dataesr/scientific-paragraphs-categorization huggingface dataset:
The quality of EEG acquisition for each subject and session can also be verified in the corresponding header file where the impedance value for each electrode at the beginning of the recording is indicated: in all recordings the impedance was kept below 20 kΩ and in most cases values are below 10 kΩ. To validate the data quality a classic analysis of EEG and fMRI patterns was performed over the 20 participants of XP2. EEG power spectrum was first estimated using a multitaper Hanning approach in the 8-30 Hz frequency band. ERDs for each block and NF run were then computed. The baseline for ERD computation was computed in the last 10 s of rest (to exclude from the computation the event related synchronization occurring after motor imagery in the first seconds of the rest block, Fig. 6 ). We performed a k-means cluster analysis on individual ERD and ERS features and identified two outliers (xp211, xp212) showing, at least in one NF run, artefactual ERD.
A boundary element method (BEM) volume conduction model based on the template MRI (semiautomatically aligned with EEG fiducials) was computed and used for inverse model estimation. Average source ERD results (Fig. 6c ) indicate a desynchronization over the precentral end postcentral left gyri (corresponding to the sensory-motor cortex of the right upper limb). fMRI analysis was performed using a processing pipeline based on Nipype, an open-source neuroimaging software that facilitates workflow reproducibility (https://github. com/nipy/nipype, version 1. 2. 0, Python 3. 6). Preprocessing steps included slice timing and motion correction, detection of motion and intensity outliers, segmentation of anatomical image and coregistration, smoothing (fwhm = 6). A fist-level and second-level GLM analysis were also performed across the three NF runs (canonical HRF, voxel-based analysis, Family-Wise error (FWE) correction p = 0.
05). Individual results were normalized to the Montreal Neurological Institute (MNI) template before performing a one-sample T-test group analysis. More details about MRI data processing can be found in the processing scripts available on github (https://github. com/glioi/BIDS_fMRI_analysis_nipype) together with schematics of the workflow steps. The motion-related and intensity outliers were detected using the ArtefactDetection tool implemented in Nipype (motion threshold = 2, intensity z-score threshold = 2): an average of 12. 1 ± 5. 3 scans per NF training run (corresponding to 3. 3% of artefactual scans per session) were detected and excluded from the following GLM analysis. In line with results in literature, average BOLD activation maps (Fig. 7 ) show significant activations in the premotor cortex and supplementary motor area (left and right), and in the left primary motor cortex during the motor NF task.
These areas are involved in the planning and imagination of movement. The left posterior parietal cortex (PPC), that plays an important role in visuo-motor coordination, was also recruited during the motor imagery NF task, in line with influencing literature  6  indicating that PPC is generally active when feedback is presented visually. Results are quite robust across the two groups (2dNF and 1dNF) that exhibit largely overlapping contrast maps, as shown in Fig. 7b. To illustrate some possible uses for data fusion and integration and to validate derivatives data, we performed two analysis, one on each dataset. The first one follows the methodology proposed in  27  to estimate sources localisation using a joint EEG and fMRI sparse model and extend the analysis to XP1 population. The second shows the methodology to learn a fMRI informed EEG filter aiming at improving NF sessions using EEG only, as proposed in  17  : we will synthetically illustrate the methodology in a data integration perspective and show representative results.
A data fusion approach for joint EEG-fMRI source estimation (XP1). To estimate source location on the XP1 dataset, we applied the symmetric bi-modal method proposed by  27  to the motor localizer (motorloc) runs. The proposed approach uses a sparse regularisation for the sources and combines the two modalities EEG and fMRI to improve the spatio-temporal resolution of the source estimation. The problem writes as follow: With S the estimated sources, G the leadfield matrix, E the observed EEG signals, B the observed BOLD signal and H a matrix linking EEG time samples to BOLD time samples using the hemodynamic response function. We chose different values for α (1 to consider EEG only, 0. 6 the optimal balance between both modalities as found in  27  and 0 to consider fMRI only), and the optimal sparsity parameter λ = 2.
