dataesr/scientific-paragraphs-categorization huggingface dataset:
Selection of chemicals and classification A test set of 85 chemicals was comprised of 39 with known teratogenic potential and 46 non-teratogenic potential. Classification of the chemicals as teratogenic or not teratogenic was based on the US FDA list of teratogenic drugs, as well as health agency opinions on specific chemicals (e. g. , EFSA opinions) and drugs (SmPC from the EMA database). Complete hazard information was then refined using REACH dossiers, EU MSDS, CLP Notifications as well as FDA categories, TGA, Shepard's Catalog, US EPA ToxRef database and Reprotox Collection. Amongst the teratogenic chemicals, 23 were drugs, which represent a significant set of chemicals for which teratogenicity data are regulatorily required, and thus exist. There were also 7 plant protection products, 5 industrially manufactured chemicals, 1 food (caffeine), 1 vitamin (trans-retinoic acid) and 2 chemicals considered as pollutants or residuals of chemical synthesis, namely benzo[a]pyrene and ethyl carbamate.
Decision tree generation A decision tree was generated with the use of python 3 with default libraries (numpy, pandas, seaborn, sklearn). The result was crossvalidated with 1000 downsamplings of our database. Training and test sets were generated by splitting the dataset with the ratio 80% training / 20% test and computing the sensitivity, specificity and cohen kappa for each 1000 downsamplings. The mean of each result was then compared to have a better trust in the model prediction. After that procedure, we observed that the resulting decision tree is the most frequent unsupervised one generated from all 1000 downsamplings. Missing values for chemicals not tested in all three assays were replaced with the use of SimpleImputer function from scikitlearn program [scikit-learn]. This imputer replaces missing values by most frequent value, which tends to make the prediction result more conservative.
e. , they were "false positives"), but for most of these (23 chemicals), the DTP concentration exceeded the C max (right-hand side of the figure). For example, the DTP concentration for       classified these as having a potential risk of teratogenicity. The DTP for the non-teratogenic chemical, penicillin G, was greater than 1000 µM, confirming that this had no potential risk for teratogenicity since the plasma C max is 134. 6 µM. The impact of incorporating the C max into the classification on the predictivity of the assay is shown in Table  5. When the C max was considered for 43 chemicals (with available plasmatic concentration values), the specificity was increased from 26. 1% to 82. 6%. The Po and the kappa value both increased very significantly compared to the same set of 43 chemicals without this correction and compared to the overall dataset of 82 chemicals.
Performance of individual assays 4. 1. 1. In silico DART decision tree In silico tools are increasing in use and acceptance, especially in providing an initial qualitative indication of the potential of a chemical to cause reproductive toxicity  [39, 58]. Indeed, QSARs may help in the evaluation of REACH chemicals and may even be used instead of experimental testing to fill data gaps  [74]. The in silico DART decision tree used here was also helpful for building the list of reference substances tested in this study, it also provided an initial assessment of the potential of reproductive toxicity which was then followed up in the in vitro assays. The applicability domain for various QSAR models varies according to the model (which may also be specific for chemical classes)  [35] ; however, the predictivity of this alert model for the chemical space.
ZET The specificity of the ZET was higher than the devTox assay and similar to that of the in silico DART model; however, the sensitivity of this assay (77. 8%) was the lowest of the three NAMs. Others have also noted a similar relatively low sensitivity of this assay (60%-70%)  [4] , while another group reported a low specificity of this assay (43%)  [69]. The lower concordance of the ZET with in vivo animal data has been attributed to the bioaccumulation in the embryo of some chemicals and a poor uptake into the embryos of others  [4, 69]. The route of exposure in the ZET is via immersion; however, chemical uptake and internal concentration kinetics can vary greatly depending on the compound's physicochemical properties. For example, some hydrophilic teratogenic agents may not accumulate fast enough in the embryo to cause their adverse effect on early stages of development.
