dataesr/scientific-paragraphs-categorization huggingface dataset:
This new result highlights the exploratory potential of the Functionnectome toward understanding the white matter support of complex cognitive systems. The language circuitry, and more so its semantic system, offers an even greater challenge as it cannot be explored in animal studies  41. The Functionnectome confirmed, for the first time in the healthy human brain, the structural-functional circuitry supporting semantic processes originally suggested by lesion studies  40  or intraoperative stimulation  41  in humans (i. e. the uncinate fasciculus, the inferior fronto-occipital fasciculus, the middle longitudinal fasciculus, and the posterior segment of the arcuate fasciculus). The Functionnectome also revealed the involvement of the anterior corpus callosum in story comprehension. Whilst some language processes require the integration of the left and right hemispheres via the posterior corpus callosum  62  , the anterior corpus callosum has been implicated in semantic disorders (e.
To upscale the validation of this network view of brain functioning, crowdsource analysis of additional data would be possible. To facilitate this application, we provide an open-source software that will allow for an easy use of the Functionnectome method to revisit already acquired fMRI datasets, either privately or publicly available (e. g. HCP  65  , UK Biobank 66 , ABIDE  67  ). The use of the Functionnectome is also not bound to the activation paradigm and can be combined with advanced fMRI statistics  7  to reveal the dynamic causal interaction between brain circuits. Additionally, the Functionnectome can leverage the wealth of MRI modalities to explore the involvement of white matter circuits in different aspects of brain dynamics. For example, it could be applied to resting-state functional connectivity or cortical thickness to open up new perspectives onto the study of functional synchronisation, cortical changes during development, and brain pathologies.
These improvements will be implemented in the Functionnectome as priors can easily be replaced in the future to incorporate novel advances in tractography. Future developments of the priors might include a separation into interhemispheric (i. e. commissural circuits), cortico-subcortical (i. e. projection circuits), and cortico-cortical connectivity (i. e. association circuits) to better disentangle brain circuitries. Finally, the use of the Functionnectome for every brain voxel can be very expensive computationally and thus time-consuming. While we recommend using this procedure, we acknowledge that not all research teams have access to the computing power required to compute the "voxel-wise" Functionnectome over several participants in a reasonable time. To circumvent this potential constraint, we provide an option within the software allowing for the use of an atlas and its parcels instead of all voxels (i.
e. 'simplified' priors) for a less computationally intensive "region-wise" analysis (see Supplementary note 1: Region-wise analysis, Supplementary Figs. 1 2 3 4 and Supplementary discussion 1 in the supplementary information for the region-wise results and discussion of each of the four tasks explored in the present study). Overall, we introduced and demonstrated the potential of the Functionnectome method and its open-source companion software (see Supplementary note 2: Functionnectome User-guide in the supplementary information) opening the field of in vivo study of the function of white matter in healthy humans. Despite the unavailability of a ground truth to completely validate the results obtained, we were able to identify strong indicators of both the sensitivity (the expected pathways were detected) and specificity (plausible activation of pathways that are yet to be formally associated with a function, e.
Methods The workflow was summarised in Fig. 7. Datasets. Three datasets derived from the Human Connectome Project 65 (HCP) were used for the study, and are publicly available: the 7T diffusion-weighted imaging (DWI) data used to generate the tractography priors (subset of 100 subjects; available at https://osf. io/5zqwg/ and http://www. bcblab. com/BCB/ Opendata. html), the 3 T task-based fMRI acquisitions (46 participants, test-retest dataset), and the 3T resting-state fMRI acquisitions (45 participants, test-retest dataset). In order to ensure that the results obtained here would be generalisable to other datasets, the 100 participants of the DWI dataset were randomly chosen as a normative population that was independent from the participants of the test-retest dataset used in the fMRI analyses.
