dataesr/scientific-paragraphs-categorization huggingface dataset:
Also, one has to note that postcranial remains are scarcer compared to skulls. Moreover, geometric morphometric datasets have to be complete, thus this led us to drop some specimens (e. g. , primate skulls due to incompleteness). The list of extant taxa, their associated locomotor behaviors as well as the numbers of specimens for each analysis are presented in Table  1  and specimens   All the rodent families and 2 of primates respectively including 17 species and 4 species are used for the predictions on the distal humeri (h_gm_x). Eight fossil specimens of D. luederitzi have been included in the analysis, coming from Napak IV, V, XV, and XXXI (NAP IV 65a, NAP IV 65b, NAP IV 65c,  NAP IV 4'03, NAP IV 12'12, NAP V 34'11, NAP XV 158'12, and NAP XXXI 21'18). All extant specimens come from the MNHN, RMCA and/or Ditsong Natural History Museum (Pretoria, South Africa).
| Geometric morphometry Pictures of the specimens have been taken with an Olympus Tough 4 camera, by the same user. Photographs were taken with the palate oriented parallel to the photographic plane for the skull, as well as the anterior face of the humerus. TPS files have been created with the TpsUtil64 software and the landmarks have been positioned on the 2D pictures with the Tps Dig232 software  (Rohlf, 2018). To test the F I G U R E 4 Position of localities of the fossil studied in red. The other numbers correspond to the localities worked by the Uganda Paleontology Expedition. T A B L E 1 List of the species used in this study, their associated locomotor behaviors  (Happold et al. , 2013; Kingdon, 2015; Shepherd & Shepherd, 2012; Viljoen, 1978)  and the numbers of specimens (Arb. , Arboreal).
| Statistical analysis Analyses to predict the locomotor behavior of D. luederitzi were performed with the R software (R Core Team, 2018), mainly with the packages FactoMineR  (Lê et al. , 2008) , MASS  (Venables & Ripley, 2002) , Ape  (Paradis & Schliep, 2019) , Geiger  (Pennell et al. , 2014)  and Geomorph  (Adams & Otárola-Castillo, 2013). We use Rrcov package to calculate Wilks' lambda to verify the significative discrimination of the locomotor groups  (Todorov & Filzmoser, 2010). To calculate typical probabilities for the LDA analyses, we used the script function from Boedeker and Kearns (  2019 ) that we have adapted to our data set. Unfortunately, this function does not take into account the impact of phylogeny, typical probabilities are not available for the PFDA analyses. The same method has been used with the geometric morphometrics, where the morphospaces are constructed with the shape of the structures analysed, characterized by the landmarks.
| Selection of the phylogenetic tree A phylogenetic tree has been obtained by compilation of different molecular phylogenies coming from the Timetree database (Figure  9 ;  Hedges et al. , 2015; Kumar et al. , 2017). The time of diversification of the taxa taken into account is given in estimated ages. Not all species used for this study were represented in the available molecular database of the Timetree website. Thus, we chose to use the divergence time of the genus representing all the species of our data set, to have a maximum number of taxa represented in the morphometric analysis. We assume that the species of a genus should have a divergence All analyses except the PFDA based on linear measurements show high percentages of good attribution (s_lm_lda = 81,67%, s_gm_lda = 97,67%, s_gm_pfda = 96,55%; s_lm_pfda = 40,47%), as well as a good discrimination between terrestrial/fossorial groups and arboreal ones including gliders, strict arboreals and jumpers.
For this method, the majority of the specimens are predicted as arboreals, but two others are categorized into the fossorial and terrestrial classes, which could highlight the possible locomotor diversity. Despite the good discriminations made by both LDA, one parameter that is not taken into account is the phylogeny. Indeed, the common history of the species can have an impact on the species distribution, possibly bringing together species that have a similar morphology just because of their close relationship. Thus, it is important to include this information, allowing the correction of the data to take into account the potential phylogenetic signal in the interpretations. Concerning the geometric morphometrics on the skull, the optimal lambda values obtained in the PFDA are equal to 0, implying the absence of a phylogenetic signal in the discrimination of the locomotor categories.
