dataesr/scientific-paragraphs-categorization huggingface dataset:
Peak lists were generated using PAVA software  66. All generated peak lists were searched against the rat subset of the UniProt database (UniprotKB 2017. 11. 01) using Protein Prospector  67. The database search was performed with the following parameters: a mass tolerance of 20 ppm for precursor masses; 30 ppm for MS/MS, cysteine carbamidomethylation as a fixed modification and acetylation of the N terminus of the protein, pyroglutamate formation from N-terminal glutamine, and oxidation of methionine as variable modifications. All spectra identified as matches to peptides of a given protein were reported, and the number of spectra (Peptide Spectral Matches, PSMs) used for label free quantitation of protein abundance in the samples. Protein networks analysis. The networks was constructed in Cytoscape using the STRING protein interaction database for mus musculus.
Proteins were included if their fold change was >2 and at least ten peptides were found in one dataset. Interactions with a confidence score above 0. 40 were included. Black line thickness was mapped to STRING interaction score and node size was set to the logarithm of the fold change. The size of the "Nfasc" node and node colors were manually set for esthetic purposes. Immunofluorescence microscopy. Cultured rat primary neurons were fixed in 4% paraformaldehyde (PFA, pH7. 2) and immunostained with the indicated antibodies. In some instances, neurons were "extracted" prior to fixation using 0. 5% TX-100 for 5 min at room temperature, or for 15 min at 4 °C. Images of immunofluorescence were captured using an Axio-imager Z1 microscope (Carl Zeiss MicroImaging) or Axio-observer Z1 microscope (Carl Zeiss MicroImaging) fitted with an AxioCam digital camera and Apotome for structured illumination (Carl Zeiss MicroImaging).
Fourteen days in vitro (DIV) neurons were fixed using 4% PFA in PEM (80 mM Pipes, 5 mM EGTA, and 2 mM MgCl 2 , pH 6. 8) for 10 min. After blocking, they were incubated with primary antibodies overnight at 4 °C, then with DNA-PAINT secondary antibodies (Ultivue) for 1 h at room temperature. DNA-PAINT imaging was performed on an N-STORM microscope (Nikon Instruments). The two channels were imaged sequentially in imaging buffer (PBS, 500 mM NaCl, 5% dextran sulfate) using the 647 nm laser at 40-60% power for 40,000 frames at 20 Hz. The first channel imaged was Mical3 with 0. 26 nM imager I2-650, then α2-spectrin with 0. 125 nM imager I1-650 (imagers from Ultivue). Statistics, reproducibility, and data presentation. All statistics were performed using Excel or GraphPad Prism software. Statistical tests are stated in each figure legend.
Acknowledgements The work reported here was supported by the following research grants:  NIH   NS044916  (MNR);  NIH   NS069688  (MNR); GM103481 (ALB),  CNRS   ATIP AO2016  (CL), and by the  Dr. Miriam and Sheldon G. Adelson Medical Research Foundation  (ALB and MNR). We thank  Dr. Jonathan Terman  ( UT Southwestern ) for insightful suggestions on Mical3 functions. We thank  Dr. Takeshi Yoshimura  for construction of the α2 spectrin-GST fusion proteins.
Data availability All data generated or analyzed during this study, including mass spectrometry data sets, are available from the corresponding author upon request. All proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD016272. A supplemental file including all PSMs is provided in Supplementary Data 1. The source data underlying Figs. 3d-f, 4b, d, g, 5g, 6b, d, i, j, and S4b, d, f, h are provided in the Source Data file.
The GCM repeatedly affirms the need for migration (and migrants) to be better documented (as in Objective 1 on data, Objective 3 on information and Objective 4 on identity documents). Border management and biometrics are also about the systematic documentation of people and flows. The GCM calls for systematic exchange of information and for improved databases, in a way inspired by 'big data' technologies. This is connected to the fight against irregular migration and to the GCM's rejection of the hidden (or underground) nature of unauthorised flows. The emphasis on knowledge and information indirectly reveals the belief according to which current migration dynamics are obscure and insufficiently documented. As  Guild (2019)  observes, this raises concerns about privacy and protection of personal data (see also  Glouftsios and Scheel 2020).