HCP data was acquired by the WU-Minn Consortium with IRB approval and informed consent from all participants, and the WU-Minn HCP Consortium Open Access Data Use Terms were respected in the present study. Acquisition parameters and preprocessing. EPI acquisitions: Full description of the acquisition parameters have been described elsewhere  69. In brief, the data were acquired on a 3 Tesla Siemens Skyra scanner using a whole-brain EPI acquisition with a 32-channel head coil. The parameters were as follows: TE = 33. 1 ms, TR = 720 ms, flip angle = 52°, BW = 2290 Hz/Px, in-plane FOV = 208 × 180 mm, 72 slices, 2. 0 mm isotropic voxels, and a multi-band acceleration factor of 8. 1200 frames per acquisition for the resting-state fMRI, and the number of frames was task-dependent for the task-based fMRI.
Each type of acquisition was acquired twice using a right-to-left and a left-to-right phase encoding. The EPI 4D acquisitions were then preprocessed through the "Minimal preprocessing pipeline" fMRIVolume  70  , which applies movement and distortion corrections and performs a registration to the MNI152 space. Additionally, the resting-state acquisitions were further preprocessed with despiking, detrending of motion and CSF, white matter and grey-matter signal, temporal filtering (0. 01-0. 1 Hz), and spatial smoothing (5 mm FWHM). Diffusion-Weighted Imaging (DWI) acquisitions and tractography. Structural connectome data were downloaded (subset of 100 subjects; available at https:// osf. io/5zqwg/ and http://www. bcblab. com/BCB/Opendata. html). This dataset was derived from the diffusion-weighted imaging dataset of 100 participants acquired at 7 Tesla by the Human Connectome Project Team 29 (http://www.
The default HCP preprocessing pipeline (v3. 19. 0)  70  was applied to the data  73. In short, the susceptibility-induced off-resonance field was estimated from pairs of images with diffusion gradient applied with distortions going in opposite directions  74  and corrected for the whole diffusion-weighted dataset using TOPUP  75. Subsequently, motion and geometrical distortion were corrected using the EDDY tool as implemented in FSL. Next, we discarded the volumes with a b-value of 1000 s/mm -2 and wholebrain deterministic tractography was subsequently performed in the native DWI space using StarTrack software (https://www. mr-startrack. com). A damped Richardson-Lucy algorithm was applied for spherical deconvolutions  75, 76. A fixed fibre response corresponding to a shape factor of α = 1.
5 × 10 -3 mm 2 s -1 was adopted, coupled with the geometric damping parameter of 8. Two hundred algorithm iterations were run. The absolute threshold was defined as three times the spherical fibre orientation distribution (FOD) of a grey matter isotropic voxel and the relative threshold as 8% of the maximum amplitude of the FOD  77. A modified Euler algorithm  78  was used to perform whole-brain streamline tractography, with an angle threshold of 35°, a step size of 0. 5 mm and a minimum streamline length of 15 mm. We co-registered the structural connectome data to the standard MNI 2 mm space using the following steps: first, whole-brain streamline tractography was converted into streamline density volumes where the intensities corresponded to the number of streamlines crossing each voxel.
Second, a study-specific template of streamline density volumes was generated using the Greedy symmetric diffeomorphic normalisation (GreedySyN) pipeline distributed with ANTs  78, 79. This provided an average template of the streamline density volumes for all subjects. The template was then co-registered with a standard 2 mm MNI152 template using flirt as implemented in FSL. This step produced a streamline density template in the MNI152 space. Third, individual streamline density volumes were registered to the streamline density template in the MNI152 space template and the same transformation was applied to the individual whole-brain streamline tractography using the trackmath tool distributed with the software package Tract Querier 80 using ANTs GreedySyn. This step produced a whole-brain streamline tractography in the standard MNI152 space.
Independent Component Analysis (ICA) is a popular data-driven method capable of extracting these networks from the resting-state fMRI data by detecting spatially independent components in the signal, and isolating their time-courses and corresponding spatial maps. We first ran a group ICA on the resting-state data (both LR and RL acquisition of all 45 subjects) to extract 20 independent components (IC), using MELODIC (multivariate exploratory linear optimised decomposition into independent components, version 3. 15), available in the FMRIB Software Library (FSL)  75. Among these 20 functional ICs, 3 were identified as noise components, leaving 17 ICs as resting-state networks. In parallel, we performed the same ICA on the anatomical priors of the Functionnectome using the region-wise probability maps concatenated over a fourth dimension.
