dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of the impacts of data sets across methods Figure  6  shows the total impact scores for the selected LCIA methods applied to the forty-five studied metals across all market processes from the ecoinvent 3. 1. 7 database. Both X-and Y-axes are in log scale. Please note that the scale of the X-axes may vary between graphs. R 2 values represent the correlation of the log-log regression of impact scores per data set. Lower R 2 values indicate that the compared methods present different impact hotspots among the covered metals. Three additional scatter plots are presented in the SI. The impact scores range over 18 orders of magnitude across the data sets. The relatively high correlations between impact scores can partially be explained by the differences in the relative size of functional units (i.
7. 1 database (e. g. , germanium and scandium). Thus, data sets are expected to present noticeably greater impact scores with the endpoint ADR method than the endpoint LPST100 method due to higher-than-average resource flows of highly dissipative, relatively expensive metals (e. g. , gallium and germanium; cf. Figure  2 ). For instance, gallium contributes an average of 0. 016% to the inventory totals for the 100 data sets with the largest difference between the total impact scores for the endpoint ADR and LPST100 methods. In contrast, gallium's average share of inventory totals is of ReCiPe 2016 (kg Fe-eq. ) LPST 100 (endpoint, kg Fe-eq. ) f R = 0. 995 0. 0054% across all of the data sets. Finally, the comparison between endpoint category totals for the LPST100 method and those of the ReCiPe 2016 method (Fig.
The midpoint CFs for the ADR and LPST methods rely on replicable results from open-source data and model  (Charpentier Poncelet et al. 2022b; Helbig and Charpentier Poncelet 2022). Endpoint CFs introduce the use of price statistics in the assessment, which is helpful to distinguish between the value of different metals but presents some limitations at this point  (cf. "Sect. 3. 1. 2"). The identified limitations are expected to be most relevant for metals with very small resource flows in the LCI or for which no resource flows are currently reported in widespread databases like ecoinvent. For instance, it was observed that byproduct metals like gallium are likely to be over-represented in the endpoint results for the ADR method. Moreover, while price statistics were here considered to be the best applicable proxy to represent the value of metals for humans as defined by the Life Cycle Initiative's taskforce on mineral resources  (Berger et al.
Data availability Data and results depicted in the article are provided in the Supporting information documents. The data and model used to compute midpoint characterization factors may be accessed online at https:// doi. org/ 10. 17605/ OSF. IO/ CWU3D  (Helbig and Charpentier Poncelet 2022). Price data and references are detailed in the article and in the Supporting information. Data from the ecoinvent database are subject to copyright and may not be divulged.
985 2 Total endpoint impacts 1E-05 1E-02 1E-08 1E+01 1E+04 1E+07 1E+10 Tellurium 1E+13 1E-08 1E-08 1E-05 1E-02 1E+01 1E+04 1E+07 1E+10 100% ADP 2015 (kg Fe-eq. ) Palladium ReCiPe 2016 (kg Fe-eq. ) 60% 80% 0% 20% 40% Inventory ADR (midpoint, kg Fe-eq. ) 1E+07 1E+10 no endpoint CFs ADP 2015 1E-02 1E+01 1E+04 1E-05 c ReCiPe 2106 LPST25 LPST100 LPST500 ADR Platinum Nickel Gold Chromium Iron R = 0. 991 2 ADR (midpoint, kg Fe-eq. ) 1E+07 1E+10 1E+04 1E-02 1E+01 1E-05 d R = 0. 927 2 Fig. 5 Inventory contributions and total midpoint and endpoint impact scores for selected LCIA methods applied to 5999 market reserves method for elements based on the cumulative production in LPST100 (midpoint, kg Fe-eq. ) data sets from the ecoinvent 3. 7. 1 database. ADP 2015: ADP ultimate 1E-08 1E-08 1E-05 1E-02 1E+01 1E+04 1E+07 1E+10 1E-08 1E-05 1E-02 1E+01 ADP 2015 (kg Fe-eq.
Grant/Award Number:  ANR-17-EURE-0017 ;  Agence Nationale de la Recherche , Grant/Award Number:  ANR-16-CE28-001401 ;  BBVA Foundation , Grant/Award Number: Leonardo-2021;  Universidad de Granada/CBUA The purpose of this article is to explore the role commitments may play in shaping our sense of joint agency. First, we propose that commitments may contribute to the generation of the sense of joint agency by stabilizing expectations and improving predictability.
