dataesr/scientific-paragraphs-categorization huggingface dataset:
QTL positions The positions of the QTLs were drawn independently with a density proportional to the gene density of each species (taking gene distributions without transposable elements; FigS2 for the gene distribution of B. rapa and FigS3 for O. sativa). Gene physical positions were downloaded from public databases (for O. sativa : https://rapdb. dna. affrc. go. jp/download/irgsp1. html, file "Gene structure information in GFF format" for the computationally predicted genes; and for B. rapa: http://brassicadb. org/brad/datasets/pub/BrassicaceaeGenome/Brassica_rapa/V3. 0/). QTL physical positions were thus sampled at random among the gene physical positions, for each replicate of our simulations. As only genetic positions in centiMorgans were used during simulations, we had to convert the QTL physical positions into their genetic positions by using the genetic map for each situation (normal, HR, and boosted recombinations for both O.
Four different values of h 2 were tested: 1, 0. 8, 0. 5 and 0. 2. Note that h 2 is defined based on the F2 population, the later populations having different variances will also have different values of h 2. In the case of genomic selection, GEBV was calculated using the estimated marker effects and the population mean. Both were estimated in the F2 population and then every fourth generation (training populations) with ridge regression best linear unbiased prediction (RR-BLUP) using the rrBLUP R package  (Endelman 2011). This corresponds to the mixed model Y = Xβ + Zu + e, where Y is the vector (Nx1) of phenotypic values calculated as for phenotypic selection, X a vector (Nx1) of 1s, Z the genotype matrix (NxN marker ) and e the vector of residuals. The genotype matrix was coded as follows: Z ij = -1 if the marker j of individual i is homozygous for the allele of parent 1, Z ij = 1 if it is homozygous for the allele of parent 2 , and Z ij = 0 if it is heterozygous.
Consequences for genomic selection programs Battagin et al. (2016) wondered about the use of increased recombination for genomic selection and speculated that large datasets would be needed. Indeed, the calibration of genomic selection models is dependent on genomic blocks shared across individuals in the population; increasing recombination reduces the sizes of these blocks. Consequently, the calibration is made more difficult and more importantly the predictive power of the model decreases faster as one accumulates crossovers throughout generations. Nevertheless, our results show a benefit of increased recombination for genomic selection programs even when using a relatively modest training population of 250 individuals. We did observe as anticipated that the accuracy of genomic prediction was reduced under increased recombination (faster break-down of the linkage blocks).
Acknowledgement We thank  S. Mezmouk ,  E. Jenczewski ,  J. C. Montamayor ,  D. Livingston ,  C. E. Durel  and  R. Mercier  for constructive comments and discussion. This work has benefited from a  French State grant  ( LabEx Saclay Plant Sciences-SPS ,  ANR-10-LABX-0040-SPS ), managed by the  French National Research Agency  under an " Investments for the Future" program  ( ANR-11-IDEX-0003-02 ) and KWS, MARS, and  Secobra  which funded the salary of ET and  Ecole Doctorale FIRE -Programme Bettencourt  for support.
The protein identified in microglia EVs were qualitatively compared to the top 100 proteins described in ExoCarta, a web-based compilation of EV markers  [34]. From the exclusive as well as over-represented proteins present in P2-EV+ sample, 29 proteins were identified in this ExoCarta database (Fig. 4e ). Among these markers, 15 were exclusive to P2-EV+ and 14 were over-represented in P2-EV+ but also detected in P3-EV-. Although it could be possible to suggest the minor presence of EVs in P3-EV-, most of them were isolated in the P2-EV+ sample. As well, the 3 proteins exclusive to P3-EV-were not associated to EV markers and possibly resulted from the elution of non-EV aggregates (not shown). Otherwise, a similar submission of exclusive and over-represented proteins from P1-EV-did not allow any identification of EV marker and could correspond to the SEC void volume.
In addition, the cross-species neurotrophic effect supported by leech microglial EVs was recently described  [25]. Because the raw data from nanoLC-MS/MS analyses were submitted to a rat protein database, the low homologies on full EVs)-activated neurons with control ones (P3-EV-) and showed exclusive proteins in each sample as well as common signatures (Fig. 6a  and Additional file 2: Table  S3 ). In both amounts of EVs used to activate the neurons (10 6 or 10 7 EVs), the comparison led to the identification of a quite similar number of exclusive and common proteins between EV-activated and naïve neurons. In presence of 10 6 or 10 7 EVs respectively, 71 or 97 proteins were exclusive to the controls whereas 45 or 37 proteins were exclusive to the EV-activated neurons. The common signatures (1033 and 1004 proteins) were independently used in a relative quantification to highlight clusters of a few proteins significantly over-represented in the EV-activated neurons (blue frames) or in control neurons (yellow frames), as described in the heatmaps (Fig.
