dataesr/scientific-paragraphs-categorization huggingface dataset:
Contrastingly, the input for the MLP is the labelled feature collection, which is derived from single pixels in an image, which is considered as more suitable when using Sentinel-2 imagery. In the past decades, mapping forest cover and type have achieved satisfactory outcomes globally and operationally using remote sensing techniques  [37, 39]. Mapping tree species at large scale has however only progressed slowly, mainly because the required remote sensing datasets are expensive to acquire, such as LiDAR and other very highresolution data  [35, 40]. Furthermore, the Faidherbia albida agroforestry multipurpose trees studied here grow in drylands where trees grow as individuals, and such trees outside of forest have been underestimated  [61]. As such, our study represents a first attempt to conduct a national scale mapping of scattered non-forest tree species.
01). Red and green bars represent down-regulated and up-regulated pathways respectively. All signi cant modulated pathways are displayed in Supplementary Figure  2. (c) Signi cant Gene Ontology (GO) biology processes enriched by differentiated expressed genes modulated by 3-oxo-C12:2-HSL. Terms were annotated using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Red and green bars represent down-regulated and up-regulated pathways respectively. (GO: Gene Ontology, KEGG: Kyoto Encyclopedia of Genes and Genomes) Differentially expressed genes were annotated by Gene Ontology (GO) analysis (Fig. 2c ). GO analysis revealed that down-regulated genes were enriched in "immune system process", "innate immune response" and "in ammatory response" categories, in accordance with the anti-in ammatory effects of 3oxo-C12:2-HSL observed in Figure  1.
Taste receptor signalling, through the second messenger IP3, activates calcium release from the endoplasmic reticulum  23. We demonstrated that 3-oxo-C12:2-HSL is able to induce calcium ux in RAW264. 7 macrophages, thus reinforcing the hypothesis that 3-oxo-C12:2-HSL signals through taste receptors. Furthermore, through a screening of all human bitter taste receptors, we identi ed six taste receptors that were activated in the presence of 3-oxo-C12:2-HSL. Some of them have numerous known ligands, such as TAS2R14 which can bind to more than 150 compounds, while others only have two known synthetic agonists like TAS2R13, according to the BitterDB database  51. Advances in knowledge of BTR involvement in in ammatory pathways are needed to link our ndings with AHLs signalling in immune cells. In the present study, we restricted our investigation to the effect of 3-oxo-C12:2-HSL on host immune cells.
RNA-Seq data work ow FastQC v 0. 11. 9  53  was used for the quality control of raw paired-end fastq data set. Illumina's adapters were removed from reads using cutadapt v2. 10 54. Trim_galore v0. 6. 4  55  was used to trim bad quality bases (Phred < 20). Salmon v 1. 4. 0 56 was used to quantify paired-end reads against a mapping-based index built from the Ensembl GRCm38 transcript set for all samples. Downstream analysis was handled in R platform v4. 0. 2  57  and its add-on packages. Quanti ed transcripts were imported into R using the package tximport v1. 16. 1 58. Gene-level DESeqDataSet object was built from previously imported transcript level abundances using the package DESeq2 59 to perform the differential expression analysis. A pre-ltering was applied to remove genes with less than 5 counts in one sample.
For visualisation, raw counts were transformed using the "variance stabilizing transformation" method implemented in the package DESeq2. For an overview of the variability among samples, Principal Component Analysis was performed on variance stabilizing transformation and normalised count dataset using the package ade4 v 1. 7-16 60. Differential expression analysis was carried out by the DESeq function of the DESeq2 package. Brie y, the size factor was estimated for each sample, the dispersion was estimated for each gene, counts were tted to a Negative Binomial Generalized Linear Model and Wald's test was conducted to test the difference in expression between biological conditions. The Benjamini-Hochberg method  61  was used for controlling the False-discovery rate (FDR). Signi cance level was xed at type I error alpha = 0.
01. Volcano plots were used to display the results of differential analysis using the ggplot2 v3. 3. 3 62 package. All signi cant gene lists were annotated for enriched biological functions and pathways using the platform through the RDAVIDWebService v1. 28. 0 63 package for gene ontology 64 and Kyoto Encyclopedia of Genes and Genomes terms 65. Signi cant terms had adjusted p-value, according to the Benjamini-Hochberg method, below 0. 05. In addition to the traditional gene by gene analysis, count dataset was analysed at KEGG pathway level using the package Ensemble of Gene Set Enrichment Analysis (EGSEA) v1. 16. 0 66 that combines results from twelve algorithms to improve the biological relevance of pathways. Thus, signi cant pathways had adjusted p-value below 0. 01. Gene expression heatmaps were produced by the pheatmap v1.