All 4D volumes are subsequently fused together by voxel-wise addition and divided by the sum of all probability maps to produce a weighted average of the voxel-wise 4D volumes. This final step ensures that all voxels have a comparable range of values (equivalent to the range of values of the BOLD signal). The final output of the algorithm is a functionnectome 4D volume. Functional data pre-processing. To compare our method with standard analyses, we explored the activation patterns of HCP fMRI paradigms for motor (n = 46), working memory (n = 45), and language (n = 44) tasks (see https:// protocols. humanconnectome. org/HCP/3T/task-fMRI-protocol-details. html and ref. 83  for a full description of the tasks protocol). Briefly, the motor tasks consisted of finger tapping (left or right hand) and toes clenching (left or right foot); the working memory task was a 2-back task; and the language task corresponded to the comparison of comprehension of high and low semantic content (stories, math problems).
Processing of neuroimaging data. The activation analysis was applied to both the original functional dataset and the functionnectome 4D volumes. Processing was identical except for the application of a traditional spatial smoothing (FWHM = 4 mm) on the functional dataset (i. e. as a usual step to improve the signal/noise ratio and misalignment). Specifically, no spatial smoothing is required for the functionnectomes 4D volumes. Usual smoothing aims at improving the signal/ noise ratio (SNR) using weighted average of the local signal, assuming that neighbouring voxels share some signal of interest. The functionnectome method combines the signal from distant yet structurally linked voxels, which has an analogous effect of improving the SNR, but is guided by actual brain circuits. The processing was done using FEAT (FMRI Expert Analysis Tool, v6.
00) from FSL (FMRIB's Software Library). First-level statistical analysis was carried out on each participant using FILM (FMRIB's Improved Linear Model)  84  with prewhitening. For each task, we used the main contrasts provided with the HCP dataset. Then, a group-level analysis was performed on all participants using the first stage of FLAME (FMRIB's Local Analysis of Mixed Effects)  85  , resulting in the z-maps (z-transformed t-maps) presented in the results (Figs. 1 2 3 4 ). Note that the assumption of identically independently distributed residuals in the linear modelling applied to the functionnectome volumes should hold true, as the signal of the voxels results from a simple linear combination of the signals (the classic BOLD time-series) for which the assumption was already considered valid.
Labelling for cortical regions and white matter pathways were added manually by expert anatomists (SJF and MTS). The visualisation of 3D structures in 2D is limited and may appear ambiguous at times but the full trajectories of pathways were considered before labelling, especially in regions of overlap between white matter structures. The 3D renderings were generated using the associated z-map. All functionnectome maps (slices and 3D renderings) were masked to remove the grey-matter parts of the volume in order to improve readability of the figures. The mask used here was generated using the segmentation provided in the HCP dataset and was composed of the voxels defined as white matter or brain stem in at least 10% of the subjects of the test-retest dataset. This very permissive 10% threshold was chosen to prevent the underestimation of the extent of the functionnectome maps.
Note that the grey-matter parts of the maps are also of interest, and were only removed here to avoid confusion between the two methods. Availability of data. All the raw anatomical and functional data are available on the HCP website. The Functionnectomes and the associated maps are available on demand to the authors. The python compatible algorithm (version 3. 6 or higher) for the Functionnectome analysis is freely available and comes with an optional GUI code.
However, there is a high variation in the daily intake of polyphenols, as it can range from less than 500  [7]  to more than 1500 mg/day  [8] , which reflects the differences in nutritional habits between individuals, but also at the population level. Epidemiological studies have reported positive effects of polyphenol intake on cardiometabolic health in humans  [9, 10]. These data initiated numerous human intervention trials to study the effects of various dietary polyphenols on cardiometabolic risk factors in subjects of different gender, age, or health status. Interventions included pure compounds and plant extracts along with various polyphenol-rich foods or beverages. Given the diversity in study populations, along with food bioactives and food matrices, the variability observed in the different outcomes is not surprising.
