dataesr/scientific-paragraphs-categorization huggingface dataset:
2D fast low-angle shot (FLASH) acquisition pilot scans were performed in three planes: axial, sagittal, and coronal. A whole-brain image was acquired based on a 3D-FLASH protocol. The echo and repetition times were set to 5. 3 and 15. 0 ms, respectively. The flip angle was 30 • with a bandwidth of 3 kHz. The field of view (FOV), covering the whole brain, was 16 mm × 14 mm × 15 mm with a matrix of 320 × 280 × 300 points, leading to an isotropic resolution of 50 µm. Eight averages were performed, leading to an experimental time of  around 2 h and 50 min. One MRI data set (WT male) was excluded from further MRI analysis due to the presence of imaging artifacts. The MRI scans were linearly (six parameters followed by 12 parameters) and subsequently nonlinearly registered using a combination of mni_autoreg tools  (Collins et al.
14 software (Olympus). A 10× objective was used. Digital images were then analyzed with ImageJ. In order to bypass the high amount of background in our images, we devised a method of signal quantification. Briefly, images were transformed from RGB to 8 bit, with a resolution of 2,560 × 1,920 pixels, and mean, median, mode, and standard deviation of the gray values in the entire picture were obtained. With these values, the signal threshold for each picture was determined by subtracting a fixed amount of standard deviations from the mode of the entire picture, which represents the background staining. The number of standard deviations was kept constant within the entire series of pictures for each antibody (2 for MBP, 2. 5 for GFAP, and 3 for Iba1). The amount of positive signal marked by the threshold was expressed as the percentage area of total ROI and compared with two-way ANOVA with sex and genotype as factors.
We also included the hippocampus, given the extensive literature on its response to GCs. We observed a decrease in MBP expression in the hippocampus, which may relate to the long-term loss of white matter integrity in remitted CS patients. We did not acquire diffusion tensor imaging (DTI) data in our mice; thus, a direct comparison is not possible. A body of literature suggests inhibiting effects of GCs on oligodendrocyte function and proliferation  (Miguel-Hidalgo et al. , 2019 ; hippocampus and pyriform cortex) and white matter  (Dunlop et al. , 1997; Alonso, 2000; Quinlivan et al. , 2000). Work in multiple sclerosis models also supports negative effects of GCs on MBP expression and white matter  (Sieve et al. , 2004; Chari et al. , 2006). The reduced MBP expression seems a logical correlation to the reduced white matter volume in the MRI data of our mice.
FUNDING This work was supported by  CONACyT  ( 440584 ),  Initiative d'EXcellence-Initiative Science Innovation Territoires Economie (IDEX-ISITE)  initiative  16-IDEX-0001  ( CAP 20-25 ) and  Agence Nationale pour la Recherche  ( ANR-14-CE12-0007-01 ;  ANR-18-CE14-0012-02 ).
1% formic acid in milliQ-grade H 2 O, to 40% solvent B, containing 80% acetonitrile and 0. 085% formic acid in mQH 2 O. The mass spectrometer acquired data throughout the elution process and operated in the DDA PASEF mode, with a 1. 1 s/cycle, with the timed ion mobility spectrometry (TIMS) mode enabled and a data-dependent scheme with full MS scans in PASEF mode. This enabled recurrent loop analysis of a maximum of the 120 most intense nLC-eluting peptides, which were CID fragmented between each full scan every 1. 1 s. The ion accumulation and ramp times in the dual TIMS analyzer were set to 50 ms each, and the ion mobility range was set from 1/K0 = 0. 6 vs. cm -2 to 1. 6 vs. cm -2. Precursor ions for MS/MS analysis were isolated in positive mode with the PASEF mode set to « on » in the 100-1700 m/z range by synchronizing quadrupole switching events with the precursor elution profile from the TIMS device.