ACKNOWLEDGMENTS We thank  Sorbonne Université  and the program Interface Pour le Vivant, allowing the funding of this doctoral research. We would like to thank the  Uganda Museum ,  Rose Nkaale Mwanja  and  Sarah Musalizi  for arranging access to the Ugandan material. We also thank  Sylvie Crasquin , Head of the UMR 7207 ( CR2P ) where this study was carried out. Authorization to carry out research in the country since 1985 was obtained from the  Uganda National Council for Science and Technology. The research was funded by the  Commission des Fouilles of the French Government , the  CNRS  ( GDRI 193 ,  PICS 1048 ),  CR2P-UMR 7207 - MNHN-Sorbonne Université. We appreciate the support of the  French Embassy ,  Kampala. Thanks  to the local leaders and our field team in Napak. Dr Martin Pickford  kindly revised the English.
Therefore, we divided the endemic species into single-island endemics (SIEs-species whose entire geographical distribution is restricted to a single island in the archipelago) and multi-island endemics (MIEs-species whose entire geographical distribution is restricted to two or more islands in the archipelago). NENs were those species found on both the mainland and on the archipelago. For each group, we used plant functional trait data and climatic variables to estimate functional distinctiveness and climatic rarity, respectively, and tested whether differences between the endemic groups differ within and between islands of different ages, as predicted by our hypotheses.
Trait data We used three plant traits: maximum plant height, maximum leaf area and maximum flower length. Plant height, a common measure of plant size, influences a plant's ability to compete for light and is important for seed dispersal  (Muller-Landau et al. , 2008; Westoby et al. , 2002). Measuring leaf size, leaf area is important for carbon acquisition, transpiration, CO 2 uptake and light interception  (Press, 1999). Flower length (proxy for flower size) is important for pollination, energy balance and transpiration  (Harrap & Rands, 2022; von Arx et al. , 2012). All three traits show relationships with environmental variables, including temperature and precipitation  (Dong et al. , 2020; Moles et al. , 2009; Paušič et al. , 2019). We used maximum trait values as they reflect the species fitness response to environmental gradients and generally have better data coverage.
Data were sourced from the literature  (Bramwell & Bramwell, 1974; Eggli, 2002; Hohenester & Welß, 1993; Muer et al. , 2016; Schönfelder & Schönfelder, 2018) , online herbarium specimens from GBIF (www. gbif. org) and JSTOR (www. plants. jstor. org), and species descriptions (see  Hanz et al. , 2022). We used maximum leaf length and leaf width values to calculate maximum leaf area using the following formula: where LA = leaf area, LL = leaf length and LW = leaf width. Leaf length and leaf width accurately estimate leaf area  (Cutts et al. , 2021; Kraft et al. , 2008; Pandey & Singh, 2011; Shi et al. , 2019). The current plant checklist for the Canary Islands places species into six categories: definitely native (including endemics), probably native, possibly native, probably introduced, introduced non-invasive and introduced invasive  (Arechavaleta et al.
Species occurrence data To estimate the mean climatic conditions within each species' range, we used species occurrence data from Atlantis 3. 1 (www. biodi versi dadca narias. es/biota), a governmental, open-access database with occurrence data of Canary Islands species. The data are presenceonly, at a resolution of 500 × 500 m and reflect sampling effort  (Hortal et al. , 2007) , but are particularly good for endemic species, for which there has been long-term sampling. Steinbauer, Field, Fernández-Palacios, et al. (2016)  provide an informative review of the data quality in their supporting information. We updated the species presence data and taxonomy using the most recent checklist for Canary Island plants-FloCan  (Beierkuhnlein et al. , 2021) , which revealed duplicates of two species: Arenaria serpyllifolia was measured twice under different names and Serapias mascaensis was included as both a species and a subspecies.
Climatic rarity We computed climatic rarity following  Irl et al. (2015). Mean annual precipitation and mean annual temperature were interpolated, at a resolution of 500 × 500 m to match the occurrence data, using data obtained from meteorological stations on the Canary Islands, provided by Agencia Estatal de Meteorología (see  Irl et al. , 2015). Precipitation was log-transformed because ecologically meaningful variation among smaller values is underemphasised when using the raw data, which are strongly right-skewed. Climatic rarity was calculated as follows. First, temperature and precipitation variables were divided into equal sized bins. We trialled using 10, 20 and 30 bins. We report the results using 20 bins as this produced slightly higher R 2 values (see supporting information S2).
