dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of NOAELs and reference doses (RfD) Comparisons of NOAELs and LOAELs obtained in vivo mainly in rats are given in the file "Table for comparisons" at https://data. mendeley. com/datasets/dk8gsx2r4j/9 in the Mendeley data repository. RfD values in this table derive from the NOAELs, by a reduction factor of 100. When only a LOAEL is available, the RfD values in obtained applying a reduction factor of 300. NOAEL and LOAEL can vary greatly according to the target organ and function. Inter-sex and interspecies differences are also observed. When different safety values were available, the reproductive and/or developmental effects were considered preferentially. This is because this study focusses on endocrine disrupting effects and essentially on reproductive hormones. However, sometimes, hepatotoxicity occurred at lower dosages than reprotoxicity.
Lee et al. bw/d and 180 mg/kg bw/d), and (  5 ) triazines (e. g. , cyromazine and terbuthylazine with respective NOAELs on reproduction or development in rat equal to 2 mg/kg bw/d and 5 mg/kg bw/d). When several NOAELs were given in the same species for the same substance and still on reproduction or development, the lowest was retained. The references used for NOAELs are at https://data. mendeley. com/datasets/dk8 gsx2r4j/9 in "Table for comparisons". Besides, the LOAEL of genistein (35 mg/kg bw/d) recorded by the NTP in rat, may be derived in a NOAEL of the same order of magnitude that safety values obtained on the same model for other isoflavones and also for diethylstilbestrol, zearalenone and β-sistosterol. Besides, p,p' DDE, nitrofen, lindane or methoxychlor also present reprotoxicity at NOAEL close to those that can be deduced for genistein.
Comparisons of exposure to synthetic and natural EDs It can be observed in Table  2  based on substances measurements that natural compounds are present in raw materials at higher concentrations than any other synthetic EDs. Therefore, consumption of plants containing high doses of natural hormone-like substances should be considered. Anyway, as already mentioned pesticides at low doses reach consumers as complex mixtures that can have either additive or antagonizing effects  (Rizzati et al. , 2016). The final overall effect depends on concentrations, pharmaco-kinetics and pharmaco-dynamics. For most of the ED-PPPs listed in table 10 at https://data. mendeley. com/datasets/dk8gsx2r4j/9, the exposure data in France are not available, especially because, as mentioned earlier, the substances have been withdrawn from the market or are no longer authorized.
Acknowledgments This work was supported by  Inserm  and  Bordeaux Sciences Agro. Assays were possible thanks to the  Regional Council of Nouvelle Aquitaine. A. L. helped with the exposure calculation and manuscript writing. S. B. helped with foodstuff assays and manuscript writing. V. L. was involved in the isoflavone assays and checked the manuscript. M. L. was involved in the PPPs analysis identifying endocrine disruptors in the scientific literature. C. B-P. initiated, coordinated, and supervised the work. She was involved in the assays, PPP analyses, isoflavone exposure calculation, and manuscript writing. None of the authors have any conflict of interest to declare. The work was funded by the  Aquitain Region. The grant number is : 2020. 809. CP.
After 572 s of injection, displacement vectors are significantly more inclined than the fault plane. This is illustrated by the normal displacement curve that shows a much sharper increase than the tangential displacement curve (Fig. 1d ). This much larger dip of displacement vectors after 572 s corresponds to a significant fault vertical opening associated with a strong fluid pressure drop (1. 2 MPa) around the injection. The displacement data are continuously logged together with pump parameters (pressure and flowrate), as well as temperature and pressure in the borehole above, between and below the packers. The pressure sensors allow for measurements over a pressure range from 0 to 10 MPa, with a 0. 001 MPa accuracy. The accuracy of the temperature sensors is 0. 1°C. During the hydraulic injection test, the injection pressure is controlled by an engine pump while flowrate, pressure, temperature and displacement variations from the two SIMFIP probes, respectively installed in the injection borehole and in the monitoring borehole, are monitored with the same acquisition station.