The increased use of genomics to detect developmental effects of a wide range of chemicals has led to its use in bioinformatics analyses to understand the biology involved in DART. Based on this evaluation, a hypothetical "human DARTable genome" has been proposed  [29]. This is analogous to the "Druggable Genome" and is a set of proteins participating in Molecular Initiating Events (MIEs) or transcriptomic/proteomic key event biomarkers of DART effects which can be targets for chemicals causing DART. It is envisaged that the human DARTable genome can help select relevant cell models to cover the biology involved in DART in early screening as part of an integrated approaches to testing and assessment (IATA) for DART  [29]. In line with this, the EU Framework program 7 project, ChemScreen, aimed to select suitable tests for use in a simple, rapid screening IATA  [70].
Correlation Analysis Pearson's r was used to calculate the correlation coefficient between the temperature sensitivities of extreme precipitation (η 99 ) and mean precipitation (η). Additionally, a null hypothesis test was performed, in which the distributions underlying the samples were uncorrelated and normally distributed. We selected a two-sided p-value to test non-correlation. For a given sample with a correlation coefficient r, the p-value is the probability that the correlation coefficient r' of random samples A and B with zero correlation would be greater than or equal to |r|. Pearson's correlation and p-value tests were performed using the SciPy library within the Python platform, following established statistical guidelines for the preferred significance level and sample size.
Acknowledgments This study was sponsored by the  National Natural Science Foundation  (Grants: 42171095; 42371123), the  Social Science Foundation of Shaanxi Province  (Grant:  2020D039 ), the  Fundamental Research Funds for the Central Universities  (Grant:  GK202201008 ), and the  Open Foundation of the State Key Laboratory of Urban and Regional Ecology of China  (Grant:  SKLURE2022-2-1 ).
Data Availability Statement We would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modelling. We thank the climate modeling groups for producing and making their model outputs available  (Eyring et al. , 2016). The data is available at https://esgf-node. llnl. gov/search/cmip6/. Codes are available from: https:// doi. org/10. 5281/zenodo. 7630041.
The mt-CO1 region was amplified using primers JB3 and JB4. 5 as already reported by Bowles et al. (31). PCR amplicons were purified and sequenced from both ends at Macrogen (Macrogen Europe, Amsterdam, The Netherlands). Alignment was performed with Clustal W integrated into MEGA v6. 0, and sequences were compared with the non-redundant database available in the GenBank R database using the software BLAST (32).
Data analysis An agreement table between each of the two copromicroscopic tests (FT and CMPCR) and the scraping one (SFCT) was displayed; the concordance was then calculated for each of the two tests as the number of samples with same result out of the total number of samples examined (% concordance). The sensitivity (Se) of copromicroscopic tests and 95% confidence interval (95%CI) was assessed for ascarids and Ancylostomatidae (only for FT), and for taeniids (both for FT and CMPCR), using SFCT as the reference standard. Analysis of the sensitivity of the two tests was performed through EpiTools (https://epitools. ausvet. com. au), using the Clopper-Pearson (exact) method for 95% CI definition. The intensity of infection [i. e. , the number of individuals of a particular parasite species in a single infected host, sensu Bush et al.
Literature search and meta-analysis To unveil the relation between the presence of eggs in the feces and the adults in the intestines, we performed a literature search to complement our findings with data generated by similar studies previously conducted. Prevalence values in other red fox populations were retrieved from literature, focusing on studies conducted exclusively in Europe and using scraping (SCT or SFCT) and copromicroscopic (qualitative flotation or quantitative techniques) approaches on the same animals. For this aim, the database Google Scholar was searched using the terms "copromicroscopic" OR "eggs" AND "red fox" as keywords. Data from single publications investigating different, geographically separated, red fox populations, were considered independently as separated datasets.
Analysis of prevalence data from literature Based on the literature search, we found five studies investigating helminthic infection in red fox populations using, at the same time, scraping and copromicroscopic techniques  (18, (36) (37) (38) (39). However, three studies  (36, 38, 39)  were excluded from the analysis because the prevalence values at necropsy and at copromicroscopy were calculated on different subsets of animals, and the prevalence values in the group of animals analyzed by both methods were not reported. In one study, the investigated red fox population was split in three different datasets, due to their geographical separation. Therefore, a total of five datasets, including ours, were used in the metaanalysis. The prevalence values reported in each study for each considered group of parasites are listed in Table  4 , along with the estimated pooled prevalence, and their concordance is displayed in Figure  1 , using a Bland-Altman graph.
