dataesr/scientific-paragraphs-categorization huggingface dataset:
Cells were irradiated with different ionizing radiation doses and the transcriptomes were assayed at different times after irradiation (see in Additional file 7: Figure  S4  and Additional file 3: Table  S2 ). Additional file 3: Table  S2  summarizes properties of each dataset (number of expressed mRNAs and miR-NAs), experimental conditions (radiation dose in Gy, time of observation) and results of model simulations described as Spearman correlation coefficient values.
Cellular processes influenced by the highest ranked miRNAs We identified the KEGG pathways terms corresponding to genes with transcripts containing sequence motifs targeted by the 30 top-ranked miRNAs. Table  5  shows the example of pathways for Me45 cells. We used DIANA-mirPath  [45]  to find these pathways, but it was not possible to find detailed information about miRNAs which have been discovered only recently. The top-ranked   Wnt signaling pathway 0. 0119  65 13  Hedgehog signaling pathway 0. 0242  27 13  FoxO signaling pathway 0. 0270  60 10  miRNAs target mRNAs in 35 KEGG pathways (p-value < 0. 05). Examples of these pathways are shown in Table  5  and all are presented in the (Additional file 9: Table  S4 ). Many of these pathways are connected with cellular signalling and responses in different diseases.
Correlation coefficients between predicted and experimentally assayed mRNA expressions decreased with time in Me45 cells, which suggests that in this cell line these interactions had the highest influence on transcriptome changes directly after irradiation, and later the influence of other mechanisms increased. However for other cell types, the correlation coefficients of predictions versus the experimental results were not changing monotonously, and the patterns were cell type specific (see Table  7 ). In addition, we performed analysis of experiments similar to ours, involving a set of cell types assayed at different times after irradiation. The datasets are available in the ArrayExpress database; some of their characteristics are listed in Additional file 1: Table  S1. The correlation coefficients between experiment and our model prediction are always less than 1 since factors different from miRNAs influence the mRNA expressions and reduce the correlation.
A summary of results is depicted in Fig. 7. Bars represent mean values with minimal and maximal values calculated based correspondingly on 3 datasets with 2 Gy radiation dose, 14 with 4 Gy, 2 with 5 Gy, 4 with 10 Gy, and 3 with 60 Gy. Comparisons of predicted and experimental results for data available in the ArrayExpress database, involving cells irradiated with different doses, showed that a good fit between model and experiment is only achieved for doses lower than 5Gy. Concluding, it seems that the regulation of mRNA expressions in irradiated cells depends on miRNA only if the direct effects of irradiation dominate cell response.
The correlation coefficients (ρ) of model predicted and experimentally assayed transcriptome expressions change in time. Me45, K562 and HCT116 cells were irradiated with 2Gy of ionizing radiation, the levels of transcripts were assayed at different time points after irradiation in microarray experiment and predicted by model simulations Summary of the study of model performance using publicly available miRNA and mRNA datasets. Values of correlation coefficient (ρ) calculated for experimental and simulation data depend on irradiation dose. Bars represent mean values with minimum and maximum values calculated based correspondingly on 3 (2 Gy), 14 (4 Gy), 2 (5 Gy), 4 (10 Gy) and 3 (60 Gy) datasets.
Funding This work was supported by the  Polish National Science Center , grant  UMO-2015/19/B/ST7/02984. Calculations were performed using the Ziemowit computational cluster (http://www. ziemowit. hpc. polsl. pl)funded by the  EU Innovative Economy Program (POIG)  project  02. 01. 00-00-166/08  ( BIO-FARMA ) and expanded under the  POIG  project  02. 03. 01-00-040/13  (Syscancer). Experiments were performed in the Silesian  BIO-FARMA -Bioinformatics Laboratory  and the  Biotechnology Centre of the Silesian University of Technology  ( POIG. 02. 01. 00-00-166/08  project).
Availability of data and materials All microarray data files are available from ArrayExpress database (Me45, K562, HCT116 +/+ and HCT116 -/-cell lines accession numbers E-MEXP-2623, and E-MTAB-5197 for mRNAs and miRNAs, respectively). ID for published datasets can be found in separate excel file (Additional file 1: Table  S1 ).
