dataesr/scientific-paragraphs-categorization huggingface dataset:
The simulated rotation curves of the LG analogs are in rough agreement with the observations. Dwarf galaxies, as well as galactic disc features (such as bars, spiral arms, etc) are resolved. The satellite galaxies have similar stellar mass dis-tributions and radial concentration as observed. The Hestia runs also produce large Magellanic cloud and M33 like objects. The present HESTIA simulations successfully reproduce many of the observed features of the local universe on scales ranging from the 10 14 M Virgo cluster more than 10 Mpc away, the local filament (within which the LG resides) on the scale of a few Mpc, the inter-LG sub-Mpc scale and all the way down to sub-virial scales within the MW and M31 galaxies. The large scales are strongly constrained by the CF2 velocity data and are robustly reproduced by the simulations.
Going down to smaller and smaller scales, the constraining power of the data diminishes and the variance within the different realizations increases. The conclusion from this work is that the wide range of LG properties reported here -including the inner structure of the MW and M31, stellar and light distribution, distribution of satellites, environment -is reproduced within the constrained variance. Some of these properties are reproduced by some of the galaxies -not necessarily all of them. This opens the door to further studies on the nature of the LG and its formation. Three immediate routes are to be pursued: the increase of the ensemble of constrained simulations (thereby studying the statistical significance of our results); the increase of resolution (thereby studying the numerical robustness of the results); and experiments with the model of galaxy formation physics.
ACKNOWLEDGMENTS NIL acknowledges financial support of the Project IDEX-LYON at the  University of Lyon  under the Investments for the  Future Program  ( ANR-16-IDEX-0005 ). NIL & PW acknowledge support from the  joint Sino-German DFG research Project  " The Cosmic Web and its impact on galaxy formation and alignment " ( DFG-LI 2015/5-1 ). NIL, MSP, and PW thank the  DAAD  for PPP grant  57512596  funded by the  BMBF. CP acknowledges support by the  European Research Council  under  ERC-CoG  grant  CRAGSMAN-646955. YH has been partially supported by the  Israel Science Foundation  grant  ISF 1358/18. HC acknowledges support from the  Institut Universitaire de France  and the  CNES. AK and GY are supported by  MICIU /FEDER (Spain)  under research grant  PGC2018-094975-C21. AK further acknowledges support from the  Spanish Red  Consolider MultiDark FPA2017-90566-tREDC and thanks Wire for 154.
MATERIALS AND METHODS Strategy for preventing cardiovascular and renal events based on arterial stiffness is a multicenter study including 25 French clinical centers, among which 12 are Excellence Centers labelled by the ESH. It is investigator initiated and driven, funded by the French Ministry of Research (PHRC 2011-K110102 / N°ID RCB: 2012-A00023-40) and Fondation de Recherche sur l'Hypertension Arterielle (FRHTA). A list of the study sites is provided in Table  S1.
Study Design The trial is defined by the protocol NCT02617238 (http://www. clinicaltrials. gov/) as a multicentre randomised two parallel groups study using a prospective, randomised, open, blinded end-point design, aiming at comparing the efficacy of a therapeutic strategy targeting the normalisation of arterial stiffness for reducing CV and renal events (PWV group), in comparison with a classical therapeutic strategy implementing the ESH-ESC Guidelines  [16]  (conventional group), in patients with primary hypertension and medium-to-very high CV risk (Figure  1 ). Since treatment of hypertension is adjusted on the repeated measures of PWV in the PWV group, SPARTE study is truly a "target driven, long-term intensified intervention trial". • Target driven study: The target is not BP such as in ACCORD  [17] , CARDIO-SiS  [18] , JATOS  [19]  or UKPDS  [20] , but arterial stiffness.
Screening and Randomization Around 60 subjects were to be enrolled in each center upon verifying the study eligibility criteria listed in Table  1. Following a parallel group study design, eligible patients are randomized one-to-one in the treatment arms. The randomization list, created by the coordination center before the start of inclusions, uses a randomized block design, and is stratified by center and categories of CV risk. The randomization is centralized and made available to centres through the web-based software CleanWeb (Telemedicine Technologies, http://www. tentelemed. com/la-solutioncleanweb/). At randomization, the clinician obtains the patient allocation group after having recorded the patient characteristics required by the algorithm. In each centre, one or more investigators are in charge of the screening.
