dataesr/scientific-paragraphs-categorization huggingface dataset:
GO term enrichment analysis Gene ontology enrichment analysis was conducted in R studio using the TopGO package  86. For analysis of proteins identified in protein interaction networks, the reference library of genes is described as Y2H-positive, or genes which showed interaction in AI1-Main  31  , PPIN-1  30  , PPIN-2  11  and the Si, Rps and Xcc interaction networks. For comparative GO enrichment, GO filtering was implemented to increase specificity and reduce redundancy. GOs were removed if >10% of the total number of genes in the Y2H-positive set (280) were annotated, and if the total number of genes in each term represented <5% of the query set. To calculate GO enrichment of DEGs after Si inoculation of Arabidopsis roots, all expressed genes in the Arabidopsis reference genome (TAIR10) were used as a reference library.
Quantification and statistical analysis All experiments were completed at least three times except where indicated in the figure legend. In these instances, the experiment would be based on at least two biological replicates. Statistical analyses of the RNAseq data and networks, including the integrated 12k_space cross-kingdom interactome map and FI-SPIN are detailed above. For comparative analyses, Si interactions identified in the 500 hormone gene search space were omitted to reduce bias. Luciferase activity data from protoplast assays was processed and analysed as described above. Due to large variation of all samples between biological replicates, luciferase activity values were normalised to Col-0 mock. The normalised values from at least two biological replicates were then used for one-or two-way analysis of variance (ANOVA) with Tukey's multiple comparison test, considering genotype, treatment and interaction, where applicable.
Unsupervised machine learning for modality fusion and network decomposition For the network decomposition, we used Parallel ICA with Fusion ICA Toolbox ((FIT, http://mialab. mrn. org/software/ t) 67 in the MATLAB 2022 environment (https://it. mathworks. com/products/matlab. html). Parallel ICA allows to combine gray and white matter together, estimating independent components for both modalities. The estimation was done with information theoretic criteria 68 and ICASSO 69,70 was run ten times with Infomax algorithm to assess the consistency of each modality. As a result, a matrix with the number of subjects and the expression of each component for every subject (loading coe cients) was generated. Finally, the independent components were converted into Talairach coordinates and plotted in Surf Ice (https://www.
nitrc. org/projects/sur ce/) for visualization. See Fig. 5  for the data processing steps through Parallel ICA and the successive analyses with brain networks and psychological variables. Neural characterization of high vs low assess which component was able to predict the high versus low anxiety group subdivision, we implemented a logistic stepwise regression in JASP  30  , using loading coe cients for each gray and white matter component in one single model, regressing out age and gender. Given that the aim of our study was to nd a covariant GM-WM circuit, we considered only covarying GM-WM components, according to the JASP correlation analysis. Additionally, trait anxiety score was checked for correlation with age, gender and education. Parallel ICA processing steps with additional predictive and inferential models Parallel ICA and additional inferential and predictive As a rst step, white and gray matter MRI images from all participants were added to the Fusion ICA toolbox.
Data availability statement Due to French regulations regarding sharing of the medical imaging data, individual raw data used for this study cannot be shared through a public repository. Rather, to have access to i-Share and MRi-Share de-identi ed data, please contact Christophe Tzourio. A request has to be submitted to the i-Share Scienti c Collaborations Coordinator (ilaria. montagni@ubordeaux. fr) with a letter of intent (explaining the rationale and objectives of the research proposal), and a brief summary of the planned means and options for funding. The i-Share Steering Committee will assess this request, and provide a response (principle agreement, request to reformulate the application or for further information, refusal with reasons). If positive, applicants will have to complete and return an application package that will be reviewed by the principal investigator, the Steering Committee, and the operational staff.
, a multi-modal brain MRI database acquired in a sample of 1870 young healthy adults (18-35 years old), undergoing university- level education. The original MRi-Share database contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility-weighted (SWI), and resting-state functional imaging modalities. The participants were recruited through the i-Share cohort study, launched in February 2013, with the approval of the local ethics committee (CPP2015-A00850-49). All research was performed in accordance with relevant guidelines and regulations, and informed consent was obtained from all participants. The exclusion criteria used for the MRi-Share database consists in (1) age over 35 years; (2) pregnancy or nursing; (3) claustrophobia; and (4) contraindications for head MRI, while the additional exclusion criteria used for the present study were the following: (1) diagnosis of bulimia/anorexia/obsessive compulsive disorder/depression; (2) daily alcohol consumption; and (3) drug consumption more than 10 times in life (cocaine, mdma, heroine etc.
