dataesr/scientific-paragraphs-categorization huggingface dataset:
However, we hope that our combined datasets will enable the wider scientific community to generate and investigate their own hypotheses, beyond our exciting presentation of this new putative cardiomyocyte cell type. Secondly, by undertaking integrated scRNA-Seq and spatially resolved transcriptomic analyses of the developing heart, we established a comprehensive spatiotemporally-defined transcriptional landscape of cell types populating the developing murine heart. Our spatially resolved transcriptomic Stereo-seq investigation identified major cardiac cell populations and structures through both unsupervised and supervised machine learning techniques. However, we note that there were some imperfect, but logical, predictions of cell types through supervised techniques (Fig. S15 ). Integration of scRNA-Seq and spatially-resolved transcriptomics data remains a nascent field, and we expect future computational work to produce more accurate integration, perhaps through inclusion of prior knowledge and combinatorial scoring based on a cell's given cassette of initially predicted labels.
Sequencing was performed with Illumina HiSeq according to the manufacturer's instructions (Illumina). FastQC was used to perform basic statistics on the quality of the raw reads. Raw reads were demultiplexed and mapped to the reference genome by 10× Genomics Cell Ranger pipeline(https://support. 10xgenomics. com/single-cell-geneexpression/) using default parameters. All downstream single-cell analyses were performed using Cell Ranger and Seurat 59 unless mentioned specifically. In brief, for each gene and each cell barcode (filtered by CellRanger  59  ), unique molecule identifiers were counted to construct digital expression matrices in detail: cellranger count takes FASTQ files performs alignment, filtering, barcode counting, and UMI counting. It uses the Chromium cellular barcodes to generate feature barcode matrices.
5: 5%, E10. 5: 5%, E12. 5: 7. 5%, E14. 5: 10%, E16. 5: 15%, P3: 20%. We normalized cell libraries through SCTransform, which accounts for preservation of differential variation between highly variant and lowly variant genes  62. We utilized Uniform Manifold Approximation and Projection (UMAP), following principal component analysis (PCA) and PCA dimension selection, to enable human-interpretable visualization of the transcriptomic space through dimensionality reduction. We visually examined the data for differences between batches of cells collected at each stage. Only P3 had significant batch differences. There are extensive suggested solutions for "correcting" batch differences  63. However, from a statistical fundamentals perspective both a priori and empirically, such methods have been shown to produce aberrant downstream results  64  and so were not used here.
PHATE preserves global and local structure in the transcriptomic space of genes observed across cells: intuitive visualisation of high-dimensional gene expression data must balance within cell type (local) and between cell-type (global) transcriptional differences: local discrimination of transcriptional differences within cell types may be obscured by preservation of global between-cell type differences. PHATE enables intuitive interpretation of datasets, particularly so for "lineage" type datasets in our experience. Cell types were assigned by comparison with literature expression profiles. Weusedthe parallel implementation of Seurat's FindMarkers [https://github. com/vertesy/ Seurat. multicore], implemented with min. pct=0. 2, logfc. thres-hold=0. 5, and test. use = "bimod" to compare differential gene expression between cell types.
The purified PCR products were used to make DNBs (DNA nanoballs) which were sequenced with a MGI DNBSEQ-Tx sequencer. Raw data processing method were explained below as previously described  23. Briefly, CID and MID were extracted from the read 1, and the cDNA sequences were extracted from the read 2 in the FASTQ files from MGI DNBSEQ Tx sequencer. CID mapping allows 1 base mismatch. MID reads with N bases or more than two low quality score bases (score <10) were removed. The remaining DNA reads were aligned using STAR against the reference mouse genome (mm10) supplemented with WPRE sequences obtained from transgenic plasmid. To each gene of each CID, reads with same MIDs were only kept one for counting. After annotation and count, gene expression matrices with CID were generated. Gene expression matrices were divided into bins with 20 × 20 DNBs for E12.
pct = 0. 1, logfc. threshold = 0. 25. Each cluster was annotated using several known cell type-specific marker genes. We assigned cluster labels using gene expression profiles and bin location. To elucidate probabilistic spatial distributions of our cell types identified in our single -cell RNA sequencing data; we combined cells identified from the cardiomyocyte lineage with those identified in the wider dataset, to give a representative transcriptional reference library of potential cell types in the developing murine heart from E12. 5 to P3. We combined: Atrial Cardiomyocytes, Ventricular Cardiomyocytes, Immature Atrial Cardiomyocytes, Immature Ventricular Cardiomyocytes, Immature Trabecular Ventricular Cardiomyocytes, Trabecular Ventricular Cardiomyocytes, Sinoatrial Node, Atrioventricular Node, Purkinje Fibres, Fibroblast-like, Smooth Muscle-like, Endothelium, Epicardium, Endocardium, Neural Crest, Haematopoetic Precursors, Immune Cells, and Platelets.
