dataesr/scientific-paragraphs-categorization huggingface dataset:
To ensure interobserver reliability, JA and AB collected behavioral data together for the first 6 days of the experiment (Cohen's kappa ± CI 95% = 0. 87 ± 0. 05;  Kaufman & Rosenthal, 2009). A third observer (OL) performed sequential rounds visiting all the enclosures every 2. 5 hr to collect data on the lizards' spatial behavior. Using scan sampling, we determined the identity and location of every lizard in sight on a scale map of the enclosure that included the six wooden pallets. Each enclosure was observed from a starting position located 1 m from the plastic barrier surrounding it for 5 min, and then walking around it (randomizing direction between consecutive visits) to record lizards that were not visible from the starting position. To balance sampling effort across enclosures, scanning of a single enclosure was restricted to a maximum period of 15 min after the first lizard was spotted.
To examine potential nontransitive relations of dominance among male color morphs, we also fitted three logistic mixed models (one for each morph) on the contest outcome of heteromorphic encounters and tested whether the probability of winning against other morphs differed from even odds. Male-female interactions were classified as reproductive when the lizards engaged in sex-specific display behaviors (i. e. , ♂: display; ♀: foot shakes, tail shake), copulatory behavior (i. e. , tail grab, mating), or prolonged physical vicinity (i. e. , coperching). To examine the effect of morph combination on the frequency of male-female reproductive interactions, we used social network analysis on the compiled version of SOCPROG  (Whitehead, 2009 ) (Appendix S1). Positional data were used to examine the putative effect of color morph on activity, space use and overlap with conspecifics.
| Statistical analyses We ran linear mixed models using the lme4 package  (Bates, 2014)  in R (R Core Team, 2019), and model selection was conducted using backward single-term deletions (p <. 05) of the saturated model followed by model comparisons via likelihood-ratio tests (at α = 0. 05). All numerical variables were centered and scaled before running the models  (Schielzeth, 2010). We checked that all response variables conformed to homoskedasticity and normality assumptions before assuming a Gaussian distribution in model fitting. For some variables that did not conform to these assumptions even after transformation, we fitted models using different distributions (Appendix S1).
However, to better accommodate for the mixed-model statistical design of our experiments, we additionally used the estimates obtained here to run a simulation-based analysis of power on the probability of detecting medium-sized (Cohen's d > 0. 5) and large (Cohen's d > 0. 8) effects with growing sample sizes  (Haenlein & Kaplan, 2004; Hoenig & Heisey, 2001; O'Keefe, 2007). We created two artificial LMMs using the simr package in R  (Green & Macleod, 2016) , one corresponding to the free-ranging population and another corresponding to the mesocosm experiment. In the former, we replicated the terms and parameters of the standardized model exploring morph differences in home range size. In the latter, we replicated the terms and parameters of the standardized model exploring morph differences in social dominance (see Appendix S1).
60, p =. 81). Movements between walls were similarly frequent among color morphs (GLMM (gamma), χ 2 = 2. 80, p =. 59). Color morphs did not differ in the total number of resightings accumulated, the mean days elapsed between consecutive resightings, or the mean distance between consecutive relocations (p >. 28; see Table  S4  for more details and effect size). We could calculate reliable estimates of linear home-and core ranges for 83 lizards, but decided to exclude mixed-morph lizards from the analyses due to their scarcity. The final dataset consisted of 70 lizards: 18 females and 52 males with at least 17 resightings (Table  S2 ). Neither sex showed significant differences in SVL among color morphs (LMM: χ 2 = 6. 61, p =. 16). Males had both larger linear home ranges and core ranges than females, and also perched higher on the stone walls (p <.
69, p =. 69; copulation attempts: χ 2 = 0. 83, p =. 83; parentage: χ 2 = 0. 32, p =. 32; Figure  7 ). We found a significant interaction of the parental morph combination over juvenile body mass (LMM: χ 2 = 12. 91, p =. 012). Splitting the dataset by female morph, we found that this result was exclusively driven by a nonsignificant tendency of yellow males to sire heavier offspring than orange males when coupled with white females (LMM: χ 2 = 6. 28, p =. 09). We found no effect of male or female morph alone on juvenile mass (LMM: χ 2 < 1, p >. 5).
E 4 Male-male competitive interactions. (a) Boxplot of social dominance by color morph. Boxes indicate the interquartile range (IQR, 50% of data). Horizontal lines represent the median, and bars extend to 1. 5 times the IQR. A jittered dot cloud shows the value of the variable of interest for each lizard in our dataset. (b) Mean plot showing the probability of winning for each morph combination according to the predicted values of the logistic mixed models. Bars extend to the CI 95%. The horizontal dotted line marks 50% probability.