Data Collection The study uses an Electronic Case Report Forms [eCRF (CleanWeb, Telemedicine Technologies)]. All information required by the protocol is recorded on the eCRF. The data collected and stored outside eCRF (source data) is transcribed in the notebooks accurately and completely. Any handwritten note on the source data is dated, marked by initials and signed. An explanation is provided for each missing data. Erroneous data found on paper documents is clearly crossed and new data is copied next to the barred information, accompanied by the initials, date, and optionally a justification by the investigator or an authorized person who has made the correction.
Data Management Data management is performed by the CRU of HEGP. A list of the variables of the study size operational data model is developed by the CRU, in connection with the study investigators. This list will allow the development by the CRU of data collection specifications and computerized database (PostgreSQL format) where data is entered after quality control. A data-management plan, developed jointly by the data-manager, the investigator and the senior statistician are implemented. After correction of errors, the database will be frozen for statistical analysis.
Material. The stimuli were 72 object-scene pairs, plus six filler pairs for TNT practice. The objects were selected from the Bank of Standardized Stimuli  64, 65  , and depicted only nonliving and nonorganic artefacts. The scenes were selected from the Nencki Affective Picture System (NAPS) database  [66] [67] [68]. Half the scenes were categorized as neutral (36 images; mean valence = 5. 79, SD = 0. 84; mean arousal = 4. 78; SD = 0. 71). In Study 1, the other half were categorized as negative, and induced feelings of either disgust (18 images; mean valence = 3. 02, SD = 0. 76; mean arousal = 6. 42, SD = 0. 58) or sadness (18 images; mean valence = 2. 96, SD = 0. 49; mean arousal = 6. 43, SD = 0. 64). In Study 2, the other half all induced disgust (mean valence = 3. 15, SD = 0. 68; mean arousal = 6.
35, SD arousal = 0. 48). Owing to study time limits, we restricted our material to two negative emotions. We selected disgust and sadness, as these dimensions are the most represented in the NAPS database  69  , referring to the discrete quotation provided  67. As we aimed to obtain a homogeneous set, where each scene depicted as unique a content as possible, we manually added non-categorized images from the NAPS to this selection. For example, some images in the database depict the same scene from different angles. We excluded these pseudoreplicates after visual inspection. The neutral and negative lists were matched on numbers of animals, faces, objects, and people. Each object was paired with a scene, taking care to avoid any pre-existing semantic association (e. g. , umbrella as object, and rain in the scene) that might bias the memory encoding.
The valence SAM then appeared underneath the picture for 7 s (see Supplementary Fig. 1 ; the data for the arousal SAM in Study 1 are not shown here). This scale ranges from 1 (deeply unpleasant; frowning face on the far left of the scale) to 9 (pleasant; smiley face on the far right). Using the computer mouse, participants selected the facial expression that most closely matches the scene's perceived valence. If participants felt neutral (neither happy nor sad nor disgusted), they were instructed to click on the square under the figure in the middle. We recorded participants' ECG throughout the 20-min task, using the Biopac MP36R Data Acquisition system and its PC software AcqKnowledge 4. 1. For the purposes of another study, participants' breathing rate and electrodermal activity were also recorded.
All the participants undertook this emotional rating task in the morning (8 a. m. ) and were familiarized with the task settings using 10 independent images selected from the NAPS database. After an initial resting block of 2 min, the 72 images used in the TNT task (including baseline items) were presented in two blocks of neutral and negative scenes, whose order was counterbalanced across participants. These two main blocks were separated by a second 2-min resting block, to prevent potential overlap and contamination between the cardiac responses to negative and neutral scenes. In Study 1, each block of neutral and negative scenes was further divided into three sub-blocks of 12 images (10 s per image, 126 s per sub-block) corresponding to the Think, No-Think, and baseline conditions. Their order was counterbalanced across participants.
Criterion test. After learning and practising with the TNT task, participants performed an attentional task for 20 min using an entirely independent image set and intended to address a different question (data not reported here). At this point, participants viewed a brief reminder of all the studied pairs (3 s each), during which they were once again asked to reinforce their knowledge of the pairings. Following this pair refresher, they underwent a final criterion test in which they had to recall the correct image in a similar way to the test/feedback procedure, but this time without any feedback and only once. This allowed us to identify items forgotten before the TNT task and exclude them from the analysis. Think/No-Think task. Participants then performed the TNT task, which was divided into eight TNT blocks, each 5 min in length.
