dataesr/scientific-paragraphs-categorization huggingface dataset:
3-250 Hz) were recorded and stored at 1000 Hz. LFP activity was recorded for 20 minutes while mice were in the cage before the saline/drug/DMSO injections. Behavioral data with automated tracking was also acquired (Neuromotive, Blackrock Microsystems Inc. , UT). Data were analyzed using custom-written MATLAB scripts. Brain network function before, during and after acute drug treatment Following 7 days of habituation, all mice were subjected to neurophysiological recordings for 3 days as follows: first day, all mice were injected with saline; second day, 8 and 7 mice were injected with drug or DMSO, respectively; third day, all mice were injected with saline. All mice were recorded in their cage for 20 minutes prior to injections. Data were collected and stored for further analysis.
Statistical Analysis For all mice, 20 minutes of home cage brain activity was compared with 20 minutes of brain activity following saline or drug/DMSO treatment. Four LFP channels for each structure were pseudorandomly chosen for analysis. LFP cross-structural coherence was calculated from the LFP pairs used for LFP oscillatory power analysis with the MATLAB (MathWorks, Natick, MA) mscohere function at a 1-second sliding window with a 1-second step. LFPs and coherence activity were averaged for mice in the 6 frequencies that demonstrated bands of increased connectivity for 20 minutes: delta 1-4 Hz (Amy-Hip, Amy-PrL, and Hip-PrL), theta 4-11 Hz (Amy-Hip and Hip-PrL), and beta 15-30 Hz (PrL-Hip). The LFP power and coherence activity data were averaged across the recording session. Data were compared before and after treatment in the two groups using a RM-ANOVA with Box-Cox transformation, followed by a Wilcoxon sign-rank test with false discovery rate correction.
QUANTIFICATION AND STATISTICAL ANALYSIS Data analysis was performed with Microsoft Excel, Imaris (Bitplane), and MATLAB; and statistical analysis was done with GraphPad Prism 7. All statistical details can be found in the figure legends and figures. p<0. 05 was the threshold of significance. Due to a poor perfusion, one data point in the experiment shown in Figure  4G  was more than 2. 5 SD greater than the mean for all 42 samples and was excluded from the analysis prior to unblinding.
Acknowledgments We are grateful to the following for their assistance and advice:  Rainbo Hultman ,  Benjamin Sachs ,  Lauren Rochelle ,  Daniel Urban ,  Scott Robertson ,  Jue Fan ,  Nicole Calakos ,  Fan Wang ,  David Piston ,  Martin Fischer ,  Francisco Robles Guerrero ,  Ramona Rodriguiz ,  William Wetsel ,  Cheryl Bock ,  Meilang Flowers ,  Thomas L. Brown ,  Lauren Burianek , and  Scott Soderling. Ki  determinations were provided by the  NIMH's Psychoactive Drug Screening Program , Contract # HHSN-271-2013-00017-C. Pharmacokinetic analysis was carried out by the  Duke Cancer Institute PK/PD Core Laboratory. This work was supported by  NIH  grants:  1R21NS081513  (PJN), 8P41EB015897 (GAJ),  2R01MH079201  and  5R37MH073853  (MGC); and  CIHR  grants:  MOP 93651 ,  12600 ,  89919  (MAMP).
Study design and participants/procedures The ANRS CO22 HEPATHER cohort, "Therapeutic Option for Hepatitis B and C: A French Cohort", is a national, multicentre, prospective, observational cohort study of patients with viral hepatitis B or C that started in August 2012 (ClinicalTrials. gov registry number: NCT01953458). The main initial objectives of this study were to quantify the clinical efficacy and safety of new hepatitis treatments in real-life. The details of the cohort have been previously specified (see references  [7]  and  [8]  for a complete description). This study was observational and the choice of treatment combination, treatment timing, and screening for HCC or the progression of fibrosis was granted by the physician. Nevertheless, national French recommendations based on the European Association for the Study of the Liver (EASL) guidelines  [9]  were followed.
Outcomes Study outcomes were all-cause mortality, subsequently classified into liver-related (LR) or non-liver-related (NLR) deaths, HCC incidence, and need for LT. The causes of death were classified by an adjudication committee comprised of two hepatologists (HF and MB) and one methodologist (CD). Adjudication was based on medical records and investigators completed a specific case report form. Data on HCC incidence included the number of lesions at diagnosis, the largest nodule size, total size, diagnostic imaging procedures, and treatment. Secondary study outcomes were SVR in the group of patients exposed to DAAs and clinical outcomes in patients with severe disease.