Bitter Taste Receptors Screening Bitter taste receptor cDNA were cloned into the pcDNA4 mammalian expression vector (Invitrogen) using full coding receptor sequences reported on UniProt database. The synthetic constructs were codon optimized (Genewiz) and combine the rst 45 amino acids of rat somatostatin type 3 receptor at the amino terminus to improve plasma membrane targeting of the receptors in the heterologous system  68. The FLAG epitope was added to the carboxy terminus without interfering with receptor functionality and can be used for immunocytochemistry. Calcium-mobilization assays were performed using human embryonic kidney cells (HEK293T) stably transfected with chimeric Gα16gust44 protein 69. Cells were seeded in black, poly-D-lysine coated 96-well plates at a density of 35000 cells/100µL in high glucose DMEM supplemented with 10% dialysed fetal bovine serum and 1% penicillin/streptomycin.
Acknowledgments This research was funded by  Fondation pour la Recherche Médicale , grant number  EQU202003010171  and by the  Association François Aupetit  (projects research on AHLs 2019 and 2021). G. Coquant was the recipient of a fellowship from  Fondation pour la Recherche Médicale   FRM ECO201806006843. D. Aguanno was the recipient of a fellowship from  CORDDIM, Ile-de-France region. The authors acknowledge the  Association François Aupetit  for its unwavering support. The authors thank  Yannick Marie  and his team at the  Plateforme de Genotypage/Séquençage of the Institut du Cerveau et de la Moelle épinière  for the RNA sequencing.
Basal and luminal subpopulations are indistinguishable in embryonic mammary glands using the EpCAM and CD49f gating strategies routinely applied to adult tissues (Figure  S1A ). Using the Seurat R package  (Stuart et al. , 2019) , unsupervised clustering of single cell expression data revealed distinct cell clusters at E13. 5, E14. 5, E15. 5 and P0, respectively (Figure  S1B ), which were manually annotated by matching enriched gene sets with known markers of mammary epithelium, mesenchyme and skin cells. With the objective of mapping MECs undergoing lineage commitment early in embryogenesis, we removed contaminating skin cells (Figure  S1B ) and performed a sub-clustering analysis of epithelial populations at each developmental timepoint. A cluster composed of proliferative epithelial cells was identified at E15.
Microscopy and image acquisition 3D imaging: Images were acquired using a LSM780 or LSM880 inverted laser scanning confocal microscope (Carl Zeiss) equipped with 25x/0,8 OIL LD LCI PL APO or 40x/1,3 OIL DICII PL APO. For standard 4-color imaging, laser power and gain were adjusted manually to give optimal fluorescence for each fluorophore with minimal photobleaching. Images were captured using the ZEN Imaging Software and processed in Fiji (ImageJ v1. 53). smRNA-FISH: images were acquired using a LSM880 with an Airyscan system. The Airyscan system has 32-channel GaAsP (Gallium Arsenide Phosphide) detectors, which allow to obtain images with enhanced spatial resolution and improved signal-to-noise ratio (SNR) than in traditional LSM systems  (Huff, 2015). A 63x/1,4 OIL DICII PL APO objective was used.
The isotype controls were the following: PE rat IgM (Biolegend, 400808), PE/Cy7 rat IgG2a (Biolegend, 400522), APC/Cy7 rat IgG2a (Biolegend, 400524) and APC rat IgG2b  (Biolegend, 400612). Complete details of the antibodies used are provided in the Key Resources Table  1. The results were analyzed using the FlowJo software (V10. 0. 7).
Image analysis and quantification For time-lapse live imaging analysis, first time-lapse reconstructions were generated using the Bio-Formats plugin  (Linkert et al. , 2010)  in Fiji (ImageJ v1. 53). Then, automated segmentation of mammary buds was performed using a custom-made segmentation model based on U-Net  (Ronneberger et al. , 2015). Segmented masks and raw image were input in the ImageJ plugin, BTrack, for tracking the growing branch tips. BTrack allows the users to remove or create new end points to manually correct the obtained tracks. We obtained the average growth rate for each branch using customized Python scripts (see Data and code availability). Statistical analyses were performed in Prism (v9. 2, GraphPad). To determine bud surface area in the presence of FGF10 in the medium, segmented masks were obtained from each timepoint using the U-Net model previously described.