FUNDING INFORMATION Fern andez-Castro's work was produced with the support of a 2021 Leonardo Grant for Researchers and Cultural Creators (BBVA Foundation), and the Juan de la Cierva Research Fellowship (IJC2019-040199-I) of the Spanish Ministry of Science. Pacherie's work was supported by the Agence Nationale de la Recherche (ANR; Grant No. ANR-16-CE28-001401) and by Ecoles Universitaires de Recherche Frontiers in Cognition (Grant No. ANR-17-EURE-0017).
, 2016). A detailed description of the in situ and remote sensing measurements taken during January and February 2014, as part of the CLACE 2014 campaign, is provided by  Lloyd et al. (2015)  and  Grazioli et al. (2015). Here we only offer a brief presentation of the datasets used in this study. Shadowgraphs of cloud particles were produced by the two-dimensional stereo hydrometeor spectrometer (2D-S;  Lawson et al. , 2006) , part of a three-view cloud particle imager (3V-CPI) instrument. The 2D-S products have been used to provide information on the number concentration and size distribution of particles in the size range of 10-1280 µm. Following  Crosier et al. (2011) , the raw data were processed to distinguish ice crystals from droplets. Removal of artifacts from shattering events was also considered  (Korolev et al.
Both case studies are associated with the passage of frontal systems over the region of interest, approaching the alpine slopes either from the NW (cold front) or the SE (warm front) direction, as shown by the vertically integrated condensed water content (ICWC; sum of cloud droplets, rain, cloud ice, snow, and graupel) in Fig. 1. For both cases, the simulation covers a 3 d period, with the first 24 h being considered sufficient time for the spin-up. A 27 s time step was used in the parent domain and goes down to 9 s in the second domain and 3 s in the third domain. Note that achieving such small time steps in the innermost domain is essential to ensure numerical stability in non-hydrostatic simulations over a region with complex orography such as around JFJ. The fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses dataset (ERA5;  Hersbach et al.
, 2020)  is used to initialize the model and provide the lateral forcing at the edge of the 12 km resolution domain every 6 h. Static fields at each model grid point come from default WRF pre-processing system datasets, with a resolution of 30 for both the topography and the land use fields. Land use categories are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover classification. Regarding the physics options chosen to run WRF simulations, the Rapid Radiative Transfer Model for General Circulation Models (RRTMG) radiation scheme is applied to parameterize both the shortwave and long-wave radiative transfer. The vertical turbulent mixing is treated with the Mellor-Yamada-Janjić (MYJ; Janjić, 2001) 1. 5 order scheme, while surface options are modeled by the Noah land surface model (Noah LSM;  Chen and Dudhia, 2001).
2. 2. 1). Here we consider that the product of these two processes can undergo shattering and generate numerous ice fragments, the number of which is parameterized after  Phillips et al. (2018). The formulation is derived by fitting multiple laboratory datasets to a Lorentzian function of temperature and a polynomial expression of the drop size. More precisely, the total number of fragments (N ) generated per frozen drop are given by the following: N = (D r ) (T ) ζ η 2 (T -T 0 ) 2 + η 2 + βT , ( 4 ) where T is the temperature (in Kelvin), and D r is the size of the freezing raindrop (in millimeters). Note that N is defined as the sum of the big fragments (N B ) and tiny splinters (N T ). Equation (  4 ) applies only to drop diameters less than 1. 6 mm, which is the maximum observed experimentally.
Model evaluation The control simulation (CNTRL), performed with the standard M05 scheme, sets the basis for assessing the validity of the model against available meteorological observations. Temperature, relative humidity, wind speed, and wind direction are obtained from the MeteoSwiss weather station at JFJ. The comparison of each meteorological variable with the results from the nearest model grid point of the CNTRL simulation is shown in Fig. 2. Note that the outputs are from the first atmospheric level of the innermost domain at ∼ 10 m above ground level (a. g. l. ; Fig. 1 ), while the first 24 h of each simulation period are considered spin-up time and are, therefore, excluded from the present analysis. The mean modeled values and standard deviations (SDs), along with the root mean square error (RMSE), and the index of agreement (IoA) between model predictions and observational data are summarized in Table  1.