A sparse EEG-informed fMRI model to improve EEG NF only (XP2). We intended to learn fMRI-NF scores from EEG signal as proposed in  17  in order to determine if fMRI information could be integrated to EEG, on the dataset XP2. The learning and testing process is illustrated in Fig. 9a. The method proposes a model able to learn fMRI-NF scores from bi-modal NF sessions, using EEG signals only. The model learns some parameters of a design matrix composed by different frequency bands between 8 and 30 Hz, and different time delays to better match fMRI delays. A mixed norm is applied (l 1 on the electrodes dimension and l 21 on the frequency bands dimension) to the model to regulate the parameters. We excluded from the analysis the two subjects with a bad ERD, as detailed above. For each subject, one of the sessions was used to learn the model, and the two other NF sessions were used to test the model.
To validate the efficiency of the prediction, we correlated the estimated fMRI-NF scores with the fMRI-NF scores of the testing sessions, Fig. 9b  shows boxplots in blue of such correlation across the dataset, for the learning phase and the testing phase. Information from fMRI could be extracted using EEG signals, as EEG signals could predict fMRI-NF scores with a median correlation of 0. 34 with the ground truth. The model is quite well adapted to the data as shown by the learning step scores. Figure  9b  also shows the correlation of the estimated EEG-fMRI-NF scores using only EEG-NF scores, with the ground truth that is the EEG-fMRI-NF scores. Figure  9c  shows an example of prediction on subject sub-xp216, the estimated NF scores have been learned on its first NF run and tested on its last run.
Usage Notes This dataset of simultaneously acquired EEG and fMRI during a NF motor imagery task has potential to shed light on the coupling model underlying the EEG and fMRI signals, to advance methodologies for multimodal data integration and test EEG de-noising methods. The design of the platform and the data collection itself represented a challenge and a veritable research advancement: this constitute a major added value of this dataset but also implies that some technical challenges during data acquisition (especially for the first experiment XP1) led to missing training sessions in some subjects. Another limitation of this dataset is the quality EEG acquired during MRI imaging. As showed in the technical validation section and in previously published work, however, average results are in line with literature thus indicating the overall quality of EEG recordings.
Even if the definition of the BIDS standard for EEG is still evolving  28  we tried to adhere to this standard to have an easily and robustly exploitable EEG-MRI BIDS dataset, that can be processed using predefined and standardized pipeline (such as fMRIprep, BIDSHandler and various other BIDS-Apps, https://github. com/BIDS-Apps). As mentioned in the previous section common and freely available software packages for EEG and MRI data processing were used to analyse data (SPM, Nipype, FieldTrip), except for EEG pre-processing for gradient and BCG artefact correction, for which the Brain Vision Analyser software (Brain Products GmbH, Gilching, Germany) was used. Other scripts used for the Technical Validation in this paper are provided by the authors as specified in the Code Availability session.
Code availability A detailed description of the bimodal EEG-fMRI NF platform is given in  20  : the platform software package for realtime analysis and visualization is well documented but not publicly available. Python pipelines for the analysis of structural and functional MRI are available on github (https://github. com/glioi/BIDS_fMRI_analysis_nipype), in form of commented jupyter notebooks. Other scripts used for the technical validation in this paper can be provided by the authors upon request.
**p < 0. 01; ***p < 0. 001; ****p < 0. 0001; ns, not significant. See also Figures  S3, S4,  and S5  and Data S1  and S2. frameshift and premature stop codon in both transcripts of the arcp-1 gene (Figures  2F  and S5A ). Second, we independently found several alleles of arcp-1 in a collection of sequenced mutants that suppress aggregation behavior of npr-1(null) animals, including two that introduced premature stop codons. The number and kind of these alleles made it likely that disrupting arcp-1 caused an aggregation defect. Consistent with this hypothesis, the aggregation defect of one strain (db1082 allele) mapped to a 1 Mb interval on chromosome III, centered on arcp-1 (Figures  2F  and S5A ). Two mutants from the million mutation project  (Thompson et al. , 2013) , harboring arcp-1 alleles (gk856856 and gk863317) that introduce premature stop codons, were also defective in aggregation and bordering (Figures  S5B  and S5C ).
