dataesr/scientific-paragraphs-categorization huggingface dataset:
How much Antarctic ice shelf basal melt rates can increase in response to global warming remains an open question. Here we describe the response of the Southern Ocean and Antarctic ice shelf cavities to an abrupt change to high-end atmospheric conditions typical of the late 23 rd century under the SSP5-8. 5 scenario. To achieve this objective, we first present and evaluate a new 0. 25°global configuration of the NEMO ocean and sea ice model. Our present-day simulations demonstrate good agreement with observational data for key variables such as temperature, salinity, and ice shelf melt rates, despite remaining difficulties to simulate the thermocline and melt variability in the Amundsen Sea. The ocean response to the high-end atmospheric perturbation includes a strengthening and extension of the Ross and Weddell gyres and a quasi-disappearance of sea ice, with subsequent decrease in production of High Salinity Shelf Water and increased intrusion of warmer water onto the continental shelves.
The simulated ice shelf melt rate pattern is shown in Fig. 5a. The melt pattern underneath the Filchner-Ronne ice shelf includes large areas of refreezing and maximum melt rates at the front of Ronne Ice Shelf and near the deepest parts of the grounding line, consistently with satellite estimates  (Rignot et al. , 2013; Moholdt et al. , 2014; Adusumilli et al. , 2020)  and high-resolution simulations  (Hausmann et al. , 2020). Near zero melt rates are simulated underneath most of Ross Ice shelf, except near Ross Island at the west end of the ice front, as reported in  (Rignot et al. , 2013; Adusumilli et al. , 2020). The warm ice shelves from Getz to Pine Island, in the Amundsen Sea, all experience local melt rates above 10 m yr -1 in agreement with the aforementioned satellite estimates. The deepest part of Pine Island shows a high melt area above 20 m yr -1 , corresponding to the one visible in satellite data  (Shean et al.
Code availability. Source code and parameter namelists for each experiments are all available here: https://github. com/pmathiot/paper_ MJ2023. Source code used to make the figure will be provided after the review process. Data availability. Data used to run the simulations are available upon request to the lead author. Monthly atmospheric forcing anomalies as well as monthly climatologies of the last 30y of PERT and REF are available on zenodo in  Mathiot and Jourdain (2023).
ment No 820575 (TiPACCs) and by by the French National Research Agency under Grant ANR-19-CE01-0015 (EIS). N. Jourdain was also funded by EU-H2020 grants No 101003536 (ESM2025) and No 869304 (PROTECT). This work was granted access to the HPC resources of CINES and TGCC under allocations A0100106035 and A0120106035 attributed by GENCI. 20 https://doi. org/10. 5194/egusphere-2023-1606 Preprint. Discussion started: 17 July 2023 c Author(s) 2023. CC BY 4. 0 License.
Results And Discussion A comprehensive dataset obtained from an aircraft measurement campaign provides excellent insight into the variability in the external cycling of NO x and its perturbation on the oxidative capacity of the atmosphere  4, 33. Nineteen research ights were conducted onboard NSF/NCAR C-130 to collect measurements of NO x , HONO, nitric acid (HNO 3 ), particulate nitrate (pNO 3 ), some alkyl nitrates, PAN, radicals (OH, HO 2 , RO 2 ), ozone (O 3 ), volatile organic compounds (VOCs), aerosol size distributions, photolysis frequencies, and other meteorological parameters, mostly in various low-NO x troposphere environments (SI), i. e. , from the pristine terrestrial and marine boundary layer (BL) to the middle FT (Fig. S1  and Table  S1 ). The data was collected from the nineteen research ights without further screening and provided a unique dataset that offers a more global representation of atmospheric variability and therefore supports our analysis strategy.
Dodds (1945)  first described the general course of female development in diploids of Musa acuminata (AA), from macrospore mother cell to mature embryo sac. The functional sacs have a characteristic bell shape with a chalazal pit, and the eight nuclei are organized into a micropylar apparatus consisting of: one oocyte; two synergids; three chalazals, which degenerate into the basal pit; and two polars close to the basal pit. Shepherd (1954)  first reported quantitative data from 'Gros Michel' (AAA group) in triploid cultivars. The author found that the frequency of occurrence of ovules with mature sacs at the time of flower opening varied from 9. 8% to 18. 0%, depending on where the samples were collected. However, many embryo sacs were abnormal in terms of nuclei number or arrangements. Later,  Simmonds (1960)  examined ovules of 'Mysore' (AAB group), 'Bluggoe' and ' Awack Legor' (both ABB group), and no embryo sac of mature aspect was found in 'Mysore' or 'Bluggoe'.