Protein Quantification and Comparison Mass spectrometry data were analyzed using MaxQuant version 1. 6. 17 (Max-Planck Institute of Biochemistry, Computational Systems Biochemistry, Heidelberg, Germany)  [42]. The database used was a concatenation of Rattus norvegicus sequences from the UniProt and-SwissProt databases (release 2020-10) and a list of contaminant sequences from MaxQuant. The enzyme specificity was that for trypsin. The mass tolerances for the precursors and fragments were set to 20 ppm. Carbamidomethylation of cysteines was set as a permanent modification, and acetylation of the N-terminus and oxidation of methionines were set as variable modifications. A second peptide search was allowed, and the minimal length of peptides was set to seven amino acids. The false discovery rate (FDR) was maintained at <1% for both peptides and proteins.
Label-free protein quantification (LFQ) was performed using both unique and razor peptides. At least two such peptides were required for LFQ. The "match between runs" (MBR) option was allowed with a match time window of 0. 7 min and an alignment time window of 20 min. For differential analysis, LFQ results from MaxQuant were quality-checked using PTXQC  [43]  and then imported into Perseus software version 1. 6. 14 (Max-Planck Institute of Biochemistry)  [44]. Reverse and contaminant proteins were excluded from the bioinformatic analysis.
Bioinformatic Analysis Protein set enrichment analysis was performed using GSEA version 4. 1. 0 (Broad institute, Cambridge, MA, USA). Proteins were ranked accordingly to the signal-to-noise metrics and enrichment was based on the weighted Kolmogorov-Smirnov-like statistic. A phenotype permutation was performed 1000 times for the analysis, and GSEA was used to identify protein sets against the Reactome pathway database. Reactome pathways defined by an FDR < 0. 25 and a p value < 0. 05 were considered significant. Proteins differentially expressed were analyzed using the WEB-based GEne SeT AnaLysis Toolkit (http://www. webgestalt. org, accessed on 12 october 2021). An over-representation analysis (ORA) against the Reactome, the Gene Ontology (GO), and the Mammalian Phenotype Ontology database was performed to identify the enriched Reactome pathways, biological processes, and possibly related phenotypes.
Statistical Analysis All data are reported as means ± standard error of the mean (SEM). Statistical analysis of all data was performed using GraphPad PRISM version 9. 0 (San Diego, CA, USA). Body-weight data were analyzed using Student's unpaired t-test. Two-way ANOVA, followed by a Newman-Keuls multiple comparisons test, was applied to the analysis of ROS assay data. Significance was set at p < 0. 05 for all tests. Sample reproducibility was analyzed by principal component analysis (PCA) performed on Log2 transformed LFQ values applying a filter of 100% valid values. Based on the PCA results, two outlier samples (1 CTRL P1 and 1 CTRL P4) were excluded from the analysis. Heat maps were built using Morpheus (https://software. broadinstitute. org/morpheus, accessed on 12 october 2021), and clustering was analyzed using raw values and columns using Pearson correlations on Z-score-transformed values.
The Microglial Proteome Early after Birth Reflects the Growth Restriction following LPD Exposure Growth restriction is one of the major features observed in animals exposed to antenatal LPD. Indeed, exposure to LPD from E1 to E17 significantly reduced the body weight of the embryos (t25 = 5. 437, p < 0. 0001) (Figure  2A ). There was also a significant reduction in body weight for LPD animals during the first postnatal day (P1 = t52 = 18. 35, p < 0. 0001; P2 = t20 = 11. 64, p < 0. 0001; P4 = t20 = 9. 02, p < 0. 0001). We performed a protein set enrichment analysis for differentially expressed (DE) proteins between CTRL and LPD microglia at P1 and P4 against the Mammalian Phenotype Ontology database. Protein sets associated with the body weight and body size were significantly enriched in LPD microglia at P1 (Figure  2B ).
