dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements L. T. Verryckt is funded by a PhD fellowship from the  Research Foundation Flanders (FWO). This project was funded by the  European Research Council Synergy  Grant;  ERC-2013-SyG-610028 IMBALANCE-P. We thank the staff of the  Nouragues Ecological Research Station , managed by  USR mixte LEEISA (CNRS; Cayenne) , and the Paracou station, managed by  UMR Ecofog (CIRAD, INRA ;  Kourou). Both research stations received support from " Investissement d'Avenir " grants managed by  Agence Nationale de la Recherche  ( CEBA :  ANR-10-LABX-25-01 ,  AnaEE-France :  ANR-11-INBS-0001 ). We are grateful to  Valentine Alt ,  Samuel Counil ,  Jocelyn Cazal ,  Gonzalo Carrillo ,  Jean-Loup Touchard ,  Anthony Percevaux ,  Benjamin Leudet  and  Stefan van Beveren  for climbing the trees to collect the selected branches.
Advances in multi-omics technologies have led to an explosion of multimodal datasets to address questions ranging from basic biology to translation. While these rich data provide major opportunities for discovery, they also come with data management and analysis challenges, thus motivating the development of tailored computational solutions to deal with multi-omics data. Here, we present a data standard and an analysis framework for multi-omics -MUON -designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible data structure, supporting an arbitrary number of omics layers. The MUON data structure is interoperable with existing community standards for single omics, and it provides easy access to both data from individual omics as well as multimodal dataviews.
Introduction Multi-omics designs, that is the simultaneous profiling of multiple omics or other modalities for the same sample or cells, have recently gained traction across different biological domains. Multi-omics approaches have been applied to enable new insights in basic biology and translational research  [1, 2]. On the one hand, the emerging multi-omics datasets result in novel opportunities for advanced analysis and biological discovery  [3]. Critically however, multi-omics experiments and assays pose considerable computational challenges, both concerning the management and processing as well as the integration of such data  [4, 5]. Major challenges include efficient storage, indexing and seamless access of high-volume datasets from disk, the ability to keep track and link biological and technical metadata, and dealing with the dependencies between omics layers or individual features.
Additionally, multi-omics datasets need to be converted into specific file formats to satisfy input requirements for different analysis and visualisation tools. While specialized frameworks for the analysis of different omics data types have been proposed, including for bulk and single-cell RNA-seq  [6] [7] [8] [9]  or epigenetic variation data  [10] [11] [12] [13] , there is a lack of comprehensive solutions that specifically address multi-omics designs. Additionally, there currently exists no open exchange format for sharing multi-omics datasets that is accessible from different programming languages. The few existing solutions for multi-omics data (Seurat  [8] , MultiAssayExperiment  [14] ) are confined to the R programming language ecosystem, and require loading the full dataset into the working memory, which precludes dealing with larger datasets.
To address this, we here present MUON (multimodal omics analysis), an analysis framework that is designed from the ground-up to organise, analyse, visualise, and exchange multimodal data. MUON is implemented in Python and comes with an extensive toolbox to flexibly construct, manipulate and analyse multi-omics datasets. At the core of the framework is MuData, an open data structure standard, which is compatible with and extends previous data formats for single omics  [9, 15]. MuData files can be seamlessly accessed from different programming languages, including Python  [16] , R  [17] , and Julia  [18]. We illustrate MUON in the context of different vignettes of its application, including analysis of combined gene expression and chromatin accessibility assays as well as gene expression and epitope profiling.
Results MuData: a cross-platform multimodal omics data container At the core of MUON is MuData (multimodal data) -an open data structure for multimodal datasets. MuData handles multimodal datasets as containers of unimodal data. This hierarchical data model generalizes existing matrix-based data formats for single omics, whereby data from each individual omics layer are stored as an AnnData  [15]  object. (Figure  1a, c ). MuData also provides a coherent structure for storing associated metadata and other side information, both on the level of samples (e. g. cells or individuals) and features (e. g. genes or genomics locations). Metadata tables can either be specific to a single stored data modality, or they can represent joint sample annotations that apply to all modalities stored in a MuData container.
