dataesr/scientific-paragraphs-categorization huggingface dataset:
Low CDNC values are also found in the measurements when the POC is present towards the end of the deployment period (Fig. 13 ). As a result of the strong vertical shear in wind speed, the time for an air parcel to leave the African continent to reach Ascension Island is shorter for aerosol higher up in the residual CBL than lower down in the CBL or in the MBL. Thus, in general, the aerosol at lower altitudes can be significantly older compared to that located at elevated altitudes (see analysis of ORACLES data by  Dobracki et al. , 2021). There is clear evidence from both  Wu et al. (2021)  and  Dobracki et al. (2021)  that aerosol higher up in the residual CBL exhibits a higher SSA (i. e. it is less absorbing on a per particle basis) than that lower down. Taylor et al. (2020)  show that the mass absorption coefficient in the CLARIFY domain does not vary significantly with altitude.
Analysis of aerosol size distributions Prior to the ORACLES, AEROCLO-sA, LASIC, and CLARIFY-2017 campaigns, airborne measurements of BBA size distributions in the region were sparse. Haywood et al. (2003)  documented the size distribution of BBA during SAFARI-2000, both close to emission source, off the coast of Namibia, and in the vicinity of Ascension Island; both of these cases are of relevance for CLARIFY-2017. Although the data presented here are quality-assured, the specific analyses performed during CLARIFY-2017 inevitably differ due to different sampling locations, sampling periods, and case studies, etc. Therefore, we present a composite of the models that are used to fit data in these studies to allow for a quantification of the error introduced by the assumptions used in each analysis.
Large-scale model-focussed investigations The data collected during the CLARIFY campaign are already proving a valuable resource for modelling studies. In addition to the modelling work in support of flight planning (Sect. 1), preparatory work funded by the project involved testing the GLOMAP-mode aerosol microphysics scheme in the Unified Model at convection-permitting resolution over the south-east Atlantic  (Gordon et al. , 2018). The scheme was evaluated against satellites and data from the ARM site at Ascension Island and shown to perform well. The Unified Model global configuration was subsequently also shown to predict properties of smoke aerosol realistically in an evaluation against CLARIFY measurements of extinction  (Che et al. , 2020)  and ORACLES 2016 measurements  (Shinozuka et al.
This highlights the importance of upper tropospheric nucleation and subsequent subsidence in subtropical areas for the local cloud regime (e. g. Clarke, 1993)  and highlights a route for significant microphysical aerosol effects on clouds, at considerable distance from anthropogenic source regions. Further work on regional modelling includes the coupling of the GLOMAP-MODE two-moment aerosol microphysics to the CASIM two-moment cloud microphysics scheme, which allows for more refined studies of the indirect effect  (Gordon et al. , 2020). The current intensity of modelling activity suggests that the CLARIFY data set will be a valuable resource for model evaluation for many years to come. The measurements obtained during the CLARIFY measurement campaign will also, together with data from OR-ACLES, LASIC, and AEROCLO-sA, contribute a key constraint on the representation of biomass burning aerosols in current climate models as part of the ongoing AeroCom Aircraft intercomparison study (https://wiki.
Despite the relative success of the CLARIFY-2017 campaign, which has addressed many of the key objectives, it is envisaged that additional analyses will be performed by the scientific community using the extensive data set of observations. As such, CLARIFY-2017 provides considerable potential legacy work that can be further exploited in the future. Data availability. All core and quality-assured non-core observational data, model forecast data, mission scientist logs, and flight summaries pertaining to the CLARIFY-2017 campaign are available from the UK's Natural Environment Research Council's central data archive at http://data. ceda. ac. uk/badc/faam/data/ (last access: 22 January 2021)  (CEDA, 2021a). Flight data for specific flights (including test flights) are available from http://data.