The plants were grown in the active banana germplasm bank at Embrapa Mandioca e Fruticultura, in the municipality of Cruz das Almas, in the state of Bahia, Brazil (12°39'S, 39°06'W, at an altitude of 225 m), with Af climate, according to Köppen's classification. Samples were collected on the day of flower opening and pollination (day 0) and three days later (day 3). Pollination was carried out using the male-fertile wild Musa acuminata accession 'Long Tavoy'. Quantitative data on embryo sacs as observations of pollen tube growth were obtained at Embrapa in 1987. At that time, few sharp enough images were obtained. In Montpellier, at CIRAD, interferential contrast techniques, based on Herr's protocol, were used to better illustrate the images obtained back in 1987 in Brazil. To prepare the samples, ovaries were stripped on the outer skin and thin inner skin, enclosing each of the three carpels.
Acknowledgments To  Cécile Dubois  from  French Agricultural Research Centre for International Development (CIRAD ), for support in the statistical analyses; and, in memoriam, to  Dr. Kenneth Shepherd  (1927Shepherd ( -2001)), for leading a collaborative project between  Empresa Brasileira de Pesquisa Agropecuária (Embrapa)  and CIRAD in the mid-1980s, whose results were only registered in the first version of a draft manuscript for over 35 years before being published here, and for the indelible mark he left on those who had the chance to work with him.
Acknowledgments We would like to thank the three reviewers for their comments and suggestions, which significantly contributed to the progress of this article. We also thank  Thomas Roulet  for his precise and constructive editorial followup. This research was initiated within the framework of the  ANR  project " ABRIR " and benefited from this project's financial and scientific support. Special thanks to  Caroline Ibos  who participated in the first explorations and discussions around this paper. Thanks to all the participants for their feedback during our various presentations. Finally, thanks to the "Lejabys",  Raymond Vacheron , and the artists who placed their trust in us and gave us their time to share their experience of the struggle and their emotions.
Finally, some statistical methods are able to deal with the causal structure existing within the exposome or with intermediary data. The internal exposome and biological layers are considered as intermediary layers between the external exposome and health. The incorporation of these intermediary layers could help to reduce the false discovery proportion in exposome-health association studies  [17] , but this requires large amounts of data (both in terms of exposure and participants). Great advances have been made to use methods that are more relevant to these large and complex datasets, and these efforts must be continued; in particular, by including more sophisticated methods addressing combined exposures and handling of longitudinal data. The exposome and respiratory health: what have we learnt from pioneer studies? Among exposome studies focusing on respiratory health and assessing a wide range of exposures belonging to at least two different exposure families, several have been based on cohorts of children.
Introduction Next-generation sequencing (NGS) has disclosed the possibility of investigating microbial biodiversity with unprecedented resolution. This 'data explosion' is both a challenge and a fascinating opportunity. Indeed, during the last 15 years, researchers in the biomedical field adopted this approach to investigate the human microbiome, i. e. , the collective ensemble of microorganism coexisting in and on the human body, in particular through the cataloging of the bacterial species in specific sites and their relative abundances  [1, 2, 3, 4] , shedding light on a plethora of micro-organisms impossible to access with traditional methods, such as culturing. All these efforts have been designed as a community resource program to develop broadly available, reference dataset and rapidly released computational and statistical tools, and clinical protocols, to serve as a catalyst for this emerging field.
This effect also manifests in the sparsity of the dataset, where most of the species appear in few of the samples. However, sparsity may not indicate ecological exclusion of the species  [16, 17]. The second effect pertains to the taxonomic profiling of DNA sequences. Due to the eventually large overlap among large regions of phylogenetically distant bacterial genomes, sequence alignment tools tend to introduce a lot of false positive species (typically found in very low abundances), which are not really present in the microbial community  [18]. This problem is typically mitigated by choosing an appropriate database for classification or by setting a relative abundance cut-off, which is arbitrarily chosen  [18]. Finally, microbiome data are affected by compositionality. Due to both technical (sequencing, sequence alignment errors) and biological variability of samples, it is often the case that taxonomic profiles differs -possibly by one or two order of magnitudes -in the number of aligned sequences, which makes the comparison between different samples difficult.