7 million paired-end reads. Reads demultiplexed using bcl2fastq v2. 20 software were preprocessed using QIIME2 suite v2020. 8 and FASTQC v0. 11. 9. Reads were first checked for low quality bases (Q30) and trimmed of primers before being merged using FASTQ-join. The merged reads were denoised, dereplicated and removed from the chimera for each sample independently using the DADA2 plugin from the QIIME2 suite. The taxonomy of the resulting Amplicon Sequence Variants (ASV) was assigned using the SILVA v. 138 database  (Quast et al. , 2012). The ASVs abundance were normalized by rarefaction without replacement to even sampling depth of 268,632 reads (Supplemental file 1: Fig. S2 ), resulting in 43,806 ASVs, and one sample was discarded from further analysis because its maximum library size was below the defined threshold.
Discussion This study was part of a trial designed to assess the effects of agroecological practices, namely the use of high botanical diversity and late mown silages in diets of dairy cows of diverse breeds, on enteric CH 4 emissions under practical conditions. Rumen fluid was sampled to assess how these practices modified the rumen microbiota and whether this could be linked to CH 4 emission. Two limitations were faced due to the study design. First, it was not possible to correct the diversity and composition of the microbiota for the confounding variable of time in the data collected in this study as the same cows were sampled throughout the duration of the study. It is known that there is a potential time effect on rumen microbiota as observed by others  (Vaidya et al. , 2020). It was however assumed that the effect of time would be minor compared to the diet and/or breed effect as long as the feed intake was similar over the weeks.
Collection of Sample Material (MRI, Plasma) Five patients were enrolled in this study (four women and one man; mean age: 38. 2 years old; mean disease duration: 6. 6 years; mean Expanded Disability Status Scale (EDSS): 2) with definite RMS6 between March 2009-September 2010 (Neurological Hospital, Hospices Civils de Lyon, France). No disease-modifying treatment was used. Enrollment, blood sample collection, and MRI acquisition were published earlier  [1]. Blood samples were obtained from the cubital vein before each scan. Plasma samples were collected without signs of hemolysis and stored in aliquots at -80 • C prior to processing. The longitudinal prospective study (ClinicalTrials. gov Identifier: NCT00861172) was approved by an ethics committee (CPP Lyon Sud-Est IV) and the French Health Products Safety Agency (AFSSAPS)  [1].
MRI Analysis of Five Patients Frequent high-resolution MRI consisted of a baseline (BL) MRI followed by seven consecutive weekly MRIs (BL to day 49) on five RRMS patients as previously published  [1]. MRI acquisitions were performed on a 3T MRI system (Achieva 3T, Philips Medical Systems, Amsterdam, The Netherlands). A standard dose of 0. 1 mmol/kg Gadobutrol (Gadovist) was administered during each MRI session 60 s before the 3D T1 acquisition and the 3D FLAIR and 3D-T1-Gd images used for this study. Analysis of MRI data included: the total number of Gd-enhancing lesions, the number of new Gd-enhancing lesions, the number of Gd-enhancing lesions with maximal Gd intensity at each time point, and the number of new FLAIR lesions. The patients were post-analysis divided based on the level of MRI activity measurement (low: no Gd-enhancing lesions; medium: 8-26 Gd-enhancing lesions; high: 116 Gd-enhancing lesions) across 49 days.
Proteomics-Data Analysis A label-free quantification (LFQ) analysis of the plasma was performed in MaxQuant 1. 5. 7. 0 by searching the data files against the Uniprot Homo sapiens reference proteome with isoforms (UP000005640, protein count 70,952). All standard settings were employed with carbamidomethyl (C) as a static modification and protein N-terminal acetylation, deamidation (NQ), and Oxidation (M) were included as variable modifications. All reported proteins were identified with an <1% FDR, to ensure only high-confidence protein identifications. Result files from MaxQuant were analyzed in Perseus v1. 6. 12. 0 where reverse hits were removed from further analysis, and the data were log2-transformed. Furthermore, more than two unique peptides were required for valid protein identification and quantification to ensure high-quality data.