75 gm -3 , which is substantially higher than the respective maximum LWC observed during the SE case (0. 30 gm -3 ). On 26 January before 12:00 UTC, all sensitivity simulations predict LWC > 1 gm -3 , which, however, cannot be validated against measurements due to missing data in the CDP time series. Note that this period is excluded from the statistics derived in Table  5. The CNTRL simulation is found to overestimate the cloud LWC, predicting 0. 42 (0. 25) gm -3 in the third quartile, which is a factor of ∼ 2 higher than the observed values during the NW (SE) case (Table  5 ). The modeled LWC in the 75th percentile is decreased by a factor of 1. 5-5 in the simulations that account for the BR process (Table  5 ), improving the agreement with observations (Fig. 3c ). The reduction in LWC is expected, considering that the higher N isg produced when BR is activated can readily deplete the surrounding droplets under liquid water subsaturated conditions through the WBF process.
Code and data availability. The WRF outputs and the microphysical observations presented in this study can be downloaded from the EnviDat data portal at https://doi. org/10. 16904/envidat. 271  (Georgakaki et al. , 2022). Meteorological data can be downloaded from the Swiss Federal Office of Meteorology and Climatology MeteoSwiss site (https://gate. meteoswiss. ch/idaweb/login. do, Meteosuisse, 2022). The updated Morrison scheme is available upon request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-1965-2022-supplement.
Acknowledgments We thank the study group of the  National Health and Nutrition Examination Survey. The study was supported by grants from the  Research Committee-Research Assistant Professorship Scheme  ( 2021-22 ; reference  SMH/NL/JF/RAP2122/0752/22en ),  Research Committee- Strategic Seed Funding for Collaborative Research Scheme  ( 2022-23 ; reference  TL/SSFCRS2223/0953/23ww ), the  Chinese University of Hong Kong ,  Hong Kong Research Grants Council -General Research Fund  (grant  165056653 ),  Health and Medical Research Fund  (reference  18190471 ),  Vice-Chancellor Discretionary Fund  ( Round III ; grant  136366853 ),  Hong Kong Research   Grants Council  ( Research Matching Grants   8601219  and  8601242 ), and a grant from the  Research Committee on Research Sustainability of Major Research Grants Council Funding Schemes  ( 3133235 ) of the  Chinese University of Hong Kong.
Conclusion We have provided a comprehensive list of metabolic diseases associated with ear disease (external ear abnormalities) or hearing loss (middle or inner ear involvement). We have also proposed a battery of standard biological explorations to aid in diagnosis based on the many possible IMDs, with a priority of treatable diseases. This represents the ninth issue in a series of educational summaries providing a comprehensive and updated list of metabolic differential diagnoses according to system involvement. The full list can be freely accessed at www. iembase. org/gamuts, and will be curated and updated on a regular basis. Supplementary data to this article can be found online at https://doi. org/10. 1016/j. ymgme. 2022. 09. 005.
The Human Cell Atlas (HCA) consortium aims to establish an atlas of all organs in the healthy human body at single-cell resolution to increase our understanding of basic biological processes that govern development, physiology and anatomy, and to accelerate diagnosis and treatment of disease. The lung biological network of the HCA aims to generate the Human Lung Cell Atlas as a reference for the cellular repertoire, molecular cell states and phenotypes, and the cell-cell interactions that characterize normal lung homeostasis in healthy lung tissue. Such a reference atlas of the healthy human lung will facilitate mapping the changes in the cellular landscape in disease. The discovAIR project is one of six pilot actions for the HCA funded by the European Commission in the context of the H2020 framework program.
ly/HCALungCZI1), LungMAP (https://LungMAP. net) and HuBMAP (https://HuBMAPconsortium. org). The discovAIR consortium is one of six pilot actions for the Human Cell Atlas funded by the European Commission in the context of the H2020 framework program. DiscovAIR aims to contribute to establishing the first draft of an integrated Human Lung Cell Atlas. In this atlas, discovAIR will contribute significantly to the multimodal single-cell omics data from healthy human lung, to mapping the cellular heterogeneity observed in single-cell RNA-seq (scRNA-Seq) datasets onto the lung tissue architecture, and to identification of changes of molecular cell phenotypes and their interactions in lung disease cohorts such as asthma, chronic obstructive pulmonary disease (COPD), pulmonary arterial hypertension (PAH), coronavirus disease of 2019 (COVID-19), and interstitial lung diseases (ILD) such as interstitial pulmonary fibrosis (IPF), enabling accelerated translational and clinical research into lung diseases.
