dataesr/scientific-paragraphs-categorization huggingface dataset:
Since then, DWI has benefitted from considerable technological advances, and the quality and number of published studies in the field of ADHD has progressively increased. A more comprehensive review with metaanalysis is therefore timely to provide a broader view of the findings, identify the most robust evidence, and highlight methodological considerations. Therefore, we conducted a comprehensive systematic review of DWI studies in ADHD, including a quality assessment of imaging data acquisition, preprocessing and analysis. We then performed, wherever possible, meta-analyses including individuals with ADHD of any age and, separately, children and adults. Finally, we conducted meta-regressions to test the effect of age, sex, and medicationnaïve status, followed by a sensitivity analysis including only highquality datasets.
Further, data on imaging data acquisition, preprocessing and analysis were extracted for the quality assessment (see below). Authors were contacted for missing data on imaging parameters included in the quality assessment. For the meta-analyses, we extracted peak coordinates and their effect sizes for FA, MD, AD and RD contrasts. In studies not providing exact effect sizes for peak coordinates, the study threshold for significance was interpreted as the effect size, as in previous reports  [11]. In cases where peak coordinates and/or effect sizes were not provided, we contacted the corresponding authors to obtain the missing data. P-values of peak coordinates were converted to t-values using the anisotropic effect size signed differential mapping (AES-SDM) utility (http://www. sdmproject. com/utilities/?show=Statistics)  [14].
Meta-analysis and meta-regressions We ran meta-analyses for diffusion metrics that had peak coordinates available from at least five suitable studies. As in previous meta-analyses  [11, 12] , we used the Signed Differential Mapping (SDM) software, version 6. 21  [15]  (https:// www. sdmproject. com/), to analyze regional differences in tract metrics between ADHD and TD control groups; and used the TBSS template for TBSS studies. Also consistently with prior studies  [11, 12] , we used a random effects model, and the same statistical threshold that was previously applied (p < 0. 005 at the voxel level with an extent threshold of 10 voxels). Meta-analyses were repeated in the pediatric sample (i. e. , children/adolescents <18 years) and adults separately; and then in children (<12 years) and adolescents separately (Supplementary Table  S2 ).
We also conducted a post-hoc sensitivity analysis only including datasets judged of high quality. Finally, we ran meta-regression analyses to test the linear effects of age, percentage of males and medication-naïve subjects. We chose these variables because they have been associated with variation in white matter characteristics  [16, 17]. Further, given that a previous report suggested that stimulant treatment could affect FA measures in children, but not adults  [16] , we tested the potential confounding effect of treatment exposure in the metaregression of age.
Quality assessment Among the included studies, 68. 7% were judged to be of low overall quality, mainly due to factors related to pre-processing (rated low-quality in 54% of studies) and/or acquisition parameters (rated low-quality in 46% of reports). Low-quality ratings for preprocessing were mainly due to lack of motion correction/ quality checks; those for acquisition to the use of non-isotropic voxels or lack of information. The quality assessment of individual studies is reported in Supplementary Tables  S4  and 5. Meta-analyses and meta-regressions Meta-analysis of TBSS studies comparing ADHD versus controls. Of the 43 TBSS studies included in the systematic review, 25 had available peak coordinates for case-control differences in FA and 18 in MD from non-duplicated datasets (see Supplementary Table  S2 ).
Therefore, these studies were included in two separate metaanalyses. The first meta-analysis included 25 TBSS studies, for a total of 32 datasets (26 in children and 6 in adults), comparing FA between 1348 individuals with ADHD and 1354 TD controls. As shown in Fig. 4  and Supplementary Table  S6 , the SDM analysis identified two clusters showing reduced FA values in the ADHD group compared with the TD control group. The right splenium of the CC, extending to the posterior cingulum, showed the most significant effect size and cluster extent (Fig. 4 ). The right body of CC exhibited the second-largest effect size and cluster extent. No clusters exhibited increased FA values in the ADHD group as compared with TD controls. The second meta-analysis included 18 studies, for a total of 23 datasets (19 in children and 4 in adults), comparing MD between 1051 participants with ADHD and 1101 controls, and did not identify any significant clusters.
