dataesr/scientific-paragraphs-categorization huggingface dataset:
Metabolomic Analysis of Single Stress Responses Along with transcriptomic analysis, we performed FT-ICR-MS analysis to study the impact of abiotic or/and biotic stresses echoed on the leaf metabolome. The analysis of all samples generated 6657 raw m/z, among which 5728 formulas were validated. Among the latter, 1152 m/z could be identified after queries in MassTrix (Supplementary File S2). Data from all NI samples were isolated at timepoint A and B subsets. Statistical analysis led to the identification of 199 and 212 significant m/z discriminants for NINAS, NIWS, NIHS, and NIWSHS conditions at timepoints A and B, respectively (Figure  S7 ). A comparison was then made between each single stress and the NINAS control (Figure  5A ). Plants 2023, 12, x FOR PEER REVIEW 9 of 25 in their combined stress experiment which we did not detect in our samples.
Metabolomic Analysis of Single Stress Responses Along with transcriptomic analysis, we performed FT-ICR-MS analysis to study the impact of abiotic or/and biotic stresses echoed on the leaf metabolome. The analysis of all samples generated 6657 raw m/z, among which 5728 formulas were validated. Among the latter, 1152 m/z could be identified after queries in MassTrix (Supplementary File S2). Data from all NI samples were isolated at timepoint A and B subsets. Statistical analysis led to the identification of 199 and 212 significant m/z discriminants for NINAS, NIWS, NIHS, and NIWSHS conditions at timepoints A and B, respectively (Figure  S7 ). A comparison was then made between each single stress and the NINAS control (Figure  5A ). WS provoked a limited down-accumulation of metabolites in leaves at timepoint A (7 m/z).
Phytopathogen Re-Isolation and Necrosis Measurement At each sampling timepoint (A and B), re-isolations were made to verify the presence of the inoculated pathogen (D. seriata or N. parvum) and its absence in the wounded controls (NI), according to the protocol of  [75]. Then, 2 months post inoculation, the length and area of external cankers and internal necrosis developed on the green stem were measured using the software ImageJ (v1. 48). For each timepoint, three plants per condition were used. 3. 4. Transcriptomic Analysis 3. 4. 1. RNA Extraction, cDNA Library Construction, and Illumina RNA Sequencing Total RNA was isolated from 3 × 50 mg of leaf powder using the PureLink Plant RNA Purification Reagent (Invitrogen, Cergy-Pontoise, France). The manufacturer's protocol was followed until the phase of separation with chloroform/isoamylic alcohol (24:1).
Read Mapping, Assembly, and Differential-Expression Analysis Raw Fastq files were quality-controlled with FastQC (v0. 11. 7  [76] ), and then trimmed for adapters using cutadapt (v1. 16  [77] ). For this analysis, the genome used was the 12X version of PN40024 (Pinot Noir  [78] ) and the annotation used was the "V2. 1 CRIBI"  [79]. A file containing all isoforms sequences described in the annotation V2. 1 CRIBI was generated by gffread (v0. 9. 12; http://ccb. jhu. edu/software/stringtie/gff. shtml, accessed on 1 March 2018). For all samples, the number of reads mapped to each isoform was counted using Salmon (v0. 9. 1  [80] ). The count per isoform was turned into a count per gene using the package tximport (v1. 8. 0  [81] ) of the software R (v3. 5. 0). Then, the differentially expressed genes (DEGs) between two groups of samples were detected using the R package EdgeR (v3.
Gene Ontology Analysis In order to characterize the DEGs in drought treatment, heat treatment, or the combination of both, GO-based enrichment tests were carried out using the software agriGO (v2. 0) (available at http://systemsbiology. cpolar. cn/agriGOv2/, accessed on 1 March 2018). Enriched GO terms were detected using singular enrichment analysis (SEA) with the Vitis vinifera "Gramene Release 50" as GO annotation reference. The significance of over-represented terms in the three categories (biological process, cellular component, and molecular function) were assessed by Fisher's exact test, adjusted by the Hochberg (FDR) method. The significance level and the minimum number of mapping entries were set at 0. 05 and 5, respectively  [84, 85].