Optogenetic Electrophysiology Optical stimulation of ChR2 light-sensitive channel. Whole hearts were paced through the activation of ChR2 light-sensitive channels. This was achieved by the delivery of 470 nm blue light pulses (5-10 ms pulse width) generated by OptoFlash (Cairn Research). Pulses were triggered by a 1401 digitiser and Spike 2 software (Cambridge Electronic Design). Approximate blue light intensity was measured with a 818-ST2 Wand Detector connected to a 843 R Power metre (both Newport Corporation, CA, USA) and expressed normalised for the area being illuminated through simulating the average light intensity reached to the surface of the tissue by mimicking the distance of fibre-heart. To monitor cardiac rhythms, we carried out ex vivo ECG analysis on langendorff perfused hearts.
Sections were examined with an Olympus BX51 research light microscope (Olympus Optical Co Ltd. , London, U. K. ) and images captured with a Zeiss Axiocam digital camera and Axiovision software (Carl Zeiss Vision GmbH, Hallbergmoos, Germany). For TEM examination, 100 nm thick sections were collected onto 150 mesh formvar/carbon-coated copper grids, stained with 2% uranyl acetate, and coated with a few nanometre layer of evaporated carbon to eliminate charging. For metal particle morphology, the samples were observed by a JEOL JEM2000 TEM (JEOL, Welwyn Garden City, UK), with STEM and EDAX Genesis 4000 Energy Dispersive X-Ray Spectrometry (EDAX, Leicester, UK) using 100 kV. Images were collected using a Getan Orius (11 megapixels) in-line digital camera (Gantan Inc. Abingdon, UK).
Acknowledgements We thank  Peter Somogyi, FRS, FMedSci, PhD, FRS  ( Department of Pharmacology, University of Oxford ) for help with imaging experiments and  Wenyuan Zhang  ( Department of Zoology, University of Oxford ) for help in preliminary bioinformatic analysis. We thank  Prof. Gillian Douglas  ( Radcliffe Department of Medicine, University of Oxford ) for providing MHC-Cre mouse line. We thank  Niloufer G Irani  and colleagues in  Micron Advanced Bioimaging Unit at Department of Biochemistry (University of Oxford ) for providing support and assistance for imaging work. We thank  Prof. Christopher L-H Huang  ( Department of Physiology, University of Cambridge ) for critical comments. We thank production team of  China National GeneBank at Shenzhen  for their assistance in providing Genebank resources.
Data availability The Dbh Cre , Dbh CreERT , Dbh CFP , Dbh f/f mouse lines are available from M. L. and X. T. groups under a material transfer agreement with the University of Oxford and Southwest Medical University. Interactive versions of certain data are included with paper. Raw sequencing data generated by Stereo-seq and nucleic acid dye staining images have been deposited to Spatial Transcript Omics DataBase (STOmics DB) with the accession number STT0000009. The raw data of single cell RNA sequence have been deposited into CNGB Sequence Archive of China National GeneBank DataBase with accession number CNP0002316.
UPLC-HRMS/MS analyses and data processing As a first step, all extracts (i. e. cyanobacteria endometabolome and macrophyte extracts) were analyzed on a UPLC-ToF system (Ultra-Performance Liquid Chromatography-Time of Flight Mass Spectrometry, Xevo G2-S ToF, Waters). Following the injection of 10 µL, the chromatographic separation was performed at 40 °C on an HSS-T3 column (150 × 2. 1 mm × 1. 7 µ, Waters) connected to an HSS-T3 security guard cartridge (10 × 2. 1 mm) by using two different gradients according to the analysis of the hydrophilic or the lipophilic fraction, respectively (Supplementary Table  S1 ). HRMS data acquisition was performed in MSe centroid mode after electrospray ionization operated in positive (ESI+) or negative (ESI-) mode (Supplementary Table  S2 ). Briefly, MSe mode consisted in the acquisition of all ion fragmentations including full scans (50-1500 m/z) at both low (6 eV) and high (10-40 eV ramp) collision energy with a scan time of 0.