The right splenium of the CC exhibited the largest effect size and cluster extent, and the right ATR showed the second-largest effect size and cluster extent. No clusters exhibited increased FA values in adults with ADHD as compared to controls. No significant cluster was identified when repeating the meta-analysis on casecontrol comparisons in MD separately in children and adults. Meta-analysis of high-quality studies. In the sensitivity analysis considering only the 6 TBSS datasets rated of high quality (Supplementary Table  S2 ), we did not observe significant differences in FA between ADHD and TD controls. Meta-regression analyses. We ran three meta-regressions including the 25 TBSS studies  (32 datasets)  comparing FA between ADHD and Fig. 4  Results of the meta-analysis of TBSS studies.
DISCUSSION We conducted the most comprehensive systematic review of DWI studies in the field of ADHD, meta-analyzing studies amenable to quantitative synthesis. The systematic review identified widespread alterations (mainly reduced FA) in individuals with ADHD as compared to controls, mostly in the fronto-striatal pathways, cingulum, and CC. The meta-analysis of TBBS studies included 32 datasets (of which six in adults) and identified the most consistent FA reduction in the right splenium (extending to the posterior cingulum), followed by the body, of the CC. Meta-regressions showed that these effects were not affected by sex or exposure to ADHD medication. However, lower FA was related to older age, and case-control differences did not survive in the pediatric metaanalysis. Conversely, the meta-analysis in adults mainly identified reduced FA in the right splenium of the CC and ATR.
g. , almost 90% of articles included in  Zhao et al. , 2022 [126]  were included in  Aoki et al. , 2018 [11] ). Our findings from 32 TBSS datasets further support the consistently reported callosal alterations in ADHD extending to the cingulum. Different mechanisms may lead to reduced FA, such as altered myelination, axonal density/diameter or fiber crossing  [127, 128]. As FA is a composite measure, we encourage future studies to consistently report additional metrics, e. g. AD and RD, as this may help understand whether white matter alterations are primary (e. g. related to myelination) or secondary to those in the gray matter from which the tract origins from (e. g. number of neurons). Both these mechanisms have been suggested as plausible in relation to ADHD pathophysiology  [113, 129].
PRISMA 2020 flow diagram for new systemaƟc reviews which included searches of databases, registers and other sources Results for each database are reported in Supplementary material, page 2 b) References of excluded studies are reported, with reasons, in Supplementary material, page 3 c) References of included studies are reported in Supplementary material, TableS2 and page 20 Identification of studies via databases and registers Identification of studies via other methods n o i t a c i f i t n e d I Records identified from (a) : Databases (n = 2581) Records removed before screening: Duplicate records removed (n = 1625) Records removed for other reasons (n = 0) Records identified from: Citation searching (from prior reviews) (n = 2) Records screened Records excluded (n = 956) (n = 795) Screening Reports sought for retrieval (n = 161) Reports not retrieved (n = 0) Reports sought for retrieval (n = 2) Reports not retrieved (n = 0) Reports excluded from systematic Reports assessed for eligibility (n = 161) review: 34 (b) No ADHD vs controls (n= 17) No peer reviewed original Reports assessed for eligibility (n = 2) Reports excluded (n = 0) articles (n = 14) No diffusion imaging (n = 3) Reports included in systematic review (n = 129) (c) Included TBSS studies eligible for MA (n = 43) TBSS studies excluded from meta-analysis: 18 (b) No group comparison data (n=3) No whole brain (n=3) TBSS studies included in meta-analysis (n=25, 32 datasets) (c) Duplicated samples (n=4) No coordinate table (n=8) a).
Quantitative proteomic analysis of the different particle subtypes We next performed a quantitative proteomic analysis by label-free mass spectrometry on 10k, sEVs, VLPs, ENPs and Mix samples from EO771, adjusted to the same amount of proteins. Proteins were identified combining the databases of mouse and murine virus proteins as in Fig. 1F. 6127 proteins in total, including 53 viral proteins, were identified by at least 3 peptides among 5 replicates in at least one type of sample (Suppl. Table  2 : VennDiagram). Venn diagram analysis showed that most of the identified proteins (3800) were shared between all groups (Fig. 3A , with Mix sample excluded from the Venn Diagram analysis for better visualization of the single fraction-specific components). Nevertheless, 303 proteins were found only in the 10k, 130 in the sEVs, 63 in the VLPs and 231 in the ENPs (Suppl.