Such a family of distributions has 2S -1 parameters (see section 4. 2. 2 of the Supplementary Material), and since S ∈ [10 2 , 10 3 ] and microbiome dataset include, typically, R ≈ 10 2 samples, fitting this model is an unfeasible task. On the other hand, the family has an analytical expression, providing a clear ecological interpretation of its parameters. As we will show, we can reduce greatly the number of free parameters by constraining the model employing relationships obtained from empirical macro-ecological patterns, as proposed by  [16]. First, we consider the TL. It takes the form of a scaling relation between the mean relative abundance of a species (among samples) and its fluctuations, i. e. σ 2 vi = Av ζ i , (3) for i = 1,. S, and where A and ζ do not depend on i. Compositionality does not affect such law if ζ = 2 , and thus the same A is found also if we consider ⃗ x, instead of the ⃗ v (for a derivation of this result, see SM section 4).
Microbiome Metadata Selection and Analysis We have selected gut microbiome data from three studies  [4, 13, 35] , where both sequencing data and sample metadata are available for H controls and three gastrointestinal tract diseases: Chron (CD), Ulcerative Colitis (UC) and Inflammatory Bowels Syndromes (IBS). We have filtered so to have a homogeneous and not biased dataset (see section 5 of the Supplementary Material for details). In general, we have selected the patients less affected (at least at the time of the study) according to medical treatments, in order to limit the impact of different drug treatments on gut microbiome. After this filtering procedure, we ended up with R H = 91 shotgun metagenomic samples from healthy control individuals and R U = 202 samples of dysbiotic microbiome.
We have implemented a computational pipeline to process the throughput sequencing metagenomic data following best practices  [38] , such as quality filtering (remove reads with Q<20) and human DNA decontamination with the NCBI (GRCH38) human genome assembly  1. The metagenomic taxonomic profiling tool we have adopted in our analysis is the Kaiju classifier  [39] , which converts metagenomic reads in all possible open reading frames (ORFs) and search for the best match in a protein database. The advantage of this procedure is that, thanks to the degeneracy of genetic code, is robust to random mutations along the genome and as such, to evolutionary divergences between the dataset and the reference catalog of genomes. As a reference species catalogue, we have used RefSeq  [40] , which contains protein sequences from complete archaeal and bacterial genomes.
Mean Abundance Distribution and Taylor's Law We find that an emergent MAD is observed in both H and U dataset. However, we observe a dependence of the shape of the distribution depending on the relative abundance cut off κ. In particular for κ < 10 -5 the MAD displays a Log-Laplace shape, i. e. P M AD (x|µ, λ) = e -| log x-µ| λ /(2λx), while for κ > 10 -5 the MAD is a Log-Normal distribution P M AD (x|µ, λ) = e -(log x-µ) 2 2λ 2 /(x √ 2πλ 2 ), the same found for OTU 16s data  [16]  In the y-axis of Figure  2  we show the BIC ratio ??: if it is greater than one, it indicates that the Laplace distribution is a better fit than the Log-Normal, while if BIC<1, then the opposite is true. We thus obtain the values of µ and λ for three different thresholds of κ. As also clear from Fig. 2 , the Laplace distribution is usually a better description for the MAD, except for large threshold where the MAD clearly dispays a Log-Normal shape (and compatibly with the OTU case [?]).
Acknowledgements This research was supported by  Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)  ( SMSEGL20SC01 ) and the  RGC General Research Fund 16304021. DC is funded by the  Research Council of Norway  under grant  275268  (COLUMBIA). ML acknowledges financial support from the  Chanel Research Chair of ENS-PSL. The numerical model modifications, analyses code and sample model data including those mentioned in text but not shown may be found on Zenodo at http://dx. doi. org/10. 5281/zenodo. 7612270.
This investigation utilizes the Weather Research and Forecasting (WRF) model: we examine the difference between the control simulation ensemble and the experimental ones where we eliminated the seasonal progression during July and August, therefore deducing the impact of the resulting SST anomalies on surface parameters (sea level pressure and surface winds) over the ocean, as well as on the transport of humidity and precipitation over the continent. The article presents the datasets and describes the experimental setup in Sect. 3, compares the model results with observations in Sect. 4, highlights the main findings in Sect. 5, and concludes with a summary of the results in Sect. 6.