The mice were fed a ND (AIN93Mi; Research diet) or a HFD (60% fat and 20% carbohydrates (kcal per 100 g), D12492i, Research diet). Treatment continued for 16 weeks. Body weight, food and water intake were recorded weekly. Body composition (lean and fat mass) was assessed weekly by using 7. 5 MHz time domain-nuclear magnetic resonance (TD-NMR; LF50 minispec, Bruker). After 15 weeks of follow-up, a subset of mice was placed in metabolic chambers for indirect calorimetry measurements. Fecal energy content. Fecal energy was measured on fecal samples harvested after a 24 h period during the final week of treatment by the use of a bomb calorimeter (Staufen, Germany). Treatment of Napepld ΔIEC mice with Akkermansia muciniphila. Data displayed in Supplementary Figure  3  come from a set of mice metabolically characterized in Everard et al.
cDNA was prepared by reverse transcription of 1 μg total RNA using a Reverse Transcription System kit (Promega, Madison, Wisconsin, USA). Real-time PCR was performed with the CFX96 real-time PCR system and CFX Manager 3. 1 software (Bio-Rad, Hercules, California, USA) using Mesa Fast qPCR (Eurogentec, Liège, Belgium) for detection according to the manufacturer's instructions. Rpl19 RNA was chosen as the housekeeping gene. All samples were performed in duplicate, and data were analyzed according to the 2 -ΔΔCT method. The identity and purity of the amplified product were assessed by melting curve analysis at the end of amplification. The primer sequences for the targeted mouse genes are presented in Supplementary Table  3. Western-blot analyses. For the detection of NAPE-PLD, tissues were homogenized with TissueLyser II (Qiagen) in RIPA buffer supplemented with a cocktail of protease inhibitors and phosphatase inhibitors (Sigma).
The revelation was performed using a chemiluminescent substrate (SuperSignal ® West Pico (ThermoScientific) and Amersham Image600 (GE Healthcare). Densitometry analysis were performed using ImageQuanTL software. Information regarding all the antibodies can be found in Supplementary table  4. Uncropped western blots are available as Supplementary Figures  7 8 9. Histology on brain. Twenty micrometer thick serial coronal cryosection were mounted on SuperFrost Plus slides (Menzel Gläser) and kept at -20 °C. For Arcuate Nucleus we harvested approximately 10 serial sections per animal from bregma -1. 22 mm to -2. 54 mm according to The Mouse Brain in stereotaxic coordinates (Paxinos, Franklin); for the Paraventricular Nucleus 6 sections per animal from bregma -0. 58 mm to -1. 22 mm. For c-Fos immunohistochemistry in PVN, after antigen retrieval (Dako S1699) by heating (2100 Antigen Retriever, from Aptum) the endogenous peroxidases were inhibited in a solution of MeOH with H 2 O 2 0.
c-Fos and POMC-expressing neurons were quantified on multiplex-stained cryosections with software applications (APP"s) using the image analysis tool Author version 2017. 2 (Visiopharm, Hørsholm, Denmark). Brightfield (blue counterstaining of tissue) and fluorescent (c-Fos/POMC/Hoechst33342) scans were first aligned using the Tissuealign add-on from Visiopharm. Regions of interest (ROI) were then manually delineated on the brightfield scans and were automatically applied to the aligned fluorescent scans. Within these ROIs, nuclei were segmented based on the Hoechst nuclear staining and classified into four categories according to their expression of c-Fos and POMC (negative, single stained for each marker, double stained). Detected cells were finally quantified using a counting frame to avoid double cell counting.
The eCBs were quantified by isotope dilution using their respective deuterated standard (showing identical retention times). The calibration curves were generated as described and the data were normalized to the average eCB content/tissue weight of the WT group 3. LC-MS/MS analyses of endocannabinoids. Data displayed in Figs. 3, 10 and Supplementary Figure  3  were obtained as follows: intestinal epithelial cells Frozen (-196 °C) jejunum samples (~10 mg) were crushed with a disposable tissue grinder, harvested with 500 µl PBS, immediately mixed with 500 µl methanol containing 5 ng of deuterated standards (see table), vortexed for 60 s then agitated 90 min at room temperature to fully denature the proteins. Samples were then centrifuged (20,000×g; 10 min, room temperature). The supernatants were collected and their methanol content adjusted to 10% with acidified water (0.
6 μm, Phenomenex) and eluted at a flow rate of 400 μl/min using a discontinuous gradient of solvent A (1 mM ammonium acetate + 0,05% acetic acid) and solvent B (acetonitrile/water; 95/5 + 1 mM ammonium acetate + 0. 05 acetic acid). Gradient was as follows: 15-35% B from 0 to 2 min, 35-75% B from 2 to 12 min, 75-95% B from 12 to 12. 1 min and kept at 95% until 17 min. The HPLC system was interfaced with the electrospray source of a Shimadzu 8050 triple quadrupole mass spectrometer and mass spectrometric analysis was done in the positive ion mode using multiple reaction monitoring using the specific mass transitions shown in Supplementary Table  11. Lipidomics analysis in the jejunum. Data displayed in supplementary tables 5-10 were obtained by using lipidomics performed in collaboration with Biocrates (Innsbruck, Austria).
The biologically most abundant members of (lyso-) glycerophospholipids, i. e. , (lyso-) glycerol-phosphocholines, glycerol-ethanolamines, glycerol-serines, glycerol-glycerols, as well as sphingolipids, i. e. , sphingomyelins, ceramides, dihydroceramides, and 2-hydroxyacyl ceramides, were quantitatively analyzed by a high-throughput flow injection ESI-MS/MS screening method. The MRM detection in positive and negative mode was performed using a AB SCIEX 4000 QTrap® tandem mass spectrometry instrument (AB SCIEX, Darmstadt, Germany). The sample preparation of 20 μl sample volume followed a MeOH/ CHCl3-liquid/liquid-extraction protocol. Five internal standards were used to compensate for matrix effects, and 43 external standards for a multipoint calibration. The quantitative data analysis was performed with Biocrates in-house software MetIDQ™ enabling isotopic correction.
97 threshold against the Greengenes database (Fig. 5c-f ) or using mother (Supplementary Tables 1-2 and Supplementary Data 1). Principal coordinate analysis was calculated using unweighted UniFrac distance. Phylogenetic tree was generated using QIIME 1. 9. 0 and visualized using GraPhIAn  51. Sequence data that support the findings of this study have been deposited in Sequence Read Archive (SRA) database with the PRJNA508476 accession code (http://www. ncbi. nlm. nih. gov/sra/PRJNA508476). Hepatic lipid content analysis by Oil Red O staining. Liver tissue was embedded in Tissue-Tek Optimal Cutting Temperature compound (Sakura Europe, Leiden, Netherlands) and flash-frozen in cold isopentane. Five micrometer-tick tissue sections were stained with Oil red O staining for lipid content analysis.
Five highmagnification fields (20×) were analyzed per mouse. Quantification of mean droplet area was performed using ImageJ software (Version 1. 50a, National Institutes of Health, Bethesda, Maryland, USA). Fibrosis analysis by Sirius red staining. Hepatic collagen content was visualized by staining paraffin sections with 0. 1% Sirius Red (Sigma-Aldrich). At least five high-magnification fields were selected at random for each mouse. Images were obtained using a SCN400 slide scanner and Digital Image Hub software (Leica Biosystems, Wetzlar, Germany). All analyses were performed in a blinded manner by the investigator and quantified using an semi-automated script in ImageJ software (Version 1. 50a, National Institutes of Health, Bethesda, Maryland, USA). Collagen content was quantified as percentage stained area per total tissue section area.
Statistical analysis. Statistical analyses were performed using GraphPad Prism version 7. 00 for Windows (GraphPad Software, San Diego, CA, USA) except for microbiota analyses as described above. If variances were significantly different between groups, values were normalized by log-transformation before proceeding to the analysis. In cases when variance differed significantly between groups even after normalization, a non-parametric test was performed. Different with P values < 0. 05 were considered significant. Reporting Summary. Further information on experimental design is available in the Nature Research Reporting Summary linked to this Article.
Acknowledgements We thank  A. Barrois ,  A. Bever ,  H. Danthinne ,  T. Duparc ,  B. Es Saadi ,  R. M. Goebbels ,  S. Matamoros ,  T. Pringels ,  V. Allaeys , (at  UCLouvain ,  Université catholique de Louvain)  and  C. Martin  (at  Université Laval ), for their excellent support and assistance. We thank  C. Bouzin  and  A. Daumerie  from the  IREC imagery platform  ( 2IP ) from the  Institut de Recherche Expérimentale et Clinique (IREC)  for their excellent help and  N. Van Baren  for the use of the Pannoramic imagery system at the  De Duve institute (DDUV, UCL, Brussels). A. E. is research associate at  FRS-FNRS  ( Fonds de la Recherche Scientifique ),  Belgium. M. R. and A. d. W. are research fellow at FRS-FNRS, P. D. C. is a senior research associate at  FRS-FNRS, Belgium. P. D. C. is the recipient of grants from the  FNRS  and  FRSM (Fonds de la recherche scientifique médicale, Belgium)  (convention J.
0084. 15 and convention  3. 4579. 11 ), Projet de Recherche  (convention:  T. 0138. 14 )and  Action de Recherche Concertée  (Communauté française de Belgique convention  12/17-047 ). V. D. M. is the holder of the Canada Excellence Research Chair on the  Microbiome-Endocannabinoidome Axis in Metabolic Health at Université Laval , which is supported by  Social Sciences and Humanities Research Council (SSHRC) , the  Natural Sciences and Engineering Research Council (NSERC)  and the  Canadian Institutes of Health Research (CIHR). This work was supported by the  FRFS-WELBIO  under grant  WELBIO-CGR-2017C-02. P. D. C. is a recipient of the  Funds Baillet Latour  (Grant for Medical Research 2015) and  ERC   Starting Grant 2013  ( European Research Council , Starting grant  336452-ENIGMO ).
At this moment, the sample involves galaxies in the redshift range 0. 04 -0. 07, at a typical radial comoving distance of ≈ 250 Mpc. Future works with data for high redshift galaxies might in a near future complete the construction of the main paths of galaxy evolution. The paper is designed as follow. In Sec. 2 we describe the WINGS data sample used in this work and we give a short review of the galaxy variables used in our analysis. Sec. 3 introduce the multivariate analysis approach, in particular the Maximum Parsimony method (Sec. 3. 1). We discuss the selection of the variables used in the phylogenetic analysis in Sec. 3. 4 and we present our results in Sec. 4 discussing three particular cases: the full sample (Sec. 4. 1), the cluster sample (Sec. 4. 2) and the field galaxy sample  (Sec.
The WINGS sample The data used in this work are those belonging to the Wide-field Imaging of Nearby Galaxy-clusters Survey (hereafter WINGS) survey. 1  WINGS is a long term research project especially designed to provide a robust characterisation of the photometric and spectroscopic properties of galaxies in nearby clusters (0. 04 < z < 0. 07)  (Fasano et al. 2006). The core of the survey WINGS-OPT  (Varela et al. 2009)  is the database of optical B and V images of 77 clusters, obtained during dark time with the Wide Field Camera (WFC, 34 × 34 ) mounted at the corrected f/3. 29 prime focus of the INT-2. 5 m telescope in La Palma (Canary Islands, Spain) and with the Wide Field Imager (WFI, 34 × 33 ) mounted at the f/8 Cassegrain focus of the MPG/ESO-2. 2 m telescope in La Silla (Chile), that have been analysed with SExtractor  (Bertin & Arnouts 1996).
The optical photometric catalogues are 90% complete at V∼ 21. 7, which translates to M * V +6 at the mean redshift of the survey  (Varela et al. 2009 ). The WINGS-OPT database includes respectively 393013 galaxies in the V band and 391983 in the B band. The WINGS database has a spectroscopic follow-up WINGS-SPE for a subsample of 48 clusters (26 in the north and 22 in the south hemisphere) with the spectrographs WYF-FOS@WHT (λrange = 3800 ÷ 7000 Å, resolution FWHM = 3 Å) and 2dF@AAT (λrange = 3600 ÷ 8000 Å, resolution FWHM = 6 Å). From these spectra we derived the redshift of 6132 galaxies  (Cava et al. 2009). A subsample of this (5299 galaxies) have been analysed with spectrophotometric techniques deriving the star formation rate s f r * at different epochs, the stellar masses M * and ages, the internal extinction A V and the equivalent widths of the main absorption features  (Fritz et al.
The adopted sample From the WINGS database, a sample of 4100 galaxies has been extracted for this work. Distances were determined from their redshifts. 2624 galaxies were determined to be members of 28 galaxy clusters (Fig. 1 ) with redshifts around 0. 05-0. 06. The remaining 1476 galaxies are background galaxies with slightly larger redshifts around 0. 15 (see Fig. 2 ). They are considered as field galaxies. This field sample is heterogeneous, since it is represented by galaxies in the field of our clusters. Binary galaxies or small groups might be present. The possible presence of subgroups have been analysed by  Ramella et al. (2007). Cluster background galaxies have been eliminated on the basis of the colour-magnitude diagrams (see  Valentinuzzi et al. 2011 ) and on  Star formation rate in M yrs -1 : stellar mass produced within the age bin 0 -2 • 10 7 yrs s f r2 idem of s f r1 for the bin 2 • 10 7 -6 • 10 8 yrs s f r3 idem of s f r1 for the bin 6 • 10 8 -5.
Jellyfish galaxies A number of "jellyfish galaxies" have been identified in the WINGS database by  Poggianti et al. (2016). These objects are outstanding examples of galaxies which show knotted arms of material resembling the tentacles of jellyfishes. They are believed to be galaxies that are still gas rich and have just entered the cluster environment. Jellyfish galaxies are found mainly in C2 and C8, with a prevalence of ≈ 10% in each group, and in F3, F9, and F13. D. Fraix-Burnet et al. : A Maximum Parsimony analysis of the effect of the environment on the evolution of galaxies Fig. 15. Histograms for jellyfish galaxies that are in our cluster (black lines) or field (grey lines) samples, for the parameter δ (see text) and the distance ∆ CC to cluster center. The groups F3 and F13 are large, and so the prevalence of jellyfish galaxies is very low, ≈2%.
Conclusions This study aims at investigating the effect of cluster environment on the evolutionary scenario of galaxies. We have used a consequent sample of homogeneous data, from the WINGS catalogue, with about 2600 galaxies which are members of clusters, and about 1500 ones which are field galaxies. We used Maximum Parsimony, a phylogenetic technique also known as cladistics. It is designed to find evolutionary paths between objects or classes and has now been often used in astrophysics. However, the sample is too large for this technique to be applied directly. We reduced the amount of objects by performing a pre-clustering using a hierarchical clustering method in order to pave the data set according to its extent and not to its density distribution. We have found that 300 pre-clusters is a good compromise between representativity and computational constraints of the MP analysis.
After a sentence ended, another blank screen occurred for 2000 ms (Fig. 1 ). After that, either a catch question was displayed, with the whole question in the middle of the screen and the yes-no answers randomly split to the left or right side, or the next trial began. In total, participants read 253 sentences (253 trials) in random order, which came from one of three lists, counterbalanced on the three levels of context constraints. The total amount of trials was divided into four blocks, separated by small breaks in-between. The length of a break was self-determined by the participants and the task could be continued by button press. In total, the data acquisition lasted 60 min.
Data acquisition. All data were acquired using a 275 axial gradiometers CTF Omega MEG system. Horizontal and vertical bipolar electrooculography (EOG) as well as electrocardiography (ECG) were recorded in order to discard eye blinks, eye movements and heart beats contaminated trials. All electrophysiological signals were low-pass filtered at 300 Hz, digitized at 1200 Hz, and stored for off-line analysis. Three coils were placed on the nasion and the left and right ear canal to register the head position with respect to the gradiometers. The coils enabled real-time monitoring of the head position throughout the experiment  41. Next to the MEG recordings, magnetic resonance images (MRIs) were obtained from 32 of the participants with a 1. 5 T or 3. 0 T Siemens system. By means of attached markers at the same anatomical locations as the head coils, the MRIs could be aligned to the MEG coordinate system.
Data preprocessing. All data were analyzed using the open-source Matlab toolbox Fieldtrip  42. From the MEG data, a time-window of interest was segmented 2 s before and after the onset of a sentence's final word for each trial. This segmentation therefore included the blank delay period just before onset of the target word, where the effect of context constraints is expected to occur, and the period after onset of the target word. The segmented data were low-pass filtered at 150 Hz. The 50 Hz line noise components were removed by using a notch filter. Artifact identification and rejection was done in three steps. First, MEG jump and muscle artifacts were identified by visual inspection of amplitude variance over trials. Second, artifacts related to eye-movements and cardiac activity were identified and removed by means of an independent component analysis (fastICA)  43  , followed by backprojection.
The independent components were visually inspected and removed from the sensor data, if they resembled heartbeat, eye-movements or blinks (as compared to the recorded EOG and ECG). Third, the resulting data were again visually inspected to remove any remaining artifacts. From this procedure, on average 11% of trials and 1. 5% of MEG sensors were excluded from further analysis. Event-Related Field (ERF) analysis. Event-related fields were investigated to observe M400 modulations after the last word onset. This correlate is the magnetic counterpart of the classical N400 measured by electroencephalography and inhabits the same time-course and response properties  44, 45. For each condition, the epochs were first low-pass filtered at 35 Hz. All ERFs were then baseline corrected based on a time window of -300 ms-0 ms relative to target word onset.
This model was used to compute the forward model according to Nolte  48. The initial co-registration between the headmodel and MEG sensors was achieved by manually identifying the anatomical landmarks of the nasion and two auricular fiducials, and was additionally refined, using the subject-specific three-dimensional digitised representation of the scalp, as obtained by a Polhemus digitizer. The source space was discretized into a three dimensional grid with a 6 mm resolution. Source reconstruction was performed using a spatial filter, which was computed by combining the cross-spectral density (CSD) matrices from all three conditions (HC, MC, LC). The CSDs were computed using the Fast Fourier transform of the data with multitapering, with a center frequency of 10 Hz or 18 Hz for the alpha (averaged over the time window 540-0 ms, relative to target word onset) and beta (averaged over 450-0 ms) frequency band respectively.