Fig. 4B ). The most significantly enriched "cellular component" GO-terms found in all samples were focal adhesion and cell-substrate junction, suggesting that areas of cell interaction with each other and with their substrate were major sources of EVs/ENPs. Other enriched GO-terms included ribosome-and nucleus-related terms, which were especially significantly enriched in sEVs and in ENPs, while terms corresponding to lumen of various internal compartments (including ficolin-1-containing compartments) were more enriched in 10K and VLPs. We next used the state-specific protein analysis (SSPA) feature of the myProMS software (as described in Materials and Methods) to identify proteins specific to or significantly enriched in one or a group of samples (Suppl. Table  2 : tab "SSPA"). GO-term enrichment analysis on the top-100 most enriched specific proteins of each sample highlighted membranes of mitochondria, ER and secretory granules in the 10k, lumen of secretory granules and lysosomes as well as membrane of secretory and coated vesicles in ENPs, and nuclear components as well as both lumen and membrane components of organelles in sEVs.
3. 2 (Wyatt Technology) was used for data analysis. Baseline subtraction was performed. For size distribution analysis the weight density method with results fitting was applied. The sphere model was used. The refractive index for EVs (1. 37) was added for correct number density estimation. Light scatter (in relative signal), diameter (in nm) and UV absorption (280nm) were plotted against time. The AF4 eluted fractions were collected during time and pooled as indicated in Suppl. Fig. 2B , and concentrated to 65 µL using Amicon Ultra-2 10K filters (Merck Life Science).
For the quantitative analyses, liquid chromatography (LC) was performed as above with an RSLCnano system (Ultimate 3000, Thermo Scientific) coupled online to an Orbitrap Eclipse mass spectrometer (Thermo Fisher Scientific). Peptides were trapped onto the C18 column at a flow rate of 3. 0 µL/min in buffer A for 4 min. Separation was performed on a 50 cm nanoviper column (i. d. 75 μm, C18, Acclaim PepMapTM RSLC, 2 μm, 100Å, Thermo Scientific) regulated to a temperature of 50°C with a linear gradient from 2% to 25% buffer B at a flow rate of 300 nL/min over 91 min. MS1 data were collected in the Orbitrap (120,000 resolution; maximum injection time 60 ms; AGC 4 x 10 5 ). Charges states between 2 and 7 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Mass Spectrometry Data analysis: For identification, the data was searched against the Mus musculus (UP000000589_10090) UniProt database and a manually curated list of murine virus protein sequences using Sequest-HT through Proteome Discoverer (version 2. 4). The database of murine virus proteins includes protein sequences from all known mouse endogenous and exogenous retroviruses (523 sequences manually extracted from Swissprot) and from endogenous MLV envelope glycoproteins (53 sequences), translated from the nucleotide sequences of proviruses annotated as previously described  (Attig et al. , 2017). Enzyme specificity was set to trypsin and a maximum of two-missed cleavage sites were allowed. Oxidized methionine, Met-loss, Met-loss-Acetyl and N-terminal acetylation were set as variable modifications.
Carbamidomethylation of cysteins was set as fixed modification. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 6 Da for MS/MS peaks. The resulting files were further processed using myProMS  (Poullet et al. , 2007)  v3. 9 (https://github. com/bioinfo-pf-curie/myproms). FDR calculation used Percolator  (The et al. , 2016)  and was set to 1% at the peptide level for the whole study. Proteins were considered expressed if identified with at least 3 peptides among 5 replicates. The label free quantification was performed by peptide Extracted Ion Chromatograms (XICs), reextracted across all conditions and computed with MassChroQ version 2. 2. 1  (Valot et al. , 2011). For protein quantification, XICs from proteotypic and non-proteotypic peptides were used by assigning to the best protein, peptides shared by multiple match groups, and missed cleavages, charge states and sources were allowed.
Median and scale normalization was applied on the total signal to correct the XICs for each biological replicate (N=5) for total signal and global variance biases. Label-free quantification (LFQ) was performed following the algorithm as described  (Cox et al. , 2014)  with the minimum number of peptide ratios set to 2 and the large ratios stabilization feature. The final LFQ intensities were used as protein abundance. For principal component analysis (PCA), data were filtered to only allow proteins with at least 3 quantified peptide ions per sample and with all missing allowed across all samples. The LFQ values of the 3200 proteins selected were log10-transformed and the remaining (27%) missing values were imputed using the R package missMDA  (Josse and Husson, 2016). (which was not certified by peer review) is the author/funder.