PDE-1 and ARCP-1 Inhibit Expression of FLP-19 Neuropeptides To investigate further how disrupting arcp-1 alters BAG function, we specifically labeled these neurons with GFP, used fluorescence-activated cell sorting (FACS) to isolate the fluorescent cells from acutely dissociated arcp-1;npr-1 and npr-1 control animals, and profiled their gene expression using RNA sequencing (RNA-seq) (see STAR Methods). Genes that are hallmarks of BAG, such as those involved in CO 2 signaling (gcy-9, pde-1, flp-17) and BAG cell fate determination (ets-5)  (Guillermin et al. , 2011; Hallem et al. , 2011) , were among the top enriched genes in our dataset (Data S4). arcp-1 itself was among the 100 most highly expressed genes in BAG. The BAG profiles highlighted significant gene expression differences between arcp-1 mutants and controls, notably changes in the abundance of mRNAs encoding neuropeptides, genes involved in ciliary intraflagellar transport, ion channels, and gap junction subunits (see Discussion; Data S4D).
The genomes of JU1249 and JU2825 were sequenced on an Illumina Hiseq4000 at 20x coverage with paired-end 150 bp reads. For each genome, the raw data were aligned to the reference genome (C. elegans WS243 masked from http://wormbase. org) and analyzed using BWA, SAMtools, Picard and Genome Analysis Toolkit (GATK)  (Li and Durbin, 2009; Li et al. , 2009; Van der Auwera et al. , 2013). The accession number for the genomic sequence data of JU1249 and JU2825 is NCBI: PRJNA514933 (https://www. ncbi. nlm. nih. gov/genome/?term=PRJNA514933). From the output BAM files, homozygous SNPs between the two strains were called and filtered with a raw read depth threshold of 10-300. Allele quantification for the III_663310 polymorphism was performed using pyrosequencing as previously described  (Duveau and Felix, 2012).
html?redirectsrc=//www. ensembl. org%2Finfo %2Fdocs%2Ftools%2Fvep%2Findex. html) (McLaren et al. , 2016). The mfP22 deletion was verified by PCR and Sanger sequencing, using primers listed in Table  S2. The accession number for the genomic sequence data of the replicate populations is NCBI: PRJNA515248 (https://www. ncbi. nlm. nih. gov/genome/?term=PRJNA515248).
Distribution of mfP22 allele in wild isolates To examine the distribution of the mfP22 deletion in C. elegans wild isolates, we monitored the presence of the deletion visually, using Tablet 1. 16. 09. 06  (Milne et al. , 2013) , for 151 isotypes with whole genome sequences in the CeNDR database  (Cook et al. , 2016). The mfP22 allele was only found in JU1249 (Data S2).
Calcium imaging L4 animals expressing a ratiometric yellow cameleon sensor were picked 24 h before imaging. Animals were glued to agarose pads (2% agarose in M9 buffer, 1 mM CaCl 2 ) using Dermabond tissue adhesive (Ethicon) with the nose immersed in a mix of bacterial food (E. coli OP50) and M9 buffer. To deliver gas stimuli, glued animals were placed under a microfluidic chamber with inlets connected to a PHD 2000 Infusion syringe pump (Harvard Apparatus) running at a flow rate of 2. 5 ml/min. An electronic valve system placed between the syringes and the microfluidic chamber allowed switching between two different gas mixtures in a controlled manner at prespecified time intervals. Imaging data were analyzed using Neuron Analyzer, a customwritten MATLAB program (code available at https://github.