05). In contrast, the ESCO3-resistant clone 18 and the fully resistant clone 23 were significantly (P = 0. 03 and P = 0. 02, respectively) less virulent than the wild-type strain, indeed only 20% of the embryos died. As the capsule and O antigen of lipopolysaccharide (LPS) are the virulence factors putatively targeted by phages for adsorption, their expression was determined. Capsular antigen K1 and O2 antigen were detected in all resistant clones (data not shown).
Plain Language Summary The ocean provides a large part of the energy for the intensification of tropical cyclones through warm sea surface temperature and sea-air heat and moisture exchanges. However, the ocean-atmosphere interactions also trigger processes which cools the sea surface temperature beneath the tropical cyclone and thus generates a negative feedback on the TC intensification. The numerical forecasts of the regional numerical weather prediction model AROME-IO are valuable guidance for the Regional Specialized Meteorological Centre for Tropical Cyclones, La Réunion. The objective of our study is to evaluate the possibility of replacing the current ocean mixed layer parameterization by a more realistic ocean able to represent more complex processes such as the explicit transport by the currents.
Currently, the atmospheric and oceanic initial conditions of most operational coupled system as the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) are produced by independent data assimilation systems. The lack of balance in the boundary layer of both the atmosphere and the ocean may then create an initial choc which perturbs the short range of the forecast. A coupled data assimilation is the solution to create balanced atmospheric and ocean initial states and it is an active domain of research with still many scientific and technical problems to be solved  (Browne et al. , 2019; Penny et al. , 2017). For a regional model without data assimilation such as AROME-OI, the initialization of the atmosphere is based on a lower resolution global analysis.
The plan of the paper is detailed as follows. Section 2 presents the forecast system AROME-IO, the experimental coupled system AROME-IO/NEMO and the description of the setups of the different numerical simulations performed in this study. The results of the simulations are analyzed in Section 3. First, the statistical impact of the 3D OA coupling and the choice of initial conditions is assessed with scores (bias and standard deviation) against the Best-Track (BT) data for all the TC simulations, then separately for TCs with slow or fast translation speed. Second, we further analyze the results of the simulations with three test cases: one in the slow propagation speed category (TC Gelena, February 2019), one in the fast propagation speed category (TC Belna, December 2019) and one for which buoy measurements enable a direct comparison of the modeled SST cooling with observations (TC Batsirai which encounters a phase of slow propagation at the beginning of February 2022).
The AROME-NEMO Coupled System The experimental coupled system AROME-NEMO which has been implemented in the context of this work combines the AROME-IO atmospheric model  (Faure et al. , 2020 ) and a regional version of the NEMO ocean model  (Madec et al. , 2019). The coupling between these two components is controlled by the OASIS3-MCT coupler  (Craig et al. , 2017). NEMO is the European modeling framework for oceanographic research, operational oceanography, seasonal forecasting and climate studies. This study uses the version 3. 6 of the code with the same ORCA grid (tripolar grid with variable horizontal resolution;  Madec & Imbard, 1996)  at the horizontal resolution of a 1/12° (about 9 km in the SWIO region) and the same 50 unevenly spaced vertical levels as the PSY4 global operational products of MERCATOR-Ocean.
The NEMO domain covers the whole oceanic part of AROME-IO. The bathymetry is based on the ETOPO1 database  (Amante & Eakins, 2009). The vertical mixing is a TKE closure scheme based on the work of  Gaspar et al. (1990)  (like the 1D OML used in this study), but with important modifications introduced by  Madec et al. (1998)  in the implementation and formulation of the mixing length scale. In the current configuration, the barometric effect on the sea surface height and the tidal forcing are switched off (default configuration). In the configuration used in this study, NEMO provides to OASIS the 1 hr mean SST with a coupling frequency of 1 hr which is commonly used for the a 1/12° resolution in the NEMO community. The SST is used to compute the air-sea fluxes at each subsequent atmospheric time step.