The protein is declared as overexpressed in the first set and underexpressed in the second. The extent of overexpression is represented by the "best delta" which is the ratio of the largest step over the biggest mean. A statistical test relying on the general linear model with a negative binomial law is also performed to estimate the significance of the difference of means between the 2 sets. Finally, the p-values obtained for the whole dataset are corrected for multiple testing according to the Benjamini-Hochberg (FDR) method. For heatmap representations, log10-transformed LFQ values for proteins with more than 3 peptides were used. Euclidean distances were calculated for the clustering of proteins (rows) and samples (columns). Pheatmap R package was used for representation. All proteins were used for suppl.
Fig4A. The top 15 or 10 most significantly enriched proteins of each sample type (proteins with delta ≥50 ranked by adjusted p-value according to SSPA) were selected for representation in Fig. 3D. The 46 quantified viral proteins were used for Fig. 3E. Proteins described as DAMPs or PAMPs in  (Hoshino et al. , 2020)  were selected for representation in Fig. 4F. GO-enrichment for Cellular Component was used in two analyses: 1) the top 100 most expressed proteins and 2) the top 100 most SSPA-specific proteins in each group of particles. Enrichr R package was used for enrichment calculations  (Kuleshov et al. , 2016)   and ggplot2  was used for representation. The mass spectrometry proteomics raw data are being deposited to the ProteomeXchange Consortium via the PRIDE  (Perez-Riverol et al. , 2022)  partner repository (dataset identifier number pending).
mCherry and OVA quantification The amount of mCherry present in each sample was quantified measuring the fluorescence (excitation 585/emission 625) of 50 µl of each sample through a spectrophotometer (SpectraMax iD3), side-by-side with a standard curve of recombinant mCherry of known concentration. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The amount of OVA in each sample was calculated from the signal intensity of the ~ 40 kDa band, in the Western blot revealed with OVA antibody, using the curve of recombinant OVA with known concentration. Intensity of the bands was quantified using Image Lab software (Bio-rad).
Cytokine bead array A customized set of 17 cytokines (IFNγ, IL2, IL5, IL4, IL6, MCP1 (CCL2), IL13, IL10, IL17a, MIP1α, TNFa, MIP1β, IL12 p70, RANTES (CCL5), MIG (CXCL9), IL1α, IL1β) reported to be secreted by DCs  (Morelli et al. , 2001)  was selected to measure in the supernatant of the MutuDC by Cytometric Bead Array (BD CBA Flex Sets) following the (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted January 28, 2023. ; https://doi. org/10. 1101/2023. 01. 27. 525863 doi: bioRxiv preprint manufacturer's recommendations. 15 µl of each culture supernatant was used for the assay. Samples were acquired on a BD FACS verse cytometer and analyzed with the FCAP Array software.
ACKNOWLEDGMENTS We thank for fruitful discussions and tools:  Drs J. Helft ,  L. Saveanu ,  H. Acha-Orbea ,  L. Evans ,  J. P. Stoye ,  G. Boncompain ,  S. Doll ,  S. Fiorentino ,  L. Alaoui ,  M. Burbage ,  M. Gros ,  M. Jouve-San Roman ,  M. Rehmsmeier ,  L. Ringrose , and  C. Basse. This work was funded by  INSERM ,  CNRS ,  Institut Curie ,  French IdEx  and  LabEx  ( ANR-10-IDEX-0001-02 PSL ), the  MSCA-ITN  grant " TRAIN-EV " agreement No  722148 , grants from french  ANR  ( ANR-18-CE13-0017-03 ;  ANR-18-CE15-0008-01 ;  ANR-18-CE16-0022-02 ),  INCa  ( INCa_16083 ),  Fondation ARC  ( PGA12021020003189_3588 ), FRM ( FDT202106013265 ,  EQU201903007925  and  DGE20121125630 ),  Cancéropôle Île-de-France  ( 2013-2-EML-02-ICR-1 ), financial support from  "la Région Île-de-France " ( N°EX061034 ) and  ITMO Cancer of Aviesan and INCa  on funds administered by  Inserm  ( N°21CQ016-00 ) for MS analysis.
(E) Quantification of size and presence ("enveloped capsids") or absence (EVs) of a VLP structure in all particles of one representative sample of 200k analyzed by cryo-EM (from images at 50,000X magnification). (F) Proteomic identification of viral proteins in 200k pellets of EO771, 4T1 and MutuDC. Blue: proteins identified by specific proteotypic peptides. Green: proteins from our curated endogenous retrovirus database. (G) Western blot shown in C) incubated with antibodies anti-MLV proteins gag and env. (H) Infectivity capacity of the 200k from EO771 cells, shown by production of pseudotyped XG7 GFP-encoding virus from Mus Dunni-XG7 cells, 14 days after exposure to the EO771 200k. Pseudotyped virus in the supernatant of Mus Dunni-XG7 was evidenced by detection of GFP expression in parental Mus Dunni exposed to this supernatant (red), as compared to cells not exposed to the supernatant (blue).