Funding This work was supported by  UK Research and Innovation  as part of the  Global Challenges Research Fund , grant number  NE/ P021077/1  ( GCRF African SWIFT ) and  Centre d'Excellence Africain en Mathématique, Informatique et TIC (CEA-MITIC). The authors gratefully acknowledge financial support from the  French Programme d'Investissements d'Avenir de l'Agence Nationale de la Recherche , through Contract  ANR-17-MPGA-0015  ( PRODUCT ).
Availability of data and materials (a statement on how any datasets used can be accessed): All reanalyses and observational data sets are freely available. We thank the NOAA / NCDC for the Reynolds SST product, Copernicus Climate Data Service for the ERA5 data, and NASA GES DISC for the rainfall TRMM data. This work was granted access to the HPC resources of IDRIS under the allocation 0292 made by GENCI. We also thank GENCI for the availability of the ERAI forcing data sets, and server and storage of the WRF outputs simulations.
Acknowledgements This research was funded in whole, or in part, by the  Wellcome Trust  ( 222932/Z/21/Z ). For the purpose of open access, the author has applied a CC BY public copyright license to any author-accepted manuscript version arising from this submission. A. S. and G. L. are supported by the  University College London Hospitals National Institute for Health Research (NIHR) Biomedical Research Centre  and funded by the  NIHR   ENHANCE study  ( NIHR203670 ). M. K. was supported by the  Wellcome Trust  ( 221854/Z/20/Z ), the  UK Medical Research Council  (MRC;  S011676/1 ,  R024227/1 ), the  US National Institute on Aging  ( R01AG056477 ) and the  Academy of Finland  ( 350426 ). E. B. L. is supported by research grants from the  US National Institute on Aging  ( UO1AG0006781 ,  RO1AG ).
Acknowledgements The authors wish to thank the  Cultural Heritage Service of the Diputación Foral de Bizkaia  for funding the 4-year multidisciplinary study project ( 2016-2020 ) " Study of rock art in Atxurra cave " directed by  Dr Diego Garate. The present study has been carried out within the framework of the research project " Before art: social investment in symbolic expressions during the Upper Palaeolithic in the Iberian Peninsula " ( PID2019-107262GB-I00 ), PI:  Diego Garate , funded by the  Ministerio de Ciencia, Innovación y Universidades (Spain) , the research projet " Scientific virtual reality for the study and dissemination of the scenarios of artistic creation in Palaeolithic caves (RealCaveART) " ( PDC2022-133124-I00 ), PI:  Diego Garate , funded by  MCIN/AEI/10. 13039/501100011033  and the  European Union Next Generation EU/PRTR , and the research project " Creation and perception in Anatomically Modern Humans: analysis of the biological, cognitive and social skills linked to the production of Paleolithic art (ArtMindHuman) " ( PID2021-125166OB-I00 ), PI:  Olivia Rivero , funded by the  Ministerio de Ciencia, Innovación y Universidades (Spain).
The 3-D hydrodynamical ocean model SYMPHONIE To  Duy et al. (2022)  built a fine-resolution configuration of the 3-D ocean circulation model SYMPHONIE  (Marsaleix et al. , 2008 (Marsaleix et al. , , 2019) )  over the Vietnam coastal region (VNC hereafter for VietNam Coast), based on a horizontal polar grid with a resolution decreasing linearly seaward, from 1 km at the Vietnamese coast to 4. 5 km offshore and with 50 vertical levels. They used the GEBCO_2014 dataset released in April 2015 at a 30 s interval (∼ 0. 9 km) and available from https://www. gebco. net, last access: 7 April 2023. We use exactly the same configuration here and show the VNC domain in Fig. 1a-b. The atmospheric forcing is computed from the 3-hourly output of the European Center for Medium-Range Weather Forecasts (ECMWF) 1/8.
Code and data availability. The code for the SYM-PHONIE model can be downloaded freely from https: //sirocco. obs-mip. fr/ocean-models/s-model/download/ and is presented in  Marsaleix et al. (2008). Sea surface temperature, currents and wind stress simulated by the ensemble over the summer of 2018 are freely available on https://doi. org/10. 5281/zenodo. 7504087  (Herrmann et al. , 2023).