7 ). Indeed, the neuronal proteome was characterized in the context of neurite outgrowth assays conducted on rat neurons to evaluate the effects of leech microglial EVs. Because miRNAs can sequester target mRNAs, their presence in microglial EVs would therefore have the effect of varying the availability of neuronal transcripts for protein translation. In order to generate a list of mRNA targets potentially regulated by these miR-NAs, two target prediction softwares were used: TargetScan and miRDB  [35, 36]. TargetScan searches for targets by sequence homology between the "seed" sequence that corresponds to nucleotides 2 to 8 of the mature miRNA and the 3′UTR of the target mRNAs. The miRDB prediction software works with an algorithm developed by analyzing thousands of target miRNA-mRNA interactions from high throughput sequencing experiments.
Other mRNAs predicted as targets in our assays encode proteins involved in neuroprotection or neuronal differentiation. The mRNA encoding the RASGRP2 molecule, also known as CalDAG-GEFI, would be regulated by miR-1705. Importantly, this protein is induced in Huntington's disease. A decrease in the level of this protein makes it possible to induce a neuroprotective effect  [57]. Still predicted to be targeted by miR-1705, the mRNA encoding the NUMBL molecule, for Numb-like protein, interacts with the Notch molecule. Many data are available on their role during the neurogenesis  [58]. These mechanisms do not seem to occur in the adult state. In addition to the neuronal context, the induction of NUMBL showed inhibition of cell proliferation and even induction of tumor cell apoptosis in colorectal cancer  [59].
Nanoparticle tracking analysis (NTA) NTA was performed using a NanoSight NS300 instrument and an automated syringe pump (Malvern Panalytical Ltd, UK). The script was adapted as follows: samples were diluted (1:100) in filtered PBS and loaded using an automated syringe pump. The infusion rate was initially fixed to 1000 for sample loading and chamber filling and then decreased to 25 for video recording. A delay of 15 s was applied to stabilize the flow before acquisition. Video captions of 60 s were performed in triplicate for each sample with a camera level setting at 13 and a screen gain at 3. The NTA 3. 2 software was used to process the recorded movies with a camera level setting at 13 and a detection threshold at 3. PBS used for EV recovery was used for negative controls. As a control for ODG experiments, 200 µl of PBS were loaded at the bottom of the tube that was then processed exactly in the same conditions as the EV-containing samples.
Quantitative gene expression analysis Real-time quantitative PCR (qPCR) reactions were performed using Platinum ® SYBR ® Green qPCR SuperMix-UDG kit (Thermo Fisher Scientific, Waltham MA, USA) on a CFX96 ™ Real-Time PCR Detection System instrument (Biorad, Hercules CA, USA) with the following program: 2 min at 50 °C, 1 min at 95 °C, and 50 cycles of: 15 s at 95 °C, 15 s at 51 °C, 20 s at 60 °C. Data were analyzed with the CFX Manager software (Biorad, Hercules CA, USA). The relative gene expression of the different miRNAs of interest were standardized using the miRNA cel-mir-39 spike-in control and were calculated using the 2 -∆∆Ct method  [65].
MS data analysis All the MS data were processed with the MaxQuant (version 1. 5. 8. 3) software using the Andromeda search engine. The proteins were identified by searching MS and MS/MS data against Rattus norvegicus database or homemade H. medicinalis database described in detail  [24]. Trypsin specificity was used for the digestion mode with N-terminal acetylation and methionine oxidation selected as the variable. Carbamidomethylation of cysteines was set as a fixed modification, with up to two missed cleavages. For MS spectra, an initial mass accuracy of 6 ppm was selected, with a minimum of 2 peptides and at least 1 unique peptide per protein, and the MS/MS tolerance set to 20 ppm for HCD data. For identification, the FDR at the peptide spectrum matches (PSMs) and protein level was set to 0.
01. A label-free quantification of proteins was performed using the MaxLFQ algorithm integrated into MaxQuant with the default parameters. The analysis of the proteins identified was performed using Perseus (version 1. 6. 2. 3) software. The file containing the information from identification was used with hits to the reverse database, and proteins only identified with modified peptides and potential contaminants were removed. Then, the LFQ intensity was logarithmized (log2[x]). Categorical annotation of rows was used to define different groups after pooling replicates. Multiple-sample tests were performed using ANOVA test with a p-value of 5% and preserving grouping in randomization. The visual heatmap representations of significant proteins were obtained using hierarchical clustering analysis.