ceda. ac. uk/badc/faam/data/2017/c026-jul-31 (last access: 22 January 2021) (CEDA, 2021b) through to http://data. ceda. ac. uk/badc/faam/data/ 2017/c056-sep-09 (last access: 22 January 2021) (CEDA, 2021c). Author contributions. JMH was the CLARIFY-2017 lead PI responsible for the overall planning and deployment and prepared the paper with input from all co-authors. JMH, SA, AB, NB, KC, HC, PF, and PS were institutional PIs who led the CLARIFY-2017 application and directed the research. JMH, SA, PAB, KB, HC, MIC, IC, ZC, ND, HG, MdG, RH, JML, FP, and JWT were involved in the operational flying during the campaign as mission scientists or operators of non-core instrumentation. Modelling support during the detachment was provided by NB, MB, HCh, DWP, and PS. Postdeployment analysis of observational and model data was carried out by PB, SA, NB, KB, HCC, HCh, MIC, IC, ZC, ND, BD, PF, HG, MdG, RH, BJ, ACJ, JML, FM, DGP, FP, KS, JWT, DWP, and HW.
Jones, Florent Malavelle, Michael I. Cotterell, and Fanny Peers were also supported by the Research Council of Norway via the projects AC/BC (240372) and NetBC (244141). Philip Stier would additionally like to acknowledge funding from the European Research Council (ERC) project (RECAP) under the European Union's Horizon 2020 Research and Innovation programme (grant agreement 724602) and the Natural Environment Research Council project NE/P013406/1 (A-CURE). Financial support. This research has been supported by the Natural Environment Research Council (grant no. NE/L013584/1 and NE/P013406/1), the European Commission (grant no. RE-CAP 724602), and the Norges Forskningsråd (grant no. 240372 and 244141). statement. This paper was edited by Peter Knippertz and reviewed by Johannes Quaas and one anonymous referee.
Acknowledgements. We would like to thank  Sarah Taylor  and  Ed Gryspeerdt  for putting together Fig. 1 at the proposal stage. Tony Radakin  and the  Joint Forces Command  are thanked for their support for the  CLARIFY  project. Andy Pittock  of the RAF was instrumental in making this project a success through the support given to us during the detachment; the can-do attitude of the RAF was very much appreciated throughout the duration of the deployment. Jonny Hobson  and the staff of the Obsidian Hotel are thanked for their hospitality, and Jonny deserves particular thanks for providing us with interesting tours of the flora and fauna of the island. The staff of  Air Task , Avalon Engineering, and  FAAM  are thanked.
Introduction The relative contribution of land ice to global mean sea level rise has considerably increased in the recent decades and is now larger than the thermosteric effect  (Nerem et al. , 2018). Amongst the different contributions, the Greenland and Antarctic ice sheets have potential to substantially raise the global mean sea level, with a weakly constrained trajectory  (Oppenheimer et al. , 2019). While observational datasets show a dramatic increase in mass loss over the last decades for both ice sheets  (Mouginot et al. , 2019; Rignot et al. , 2019) , there is an urgent need for robust assessment of future sea level rise by projections obtained with numerical models. Most of the time, these projections involve comprehensive ice sheet models that compute the ice thickness change that results from evolving forcings, such as climate change.
In doing so, the basaldrag coefficient and the temperature at the base are consistent with each other. For this work we performed more than 10 thermal-equilibrium experiments, each one followed by five iterations of 200 years. At the end of the iterative process, we use the last inferred basal-drag coefficient together with the corresponding thermal state to run a short relaxation experiment of 20 years. The end of this relaxation experiment defines our initial state, which is used to begin the historical experiment hist and the control experiment ctrl (see Sect. 2. 3). Our ice thickness and bedrock topography of reference are from the BedMachine v. 3  (Morlighem et al. , 2017). This dataset is used as a target for our iterative procedure to infer the basal-drag coefficient. It is also used as the starting topography for the short relaxation that defines our initial state.
Our present-day reference climate forcing, namely annual near-surface air temperature and annual surface mass balance, comes from the MAR v3. 9  (Fettweis et al. , 2013 (Fettweis et al. , , 2017) )  forced at its boundary by MIROC5, averaged over the 1994-2015 period. On top of this climate forcing, we also add a strongly negative surface mass balance term of -15 m yr -1 outside the present-day ice mask in the observational dataset in order to avoid inconsistencies between the climate forcing and the initial ice sheet geometry. This present-day reference climate forcing is used for the initialisation procedure and for the control experiment ctrl. The model is run on a Cartesian grid at 5 km resolution, covering the Greenland ice sheet and using a stereographic projection. Since 5 km is too coarse to represent Greenland's floating ice tongues, the sub-shelf melting rate has been set to a large value (200 m yr -1 ) to discard simulated floating points.