2 s. Lockspray solution (Leucine Enkephalin, Waters) was infused every 60 s at 10 µL/min in parallel with the sample to correct mass drift during the acquisition (Lockmass: 556. 2771 in ESI+ and 554. 2615 in ESI-). Data processing (i. e. preprocessing and further filtration) is described in Supplementary Material (Section 1. 2. 3). Since the reliability of MSe mass spectra with available MS2 libraries was low (see below), a limited number of relevant samples (i. e. extracts of cyanobacteria from only two conditions: control and exposed to 10 g/L) were re-injected on UPLC-Qexactive+ instrument (ThermoScientific) in order to acquire actual MS2 allowing to improve the annotation of metabolites and associated pathways. The chromatographic conditions were similar to those of UPLC-TOF (Supplementary Table  S1 ).
Chemometric analyses and aggregated metabolomic dose-responses The metabolomic datasets were analyzed using the statistical analysis tool from MetaboAnalyst 5. 0 and DROMICS  [41]. Prior to the chemometric analyses in MetaboAnalyst, data were processed using the interquartile range (IQR) in order to keep only the 5000 more significant features. Then, data were normalized (sample's median), transformed (cube root of variable values), and scaled (variable value pareto) to obtain homoscedasticity. For the endometabolome datasets, unsupervised (PCA, HCA) and supervised (PLS-DA) multivariate analyses were implemented to get a comprehensive picture of the metabolomic response and to test if the metabolomic fingerprints differed between the tested concentrations. Such analyses were performed for all the datasets, individually.
To take advantage of the serial dilution-based screening strategy, DROMICS  [41]  was used in order to provide aggregated metabolomic dose-responses, as the empirical cumulative distribution frequency (ECDF) of the benchmark dose 1SD (BMD 1SD ) of each feature (see Supplementary Section 1. 2. 3). To do so, the entire dataset (i. e. without IQR filtration) was first normalized, transformed, and scaled. In DROMICS, the features were first filtered according to their responsiveness (i. e. Quadratic trend test, FDR = 0. 01) and then categorized according to their trends (increase, decrease, U-shape, Bell-shape) with increasing concentration of the extract (see Supplementary Section 1. 2. 3). The responsive features fitting with the proposed quadratic models were selected for annotation.
Metabolite annotation Metabolite annotation was performed using MS-DIAL combined with MSClean-R, allowing the multiplexing of positive and negative data and the clustering of adducts, the use of an in-house MS2 library (kind gift from G. Marty), and the further im-plementation of software (MS-Finder, SIRIUS 5) for in silico machine learning and molecular networking (Supplementary Table  S6  and Supplementary Figure  S1  and S2 ). To this end, only a part of the HRMS-TOF raw data was processed including those from the cyanobacteria endometabolome (control and at 3. 3 g/L conditions) and the macrophyte extract (3. 3 g/L). Briefly, the datasets were processed with the suspect list from DROMICS and an in-house database covering central metabolism. For both processes, the signals were cleaned using MS-CleanR and further annotated using MS-Finder and SIRIUS.
In both softwares, several databases (Puchem, COCONUT, HMDB, CheBi, Biocyc, etc. ) were explored from in silico fragmentation and machine-learning, based on the simplified molecular-input line-entry system (SMILES). In particular, Sirius was used to take advantage of its machine-learning algorithm to compare our experimental MS2 fragments with the predicted ones. Since the match between TOF spectral data and the in-house library was low (data not shown), some samples were analyzed on an UPLC-Qex+ to obtain more accurate MS2, allowing to improve the number of matches with MS2 spectra from experimental acquisition or in silico and machine learning tools. These HRMS data were directly processed using MS-DIAL combined with MS-cleanR, MS-Finder, and SIRIUS. Details on processing parameters are provided in Supplementary Material (Supplementary Table  S5 , Supplementary Figures  S1  and S2 ).
In the best case, the metabolites were putatively annotated at Level 2a (i. e. probable structure due to library MS2 spectrum match) while most of them were at L3 (tentative candidates related to match in silico or machine learning based fragmentation) according to the confidence scale of Schymanski et al. [42]. Also, SIRIUS allowed annotation at the pathway and superclass levels based on a neural network algorithm (Natural Products Classifier,  [43]. The putative annotations of all the datasets (Hydro ESI+/from both LC-TOF/LC-Qex, Lipo ESI+/-from LC-TOF) were finally merged in a single sheet and further filtered to obtain a list of putative metabolites, pathways and superclasses (∆ppm < 10; ZODIAC score > 0. 4; Single putative SMILE from SIRIUS).