To remove possible metal residues, the substrate-electrode samples were placed in acetone overnight. A flake of NPG was picked up by an elastic polydimethylsiloxane (PDMS) stamp, and it was transferred onto the active region defined between a pair of Au film electrodes. Material Characterization: The SEM and HR-TEM images were taken by Seron AIF 2100 and TECNAI F20, respectively. The NPG structure was studied using an Thermo Nicolet Almega Dispersive Raman spectrometer (Nd:YLF laser with an excitation wavelength of 532 nm and a power of 100 mW). The XRD data were obtained using INEL Equinox 3000, and the XPS data were measured using an Al Kα X-ray source at 1486. 6 eV. Electrical Characterization: The fabricated NPG memory devices were characterized using an electrical probe station equipped with a semiconductor source-measure unit Keithley model 2400 at a room temperature and under ambient air.
Acknowledgements This work was supported by the  Alliance Program (T. I. E. R grant) of  Columbia University , by the  Gachon University  research fund of 2021 ( GCU-202106390001 ), and by the  National Research Foundation of Korea (NRF)  grant funded by the  Korean government (MSIT)  ( NRF-2019R1C1C1003356 ). The authors acknowledge the technical support of  Prof. Miyoung Kim  at  Seoul National University. This article was amended on March 31,  2022  to correct Table 1: the  ON/OFF ratio for Al/GO/n-Si and Al/GO-Au NPs/ITO , and the fabrication technique for Ag/GO/ITO were corrected.
orientalis correlate best with future observations of M. intestinalis suggesting that M. orientalis prevalence drives M. intestinalis prevalence. On the other hand, negative shift values giving the best correlation might rather suggest that M. intestinalis declines for unknown reasons and M. orientalis is taking advantage of the emptying niche in the intestine of mussels. From the abundance data (i. e. the number of parasites found in each host) we also calculated parasite aggregation and the likelihood of finding a mate in an infected host. Aggregation was expressed as the variance-to-mean ratio calculated from the number of parasites found in each host for each sampling time point in all three host-parasite combinations (M. intestinalis in mussels, M. orientalis in mussels and M. orientalis in oysters).
A variance-to-mean ratio of 1 indicates that data is distributed according to a Poisson distribution. Higher values indicate a higher degree of aggregation resulting from a clumped distribution with only few hosts carrying many parasites while many hosts show low infection intensities or no infection at all  (Barbour and Pugliese, 2000). The likelihood of finding a mate, i. e. meeting a conspecific of the opposite sex, was calculated from binomial proportions of males and females assuming the simplest case of a 1:1 sex ratio based on the collected abundance data. In other words, if a parasite ends up in a host with only one other parasite, it has a 50% chance that the other specimen is of the opposite sex, whereas there is already a 96% chance to encounter the opposite sex when 5 parasites were already present.
orientalis was not found on Sylt at the time both experiments were conducted (2014 and 2016, Figure  2 ). Infection rates were analyzed with binomial generalized linear mixed models (GLMM) using the R package lme4. The number of successful against failed infections from all ingested copepodites in each mussel was used as the binomial response. We recorded infection intensities for both parasites in co-infections separately and fitted "mussel ID" as a random intercept to account for the two infection intensities coming from the same host. We fitted "infection type" (single vs. co-infection) testing for differences in infection rates between single and co-infections, "parasite species" (M. intestinalis vs. M. orientalis) testing for differences in infection success between both parasite species and the interaction term describing whether differences between single and co-infections were dependent on the focal parasite species.
Pairwise differences between treatment combinations were tested by Tukey-Posthoc test implemented in the multcomp package  (Hothorn et al. , 2008). For the sequential infections we used two rounds of infection separated by 20 days. To account for using different batches of copepodites between both infection rounds, we conducted single species infections with 24 copepodites of both parasites at both time points using a different set of hosts each time. The sequential infections consisted of mussels, which were either exposed twice (mixed infections) or once (single species infections) in all possible combinations resulting in 8 treatment groups of 10 mussels each (n = 80). We dissected the sequential infection experiment in two rounds of 5 individuals per treatment group to allow size differences between parasites to increase between first and second infections.