The Gray's test was used to compare the cumulative incidences between treated and untreated patients. Categorisation of continuous covariates was based on previously determined clinically relevant thresholds (all biological parameters) or quartile divisions. Missing covariate values were handled using indicators for missing data in the multivariate model. To better characterise the potential effects of SVR in patients exposed to DAAs compared with untreated patients, the exposure period was divided into: (1) on-treatment period (from the first to last day of DAA treatment, extended for 3 months), and (2) the period with a measurable SVR status (from 3 months after the last day of DAA treatment to the end of followup). These were regarded as time-dependent covariates in the Cox models. All analyses were performed with SAS 9.
6% in the first two treatments (BBCH00 and BBCH14) and 0. 5% for the last three (BBCH73, BBCH 81-83, BBCH97-99). To ensure the optimal crop coverage and to avoid product wastefulness, different volumes from 250 to 400 L•ha -1 were sprayed for the five treatments, in agreement with the vines canopy development (Table  1 ). From 2015 to 2017, annual surveys were carried out at harvest (end of August-September) for Esca foliar symptoms, both chronic (GLSD, Figure  2A, D ) and severe (apoplexy-Figure  2B, C )  [13]. From 2018 onwards, vines were analyzed three times per year for Esca symptoms, from pea-sized berries to harvest. Data related to the Esca presence and symptom severity were reported on a map of the vineyard, for each individual vine. These data were useful to determine which vines could be considered not affected by Esca disease and its spread in the studied vineyard over the years.
Thus, both annual (ratio between symptomatic plants of the year and the total observed) and cumulative (ratio between symptomatic plants that showed symptoms the previous year added to those showing symptoms in the year of survey and the total observed) Esca incidence were calculated. Vines from the transition rows between Control and LC2017 plots (i. e. , buffer zones) were not considered for the calculation. Esca cumulative incidence data were subject to statistical analysis by using a Mann-Whitney non-parametric test. For annual incidence, we decided to only focus on the trend and did not perform statistical analysis related to the erratic expression of foliar symptoms. (C) apoplectic cane beside other healthy (partial apoplexy) within the same vine; (D) a vine showing contemporary GLSD (red circle) and apoplexy symptoms.
The bioinformatic processing of reads was performed through a QIIME-based custom bioinformatics pipeline (Patent WO2017096385). A first quality control was used to remove adapters and chimeras  [21]. After that, reads were trimmed out from when they did not reach the appropriate quality parameters and then the operational taxonomic units (OTU) clusters were performed using 97% identity. Taxonomy assignation and abundance estimation were obtained comparing OTU clusters by using the SILVA database, version 132  [22]  and UNITE database version 7. 2  [23]  as taxonomic references and for bacterial and fungal microorganisms, respectively. Samples were analyzed for the Alpha-and Beta-diversity separately and by using the R package vegan  [24]  and using the OTUs counts. Regarding the Alpha-diversity, Shannon's index and observed richness were calculated and plotted against the conditions in analysis (treatment and wood type) and by using a two-way ANOVA test (p < 0.
Samples were introduced into the microelectrospray source at a flow rate of 120 µL•h -1. Spectra were acquired with a time domain of 4 mega words over a m/z mass range of 100 to 1000, and 300 scans were accumulated per sample. MS data (m/z) were submitted to NetCalc algorithm to get compound elemental chemical formula and to Masstrix (http://masstrix3. helmholtz-muenchen. de/masstrix3/, accessed on 8 October 2020) to obtain identification against Vitis vinifera databank (negative mode, 1 ppm error). The list was, thereafter, manually curated to remove isotopic forms ( 15 N, 18 O) and to revised wrong formula assignations. Statistical analysis was performed with the Perseus software (http://www. perseus-framework. org, accessed on 2 October 2019) that allowed us to identify significant m/z per conditions with multivariate ANOVA analysis (p < 0.
05). In a global approach encompassing all organs (leaves, canes, woods (two zones) and grapes (juice), the list of m/z was reduced to organ-significant ones and used to generate hierarchical clustering (HCA). In a second approach, the dataset list was split per organ in order to determine symptoms-m/z discriminant (p < 0. 05). Those organ subsets of m/z were then used to build principal component analysis (PCA) (RStudio (http://www. rstudio. com/, accessed on 28 August 2019) and Venn diagram. For specific sample comparison, elemental formula were classified into putative chemical functional categories (lipids, peptides, amino sugars, carbohydrates, nucleotides and phytochemicals/aromatic compounds) using the multidimensional stoichiometric constraints classification (MSCC) described by Rivas-Ubach et al.