The analyses were conducted using a Zeiss Axiovert 200 M with a 63 × 1. 4 numerical aperture oil immersion objective. The neurite length was measured with NeuriteTracer ImageJ software program  [67]. For all assays, the significance was calculated by one-way ANOVA followed by Tukey post hoc test  [19]. Additional file 2: Table  S1. List of exclusive and common proteins represented in the Venn diagrams (shown in Fig. 4 ) corresponding to Perseus analysis generated from the analysis of SEC fractions. Table  S2. List of over-represented proteins identified in specific clusters after Perseus analyses (extracted from the two heatmaps shown in supplementary Fig. 4c  and d ) generated from the analysis of SEC fractions. Table  S3. List of exclusive proteins represented in the Venn diagrams (shown in Fig.
Acknowledgments The authors would like to thank  Loic Brunet  and  Anne-Sophie Lacoste  for TEM observation. The TEM observations were performed on a device of the BICeL-Campus CS core facility acquired through a funding from the  Agence Nationale de la Recherche ,  ANR  ( 10-EQPX-04-01 ). The authors gratefully acknowledge  Terry Gaasterland ,  Lee Edsall ,  Michael Baker  and  Eduardo Macagno  from  UCSD in la Jolla  for providing the Hirudo medicinalis assembled genome.
DISTRIBUTION OF THE hmqABCDEFG OPERON IN THE BURKHOLDERIA GENUS Based on complete and draft genome sequences available in the Burkholderia Genome database (http://www. burkholderia. com), we have performed two complementary analyses with hmqABCDEFG of B. ambifaria strain AMMD as a reference: (1) an alignment sequence analysis () and (  2 ) an orthology analysis. The BLAST analysis was run for the nucleic acid sequence of each gene composing the hmq operon, using the default settings  (Johnson et al. , 2008; Winsor et al. , 2008). The orthology analysis consisted in examining the similarity of the protein sequence and the flanking gene regions by the pair-wise Diamond searches  (Winsor et al. , 2008; Buchfink et al. , 2015). While that method may be more accurate than a BLAST analysis, an orthology analysis could miss some positive results if the hmqABCDEFG operon is in a different genomic location than the reference.
Thus, a combination of both methods was selected. Using the two analyses, we screened: (1) the seven genes composing the hmqABCDEFG operon based on the percentage of identity and (2) the species having all seven genes present in their genome. Sequences of the hmqABCDEFG operon are well conserved, with a nucleotide identity between 70 and 100%. Importantly, we only found instances of a hmqG homolog in a genome if it belonged to a hmqABCDEFG operon. Based on both analysis methods, the hmqABCDEFG operon was found in 6 out of 21 Bcc species: B. cepacia, B. ambifaria, B. pyrrocinia, B. ubonensis, B. contaminans, and B. stagnalis, plus in some still unclassified Burkholderia spp. -having at least one sequenced genome present in the Burkholderia. com database (Table  1 ). Furthermore, some strains of B.
THE SYNTENY OF THE hmqABCDEFG OPERON IN BURKHOLDERIA Given that the hmqABCDEFG operon has a heterogeneous distribution in Bcc, we decided to examine its genomic context. The number of complete genome sequences being limited, the analyses was performed on B. ambifaria AMMD, B. ambifaria MC40-10, B. cepacia ATCC25416, B. contaminans MS14, B. pyrrocinia DSM 10685, B. ubonensis MSMB22, B. pseudomallei K96243, and B. thailandensis E264, all available in the Burkholderia Genome database  (Winsor et al. , 2008). A multiple whole-genome alignment using progressiveMAUVE software was done to view conserved regions surrounding the hmqABCDEFG operon (Figure  1B )  (Darling et al. , 2010). By this analysis, we defined three groups: (1) B. ambifaria, (2) B. contaminans, B. pyrrocinia, B. cepacia, and (3) B.
, 2018; Noonan et al. , 2014). A single train was applied sequentially to each amygdala in experiment 1 and to the midline structure, ACC, in experiment 2. The impact of TUS was determined by examining brain activity over an 80-min period starting $30 min after the 40-s stimulation train began (STAR Methods). Activity was recorded not just from the stimulated site but from across the entire brain using functional magnetic resonance imaging MRI (fMRI). fMRI data from the stimulated animals were compared with data from an additional group of control individuals (n = 9) that had received no TUS. Note that depth of anesthesia and the delay between sedation induction and data acquisition were similar between the TUS and control groups (0. 7%-0. 8% and 0. 7%-1% range of expired isoflurane concentration, 1.