In a similar manner, MuData containers can be used to store derived data and analysis outputs, such as cluster labels or an inferred sample embedding (Figure  1b ). MuData objects are serialised to HDF5  [19]  files by default -the industry standard for storing hierarchical data. Individual omics layers are serialised using the existing AnnData serialisation format, thus permitting direct access to single omics using existing toolchains that build this data standard (Figure  1c ). Basic access to MuData objects is possible from all major programming environments that support access to HDF5 array objects. Additionally, MUON comes with dedicated libraries to create, read and write MuData objects from Python, R, and Julia. These tools facilitate the exchange of multi-omics data across platforms, and ensure consistent file format definitions.
MUON: a framework for multimodal omics data The MUON framework allows for managing, processing, and visualising multi-omics data using the MuData containers. Existing workflows developed for single-omics can be reused and applied to the contents of a multi-omics container. For example, individual modalities of the simultaneous gene expression and chromatin accessibility profiling  [20]  can be processed using existing RNA  [21]  and ATAC  [22]  workflows. In this manner, canonical processing steps, including quality control, sample filtering, data normalization and the selection of features for analysis can be transferred from single-cell omics analysis (Figure  2a ). The integration of multiple modalities within a MuData container facilitates the definition of multi-omics analysis workflows, allowing to flexibly combine alternative processing steps (from left to right in Figure  2b ).
g. using methods such as similarity network fusion, SNF  [30] , or WNN  [29] ). Finally, the latent or neighbourhood representations can serve as a starting point for downstream analysis and interpretation. For example, UMAP  [31]  can be directly applied to cell neighbourhood graphs to generate nonlinear embeddings of cells. Similarly, the alternative cell neighbourhood graphs can be used as input for identifying connected components and thereby putative cell types (e. g. using multiplex community detection techniques  [32] ). The flexibility to choose and control individual processing steps in MUON makes it possible to compose tailored workflows for a particular dataset.
Such visualisations can be generated from MuData objects without loading all the data into memory. As a second example, we considered CITE-seq  [35]  data, which comprises gene expression and epitope abundance information in the same cells. To process the latter, specialized normalisation strategies for denoising and scaling  [36]  are available. Normalised protein counts can then be used to define cell types, akin to gating in flow cytometry  [37]  (Figure  3c ). Once the count matrices are processed, these can be integrated using alternative multimodal options (Figure  2 ). For instance, using both modalities for cell type annotation as well as for dimensionality reduction allows to attribute the distinction between naïve and memory T cells to the abundance of CD45 isoforms RA and RO at the protein level (Figure  3d ).
Discussion Multimodal omics designs are increasingly accessible, allowing for characterising and integrating different dimensions of cellular variation, including gene expression, DNA methylation, chromatin accessibility, and protein abundance  [3, 38, 39]. MUON directly addresses the computational needs posed by such multi-omics designs, including data processing, analysis, interpretation, and sharing (Figure  1 ). Designed for the Python ecosystem, MUON operates on MuData objects that build on community standards for single-omics analysis  [9]. Serialization to HDF5 makes MuData objects accessible to other programming languages, including R and Julia. MUON is designed in a modular fashion, which means that existing methods and tools for processing individual omics can be reused to design more complex analysis workflows (Figure  2 ,3).
At the same time, the software facilitates combining single-omics analysis methods with a growing spectrum of multi-omics integration strategies to define novel multi-omics workflows. Looking ahead, MUON will be a robust platform to build upon and support future developments. On the one hand, handling novel assays for multi-omics that are emerging can be integrated. For example, mRNA and proteins can be assayed together not only with CITE-seq  [35]  but also with QuRIE-seq  [40]  or INs-seq  [41]. Moreover, trimodal assays such as scNMT-seq  [42]  or TEA-seq  [43]  allow to generate data beyond just two modalities and can be handled with MUON, which is designed to manage an arbitrary number of modalities. On the other hand, the complexity of experimental designs is rapidly increasing  [44, 45].