These spatially variable gradients were evaluated with the MAR model with the method of  Franco et al. (2012). Ice-ocean interactions for the Greenland ice sheet are most of the time poorly represented amongst participating ISMIP6-Greenland models. This is mostly due to the fact that the spatial scale needed to represent such interactions is out of reach for most models. This is also the case for GRISLI, where the 5 km resolution grid is too coarse to capture marine-terminating outlet glaciers. To cope with this problem, retreat masks for outlet glaciers have been made available in ISMIP6-Greenland. They were obtained with simple parameterisations calibrated and tested against observational datasets  (Slater et al. , 2019). These masks provide, for a given resolution, the fraction of the grid that becomes ice-free and they are used to impose a specific retreat rate of the marine front.
Finally, the initial condition chosen for the ice sheet model remains an important question for ice sheet modelling. In particular, assuming an ice sheet in equilibrium with present-day climate for the initial condition, as done here but also in most participating IS-MIP6 models, could lead to an underestimation of the future mass loss. Data availability. The GRISLI outputs from the experiments described in this paper are available in the Zenodo repository with digital object identifier https://doi. org/10. 5281/zenodo. 3784665 (Quiquet and Dumas, 2020). The outputs in the Zenodo repository are the standard GRISLI outputs on the native 5 km grid, and, as a result, they may slightly differ from the post-processed outputs available in the official CMIP6 archive of the Earth System Grid Federation (ESGF).
Acknowledgements. We thank the  Climate and Cryosphere (CliC)  effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoting ISMIP6. We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP5  and  CMIP6. We thank the climate modelling groups for producing and making available their model output; the  Earth System Grid Federation (ESGF)  for archiving the CMIP data and providing access; the  University at Buffalo  for IS-MIP6 data distribution and upload; and the multiple funding agencies who support  CMIP5 ,  CMIP6  and  ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and the IS-MIP6 dataset preparation group for their continuous engagement in defining ISMIP6.
Furthermore, it is important to know what has been identified during the past 15 years as putative protein biomarkers of tenderness and prudent to explore how we move forward in the discovery pipeline with a robust list of candidate protein biomarkers to validate their veracity in predicting beef tenderness using representative populations of cattle across the globe. Considering the vastness of the data collected thus far and the need to establish a biologically meaningful and comprehensive list of biomarkers for tenderness, our aim was to use an integrative "omics" ('integromics') approach to integrate proteomics datasets from independent proteomicbased studies. Using a meta-analysis approach with data collected from the Longissimus muscle we propose for the first time an updated, effective panel of 33 robust biomarkers of beef tenderness using a lower cut-off of proteins appearing in at least four independent studies.
Literature search strategy A systematic computerized search of the Google Scholar, Pubmed. gov (NCBI), Scopus and Web of Science (Clarivate Analytics) databases was performed as depicted in Fig. 1  using an existing search strategy  (Gagaoua, Hughes, et al. , 2020). All the eligible proteomic-based studies that investigated beef tenderness from original papers published up to 1 April 2020 were thus identified, and organized into a spreadsheet reference manager. The search keywords that were used were included in Fig. 1. For all relevant articles, the records retrieved with the related article links in the databases were further screened, and the reference lists of each individual paper were also carefully checked for any other relevant studies.
Selection of publications: inclusion/exclusion criteria and quality assessment The first search using the entire databases yielded a total of 4201 publications (Fig. 1 ). We then detected and deleted the duplicates manually using Reference Manager and then one person (the first author) screened the title and abstract of 2160 studies for relevancy based on exclusion of any studies not on bovine skeletal muscle proteome (Fig. 1 ). From this, 178 full-text publications were deemed suitable and the relevant articles were retrieved, provided they were in English and published in peer-reviewed journals, to ensure the methodological quality of the studies. The publications for inclusion in the integromics were selected and further examined, to determine whether they should be included based on the criteria shown in Fig.