However, scatterometer wind measurements are limited to about 25 m/s. Recently, the experimental use in near-real time of SAR wind data allowed a direct estimation of very strong TC winds (50-70 m/s) at a resolution of about 3 km  (Duong et al. , 2021). At the end of each tropical cyclone season, the RSMC re-examines and possibly updates the objective analyses which had been established in real time using new information which were not available during the initial TC monitoring. The RSMC "best-tracks" (later referred to as BT) digital data are then mailed to the National Climatic Data Center (Asheville-USA) for integration within the global IBTrACS database and to some other interested Weather Centers. The access to this database is also possible via the RSMC website. The BT data provide the minimum of pressure and the maximum wind (10 min average) for each TC in the SWIO, every 6 h during its life cycle.
Data Availability Statement The atmospheric model used in this study is AROME Cycle 43t2 which has been the operational version of the AROME-overseas from July 2019 to June 2022. AROME is available for non-commercial research purpose upon signature of a license agreement, see http://www. accord-nwp. org/?ACCORD-MoU-2021-2025. The ocean model is NEMO, version 3. 6, which can be downloaded at https://www. nemo-ocean. eu. All the material which is necessary to reproduce the numerical simulations has been published on zenodo. org with the https://doi. org/10. 5281/ zenodo. 7241990. The SAR data have been uploaded from https://cyclobs. ifremer. fr/app/ and the data from the ARGO drifters from http://www. coriolis. eu. org/Data-Products/Data-selection. The Best Track data have been extracted from the Best Track database of the Direction Régional de l'Océan Indien (DIROI) of Météo-France.
TNO Spatially-resolved emission inventory GHG emissions inventories remain an important source of information that allows governments, policymakers, and corporations to i) understand their carbon footprints and ii) develop Climate Action Plans in order to reduce their GHG emissions. In this study, we use the 1 km x 1 km resolution activity-based emissions inventory developed by TNO. The TNO inventory has been downscaled from national data to the urban scale using spatial proxies  [50]. It is based on nationally-reported emissions to UNFCCC and the European Monitoring and Evaluation Programme (EMEP), spatially distributed according to various proxies such as population density and road network maps. The 1 km x 1 km version was prepared within the European CHE and VERIFY projects (https:// verify.
Projections of gridded CO 2 emissions for 2030 and 2050 For the projection of the gridded CO 2 emissions for the target years 2030 and 2050, the first step was to reconcile the sectors of the TNO 2019 inventory with the city's climate action plan. We chose to base the study on the 2019 TNO inventory to avoid the impact of the COVID-19 pandemic on the activity data for the year 2020 and after. The second step was to detail and quantify the reduction targets based on the climate plans, and the third was to subtract those avoided emissions from each sector in order to create future emission maps. For Munich, sector harmonization was especially important as the TNO sector classification differs from the Munich sectors. Therefore, Munich's climate action items were re-distributed according to the TNO inventory sectors.
Funding This work is been funded by the  ICOS   PAUL  project:  PAUL, Pilot Applications in Urban Landscapes -Towards integrated city observatories for greenhouse gases (ICOS Cities) , funded by the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement No  101037319. T. Lauvaux was supported by the project  CIUDAD  ( Make Our Planet Great Again program ,  CNRS ) and the  Thomas Jefferson Fellowship program  ( French embassy in the US).
, 2018)  and fishing pressure  (Murgier et al. , 2021)  and it is often challenging to disentangle this myriad of impacts on the plankton community. This rapidly changing marine region supports important fisheries, encompasses a range of contrasting pelagic habitat types, including river plumes, coastal, shelf, and oceanic areas, and also benefits from some of the longest and highest density biological time series in the world  (McQuatters-Gollop et al. , 2022) , making it a perfect location to investigate how climate change and variation in anthropogenic pressures have impacted plankton communities. To detect important changes in ecosystem functioning, complex longterm plankton monitoring datasets can be summarised into ecologically meaningful lifeforms (i. e. planktonic organisms which fulfil the same important functional role in their environment)  (Margalef, 1978; McQuatters-Gollop et al.