Fund and  Portuguese Ministério da Educação e Ciência  ( SFRH/ BPD/94582/2013 ). P. C. was supported by a  RyC Research Fellowship  from the  Spanish Government  ( RYC-2013-12998 ). The study was supportedin part by grant  PID2019-104721GB-I00  of the  Spanish Ministerio de Ciencia e Innovación  to EF and GPL,  FEDER  through the  COMPETE program  (ref. 008929), the  Swedish Research Council  ( 2017-03846 ), and Portuguese national funds through the  FCT  project  PTDC/BIA-EVL/30288/2017-NORTE-01-0145-FEDER-30288 , cofunded by NORTE2020 through Portugal 2020 and FEDER Funds and by  National Funds through FCT , by the  Laboratoire d'Excellence (LABEX)   TULIP  ( ANR-10-LABX-41 ) and the  INTERREG POCTEFA ECTOPYR  ( EFA031/15 ). This work has also benefitted from state aid managed by the  French national research agency  under the  Future Investments Programme  bearing the reference  ANR-11-INBS-0001AnaEE -Services.
Tropical Cyclones (TCs) are among the most devastating atmospheric phenomena, with drastic socio-economic consequences in coastal regions. Accurately projecting TC activity changes in a global warming context is hence a crucial challenge. The last Intergovernmental Panel on Climate Change (IPCC) assessment  1  indicates "that it is likely that global frequency of TCs will either decrease or remain essentially unchanged", and that an increase in the frequency of most intense TCs for the 21 st century is "more likely than not". This assessment 1 also stresses the low-confidence level in basin-scale TC projections, given the contrasting results from existing studies. Therefore, TC projections are still uncertain despite their crucial importance. The future climate response to anthropogenic forcing is generally derived from the analysis of simulations from the Coupled Model Intercomparison Project (CMIP) database  2.
Forcing a high-resolution atmospheric model with those biased boundary conditions induces systematic biases on the simulated TC climatology, and probably hampers the future projections  13, 14. A commonly-used strategy to alleviate this shortcoming is to prescribe "climate change anomalies", i. e. atmospheric simulations are forced by observed present-day SSTs, onto which the anomalous ensemble mean sea surface warming pattern from CMIP5 projections is added  15. This approach is sometimes referred to as pseudo-global warming (PGW) downscaling, although this term was also used to describe the application of more ad-hoc anomalies  16. Strong uncertainties in regional projections of future TC activity also arise from the various projected SST warming patterns for a given scenario in the CMIP database  [17] [18] [19] [20] [21].
The modelled TC activity in the PD simulation is compared to the TC activity inferred from the latest version (v03r09) of the International Best Track Archive for Climate Stewardship (IBTrACS) database  57 over 1980-2016. tc diagnostics. TGCI is a cyclogenesis index built from 4 environmental parameters, which can be written ) are obtained as a result of the best fit of the multi-linear regression with the simulated climatological TC genesis in PD using the environmental parameters derived from the PD simulation. In a similar way as in Camargo et al. 62  , this ensures that the TGCI is fitted to capture the cyclogenesis climatology simulated in the PD simulation. Following Zhang and Wang 42 , the contribution of each factor to TC genesis changes are derived from a Taylor's series expansion of the TCGI terms.
Acknowledgements C. Dutheil  and  C. Menkes  thank funding from  European Project INTEGRE, Fonds Pacifique VARAPP , the  New Caledonian Governement and SPREP. The authors thank the  Institut de Recherche pour le Développement (IRD)  for supporting all authors except for  M. Bador ,  N. C. Jourdain  and  A. Peltier. MB was supported by the  ARC Centre of Excellence for Climate Extremes  ( CE170100022 ). The authors acknowledge the  Pôle de Calcul et de Données Marines (PCDM)  for providing DATARMOR storage and computational resources (http://www. ifremer. fr/pcdm). This work also used GENCI-TGCC computing resources and benefited from developments done during the PULSATION  ANR-11-MONU-0010  project of the  French National Research Agency (ANR). The scripts used to calculate the CMIP5 anomalies and to apply them to WRF's boundary conditions are available on http://github.