PCR reactions were performed in duplicate on three biological replicates per condition. The data were analyzed using the Bio-Rad CFX Manager software (v3. 0), and the relative gene expression levels were determined by the method of  [87] , with EF1-α and 39SRP as internal reference genes. The results were log 2 -transformed and correspond to the means ± standard error of the biological triplicate in most cases, except for the study of water stress effect in which six biological replicates were considered. Contrasting with the Illumina RNA-Seq results, to study the effect of abiotic stresses, the results for timepoints A and B were kept separated and the controls used for the qRT-PCR analysis were the non-stressed plants (three plants) of the same kinetic time. To investigate the infection effect, the controls were similar to the ones used previously in the RNA-Seq analysis.
Data Analysis Spectra were externally calibrated on clusters of arginine (10 mg•L -1 in methanol). Further internal calibration was performed for each sample by using a list of ubiquitous fatty acids and recurrent wine compounds, allowing mass accuracies of 0. 1 ppm  [89]. The m/z peaks with a signal-to-noise ratio (S/N) of 4 and higher were exported to peak lists. All m/z detected were given an identification number (ID) in order to simplify further data processing. Several step procedures were then applied on the data lists (m/z). First, Perseus software (https://maxquant. net/perseus/) selected m/z when seen at least three times in datasets. These m/z coupled to their respective peak intensities were submitted to statistical analysis with Perseus software, which performed hierarchical clustering and multivariate ANOVA analyses between samples groups under a p < 0.
05 to get significant m/z. All significant m/z were then submitted to compound identification using MassTrix software (http://masstrix3. helmholtz-muenchen. de/masstrix3/ accessed on 1 June 2018), which proceeds to search against KEGG, HMDB and LipidMaps databases (at 1 ppm tolerance value), on the Vitis vinifera organism. Compounds identified as isotopes (C13, N15, O18, etc. ) were kept in the list of interest. In parallel, m/z were subjected to the NetCalc algorithm and an in-house software tool to obtain elemental chemical formulae  [90]  validated by van Krevelen diagram calculation, as previously described  [34]. The m/z with reliable row formulas were manually classified into predicted functional categories (lipids, peptides, amino sugars, carbohydrates, nucleotides, phytochemicals, and NM when not matching or drug classified) according to the KEGG annotations.
The category "phytochemicals" corresponds to secondary metabolites. Raw formula agreements on identified compounds between NetCalc and MassTrix were verified and revised when necessary. Indeed, wrongly calculated formulas encompassed isotopes and other elements (Br, Cl, F, Ca, etc. ) containing molecules. These compounds were considered and specified in tables but were not revised or reclassified and left as the NM functional category. Top disease-specific compounds relative to control were sorted among the identified compounds by varying t-test parameters such as S0 (diseased:control fold parameters) and FDR to more stringent values in Perseus software. The log 2 (fold change) is expressed in comparison to the corresponding non-inoculated plant (NI) subjected to the same abiotic stress. Indeed, the expression of samples at timepoint A are expressed comparatively to the samples at timepoint B.
the "MEG-MaSC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fictional stories from the Manually Annotated Sub-Corpus (MASC) intermixed with random word lists and comprehension questions. We time-stamp the onset and offset of each word and phoneme in the metadata of the recording, and organize the dataset according to the 'Brain Imaging Data Structure' (BIDS). This data collection provides a suitable benchmark to large-scale encoding and decoding analyses of temporally-resolved brain responses to speech. We provide the Python code to replicate several validations analyses of the MEG evoked responses such as the temporal decoding of phonetic features and word frequency.
While large and high-quality functional Magnetic Resonance Imaging (fMRI) datasets related to language processing have recently been released  37, 38  , there is currently little publicly available high-quality temporally-resolved brain recordings acquired during story listening. The most extensive databases of such data include: • van Essen et al. 39  : 72 subjects recorded with fMRI and MEG as part of the Human Connectome Project, listening to 10 minutes of short stories, no repeated session 39   41  Until the present release of our dataset, there existed no public magneto-encephalography (MEG) with (1) several hours of story listening (2) multiple sessions (3) a systematic audio, phonetic and word annotations (4) a standardized data structure. Thus, our dataset offers a powerful resource to the scientific community.