The sources of carbon and nitrogen, all being assumed to come from outside the fruit pericarp, mainly the phloem sap, have been simplified from the main metabolites common to at least two species. The carbon sources were sucrose, sorbitol and glutamine, while the nitrogen sources were glutamine and nitrate. All cofactors were defined as internal metabolites, which means that they were balanced, thus constraining the metabolic model not only through the carbon and nitrogen balance but also through the redox and energy status. The network of reactions schematised in Figure  3  is given as a list of the stoichiometric reactions (Table  S1 ) and metabolites (Table  S2 ) and provided as a Systems Biology Markup Language (sbml) file (Appendix S1). A previous dataset for biomass composition of eight fruit species throughout their development  (Roch et al.
Finally, the constraint-based model was solved to calculate metabolic fluxes throughout fruit development using the objective function of flux minimisation (minimisation of the sum of the squares of internal fluxes) in agreement with the principle of 'minimal effort' Ó 2023 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd. , The Plant Journal, (2023), doi: 10. 1111/tpj. 16409 (Holzh ütter, 2004). To be able to perform multivariate analyses, flux data were generated at 30 developmental stages equally distributed throughout development. For each fruit species, time was normalised by the time to maturity (Table  S6 ). Finally, the contrasted calculated fluxes, both external and internal, were analysed to bring out specific reactions and pathways supporting growth traits.
Although such allometric approach remains rare, it is very promising. For example, when questioning phenotypic differences between organisms with three genome-wide datasets of human-mouse orthologs arising from a variety of sources in protein divergence  Morata et al. (2013)  identified a relationship between protein divergence and isoform multiplicity co-occurrence and explained its origin in terms of a simple gene-level property. The implications of these findings could be used to analyse the molecular basis of differences between species, as they provide a plausible explanation regarding the balance between function and cost of synthesis constraints. The duration of the development of certain fruits, in particular the fruits of many woody plants in which it can exceed 200 days, can be seen as an important trait.
The five biological replicates of pericarp samples were then deep-frozen in liquid nitrogen and stored at À80°C before cryogrinding, lyophilisation and biochemical analyses. Lyophilisation allowed measurement of the dry matter content. Sample dry matter was calculated from the dry matter content and measured fresh mass. Growth data were added from published studies for eight other fruit species: atemoya (https://www. ttdares. gov. tw/en/ theme_data. php?theme=what_we_do&sub_theme= achievements&id=6178), avocado  (Liu et al. , 1999) , blood orange  (Chen et al. , 2022) , mango  (Tandon & Kalra, 1983) , pear  (Oikawa et al. , 2015) , strawberry  (Cakpo et al. , 2020) , sweet cherry  (Gibeaut et al. , 2017) , and tomato  (Biais et al. , 2014).
The corresponding fluxes (accumulation and/or degradation) were then calculated by calculating the first derivative of the fitted functions. These fluxes were used as lower and upper bounds to constraint the resolution of the model. In the Ó 2023 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd. , The Plant Journal, (2023), doi: 10. 1111/tpj. 16409 case of reversible enzymes, the lower bound was negative. Growth and DW-to-FW ratio were also fitted in order to further convert fluxes from a fruit-basis to a gram-basis. In the case of amino acids, the total concentration of amino acids was split into four groups defined by their precursors (Table  S5 ). Regressions of the experimental data were performed using the R shiny interface EasyReg (https://sprigent.
Statistical analyses Hierarchical clustering analysis of flux data with RGR used to select three clusters was performed with MultiExperiment Viewer (RRID:SCR_001915, MeV4. 9. 0). PCA of flux data of real fruits was performed using BioStatFlow web tool (http:// biostatflow. org; Jacob et al. , 2020)  after mean-centring and unit-variance scaling. The data comprised 119 flux variables for the 240 samples (eight fruit species at 30 stages). The PCA loading values were then used to project the 96 virtual fruits at 30 stages on the PCA scores plot, using the 119 calculated flux data for the 2880 virtual samples (96 virtual experiments at 30 stages). The scores were plotted in 3D using MATLAB scatter3 (Mathworks R2018). Relationships between variables were studied using Pearson correlations, and their p-values calculated using MATLAB.