ACKNOWLEDGMENTS We thank  Roger Pocock  ( Monash University, Australia ),  Niels Ringstad (New York University School of Medicine, USA) , and the  Caenorhabditis Genetics Center , funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ), for strains. We gratefully acknowledge  Tim Stevens ,  Gurpreet Ghattaoraya , and members of the de Bono and Schafer labs for advice and comments. This work was funded by an  Advanced Grant  from the  European Research Council  to M. d. B ( ACMO 269058 ) and by the  Medical Research Council (United Kingdom). I. B. received an  EMBO   Long-term Fellowship  ( ALTF 387-2015 ) and  EU Marie-Curie Individual Fellowship  ( MSCA-IF 704383 ) and is a fellow of the  Research Foundation-Flanders (FWO). Work in the Felix lab was funded by a grant from the  Fondation pour la Recherche Medicale  ( DEQ20150331704 ).
Behavioral assays All experiments used young adult hermaphrodite animals, therefore sample stratification was not required within each genotype/condition. For most experiments, measurements were scored using an automated algorithm so blind scoring was not undertaken: see each subsection for details. For details of statistical tests, see the relevant Figure legend for each experiment and also the subsection ''Quantification and Statistical Analysis. '' All recordings that passed the automated analysis pipeline were included in the final dataset. For rescue and RNAi experiments, behavioral responses and phenotypes were confirmed by testing at least two independent transgenic strains.
Videos were analyzed in Zentracker, a custom-written MATLAB software (https://github. com/wormtracker/zentracker). All worms in the field of view were analyzed except those in contact with other animals. Speed was calculated as instantaneous centroid displacement between successive frames. Omega turns were identified as described  (Laurent et al. , 2015). In total 2-4 assay plates with 20-30 animals per plate were tested per day, and each genotype or condition was assayed in at least two independent experiments. As locomotion measurements were conducted using an automated algorithm, genotypes were not blinded prior to analysis. Devices). To reduce photobleaching an optical density filter 2. 0 or 1. 5 was used. Excitation light was passed through an excitation filter for CFP (438/24-25, Semrock) and a dichroic filter for YFP (DiO2-25x36, Semrock).
Frozen worm pellets ($10 g) were pulverized using a Freezer/Mill (SPEX SamplePrep). Crude extract was clarified at 4 C for 10 min at 20,000 g, and again for 20 min at 100,000 g with a TLA-100 rotor (Beckman Coulter). For immunoprecipitation, samples were incubated with GFP-Trap (ChromoTek) for 4 h at 4 C, then washed 3 times with 50 mM HEPES, 100 mM KCl. Purified complexes were eluted in SDS-sample buffer at 95 C and further fractionated by SDS-PAGE prior to mass spectrometry analysis. Proteins were identified by Orbitrap-mass spectrometry and MASCOT database searching. Gel samples were destained with 50% v/v acetonitrile and 50 mM ammonium bicarbonate, reduced with 10 mM DTT, and alkylated with 55 mM iodoacetamide. Digestion was with 6 ng/ml trypsin (Promega) overnight at 37 C, and peptides extracted in 2% v/v formic acid 2% v/v acetonitrile, and analyzed by nano-scale capillary LC-MS/MS (Ultimate U3000 HPLC, Thermo Scientific Dionex) at a flow of $300 nL/min.
A C18 Acclaim PepMap100 5 mm, 100 mm x 20 mm nanoViper (Thermo Scientific Dionex), trapped the peptides prior to separation on a C18 Acclaim PepMap100 3 mm, 75 mm x 250 mm nanoViper. Peptides were eluted with an acetonitrile gradient. The analytical column outlet was interfaced via a nano-flow electrospray ionisation source with a linear ion trap mass spectrometer (Orbitrap Velos, Thermo Scientific). Data dependent analysis was performed using a resolution of 30,000 for the full MS spectrum, followed by ten MS/MS spectra in the linear ion trap. MS spectra were collected over a m/z range of 300-2000. MS/MS scans were collected using a threshold energy of 35 for collision-induced dissociation. LC-MS/MS data were searched against the UniProt KB database using Mascot (Matrix Science), with a precursor tolerance of 10 ppm and a fragment ion mass tolerance of 0.
