dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, discovAIR will establish a lung cell perturbation map to chart cell-state transitions between health and disease (see Figure  1 ). Lung diseases are highly heterogeneous, and several phenotypes as well as endotypes can be observed for nearly all chronic lung diseases. The scope of discovAIR is to provide proof-of-principle data that a high-quality reference cell atlas of healthy lung and can be used in combination with datasets from lung tissue of patients with lung disease to identify disease-associated cell states and cell-cell interactions, and the healthy-to-diseased cellular trajectories can be further characterized using the perturbation atlas. Therefore, discovAIR has chosen to include relatively small numbers of samples from patients with lung disease (10 tissue samples per disease condition).
In addition, discovAIR has selected very specific disease groups, such as adult patients with (physician diagnosed) childhood-onset asthma without a history of cigarette smoking, in an attempt to minimize the chance that heterogeneity of the disease obscures any reproducible disease-associated transcriptional cell states in the final dataset. The discovAIR perturbation atlas will use primary epithelial, immune and endothelial cells, as well as precision-cut lung slices, all isolated from lung tissue of healthy donors, and stimulated in advanced in vitro culture models  [40, 41]. Time-series analysis of the ex vivo stimulated primary cells by scRNAseq will allow the identification of transitional cell states in the cellular trajectories of healthy cells towards a potentially disease-associated transcriptional cell state.
The discovAIR project focusses on testing this perturbation atlas concept in epithelial, endothelial and immune cells given the availability of well-developed cell culture models available to the consortium. However, other cell types such as mesenchymal cells including fibroblast subsets and smooth muscle cells are likely to play key roles in disease inception, progression and exacerbations, and will need to be studied in a similar approach in future follow-up projects if the concept of the perturbation atlas is validated. Integration of the datasets from ex vivo cultures of healthy primary lung cells and tissues with the scRNA-seq datasets acquired in lung tissue samples obtained from patients with lung disease is expected to allow the identification of the optimal in vitro proxy for the diseased cell states observed in vivo.
These studies are expected to further uncover cellular mechanisms of disease, guide identification of potential targets for preventive or therapeutic intervention and reveal biomarkers that can be used to detect the presence of these specific diseased cell states for use in diagnosis or treatment response monitoring. Given that discovAIR is a 2-year pilot project, the approach has a strong focus on transcriptomic data to establish the first draft of the Human Lung Cell Atlas in health in disease. Notwithstanding the significant progress beyond the state-of-the-art such an Atlas would entail, these foundational transcriptomic datasets will need to be validated at the protein level, both for the Human Lung Cell Atlas describing the transcriptional heterogeneity in healthy lung tissue as well as for the changes therein associated with chronic lung disease, to be able to achieve their full impact for the respiratory community and patients with lung disease.
Taken together, the multimodal healthy lung tissue datasets, state-of-the-art integration methods, the spatial mapping of healthy lung tissue and the disease cohorts in combination with the perturbation atlas will allow discovAIR to generate a first draft of the Human Lung Cell Atlas (V1. 0) as a standard reference for the respiratory community. This will not only encompass a healthy reference Lung Cell Atlas combining multimodal omics and spatial datasets, but also a comprehensive description of the changes thereof with disease, and the cellular trajectories that might lead to the acquisition of the unique, disease-associated cell states. This will be a key asset for the respiratory community and is expected to facilitate progress in regenerative and precision medicine for lung disease, and guide identification of novel candidates for precision diagnostics and curative interventions.
Representatives from the pharmaceutical industry were highly interested to use the Lung Cell Atlas to map gene expression to specific locations in the bronchial tree or parenchyma, and to guide design of drug delivery methods for targeted therapies. Furthermore, detailed insight into the cellular transcriptomes, behaviors and interactions in the different regions of the lung and in subtypes of disease could accelerate drug design for precision medicine. The industry representatives further stressed the importance of access to the raw data and future expansions of the Lung Cell Atlas with, for instance, single-cell epigenetic or proteomic datasets, as well as datasets from a large number of lung diseases.