Implementation of muon Muon has been implemented in the Python programming language and builds on a number of existing numerical and scientific open-source libraries, in particular NumPy  [47] , Scipy  [48] , Sklearn  [49] , Pandas  [50] , h5py  [51] , AnnData  [15]  and Scanpy  [9]  for omics data handling, MOFA+  [28]  for multimodal data integration and matplotlib  [52]  and seaborn  [53]  for data visualisation. The weighted nearest neighbours (WNN) method has been implemented following  [29]  describing the original method and  [43]  describing its generalisation to an arbitrary number of modalities.
Processing gene expression and chromatin accessibility data Single-cell multiome ATAC + gene expression demonstration data for peripheral blood mononuclear cells (PBMCs) from a healthy donor with granulocytes removed through cell sorting processed with ARC 1. 0. 0 pipeline was provided by 10X Genomics (https://support. 10xgenomics. com/single-cell-multiome-atac-gex/datasets). Log-normalisation was used for both gene and peak counts, and respective values for highly variable features scaled and centered to zero mean and unit variance were then used as input to discussed algorithms such as PCA, as implemented in scikit-learn  [49]  and scanpy  [9] , or MOFA+  [28]. Differentially expressed genes and differentially accessible peaks were identified with respective functionality in scanpy and were used to compile gene lists for cell type identification.
The respective vignettes are available at https://muon-tutorials. readthedocs. io/en/latest/single-cell-rna-atac. Processing CITE-seq data CITE-seq data for PBMCs from a healthy donor was provided by 10X Genomics (https://support. 10xgenomics. com/single-cell-gene-expression/datasets/3. 0. 2/5k_pb mc_protein_v3). Log-normalisation was used for gene counts, and dsb  [36]  was used to denoise and scale protein counts. Respective values for highly variable features scaled and centered to zero mean and unit variance were then used as input to discussed algorithms. The respective vignettes are available at https://muon-tutorials. readthedocs. io/en/latest/cite-seq.
Microfluidic vRootchip A microfluidic chip, vRootchip was used to analyze root tip growth in real-time. The manufacturing of the chip, sample preparation procedure, and data analysis of root tip growth was performed as described previously  [16]  and according to Li and Verstraeten et al. (unpublished). vRoot-chip was used with 10 nM IAA treatment in ¼ MS and 0. 1 % sucrose. For imaging, the vertical confocal microscopy setup was used as described previously  [16, 48]  and according to Li and Verstraeten et al. (unpublished). The 10 nM IAA solution was supplemented with the cell-impermeable fluorescent dye Tetramethylrhodamine isothiocyanate-Dextran  [16].
Root gravitropic assay For measurements of root gravitropic curvature kinetics, 4-d-old seedlings were placed on plates with ½ MS and rotated 90 • and roots were imaged using a vertically placed flatbed scanner (Epson Perfection V370 Photo). Multiple plates were held in place on the scanner by a custom-made holder. Max. 12 ROIs of the seedlings were automatically imaged with a resolution of 1200 dpi in 10 min time intervals using an AutoIt script for 8 h. In ImageJ, the time-lapse movies of the seedlings were manually cropped and registered (stabilized) using the Fiji plugin "StackReg" in "Rigid body" mode.
Root growth (RG)-tracker We developed a custom MATLAB application named RG-tracker (https://research-explorer. app. ist. ac. at/librecat/record/8294) with a graphical user interface that allows entirely automated root growth analysis and tracking of the root tips. Root tips were segmented based on the pixel classification workflow of Ilastik  [52] , which only requires manual retraining in case the imaging conditions change drastically. For each point in time, the positions of the root tips were determined by segmenting the tip-probability output, performing particle size filtering and calculating the center of mass. The root tips were then tracked over time by solving the linear assignment problem using the Hungarian algorithm (Munkres). The tracking algorithm can deal with gaps in the root tip detection and both the gap closing and the maximum linking distance can be specified in the GUI.
Completed tracks are filtered by minimum track length, duration and maximum growth speed to remove miss-detections and then presented to the user as an overlay of raw data, tip segmentation and tip tracks. At this point the user can exclude additional tracks from further analysis and export the overlay of the tracks and the root time-lapse. The x/y coordinates of each root tip, growth speed, direction of growth, growth angle and root length are then calculated for each point in time and exported for further analysis. All experiment specific parameters such as the segmentation threshold, particle size, and track filters can be saved and together with the segmentation project file form the complete data set to clearly recapitulate the data analysis.