To conduct a principal component analysis (PCA), missing values (i. e. , proteins where a quantification value was not obtained for a given replicate analysis) were imputed with values from a normal distribution (width 0. 3 and downshift 1. 8) to simulate signals from low abundant proteins. All raw data and unfiltered search data have been made public through the PRIDE ProteomeXchange consortium with the dataset identifier PXD035422  [25].
EV Array Analysis-Interpretation and Statistical Analysis of Data Data analysis: Creation of graphs and statistical calculations were carried out using GraphPad Prism (version 6. 04, GraphPad Software, Inc. , San Diego, CA, USA), SigmaPlot (version 11, Systat Software Inc. , San Jose, CA, USA), and Excel (version 2013, Microsoft, Redmond, WA, USA). Heatmaps were produced using Genesis (version 1. 7. 6, IGB TU Graz, Graz, Austria). For a given antibody spot, the signal intensity was calculated as the mean signal of triplicate spots in relation to the sample signal of the negative spot (PBS) in triplicate. For each spot, the signal intensity was calculated by subtracting the mean of the background (no sample/blank, washing buffer) from the mean of the foreground (spot signal). Before visualization and calculation of linearity, the antibody signal intensities were converted to log space by log2 transformation.
Statistical Analysis A PCA with two components was made to visualize the major source of variation in the plasma protein expression, and volcano plots were made to show log2 fold change in plasma proteins between cytokines and proteome profiles, respectively. Benjamini-Hochberg adjusted p-values smaller than 0. 05 were considered significant. Heatmaps with hierarchical clustering were also made in Perseus  [29]. A sparse partial least squares discriminant analysis (sPLS-DA) was adapted from the MixOmics R package to identify what proteins could discriminate between the three groups of MRI activity (low, medium, high) according to multiplex and proteomics grouping  [30]. The performance of an initial sPLS-DA model with 5 components was assessed using the perf function with 3-fold cross-validation and 50 repeats to identify the optimal number of components.
Conclusions In conclusion, we used a unique dataset of weekly MRI with gadolinium combined with proteomics, multiplex assay of soluble inflammatory and EV markers in serial plasma samples to address the relationship between systemic inflammatory changes and acute lesion evolution in the brain. The proteomic profiling of plasma samples revealed a low intra-variability of the individual patients indicative of large patient-to-patient variability. We found that altered key processes were associated with acute adaptive and innate inflammatory responses, neutrophil degranulation, complement, and coagulation pathways. Several analyses of our dataset indicated the association of systemic IL-17 with the evolution of new Gd-enhancing and FLAIR lesions on MRI suggesting that this cytokine and the Th17 pathway may be important in BBB disruption and initiating inflammation in the brain of patients with MS.
However, the tumors on the back (orange, Figure  3A ) did not cluster with this group, instead forming a second cluster distant from the developmental trajectory. To identify genes in VGLL2-NCOA2 tumors that account for this clustering with segmentation, we performed a differential gene expression analysis from the RNA-seq data. First, the cohort of n=18 CMV-VGLL2NCOA2 driven zebrafish tumors was compared to n=7 mature zebrafish skeletal muscle samples, identifying 385 differentially regulated genes. Next, n=3 samples of embryonic zebrafish during somitogenesis (1-4 somites, 14-19 somites, and 20-25 somites) were compared to mature zebrafish skeletal muscle; and 303 differentially regulated genes were found. Then, these 385 and 303 gene sets were intersected to identify 27 genes shared in both development and tumor datasets as differentially expressed when compared to mature skeletal muscle (Figure  4A ).
Trim Galore (https://www. bioinformatics. babraham. ac. uk/projects/trim_galore/) was used for quality and adapter trimming. The qualities of RNA-sequencing libraries were estimated by mapping the reads onto zebrafish reference dataset (Genome Reference Consortium Zebrafish Build 11, GRCz11) using Bowtie (v2. 3. 4. 3)  52. STAR (v2. 7. 2b)  53  was employed to align the reads onto the zebrafish genome GRCz11, SAMtools (v1. 9)  54  was employed to sort the alignments, and (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted September 16, 2021. ; https://doi. org/10. 1101/2021. 09. 15. 460544 doi: bioRxiv preprint 21 HTSeq Python package  55  was employed to count reads per gene.