We observed similar, but less pronounced, protein set enrichment in P4 LPD microglia (Figure  2B ). We then focused on the protein set "abnormal prenatal body size (MP: 0010866)" enriched in both P1 and P4 microglia. Log2 fold change (FC) values showed the deregulation of MP: 0010866 proteins at both P1 and P4, either down or upregulated in microglia by LPD exposure (Figure  2C ). The major deregulation of this protein set by LPD exposure observed at P1 was reduced at P4 (Figure  2C ). The MP: 0010866-associated proteins were analyzed against the Reactome pathway database to better understand their biological role. The analysis showed these proteins to be involved in the regulation of apoptotic processes and the NOTCH3 signaling pathway (Figure  2D ).
The Microglial Proteome Early after Birth Reflects the Growth Restriction following LPD Exposure Growth restriction is one of the major features observed in animals exposed to antenatal LPD. Indeed, exposure to LPD from E1 to E17 significantly reduced the body weight of the embryos (t 25 = 5. 437, p < 0. 0001) (Figure  2A ). There was also a significant reduction in body weight for LPD animals during the first postnatal day (P1 = t 52 = 18. 35, p < 0. 0001; P2 = t 20 = 11. 64, p < 0. 0001; P4 = t 20 = 9. 02, p < 0. 0001). We performed a protein set enrichment analysis for differentially expressed (DE) proteins between CTRL and LPD microglia at P1 and P4 against the Mammalian Phenotype Ontology database. Protein sets associated with the body weight and body size were significantly enriched in LPD microglia at P1 (Figure  2B ).
Protein Sets Associated with Inflammation Are Deregulated in the LPD Microglial Proteome A deregulated inflammatory response at the transcriptomic level has been previously reported in microglial cells isolated from LPD animals during the first postnatal days  [25, 26]. We performed a GSEA analysis against the Reactome database focused on Reactome pathways involved in inflammation and significantly upregulated in LPD microglia. Exposure to an LPD induced significant upregulation of proteins associated with the "NFkB signaling" and "interleukin 1 signaling" (R-RNO-9020702) pathways in both P1 and P4 LPD microglia. Similarly, we observed increased expression of proteins involved in the "cellular response to hypoxia" (R-RNO-1234174) and the "regulation of apoptosis" (R-RNO-169911) in the P1 and P4 LPD microglial proteome (Figure  3A ).
There was significant upregulation of the Reactome pathway "interleukin 17 signaling" (R-RNO-448424) in LPD microglia relative to CTRL only at P1, whereas the expression of proteins associated with "antigen processing cross presentation" (R-RNO-1236975) and "lysosome vesicle biogenesis" (R-RNO-432720) increased only at P4 in LPD microglia (Figure  3A ). Finally, proteins differentially expressed between CTRL and LPD microglia at P1 and P4 were analyzed against the gene ontology (GO) database targeting biological processes associated with inflammation. Among the significantly enriched biological processes, 8% were found to be associated with the development of an immune response and antigen processing. Proteins involved in the regulation of "lysosomal transport" (GO: 0007041) and "transport between endosome and lysosome" (GO: 0032510) were overexpressed in the LPD P1 microglial proteome relative to that of CTRL microglia (Figure  3B ).
We found pathways associated with inflammation to be significantly upregulated at P4 relative to P1 by GSEA analysis against the Reactome pathway database. Proteins involved in "NFkB signaling", "interleukin 1 signaling" (R-RNO-9020702), and "antigen processing cross presentation" (R-RNO-1236975) were upregulated in both CTRL and LPD microglia at P4. Similarly, proteins associated with cell-cycle-related pathways were found to be upregulated in P4 microglia relative to P1 for both CTRL and LPD microglia (Figure  5E ). Aside from these similarities, upregulation of several other Reactome pathways, including "nucleotide excision repair" (R-RNO-1236975), "DNA repair" (R-RNO-73894), "global genome nucleotide excision repair gg ner" (R-RNO-5696399), "oxidative stress induced senescence" (R-RNO-2559580), "cellular senescence" (R-RNO-2559583), and "epigenetic regulation of gene expression"(R-RNO-212165), occurred only in LPD P4 microglia (Figure  5E ).