Cumulative Incidence from 2015 to 2019 The count of the new symptomatic plants over the years led us to obtain a more realistic evaluation of the Esca spread in the vineyard and on the effect of LC2017 treatments on its expression. The first data on Esca cumulative incidence (2016, Figure  4 ) showed a uniform expression, with almost similar values between control and LC2017-treated plots (12. 77% and 12. 92%, respectively). This uniformity changed progressively over the 5-year survey period: in 2019, the cumulative Esca incidence reached 22. 93% in the control plots while it was 21. 81% in the treated plots. Contrary to annual incidences, in which the year's higher values were recorded either in treated or control plots, the cumulative Esca incidence values since 2017 have always been higher in controls, with increasing differences from those of treated plots.
chlamydospora and P. minimum and thus reduce Esca foliar symptoms in the field. However, no putative m/z corresponding to scytalone or isosclerone could be found in our FT-ICR-MS data list, making this hypothesis unable to be checked. To the best of our knowledge, our study and those of Di Marco et al. [33]  are the only ones to have tested a copper-based product beyond the role of pruning wound protector for GTDs control, also verifying its effect on leaf symptom expression in the field. Regarding the bacterial part of our microbiome analysis, the presence of Aureobasidium pullulans in the healthy wood is notorious. This strain was first reported as a potential biocontrol agent against Eutypa lata  [41]  and, more recently against Diplodia seriata in an in vitro study by Pinto et al. [42].
This recovered status of the Esca-affected plants treated with LC2017 was previously observed  [49]  in leaves of vines treated with sodium arsenite, now forbidden but able to control the emergence of Esca foliar symptoms in GTD-infected plants. This result could be related to the lower incidence of GLSD symptoms observed in the treated plots, thus strengthening the hypothesis that LC2017 eventually enlarges the asymptomatic phase of Esca-infected vines. HCA analysis of the whole metabolomic dataset straightforwardly discriminated the sample types (organ/tissue/juice) rather than the three modalities studied (H, DS, DAT). Thanks to analyses performed for each sample type data set, we reported a differential accumulation of metabolites between the three modalities, especially in leaves and canes, but also in wood samples.
Diversification of VANC-dependent anti-silencing systems within and across species In order to maintain the functionality of the recognition system, diversification of short motifs within VANDALs needs to be accompanied by parallel variations in their cognate VANC proteins. However, investigating the evolution of TE-encoding proteins is challenging because of the lack of reliable transcript annotations over TE sequences in reference genomes. To circumvent this limitation and determine the whole repertoire of VANDAL-encoded VANC proteins in A. thaliana, we carried out deep long-read Nanopore sequencing of cDNAs from ddm1 mutant plants (see Materials and Methods). By combining this high-quality dataset with a previous "gene-like" annotation of TEs  (Panda & Slotkin, 2020) , we could identify 160 VANDAL-encoded transcripts together with their orientation, precise transcription initiation, and termination sites as well as their exon-exon boundaries  (Fig 3A).
Detection of conserved domains in the Pfam database using Hidden Markov Models show that most VANC-like proteins contain the domain DUF1985, either alone or in association with the Ulp1 or DUF287 domains  (Fig 3C) , and these combinations broadly explain the phylogenetic clustering. Remarkably, while Ulp1 domain is conserved across the tree of life (Appendix Fig S7A ) and is associated with de-sumoylation activities  (Johnson, 2004) , DUF1985 and DUF287 have uncharacterized functions and are almost exclusively encoded by Mu-like elements (MULEs), named after the Mutator (Mu) DNA transposon of maize  (Robertson, 1978). 2007). Indeed, we found that this TE encodes a VANC-like protein containing both DUF1985 and Ulp1 domains  (Fig 3F)  and carries outside of its coding sequences a high density of the quasipalindromic 11 bp motif TAAACGATCGT, arranged in a one-helixturn periodicity (Fig  3G  and H ).