; https://doi. org/10. 1101/2021. 09. 15. 460544 doi: bioRxiv preprint 22 data used for this analysis are available at https://portal. gdc. cancer. gov/projects. RNA-seq data from n=42 fusion-positive and fusion-negative rhabdomyosarcoma is from Xu et al 2018  63. RNA-seq data from n=396 adult skeletal muscle samples are from the Genotype-Tissue Expression Project (GTEx). The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on March 2018. The same computational analysis steps based on hg19 human reference genome data, as detailed in the RNA-seq and analysis section, were applied to process these tumor and muscle datasets side by side to minimize computational batch effects.
Acknowledgements We thank the  UT Southwestern Histopathology core  for excellent services and for their expertise, and the  Nationwide Children's Hospital Animal Resources Core  for their exceptional zebrafish husbandry. G. C. K is supported by an  Alex's Lemonade Stand Foundation "A" Award  and a  V Foundation for Cancer Research   V Scholar Grant. J. F. A. was supported by grant  RP120685-P3  from the  Cancer Prevention and Research Institute of Texas (CPRIT)  and by  Curing Kids Cancer. L. X. is supported by  Rally Foundation ,  Cancer Center Support  Grant  P30 CA142543  and  RP180805  from  CPRIT. F. T. is supported by the  Institut National de la Recherche Médiacale (INSERM). S. W. was supported by a grant from the  Fondation Nuovo-Soldati pour la recherche en cancérologie. (which was not certified by peer review) is the author/funder.
Neutron Source Monte-Carlo ray tracing simulations were carried out to evaluate the instrument performance using McStas  [14]  software. In Table  1 , the main parameters for the neutron source are shown for two scenarios. The first one is the projected flux for the High Brilliance Neutron Source (HBS)  [4, 15]. The second one is the best-case scenario expected for the JULIC Neutron Platform which is currently only operating at a very low power. The bi-spectral source is modeled with three components (Maxwellian) corresponding to the coldmoderated neutrons (60 K), the thermal-moderated neutrons (305 K), and the under-moderated ones (UM). This last component represents the neutrons that are not properly moderated by the thermal premoderator and the cold moderator. The flux corresponding to each component (cold, thermal, and under-moderated) is detailed in Table  1  and the simulated spectrum is shown in Figure  3.
Overall, the GRM treatments did not significantly affect any of the observed barrier properties, confirming the integrity and functionality of the tight junctions. TEER and P app results related to RGO are reported in Figure  S7. 8] [49] [50]  For this reason, following the exposure of bEnd. 3 cells to either FLG or GO, we explored the cell proteome by obtaining label-free high-resolution LC-MS data on the cell lysates. We quantified a total of 5153 protein groups in all three groups (control, FLG, and GO), with a total protein abundance profile that spans over roughly 4. 5 logs. The full list of quantified proteins is reported in Supplementary File P1 and can also be found in the PRIDE database (data set identifier PXD038297). We detected only minor changes in the bEnd. 3 proteome following exposure to GO (134 proteins) and FLG (43 proteins), corresponding to about 2.
■ ACKNOWLEDGMENTS We thank  Dr. Michele Dipalo  ( Istituto Italiano di Tecnologia, Genova, Italy ) for help with Raman measurements. We also thank  Ilaria Dallorto ,  Rossana Ciancio ,  Diego Moruzzo , and  Arta Mehilli  for administrative and technical help. The project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 881603 Graphene Flagship Core3  (to F. B. ), the  Italian Ministry of Foreign Affairs and International Cooperation  (Grant Agreement No. MAE00694702021-05-20  to F. B. ), and  IRCCS Ospedale Policlinico San Martino, Genova, Italy  ( Ricerca Corrente  and "5x1000" to F. B and V. C. ).