Strategy for Literature search and Data Extraction All literature relevant to the effect of polyphenols on gene expression and cardiometabolic endpoints in human trials was searched and obtained using the Preferred Reporting Items for Systematic Reviews (PRISMA) statement guidelines with a predetermined search strategy  [32]. A comprehensive search on PubMed and Web of Science, using Medical Subject Headings (MeSH) and Boolean operators where appropriate, was conducted in July 2018, with an update in November 2019. The search included keywords referring to bioactives (polyphenols, flavonoids, flavanols, flavanones, epicatechin, catechin, procyanidin, anthocyanins, resveratrol, hydroxytyrosol, extracts, fruits, juice, grapes, citrus, pomegranate, apple, tea, coffee, cocoa, olive oil, chocolate, berries, isoflavones, daidzein, equol, hesperetin), type of studies (human, clinical trials, randomized, patients, volunteers, males, females), nutrigenomic analysis (nutrigenomic, genomic, genome, gene, gene expression, transcription, mRNA, messenger RNA, RT-PCR, PCR-arrays, microarrays, macroarrays, epigenome, miRNA, ncRNA), and target tissues (peripheral blood mononuclear cells, T-cells, B-cells, lymphocytes, monocytes, blood, blood cells, platelets, adipose tissue, adipocytes, liver, plasma, serum).
Following the identification of the publications using the aforementioned terms, the search results were narrowed down by only selecting studies with a targeted approach published in English language. Inclusion criteria for data extraction were as follows: human intervention studies of cardiometabolic disease or cardiometabolic risk factors where polyphenols were used for intervention, and modulation of gene expression was studied at mRNA and/or miRNA level, which was analyzed with RT-PCR methods. Furthermore, in order to be included in our analysis, the publications should have reported at least one beneficial cardiometabolic outcome, such as: improved plasma lipid status (total cholesterol, triglycerides, HDLcholesterol, LDL-cholesterol, apoA1, apoB, or oxLDL, etc. ), improved oral glucose tolerance test, decreased fasting glucose, glycated hemoglobin, insulin resistance, blood pressure, body weight, waist circumference, systemic inflammation (circulating CRP, TNFα, interleukins, etc.
), or decreased oxidative stress. Studies reporting an unfavorable cardiometabolic outcome, for example, increased LDL-cholesterol, were excluded from our analysis. In addition, studies that did not include a control of polyphenol intervention, or studies of co-interventions (for example, co-interventions of polyphenols with vitamins or exercise), were also excluded. A template for data extraction was designed specifically for this study. The template was pilot tested to verify the accuracy of the data extraction. The final version of the template was distributed among the participants. Extracted data include: details about the paper (PMID, authors, year of publication, title), information about the study population (gender, age, number of participants, health status), information about the intervention (bioactive/s, dose and duration of intervention), study design, information about the cardiometabolic outcomes (including both studied and significantly modulated), and information about gene expression (mRNA and/or miRNA, analytical method, tissue/cells analyzed for gene expression, gene symbol as reported in the paper, official gene symbol, and official gene name).
Bioinformatic Analyses Pathways enrichment analyses were conducted using GeneTrail2 (https://genetrail2. bioinf. uni-sb. de/, accessed on 24 January 2020)  [33] , version 1. 6, as a platform to access KEGG and BioCarta databases, using the following settings: over-representation analysis; null hypothesis (for p-value computation)-two-sided; method to adjust p-values-Benjamini-Yekutieli; significance level-0. 05. All supported genes in the NCBI database were taken as a reference. Interactions between functional groups of genes were analyzed with Cytoscape software (version 3. 7. 2; https://cytoscape. org/, accessed on 1 June 2020)  [34] , using the application ClueGO  [35] , connected to CluePedia  [36]. Protein-protein interactions (PPIs) were analyzed using the database STRING (https: //string-db.
org/, accessed on 25 January 2020)  [37] , version 11. 0, applying the following settings: confidence; text-mining, experiments, databases, co-expression; high confidence-0. 700; no more than 10 interactions in the first shell and no more than 5 interactions in the second shell. The obtained protein network was organized in three clusters of functionally interconnected proteins. Transcription factors that regulate the expression of polyphenol modulated genes were identified using Enrichr (https://amp. pharm. mssm. edu/Enrichr/, accessed on 25 January 2020)  [38, 39] , as a platform to access the database TRRUST Transcription Factors 2019  [40]. Interactions between the transcription factors and the genes they regulate were visualized with Cytoscape software (version 3. 7. 1; https://cytoscape.