Electro-oculograms were recorded using four electrodes placed vertically and horizontally around the eyes. EEG data were processed offline using Netstation 4. 4. 2 (Electrical Geodesics Inc. , Eugene, OR, USA). The signal was filtered using a 1-Hz Kaiser FIR first-order high-pass filter (which ensures a linear phase and no distortion in the bandwidth), in order to discard DC and very slow waves. EEG preprocessing (study 2). EEG analyses were performed using Version 0. 18. 1 of the MNE library  75, 76  implemented in Python 3. 6, following recent recommendations  [77] [78] [79]. The EEG pipeline relies on automated steps, in order to promote the sharing of results and subsequent replication. We removed 13 peripheral electrodes (F11, FT11, T9, TP11, P09, I1, Iz, I2, P010, TP12, T10, FT12, F12) from the 115 EEG electrodes, owing to poor impedance quality or lack of contact with the scalp.
We low-pass filtered the raw data at a 30-Hz cutoff frequency using a FIR filter and referenced them to a common average reference. Poor sensors were corrected using the RANSAC algorithm implemented in the Autoreject library  80. Cue onset was adjusted for the delay (15 ms) between trigger generation and the display of the image on the screen in the Faraday cage, measured using photodiodes during preliminary tests. We then segmented the trials from -1,500 ms to 4,000 ms around the cue onset, and applied ICA correction to reduce eye blinks and muscular artefacts, based on visual inspection of the ICA components. Finally, the remaining artefacts were detected and corrected using Version 0. 1 of the Autoreject library  80  with the toolbox's default parameters.
Time-frequency analysis (Study 2). Our analysis focused on the contrasts between the No-Think and Think conditions during the TNT task, and between successful suppression (i. e. , nonintrusion) and intrusive trials in the No-Think condition. For the Think condition, we only selected trials where participants reported successful memory reactivation (feedback = 1). We kept all available No-Think trials and extracted the time-frequency representation for each epoch, using the multitaper method between 3 and 30 Hz (27 bins). After this procedure, we epoched the epochs from -500 to 3,000 ms around the onset of the reminder, to avoid edge artefacts, and baseline-corrected them using the percentage method, based on the 500 ms preceding the cue onset. The resulting time-frequency data were then down-sampled to 20-ms time bins for further analysis.
Statistical analysis. Analyses were performed using custom Python scripts (Python 3. 6) together with specific modules for EEG analyses  75  , the Pingouin statistical module  81  , and R packages (R Version 3. 4) for factorial analyses  82  and effect size calculation  83. Figures, scripts, statistical analyses and part of the data reported in this paper are available at https ://osf. io/pjv6d /. At the behavioral level, items that were not correctly recalled during the criterion test before the TNT phase were systematically excluded from later analyses (Study 1: 1. 2%; Study 2: 1. 1%). We analyzed behavioral results with an analysis of variance (ANOVA) incorporating all the available factors, and tested the suppression-related effects (e. g. Baseline vs No-Think) with planned comparisons.
We performed two-sided comparisons when no a priori hypothesis was formulated, and one-tailed comparisons when we expected to observe specific effects. To improve the clarity of our presentation, the figures do not report all the possible comparisons-only the relevant effects. For the ECG recordings, we compared neutral and emotional heart rate curves, running a paired t test at each time point. We used the fdr_correction() function implemented in MNE Python  75  to correct for multiple comparisons. For each group, we set the significance threshold at 0. 05 to assess the effect of emotions in the first session, and used a one-tailed t test to measure the effect of suppression in Session 2. For the EEG recordings in Study 2, we tested for statistical difference using nonparametric clustering statistics  84  as implemented in MNE-Python, specifying the connectivity matrix to account for the distance between the electrode sites.