Conclusions The Human Lung Cell Atlas is a shared goal of several international consortia, all of which are represented in the Lung Biological Network of the Human Cell Atlas. The discovAIR consortium is the main European consortium active within HCA-Lung, and is one of the six research and innovation actions funded by the European Committee in the Horizon2020 framework program. The discovAIR consortium aims to contribute to the goals and efforts of the Lung Biological Network of the Human Cell Atlas by providing the multimodal characterization of healthy lung tissue, including spatial mapping of cell types and cell states onto the tissue architecture. This will allow discovAIR to generate a first draft of the Human Lung Cell Atlas as a standard reference for the respiratory community. In addition to the healthy reference Lung Cell Atlas combining multimodal omics and spatial datasets, discovAIR will also provide a first description of the changes thereof with several lung diseases, and the cellular trajectories that might lead to the acquisition of the unique, diseaseassociated cell states.
This will be a key asset for the respiratory community and is expected to facilitate progress in regenerative and precision medicine for lung disease, and guide identification of novel candidates for precision diagnostics and curative interventions. DiscovAIR will develop portals for data exploration and analysis suitable for the academic and industrial end-users, as well as information portals for patients, their families and the interested lay audience in collaboration with the ELF, an ambassador organization for patients with lung disease. All discovAIR methods, datasets and atlases will be made freely available and serve as a basis for further expansions and updates by the Lung Biological Network of the Human Cell Atlas consortium. Future iterations of the Lung Cell Atlas are expected to increase the genetic diversity of the atlas, to incorporate fetal and pediatric lung development, to expand the number of diseases and diseased samples included in the atlas and to further develop the interactive and integrated features of the Human Lung Cell Atlas across transcriptomic, epigenomic, proteomic and spatial data modalities, evolving into a standard reference for the respiratory community.
In the perturbation atlas, stimulations of ex vivo cultured primary cells or precision-cut lung tissue slices will be used to map cell state trajectories of healthy cells to diseased cell states (G). These novel datasets will be ingested into next iterations of the integrated lung cell atlas, used to establish consensus annotations for the different lung cell types and submitted to the appropriate data repositories (H) as open data using novel visualization modalities for the spatial datasets. Finally, the integration of the lung cell atlas (including datasets from lung disease) and the perturbation atlas will generate novel insights into mechanisms of disease, help guide identification of drug targets and biomarkers for disease inception or treatment response.
Acknowledgements This work is supported by the  European Union 's  Horizon2020 Research and Innovation Program  under grant agreement no. 874656  (discovAIR) to ST, KSP, SL, WT, JL, MvdB, MS, PH, JD, IP, JS, HBS, PB, MvP, FJT, CS, KBM and MCN, and a  Seed Network grant  from the  Chan Zuckerberg Initiative  to PB, HBS, KBM, AVM, MCN and FJY. MvdB and MCN are also supported by grants (no. 5. 1. 14. 020  and  4. 1. 18. 226 ) from the  Netherlands Lung Foundation. KBM and SAT are also supported by  Wellcome  ( WT211276/Z/18/Z  and Sanger core grant  WT206194 ). JS is also supported by an  ERS/RESPIRE4 Marie Sklodowska Curie fellowship  ( R4202007-00844 ). PB was also supported by  Institut National contre le Cancer  ( PLBIO2018-156 ),  FRM  ( DEQ20180339158 ),  Inserm Cross-cutting Scientific Program HuDeCA 2018 , and the  National Infrastructure France Génomique  ( Commissariat aux Grands Investissements ,  ANR-10-INBS-09-03 ,  ANR-10-INBS-09-02 ).
outside the submitted work;  Dr. Koppelman  reports grants from  Lung Foundation of the Netherlands, GSK , Vertex, TEVA the  Netherlands ,  UBBO EMMIUS Foundation ,  European Union  ( H2020 grant ), outside the submitted work and he has participated in advisory board meetings to GSK and PURE-IMS, outside the submitted work;  Dr. van den Berge  reports research grants paid to  UMCG  from  GlaxoSmithKline ,  Genentech ,  Roche ,  Novartis , outside the submitted work;  Dr. Nawijn  reports grants from  European Commission , grants from  Chan Zuckerberg Initiative , and grants from The  Netherlands Lung Foundation , during the conduct of the study; grants from  GSK Ltd , outside the submitted work. All other authors do not have a conflict of interest.