org/, accessed on 1 June 2020)  [41]. To identify mRNAs regulated by polyphenol modulated miRNAs, i. e. , predicted miRNA targets, we used miRBase (http://www. mirbase. org/, accessed on 8 February 2020)  [42] , as a platform to access the following databases: TargetMiner, TargetScanVert and miRDB. For ID conversion of genes that were retrieved with the database TargetMiner, we used the ID convert tool of the database SYNGO (https://syngoportal. org/, accessed on 8 February 2020)  [43]. InteractiVenn (http://www. interactivenn. net/, accessed on 8 February 2020)  [44]  was used as a tool to retrieve the predicted miRNA targets that are in common for the selected databases, which were subsequently used for pathways enrichment and integration analyses. Interactions between miRNA targets were visualized with Cytoscape software (version 3.
Networks were constructed and visualized using Cytoscape software (version 3. 7. 1; https: //cytoscape. org/, accessed on 1 June 2020)  [41]. Data preparation was performed with the use of several R packages, including splitstackshape (https://github. com/mrdwab/ splitstackshape, accessed on 16 June 2020), data. table (https://github. com/Rdatatable/ data. table, accessed on 16 June 2020), dplyr (http://dplyr. tidyverse. org, accessed on 16 June 2020; https://github. com/tidyverse/dplyr, accessed on 16 June 2020), and string (http: //stringr. tidyverse. org, accessed on 16 June 2020; https://github. com/tidyverse/stringr, accessed on 16 June 2020). Pathway networks were built separately for pathways enriched in each category and pathways considering all categories' components together in a global pathway enrichment analysis.
To obtain the pathways with the highest degree (number of connections of one node to other nodes), the Cytoscape Network Analyzer application was used (http://apps. cytoscape. org/apps/networkanalyzer, accessed on 16 June 2020). For integrative analysis of extracted mRNAs, their associated TFs, and extracted miRNAs, we used the 3D-layer option in the bioinformatic tool OmicsNet (https://www. omicsnet. ca/, accessed on 10 February 2020)  [47, 48]. Official gene names and symbols were identified using GeneCards (https://www. genecards. org/, accessed on 22 May 2020)  [49]. Where applicable, protein names were searched in the UniProt database (https://www. uniprot. org/, accessed on 22 May 2020) [50].
Studies, Bioactives and Differentially Expressed Genes A comprehensive search on PubMed and Web of Science, conducted in July 2018, with an update in November 2019, resulted in 8678 documents. After removal of duplicates, and using "Human" and "RCT" and "Clinical study" and "Clinical trial" and "Controlled Clinical Trial" and "Multicenter Study" filters, this number dropped to 465 manuscripts, which were further screened for eligibility. Based on titles and abstracts, 89 papers were selected for data extraction and distributed among the participants (Figure  1 ). After detailed analysis of the full text, and applying the predefined inclusion/exclusion criteria, we identified seven papers that report significant changes in gene expression accompanied with at least one favorable cardiometabolic outcome in controlled human intervention studies with polyphenols  [51] [52] [53] [54] [55] [56] [57].
As for the modulation of gene expression, we identified 58 differentially expressed genes at mRNA level (one duplicate was removed), and 5 differentially expressed miRNAs (Table  1 ), which were subjected to bioinformatic analyses. To better understand the biological meaning of genes extracted from the literature, we first conducted bioinformatic analyses to identify pathways significantly associated with polyphenol modulated genes. Using GeneTrail2 as a platform to access KEGG and BioCarta databases, we identified N = 71 KEGG and N = 42 BioCarta pathways that are significantly associated with extracted genes, modulated at the mRNA level. Among KEGG pathways retrieved with this analysis, N = 32 pathways are related to cellular processes, whereas the others are related to various diseases. Among the top 25 BioCarta pathways, N = 24 are related to cellular processes.