Etiolated hypocotyl growth To analyze the growth of etiolated hypocotyls, the seedlings were recorded at 12 h intervals for 120 h in a dark chamber equipped with an infrared light source (880 nm LED; Velleman, Belgium) and a spectrumenhanced camera (EOS035 Canon Rebel Xti, 400DH) with built-in clear wideband-multicoated filter and standard accessories (Canon) and operated by the EOS utility software. The hypocotyl length was measured using ImageJ.
Acknowledgments We would like to acknowledge Bioimaging and  Life Science Facilities  at  IST Austria  for continuous support and also the  Plant Sciences Core Facility of CEITEC Masaryk University  for their support with obtaining a part of the scientific data. We gratefully acknowledge  Lindy Abas  for help with ABP1::GFP-ABP1 construct design. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  [grant agreement no. 742985 ] and  Austrian Science Fund (FWF)  [I  3630-B25 ] to J. F. ;  DOC Fellowship of the Austrian Academy of Sciences  to L. L. ; the  European Structural and Investment Funds, Operational Programme Research, Development and Education -Project  " MSCAfellow@MUNI " [ CZ. 02. 2.
The column outlet was connected to a triplequadrupole MS system (SCIEX API 4000 QTRAP; MDS Analytical Technologies/Applied Biosystems) with an atmospheric pressure chemical ionization source (set to 500°C). Multiple reaction monitoring in positive ion mode was used to detect RE species with settings and mass-to-charge (m/z) transitions as described before  (Ajat et al. , 2017). Chromatographic peaks were integrated and quantified using Analyst software version 1. 4. 3 (Applied Biosystems). To measure other neutral lipids (sterols, TAGs, SEs), samples were injected and separated on a Kinetex/HALO C8 column (2. 6 µm, 150 × 3. 00 mm; Phenomenex). A gradient of methanol/H 2 O (5:5 vol/vol; solvent A) and methanol/isopropanol (8:2 vol/vol; solvent B) was generated by an Infinity II 1290 ultra-HPLC system (Agilent Technologies) and with a constant flow rate of 600 µl min -1 (0 min, 100% A; 2 min, 0% A; 8 min, 0% A; 8.
Acknowledgments We thank  Shreyas Sinha  and  Elmon Meijering  for their assistance during the initial phase of experiments. Microscopic images were acquired at the  Center of Cellular Imaging, Faculty of Veterinary Medicine, Utrecht University. We thank  Esther van 't Veld for technical assistance. Lipid analyses were performed at the  Lipidomics Centre, Faculty of Veterinary Medicine, Utrecht University. We thank  Jeroen Jansen  for technical assistance. EM for quantification of LD size in yeast (Fig. 4) was performed at the  Electron Microscopy Core Facility  at the  University of Texas Southwestern Medical Center. We thank  Alain de Bruin ,  Simon de Neck , and Elsbeth van Liere at the  Dutch Molecular Pathology Center (Utrecht University ) for expert help and analysis of seipin KO livers.
systematic vascular screening from head to pelvis in all patients with FMD. 1  Although neurological manifestations of FMD are rare and generally considered as benign, severe complications, including ischemic and hemorrhagic stroke, may occur. 3, 5 ymptomatic or asymptomatic cervical (carotid or vertebral) artery dissection (CeAD) is a frequent manifestation of FMD. Thus, recent data from the United States Registry for Fibromuscular Dysplasia (US FMD Registry) show that the prevalence of CeAD is estimated to be 21%, with a higher prevalence of carotid (16%) than vertebral (5%) artery dissection. Additionally, among patients with a neurological presentation leading to diagnosis of FMD, the prevalence of CeAD is even greater, being 27% in the ARCADIA (Assessment of Renal and Cervical Artery Dysplasia) registry.