Bisulfite conversion, BS-seq libraries, and sequencing (paired-end 100 nt reads) were performed by BGI Tech Solutions (Hong Kong). For WGBS of transgenic plants, bisulfite treatment and library preparation were conducted as previously described  (Fu et al, 2013). In all cases, paired-end reads were trimmed using Trimmomatic program (version 0. 33) with the following parameters "ILLUMINACLIP:TruSeq3-PE. fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36"  (Bolger et al, 2014). Mapping of trimmed sequences to Arabidopsis reference genome (TAIR10) with option "-n 1 -l 20," removal of identical reads, and counting of methylated and unmethylated cytosines were performed by Bismark ver. 0. 15. 0  (Krueger & Andrews, 2011). MethylKit package v0. 9. 4  (Akalin et al, 2012)  was used to calculate differential CG methylation in 100 bp non-overlapping windows (DMRs) between epiRILs and wild type.
Significance of calculated differences was determined using Fisher's exact test and Benjamin-Hochberg (BH) adjustment of P-values (FDR < 0. 05) and methylation difference cutoffs of 40%. Metaplots of mCG across wt-derived VANDAL copies shown in Fig  1D  and Appendix Fig S4 were performed using deeptools v3. 4. 0 and DNA methylation data from wt, ddm1, and the indicated epiRIL in each case. DNA methylation ratio in 120 bp bin for each cytosine context was calculated and compared between WT and VANC1-TG. Bins whose change in CG methylation ratio was 0. 5 or more were determined as CG-hypoDMRs. Significance of decrease in DNA methylation for TEs in each cytosine context (Figs  4D  and 5E ) was accessed by value (Mn/Cn À Mt/Ct)/(1/√Cn + 1/√Ct), where Mn, Cn, Mt, and Ct are methylated cytosine (M) and total cytosine (C) counts mapped for each TEs in the non-transgenic (n) and transgenic (t) plants, respectively  (Fu et al, 2013).
Short motif detection DNA sequences in epiRILs CG-hypoDMRs and within annotated VANDALs were extracted using fastaFromBed command (bedtools) and analyzed by meme  (Bailey, 2011)  with the following parameters: -dna -oc -nostatus -time 18000 -maxsize 60000 -mod anrnmotifs 3 -minw 8 -maxw 12 -revcomp. For CUMULE (GenBank: AY524004), DNA sequences outside coding regions, predicted using GENSCAN  (Burge & Karlin, 1997) , were extracted and processed as described above. Presence of specific short-sequence motifs across all VANDAL copies was evaluated using fimo script. Isolated, intermediate (2-3 motifs/1kbp) and high (+4 motifs/1 kbp) density of motifs were determined by counting the number of motifs in 1,000 bp windows. Distance between consecutive motifs was obtained using closestBed command (bedtools) and the frequency distribution of these distances was represented as a heatmap.
Palindromic Index for motifs was calculated as the average fraction of palindromic DNA within motif instances using an in-home script (accessible at https://github. com/LeanQ/palindromes). DNA sequences in VANC1 CG-hypoDMRs overlapping VANDAL1/2/1N1/ 2N1 (N = 325) were used for prediction of VANC1-targeted motifs by DREME script of MEME software version 4. 11. 0  (Bailey, 2011). model (mqmsacn)  from the R/qtl package. Genome-wide significance was determined empirically for each trait using 1,000 permutations of the data. LOD significance thresholds were chosen to correspond to a genome-wide false-positive rate of 5%. To summarize epiQTL results obtained for the different copies of the same VANDAL family, LOD scores were first transformed to P-values using the following function in R: pchisq(LOD*(2*log(10)), df = 1, lower.
Functional annotation of TE-encoding genes Long reads from ddm1 plants were mapped on the Arabidopsis reference genome (TAIR10) using minimap v2. 11-r797 (H. Li, 2018)  with the following options -ax splice -G 30k -t 12 and STAR v2. 5. 3a  (Dobin et al, 2013)  with the following options --outFilterMultimapNmax 50 --outFilterMatchNmin 30 --alignIntronMax 10000 --alignSJoverhangMin 3, respectively. Previously published short reads  (Oberlin et al, 2017)  were also mapped on the Arabidopsis reference genome (TAIR10) using STAR  (Dobin et al, 2013). Transcript annotation was performed using the FLAIR pipeline  (Tang et al, 2020)  available at https:// github. com/BrooksLabUCSC/FLAIR. First, splicing junctions based on short-read sequencing data were extracted using "junc-tions_from_sam. py" script and used to correct ONT long reads using "flair.
py correct" script. Transcript isoforms were then detected using the "flair. py collapse" script, and transcripts supported by at least five long reads were retained. Annotated transcripts overlapping VANDAL elements were extracted using intersectBed and translated in silico using getorf vEMBOSS:6. 6. 0. 0. Putative VANCs proteins were identified by BLAST against the functionally characterized VANC21 and VANC6  (Hosaka et al, 2017). Conserved protein domains were detected using HHMscan (https://www. ebi. ac. uk/Tools/hmmer/ search/hmmscan) against the Pfam database.