Application to a virtual graph Our study focuses on changes in landscape graphs, not on graph construction: abundant literature explains how to construct landscape graphs so we insist on only on a few key points related to construction in our Discussion. We created a virtual graph composed of 14 patches of different sizes (from 3 to 70 ha) linked by 18 connections with various connection probability values (from p=0. 1 to 0. 8, Figure  1 ). We generate a graph with a specific layout to be as instructive as possible. The total patch surface area (S) was 350 ha and the amount of reachable habitat according to EC was 174. 9 ha (Table  1 ), because the probability of connection between all the patches were below 1. All network connectivity analyses were performed with the Conefor software  (Saura and Torné, 2009).
Phylogenetic analysis of Anopheles APL1 gene copy number The APL1 locus was manually sequenced and sized by PCR in the Anopheles stephensi SDA-500 colony housed at Institut Pasteur. Strategy and primers used are indicated in Additional file 1: Figure  S1. The annotated An. stephensi APL1 genes in the VectorBase genome database  [26]  are ASTE016290 in the An. stephensi SDA-500 strain and ASTEI02571 in the An. stephensi Indian strain. The VectorBase assemblies and annotations used, current as of January 2019 were: SDA-500 strain, assembly AsteS1, gene set: AsteS1. 7, dated 22 October 2018; and Indian strain, assembly AsteI2, gene set AsteI2. 3, dated 21 February 2017. For phylogenetic analysis of APL1 copy number as presented in Additional file 2: Figure  S2 , APL1 orthologues for all Anopheles species genome assemblies were obtained from VectorBase and sequence was extracted for a window of 60,000 base pairs (bp) centered on the APL1 orthologue(s).
Mosquito mortality curves Mosquito mortality was monitored in cages of at least 50 mosquitoes, recorded every 2 days until all mosquitoes died. Treatment with dsRNA was performed in 3-dayold mosquitoes and recording of mortality began 4 days after dsRNA injection in 7-day-old mosquitoes. Bloodfeeding with or without P. yoelii was done 4 days after dsRNA injection in 7-day-old mosquitoes and recording of mortality began 3 days following the normal or infected blood meal in 10-day-old mosquitoes. Beginning at adult emergence, mosquitoes were maintained with 10% sucrose and in the case of antibiotic treatment, supplemented with penicillin 62. 5 µg/ml, streptomycin 100 µg/ml and gentamicin 50 µg/ml. Two to three replicates were performed for each condition tested. A Cox proportional hazards regression model was fitted to the data using treatments as predictor terms  [33, 34].
Acknowledgements We thank the  Institut Pasteur core facility , the  Center for the Production and Infection of Anopheles (CEPIA) , for gametocyte culture and mosquito infection and  Corinne Genève ,  GGIV Institut Pasteur , for rearing and infecting mosquitoes. We thank  Olivier Sylvie ,  Inserm  unit on Molecular biology and immunology of malaria liver infection, for provision of P. yoelii fluorescent strain delta-p230p-GFP.
Funding This study received financial support to KDV from the  European Commission ,  Horizon 2020 Infrastructures # 731060 Infravec2 ;  European Research Council , Support for frontier research, Advanced Grant # 323173  AnoPath;  Agence Nationale de la Recherche , # ANR-19-CE35-0004  ArboVec; and  French Laboratoire d'Excellence  " Integrative Biology of Emerging Infectious Diseases " # ANR-10-LABX-62-IBEID  and to MMR from  National Institutes of Health ,  NIAID  # AI121587. Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In the case of pH, representing these impacts does not necessarily increase the need for additional site level inputs but representing all of the biochemical pathways that contribute to the products and reactants involved in denitrification may entail detailed measurements that are not routinely performed. More complete exploitation of established and emerging observational data provides further opportunity to inform model selection, optimal complexity, and improvement. Ensemble models have been used more extensively for crop than N 2 O and denitrification modeling  [62] , but recent evaluations suggests that this approach could be promising  [12, 63]  especially when comparing results with global databases derived from rigorous field studies (e. g. Ref. [64] ). While the vast majority of field studies reporting N 2 O emissions used soil surface based bottom up measurements, N 2 O fluxes from tower based top down methods are becoming increasingly available and have the advantage of integrating fluxes over large time scales and spatial scales ranging from field (e.
g. Ref. [28 ] ) to continental scales  [65 ]. Running models at landscape and larger scales for comparing with top down data relies on availability of spatially and temporally resolved model input databases. Fortunately, land cover, soils, and weather data required for model inputs are available at near complete spatial coverage for countries such as the USA [66], Canada  [67]  or at the European level  [68]. Although extensive N 2 O data are valuable for model comparisons, they are limited because other processes such as nitrification directly contribute to observed N 2 O fluxes while various Nr transformation indirectly contribute to observed N 2 O. Measurements of isotopic composition and intramolecular distribution of 15 N in the N 2 O molecule can be used to constrain Nr turnover  [69].