Weakly nonlinear models have been developed and used for wave simulation and prediction based on expanding Eulerian wave properties up to the second-order in wave steepness (e. g. ,  Zhang et al. , 1996 Zhang et al. , , 1999)) , which for instance allows separating free-and bound-wave components in wave measurements. A model based on the Modified NonLinear Schrödinger (MNLS) equation, which simulates third-order wave properties such as phase speed, was used to predict bichromatic waves  (Trulsen and Stansberg, 2001) , then extended to both onedirectional and directional irregular seas  (Simanesew et al. , 2017). The latter study showed that the MNLS equation is able to provide satisfactory predictions of long-crested irregular waves, but the lack of directional input data prevented properly estimating its prediction performance for short-crested waves with increasing directional spread.
Experimental and numerical frameworks Applications presented hereafter are based on surface elevations measured in laboratory experiments and computed in corresponding numerical simulations. Both data sets are referred to full scale wave parameters, but they are both performed at a  * = 1:50 geometric scale (corresponding time scale is  * = √  * ≈ 7. 06 under Froude scaling), for long-crested wave trains generated in the oceanic 3D tank of École Centrale de Nantes (ECN), which is 50 m long, 30 m wide, and  = 5 m deep. Waves are generated at one side of the tank by 48 individual rotating flaps, and absorbed by a beach at the other extremity. Numerical simulations are performed using the open-source code HOS-NWT 1  developed at ECN. It makes use of the HOS method to simulate a numerical wave tank, and has been extensively used and validated against real wave tank experiments  (Bonnefoy et al.
Fig. 10  shows time series of surface elevations for these wave groups in cases A, E and H, compared to predictions of the linear (LWT), linear corrected (LWT-CDR), and nonlinear (ICWM) models, using experimental (a, c, e) and numerical (b, d, f) data. For each case, only small differences due to experimental noise can be seen between the experimentally and numerically generated reference surfaces. While there is an overall agreement between the reference and predicted surfaces, differences in wave phase and elevation increase with wave steepness, due to cumulative effects of nonlinearity during wave propagation. Accordingly, for the smallest wave steepness (case A), all three models predict the same surface elevation, in good agreement with references, particularly for numerical data (b) for which predictions almost perfectly overlap the HOS solution, but predictions become increasingly different between the three algorithms, the larger the characteristic wave steepness.
Influence of experimental noise on wave prediction The prediction misfits based on numerical and experimental reference data are compared in Fig. 15 , at gauges 20, 21, and 22, for all cases listed in Table  1 , using the linear (LWT) or nonlinear (ICWM) algorithms. To better assess the effect of experimental noise on the prediction misfit, a ''noisy numerical dataset'' was created by adding to the numerical data a noise signal having the same spectral shape (or NSR) as that analyzed for the experiments (Fig. 5b ), scaled by the measured noise amplitude   (Fig. 5a ), with independent random phases for each wave gauge. As would be expected, for both algorithms, the prediction misfits are larger at all wave gauges using experimental data, as compared to noise-free numerical data, particularly for cases with a lower steepness, which have relatively larger noise levels.
Note that higher-order wave models (e. g. , HOS, MNLS) are expected to further improve the prediction accuracy, but only provided that the assimilation procedure yields the correct initial model parameters, which becomes increasingly complex and time-consuming when the model order increases. Using such models to propagate waves will also require a much larger computational effort. Lessons learned in this study will help applying our prediction algorithms to data acquired at sea using optical sensor systems, such as LIDARs (e. g. ,  Belmont et al. , 2007; Nouguier et al. , 2014). As the method described here is directly applicable for predicting directional wave fields, further investigation of the performance of our algorithms for short-crested waves will be pursued in future work.
Acknowledgments This work was supported by  Naval Group and École Centrale de Nantes (ECN)  through their  Joint Laboratory  for  Marine Technology , and by the  PREDEMO-NAV  project grant  ANR-15-ASTR-0006 , of the  French Agency for National Research. SG also acknowledges support from the  US National Science Foundation, Physical Oceanography Program  grant  OCE-1947960  to the  University of Rhode Island. The ECN ocean tank staff is gratefully acknowledged for its support in the experimental part of this work. The authors would also like to thank the reviewers for their constructive comments and suggestions to improve the initial manuscript.
Furthermore, in support of these field campaigns, the local sounding network was enhanced by the addition of high-frequency fixed and mobile site launches during intense operational periods (IOPs), and a 3-D Lightning Mapping Array (LMA) operated in Córdoba, central Argentina. All these observations provided an unprecedented dataset in this significant region. More information about the dataset is provided in Section 2. The availability of the new high-resolution GOES-16/17 satellites presents a unique opportunity to investigate the time evolution of severe weather from a satellite perspective. In particular, this set of observations could revolutionize convection studies in SESA given the clear improvement that it represents to the previously available 10-or 15-min resolution satellite information in the region.