Finally, studies were excluded that reanalyzed the same dataset or a subset of the same database by applying different statistical methods and we kept only one study complying with our criteria. The experimental design of each study was carefully screened including the identified proteins and the direction of change and/or associations with beef tenderness evaluated either by sensory trained panelists or WBSF. The quality assessment of the experimental designs (Table  S1 ) and proteomics-based methodologies used to retrieve the putative protein biomarkers of beef tenderness were evaluated following the guidelines of the Newcastle-Ottawa scale. This quality check revealed that most (96%) of the eligible studies used good data by reporting the information needed in the context of the discovery of beef tenderness biomarkers.
Our quality assessment check provides an overview of the quality of the publication and proteomic study reporting but does not address the quality of the proteomic data itself. There are a certain variations in reporting proteomic analytical details independent of whether a journal provides guidelines. Aggregating data from multi-platform proteomics datasets from a qualitative point of view is the main objective of the present integromics study. Such variations are not a major hindrance, as the direction of changes in protein abundance in association with beef tenderness traits is the information needed.
Database preparation: data extraction and proteomic data aggregation A total of 22 papers, which included 28 proteomics experiments (888 animals; Table  1  and Table S1 ) fulfilled the above criteria and were included in this integromics to create the first repertoire of beef tenderness biomarkers  (Baldassini et al. , 2015; Beldarrain et al. , 2018; Bjarnadottir et al. , 2012; Boudon et al. , 2020; Carvalho et al. , 2014; D'Alessandro et al. , 2012; D'Alessandro et al. , 2012; Gagaoua, Bonnet, Ellies-Oury, et al. , 2018; Gagaoua, Bonnet, & Picard, 2020; Gagaoua, Monteils, et al. , 2017; Guillemin et al. , 2011; Jia et al. , 2009; Kim et al. , 2008; Laville et al. , 2009; Malheiros et al. , 2019; Morzel, Terlouw, Chambon, Micol, & Picard, 2008; Picard et al. , 2014; Picard & Gagaoua, 2020a; Sierra et al.
, 2012; Silva et al. , 2019; Zapata et al. , 2009; Zhao et al. , 2014). Supplementary files in these papers were examined for details on the experimental designs and all the relevant information were summarized in Table  S1. Data from various other publications including reviews, which did not meet the selection criteria but are still of relevance (e. g. , putative proteins reported in other muscles or beef qualities) for the broader context, were taken into consideration in the discussion. Data collection from the 22 publications was performed according to a protocol that allows for the creation of a full database using spreadsheets in Microsoft Excel with the following information extracted from each study in the order of the publication year (Table  S1 ): experiment number (from the oldest Exp.
1_ref 1 published in 2008 to the newest publication in 2020 as Exp. 28_ref 22), first author's name, publication year, journal, hyperlink Digital Object Identifier (DOI) of the paper, breed, gender or animal category, age at slaughter, sampling position of the steaks and time, ageing time of the steaks, conditions of the evaluation of tenderness using sensory panelists or WBSF measurements, end-point cooking temperature (maximum internal temperature achieved when a sample is cooked), number of samples included in the experiment, type of the protein extracts (myofibrillar, sarcoplasmic or both), the proteomic-based platform used and associated details, the selection approach of the differential proteins and finally the number of putative biomarkers that were retrieved by our criteria. Key information regarding the proteins of interest (unique gene names (GN)) and their relationships with beef tenderness were annotated by giving their UniprotID accession numbers, GN, full name of the proteins, direction of change or associations (positive (•) blue color, negative (■), red or both directions (♦) orange) with the tenderness scores or WBSF values collected (Table  1 ).