RNA-seq data analysis Prior to analysis the raw files were pre-processed using Bowtie2 version 0. 11. 0 to remove ribosomal RNA that mapped to a ribosomal RNA sequence library (Wormbase, WS255). Additionally, FASTQ files relating to the same sample but sequenced over multiple flow cell lanes were concatenated to give a single file. Custom rRNA_remover and rna_seq_lane_merger scripts were used (available on GitHub: https://github. com/lmb-seq/RNA-Seq_utilities). The files were then processed by PRAGUI -a Python3 pipeline for RNA-seq data analysis. PRAGUI automates analysis by incorporating widely used RNA-seq processing packages including: Trim Galore, FastQC, STAR, DESeq2, HTSeq, Cufflinks and MultiQC. PRAGUI can be found at: https://github. com/lmb-seq/PRAGUI. The following parameters were used with PRAGUI: DESeq2 analysis (labeled as ''DESeq''), unstranded paired-end library, worm e9 Neuron 105, 106-121.
e1-e10, January 8, 2020 organism with C. elegans genome fasta file and canonical gene set gtf file (Wormbase, WS255), STAR arguments set to ''-outSAMstrandFieldDESeq intronMotif-readFilesCommand zcat -c-outSAMtype BAM SortedByCoordinate,'' mapq set to 20. All other PRAGUI parameters were kept default. 5. 5 À 17 million reads were obtained per sample and mapped to the C. elegans genome. Sequences are deposited at GEO (GSE135687).
DATA AND SOFTWARE AVAILABILITY Datasets The genome sequencing data of JU1249 and JU2825 is available on NCBI: PRJNA514933 (https://www. ncbi. nlm. nih. gov/genome/? term=PRJNA514933). The genomic sequence data of the replicate populations for QTL mapping is available on NCBI: PRJNA515248 (https://www. ncbi. nlm. nih. gov/genome/?term=PRJNA515248). Sequence data from the RNA-Seq analysis of sorted BAG neurons is deposited on GEO: GSE135687 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE135687).
Locomotory assays Videos of locomotory assays were analyzed in Zentracker, a custom-written MATLAB software available on https://github. com/ wormtracker/zentracker. Calcium imaging Recordings were analyzed using Neuron Analyzer, a customwritten MATLAB program available at https://github. com/ neuronanalyser/neuronanalyser. RNA-seq analysis Codes for removing rRNA sequences from datasets and for concatenating FASTQ files relating to the same sample but sequenced over multiple flow cell lanes, are available on GitHub: https://github. com/lmb-seq/RNA-Seq_utilities. The git repository for PRAGUI can be found at: https://github. com/lmb-seq/PRAGUI.
elegans: Strain AX7324: arcp-1(db1082) III; npr- Caenorhabditis Genetics Center This paper Wormbase: OP50; RRID: WB-STRAIN:OP50 AX7324 Chemicals, Peptides, and Recombinant Proteins 1(ad609) X 5x outcrossed Dermabond tissue adhesive for worm glueing C. elegans: Strain AX6723: arcp-1(db1082) III; npr- Ethicon This paper Cat#AHV12 AX6723 1(ad609) X; dbEx975 [arcp-1p::arcp-1b::sl2::gfp; unc-Critical Commercial Assays SuperScript II reverse transcriptase 122p::rfp] KAPA Hifi HotStart kit 1(ad609) X; dbEx1050 [arcp-1p::arcp-1b::sl2::mKate; C. elegans: Strain AX7094: arcp-1(db1082) III; npr- Invitrogen KAPA Biosystems This paper Cat#18064-014 Cat#KK2601 AX7094 Ampure XP beads lin-44p::gfp] Beckman Coulter Cat#A63881 Nextera XT DNA sample preparation kit C. elegans: Strain AX6720: arcp-1(db1082) III; npr- Illumina This paper Cat#FC-131-1096 AX6720 Deposited Data 1(ad609) X; dbEx974 [arcp-1p::arcp-1a::sl2::gfp; Genome sequence data of JU1249 and JU2825 unc-122p::rfp] This paper NCBI: PRJNA514933 Genome sequence data of replicate populations for C.