No suitable and available datasets were excluded: data from all TUS sessions where the sonication could be focused on the target coordinates were included. Sample sizes could not be predetermined statistically in the absence of a prior literature reporting relevant expected effect sizes; instead we adopted sample sizes similar to those reported in previous publications detailing interventional macaque functional magnetic resonance imaging (fMRI) studies  (O'Reilly et al. , 2013). Data collection and analysis were not performed blind to the conditions of the experiments.
Scanning commenced 1. 53 hours (SEM: 4 minutes) and 2. 38 hours (SEM: 4 minutes) after anesthesia induction in TUS and control sessions, respectively. In both cases data collection commenced when the clinical peak of ketamine had passed. Anesthesia was maintained, in accordance with veterinary recommendation, using the lowest possible concentration of isoflurane to ensure that macaques were anesthetized. The depth of anesthesia was assessed and monitored using physiological parameters (heart rate and blood pressure, as well as clinical checks before the scan for muscle relaxation). During the acquisition of the functional data, the inspired isoflurane concentration was in the range 0. 8%-1. 1%, and the expired isoflurane concentration was in the range 0. 7%-1%. Isoflurane was selected for the scans as it has been demonstrated to preserve rs-fMRI networks  (Neubert et al.
, 2015; Sallet et al. , 2013; Vincent et al. , 2007). Macaques were maintained with intermittent positive pressure ventilation to ensure a constant respiration rate during the functional scan, and respiration rate, inspired and expired CO 2 , and inspired and expired isoflurane concentration were monitored and recorded using VitalMonitor software (Vetronic Services Ltd. ). Core temperature and peripheral capillary oxygen saturation (SpO 2 ) were also constantly monitored throughout the scan. A four-channel phased-array coil was used for data acquisition (Dr. H. Kolster, Windmiller Kolster Scientific, Fresno, CA, USA). Whole-brain BOLD fMRI data were collected from each animal for up to 78 minutes. All fMRI data were collected using the following parameters: 36 axial slices; in-plane resolution, 2 3 2 mm; slice thickness, 2 mm; no slice gap; TR, 2000 ms; TE, 19 ms; 800 volumes per run.
Macaque MRI preprocessing The preprocessing and analysis of the MRI data  (Verhagen et al. , 2019)  was designed to follow the HCP Minimal Processing Pipeline  (Glasser et al. , 2013) , using tools of FSL  (Jenkinson et al. , 2012 ; https://fsl. fmrib. ox. ac. uk/fsl/fslwiki), HCP Workbench (https://www. humanconnectome. org/software/connectome-workbench), and the Magnetic Resonance Comparative Anatomy Toolbox (MrCat; www. neuroecologylab. org). The T1w images were processed using tools of FSL in an iterative fashion cycling through brain-extraction (BET), RF bias-field correction, and linear and non-linear registration (FLIRT and FNIRT) to the Macaca mulatta F99 atlas  (Van Essen and Dierker, 2007). The application of robust and macaque-optimized versions of BET and FAST also provided segmentation into gray matter, white matter, and cerebral spinal fluid compartments.
Segmentation of subcortical structures was obtained by registration to the D99 atlas  (Reveley et al. , 2017). The first 5 volumes of the functional EPI datasets were discarded to ensure a steady RF excitation state. EPI timeseries were motion corrected using MCFLIRT. Given that the animals were anesthetized and their heads were held in a steady position, any apparent image motion, if present at all, is caused by changes to the B0 field, rather than by head motion. Accordingly, the parameter estimates from MCFLIRT can be considered to be 'B0-confound parameters' instead. Each timeseries was checked rigorously for spikes and other artifacts, both visually and using automated algorithms; where applicable slices with spikes were linearly interpolated based on temporally neighboring slices. This procedure identified an epoch with strong noise contributions at the end of the last run of a single rs-fMRI session following amygdala TUS.