Results: database description A computational workflow allowed aggregation of 124 proteins (unique GN) from the 28 proteomic-based experiments, several of which were common among the studies (Table  1 ) and across the animal categories (Table  S2 ). Uncharacterized proteins (n = 2) were not included. The list of DAP provided by authors were based on fold-change and/or p-value and/or significant correlations/regression as determined for each study (Table  S1 ). In fact, the selection criteria for proteins retrieved in the 28 experiments differed somewhat; two studies used a fold-change, 16 used differences in protein abundance between extreme groups of tenderness and 10 used regression analyses based on significant correlations (P < 0. 05). Data collected from the 28 experiments (n = 888 animals) were from different animal categories (genders): 15 experiments used bulls (n = 371 animals), five used cows (n = 326), four used steers (n = 46), three used both bulls and steers (n = 135, the authors mixed the animals, thus we coded this category as bulls+steers) and only one study used heifers (Table  S1 ).
ID conversion Considering the limitation of the Gene Ontology (GO) annotation in bovine databases, the gene and protein identifiers were converted on Uniprot Retrieve/ID Mapping (https://www. uniprot. org/uploadlists) to satisfy the GO analysis input format. The Gene Symbol, Ensembl ID and UniProt KB AC/ID were then converted to the orthologous human EntrezGeneID. Both bovine and human UniprotIDs were therefore indexed in the database.
Categorization of the proteins into biological pathways A functional annotation of the 124 proteins into the main biological pathways (Table  1 ) was performed first using the information provided by UniProtKB (https://www. uniprot. org/) for each protein and confirmed by PANTHER (Protein ANalysis THrough Evolutionary Relationships) classification system based on the human UniProtIDs of the proteins. PANTHER allows the browsing of a manually curated database by functions. The accuracy of protein function prediction was estimated using Hidden Markov Models that were constructed for each functional protein group. A manual annotation process was performed to allocate the 124 proteins to 6 main biological pathways (Table  1  and Fig. 2 ): (i) muscle contraction, structure and associated proteins (n = 35); (ii) energy metabolism (n = 29); (iii) heat shock proteins, HSPs (n = 11); (iv) oxidative stress proteins (n = 11); (v) proteolysis (n = 2) and (vi) other proteins including regulation of cellular processes, binding, apoptosis and transport (n = 36).
Identification of top proteins common to beef tenderness From the list of 124 putative protein biomarkers, a cut-off of two or more datasets (≥ 2) was applied to identify common proteins across studies regardless of the animal category, leading to a panel of 64 proteins that were reported in at least two independent proteomic experiments (Fig. 2 ). These proteins were ranked by the number of times they were identified from the highest to the lowest (Table  1 ) in each of the five biological pathways to which they belong (Fig. 2C ). Sixty proteins were identified only once among the 28 experiments (Table  1  and Fig. 2A ). Further cut-offs were applied to rank the protein biomarkers and the results of this analysis are shown in Fig. 2A , illustrating the 33 robust protein biomarkers that were identified ≥4 times and their rank order distribution (graph in the insert of Fig.
Protein-Protein Interactions (PPI) Proteins are known to rarely act alone, thus the study of protein networks are essential for a wide range of cellular processes and form a network of great complexity. STRING database ver. 11 at https://stringdb. org/ was used to describe the Protein-Protein Interactions among the 64 proteins with cut-off ≥2 (Fig. 3 ). The construction default settings required as a minimum interaction score confidence of 0. 6 and 4 criteria for linkage (co-occurrence, experimental evidences, existing databases and text mining) were used. Due to the limitation of the GO annotation of genes in bovine, we used human UniprotIDs. In line with our expectations, the network in Fig. 3  clearly confirms the interconnectedness of the biological pathways underlying beef tenderization, with the pivotal role of structural and contractile proteins, as a consequence of their breakdown during proteolysis  (Lana & Zolla, 2016).
Pathway and process enrichment analysis The 64 proteins were further used to perform a pathway and process enrichment analysis (Fig. 4 ) and identify significant hub modules created by these proteins using the intuitive web tool Metascape® (https:// metascape. org/) based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, GO Biological Processes, and Reactome gene sets (Table  2  and Fig. S2 ). The tool uses the hypergeometric test and Benjamini-Hochberg p-value correction algorithm to display the first 20 statistically significant enriched ontology terms  (Zhou et al. , 2019). In this study, the terms with a P-value < 0. 05, a minimum count of 3, and an enrichment factor > 1. 5 (ratio between the observed counts and the counts expected by chance) were considered. Representative terms with a similarity score > 0.