The roadmap of the Lung Biological Network The identification of the pulmonary ionocyte as a previously unknown cell type present in healthy lung tissue  [8, 9] , sparked a number of studies mapping the cellular heterogeneity of healthy lung tissue with increasing spatial and cellular resolution, and taking into account smoking as one of the most important factors driving specific cell states in lung tissue  [3, [11] [12] [13] ]. An overview of current lung tissue scRNA-Seq datasets and their availability is provided in Appendix 1. Progress in the Lung Cell Atlases has also recently been summarized  [14]. Notwithstanding the importance of these foundational datasets for the respiratory community, HCA-Lung aspires to move well beyond this current state-of-the-art that is hampered by relatively low number of donors, poor coverage of different ethnicities and ancestral diversity as well as age range of tissue donors, limited resolution across the CCF and lack of spatial mapping of the identified cell states onto the tissue architecture  [4, 6].
Therefore, HCA-Lung aims to provide a true reference atlas of the lung that captures the heterogeneity of cell states associated with location within the organ, as well as the variation of cell states that can be observed within healthy lung tissue, as a function of genetic, demographic and geographical variables. One approach to establish such a reference atlas is to leverage recent advances in computational data integration techniques  [15] [16] [17] [18] [19]  to integrate the currently available single-cell RNA-seq datasets into a single embedding, thereby establishing an integrated Lung Cell Atlas that captures most of the available data and identifies undersampled regions and cell populations, and efficiently corrects for batch effects. In addition to capturing the full heterogeneity in transcriptional cell states of lung cells, HCA-Lung aims to incorporate a much larger variation in ethnicity and ancestral diversity, age range and geographical location of tissue donors, as well as multiple layers of omics data into an advanced draft of the Lung Cell Atlas, including epigenetic features such as CpG methylation, chromatin accessibility and histone modifications, or proteomic features at single-cell resolution.
Leveraging the recent advances in single cell joint profiling protocols, paired modalities can enrich our RNA reference using transfer learning approaches that map the RNA measurements onto each other  [20]. Finally, the lack of a systematic description of molecular cell phenotypes with spatially resolving methods is severely hampering the interpretation of the wealth of data generated by the single-cell omics approaches. Clearly, adding such spatial maps of healthy lung tissue is one of the current priorities of HCA-Lung. Merging spatial datasets with an integrated Lung Cell Atlas based on multiple layers of omics data will then establish a true Human Lung Cell Atlas of healthy lung tissue, which will be an invaluable resource for basic, translational and clinical research into lung and its diseases.
To address this shortcoming, discovAIR will combine multimodal single-cell profiling of lung cells with multimodal spatial mapping of the lung cell types and their molecular states onto the lung tissue architecture (see Figure  1 ). Moreover, discovAIR partners will integrate multiple lung tissue single-cell datasets into a single embedding, establishing the first draft of an integrated Lung Cell Atlas, allowing cell-type specific analysis of the transcriptomic variation explained by demographic covariates, as was recently piloted for a limited number of SARS-CoV-2 cell entry genes by a highly collaborative HCA-Lung effort  [32]. We have recently benchmarked computational approaches for data integration of existing single-cell RNA-seq datasets  [19]. We will use this framework to identify the best-suited method for integration of available scRNA-seq datasets from healthy lung tissue.
The resulting integrated dataset will be presented as the core reference of healthy lung tissue, and can be used as the first draft of an integrated Lung Cell Atlas. This atlas can then be used as a reference to which newly generated datasets from either healthy or diseased lung tissue can be compared using transfer learning methods such as scArches  [20]. Taking this approach is especially powerful, as it allows unified use of cell type labels, direct comparisons of cellular composition across datasets and -in case of diseased tissue -direct identification of unique, disease-associated cell states absent from the healthy reference. Moreover, the integrated Lung Cell Atlas can help to structure discussions around cell type label harmonization and mapping of the hierarchical cell type labels used for data integration (see Figure  1 ) to existing ontologies of the cells of the lung  [33].