, 2020b). The critical role of plankton to ecosystem health and sustainable use also makes them integral to policy-related reporting  (Aubert et al. , 2017; Brander et al. , 2003; Rombouts et al. , 2019; Tett et al. , 2008). The OSPAR Convention is an international agreement among 15 "contracting parties" (including 14 nation states and the European Union) for protecting this shared marine environment and co-managing human activities sustainably. To assess the status of pelagic habitat biodiversity, contracting parties routinely submit plankton monitoring data to OSPAR. Our study summarises data submitted to OSPAR by eight contracting parties, expanding on spatial patterns of long-term change described in  Bedford et al. (2020b) , incorporating additional plankton datasets over a broader spatial extent, and using an improved set of ecologically relevant assessment units to divide the North-East Atlantic.
Plankton monitoring datasets Datasets were submitted to OSPAR by contracting parties through a 2021 data call to support OSPAR's Quality Status Report 2023  (QSR 2023) , which aims to assess the environmental and biodiversity status of the North-East Atlantic. Each dataset was subjected to institute-specific quality control procedures to ensure consistency and accuracy of counting and methodology (see Supplemental methods: Quality control for plankton datasets). Once received by OSPAR, datasets were uploaded to the online Plankton Lifeform Extraction Tool (PLET), allowing for taxa and samples to be aggregated into monthly-averaged lifeform abundances, as described in  Ostle et al. (2021). While biomass can often be a more informative metric than abundance for detecting changes in plankton communities, particularly for measuring impacts on the food web, determination of biomass based on standardised estimates of organism volume or carbon content were beyond the scope of this paper due to incomplete size information for the >1800 taxonomic groups assessed.
Even so, due to the shared functional role of taxonomic groups within each lifeform it is likely that large variation in their abundance will still generate important impacts on marine food webs. The data that support the findings of this study are openly available from The Archive for Marine Species and Habitats Data (DASSH), at https://www. dassh. ac. uk/lifeforms/. Two types of data were used to support this analysis. Spatially extensive open water surveys, such as the Continuous Plankton Recorder (CPR), are useful for examining the direction and magnitude of changes in selected lifeform abundances over time across large pelagic habitats, revealing spatial patterns of change  (Bedford et al. , 2020b). Additionally, fixed-point, full depth time-series stations provide complementary information on a larger fraction of the plankton size spectrum and in the more nearshore habitats that are particularly valuable for studying anthropogenic pressures such as eutrophication  (McQuatters-Gollop et al.
, 2019)  and pollution  (Serranito et al. , 2016). To use datasets from multiple sources effectively, procedures were implemented to ensure the comparability of results. Datasets were not combined due to differences in sampling, analysis, and enumeration methods. Instead, all datasets were analysed separately, using an identical set of methods. This way it was possible to compare trends derived across multiple datasets since each dataset maintained an internally consistent sampling and enumeration methodology. The analysis was primarily restricted to OSPAR Regions II (Greater North Sea), III (Celtic Seas) and IV (Bay of Biscay and Iberian Coast). Datasets were required to contain samples collected during and before a contemporary "assessment period" to represent current conditions. For this analysis, we selected the period between 2015 and 2019.
Time-series were limited to the end of 2019 to maintain consistency among datasets, although initial date varied. Due to interannual variability in plankton monitoring data, time-series needed to be of sufficient length (i. e. ≥10 years) to facilitate identifying statistically significant trends. While the CPR data typically covered 1960 to 2019, many of the coastal time-series commenced in the 1980s or later. Histograms displaying the distribution of samples through time for each time-series can be located in the Supplementary Materials for phytoplankton ( While 19 institutions from 9 contracting parties submitted datasets to be used for the QSR 2023, analysis was limited to 24 datasets from 15 institutions in 8 countries (Fig. 1 ; Table  S 1 ). The table in Supplementary Materials contains DOI links to detailed metadata for each dataset, wherever this was available.