Because there were no quantitative differences in infection rates between both time points, we analyzed the whole dataset together. We fitted a model similar to simultaneous infections for sequential infections. Next to the random factor of "mussel ID", we fitted the fixed effects "parasite species" (M. intestinalis vs. M. orientalis), the modified three-level factor "infection type" (single infection in either the 1st or 2nd infection round vs. con-specific infection using the same species in both infection rounds vs. hetero-specific infection using the other parasite species in the 2nd round) and the additional factor "infection round" (1st infection round vs. 2nd infection round). These main effects tested for differences between the factor levels of each factor, while the three-way interaction between parasite species, infection type and infection round tested whether either parasite species reacted differently on the prior presence of the same or other parasite species.
ACKNOWLEDGMENTS The authors would like to thank  Anouk Goedknegt  and  David Thieltges  for their help with mussel collections on Texel,  Kaibil Escobar Wolf  and  Timm Kress  for help with building the experimental setup,  Sonja Anslinger  and  Eike Petersen  for technical support in the lab, and  Karsten Reise  for helpful comments on the draft of this manuscript.
Analysis of nasopharyngeal proteins Total IgA, IgM, IgG1, IgG2, IgG3 and IgG4 were determined using the Bio-Plex Pro Human Isotyping Assay Panel (Biorad, Hercule, CA, USA). Data were acquired on a Bio-Plex 200 System (Bio-Rad) and analyzed with Bio-Plex Manager v5 (Bio-Rad). IgA1 and IgA2 subclass were measured by Simoa (Quanterix). Total IgD were determined using a ELISA kit (MBS564048, Mybiosource, San Diego). Total IgE were determined using a ELISA kit (88-50610, Invitrogen, Massachusetts). Data were collected with the Multiskan Spectrum (Thermo Fisher Scientific). Cytokines were quantified by Simoa® Cytokine 3-Plex B, Discovery or Advantage Kits (Quanterix) except IFN-γ and IL-17F that used Quanterix Homebrew assays. Nasopharyngeal mucin levels were analyzed using a MUC5AC ELISA Kit (NBP2-76703, Novus Biologicals, diluted 1/50).
Accordingly, Shannon and Simpson diversity indices were reduced in these GC/JAK3 patients (Figure  5C ). We further applied a non-metric multidimensional scaling (NMDS) using Bray-Curtis distances and found a similar distinctive clustering of GC/JAK3 patients not treated with IVIG (Supplementary Figure  5A ). In contrast, SCID-other patients with more complete hematopoietic reconstitution showed nasopharyngeal microbial communities that were more similar to healthy controls (Figures  5A-C , Supplementary Figure  5B ). Total bacterial load was also increased in GC/JAK3 patients not treated with IgRT compared to healthy controls and other SCID patients (Supplementary Figure  5C ). Nasopharyngeal microbial communities in these different SCID patients were further characterized by annotation of the 16S rRNA datasets.
pneumoniae abundance (Figures  6B, C ). These results suggest that nasopharyngeal mucus plays a role in homeostasis of local commensal microorganisms and is selectively reduced following HSCT for GC/JAK3. We next looked for possible correlations in the cytokine dataset that could associate with reduced MUC5AC levels in these SCID patients. The reduction in nasopharyngeal IgA and type 2 cytokine levels in GC/JAK3 patients (Figures  2, 3 ) paralleled the observed reductions in MUC5AC (Figure  6D, E ), suggesting a possible link. Type 2 cytokines are known to regulate barrier immunity by promoting mucus production  29, 30  but other factors stimulate epithelial cell renewal and differentiation, including IL-22  56. IL-22 levels were correlated with MUC5AC levels (Supplemental Figures 6C; see Supplemental Figures 6D for all 2-parameter correlations).