Among Reactome pathways downregulated during development, we found that "mitochondrial translation" was significantly downregulated in P4 CTRL microglia rela- We found pathways associated with inflammation to be significantly upregulated at P4 relative to P1 by GSEA analysis against the Reactome pathway database. Proteins involved in "NFkB signaling", "interleukin 1 signaling" (R-RNO-9020702), and "antigen processing cross presentation" (R-RNO-1236975) were upregulated in both CTRL and LPD microglia at P4. Similarly, proteins associated with cell-cycle-related pathways were found to be upregulated in P4 microglia relative to P1 for both CTRL and LPD microglia (Figure  5E ). Aside from these similarities, upregulation of several other Reactome pathways, including "nucleotide excision repair" (R-RNO-1236975), "DNA repair" (R-RNO-73894), "global genome nucleotide excision repair gg ner" (R-RNO-5696399), "oxidative stress induced senescence" (R-RNO-2559580), "cellular senescence" (R-RNO-2559583), and "epigenetic regulation of gene expression"(R-RNO-212165), occurred only in LPD P4 microglia (Figure  5E ).
We focused on the developmental program of microglial cells, and our data show alterations of the microglial proteome within the first postnatal days, with abnormal expression of protein sets associated with the immune system and inflammation-related immune response. The evaluation of cell purity is a critical point to assess before the proteomic analysis of samples. In accordance with results previously reported at the RNA level [27], microglia represented the main cell type and there was only minor contamination with astrocytes and oligodendrocytes in the proteome dataset generated from CTRL and LPD microglia at P1 and P4. In contrast, some neuronal proteins were found to be expressed in our proteome dataset, although immunohistochemical analysis did not show neuronal contamination. This apparent discrepancy between the in vitro results and the proteome dataset can be explained by synaptic pruning and the phagocytosis of dying and living neurons by microglia  [11] [12] [13] [14] [15] [16] 47].
Nutrients 2021 , 17 Figure 5. 2021175 Figure 5. Early developmental changes in the microglial proteome are modified by exposure to LPD. (A,C) Heat maps showing the clustering in (A) CTRL at P1 and P4 and (C) LPD at P1 and P4. (B,D) Volcano plot showing differentially expressed proteins between (B) CTRL P4 and CTRL P1 and (D) LPD P4 and LPD P1. Upregulated proteins are depicted in red, downregulated proteins in green, and unchanged in black. (E) GSEA analysis performed against the Reactome pathway database. Upregulated protein sets in CTRL P4 vs. CTRL P1 and LPD P4 vs. LPD P1 are reported. (F) GSEA analysis performed against the Reactome pathway database. Downregulated protein sets in CTRL P4 vs. CTRL P1 and LPD P4 vs. LPD P1 are reported. * p < 0. 05 vs. respective P1 time point.
Stochastic simulations For verification of analytical results we use particle-based stochastic numerical simulations (MCell software  52  ). In MCell diffusion of individual molecules is modeled using Brownian dynamics, while chemical reactions occur due to the collision of molecules and follow Poisson distribution. All the parameters for simulations are identical to the parameters of analytical solution. The presynaptic domain of radius 300 nm and sensors are modeled as spheres, intersected by a reflecting plane in the origin (Fig. 2B ). The sensor is located in the origin of the volume and has a radius of 5 nm. Depending on the context of the simulation, the Ca 2+ input, number of calcium channels and the distance between the sensor and calcium channels were manipulated; for instance, for the computation of the occupancy probability by a single ion, it was released at time 0 from a single source.