METHODS Any anonymized data not published within the article can be shared at the request of other investigators for purposes of replicating procedures and results. The design and methods of the ARCADIA study have been published elsewhere. 6 In brief, included patients were women or men aged ≥18 years diagnosed with renal, cervical, or intracranial artery FMD, who were prospectively recruited at 16 university hospitals in France and Belgium from 2009 to 2014. The diagnosis of FMD had to be established in the vascular bed, where the condition was presumed to be symptomatic, by recent (within 2 years before inclusion) computed tomography angiography, magnetic resonance angiography, or digital subtraction angiography. FMD was diagnosed as nonatherosclerotic arterial encroachment or stenoses affecting the trunk or branches of medium-size arteries, in the absence of aortic wall thickening, inflammation, and known syndromic arterial disease.
In view of our findings on the association between CeAD and sex observed in the ARCADIA registry, we undertook a systematic review of studies of patients with FMD reporting data on CeAD and sex. We report our study according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. 15 e used a search strategy using the keyword "fibromuscular dysplasia" in PubMed from inception to January 31, 2020. Two reviewers (C. A. and M. B. ) independently screened all titles and the abstracts, and then the full texts of potentially eligible studies to extract data on sex and CeAD in patients with FMD. If there were multiple publications from a study cohort, we included only the publication with the largest amount of data relevant to this review. We did not restrict inclusion by language of publication or sample size.
We also added unpublished data from the European/International FMD Registry (A. Persu, PhD, unpublished data, 2019) and US FMD registry (J. W. Olin, PhD, unpublished data, 2019). Although there was no peer review on the latest data from these 2 registries, FMD diagnosis and patients' clinical and imaging characteristics were assessed by experienced physicians in both registries. Two reviewers (C. A. and M. B. ) assessed risk of bias at the study level on the basis of whether the inclusion was prospective or retrospective, the characteristics of the population (setting, mean age, race, proportion of history of hypertension), and FMD type. We relied on the authors' definition of CeAD. Risk of bias across studies was not assessed.
Additionally, we estimated the risk of selection bias for the association between multisite involvement and CeAD in FMD to be high in the literature, because the prevalence of multisite involvement is highly dependent of the use of systematic imaging from the brain to the pelvis. No valid study has reported reliable data on multisite involvement in patients with FMD, stratified by presence of CeAD. Our search in PubMed identified 2707 titles, of which 177 abstracts were screened, leading to the assessment of 173 full texts. In addition, we added unpublished data on sex and CeAD from the US FMD Registry and the European/International FMD Registry (2 independent populations) and those from ARCADIA. A total of 171 studies were finally excluded, leaving 5 independent studies  18, 19  for inclusion in the meta-analysis (Figure  1  and Table  5 ).
Acknowledgments Drs Arnaud  and Boulanger: statistical analysis, interpretation of results, drafting of the manuscript. Dr Lorthioir: statistical analysis, interpretation of results, revision of the manuscript. Dr Amar: data collection, revision of the manuscript. Drs Azarine ,  Boyer ,  Di Monaco ,  Kastler, Oppenheim , and Thony: data collection. Dr Chatellier: concept and design of the study. Dr Jeunemaitre: concept and design of the study, revision of the manuscript. Dr Mousseaux : concept and design of the study, data collection, revision of the manuscript. Drs Persu  and Olin: data collection, interpretation of results, revision of the manuscript. Dr Azizi : concept and design of the study, interpretation of results, revision of the manuscript. Dr Touzé: concept and design of the study, data collection, interpretation of results, drafting of the manuscript.
, 2011). Random forest regression was used to identify climatic variables influencing cast production in each vegetation unit. Random forest is a nonparametric method, which consists of a large number of individual tree models trained from bootstrap samples of the data  (Breiman, 2001). The results of all individual trees are aggregated to make a single prediction. The variables tested in were total precipitations, precipitations > 20 mm h -1 , air temperature, soil temperature and soil water tension from 10 to 150 cm depth. The dataset for each vegetation unit represented 115 measurements of cast production and was randomly split into calibration dataset (80%) and validation dataset (20%). The optimization of parameters for the final models was based on minimizing the root mean square error (RMSE) between the measured and the estimated value of the production of casts for the calibration dataset based on 10-fold repeated crossvalidation with five repetitions.