Conflict of Interest statement Dr. Theis reports personal fees and non-financial support from Cellarity, and from Dermagnostix, during the conduct of the study; Janine Schniering was supported by a ERS/EU RESPIRE4 Marie Sklodowska Curie Postdoctoral Fellowship; Dr. Lundeberg reports personal fees from 10x Genomics Inc, outside the submitted work; P. Powell and J. Denning are employees of the European Lung Foundation; Dr. Timens reports personal fees from Merck Sharp Dohme, personal fees from Bristol-Myers-Squibb, outside the submitted work; Dr. Nilsson reports personal fees from 10x Genomics,.
All available data mining options need to be exploited for the most robust annotation strategy. Putative metabolite identification can be made based on mass-to-charge ratio (m/z) of the mass spectral ion and options for complementary approaches for further annotation: mass spectral database searching, in silico fragmentation tools, and orthogonal coupled techniques including retention time matching and ion mobility spectrometry (IMS) are advancing  [17, 19]. Particularly, in a HTPP context, new ultrafast separation approaches giving shorter retention times coupled to high-resolution mass spectrometry (MS) are potentially valuable. Several IMS variants have so far been poorly exploited for crops  [20]. However, for certain applications, IMS could potentially replace, or be used in parallel to, liquid chromatography (LC)-MS methods since IMS-MS has advantages relating to increased resolution and analysis speed and has greater potential to separate and annotate structurally similar (isomeric) metabolites not possible with standard LC-MS  [21].
There are also indications the technology may be more suited for portable devices  [20]. The most recent variant, structures for lossless IM (SLIM), using a highly advanced form of IMS may become orders of magnitude faster and could make it highly attractive in a HTPP context  [22]. The chemical space of small molecules is currently spread across multiple databases, and the number of compounds reported exceeds 120 million. Current estimations of the number of metabolites in living organisms is approaching 1 million  [13] , yet the majority of detected metabolites remains unknown. Standardization practices are also still lacking although increasing efforts are being made to rectify this  [13, 23]. The first significant steps have already been taken but there is still some way to go before we reach an automated approach towards fully annotated metabolite profiling  [19, 24].
Metabolomics data analysis and integration challenges Metabolomics is much more than a new provider of proxies for performance predictions and has the potential to facilitate deep insights into the genotype-phenotype interconnection. While there is a plethora of data being collected over a multitude of omics-based approaches in plants, the higher-level integration of these data still requires a set of strategies and approaches spanning various disciplines. To better grasp this challenging task of combining multiple data sets in a biologically relevant context, it is critical to describe fundamentally, three levels of data integration: conceptual, statistical, and model-based methods  [75]. Conceptual integrations are where omics data sets are analyzed separately, and the results are compared and matched to reach biologically relevant conclusions.
All these methods focus on the relationships between paired transcriptsmetabolite levels to find pairs either positively (both variables increased) or negatively (one increased and one decreased) correlated. Perez de Souza et al. [77]  used Pearson's correlation for integration of metabolites with transcripts to identify genes in the common bean participating in specialized metabolism. These types of calculation are supported by the R package IntLIM, which utilizes a linear model-based approach (Tools 1-3, Table  1 ). A simple method of data integration is the concatenation-based approach, which applies random forest, self-organizing maps, and k-means clustering to combine two data matrices (see Tools 4-6, Table  1 ). Glaubitz et al. [78] employed self-organizing maps to identify clusters of genes and metabolites connected to high temperature tolerance in rice.