Datasets This work examines a case study of severe weather that took place on December 11, 2018 in east-central Córdoba Province in central Argentina during the RELAMPAGO-CACTI field campaigns. RELAMPAGO was a joint project between the U. S. , Argentina, and Brazil. During its IOP (from 1 November -18 December 2018) RELAMPAGO consisted of a suite of fixed and mobile assets that included radars, radiosondes, flux towers, and mobile mesonets that were deployed primarily in Córdoba Province (Fig. 1 ). observations available during this day captured most of the storm's lifecycle. In particular, all sensors captured convective initiation and supercellular development with high spatiotemporal resolution. Unfortunately, this event occurred outside of the mobile asset domain, and thus it was not sampled by this set of instruments.
Advanced Baseline Imager (ABI) ABI is the primary instrument on the NOAA/NASA GOES-16/17 satellite. ABI is a multichannel passive imaging radiometer designed to observe Earth with higher temporal, spatial, and radiometric resolution than previous imagers. This instrument has multiple scan modes available with different temporal and spatial resolutions. The mode used during most of RELAMPAGO-CACTI was Mode 3, which provides a Full Disk image every 15 minutes. Also available for these field campaigns was the MDS mode, which provides images every minute with a resolution of 1 km by 1 km at the satellite sub-point. ABI has 16 different spectral bands, two visible channels, four near-infrared channels, and ten infrared channels. Of particular interest to this work, data from 10. 3-μm band with 2-km spatial resolution and from the 0.
(2013) HID algorithm available in the CSU_RadarTools open-source software package (https://github. com/CSU-Radarmet/CSU_RadarTools). This algorithm was applied to the dual-polarization RMA1 dataset in polar coordinates and gridded using the nearest neighbor approach onto a 1-km horizontal and vertical resolution grid from surface to 20 km height. KDP was estimated following a finite impulse response (FIR)-based KDP estimation algorithm also available in the CSU_RadarTools package and based on Hubbert and Bringi (1995) and Lang et al. (2007) work. 3. 2. 2 Cell IdentificationStorm identification using radar data is an important facet of forecasting the location and strength of severe weather events. Detecting storms and calculating their properties (centroid position, volume, etc. ) is an essential part of severe weather warning operations.
In addition to providing useful nowcasting capabilities, storm identification and tracking can also provide information suitable for the study of the physical mechanisms of storm evolution (e. g. , Dixon and Wiener 1993). In this work, a cell was identified from the radar dataset as a contiguous region with Ze ≥ 35 dBZ and area exceeding 25 km 2 (e. g. , Roberts and Rutledge 2003). Then, the centroid of each cell was computed following the scikit-image image processing toolkit (van der Walt et al. 2014 ).
These fast-evolving cooling and warming signatures could be analyzed in more detail with high resolution modeling studies capable of representing the storms' dynamics providing enhanced knowledge of storms' dynamics. It is important to note that the pulsing behavior in MinIR is smoothed to an almost indistinguishable signal when analyzing observations with coarser time resolution (lower than 6 or 7 min). This work demonstrates the need for more studies of severe weather events using observations with high spatio-temporal resolution. Such studies should help confirm whether the patterns seen in this work are consistent throughout a larger convective dataset. The extensive MDS dataset collected in RELAMPAGO-CACTI field campaigns could advance our understanding of storms' evolution in one of the regions of the world with the most intense convection.
Primer oligonucleotides have been described in  16, 17. ChIP-seq, RNA-seq and NGS NGS sequencing of XP genes. Sample preparation for ChIP-seq and RNA-seq as well as NGS sequencing was described in  17, 25. The raw and processed ChIP-seq data are available at GEO: http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE87562. Gene expression was uploaded at GEO: http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE87540). Sample preparation and NGS sequencing of XP genes were described in the same way as in  26. All primer sequences are available upon request.
Acknowledgements We are grateful to  E. Compe  for fruitful discussions. We thank  B. Jost  and his team for RNA-seq and ChiP-seq sequencing as well as the IGBMC cell culture facilities. A. E. was sponsored by the  ERC  and  AFM  grants; M-A. R. was supported by  French Ministère des Affaires Etrangères  and by an  Inserm Young investigator fellowship  and F. C. by a  Marie Curie  fellowship. This study was supported by the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d' Avenir   ANR-10-IDEX-0002-02. This work is dedicated to Lydie et  Guy Dutan.