Towards the end of the project, discovAIR will establish a second draft of this integrated Lung Cell Atlas incorporating all newly generated data within the consortium as well as data from the larger HCA Lung Biological Network community. The newly generated dataset from the discovAIR project will entail scRNA-seq data from at least 60 healthy controls spanning a large age range, both sexes and multiple ethnicities, as well as the detailed characterization of the 5-location dataset from healthy donor lung tissue encompassing multimodal data as well as matching spatial datasets. It is important to bear in mind that any tissue sample obtained for building the reference atlas of healthy lung tissue will be derived from either deceased individuals whose lungs are suitable for organ transplant, lung resection programs in routine clinical care (often as part of cancer treatment) or bronchoscopy studies performed for research purposes on healthy volunteers.
Of these, only the latter source can be considered to reflect truly healthy, fresh lung tissue. The integrated Lung Cell Atlases will be made freely available to the respiratory and scientific community to ensure optimal impact of the discovAIR efforts. In addition to these integrated atlases of scRNA-seq data, discovAIR aims to develop innovative visualisations for spatial datasets, including 3D reconstruction of lung tissue architecture and molecular phenotyping of local cellular neighbourhoods by multiplexed sm-FISH or immunofluorescence analysis. Most datasets with a large number of biological replicates sample only a limited number of locations within the organ, and use a single-omics technique. To contribute to a more balanced coverage of multimodal sampling across the CCF in sufficient numbers of donors, discovAIR aims to establish a reference dataset offering an in-depth exploration of healthy lung tissue from 5 deceased transplant organ donors, each sampled at 5 discrete locations (see Figure  1 ) using both scRNA-Seq and scATAC-Seq to characterise their constant and variable features, as well as multimodal spatial profiling of lung cells on adjacent tissue sections, to map their organisation in a three dimensional tissue architecture, and their local and distant interactions.
The generation of in-depth transcriptomic data will allow us to predict cell-cell interactions and the receptor-ligand pairs mediating these. The discovAIR approach will also allow validation of these predicted cell-cell interactions by spatial data that identifies, in a quantitative manner, cellular interactions. The discovAIR project has selected 4 different spatially resolving methods. Of these, two are probebased, allowing validation of the spatial expression pattern of a (limited) gene set, selected on the basis of the scRNA-seq data, by mapping these onto the tissue architecture. The other two methods are sequencing-based spatially resolving methods, allowing detection of spatial gene expression patterns without limiting the analysis to a prior selection of genes. The two probe-based methods to map the transcriptional variation and cell-type heterogeneity observed in the single-cell datasets onto the spatial architecture of lung tissue differ in sensitivity and multiplexity.
First, we will use a panel of 64 probes in the sm-FISH based method SCRINSHOT  [34] , that has high sensitivity, but depends on sequential rounds of hybridization and imaging of the same tissue section to achieve multiplexity. We have established two probe panels based on the transcriptional variation observed in a similar dataset covering these same 5 locations. One probeset was designed for airway wall-resident cell types and one for cell types of the lung parenchyma. In addition to SCRNSHOT, we will map the cell types and their molecular phenotypes in high detail with a panel of 150 probes using in situ sequencing (ISS), an alternative approach that has higher multiplexity, but not the sensitivity of SCRNSHOT  [35]. Probe selection for both SCRINSHOT and ISS was performed using a multi-objective computational approach, which optimizes our ability to discern cell types of interest while recovering most of the transcriptional variation observed in these tissue samples with the selected probes (Appendix 2: the current discovAIR smFISH gene lists for probe design in ISS and SCRINSHOT).
The RNA or ATAC sequencing data generated in this way will be correlated to the spatial location of the individual nuclei to provide insights on the distribution of particular cell populations across the human lung tissue. These datasets will be integrated with the matching snRNA-seq as well as the SCRINSHOT and ISS data, and 3D Visium data for the 2 locations where this is available. Together, this will generate a spatial map of cell types and cell states of the healthy human lung, including 3D reconstruction of small airways and lung parenchyma. Finally, DiscovAIR aims to provide a detailed profiling of the cellular trajectories of healthy adult lung cells towards the disease-associated cell states. To this end, discovAIR will study a (limited) number of chronic and/or inflammatory lung diseases: asthma, COPD, ILD, PAH and COVID-19.