Acknowledgements This article evolved from a workshop titled ' Climate Change ,  Reactive Nitrogen ,  Food Security and Sustainable Agriculture ' held at the  Karlsruhe Institute of Technology in Garmisch-Partenkirchen, Germany , on  15-16 April 2019 , and which was sponsored by the  OECD Co-operative Research Programme: Biological Resource Management for Sustainable Agricultural Systems  whose financial support made it possible for the authors to participate in the workshop.
g. ,  [3, 4] ). To understand the evolution of these odonate life history traits we used transcriptome sequences from 105 dragonfly and damselfly species along with a comprehensive fossil dataset (using newly assessed fossils in combination with those from Kohli et al. [5]  (Supplementary Table  S1 , Table  S7 , Figure  S3 ) to produce a well resolved time calibrated phylogeny of Odonata (Figure  1  and 2 ). The tree topologies we recovered using amino acids, and two nucleotide datasets (AA, Figure  1 ; NT2, NT123 Supplementary Figures  S1  and S2 ) are congruent with respect to suborder monophyly and interfamilial relationships. All branches were recovered with maximal statistical non-parametric bootstrap support with the exception of two clades, within Anisoptera (Figure  1 ). Odonata are recovered as an ancient lineage, which probably originated during the Carboniferous.
Acknowledgements We thank  Daniela Bartel  for making the ortholog set, and  Robert Waterhouse  for analytics support. We thank  Ondrej Hlinka  for bioinformatics assistance, and  Karen Meusemann  for running phylogenetic pre and main analysis and editing the manuscript. Thank you to  Alexander Blanke  for leading the initial Transodonata 1KITE-working group. We thank all who provided specimens  Alex Blanke ,  Akira Nishida ,  Chris Manchester ,  Daichi Kato ,  Guenther Theischinger ,  Jeanne Wilbrandt ,  John Trueman ,  Julian Glos ,  Kai Schütte ,  Kaoru Sekiya ,  Karen Meusemann ,  Manfred Niehuis ,  Martin Kubiak ,  Matthew Gimmel ,  Melissa Sanchez-Herrera ,  Miai Sakai ,  Mika Sugimura ,  Mitsutoshi Sugimura ,  Ola Fincke ,  Osamu Tanabe ,  Ralph S. Peters ,  Reiner Richter ,  Richard A.
Intracellular cytokine staining and analysis by flow cytometry All cultures have been checked for viability using fixable viability dye eFluor 660 (Thermofischer Scientific). For intracellular staining, T lymphocyte cultures were stimulated with a cocktail of PMA (50 ng/ml) and ionomycin (1 µM) for 5h at 37 °C. Brefeldin A (10 μg/ml) was added for 2h after PMA-ionomycin stimulation. T lymphocytes were then stained with antibodies to the following markers: anti-IL-17 (2. 4 µg/ml, 12-7179-42) and anti-IL-22 (1. 24 µg/ml, 11-7229-41) (Thermofischer Scientific). Specificity of each labelling was checked using specific isotype controls. The intracellular staining was realized by using a Cytofix/Cytoperm Kit Plus (BD Biosciences) according to the manufacturer's instructions. Data were acquired using a BD LSR Fortessa flow cytometer and the data were analysed using FlowLogic software (Miltenyi Biotec).
Statistical analysis Statistical analysis was performed using GraphPad Prism version 6. 00 (GraphPad Software, San Diego, California, USA). Results were expressed as means +/-SEM. Multiple group was compared to control group used one-way ANOVA, Kruskal wallis Dunn's multiple comparison tests. P-values < 0. 05 were considered significant (indicated by asterisks): *p < 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 000: differences between bisphenol treatments vs vehicle control. $ <0. 05: differences between bisphenol concentrations.
A reactive all-cause mortality surveillance Since 2005, the French public health agency (Santé Publique France) has been following up the all-cause mortality routinely, based on the administrative data transmitted by the French National Institute for Statistic and Economic Studies (INSEE). The mortality surveillance is based on ca 3,000 civil status offices of city halls dispatched all over the territory (including overseas)  [1]. For each death, administrative information is collected (age, sex, date and place of death, date of registration). The medical causes of death are not recorded. This sample from 3,000 cities represents 77% of the total French mortality, varying from 42% to 98% in the 100 districts  [1]. In order to analyse mortality at the level of all cities, the number of deaths recorded from the sample in each district was divided by the coverage rate of the system at the district level.