Metabolomic fingerprint and associated aggregated dose-response Results from exploratory PCA and HCA performed on each dataset (i. e. Hydro-ESI+/-, Lipo-ESI+/-) revealed that the metabolomic fingerprints differed between the different concentrations tested (Supplementary Figure  S3 ). In particular, HCA and associated heatmaps revealed how the highest concentrations (10 and 3. 3 g/L), clustered together, triggered a shift in the metabolome of Microcystis aeruginosa (Supplementary Figure  S3 ). Hence, HCA results revealed two main metabolite clusters in all datasets: one with high intensities at the highest concentrations and low intensities for the control, and another cluster with opposite patterns. Further PLS-DA results confirmed for all datasets the clear discrimination between the different conditions tested, showing significant (high R 2 and Q 2 values) and distinct distributions along component 1.
This axis explained 34. 7% (HydroESI+) and 71. 6% (LipoESI-) of the total covariance (Figure  3 ). The further implementation of DROMICS allowed us to categorize the features according to their quadratic trends (i. e. Increase, Decrease, U-shape, Bell) and to provide an aggregated response (ECDF) according to their sensitivity to the extract (i. e. BMD 1SD ), revealing interesting patterns for each dataset (Figure  4  and Supplementary Figure  S4 ). First, the full aggregated ECDF showed that 50 to 75% of the metabolome responded at concentrations higher than 1 g/L of Lagarosiphon major extract (Figure  4 ). Then, for all datasets, increasing and decreasing trends were the most represented (>60% of total signal), while increasing features were more frequent than decreasing ones except for the lipophilic ESI-(Supplementary Figure  S4 ).
Candidate pathways and associated metabolites By processing both LC-TOF and LC-Qexactive HRMS data in MS-Dial combined with MS-cleanR and further MS-Finder and SIRIUS annotation tools, some putative metabolites and associated pathways have been preliminary annotated. Overall, in case of a poor match between our spectra and the in-house library, MS-Finder and mostly Sirius proposed a putative formula and structure (Smile, InchI) for 588 signals in the hydrophilic phase, and for 274 signals in the lipophilic phase following a significant quadratic trend (Inc, Dec, U, Bell) or showing    S6  and S7 ). Figure  5  summarizes the contribution of these metabolites to different NPC pathways  [43]. The most represented pathways were the amino acids and peptides (37. 1%) followed by the alkaloids (22%), the fatty acids (21.
Uncertainties regarding the interpretation of metabolomic data First of all, as we subtracted from the cyanobacteria endometabolome the signals from macrophyte extracts, it is likely that primary metabolites occurring in both matrices have been removed from the dataset, limiting the assessment of L. major effects on M. aeruginosa metabolism. Conversely, the lipophilic phase signals of the endometabolome were not filtered and could then be "contaminated" by the macrophyte extract. Nevertheless, the hydrophilic extraction of L. major matrix logically limited the content of lipophilic chemicals that could be further partitioned in the lipophilic phase of M. aeruginosa endometabolome. Thus, even if it cannot be excluded that increasing signals in the lipophilic phase were at least partly due to L.
Acknowledgments Support from  INRAE  and  Carnot Eau & Environnement  is gratefully acknowledged. The authors thank  G. Marty  for providing the in-house MS2 library in the frame of MTH2. 0 project ( ANR 11-INBS-0010 ). This work was carried out with the support (equipment and personnel) provided by the  XPO scientific infrastructure (LIFE research infrastructure - Living in Freshwater and Estuaries, INRAE ) of EABX research unit, and by the  Bordeaux Metabolome scientific insfrastructure ( MetaboHUB research infrastructure ).