Cardiac inhibition is linked to successful suppression of unwanted emotional memories. Results from Study 1 revealed a contrasting pattern, for while cardiac inhibition was found for disgusting items, suppression of sad memories induced a stronger deceleration. Given that we observed a stronger cardiac deceleration in response to negative scenes in the pre-TNT assessment, this post-TNT deceleration points to a stronger emotional reaction following memory suppression. Images associated with sadness were rated as more intrusive than disgusting ones, and were not associated with suppression-induced forgetting following the TNT phase (see Fig. 2 ). This pattern of findings suggests that direct suppression was ineffectual for these stimuli. This difference cannot be explained by a lower emotional valence, based on the ratings provided by both the NAPS database  67  and our own participants.
For instance, a recent MAKER-based optimized pipeline tested on 39 budding yeast genomes missed only 3. 9% of genes and 4. 8% of exons, on average  (SHEN et al. 2018). However, the results were poorer in intron-rich species, for which gene annotation is challenging. Even when RNA-seq data are available, it is still very difficult to correctly predict the exon-intron structure primarily because fungal exons can be extremely short  (JANBON et al. 2014) , but also because these genomes are compact. Thus, when tested on fungal data sets, de novo transcriptome assemblers like Trinity  (GRABHERR et al. 2011)  or Cufflinks  (TRAPNELL et al. 2010)  tend to predict very large transcripts with no biological relevance. Nevertheless, several pipelines have been published and sequencing centers like the Joint Genome Institute (JGI) and the Broad Institute have developed specialized pipelines to produce annotation drafts, which are very useful in large-scale comparison analyses  (HAAS et al.
Pipeline selection The RNA-seq mapped reads from C. neoformans H99 and C. deneoformans JEC21 from the EXPO30 condition (exponential growth at 30 C) were tested in the three pipelines for gene prediction. BRAKER1  (HOFF et al. 2016)  was performed with the default parameters plus the exclusion of alternative transcripts (--alternatives-from-evidence=false) using the three replicates (A, B, and C) as RNA-seq source. Cuff-CQ  (Cufflinks v2. 1. 1 ((TRAPNELL et al. 2010 )) /Coding Quarry v2. 0 (TESTA et al.
2015) ) and C3Q (Cufflinks v2. 1. 1/Cuffmerge/Coding Quarry v2. 0) were tested with the basic parameters: minimum intron length (30); maximum intron length (4000); minimum isoform fraction (0. 9); and overlap radius (10). The merged BAM file generated by the three replicates (A, B, and C) and used in the Cuff-CQ pipeline was obtained with Samtools merge. C3Q was performed separately for the three BAM files; the GTF files generated by the three predictions (for replicates A, B, and C) were then combined by Cuffmerge and the resulting transcripts were processed by CodingQuarry. The evaluation of the pipeline sensitivity and precision for gene prediction was performed by comparing the predicted annotations against the H99 and JEC21 reference annotations  (WALLACE et al. , 2020)  with the GFFCompare program (PERTEA AND PERTEA 2020).
Cufflinks parameters selection The selection of the best Cufflinks parameter combination was also performed with EXPO30 RNAseq libraries from C. neoformans H99 and C. deneoformans JEC21 according to the C3Q pipeline. For this, the Cufflinks transcript assembly generated for each EXPO30 replicate (A, B, and C) was tested with fixed and variable parameter combinations (Table  1 ). Subsequently, as established for the C3Q pipeline, the predicted GTFs were merged and processed by CodingQuarry. All combinations include minimum intron length 30; maximum intron length 4000; and minimum isoform fraction 0. 9; since we want to remove all isoforms. The variable parameters include: pre-mRNA fraction 0. 15 to 1. 0; small anchor fraction 0. 0; minimum fragments per transfag 1; overlap radius 1, 10 and 100; 3' trimming (--trim-3-avgcov-thresh and -trim-3-dropoff-frac) 0.
Evaluation of the effect of the sequencing depth on gene prediction quality The evaluation of the effect of the sequencing depth on gene prediction was performed by down sampling the three RNA-seq libraries from the EXPO30 condition (replicates A, B, and C) with the tool PositionBasedDownsampleSam from Picard package (https://broadinstitute. github. io/picard/). In this analysis, C. neoformans H99 and C. deneoformans JEC21 were used. According to a random algorithm that downsamples BAM files, we used defined fractions of  1, 5, 7. 5, 10, 15, 20, 30 and 40  million reads for each replicate. Subsequently, the predictions were performed according to the C3Q pipeline with the Cufflinks "Q" parameter combination using the downsampled files. Evaluation of the sensitivity and specificity of gene prediction was performed by comparison of the predicted annotations against H99 and JEC21 reference annotations with the GFFCompare program.