Acknowledgments We thank  Sean Kennedy  and  Laurence Motreff  ( Biomics Platform ) for 16S rRNA sequencing,  Amine Ghozlane  and  Emna Achouri  ( HUB ) for assistance with sequencing data analysis and the  Di Santo laboratory  for discussions. This study was supported by grants from the  Institut Pasteur, INSERM ,  ANR  ( 15-CE15-000-ILC3_MEMORY ) and  ERC  ( 695467-ILC_REACTIVITY ). P. Gonçalves was supported in part by the  Labex Milieu Intérieur  ( ANR 10-LBX-69 MI ). The  Biomics Platform is supported by France Génomique  ( ANR-10-INBS-09-09 ) and IBISA.
6745, 6 j i i where the median absolute deviation (MAD) is the residuals from their median. We use the median because it is more robust to outliers; the constant 0. 6745 makes the estimate unbiased for the normal distribution. The Hat matrix leverages (h j ) adjust the residuals by reducing the weight of high-leverage data points  (Vinnakota & Bugenhagen 2013). The tuning parameter (g) is set to 2. 385 (Cauchy weight value). As mentioned, the flux-transport model is based on two data sets: (a) from 1996 to 2010 April, the model is based on MDI/ SoHO data; and (b) from 2010 May to 2019, the model is based on HMI/SDO data. The solar images associated with these data sets have different spatial resolutions. To reduce inconsistencies in the detection of active regions, we estimate the free parameters separately for each solar cycle, transitioning between the two instruments shortly after the 2008 solar minimum.
Acknowledgements We thank  Ricardo Santiago  for help with data collection,  Nathalie Parthuisot  for help in fly care,  Arnaud Pocheville  and  Laure-Anne Poissonnier  for discussions,  Antoine Wystrach ,  Brice Ronsin  and  Patrick Arrufat  for the help with photos and  Zachary Garfield  for proof reading. Study supported by the  "Laboratoires d'Excellence (LABEX) "  TULIP  ( ANR-10-LABX-41 ), the  Toulouse Initiative of Excellence "IDEX UNITI"  ( ANR11-IDEX-0002-02 ) transversality grant and the  MoleCulture  ( ANR-18-CE37-0015 ) to GI and ED. MM's salary was provided by a grant from the  French Ministry  of higher education and research, at the  Laboratoire Évolution & Diversité Biologique  (EDB). SN acknowledges IAST funding from the  French National Research Agency (ANR)  under the Investments for the  Future (Investissements d' Avenir) program  ( ANR-17-EUR-0010 ).
Data availability The datasets generated during the current study are available in the Dryad repository, https:// doi. org/ 10. 5061/ dryad. 4qrfj 6qdv. https:// datad ryad. org/ stash/ share/ 8EBl4-bROo_ gyPJaD_ GuuGV xhesH ZWbfm xOrIn XbByk.
Thus, when replacing the defined numbers of boilers with ASHPs for each energy carrier in the scenario, we start with the dwellings built before 2011 that have the highest level of energy efficiency i. e. , a BER of A. A record is made of the number of dwellings for each BER category in which an ASHP is then deployed (See Table  6 ). However, in this scenario there is no spatial targeting for the deployment of heat pumps, rather our approach parses a database of Irish dwellings looking for 320,000 dwellings with oil boilers, 40,000 dwellings with natural gas boilers and 20,000 dwellings each with solid-fuel boilers and electric storage heating systems whose BER's are closest to B2. The resultant spatial distribution of heat pumps in Scenario 1 is thus dependant on where the most efficient dwellings are currently located rather than any other geographic factor.
Emission factors for CO 2 are listed in Table  2  while data on emission factors for PM 2. 5 and SO X are Tier 1 Emission Factors (EPA, 2018b). Those for PM 2. 5 are listed in Table  3. The tables of Tier 1 Solid Fuel combustion Emission Factors are from the EMEP/EEA air pollutant emission inventory guidebook -2016, a guidebook which supports the reporting of emissions data under the UNECE Convention on Long-range Transboundary Air Pollution (CLRTAP) and the EU National Emission Ceilings Directive. On the matter of condensables, the guidebook states that the TSP, PM 10 and PM 2. 5 emission factors have been reviewed and that it is unclear whether they represent filterable PM or total PM (filterable and condensable) emissions. Thus, a definitive statement on the inclusion or exclusion of condensables cannot be provided for these emission factors.