elegans: Strain N2: C. elegans Bristol strain This paper Caenorhabditis Genetics Center NCBI: PRJNA515248 RRID: WB-STRAIN:N2_(ancestral) QTL mapping C. elegans: Strain MY10: C. elegans wild isolate Caenorhabditis Genetics Center RRID: WB-STRAIN:MY10 RNA-Seq data of sorted BAG neurons C. elegans: Strain JU1247: C. elegans wild isolate This paper M. -A. Felix, (Troemel et al. , 2008) GEO: GSE135687 RRID: WB-STRAIN:JU1247 Experimental Models: Organisms/Strains C. elegans: Strain AX6901: arcp-1(db1082) III; npr- This paper AX6901 C. elegans: Strain AX1796: glb-5(Haw) V; npr-1(g320) X 1(ad609) X; dbEx1002 [fosmid arcp-1p::arcp-1::sl2::gfp; unc-122p::rfp] de Bono lab; Persson et al. , 2009 AX1796 C. elegans: Strain LSJ1: Bristol strain C. elegans: Strain AX6766: arcp-1(db1082) III; npr- Caenorhabditis Genetics Center This paper RRID: WB-STRAIN:LSJ1 AX6766 C.
e1-e10, January 8, 2020 B Animals B Microbe strains d METHOD DETAILS B Molecular biology B Genotyping of natural polymorphisms B Behavioral assays B Selection-based QTL mapping B Distribution of mfP22 allele in wild isolates B Confocal microscopy and image analysis B Calcium imaging B Immunoprecipitation from C. elegans B RNA-seq of sorted BAG neurons d QUANTIFICATION AND STATISTICAL ANALYSIS d DATA AND SOFTWARE AVAILABILITY B Datasets B Codes STAR+METHODS KEY RESOURCES TABLE C. elegans: Strain JU1088: C. elegans wild isolate Caenorhabditis Genetics Center RRID: WB-STRAIN:JU1088 C. elegans: Strain JU1248: C. elegans wild isolate M. -A. Felix; Troemel et al. , 2008 RRID: WB-STRAIN:JU1248 C. elegans: Strain JU1543: C. elegans wild isolate M. -A. Felix RRID: WB-STRAIN:JU1543.
Although the reference genome of Solanum tuberosum Group Phureja double-monoploid (DM) clone is available, knowledge on the genetic diversity of the highly heterozygous tetraploid Group Tuberosum, representing most cultivated varieties, remains largely unexplored. This lack of knowledge hinders further progress in potato research. In conducted investigation, we first merged and manually curated the two existing partially-overlapping DM genome-based gene models, creating a union of genes in Phureja scaffold. Next, we compiled available and newly generated RNA-Seq datasets (cca. 1. 5 billion reads) for three tetraploid potato genotypes (cultivar Désirée, cultivar Rywal, and breeding clone PW363) with diverse breeding pedigrees. Short-read transcriptomes were assembled using several de novo assemblers under different settings to test for optimal outcome.
For cultivar Rywal, PacBio Iso-Seq full-length transcriptome sequencing was also performed. EvidentialGene redundancy-reducing pipeline complemented with in-house developed scripts was employed to produce accurate and complete cultivar-specific transcriptomes, as well as to attain the pan-transcriptome. The generated transcriptomes and pan-transcriptome represent a valuable resource for potato gene variability exploration, high-throughput omics analyses, and breeding programmes.