Results The following analysis is based on 1977 subjects that completed the experiment (393 in T1, 398 in T2, 400 in T3, 392 in T4, and 394 in T5). 16  We asked one attention question and controlled for the duration that each subject took to complete the survey. In Appendix A, we present the results for the restricted sample that did not fail the attention question and responded in a duration no lower than one-half of the treatment mean duration per turnout decision. In a nutshell, our main results are the same with the two samples. In the final part of the experiment, we collected some social demographic measures. Table  1  summarizes subjects' characteristics. Two features of this data are worth mentioning. First, the data shows no systematic differences across treatments. Empirical tests reported in Appendix B show that, with few exceptions, the differences in observed covariates across treatments are small and the sample is mostly balanced on these variables.
Acknowledgements We are grateful to the Associate Editor in Charge and two anonymous referees for their valuable comments and suggestions that greatly improved the paper. We are also grateful to  Thomas Gall ,  Georg Granic ,  Nicolas Gravel ,  Max Kwiek ,  Jean Mercier Ythier ,  Benoît Tarroux ,  Michael Vlassoupoulos , and the participants at the  ESA 2021 Global Online Conference  and the  RES 2023 Annual Conference  for valuable discussions, written comments and suggestions. We thank  Thibault Vercoutere  for his excellent research assistance. Finally, the financial support of the research project " ValFree" (The Value of Choice , grant No. ANR-16-CE41-0002-01 ) and the research project " FrontCog " (grant No. ANR-17-EURE-0017 ) of the  French National Agency for Research (ANR)  are gratefully acknowledged.
Extensive efforts are needed to improve our scientific understanding of the effective regulation of aerosol loading in the IGP. In this study, we aim to provide new mechanistic insights into aerosol formation and mitigation by integrating the ISORROPIA-II thermodynamic equilibrium model with the dataset of precursor gases (HCl, HNO 3 , and NH 3 ) and inorganic constituents (Cl -, NO 3 -, SO 4 2-, Na + , NH 4 + , K + , Ca  2+  , and Mg 2+ ) of PM 1 and PM 2. 5 acquired using the first deployment of the MARGA-2S instrument in the IGP (Supplementary text S1). We explore a mathematical framework to investigate the sensitivity of gas-to-particle partitioning of aerosols to different parameters like gaseous precursor concentrations (HCl, HNO 3 , and NH 3 ), pH, and ALWC, using sigmoidal curves and "sensitivity regimes" of aerosols.
Results Evaluation of the ISORROPIA-II model simulations and sensitivity analysis. The ISORROPIA-II model was run in the "forward" mode using the hourly measured total (gas + particle) species concentrations instead of only particle-phase concentrations as input, as it is considered to be more accurate  21, 22. As shown in Figure  S1 , the predicted PM 1 NH 4 + (r = 0. 97), NO 3 -(r = 0. 93), Cl -(r = 0. 98), PM 2. 5 NH 4 + (r = 0. 87), NO 3 -(r = 0. 98), Cl -(r = 0. 99), and gas-phase NH 3 (r = 0. 93) showed very good correlation with the measured concentrations, confirming the reliability of the ISORROPIA-II model simulations at a range of temperatures (278-298 K). The data points are color-coded by ambient temperatures to investigate how temperature variability can alter the ISORROPIA-II predicted gas and particle-phase concentrations.
These errors sometimes could propagate to cause significant uncertainties in the model simulations. It can be seen that, at almost all times during the study, the concentrations of the measured parameters were much higher than the range of errors, causing them to be unlikely the primary source of bias in the analysis. It is crucial to note that the analytic error in monitoring individual species cannot be evaluated in the absence of specifically designed quality-control experiments. However, to ensure the quality of the data, observational hours were excluded from the study when measurements did not meet the quality assurance and quality check (QA/ QC) criteria  19. As a result, only 1100 h of correct observational datasets were used for the model simulations, and other datasets are not included in the analysis of this paper.
5 in Table  S4. These results collectively emphasize the robustness and reliability of the ISORROPIA-II model simulations. In regions like Europe, where HCl and Cl -concentrations are very low, the assumption of fixed HCl + Cl -may not have a grave impact, but in a region like Delhi, where observed HCl + Cl -concentrations varied between 2. 11-112. 21 µg m -3 and 2. 31-186. 31 µg m -3 for PM 1 and PM 2. 5 respectively, the consideration of actual ambient concentrations in ISORROPIA-II model simulations seems to be of utmost importance. The fixed concentration of 25 µg m -3 cannot accurately represent real-world conditions. Previous studies have also emphasized that when gas-phase data are not available, running ISORROPIA-II in the forward mode, with only aerosol concentrations as input, results in the repartitioning of ammonia in the model, causing bias in the predicted pH and ALWC  3, 21.