The country total emissions were provided for 10 anthropogenic source-sectors. To produce the meteorological data for both Europe and Ireland domains we used the Weather Research and Forecast Model (WRF) version 3. 9. 1 with NCEP FNL input data in a nested system. As the work presented in this paper focuses on residential sector heating systems, we present results for the heating season part of the year which is October to March. Fig. 4  shows model results of concentrations of PM 2. 5 across the island of Ireland for the heating season in 2015. This is the reference against which the two scenario results are measured.
1 • resolution  (Johansson et al. , 2017). This inventory is based on individual ship position, speed and direction recorded every 6 min through the ship-AIS system by all commercial vessels. This information is coupled with IMO registry ship engine information to produce emissions. The emissions for the transport sector are spatially distributed across the country at road network level based on annual average daily traffic values (AADTs) and commonly available information from existing geographical data, census data, traffic data and vehicle fleet data  (Fu et al. , 2017). For the reference year and both scenarios, estimated residential sector emissions of PM 2. 5 and SO X are distributed at Small Area Level based on both primary and secondary fuel use patterns across Ireland, which are calculated using census data and CSO Household Budget survey data by EnvEcon  (Kelly et al.
, 2020; Ó Broin et al. , 2019). For Northern Ireland (UK), the TNO-MACC (Kuenen et al. , 2011) was used. These data have a spatial resolution of approximately 7 × 7 km 2 , rescaled to match the official EMEP country total emissions. For scenarios 1 and 2, residential emissions for PM 2. 5 and SO X were recalculated using the same methodology as above.
Although the WRF/EMEP model is zoomed down to a fine scale, a grid square average concentration of 2 km × 2 km will not be able to reproduce the highest peak levels observed at a monitoring station. Furthermore, comparisons with observational data always raise the question of representativity. Particularly in urban and suburban areas, where large spatial gradients in the concentration fields will make it harder to find sites that are representative of a larger domain. The results from Clare Ennis show a significantly poorer agreement between modelled and observed concentrations both with respect to the absolute levels and the timeseries development. The measured PM 2. 5 level at that site is substantially higher than modelled. Fig. 6  presents five statistical metrics for each of the six stations as calculated by the R function 'ModStats' in the openair package  (Carslaw and Ropkins, 2012).
Acknowledgement This work builds on work from an  Irish EPA  project  -CON+AIR: Addressing Conflicts of Climate and Air Pollution  (  Ó Broin  et al. , 2019). The EnvEcon team have been supported by the  Department of Environment, Climate and Communications (DECC). The paper is dedicated to the late  Prof Martin Williams  who was an invaluable member of the  CON+AIR  Project  Steering Committee  and who passed away in September 2020.
Data availability Key data used in this work can be found at https://eparesearch. epa. ie/safer/the Secure Archive For Environmental Research Data hosted by the Irish Environmental Protection Agency (epa. ie); at https://www. cso. ie/en/census/census2016reports/the database of the Irish National Census of 2016 hosted by the Irish Central Statistics Office (cso. ie); and at https://www. seai. ie/data-and-insights/seai-statistics/key-publicati ons/energy-in-ireland/which are sector based energy use statistics from the Sustainable Energy Authority of Ireland (seai. ie).
The calculations were performed using the statistical software program package XLSTAT 2019. A single sample of C. brevipes, sourced locally in Cameroon, was evaluated using the Specialty Coffee Association (SCA) protocol and scoring system (https://sca. coffee/research/protocols-best-practices) and sensory terminology of the SCA Coffee Taster's Flavor Wheel. 18 he sample was assessed by a panel of three judges, with a Q-Grader leading the panel and providing a brief, narrative assessment. Further details of the methods used can be found in Davis et al. 5.
Coffea brevipes Coffea brevipes was named by Hiern in 1896 based on a sample collected in West Cameroon, by Gustav Mann in 1862. 39   and 'