dataesr/scientific-paragraphs-categorization huggingface dataset:
The oscillation phase was acquired using the angle of the imaginary part of the transform. The LFP was band-pass filtered in the band of 30-50 Hz (unless indicated otherwise). To band-pass the LFP signals, we used a FIR (Finite Impulse Response) filter using the Kaiser window method with a 60 dB stop-band attenuation and a 5Hz width from pass to stop transition  [34]. To implement the filter we used the following functions from the Pythonbased ecosystem Scipy: signal. kaiserord, signal. lfilter and signal. firwin [35].
Human recordings analysis In this paper, aiming to constrain our computational models to observed experimental features, we extend the human data analysis performed in  [20, 21] , focusing on awake states. The data was acquired extracellularly in patients suffering of intractable epilepsy, who had multielectrode arrays implanted during therapeutic procedures. The arrays registered simultaneously local field potential (LFP) and unit activity. We considered here one patient for which the recording was very stable, and in which several periods of wakefulness could be analyzed. In each electrode, Gamma rhythms were identified and neural activity was characterized with respect to the Gamma cycles. Fig 2A illustrates a specific instant in which Gamma bursts were observed in most of the electrodes (spiking activity and the respective electrode band-filtered LFP are shown).
In the coincidence detection mode, we found that the response was only weakly modulated by the phase of the Gamma. This was assessed by comparing the Gamma oscillation to a sinusoidal control input, in which case the response was clearly phase-dependent. In agreement with the integrative mode, the responsiveness measured in the coincidence detection mode protocol was generally higher for the AI-like states. In addition, in the coincidence detection mode, among the three models, the ING Network is the only one that presents a similar responsiveness between Gamma and AI states, which stresses again the importance of network topology on networks behaviors. A smaller responsiveness during Gamma states is somehow surprising since neurons are in general more depolarized in this state and additionally increase their firing, as we showed in our data analysis.
E: Network activity for the parameters indicated with a start in A, B, C and (p = 60% and Q i = 5 nS). The raster plot of the whole network (e1), the population frequency (e2), the membrane potential of 3 randomly chosen neurons (e3) and the power spectrum of the population frequency (e4) are indicated. The population frequency is calculated as the total number of spikes (spikes of the whole network) in a time bin of 1 ms, divided by the duration of this time bin. Because of the exclusive presence of inhibitory neurons and its high level of recurrent inhibition, this network is capable of generating Gamma rhythms with frequencies around 70Hz by means of an ING mechanism. (TIF) S3 Fig. Phase -locking statistical test. A and B: Phase distribution of two randomly picked cells from the human recordings (Data segment 1): one excitatory (A, green) and one inhibitory (B, red).
The phase distribution of each cell was fitted to a Von Mises curve, which allowed the estimation of its preferred phase y VM. The phase distribution of each neuron was tested for circular uniformity using a Bonferroni-corrected Rayleigh test  [36, 37]. C and D: Rayleigh Z calculated for all recorded neurons: excitatory (C, green) and inhibitory (D, red). A neuron was considered phase-locked if the circular uniformity at P < 0. 01, (Z > Z c ) could be rejected. In these plots, neurons were ordered according to their Z value and not according to their original indexes. The activity of each neuron inside and outside Gamma bursts in all 5 data segments were quantified. Taking into account that each data segment had a different duration, containing a different total Gamma duration, and that some neurons were silent in some data segments, each neuron was analyzed individually, taking into account the percentage of the total amount of time in which the neuron was active.
Funding: A. D was supported by the  Centre National de la Recherche Scientifique (CNRS)  and the  European Community  ( Human Brain Project ,  H2020-785907 ). E. S. acknowledges a PhD fellowship from the  E ´cole des Neurosciences de Paris (ENP)  and from the  Fondation pour la Recherche Medicale (FRM)  -grant  FDT202012010566  -and the financial support from  La Fondation des Treilles. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CNRS: www. cnrs. frHuman  Brain  Project: www. humanbrainproject. euENP: www. paris-neuroscience. frFRM: www. frm. orgLa  Fondation des Treilles : https://www. les-treilles. com/la-recherche/le-prix-jeune-chercheur/.
(1-hexanol and 1-nonanol were used, alternatively for different bees; data were pooled after checking for any significant effect of the odorant used). Sucrose (unconditioned stimulus, US: same solution as for feeding) was delivered to the antennae using a toothpick, 3 s after CS onset, for 3 s. This triggered the bee's reflex extension of the proboscis to lick the reward. Whenever the animal already responded to the CS (conditioned response), it was directly allowed to feed upon US onset. Successive learning trials were separated by 10-min intervals to facilitate memory consolidation  54. Memory was assessed by placing the animals again in the conditioning setup, and by presenting them the CS without the US  72. The presence or absence of a conditioned response was recorded. In case of no response, sucrose was applied to the antennae at the end of the test, to control for the intact motor response.
Acknowledgments We thank the  Winterbourne garden (Birmingham)  and  Lucie Hotier  ( Toulouse ) for providing bees,  Roland Arnold  and  Reinhard Stöger  for discussions and comments on the manuscript. For this work we acknowledge funding from the  Sukran Sinan Fund , the  Genetics Society , the  Biochemical Society and BBSRC. JMD acknowledges funding from the  CNRS  and  Université Paul Sabatier.
Actomyosin-Based Contractility Is Essential for the Establishment of the Cell Rear. To determine the mechanisms that direct the proteins listed above to the cell rear, we investigated their dynamics at the cell periphery. To this end, we made use of the Bioflow software  (44). Briefly, Bioflow is a computational tool that allows  estimating material flow velocities by extracting the motion of fluorescent signals. For this analysis, we followed the dynamics of Esyt2a, benefiting from its more heterogeneous distribution, which is advantageous to tracking. We compared the dynamics of Esyt2a to the movement of a cytoplasmic fluorescent protein that we measured simultaneously. Interestingly, contrary to the cytoplasmic flow to the cell front, Esyt2a flow toward the rear was clearly observed (Fig.
To express proteins in germ cells, the corresponding sequence was cloned upstream of the nanos3 3′ UTR  (65). For global protein expression the sequence was cloned upstream of a Xenopus globin 3′ UTR. Embryos were kept in 0. 3× Danieau's solution and incubated at 25°. Image Acquisition. Fluorescence microscopy was performed using a Carl Zeiss Axio Imager Z1 equipped with a Retiga R6 camera or a Carl Zeiss Axio Imager Z1 equipped with a Spot Pursuit 23. 0 1. 4 MP monochrome camera. Spinning disk microscopy was performed using a Carl Zeis Axio Imager Z1 coupled to a Yokogawa CSUX1FW-06P-01 spinning disk unit and a Hamamatsu Orca-Flash4. 0LT C11440 camera. Time-lapse movies were acquired using Visi-View software. The confocal microscopy images were generated using a Carl Zeiss LSM 710 microscope and Zen software.
Protein Polarization and Polarity Difference Angle Calculation. To determine the angular polarity index, we generated an intensity-based Matlab (Mathworks) program that follows the approach described in a recent study  (35)  (for details see SI Appendix, Extended Methods, Protein Polarization and Polarity Difference Quantification). Photoactivatable Rac1-Based Experiments. MO directed against cxcl12a, Ezrin-YPet. nos3′UTR RNA, or Ypet. esyt2al. nos RNA and either the photoactivatable version of Rac1 fused to the 3′ UTR of nanos3 or control RNA, were injected in one-cell-stage kop:lifeact. mCherry. nos3′UTR-cry:DsRed embryos. The 63× movies were acquired using a Carl Zeiss LSM 710 confocal microscope and the photoactivation was performed using the bleaching module in the Zen software. Photoactivation was initiated after five frames with a 456-nm laser using a circular region of interest (ROI) with a diameter of 5 μm (120 to 240 iterations, 100% laser power) and was repeated every four frames.
Embryos were imaged starting from 30 min after transplantation. The direction of the front relative to the chemokine source was determined by measuring the angle between the line connecting the position of the chemokine source to the center of the cell and the line connecting the center of the cell to the cell front. An angle of 0°indicated perfect alignment of the front toward the chemokine source. The angles were plotted into a rose plot generated in Python. Deconvolution. The time lapse movies from Figs. 1 F and G, and 2 A and B, and 3A and SI Appendix, Fig. S3D  were deconvolved using the software Huygens Professional. Statistics and Reproducibility. Statistical analysis was conducted using GraphPad Prism 6. 0d software. The statistical test used, the n and the P values are indicated in the figure legends.
ACKNOWLEDGMENTS. This work was supported by the  European Research Council (ERC, CellMig , no. 268806  to E. R. and PolarizeMe, no. 771201  to T. B. ), the  Deutsche Forschungsgemeinschaft (DFG ,  RA863/11-1 ,  SFB 1348 , and  CRU326 ), and the  Cells in Motion Cluster of Excellence  ( EXC 1003-CIM ). N. S. G. is the incumbent of the  Lee and William Abramowitz Professorial Chair of Biophysics  and this research was supported by the  Israel Science Foundation  (Grant  1459/17 ). A. B. -P. is a member of the  Pasteur-Paris University (PPU) International PhD Program , funded by the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie  grant agreement no. 665807 , and by the  Institut Carnot Pasteur Microbes & Santé  ( ANR 16 CARN 0023-01 ). We thank  Klaus Hahn  for the photoactivatable Rac construct.
Hybridization of fragmented and labeled ssCDNA samples was done using GeneChip TM Hybridization oven and the arrays were washed then stained using GeneChip TM Fluidics Station. The arrays were scanned using GeneChip TM Scanner 3000 7G  (Thermo Fisher Scientific, Santa Clara, CA). Quality control of the microarrays and data analysis were performed using Thermo Fisher Scientific Transcriptome Analysis Console software version 4. 0. 2.
Quantitative Data Analysis Pair-wise comparisons between biological conditions were applied using specific contrasts. A correction for multiple testing was applied using Benjamini-Hochberg procedure (BH,  Benjamini and Hochberg, 1995, PubMed ID 24913697)  to control the False Discovery Rate (FDR). Probes with FDR-adjusted P < 0. 05 were considered to be differentially expressed between conditions. All raw and normalized data are available in GEO database under accession series number: GSE156116.
Quantitative Data Analysis The samples then underwent quantitative measurements using isobaric-labeled LC-MS/MS. Raw data were analyzed using Proteome Discoverer 2. 2 (Thermo Fisher Scientific) using the default MS3 SPS method. All MS/MS samples were analyzed using Sequest HT to search all human sequences from Uniprot 1  and 110 common laboratory contaminants 2  plus an equal number of reverse decoy sequences assuming the digestion enzyme trypsin. Sequest-HT was searched with a fragment ion mass tolerance of 0. 20 Da and a parent ion tolerance of 10. 0 PPM. Carbamidomethyl of cysteine and TMT10 plex of lysine were specified in Sequest-HT as fixed modifications. Oxidation of methionine and acetyl of the n-terminus were specified in Sequest-HT as variable modifications. Quantitate Label Based Quantitation (iTRAQ, TMT, SILAC, etc.
Bioinformatic Analysis and Multi-Omics Integration Genes, Targets, and Proteins Distribution Plots Data visualization was performed in Manhattan plots to show chromosomal localizations of differentially expressed transcripts and Venn Diagrams to show all possible logical relationships between multiple omic sets. Venn diagrams were used to visualize the relationships between differentially expressed (DE) proteincoding genes (mRNAs), miRNA targets, lncRNA targets, and DE proteins observed in TNF-α vs. control and VALs vs. TNF-α group comparisons. Both plots, Manhattan and Venn diagram, were built using R software packages, the Sushi package 3   (Phanstiel et al. , 2014)  and the VennDiagram package 4   (Chen and Boutros, 2011) , respectively. Conversely, associations between genes and groups were searched using unsupervised hierarchical clustering of the samples and the differentially expressed probes using the Pearson correlation as the distance metric and Ward's method for agglomeration of clusters.
Databases-Predicted miRNA Targets For each miRNA identified as differentially expressed in TNFα vs. control and VALs vs. TNF-α group comparisons, we performed a target analysis. miRNA targets were predicted using three prediction databases miRTarBase  (Hsu et al. , 2011) , mirRDB  (Chen and Wang, 2020) , and TargetScan  (Agarwal et al. , 2015) , and only targets identified in at least two of the databases were considered as putative targets for our analysis. For each database, we always chose the most stringent option among those proposed by the database.
Databases-Predicted lncRNA Targets For each lncRNA identified as differentially expressed in TNFα vs. control and VALs vs. TNF-α group comparisons, we also performed a target analysis. lncRNA target transcripts were predicted by combining the results of two prediction databases from Rtools web server: lncRRISearch 6   (Fukunaga et al. , 2019)  and RNARNA 7. Targets identified in these two databases were considered as putative targets and used in our analysis. For each database, we always chose the most stringent option among those proposed by the database.
Transcription Factors Analysis Transcription factors (TFs) potentially involved in the regulation of the expression of identified mRNAs and whose activity can be affected by epicatechin metabolites were searched using enrichR online tool 9   (Kuleshov et al. , 2016). The TFs were searched within TRRUST 10  (transcriptional regulatory relationships unraveled by sentence-based text-mining) database, a manually curated database of human and mouse transcriptional regulatory networks that contain 8,444 and 6,552 TF-target regulatory relationships of 800 human TFs and 828 mouse TFs, respectively, that have been derived from 11,237 Pubmed articles.
Docking Analysis Molecular docking analysis was employed to explore the potential interaction/binding between identified transcription factors and cell signaling proteins regulating the activity of the transcription factors identified, and the two metabolites, 5-(4 -Hydroxyphenyl)-γ-valerolactone-3 -sulfate (γVL3 S) and 5-(4 -Hydroxyphenyl)-γ-valerolactone-3 -O-glucuronide (γVL3 G). The 3D structures of metabolites were obtained from the PubChem database 11  and the three-dimensional structure of the proteins was obtained from the Protein Data Bank (PDB) database. Docking calculations were carried out using Blind Docking server 12   (Sanchez-Linares et al. , 2012).
Pathways Network The pathways identified in the step were used to build a network; two pathways were contemplated interconnected if at least one of the genes were common to both. Networks were built and visualized using Cytoscape software (version 3. 7. 1) 14 (Su et al. , 2014). Data preparation was performed with the use of several 13 https://genetrail2. bioinf. uni-sb. de/ 14 www. cytoscape. org/ R packages included splitstackshape 15  , data. table  16  , dplyr 17  , and string 18,19. Pathway networks were built independently for pathways enriched in each omic layer and pathways identified from a global pathway enrichment analysis, considering all omic layers components together. To identify six pathways with the highest degree (number of connections), the Cytoscape Network Analyzer application was used 20.
Networks of Pathways Related to Endothelial Function Endothelial function-related pathways were selected to construct a network. The endothelial function pathway-specific network was performed in Cytoscape software. As a first step, we obtained a list of genes involved in selected pathways from Kyoto Encyclopedia of Genes and Genomes (KEGG) database  (Kanehisa and Goto, 2000). Subsequently, through a series of intersection and non-intersection functions in R, we obtained the mRNAs and proteins involved in the endothelial function pathways, which were also identified to be differentially expressed in our study. Finally, we identified endothelial function pathway components that were targets of miRNAs or lncRNAs. Multilayer Integration and Representation mRNA, miRNA, lncRNA, Proteins, and Target Interaction Networks Visualization of the interactions between mRNA-TFs, miRNAstargets, lncRNA-targets, and protein-protein was performed in Cytoscape software.
Separate networks for each omic layer and a global network with all integrated interactions were made. Smaller networks were constructed to represent the miRNA targets, lncRNA targets, and DE proteins intersected with mRNAs DE in our study. We identified quantitatively changed mRNAs, miRNAs, and their targets, lncRNAs, and their targets, together with proteins DE in our study and mapped them to the global map provided in the KEGG pathway database, to include a comprehensive pathway topology.
4% to lncRNAs (86 lncRNAs) (Figure  1B ). Regarding proteomic data, we observed 464 significantly differentially expressed proteins out of a total of 5,565 (Figure  1B ). Subsequently, miRNAs and lncRNAs target gene predictions from the database analysis identified 4125 miRNA targets and 239 lncRNA targets. Thirty-one of these targets were shared between both omic layers (miRNA and lncRNA targets), of which three corresponded to the mRNA and one to the protein category. The number of component intersections between all omic layers is shown in Figure  1C. For instance, 175 miRNA targets belong to the mRNA category, three of them are shared by lncRNA targets and six by proteins, six total lncRNA targets belong to the mRNA category, and 24 total proteins belong to the category of mRNAs. We extrapolated the nutrigenomic modification data to build a network of interactions, that is considering DE mRNAs, DE miRNA and their targets, DE lncRNA and their targets, and DE proteins.
γ-Valerolactones Modulate the Expression of miRNA in HBMEC MiRNA expression was also identified as modulated following exposure of HBMEC to γ-valerolactones, suggesting the capacity of these metabolites at low concentrations to modulate the expression of small non-coding RNAs (Supplementary Table  2 ). The search for their putative targets from the databases for six differentially expressed miRNAs, hsa-miR-6730-3p, hsa-miR-6730-5p, hsa-miR-3661-3p, hsa-miR-3661-5p, hsa-miR-6746-3p, hsa -miR-6746-5p, allowed us to identify 1360 target genes, including ARSB, CXADR, FREM1, or HAP1. Among them, five targets are in common with differentially expressed mRNAs (Supplementary Figure  3A ). Figure  5A  shows the network topology of miRNA-target interactions which revealed at least four miRNA hubs for the hsa-miR-6730-5p, hsa-miR-6730-3p, hsa-miR-6746-5p, and has-miR-6746-3p.
lncRNA Expression Modulation by γ-Valerolactones Microarray analysis has also shown for the first time that exposure of HBMEC to γ-valerolactones can also modulate the expression of another group of protein non-coding RNA, which are long-non-coding RNAs. We observed a change in expression of 79 lncRNAs (Supplementary Table  3 ). A search of databases for their targets allowed us to identify 364 lncRNA-targets. Among these 364 targets, 4 (UBC, C11orf95, PLCB1, BHLHE40) are in common with protein-coding genes and 14 (IL6ST, ABHD12, HIPK2, PLEKHG2, PSD4, ZNF37A, AMOT, POU4F1, UBC, CELF3, AGO1, EFNA3, BCL7A, PRDM2) with miRNAs targets (Supplementary Figure  3A ). In Figure  6A  lncRNAtargets interactions topology is shown which identified several clusters of genes, such as for lncRNA RP11-386G11.
Transcription Factor and Interactome Analysis Using 3D in silico Modeling for γ-Valerolactones Using gene expression analysis data, the Ttrust database was searched using the EnrichR tool to identify potential TFs whose activity could be affected by γ-valerolactones and involved in the observed changes in the expression of genes. Among the most significant TFs identified were NF-κB1, cJUN, STAT2, IRF1, or FOXO4 (Figure  8A ). Following this analysis, we aimed to identify if γ-valerolactones could have a binding affinity with these TFs, binding that may affect their activity and consequently result in changes in expression of genes as we observed, under the hypothesis that these metabolites can enter into the cells. Using this approach, we observed that γvalerolactones have the potential to bind to a transcription factor, an interaction that could affect their activity and induce changes in the expression of genes, as observed using our microarray analysis.
Experimental Design The experiment consisted of a modified Sternberg paradigm, using a database of 153 female and 153 male grayscale photos of faces, which had been rated as neutral and tested in fMRI and intracranial recordings of working memory as described in  Axmacher et al. (2007). Face stimuli were homogeneous in contrast and congruent with the face ethnicity of the participants  (Esins et al. , 2015). These stimuli were presented in three different conditions: (i) In the low load condition, subjects were presented with one face (either male or female) and three scrambled pictures. (ii) In the medium load condition, two faces and two scrambled pictures were shown. (iii) In the high load condition, four faces were presented. Each trial started with the presentation of a picture (face or scrambled) for 800 ms with an interval of 1,000 ms before the next picture was shown, leading to a duration of 6,200 ms for the picture presentations (4 × 800 ms + 3 × 1,000 ms).
Subjects were asked to respond as fast and accurately as possible and did not receive feedback on their performance during the experiment. Proportion of match/non-match trials was 50:50; thus, a total of 238 (196 for presentation/42 for new probes) unique unscrambled faces were selected out of the 306 available. An overview of the different conditions is shown in Figure 1A  (Axmacher et al. , 2007). The paradigm was programmed with E-prime (Psychology Software Tools, Pittsburgh, PA) and presented to the subjects via video goggles (NordicNeuroLab, Bergen, Norway) in the scanner. Video goggles were adapted for visual acuity.
Functional Magnetic Resonance Imaging Data Acquisition The study was conducted on a 3 T Siemens TRIO MRI scanner (Siemens Medical, Erlangen, Germany). Both T1 structural volume images (TR/TE/TI, 1,570/2,75/800 ms; 160 slices; matrix 256 × 256 mm 2 , spatial resolution 1 × 1 × 1 mmł voxels) as well as T2 * -weighted axial echo-planar images with BOLD contrast [gradient echo; TR/TE, 2,700/33 ms; 36 axial slices parallel to anterior commissure-posterior commissure (ACPC) plane; acquired in ascending direction; matrix 64 × 64 mm 2 , field of view 230 mm, slice thickness 2 mm; inter-slice gap 0. 5 mm; spatial resolution 1. 8 × 1. 8 × 2 mmł voxels] were acquired. The experiment contained two functional sessions, the functional localizer followed by the main experiment.