ACKNOWLEDGEMENTS We thank  Léa Roch  for fruit cultures, harvesting and previous analyses, the  Bordeaux Metabolome Platform  (https://doi. org/10. 15454/1. 5572412770331912E12 )for providing data,  Daniel Jacob  for managing data,  Gilles Vercambre  for thermal time calculations, and  Jean-Pierre Mazat  for his critical reading. This work was supported by  FRIMOUSS  ( ANR-15-CE20-0009-01 ),  MetaboHUB  ( ANR-11-INBS-0010 ),  PHENOME  ( ANR-11-INBS-0012 ),  GLOMICAVE  ( H2020-EU. 2. 1. 4DO: I10. 3030/952908 ) and GPR Bordeaux Plant Sciences in the framework of the  IdEX Bordeaux University 'Investments for the Future' program.
Composition of proanthocyanidin monomers and dimers Wines were filtered (0. 45 µm) and 10 µL were then injected in a Vanquish HPLC system (ThermoFischer Scientific, Waltham, MA, USA) with a Thermo-Finnigan UV-Visible detector (UV-vis 200), a Vanquish autosampler and a Vanquish ternary pump coupled to a Chromeleon data system software. Separation was performed on a reverse-phase Lichrosphere 100-RP18 (250 mm x 2 mm, 5 µm; Merck, France) column  [28]. The elution solvents were water-acidified with formic acid 0. 5% (solvent A) and acetonitrile-acidified with formic acid 0. 5% (solvent B) and the flow rate of 1 mL/min. Gradient was as follows: from 5% to 18% B in 30 min, 100% B for 1 min, 100% B for 7 min, from 100 to 5% B in 1 min, 5% B for 3 min. Eluting peaks were monitored by a UV-detector at 280 nm and a fluodetector (λexcitation = 280 nm, λ emission = 320nm).
Composition of anthocyanin Red wines were first filtered (0. 45 µm) and 20 µL were injected in a Thermo Scientific Accela (Thermo Fisher Scientific, Waltham, MA, USA) HPLC with an Accela 600 pump module and a UV-Visible diode array detector and Xcalibur Software  [30]. The column used was a reverse-phase C18 Nucleosil (250 x 4. 6 mm, 5 µm). Solvent A was water/formic acid (95:5, v/v) and solvent B was acetonitrile/formic acid (95:5, v/v). The mobile phase gradient with a flow rate of 1 mL/min was as follows: from 10% to 35% B in 25 min, 100% B at 35 min, 100% B from 35 to 40 min, 10% B at 41 min, and then 10% B for 4 min before the next injection. Eluting peaks were monitored at 520 nm. Peaks were identified by comparison with injected external standards and previous results  [29]. Concentrations were expressed as mg/L malvidin 3-O-glucoside equivalents.
Estimates of the economic returns to research using such an approach have been produced periodically  (Alston et al. 1995 , Raitzer & Kelley 2008) , and there remains some demand for these aggregate numbers. However, the methods used to generate such internal rate of return (IRR) calculations are heavily dependent on strong assumptions, sometimes leading to numbers that are implausibly large  (Hurley et al. 2016 , Rao et al. 2020). Hurley et al. (2016)  note that the calculation of IRRs is clearly inconsistent with the realities of the benefit and cost streams associated with agricultural research. Rao et al. (2020)  propose a modified internal rate of return; building on aggregate evidence from 2,829 evaluations in the database of the International Science and Technology Practice and Policy (InSTePP, v3.
One practical approach toward this goal is to focus on a few key countries where large investments have occurred as a first step toward catching up after years of neglect, following the template set by the "Shining a Brighter Light" study  (Kosmowski et al. 2020). This helps the international agricultural research community to reconnect with its historical track record of collecting longitudinal data, best illustrated by the large body of literature resulting from the longitudinal ICRISAT villages data sets. The potential for a new generation of longitudinal studies lies in combining carefully implemented geolocated surveys featuring DNA fingerprinting of the major crops and livestock, reliable data on farmers' management practices, and detailed socioeconomic data, with information on the policy and institutional environment.