Deterministic simulations The release rates can be simulated using a 5-state model of Ca 2+ triggered vesicle fusion  22  : V 0 5k on k off b 0 V 1 4k on 2k off b 1 V 2 3k on 3k off b 2 V 3 2k on 4k off b 3 V 4 k on 5k off b 4 V 5 γ → F, (30) where V i denote the binding states of the sensor (i. e. , the sensor with i calcium ions bound, and V 0 meaning the unbound state), and F is the fused state of the vesicle. The conventional values of the parameters are  22  : k on = 127 mM -1 ms -1 , k off = 15. 7 ms -1 , b = 0. 25, γ = 6 ms -1. We used these values for plotting Fig. 1. For this purpose, the system of ordinary differential equations describing this model was integrated using forward Euler scheme in a custom Matlab routine. The input Ca 2+ transients are results of the spatial deterministic simulations for the channel-vesicle arrangement as in Fig.
Acknowledgements This study was supported by the  Centre National de la Recherche Scien-tifique, Fondation pour la Recherche Medicale (Equipe FRM) ,  Agence Nationale de la Recherche  ( ANR-2010-BLANC-1411 ,  ANR-13-BSV4-0016 ,  ANR-17-CE16-0019 , and  ANR-17-CE16-0026 ), and  Ile de France  ( Domaine d'Interet Majeur  (DIM) MALINF:  DIM120121 ). M. R. was supported by the  Pasteur Paris University (PPU) doctoral program. The laboratory of D. D. is a member of the  Bio-Psy Laboratory of Excellence. We would like to thank  Yukihiro Nakamura  for sharing simulated calcium transients,  Jean-Baptiste Masson  and  Nelson Rebola  for the comments on the manuscript and discussions.
Methods Brain microvascular networks. We use a first large postmortem data set (~15,000 vessel segments in a ~1 mm 3 region) from the mouse vibrissa primary sensory (vS1) cortex obtained by  3, 70  (see Supplementary Note 1 and Supplementary Movie S1) and previously used for simulation studies by  8, 43, 64, 71, 72. Details on the procedures used for correcting the vessel's diameters to match the in vivo distributions and for classifying vessels into arterioles, capillaries, and venules are given in the Supplementary Material from  8. For comparison, we use a second dataset from the same cortical region, obtained from another mouse  3, 70  , as well as dense, space-filling networks of increasing complexity, including randomly generated networks, which reproduce well the spatial and functional statistics of the mouse capillary bed  39  (Supplementary Notes 3 and 8).
Resolving these velocity profiles within capillaries does not change much the travel time distributions and only slightly modifies the early travel times  64. Hence, at the network scale, the intravessel flow variability is negligible compared to the inter-vessel variability. All numerical procedures for blood flow simulations have been implemented in a custom-built C++ code  74  and the post-processing algorithms for the Lagrangian analysis have been developed in Python (V2. 7). First-order kinetics models of oxygen supply and amyloid-β clearance. In order to evaluate the characteristic times associated with oxygen supply and amyloid-β clearance, and following  14, 54, 75, 76  , we assume that they can both be described by the following first-order kinetics models (see Supplementary Notes 9 and 10): ∂c O 2 ∂t ¼ Àk O 2 c O 2 ð11Þ and dðc A β Þ dt ¼ dðc A β À c T A β Þ dt ¼ Àk A β ðc A β À c T A β Þ ð12Þ where k À1 O 2 ¼ τ O 2 r and k À1 A β ¼ τ A β are, respectively, the characteristic times for oxygen consumption in the brain tissue and for amyloid-β clearance.
Acknowledgements Research reported in this publication was supported by the  European Research Council  under  ERC  grant agreements  615102  (BrainMicroFlow) and 648377 (ReactiveFronts) and by the  NIH  (awards  R21CA214299  and  1RF1NS110054 ). We gratefully acknowledge  P. Blinder ,  P. Tsai , and  D. Kleinfeld  for sharing anatomical networks,  C. B. Schaffer  and  N. Nishimura  for inspiring discussions,  N. Nishimura  for critically reading a previous version of this manuscript, and  M. Berg  who developed the network flow solver. The funders had no role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript.