Generated masks were manually checked and corrected against raw data for consistency prior to extracting area measurements. Surface area was measure for each timepoint and statistical analyses were performed in Prism (v9. 2, GraphPad). For smRNA-FISH dot counting, the Find Maxima tool in Fiji (ImageJ v1. 53) was used to find the highest peak values in the images using a previously specified threshold. Then, a custom ImageJ macro was coded to create 3 parallel regions of interest (ROIs) with a ring-shaped surface. Finally, the number of dots in each ROI was calculated for each smRNA-FISH probe. The percentage of dots in each ring was calculated as the ratio of number of dots in a specific ROI to the total number of dots in the 3 ROIs (outer, middle and internal ring). Statistical analysis was performed in Prism (v9.
scRNA-seq data processing and cluster analysis Single cell capture and library construction were performed using the 10x Genomics Chromium Single Cell 3' v3. 1 kit following the manufacturer's instructions, for samples of different developmental stages. The libraries were sequenced with an Illumina NovaSeq 6000 sequencer by the Next Generation Sequencing platform of Institut Curie. Data pre-processing and quality control: The 10x Genomics Cell Ranger Single-Cell Software Suite was used for demultiplexing, read alignment and unique molecular identifier (UMI) quantification (http://software. 10xgenomics. com/single-cell/overview/welcome). The pre-built mm10 reference genome obtained from the 10X Genomics website was used to align the reads. Then, the count matrices were individually loaded for each sample in R and analyzed using the Seurat package v4.
All the Uniform Manifold Approximation and Projection (UMAP) plots  (McInnes et al. , 2018)  were computed using the "RunUMAP" Seurat function with default Seurat parameters. Cell cluster identification: Cell clustering was performed using a two-step wise approach, using the "FindNeighbors'' and "FindClusters'' functions, respectively. The "FindClusters" function was used to set the resolution parameter to 0. 8. Differential expression analysis: Cell-type marker genes for each cluster were identified using the function "FindAllMarkers" function in Seurat, with detected in minimum cell fraction > 10% and logfold change > 0. 1. Then, cell clusters were manually annotated based on cell type specific markers that are known to be enriched in each cell population. Cell proliferative clusters were identified by using the following list of genes: '  Pclaf', 'Ncapg2', 'Smc2', 'Tyms', 'Tuba1b', 'Hmgb2', 'Top2a', 'Tacc3', 'Cenph', 'Cdk1', 'Tubb5', 'Diaph3', 'Cenpf', in order  to compute an expression score using the Seurat function 'AddModuleScore'.
Signature construction: a single-cell ID score for "basal-like" and "luminal-like" cells was calculated based on previously published transcriptomic analyses of adult MECs  (Kendrick et al. , 2008). The scores were computed using the Seurat function "AddModuleScore". 3D trajectory and pseudotime analysis: For this analysis, only the epithelial cell clusters from E13. 5, E14. 5, E15. 5, and P0 were considered. The pre-processing steps previously described were reapplied (normalization, PCA, and basal and luminal score). Epithelial cells were then mapped in a 3D space including the luminal score and basal score on the x-axis and the PC related to developmental time on the y-axis. For each cell cluster, the coordinates of the center in the 3D space with the median for each dimension were calculated and called "pseudo-bulks".
Cell-cell interaction analysis: The cell-cell interaction analysis was performed using the CellPhoneDB version 3. 0. 0  (Vento-Tormo et al. , 2018)  with a p-value threshold of 0. 01. The CellPhoneDB database is publicly available at https://www. cellphonedb. org/. It is a curated database of ligand-receptor interactions that allow to predict cell-cell interactions in transcriptomic data. CellPhoneDB was used on our scRNA-seq E15. 5 dataset between both mesenchymal clusters (sub-epithelial cluster and dermal mesenchyme cluster) against the basal-like epithelial cell cluster.
Data and software availability Customized scripts and instructions are available from Github: https://github. com/Fre-Team-Curie/Embryo-mammary-gland. The single cell RNA sequencing data have been deposited in NCBI's Gene Expression Omnibus (GEO) repository and are accessible through GEO Series accession number GSE210594. All other data supporting the conclusions of this study are provided in the main text or the supplementary materials.