Ocean color remote sensing offers two decades-long time series of information on phytoplankton abundance. However, determining the structure of the phytoplankton community from this signal is not straightforward, and many uncertainties remain to be evaluated, despite multiple intercomparison efforts of the different available algorithms. Here, we use remote sensing and machine learning to infer the abundance of seven phytoplankton groups at global scale based on a new molecular method from Tara Oceans. Our dataset is to our knowledge the most comprehensive and complete, available to describe phytoplankton community structure at global scale using a molecular marker that defines relative abundances of all phytoplankton groups simultaneously. The methodology shows satisfying performances to provide robust estimates of phytoplankton groups using satellite data, with few limitations regarding the global generalization of the method.
In the following work, we address this limitation by using Tara Oceans' phytoplankton observations. These data are, to date, the most comprehensive and harmonized data available on the phytoplankton taxonomic community structure on a global scale, as obtained from metagenomics reads of a single-copied gene present across all phytoplanktonic groups, an approach that provides an unbiased picture of phytoplankton cell abundances  (Pierella Karlusich et al. , 2022). We employ these data alongside satellite-derived parameters to train an unsupervised machine learning algorithm to discern the non-linear relationship between the phytoplankton taxonomic community structure and the optical signal perceived by the Ocean color satellite sensors, together with the physical environment. This new methodology allowed us to monitor the spatio-temporal variability of seven phytoplankton groups between 1997 and 2021.
Materials In this section, different datasets are presented, each playing an important role in either the training of the algorithm or the validation and evaluation of global patterns of the outputs. Therefore, three datasets are employed. The first is the input dataset which allies between the in-situ information on phytoplankton groups from the Tara Oceans expedition and their associated satellite matchups. The second corresponds to an independent dataset based on global in-situ HPLC measurements, used to compare the outputs of the presented algorithm and the DPA approach to estimate phytoplankton groups. And last, the third dataset compiles two operational satellite-derived products on phytoplankton groups' abundance and therefore are used to compare large scale patterns and evaluate any discrepancies.
Metagenomic read abundance of the psbO gene The psbO gene encodes the manganese-stabilizing protein, of around 270 amino acids, a core subunit of photosystem II (PSII) and unique to organisms carrying out oxygenic photosynthesis. The psbO gene is single copy in the vast majority of Eukaryotes 85 and Prokaryotes. The reads mapping psbO were retrieved from the metagenomes and used as a proxy of phytoplankton relative cell abundance Pierella  Karlusich et al. (2022). Given that five major organismal size fractions were collected by Tara Oceans  (0. 22-3um, 0. 8-5um, 5-20um, 20-180um, 180-2000um) , we formatted the data into major phytoplankton groups based on their 4 https://doi. org/10. 5194/egusphere-2022-1421 Preprint. Discussion started: 14 December 2022 c Author(s) 2022. CC BY 4. 0 License.
taxonomy. For every station, we pooled the results obtained for the five-size fractions into a single aggregated sample. We discarded the sampling stations where collected size fractions did not cover the full range of sizes so as not to bias the results. We then split this composite dataset composed by samples collected at 211 different stations into seven main phytoplankton groups based on DNA reads taxonomic assignation: diatoms (Bacillariophyta, hereafter referred to as Diat), dinoflagellates (Dinoflagellata, Dino), green algae (Chlorophyta, Green), haptophytes (Haptophytina, Hapto), pelagophytes (Pelagophyceae, Pelago), cryptophytes (Cryptophyta, Crypto), and prokaryotes mainly corresponding to Cyanobacteria (Fig. 1 ). The psbO read abundances of these main groups, which cover oligotrophic to eutrophic waters (Chla from 0.
Global Phytoplankton HPLC pigment dataset In order to compare the output of our methodology with an independent dataset, a global HPLC dataset has been compiled, regrouping 12 000 HPLC observations originating from several HPLC datasets between 1997 and 2014 (Fig. (2013). This HPLC dataset was collocated with satellite Globcolor and CCI matchups. This HPLC dataset contains the most used phytoplankton pigments to identify major phytoplankton groups; Fucoxanthin (Fuco), Peridinin (Perid), Alloxanthin (Allo), Zeaxanthin (Zea), Chlorophyll-b (Chlb), 19. The different phytoplankton groups and their associated pigments are shown in table  2. The objective of this dataset is to allow an independent global comparison of diagnostic pigments (DPA) vs. Satellite estimated phytoplankton groups. An average Chla fraction value for each phytoplankton group was calculated using the three sets of coefficients presented in table 2  (Uitz et al.