Statistics SKAT-O statistics were calculated using the SNP-Set (Sequence) Kernel Association Test, originally developed by  Wu et al. (2011). The test is implemented and maintained in an R package "SKAT" by Seunggeun Lee. This R package is in turn integrated in the Variant Association Tools (VAT) package. The method aggregates individual SNP score statistics in a SNP set and efficiently computes SNP-set level p-values. The SKAT-O statistics were calculated using all variants with types: "non-synonymous, " "splicing, " "stoploss, " "stopgain" or "ncRNA" (using VAT). Only genes with p < 0. 005 are reported. C-alpha statistics were calculated using C-alpha test for unusual distribution of variants between cases and controls, developed by  Neale et al. (2011). The C-alpha test is a twotailed test.
This test is integrated in the VAT tools package and also in the GEMINI framework  (Paila et al. , 2013)  for exploring genome variation. VAT tools was used to calculate the C-alpha statistics using 500,000 permutations for all variants with type: "non-synonymous, " "splicing, " "stoploss, " "stopgain, " or "ncRNA. " Only genes with p < 0. 005 are reported. GEMINI was used to calculate the C-alpha Z-scores (without permutations) using only non-synonymous SNPs. Only genes with Z-score < -5 or > 5 are reported.
Pathway Analysis Reactome pathways on the genes flagged as significant by the SKAT-O and/or C-alpha statistics was performed using 2   (Jassal et al. , 2020). The tool automatically performs an overrepresentation analysis. The main goal of the pathway analysis in this study is however to group the genes of interest into pathways and explore the possible involvement of these pathways in PXE phenotype modulation. Further, functional data for the significant genes was searched using the Pubmed database. Gene interaction networks were plotted using Genemania and Cytoscape.
Additionally, for each patient, dermal fibroblasts were seeded in triplicate and used as negative control. IL1B expression was evaluated by Quantitative PCR a LightCycler 480 (Roche, Vilvoorde, Belgium) using hypoxanthine phosphoribosyltransferase 1 (HPRT1) and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase (YWHAZ) as reference genes. Q-PCR data was analyzed using the qBase + software (Biogazelle, Zwijnaarde, Belgium). Differential gene expression was determined using the Ct method. To quantify statistical significance of differential expression, a 2sided Mann-Whitney U test was performed (non-parametrical test, not assuming an underlying normal distribution) using the Qbase + software. The significance level was set at 0. 05.
FUNDING ED was a Ph. D. Fellow of the  Research Foundation-Flanders (Belgium)  ( FWO14/ASP/084 ). This work was supported by a research grant from the  Research Foundation-Flanders (Belgium)  ( G. 0241. 11N )and a  Methusalem grant  ( BOF08/01M01108 ) from the  Ghent University. OV is a Senior Clinical Investigator of the  Research Foundation-Flanders (Belgium).
Cryo-EM data collection, processing, model building and refinement Cryo-EM grids were prepared with a Vitrobot Mark IV (FEI). Quantifoil R1. 2/1. 3 holey carbon gold grids (SPI) were glow-discharged for 30s. Then 3. 5 μL of 7 mg/ml protein sample was pipetted onto the grids, which were blotted for 3s under blot force -3 at 100% humidity and frozen in liquid nitrogen cooled liquid ethane. The grids were loaded onto a 300 keV Titan Krios (FEI) with a K3 direct electron detector (Gatan) and an energy filter. Images of the ghrelin-GHSR-Gi complex dataset were recorded with SerialEM 45 in superresolution mode with a pixel size of 0. 649 Å and a defocus range of -0. 8 to -1. 6 μm. Movies were recorded during a 2. 1 s exposure with 30 ms subframes (70 total frames) at a dose rate of 1. 176 e/Å 2 /frame.