014; Stafford et al. , 2014; Upadhyay et al. , 2011) , or the organisation of murine RSNs  (Jonckers et al. , 2011) , have stark consequences for the interpretation of the results. To establish standards and points of comparison in rodent fMRI, a growing need in the field, we carried out a multi-centre comparison of mouse rsfMRI datasets. Datasets representative of local centre acquisitions were analysed with a common pre-processing pipeline and examined with seed-based analyses (SBA) and independent component analyses (ICA), two common brain mapping methods used to investigate RSNs.
Resting-state fMRI acquisition All animal experiments were carried out with explicit permits from local regulatory bodies. Seventeen datasets, consisting of 15 individual pre-acquired rsfMRI scans each, were acquired with parameters reflecting each centre's standard. A dataset is defined here as a collection of scans acquired under one protocol by one group of individuals on a specific piece of equipment. A scan is a single rsfMRI acquisition obtained from an individual mouse. A summary of the equipment, acquisition parameters, and animal handling procedures are listed in Supplementary Table  1. Scans were acquired on dedicated Bruker magnets operating at 4. 7 T (N ¼ 1 dataset), 7 T (N ¼ 8), 9. 4 T (N ¼ 6), or 11. 7 T (N ¼ 2), with either room-temperature coils (N ¼ 7) or cryoprobes (N ¼ 10).
Gradientecho echo planar imaging (EPI) sequences were used to acquire all datasets, with repetition time (TR) ranging from 1000 ms to 2000 ms, echo time (TE) from 10 to 25 ms, and number of volumes from 150 to 1000. Acquisitions were performed on awake (N ¼ 1) or anaesthetized C57Bl/6 J mice (both male and female) with either isoflurane 1-1. 25% (N ¼ 5), halothane 0. 75% (N ¼ 1), medetomidine 0. 1-0. 4 mg/kg bolus and 0. 2-0. 8 mg/kg/h infusion (N ¼ 5), or a combination of isoflurane 0. 2-0. 5% and medetomidine 0. 05-0. 3 mg/kg bolus and 0-0. 1 mg/kg/h infusion (N ¼ 5). Awake mice were fitted with a non-magnetic head implant to fix their heads to a compatible cradle  (Yoshida et al. , 2016). Animals were either freely-breathing (N ¼ 12) or mechanically ventilated (N ¼ 5). Datasets are publicly available in the BIDS format on openneuro.
Data pre-processing Volumes were analysed in their native resolution. First, image axes were reoriented into LPI orientation (3dresample, Analysis of Functional NeuroImages, AFNI_16. 1. 26, https://afni. nimh. nih. gov) (Cox, 1996). Temporal spikes were removed (3dDespike), followed by motion correction (3dvolreg). Brain masks (RATS_MM, https://www. iibi. uiowa. edu) (Oguz et al. , 2014)  were estimated on temporally averaged EPI volume (fslmaths). Motion outliers were detected based on relative framewise displacement (FWD) estimated during motion correction. Volumes with spikes or FWD greater than 0. 1 mm, corresponding to approximately 0. 5 voxel of the average in-plane resolution, were labelled in a confound file to be excluded from later seed-based and dual-regression analyses. Linear affine parameters and nonlinear deformations with greedy SyN diffeomorphic transformation (antsIntroduction.
sh) were estimated relative to a reference T2 MRI template  (Dorr et al. , 2008)  registered to the Allen Institute for Brain Science (AIBS) Common Coordinate Framework space (CCF v3, © 2004 Allen Institute for Brain Science. Allen Mouse Brain Atlas. Available from: http://www. brain-map. org/) (Lein et al. , 2007)  and resampled to 0. 2 Â 0. 2 Â 0. 2 mm 3. The normalisation to AIBS space was carried out on brain-masked EPI using ANTS (Advanced Normalisation Tools, http://picsl. upenn. edu/software/ants/) (Avants et al. , 2014 (Avants et al. , , 2011)). Anatomical scans corresponding to each EPI acquisition were not available in all cases. Despite this limitation, plausible registrations of EPI directly to a T2 MRI template were rendered possible due to the relatively simple structure of the lissencephalic cerebrum and high EPI quality.
3 corresponding to p < 0. 01 uncorrected) overlapping with vascular structures (Supplementary Figs. 1b  and 2 ). These vascularassociated components were identified in a subset of scans supporting the notion of vascular signal confounds. Inverse transformations were applied to each mask. Average time series within masks were extracted (fslmeants) and regressed out (fsl_regfilt). Then, spatial smoothing was applied using an isotropic 0. 45 mm kernel (3dBlurInMask) corresponding approximately to 1. 5 x voxel spacing along the dimension of the lowest resolution. Finally, bandpass filtering (0. 01-0. 1 Hz) was applied (3dBandpass). The bandpass filter was applied to all datasets to enhance comparability between datasets, despite indications that medetomidine leads to a shift in resting fluctuation frequencies  (Grandjean et al.
Statistical analysis and data representation Voxelwise statistics were carried out in FSL using either nonparametric permutation tests (randomise) for across-dataset comparisons (5000 permutations and voxelwise correction), or uncorrected parametric one-sample t-tests for within-dataset comparisons (fsl_glm). The decision to present within-dataset comparisons using uncorrected parametric statistics is motivated by the desire to mitigate false-negative rates, and hence avoiding to reject potential FC. This is however done at the expense of enhanced false-positive rates. Within-dataset comparisons are summarised as "overlap maps", i. e. the percentage of overlapping datasets exhibiting significance in one-sample t-tests at a given voxel. To account for the false-positive rates imposed by our significance threshold and for clarity in their representation, overlap maps are presented as overlays with a 30-100% range.
For additional clarity, the statistical maps relative to each dataset are detailed in the supplementary materials for one representative SBA. Voxelwise statistical maps are shown as colour-coded t-statistics overlays on the ABI template resampled at 0. 025 Â 0. 025 Â 0. 025 mm 3 using MRIcron  (Rorden et al. , 2007). Statistical analysis carried out on parameters extracted from ROIs was performed in R (v3. 4. 4, "Someone to Lean on", R Foundation for Statistical Computing, Vienna, Austria, https://R-project. org) using a linear model (lm). A simplified model was designed to include the following fixed effects: breathing conditions (2 levels: ventilated or free-breathing), sedation conditions (4 levels: awake, isoflurane/halothane, medetomidine, medetomidine þ isoflurane combination), SNR (continuous variable), and mean FWD (continuous variable).
Dataset description and pre-processing validation A total of 17 datasets, each consisting of 15 individual rsfMRI scans, were included in this study. Dataset selection was restricted to gradientecho echo planar imaging scans acquired on healthy and wild-type C57Bl/6 J mice, any gender, any age, and any sedation protocol (Supplementary Table  1 ). Cortical SNR ranged from 17. 04 to 448. 56, while tSNR ranged from 8. 11 to 112. 68 (Fig. 1a, b ). A comparison between SNR and tSNR indicated a positive association between the two measures (Pearson's r ¼ 0. 75, t ¼ 18. 30, df ¼ 253, p ¼ 2. 2e-16). Due to the lack of orthogonality between the two factors, only SNR was considered in the remaining analyses. Mean FWD ranged from 0. 0025 mm to 0. 1500 mm (Fig. 1c ). A summary of representative estimated motion parameters is shown in the supplementary material (Supplementary Fig.