06 U/µL of Taq DNA polymerase. PCR running conditions were as follows: initial denaturation at 94°C for 4 min followed by 36 cycles each at 92°C in 30 s, 1 min at 52°C, 45 s at 72°C, and with a final extension step at 72°C for 5 min. Electropherograms were analyzed, and allele sizes were determined using GeneMapper ® software version 4. 1 using GeneScan 600 LIZ as a size standard (Applied Biosystems). Four out of the eight primers failed to amplify the target loci (mPoCIRE01, mPoCIRE04, mPoCIRH02, and mPoCIRE09), and two showed little polymorphism (Table  2 ). These eight loci were considered noninformative and showed insufficient variability to be used for genetic studies of P. erinaceus.
(2006)  for P. officinalis (54°C). This first test was performed on an ABI 3500XL sequencer (Life technologies) using genomic DNA extract from eight individuals selected from different countries (Table  1 ). The analysis of electrophoregrams with GeneMapper ® software version 4. 1 using GeneScan 600 LIZ as a size standard (Applied Biosystems) allowed determining allele sizes. Among the 30 primer pairs tested, 17 were selected. Indeed, we eliminated primers with profiles that were difficult to read on GeneMapper®, or with no or little polymorphism. The 17 selected primers are shown in Table  4  and were used for screening the remaining individuals in order to calculate genetic parameters. Genetic parameters including alleles' number per locus, number of private alleles, observed heterozygosity (Ho), and expected heterozygosity (He) were computed using GenAIEx software version 6.
, 2016). However, inhibition of classical pro-inflammatory pathways, such as the acute phase response and nuclear factor κB (NF-κB) signaling, as well as of TREM1 signaling, which sustains inflammation  (Owens et al. , 2017) , was also predicted (Figure  5E ; Table  S5 ), suggesting that Csf1r +/-microglia are not pro-inflammatory. Analysis of the biological processes affected by Csf1r heterozygosity predicted active neurodegeneration, increased cellular protrusions and microtubule dynamics, as well as elevated paired-pulse facilitation of synapses (Figure  5F ; Table  S6 ). ) To further explore how these transcriptomic changes are relevant to the neuropathology, we intersected our gene list with publicly available datasets showing changes in the microglial transcriptome in other mouse models of neurodegenerative disease, including models of Alzheimer's disease (presenilin/amyloid precursor protein [PS-APP], AD 5xFAD, APPswe/ PS1dE9), tauopathy (Tau_P301S), amyotrophic lateral sclerosis (ALS), rapid neurodegeneration (CK-p25), and spinocerebellar ataxia (Mfp2 -/-)  (Friedman et al.
The cells were stained using the antibodies diluted 1:200 in FACS buffer and incubated overnight at 4°C in the dark. Cells were subsequently washed twice with FACS buffer, resuspended in 1ml FACS buffer containing 2. 5 μg/ml DNase I and analyzed in a MoFlo Astrios EQ (Beckman Coulter, IN). The antibodies used for staining are listed in the Key Resources Table and the gating strategy utilized to identify each cell type is shown in Figure  S3. Data were analyzed using FlowJo version 10. 6. 1. Microglia Isolation and RNA-Seq Analysis-Microglia were isolated by FACS  (Bennett et al. , 2016). The RNA was extracted using a QIAGEN RNeasy Plus Micro kit and stored at -80°C prior to analysis. We obtained 150 bp paired-end RNA-Seq reads from an Illumina NextSeq 500 instrument. For 3 biological replicates of wild-type, Csf1r +/-, Csf2 +/-, and 2 biological replicates for Csf1r +/-;Csf2 +/-samples, an average of ~46 million pairs of reads per sample was obtained.
The computational pipeline for identifying differentially expressed genes (DEGs) has been described previously  (Wang et al. , 2017). Briefly, adapters and low quality bases in reads were trimmed by trim_galore: (http:// www. bioinformatics. babraham. ac. uk/projects/trim_galore/). The Kallisto (v0. 43. 1) software  (Bray et al. , 2016)  was employed to determine the read count and transcripts per kilobase million (TPM) for each gene that was annotated in the GENCODE database (vM15)  (Mudge and Harrow, 2015). Then we summed the read counts and TPM of all alternative spliced transcripts of a gene to obtain gene expression levels. To identify DEGs, 14,739 expressed genes with an average TPM > 1 were selected in any of the wild-type, Csf1r +/-, Csf2 +/-and Csf1r +/-;Csf2 +/-samples, using the software DESeq2  (Love et al.