Towards an in silico plant model In silico plant modelling relies upon a computational model that links gene networks, metabolic pathways, and cellular, tissue and organ compartments and enables simulations that range from metabolite flux to plant development. The creation and development of in silico models is the subject of large international projects  [92]. To achieve the goal of a virtual plant, models are constructed using two components: analytical data (transcripts, proteins, metabolites) alongside a computational component. The model may include parametric metabolic networks for the plant, which can be used to study the reaction flows based on flux balance analysis (FBA)  [93]. The curated and accurate models for FBA can be deposited as SBML files in repositories such as PlantSEED.
This repository currently contains models for 39 organisms  [94]  and can be used by anyone wishing to build their own models. The FBA methods use the COBRA (constraint-based reconstruction and analysis) approach implemented in the MATLAB or Python software environment  [95]. In contrast to tools requiring one to master programming languages, MetExplore  [96]  and Escher-FBA  [97]  are user-friendly online platforms for calculating fluxes, their visualizations and sharing with other users. FBA could be used to discover the alternative pathways or identify mutations of genes involved in metabolomic pathways  [93]. The number of plant organisms with reconstructed metabolism models or pathways is limited  [94]. Moreover, a vast number of metabolites, particularly specialized metabolites and lipids, do not currently have annotated predicted biosynthetic pathways.
In order to address this issue, network-based integration can assist in connecting the metabolites with enzymes or genes in pre-existing networks, enabling their further analysis and visualization  [98]. Networks can be constructed using constraint-, correlation-, and co-expression-based models  [99, 100]. Network analysis was used to discover a gene cluster associated with anthocyanin biosynthesis and potato pigmentation  [101] , phenolic biosynthesis  [55, 102] , or genes/metabolites involved in tomato resistance to pathogens  [103]. Network-based integration is part of several online and R-based tools including Tools 16, 18-25 (Table  1 ) or implemented in the CytoScape environment  [99]. Online  [121]  and transcripts after exposure to pathogen attack  [83]. These methods are implemented in the free software tools 6-10 (Table  1 ).
Trends in Plant Science OPEN ACCESS approaches to plant-based studies  [87, 88]. The application of machine learning and network analysis has already been able to predict accurately biochemical pathways in tomato using metabolite data  [89]. The free R package mixKernel is one example that implements machine learning methods for use in data integration  [90]. For example, Knoch et al. [91]  compared the canola phenotype predictability for different data sets (genes, transcripts, metabolites) and their combinations. The metabolomics data showed low prediction accuracy. However, combining metabolomics and transcriptomics data improved prediction.
This kernel density estimate therefore represents the realistic dispersal distribution of diaspores given that they detach from the parent plant and that the pappus morphs depending on the humidity. We compared this to a hypothetical version in which the pappus cannot morph by combining Models 1 and 2 in the same manner. In our dataset, dry weather is three times more frequent than wet. Diaspores are 8 times less likely to detach in wet weather versus dry when morphing occurs and 2. 5 times less likely when the pappus remains open. As a result dispersal during wet weather is rare in both morphing and open conditions so the distribution from Model 1 therefore dominated the overall kernel density estimates in both cases. To compare the models we found that the Jensen-Shannon divergence was 0.
Pappus angle measurements Angles of each hair from the horizontal axis were calculated at a 2 mm radius from the centre of the pappus, with the beak aligned to the vertical axis. Source data and analysis of 3D micro-tomography CT scans were identical to the ones used in  Cummins et al. , 2018. For all other pappus angle measurements, the angle was measured from 2D microscope or macrocamera images. The pappus was aligned perpendicular to the objective/lens and pappus angle measured as the angle between the outermost hairs.
88 mm) and a window overlap of 64 pixels. Data were filtered by excluding vectors of more than 2 standard deviations and a normalised median filter  (Westerweel and Scarano, 2005)  with a minimum normalisation level (ϵ) of 0. 1 pixels and a detection threshold of 3. Vectors were interpolated where missing, and the mean vector field of all 50 pairs of images was calculated. Noisy rows of data from the images were excluded from further analysis. The first row of the image (row size defined by the window size), which was always noisy, was removed. Other noisy rows were identified by fitting an autoregressive integrated moving-average model (R package 'tsoutliers') to the profiles of streamwise velocity (u z ) across the r direction of the image. The velocity profiles with outliers were considered noisy.