Methods: The One Health European Joint Programme simulation exercise (OHEJP SimEx) aimed at practicing the OH capacity and interoperability across public health, animal health and food safety sectors in a challenging outbreak scenario. The OHEJP SimEx was delivered through a sequence of scripts covering the different stages of a Salmonella outbreak investigation at a national level, involving both the human food chain and the raw pet feed industry. Results: A total of 255 participants from 11 European countries (Belgium, Denmark, Estonia, Finland, France, Italy, Norway, Poland, Portugal, Sweden, the Netherlands) took part in national level two-day exercises during 2022. National evaluations identified common recommendations to countries aiming to improve their OH structure to establish formal communication channels between sectors, implement a common data sharing platform, harmonize laboratory procedures, and reinforce inter-laboratory networks within countries.
To succeed in this aim, a challenging outbreak scenario with a zoonotic disease that typifies the OH concept and that was relevant across Europe was required. Therefore, a Salmonella outbreak scenario was developed, which included both human food and pet feed supplies specifically to provide the opportunity to share experiences and perspectives across sectors, evidencing the added value of applying a OH approach, while also providing an opportunity to test a food tracing software tool: The FoodChain-Lab (FCL) web application. By assisting countries to identify current gaps in their OH approach to a foodborne outbreak and defining strategies to tackle them, the OHEJP SimEx outcomes have resulted in recommendations suitable for all countries to assist in defining a national roadmap for future outbreak preparedness plans.
The OHEJP SimEx project team developed a realistic scenario which could be executed in multiple European countries including the following criteria: 1. The pathogen must be relevant across Europe 2. The pathogen must satisfy the One-Health focus spanning AH, PH and FS 3. Country level focus 4. Cross sectoral collaboration focus 5. Data sharing focus This exercise was included as an implementation activity in One Health EJP. All One Health EJP partner institutes were invited to participate in the OHEJP SimEx. In addition, the institutes were encouraged to invite other institutes from outside the One Health EJP consortium, e. g. , to cover up for missing sectors or to better represent the national outbreak management. Participation of institutes was on a voluntary basis. The original request and subsequent reminders to participate in the exercise were sent out by email to the following groups within the One Health EJP: Scientific Steering Board members, representatives from the Programme Managers' Committee and Project Leaders.
Providing the flexibility to tailor the scenario allowed the NEL and LELs to ensure maximum relevance for the training audience. FoodChain-Lab web application is a food tracing software jointly developed by the German Federal Institute for Risk Assessment (BfR), EFSA, One Health EJP COHESIVE and other European projects, which allows to model, visualize, and analyze complex food supply chain networks  (21, 22). This tool was included in the OHEJP SimEx as a practical tracing exercise, for the TA to establish possible contamination sources and transmission chains. Inclusion of FCL which could be accessed by all sectors highlighted the advantages of having an intersectoral tool when deciding on the implementation of control measures like product sampling and batch recall. A final meeting at the end of the exercise allowed the TA to review and discuss the challenges encountered during the simulated outbreak investigation and management.
Evaluation of outcomes The OHJEP SimEx evaluation was designed to assess the success of the objectives to identify any limitations on data sharing, develop intersectoral communication strategies and increase the mutual understanding between sectors. By identifying cooperation gaps, the evaluation also provided evidence to support the improvement of future foodborne outbreak management strategies with a OH approach. The LEs attended a training session, delivered by the OHEJP SimEx project team prior to conduction to prepare and support them. This included how to conduct After-Action Review (AAR; Hot debrief). The guided AAR discussions covered the chronological narrative of the conduction, focusing on the most relevant decisions to highlight the strengths and weaknesses identified. Hot debriefs provided participants with the vital opportunity to share their thoughts while the experience is still fresh, avoiding missing relevant details.
The OHEJP SimEx project team provided a template for the national report to ensure a level of consistency in the information provided. The OHEJP SimEx project team analyzed the national reports to identify common problems, major gaps, and current best practices. However, because the report template did not explicitly request answers to a series of questions, the data presented below was compiled from the information provided and may not represent a complete picture. By compiling and analyzing data from all the evaluation outcomes, we have provided a comprehensive analysis and summarized a list of recommendations for the improvement of OH approach to foodborne outbreaks as well as suggestions for future OH simulation exercises. In addition, the national experiences were shared at an internal One Health EJP Scientific Steering Board meeting (28th of September 2022) and at a dedicated Joint SimEx/Dissemination Workshop ' A One Health simulation exercise as a roadmap for future foodborne outbreak preparedness' (6th December 2022) that was targeted to stakeholders.