Acknowledgements The authors thank the  French institute for statistics and economic (INSEE)  for their contribution to the syndromic surveillance system SurSaUD, by transmitting mortality data daily and their action to ensure stability and quality of data along the epidemic period. They also thank the regional units of the agency for their contributions to the surveillance system, in particular the following referents:  Audrey Andrieu ,  Nahida Atiki ,  Lynda Badjadj-Kab ,  Anne Bernadou ,  Oriane Broustal ,  Sonia Chene ,  Delphine Casamatta ,  Elise Daudens-Vaysse ,  Jamel Daoudi ,  Joël Deniau ,  Marlène Faisant ,  Erica Fougere ,  Noémie Fortin ,  Céline Francois ,  Florian Franke ,  Gaëlle Gault ,  Guillaume Heuze ,  Ghislain Leduc ,  Nadège Marguerite ,  Laure Meurice ,  Annie-Claude Paty ,  Mathilde Pivette ,  Jérôme Pouey ,  Leslie Simac ,  Pascal Vilain ,  Nicolas Vincent.
Methods and data 2. 1 Site description Surface in situ CO measurements in eight cities over the HTP are involved in this study. The geolocations of these cities are shown in Fig. 1  and summarized in Table  1. Ngari locates in the western, Diqing and Qamdo locate in the eastern, and the rest cities all locate in central eastern of the HTP. Ngari, Shigatse, Lhasa, Shannan, and Nyinchi are adjacent to the Himalayas region, and Naqu, Qamdo, and Diqing are relatively far away from the Himalayas region. Generally, these cities represent the most developed and populated areas over the HTP. The altitude of these cities ranges from 3. 1 to 4. 5 km a. s. l. and the population ranges from 110 to 770 thousand. The surface pressure of these cities is about 600 hPa or less throughout the year (Table  1 ).
Surface CO data and meteorological data Routine in situ measurement of surface air qualities over the HTP started in 2015, which are organized by the China National Environmental Monitoring Center (CNEMC) network funded by the Chinese Ministry of Ecology and Environment (http://www. cnemc. cn/en/, last access: 22 March 2020). The CNEMC network has monitored six surface air pollutants (including CO, O3, NO2, SO2, PM10, and PM2. 5) at 23 sites in eight cities in Ngari, Lhasa, Naqu, Diqing, Shigatse, Shannan, Nyingchi, and Qamdo over the HTP (Table  1 ). Each city has at least two measurement sites. Surface CO volume mixing ratio (VMR) measurements at all sites are based on similar gas correlation filter infrared analyzers (http://www. cnemc. cn/en/, last access: 22 March 2020). The hourly mean datasets have covered the period from January 2015 to present for all measurement sites in the eight cities (Table  1 ).
We first applied filter criteria following that of  (Lu et al. , 2019)  to remove unreliable measurements. The resulting measurements at all measurement sites in each city are then averaged https://doi. org/10. 5194/acp-2020-810 Preprint. Discussion started: 9 September 2020 c Author(s) 2020. CC BY 4. 0 License. to obtain a city representative dataset. The 3D back trajectories calculated using HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (http://ready. arl. noaa. gov/HYSPLIT. php, last accessed on 23 May 2020) are used to determine the transport trajectories  (Wang, 2014; Draxler et al. , 2020). The input gridded meteorological data were from the Global Data Assimilation System (GDAS-1) operated by the US National Oceanic and Atmospheric Administration (NOAA) with a horizontal resolution of 1∘latitude × 1∘longitude and 23 vertical grids from 1000 to 20 hPa (https://ready.
Interannual variability Biweekly mean time series of surface CO VMR over the HTP from 2015 to 2020 along with the fitted results by using the regression model   () are shown in Fig. 4. Generally, the measured and fitted surface CO VMR over the HTP are in good agreement with a correlation coefficient (r) of 0. 81 -0. 93. The measured features in terms of seasonality and interannual variability can be reproduced by the regression model. Seasonal enhancements calculated as equation (  3 ) disclosed that large seasonal enhancements typically occur around November -December and April -May which correspond to the timings of the seasonal peaks for most cities. The trend in surface CO VMR from 2015 to 2020 over the HTP spanned a large range of (-21. 6 ± 4. 5) % to (11. 9 ± 1. 38) % per yr, indicating a regional representative of each dataset.