Accordingly, for one animal the last 254 volumes (out of a total of 2,400) were removed from further analysis. Brain extraction, bias-correction, and registration was achieved for the functional EPI datasets in an iterative manner, similar to the preprocessing of the structural images with the only difference that the mean of each functional dataset was registered to its corresponding T1w image using rigid-body boundary-based registration (FLIRT). EPI signal noise was reduced both in the frequency and temporal domain. First, the functional time series were high-pass filtered at 2000s. Temporally cyclical noise, for example originating from the respiratory apparatus, was removed using band-stop filters set dynamically to noise peaks in the frequency domain. Remaining temporal noise was described by the mean time course and the first two subsequent principal components of the white matter (WM) and cerebral spinal fluid (CSF) compartment (considering only voxels with a high posterior probability of belonging to the WM or CSF, obtained in the T1w image using FAST).
To examine the spatial extent of the neuromodulatory impact of TUS on activity coupling we extracted for every point in the brain, both subcortically and on the cortical surface, its average coupling strength with the fingerprint targets (Figure  2 , sub-panel i), excluding the amygdala and ACC. This approach allowed the creation of a quantified spatial map of the difference in average coupling between the control state and amygdala TUS, and between the control state and ACC TUS. For regions affected by TUS this difference will be large, while for all other regions this difference is close to zero. Statistical inferences on the anesthesia levels and associated physiological parameters were drawn in the context of generalized linear mixed-effects (GLME) models. These models considered the intercept, the TUS condition (control, amygdala, ACC), and the resting-state fMRI run index (1, 2, or 3) as fixed effects and the intercept and slope grouped per animal as random effects with possible correlation between them (as implemented in MATLAB, Mathworks, Natick, USA).
ACKNOWLEDGMENTS Funding for this study was provided by a  Wellcome Senior Investigator Award  ( WT100973AIA ), the  MRC  (program grants  MR/P024955/1  and  G0902373 ), a  Wellcome Trust Henry Dale Fellowship  ( 105651/Z/14/Z ), the  Wellcome Trust  ( UK Grant   105238/Z/14/Z ), The  Wellcome Centre for Integrative Neuroimaging  ( 203139/Z/16/Z ), the  BBRSC  (grant  BB/N019814/1 ), the  NWO  (grant  452-13-015 ), the  Bettencourt Schueller Foundation , and the  Agence Nationale de la Recherche  under the program '' Future Investments''  ( ANR-10-EQPX-15 ).
Data was analysed using FCS express6 (DeNovo Software). All samples were gated using forward vs. side scatter to eliminate debris. Bioinformatic analyses. To assess the transcriptome-wide methylation levels and to create the methylation level heatmaps, Trim Galore! (v0. 4. 0) with parameters "--stringency 3 -e 0. 2 -a TGGAATTCTCGGGTGCCAAGGA" was used to remove sequencing adapters and reads shorter than 20 nucleotides. Alignment to the hg38 reference genome was done with Bismark (v0. 14. 4) with parameters "-n 2 -l 50 --un --ambiguous --bowtie1 --chunkmbs 2048", to allow for up to two mismatches and to save unaligned and ambiguously mapping reads separately. Seqtk with parameters "-e 3" was used to remove the last three bases (a potential 'CCA' tail) from the unaligned and ambiguous reads followed by a second alignment attempt using Bismark.
05 were considered to be differently methylated. Samtools merge was used to combine aligned reads from all three steps. Reads with >1/3 methylated cytosines were discarded as they are likely artifacts from highly structured regions. The bismark_methylation_extractor with the "--bedGraph --counts --CX_context" options was used to extract methylated cytosines. Sequencing data from patient cells are deposited on dbGaP under the accession number phs000645. v4. p1. The sequencing data are available on GEO with the accession numbers GSE122600 and GSE125046. Gene set enrichment analyses were done using GOrilla [http://cbl-gorilla. cs. technion. ac. il/]  64. As running mode, we used "two ranked lists of genes" using all identified binding proteins as background list. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank everybody who provided us with reagents, in particular we thank  James Stevenin  for sending us recombinant SRSF2. We gratefully acknowledge the support of all the  Wellcome -MRC Cambridge Stem Cell Institute core facility  managers. This work was funded by  Cancer Research UK  ( CR-UK ) and the  European Research Council (ERC). Parts of this research in Michaela Frye's laboratory was supported by core funding from  Wellcome  and  MRC  to the  Wellcome-MRC Cambridge Stem Cell Institute. Juri Rappsilber 's laboratory was supported by a  Wellcome Trust   Senior Research Fellowship  ( 084229 ). Gracjan Michlewski 's laboratory was supported by the  MRC Career Development Award  ( G10000564 ),  Wellcome Trust   Seed Award  ( 210144/Z/18/Z ) and  Wellcome Trust   Centre for Cell Biology Core Grants  ( 077707  and  092076 ).