The story order for each participant can be found in 'participants. tsv'. This participant-specific order was used for both recording sessions. Our motivation for running two identical sessions was to (i) give researchers the ability to average the data across the two recordings to boost signal-to-noise; (ii) provide a like-for-like data reliability measure; (iii) give the opportunity for matched train and test datasets if attempting to run cross validated analyses. To ensure that the participants were attentive to the stories, they responded, every ∼3 min and with a button press, to a two-alternative forced-choice question relative to the story content (e. g. 'What precious material had Chuck found? Diamonds or Gold'). Participants performed this task with an average accuracy of 98%, confirming their engagement with and comprehension of the stories.
In addition, a very small fraction (<1%) of non-words were inserted into the natural sentences, on average every 30 words. We decided to include non-words to allow comparisons between phonetic sequences that do and do not have an associated meaning. Hereafter, and following the BIDS labeling  45  , each "task" corresponds to the concatenation of these sentences and word lists. Each subject listened to the exact same set of four tasks, in a different block order. Preprocessing. MEG. The MEG dataset and its annotations are shared raw (i. e. not preprocessed) organized according to the Brain Imaging Data Structure 45 MNE-BIDS  46. MRI. Structural MRIs were collected with separate averages of the T1w images using 3D MPRAGE sequence with 0. 8 mm isotropic resolution (FOV = 256 mm, matrix = 320,208 sagittal slices in a single slab), TR = 2400 ms, TE = 2.
22 ms, TI = 1000 ms, FA = 8 degrees, Bandwidth (BW) = 220 Hz per pixel, Echo Spacing (ES) = 7. 5 ms, phase encoding undersampling factor GRAPPA = 2, no phase encoding oversampling. To avoid subject identification, the T1-weighted MRI anatomical scan was defaced using PyDeface  47  (https:// github. com/poldracklab/pydeface) and manually checked. For four subjects (02, 06, 07, 19) we were unable to record structural MRIs, and so instead we provide the scaled FreeSurfer average MRIs in their place. The alignment between the spaces of (1) the head-position coils, (2) the MEG sensors and (3) the T1 MRI was co-registered manually with MNE-Python  48. Stimuli. We include in the dataset: the original stories ('stimuli/text'), the stories intertwined with the word lists ('stimuli/text_with_wordlists') and their corresponding audio tracks ('stimuli/audio').
Computing environment. In addition to the packages mentioned in this manuscript, the processing of the present data is based on the free and open-source ecosystem of the neuroimaging community. In particular, we used: • MNE BIDS  46  (https://mne. tools/mne-bids) • Bids-Validator (https://github. com/bids-standard/bids-validator) • Nibabel 49 (https://nipy. org/nibabel/) • Scikit-Learn 50 (https://scikit-learn. org/) • Pandas 51 (https://pandas. pydata. org/).
Data Records The dataset is organized according to Brain Imaging Data Structure (BIDS) 1. 2. 1  45  and publicly available on the Open Science Framework data repository 52 https://doi. org/10. 17605/OSF. IO/AG3KJ under a Creative Common Licence 0. An image of the folder structure is provided in Fig. 1. The detailed description of the BIDS file system is available at http://bids. neuroimaging. io/. In summary, • '. /dataset_description. json' describes the dataset • '. /participants. tsv' indicates the age and gender of each participant, the order in which they heard the stories, whether they have an anatomical MRI scan, and how many recording sessions they completed • '. /stimuli/' contains the original texts, the modified texts (i. e. with word lists), the synthesized audio tracks. • Each'.
/sub-SXXX' contains the brain recordings of a unique participant divided by session (e. g. 'ses-0' and'ses-1') • In each session folder lies the anatomical and the meg data, and the timestamp annotations (see Fig. 4 ). • Sessions are numbered by temporal order (s0 is first; s1 is second). • Tasks are numbered by a unique integer common across participants. • The dataset can be read directly with MNE-BIDS  46.
technical Validation We checked that the present dataset complies with the standardized brain imaging data structure by using the Bids-Validator (https://github. com/bids-standard/bids-validator). MEG recordings are notoriously noisy and thus challenging to validate empirically. In particular, MEG can be corrupted by environmental noise (nearby electronic systems) and physiological noise (eye movement, heart activity, facial movements)  53. To address this issue, several labs have proposed a myriad of preprocessing techniques based on temporal and spatial filtering  54  and trial and channel rejection  55. However, there is currently no accepted standard for the selection and ordering these preprocessing steps. Consequently, we here opted for (1) a minimalist preprocessing pipeline derived from MNE-Python's default pipeline  48  followed by (2) median evoked responses and (3) standard single-trial linear decoding analyses.