Acknowledgements The authors thank  S. Tajbakhsh  for the mTmG reporter line. We acknowledge the flow cytometry and cell sorting platform at  Institute Curie  for their technical support, the in vivo experimental facility for help in the maintenance and care of mouse colonies, the ICGex NGS platform of  Institut Curie  (supported by grants  ANR-10-EQPX-03 ,  Equipex  and  ANR-10-INBS-09-08 ,  France Génomique Consortium , from the  Agence Nationale de la Recherche  " Investissements d'Avenir" program , by the  Canceropole Ile-de-France  and by the  SiRIC-Curie program   -SiRIC Grant   INCa-DGOS-4654 ), the  Cell and Tissue Imaging Platform-PICT-IBiSA  (member of  France-Bioimaging ,  ANR-10-INBS-04 ) of the  Genetics and Developmental Biology Department  ( UMR3215/U934 ) for their expertise.
The authors specially thank  Olivier Leroy  and  Anne-Sophie Mace  for image analysis support. We are grateful to members of the  Fre laboratory  for support and constructive discussions. This work was supported by  Paris Sciences et Lettres (PSL* Research University)  (grant #  C19-64-2019-228 ), the  French National Research Agency (ANR)  grant numbers  ANR-15-CE13-0013-01  and  ANR-21-CE13-0047 , the  Medical Research Foundation FRM  " FRM Equipes "  EQU201903007821 , the  FSER  ( Fondation Schlumberger pour l'éducation et la recherche )  FSER20200211117  and by  Labex DEEP   ANR-Number 11-LBX-0044  to S. F. C. C. was funded by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  666003  for PhD fellowships and the  Medical Research Foundation  FRM Project  12917.
Forward Model In order to generate simulated EEG data ('  ') from simulated cortical time series ('  '), we solved the forward problem as follows: 𝑋 𝑡 = 𝐺 × 𝑆 𝑡 (1) where '' is the lead field matrix to be computed, which describes the electrical and geometrical characteristics of the head. To this end, T1 magnetic resonance imaging MRI (Colin27 template brain,  (Holmes et al. , 1998) ) was spatially aligned with head coordinates and segmented. Thus, realistically shaped shells representing brain, skull, and scalp surfaces were prepared to build a realistic volume conduction head model using the Boundary Element Method (BEM) provided by the OpenMEEG package (Fieldtrip,  (Gramfort et al. , 2010) ). In this work, we used 257 electrodes density (EGI, Electrical Geodesic Inc. ) for EEG electrode configuration.
'' is inversely proportional to the norm of lead field vectors. 𝐵 𝑖𝑗 = { (𝐺 𝑖 𝑇 𝐺 𝑖 ) 1 2 ⁄ 𝑖𝑓 𝑖 = 𝑗 0 𝑖𝑓 𝑖 ≠ 𝑗 (5) In this work, we used the Matlab function implemented in Brainstorm toolbox codes (https://neuroimage. usc. edu/brainstorm) to compute wMNE  (Tadel et al. , 2011) , with the signal to noise ratio set to 3 and depth weighting value to 0. 5 (default values).
Phase-locking value (PLV) For each trial, the phase-locking value  (Lachaux et al. , 2000)  that characterizes the phase relationship between two signals () and () is expressed as follows: 𝑃𝐿𝑉(𝑡) = | 1 𝛿 ∑ 𝑒 𝑗(𝜑 𝑦 (𝑡)-𝜑 𝑥 (𝑡)) 𝑡+ 𝛿 2 𝑡- 𝛿 2 | (7) Where   () and   () represent the instantaneous phases of signals y and x respectively derived from the Hilbert transform at time window t. The Matlab function used to calculate PLV following Equation (  6 ) is available on GitHub (https://github. com/judytabbal/dynCOALIA).
Amplitude Envelope Correlation (AEC) The amplitude envelope correlation (AEC) is calculated using the Hilbert transform of regional time series. Pearson correlation is then computed between the amplitude envelopes of two pairs of regions  (Brookes et al. , 2016; Hipp et al. , 2012)  for each trial. Refer to (https://github. com/judytabbal/dynCOALIA) for Matlab code implementation. As a result, the output dimension of the dFC tensor was [ ×  × ] for every trial/subject. This tensor was unfolded into a 2D matrix [( * -1 2 ) × ] by removing redundant connections due to symmetry, followed by mean row subtraction. Finally, all trials/subjects dFC matrices were concatenated along temporal dimension and a group dFC matrix is constructed denoted ''.
temporal Independent Component Analysis (tICA) tICA approach is already used by several studies  (O'Neill et al. , 2017; Yaesoubi et al. , 2015)  to derive k brain states that are 'statistically mutually independent' in time. Here, we evaluated tICA using three prominent ICA sub-methods: (1) JADE (Rutledge and Jouan-Rimbaud Bouveresse, 2013), (2) FastICA  (Langlois et al. , 2010) , and (3) PSAUD  (Becker et al. , 2017). Briefly, FastICA is based on information theory, while JADE and PSAUD tend to optimize contrast functions based on high statistical order cumulants of the data. Technically, we adopted the following functions; 'jader' for JADE  (Cardoso, 1999) , 'icasso' for FastICA  (Himberg and Hyvarinen, 2003) , and 'P_SAUD' for PSAUD  (Becker et al. , 2017) , implemented in Matlab (The Mathworks, USA, version 2019a).