Comparison of DEGs With Other Datasets-The DEG lists from Csf1r +/-or Csf2 +/- samples were compared to the DEG lists generated from other studies of microglia transcriptome changes associated with neurodegeneration  (Mathys et al. , 2017; Keren-Shaul et al. , 2017). In  Keren-Shaul et al. (2017)  and  Friedman et al. (2018) , DEGs were defined as FDR < 0. 05, as described in the original papers, while in  Mathys et al. (2017)  DEGs were defined by |z| > 2. The log2(fold-change) of those DEGs were used to generate heatmaps. MRI Imaging-Mice were imaged on an Agilent Direct Drive 9. 4 T MRI system (Agilent Technologies, Santa Clara, CA) as previously described  (Chitu et al. , 2015). Mice were anesthetized with 1. 5% isoflurane in room air, and respiratory rate and oxygenation saturation were monitored and maintained within normal ranges, while body temperature was maintained at 39°C, using a warm air circulator (SA instruments, Bayshore, NY).
The ratio between the diameter of an axon and the mean diameter of the myelinated fiber (G-ratio) was determined on 200 randomly chosen fibers (3-6 animals/ genotype) using ImageJ software (imagej. net). Age-related ultrastructural changes were identified according to the description provided by Peters and Sethares (Alan Peters and Claire Folger Sethares, The fine structure of the aging brain (http://www. bu. edu/agingbrain)) and quantified in 15 different microscopic fields / mouse (3-6 animals/genotype; average neurons/genotype 1118; range 929-1498). Immunofluorescence Staining of Brain Sections-Brain sections (30 μm thick) were obtained as described  (Chitu et al. , 2015)  and stained using antibodies to ionized calcium binding adaptor molecule 1 (Iba1) (rabbit IgG; Wako Chemicals, Richmond, VA or goat IgG; AbCam, Cambridge, MA), cystatin F (rabbit IgG; Fisher, Pittsburgh, PA), poly(ADP-ribose) (mouse monoclonal, Millipore, Billerica, MA), myelin basic protein (mouse monoclonal, BioLegend, Dedham, MA), PDGFRa (goat polyclonal, Minneapolis, MN), CC1 (a mouse antibody to APC reacting with Quaking 7, Millipore, Danvers, MA) and NeuN (mouse monoclonal, Millipore, Danvers, MA).
Rat anti-P2ry12 was a gift from Dr. Oleg  Butovsky (Harvard Medical School). Secondary antibodies, conjugated to either Alexa 488, Alexa 594 or Alexa 647, were from Life Technologies (Grand Island, NY). Images were captured using an Nikon Eclipse TE300 fluorescence microscope with NISElements D4. 10. 01 software. Quantification of cell numbers was performed manually. Quantification of fluorescent areas was performed using ImageJ. Images were cropped and adjusted for brightness, contrast and color balance using Adobe Photoshop CS4. Microglia morphometry-Brain sections (30 μm thick) were obtained and stained with Iba1 as described in the previous section. Z series stacks were acquired using a Leica SP5 Confocal microscope at 40x magnification with a 2 μm interval between images. Morphometric analysis of microglia was carried out in FIJI on maxiumum intensity projections of tissue sections from 3 mice/genotype using the protocol described by  Young and Morrison (2018).
QUANTIFICATION AND STATISTICAL ANALYSIS Statistical Analyses-Statistical analysis was performed using the GraphPad Prism 7 software (GraphPad, La Jolla, CA, USA). Data were screened for the presence of outliers using the ROUT method, assessed for Gaussian distribution by D'Agostino-Pearson omnibus normality test and analyzed using Student's t test, one-way ANOVA, the Kruskal-Wallis test or two-way ANOVA, as appropriate. Pairwise differences were identified using post hoc multiple comparison tests. The level of significance was set at p < 0. 05. Data within each group are presented as averages ± standard error of the mean (SEM). Only those differences that have reached statistical significance are indicated on the figures. Sample sizes for each experiment are indicated in the figure legends and more information can be found in Table  S9.