Minimal preprocessing. For each subject separately, and using the default parameters of MNE-Python, we: • bandpass filtered the MEG data between 0. 5 and 30. 0 Hz with raw. load_data(). filter(0. 5, 30. 0, n_jobs = 1), • temporally-decimate the data 10x, segment these continuous signals between -200 ms and 600 ms after word and phoneme onset, and apply a baseline correction between -200 ms to 0 ms with mne. Epochs(tmin = -0. 2, tmax = 0. 6, decim = 10, baseline = (-0. 2, 0. 0)), • and clip the MEG data between fifth and ninety-fifth percentile of the data across channels. Evoked. Figure  2  displays the median evoked responses across participants and words onset and after phoneme onsets, respectively. Both of these topographies are typical of auditory activity in MEG  36.
Decoding. For each recording independently, our objective was to verify the alignment between the word annotations and the MEG recordings. To this end, we trained a linear classifier W ∈ Rd across all d = 208 magnetometers (X ∈ Rn × d), for each time sample relative to word (or phoneme) onset independently, and for each subject separately. The classifier consisted of a standard scaler, followed by a linear discriminant regression implemented by scikit-learn 50 using model = make_pipeline(StandardScaler(), LinearDiscriminantAnalysis()) • decode high versus low median zipf-frequency of each word, as defined by the WordFreq package  56. • decode whether the phoneme is voiced or not. The decoding pipeline was trained and evaluated using a five-split cross-validation scheme (with shuffling) using cv = KFold(5, shuffle = True, random_state = 0) The scoring metric reported is Pearson R correlation between the continuous probabilistic output of the classifier on each trial, and the ground truth label (high vs.
low for word frequency; voiced vs. voiceless for voicing). The full decoding pipeline can be found in the script check_decoding. py. The results displayed in Fig. 3  show a reliable decoding at the phoneme and at the word level, across both subjects and tasks (i. e. stories). The success of our decoding analysis demonstrates: (i) the data have been correctly time-stamped relative to phoneme and word onset, in order to elicit a zero-aligned decoding timecourse; (ii) the data contain reliable signals that contain speech-related properties, suitable for further investigation; (iii) information at multiple levels (phonetic and lexical) are present in the data, allowing users to test hypotheses at different linguistic levels of description. We anticipate that encoding models would provide equally compelling results.
Fig. 1  schematically illustrates several potential complex data distribution scenarios. In the present work, we assess to what extent complex data structures are present at the level of SBM features (CT, SA and MC) in a dataset of patients with ET and matched HCs. To do so, we resort to probabilistic modelling: to capture dependences between morphometric features, we model each group's data with multivariate Gaussian distributions, which are parameterized not only by a mean vector (reflective of average morphometry values across subjects), but also by a covariance matrix (which jointly accounts for feature variance and cross-feature dependences). By comparing the fitting quality of a single Gaussian model to that of a mixture of Gaussians, we can explore whether a given brain region shows evidence for a multimodal data structure.
Data acquisition Native T1-weighted structural images were acquired for all subjects on the same head-only 3 T machine (SIEMENS SKYRA, Munich, Germany, 32-channel receive-only phase-array head coil). For ET patients, data was collected both at baseline and one year after thalamotomy. The acquisition parameters were as follows: TR/TE = 2300/2. 98 ms, isotropic voxels of 1 mm 3 , 160 slices. Scanning was performed in a drugnaïve state (drugs having been stopped at least 3 days beforehand).