A z portion of the image with no more than one consecutive noisy row was considered to form the vortex region and was used for further analysis. Analysis was also limited in the r direction by excluding regions of data that fell outside of the limits of the dandelion pappus width. The coordinates of the central point of the pappus and the pappus angle were measured from images. The pappus limits were calculated assuming the pappus formed a triangular shape with hairs that were 7. 41 mm long  (Cummins et al. , 2018). The point P of minimal flow was determined by finding where u z along the z-axis was minimal at the r location of the centre of the pappus. Let z p be the z-coordinate of P, D the diameter of the pappus and τ the pappus angle. We found that zp D = 0. 24 + 0. 0039 τ , p=0. 001, R 2 = 0.
Meteorological data Hourly meteorological data was obtained from the Integrated Surface Database from the National Centers for Environmental Information (USA). We compared meteorological data from five locations across the UK (Edinburgh, Manchester, Nottingham, Norwich, and Exeter) and found the wind speed profiles to be largely similar (Figure  S4 ). For all further analysis and modelling, we used the meteorological data from Edinburgh. Hourly data for a 30-year period  were acquired for Edinburgh (55. 950-3. 373 ) and filtered according the following criteria: entries were excluded if hourly wind speed, dewpoint, or temperature were missing; data were only used from the beginning of April to the end of October in each year (the period of dandelion reproduction); occasional data measured between hours were excluded.
We simulated dispersal under dry (RH < 90%) and wet conditions (RH > 90%) using the respective wind speed distributions. As dandelion pappi can partially close at RH > 70% (Figure  1 -figure supplement 1b) and field-measured RH > 90% is a good proxy for wetness on plant surfaces  (Sentelhas et al. , 2008) , we used RH = 90% as a cut-off point. Diaspore falling velocities for dry and wet conditions were 0. 3 m s -1 and 0. 7 m s -1 , respectively, corresponding to the measured values from our drop tests. Detachment probabilities for dry and wet weather were based on the wind tunnel detachment assays described above. Alternative parametric models were fitted to the detachment data using the R package 'flexsurv'  (Jackson, 2016). According to the Akaike information criteria (AIC), a generalised gamma model fit the data best and performed better than Weibull, exponential, log-normal, loglogistic, generalised F, and Gompertz models.
Data analysis and statistics All measurements were carried out on separate individual diaspores from different infructescences except detachment assays, in which multiple diaspores from each infructescence were counted. Detachment assays were analysed by fitting Kaplan-Meier survival models to the data, with logrank tests used to compare treatments (R package 'survival'). For falling velocity tests, the following model was fitted to the data where the falling velocity is proportional to cos -2/3 τ , similarly to  Meng et al. , 2016 , and dry and wet groups were compared using Student's two-sided two-sample t-test. For PIV data, linear regression models were fit to the data. For the plot of the relationship between C D and Re, loess regression with a span of 0. 8 was used to illustrate the trends in the data.
Acknowledgements We thank  Alex Twyford ,  Jonathan Silverton , and  Richard Milne  for helpful discussion on the project and manuscript. This work was funded by  Leverhulme Trust  ( RPG-2016-255 ) to IMV, EM, and NN and ( ECF-2019-424 ) to MS. MRB was funded by the  BBSRC  ( P011586/1  and  T006153/1 ); JMB under  UKCEH National Capability  project  06895 ; IVM by the  European Research Council  ( H2020 ERC-2020-COG 101001499 ); and NN by the  Royal Society  ( UF140640  and  URF\R\201035 ).
Funder Grant reference number Author Leverhulme Trust  RPG-2016-255   Enrico Mastropaolo Ignazio M Viola Naomi Nakayama  Leverhulme Trust  ECF-2019-424   Madeleine Seale Biotechnology  and  Biological Sciences Research Council   P011586/1  and  T006153/1   Michael R Blatt European Commission   ERC-2020-COG 101001499 Ignazio   M Viola Royal Society   UF140640  and  URF-R-201035   Naomi Nakayama The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.