Non-Negative Matrix Factorization (NMF) Nonnegative matrix factorization (NMF) is an unsupervised machine-learning technique  (Lee and Seung, 1999 ) that imposes a 'positivity' constraint on the decomposed factors. In this work, we selected the Alternating Least Squares (ALS) algorithm that has previously shown reliable performance in the fMRI context  (Ding et al. , 2013)  with 100 times replications. For this purpose, the 'nnmf' Matlab function was used  (Berry et al. , 2007).
Kmeans Clustering Kmeans is one of the simplest clustering approaches  (Lloyd, 1982). Based on feature similarity, Kmeans assigns each time point to one of the k centroids clusters, and the frequency of occurrence of each cluster at each time window is then calculated across all trials/subjects  (Allen et al. , 2014). In this study, we used L1 distance with 100 times replications and random centroid position initialization. We adopted the 'kmeans' function incorporated in Matlab  (Mucha, 1986).
Source localization: wMNE vs. eLORETA To first visualize results at the cortical level, we illustrate reconstructed brain sources in Figure  3  at simulated time intervals for each inverse model (wMNE and eLORETA) along with both source space resolution (LowRes vs. HighRes), where 'LowRes' denotes the lead field calculation on 66 Desikan regions directly while 'HighRes' refers to lead field calculation on high-resolution cortex followed by projection (average) on 66 Desikan regions (see Materials and Methods). Results shown were averaged over trials and subjects. For inverse methods quantification, we calculated the precision metric averaged over subjects' data (see Materials and Methods) at each simulated time interval between each method and reference sources. In our case, reference sources correspond to the simulated LFP cortical-level data generated by the COALIA model (as illustrated in the first row of Figure  3 ).
One explanation might be related to the induced blurring effect between close sources when using a high-resolution source grid, which might increase the cross-talk between specific cortical locations  (Maldjian et al. , 2014; Schoffelen and Gross, 2009). Overall, performance analysis showed promising results for most dimensionality reduction methods regarding their ability to derive dominant brain states. In this work, global similarity results highlight the performance of ICA subtypes methods relative to other methods. These findings are in line with a recent comparative study between source separation methods applied to three independent real MEG datasets  (Tabbal et al. , 2021). Particularly, in this work, it is noteworthy to point out that PCA, NMF, and Kmeans exhibit fragility in the temporal domain reflected by relatively low temporal similarities at several intervals (Figure  S3 ).
3-10. 0 keV band with a spectral resolution of ∆E ∼ 0. 3 keV near E = 6 keV and arcsecond angular resolution. The Chandra data were used to identify regions where the reflected emission is strong and to extract spectra from these regions. The comparison of the X-ray (4-8 keV) surface brightness maps of the Galactic Center region obtained by Chandra and IXPE (angular resolution < 30") is shown in Fig. 1  (technical details about the data reduction of the IXPE and Chandra observations are described in the Methods section). We excluded the 2-4 keV band as it is entirely dominated by unpolarized plasma emission  [21] [22] [23]. Based on the Chandra image only, we pre-selected a circular extraction region with a radius 4. 5 ′ centered on the Sgr A complex. This choice was driven by the desire to collect as many "reflected" photons as possible and, at the same time, avoid the very edges of the IXPE detectors.
The total model is built from several components that are detailed in the Methods section. All of them, except for the component representing the reflected emission from the molecular cloud, are assumed to be constant in time, i. e. staying the same for all three datasets. The reflected emission, in turn, consists of a combination of the Compton scattered and photo-absorbed continuum, and fluorescent emission lines, calculated self-consistently based on the Monte-Carlo calculations of the reflection spectrum  [25]. Among all emission components, only the continuum part of the reflected spectrum is predicted to be polarized  [16, 22, 26] , so that the Stokes parameters Q and U of the emission measured by IXPE need to be related to the reflection continuum contribution I refl,c , and not to the total observed intensity I.