The Copernicus Atmosphere Monitoring Service has recently produced a greenhouse gases reanalysis (version egg4) that covers almost two decades from 2003 to 2020 and will be extended in the future. This reanalysis dataset includes carbon dioxide (CO2) and methane (CH4). The reanalysis procedure combines model data with satellite data into a globally complete and consistent dataset using the European Centre for Medium-range Weather Forecasts' Integrated Forecasting System (IFS). This dataset has been carefully evaluated against independent observations to ensure validity and point out deficiencies to the user. The greenhouse gas reanalysis can be used to examine the impact of atmospheric greenhouse gases concentrations on climate change, such as global and regional climate radiative forcing, assess intercontinental transport, and also serve as boundary conditions for regional simulations, among other applications and scientific studies.
sciamachy. org ), the Infrared Atmospheric Sounding Interferometer (IASI, www. eumetsat. int/iasi) and The Thermal and Near Infrared Sensor for Carbon Observation (TANSO, www. eorc. jaxa. jp/GOSAT/instrument_1. html) instruments with IFS model simulations of  CO2 and CH4 (Agustí-Panareda et al. , 2022). The dataset is based on a consistent and stable model version to provide a homogenous, continuous and gapless record of the CO2 and CH4 in the entire atmosphere since 2003. The IFS includes a forecasting model and a data assimilation system combined. The data assimilation system also integrates meteorological observations as in the fifth generation of ECMWF meteorological reanalyses, ERA5  (Hersbach et al. , 2020) , to best constrain the atmospheric variability of greenhouse gases  (Massart et al.
, 2014 (Massart et al. , , 2016)). The forecasting model provides a 3-dimensional representation and evolution of the atmospheric CO2 and CH4 and meteorological variables  (Agustí-Panareda et al. , 2019). At the model surface the greenhouse gases are forced by a set of surface fluxes and emissions. Such modelling configuration allows to produce a realistic representation of the spatio-temporal variability of greenhouse gases in the atmosphere over a wide range of scales from hours to seasons and from local to global  (Agustí-Panareda et al. , 2022). Figure  1  showcases the global evolution of CO2 and CH4 represented by the CAMS GHG reanalysis data set over the period 2003-2020 and the span of the used satellite data. The seasonal averages illustrate the spatial and temporal variability information contained in the reanalysis dataset which can be exploited for a range of applications in atmospheric sciences.
The reanalysis cycling chain The reanalysis production chain is illustrated in Fig. 2. It is a cycling procedure based on a 12-hour data assimilation window that involves four main parts: -Satellite retrievals of CO 2 and CH 4 (see section 2. 2) as well as NWP observations  (Hersbach et al. , 2020). -Surface fluxes (see section 2. 3) that constitute the sources and sinks of CO2 and CH4 in the atmosphere are compiled from various sources. They provide the surface boundary condition for the tracer transport model. -A model forecast (see section 2. 4) that provides a 4-dimentional representation of the state of the greenhouse gases over space and time, along with other meteorological variables, during the 12-hour analysis window (from 09:00 to 21:00 and 21:00 to 09:00 UTC). The forecasts are initialised with the previous analysis, except for the first forecast for the initial date, which is initialised with atmospheric molar fractions from the CAMS inversion dataset  (Chevallier et al.
Satellite GHG observations The satellite measurements of radiances (L1 data) are processed by satellite retrievals developed by various data providers to derive information on the total and partial atmospheric column of CO2 and CH4 dry mole fraction (L2 data). In the CAMS GHG reanalysis only L2 products were used for CO2 and CH4. With nadir looking satellite instrument geometries the L2 data provide vertically integrated content with vertical sensitivity functions called either averaging kernel when an optimal estimation approach  (Rodgers, 2000)  is used or weighting functions, that provide information on where the retrieval sensitivity is located along the vertical. The satellite products assimilated in this reanalysis are all provided with averaging kernel and prior information or weighting functions  (Massart et al, 2014 (Massart et al, , 2016)).
The CO2 and CH4 column products are retrieved by the University of Bremen  (Reuter et al. , 2011)  and the Netherland Institute for Space Research (SRON)  (Frankenberg et al. , 2011) , respectively. Both of L2 products are delivered by the ESA GHG-Climate Change Initiative  (Buchwitz et al, 2015)  and the C3S Climate Data Store (https://cds. climate. copernicus. eu). • TANSO-FTS -GOSAT: The Thermal And Near infrared Sensor for carbon Observations -Fourier Transform Spectrometer (TANSO-FTS) instrument onboard the Greenhouse Gases Observing Satellite (GOSAT) satellite has been developed by the Japan Aerospace Exploration Agency (JAXA) and it was launched in January 2009. TANSO-FTS measures radiances in the short-wave infrared band that provide information of total-column CO2 and CH4 mole fractions.