008 amu after initiation of the Si peak. All data reduction was conducted offline using the Iolite data reduction software  60. Background intensities were interpolated using a weighted linear spline, and changes in mass bias with time were interpolated using a smoothed cubic spline. During separation of Si by cation exchange chromatography, several additional anionic species may pass through the column. Consequently, distinct chemical differences in the Si separates between meteorite groups may cause mass bias between the different objects analysed. Matrix effects imparted by SO 4  2-ions that are not retained by the cation exchange resin are suppressed by doping samples and standards with a sulfur solution; however, other species such as PO 4  -may be problematic. To this effect, we determined the concentration of residual impurities in the Si fraction using an iCAP ICP-MS located at the Centre for Star and Planet Formation, using multi-element calibrated reference solutions.
We determined the concentration of impurities for four samples representing the different sample matrices investigated in our study, namely, the BCR-2 terrestrial standard, the Orgueil carbonaceous chondrite, the QUE 97014 eucrite and the Krasnojarsk pallasite. No impurities above percent level relative to Si were detected, with Na, P, Cr and Mo being the only impurities present above 0. 1% (Extended Data Table  1 ). To assess the effect of these impurities on our measurements, NBS-28 standard solutions doped with 2% Na, P, Cr and Mo (relative to Si) were measured using the same analytical approach employed for all samples. This level of contaminants is 3-10 times greater than observed in our samples. No measurable effects were detected on the δ  29  Si or μ  30  Si values within the uncertainty of our measurements (Extended Data Table  2 ) and, thus, we conclude that the level of impurities present in our sample is negligible.
To assess the long-term reproducibility of our measurements, the deviation of individual μ  30  Si measurements from the mean μ  30  Si value of the sample (as reported in Supplementary Table  1 ) is calculated for all terrestrial standard and Orgueil measurements, following an approach similar to that utilized by ref. 61. The included samples were measured over a 19-month period and are therefore expected to give an accurate prediction of long-term external reproducibility. Extended Data Fig. 6  shows a histogram plot of μ  30  Si deviations from the sample mean for 10 terrestrial standard and 10 Orgueil analyses, which corresponds to a total of 148 individual analyses. The histogram does not include statistical rejection of measurements, but only manual rejection owing to non-statistical reasons such as abrupt changes in sample uptake or samples running out of solution towards the end of an analysis.
Mass-dependent Si isotope composition of meteorites Bulk-rock mass-dependent Si isotope data are reported as δ  30  Si values for all measured meteorites in Supplementary Table  1  and Extended Data Fig. 7. The long-term reproducibility of our measurements was evaluated using mass-dependent isotopic compositions of three terrestrial basalt standards: BHVO-2, BIR-1 and BCR-2. Isotopic analysis of these terrestrial rock standards includes multiple dissolutions of the same rock powder, as well as chemical purification of Si performed in several independent sessions over a 19-month period. The standards were measured under the same analytical conditions as the meteorites and repeated over many sessions. Repeat measurements of BHVO-2 returned an average δ  30  Si of -0. 26 ± 0. 02 (2 s. e. , n = 6), in good agreement with previously reported values.
Calculation of mass-independent values of SiC grains The average mass-independent μ 30 Si, ε  94, 95  Mo and ε  96  Zr isotope compositions of presolar SiC grains are calculated using data from the presolar grain database  19  and ε 145 Nd compositions were calculated using data from ref. 76. First, the raw isotope ratios for mainstream, X, Y and Z SiC grains are calculated from their mass-dependent values. Mass-dependent effects are then corrected for using true  29  Si/ 28 Si = 0. 05080, 98 Mo/ 96 Mo = 1. 45711,  90  Zr/ 94 Zr = 2. 96030 and 146 Nd/ 144 Nd = 0. 7219 ratios for internal normalization, assuming an exponential law. The μ  30  Si, ε  94, 95  Mo, ε  96  Zr and ε 145 Nd compositions of each SiC grain is then the deviation of the mass-fractionation corrected ratios from the solar ratios.