Small RNA analysis Small RNA data from Col-0 wild-type inflorescence were obtained from  Creasey et al (2014). Reads were trimmed using the Trimmomatic program (version 0. 33) and first mapped on AT1TE56425 sequence using Bowtie2 with the following parameters: --local -very-sensitive. Mapped 24-bp-long reads were then extracted using Samtools and aligned on the collection of VANDAL1 and VANDAL1N1 sequences, excluding AT1TE56425, using Bowtie2 with the following parameters: --local --very-sensitive -k 10.
Acknowledgements We thank members of the  Kakutani and Colot groups  and especially  Raku Saito  for discussions and critical reading of the manuscript. This work was supported by  Japanese Society for the Promotion of Science (JSPS) KAKENHI  grant numbers  JP18K06348  to TS,  26221105 ,  15H05963 , and  19H00995  to TK,  Japan Science and Technology Agency (JST) CREST  Grant ( JPMJCR15O1  to TK), grants from the  Centre National de la Recherche Scientifique (IRP SYNERTE , to LQ), the  European Union Seventh   Framework Programme Network of Excellence EpiGeneSys  (Award  257082 , to VC), and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 948674  to LQ). PB was supported by a postdoctoral fellowship (code  SPF20170938626 ) from the  Fondation pour la Recherche M edicale (FRM).
Data availability Original Scripts are available on GitHub (https://github. com/LeanQ/ palindromes). WGBS of epiRILs is available at the European Nucleotide Archive (ENA) under project PRJEB47214 (https://www. ebi. ac. uk/ena/browser/view/PRJEB47214) and NCBI Gene Expression Omnibus (GEO) as GSE62206 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE62206). WGBS of VANC1 transgenic lines has been deposited in the DDBJ under project PRJDB12220 (https://ddbj. nig. ac. jp/resource/bioproject/PRJDB12220). Expanded View for this article is available online.
Purpose: Magnetic resonance imaging guided radiotherapy (MRgRT) allows treatment plan adaptation on the MRI of the day. For dose calculations, a structure-specific bulk relative electron density (RED) overwrite derived from a planning computed tomography (CT) poses as one possible treatment workflow. However, this approach introduces uncertainties due to assignment of mean densities and requires a planning CT. The aim of this study was to investigate the uncertainty of the used patient-specific (PSCT) dose calculation in contrast to the correct calculation on a CT and compare to MR-only workflows using population-based bulk ED (PBCT) and artificial intelligence-based pseudo-CTs (AICT). Methods: Twenty primary prostate cancer patients treated on the 1. 5 T MR-Linac were chosen from the clinical database, based on best visual congruence between the planning CT and daily MRI.
The existing concepts for MR-only RTP can be classified into voxel-based, atlas-based, and hybrid approaches  [13]. Atlas-based methods use a correlation of the MRI to a database of CT patient data, followed by a deformable image registration. However, even though Chen et al. reported a gamma pass rate (2 mm/2%) of more than 98%, the required calculation time of 30 min does not make this approach suitable for online MRgRT  [14]. In a further clinically introduced hybrid-based method, an mDixon MRI sequence was used to separate segments of the MRI into water, fat, and in-phase images  [15]. These separated images were used as a basis for a classification algorithm in combination with an atlas for the generation of a pseudo-CT  [16]. Recently, with the introduction of AI into clinical radiotherapy, Maspero et al.
Patient Data and Imaging All patients included into this study were treated at the University Hospital Tübingen between January 2019 and December 2021 under a phase 2 feasibility trial (NCT04172753) at the 1. 5 Tesla MR-Linac (Unity MR-Linac, Elekta AB, Sweden). All patients gave written informed consent. For all patients, the planning CT and MRI at the 1. 5 Tesla MR-Linac were acquired with the same positioning devices. CT images were acquired with a Philips Brilliance BigBore CT scanner (Philips Healthcare, Best, the Netherlands), whereby the imaging parameters for the CT scans were as follows: 120 kV, 920-924 ms exposure time, 113-223 mA tube current, 512 × 512 pixels image size, 2 mm slice thickness, and 1. 17 × 1. 17 mm 2 pixel spacing. Corresponding MRIs for the assessment of dose calculation uncertainty and AICT model generation were acquired using a T2-weighted MRI sequence with the following imaging parameters: echo time (TE) 277.