Regression of covariates of no interest To account for the confounding impacts of age, gender and total grey matter volume in our analyses, a mixed-effects model strategy was employed. We selected this approach because although the present work only focuses on the morphometric profiles of ET patients in their baseline state, MR imaging was also performed for each subject one year after Gamma Knife stereotactic radiosurgical thalamotomy of the Example cases in which two distributions of data points are schematically illustrated (green and blue) in a two-dimensional space. Standard univariate statistical group comparison approaches could only resolve a significant group difference for top row cases (and in the third one, would not capture the clustered nature of the data). The data may differ across groups in several more complex ways: in terms of variance (middle left), covariance (center), mean in a way that covariance prevents significance to be established with univariate tests (middle right).
It may also exhibit a clustered nature such that group comparison captures no difference (bottom left), complex mixes of all the above (bottom middle), or nonlinear properties that Gaussian modelling cannot accurately capture (bottom right). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) ventro-intermediate nucleus of the thalamus. As mixed-effects models can account for within-subject variance on top of cross-subject variability in longitudinal data, we reasoned that it would be the most accurate way to describe our complete dataset. For each region, each morphometric feature (CT, SA or MC for cortical areas and volume for non-cortical ones) was modelled as: M i,s = β 0 + β 1 A s + β 2 G s + β 3 V s + ∑ 2 k=1 β 3+k I[C k+1 ] s A s + β 5+k I[C k+1 ] s G s + β 7+k I[C k+1 ] s V s + b 0,s + ∊ i,s.
Gaussian mixture model A dataset exhibiting a multimodal data structure can be represented as a Gaussian mixture model (GMM). We define K as the number of mixed Gaussians. If x s is a realization from a GMM, we have: p ( x s ⃒ ⃒ ⃒{μ k , Σ k } k=1,⋯,K , π ) = ∑ K k=1 π k N (x s |μ k , Σ k ). In the above, each Gaussian is parameterized by a mean vector and a covariance matrix, while π is a K-element vector that summarizes the respective weighting of the Gaussians and satisfies ∑ K k=1 π k = 1. The GMM can be solved with the expectation-maximization (EM) algorithm  (Moon, 1996) , as detailed in the Supplementary Material. Following convergence, the LL of a previously unseen data point z can be evaluated as: L L G (z) = ∑ S s=1 log ( ∑ K k=1 πk N (x s |μ k , Σk ) ).
Leave-one-out CV was performed. After determining the parameters of the HC and ET multivariate Gaussian distributions (μ HC , ΣHC , μET and ΣET ), to quantify how individual ET morphometric profiles stand out from those of the overall population of patients, we computed L L N ( z (ET) s ⃒ ⃒ ⃒μ ET , ΣET ) , where z (ET) s is the regional data for patient s. Higher and lower values denote more typical subjects and more "outlier" subjects, respectively. We performed PLS analysis in two settings to evaluate the associations between the morphometric data and clinical symptoms. We considered clinical information (baseline ADL, TSTH and head tremor scores, symptoms' duration and family history of ET) as behavioral variables. As imaging variables, we considered either the concatenated morphometric values across regions (for a total of 3P cort + P noncort variables), or the LL of ET samples to be realizations of the ET distribution (P variables).
mathworks. com/matlabcentral/fileexchange/34087-cbrewer-, and PLS analysis was performed with the myPLS toolbox (https://github. com/danizoeller/myPLS). All other analytical steps described above were performed with custom scripts and MATLAB2020b (MathWorks, Natick, USA). All the scripts used in this work are freely available at the following link: https://github. com/TiBiUan/SCA_IndividualDifferences. git. The data that support the findings of this study are available from the corresponding author upon reasonable request.
g. , two high-scorers may be deemed more similar than two low-scorers for an identical absolute difference in score  (Finn et al. , 2020). Our comparison between multivariate Gaussian and GMM representations additionally enabled to assess whether cross-subject heterogeneity may manifest itself through separate clusters of data points. In most cases, this was not the case, as the multivariate Gaussian model outperformed the GMM. One could then assume that heterogeneity involves a continuum of alterations rather than a multimodal data distribution, but it should be emphasized that our comparison only considered K = 2 clusters, owing to the limited size of our dataset (S = 34 ET samples). Indeed, each additional cluster would require 7 additional parameters to be estimated (assuming d = 3). Future studies should clarify, on extended datasets, whether larger numbers of clusters may provide an optimal fit for some brain regions.