Characterization of novel and missed loci The identification of novel and missed loci was performed with the GFFCompare program using the and GFFCompare. In this process, we selected for transcripts predicted by Cufflinks with supporting RNA-seq evidence. Of these selected transcripts, the longest transcript was chosen. For the other genes predicted only from genome sequencing (without RNA-seq evidence), the longest transcript was selected. We assessed the sensitivity and specificity of the C3Q predictions for C. neoformans H99 and C. deneoformans JEC21 against their reference annotations to analyze the effect of dubious sequence deletion. Filter combinations with the low numbers of remnant novel transcripts and smaller reduction in the prediction quality parameters were favored. For a better understanding, the C3Q pipeline with the "Q" Cufflinks parameters, the subsampled BAM from RNA-seq libraries for all the sequenced conditions ("ES3037M" for C.
Retrieval of deleted and non-predicted loci The mapping of C. neoformans H99 protein sequences in the C. deneoformans JEC21 genome and JEC21 protein sequences in the H99 genome by Exonerate v2. 2. 0 program (https://www. ebi. ac. uk/about/vertebrate-genomics/software/exonerate) with the following parameters ( protein2genome --percent 30 --bestn 1 --minintron 30 -maxintron 4000 -showalignment false --showvulgar false --showtargetgff true --refine region --subopt false) was performed to recover sequences deleted in the previous filtering step with conserved orthology in Cryptococcus. For this purpose, the mapped gene coordinates matching previously predicted sequences (GFFCompare program) were used to add these deleted genes to the annotation with an in-house AWK script. The addition of non-predicted genes was performed by comparing the mapped protein sequence coordinates and the genomic regions without predicted genes.
Automatization of the C3Q pipeline The C3Q pipeline, an automatic gene predictor, was built with Python3 code (C3Q_genepredictor. py) and is available in Github (https://github. com/UBTEC/C3Q) The C3Q pipeline includes all established parameters for Cryptococcus genome annotation (C3Q6 protocol): -The Cufflinks assembly of transcripts for each RNA-seq library. -The merging of the generated GTF files by Cuffmerge. -The GTF conversion to GFF format (needed for CodingQuarry). -The training and genome prediction by CodingQuarry, using the merged GFF file and the reference genome. -The sequence filtering: deletion of small transcripts up to 150 nt and intronless transcripts up to 300 nt; deletion of genome-predicted sequences without supporting reads and deletion of alternative transcripts from multi-transcript loci.
Gene orientation analysis To determine the frequency of tandem genes with the same orientation, we searched for groups of two, three, four, or five genes assigned to the same DNA strand in the GFF file from C. deuterogatti R265 (with our new annotation), C. neoformans H99 (Genome assembly reference GCF_000149245. 1), and C. deneoformans JEC21 (Genome assembly reference GCF_000091045. 1) annotations. This was performed by analyzing the orientation of each gene pair in the GFF file from R265, H99 and JEC21, recording the frequency of genes converging (tail-to-tail), diverging (head-tohead), and in the same orientation (head-to-tail) in the whole genome and for each chromosome. This was executed using an in-house Python script (script gene_organization_analysis. py).
Antisense transcription analysis To evaluate the antisense transcription in the genomes analyzed, we first generated a reversed annotation, which consisted of a GFF file with genes assigned to the opposite strand of their actual annotation. With the annotation and the reverse annotation, we analyzed the percentage of antisense transcription for each protein coding gene using the software HTseq-count using the following attributes (-f bam -r pos -s yes -t CDS -i ID -m intersection-nonempty --nonunique none (. bam) (. gff)) and the distinct RNA-seq libraries. Sense/antisense counts ratios for each gene for each condition were plotted. The script used for generation of a reverse GFF is available (reverse_gff. py).
Availability and accession number Raw and summarized sequencing data are available at SRA with the accession number: PRJNA660459. The C3Q pipeline is available in Github (https://github. com/UBTEC/C3Q); Supplemental files available at FigShare. The final annotation of C. deuterogattii genome was submitted to NCBI and is available on accessions CP025759. 1 to CP025772. 1.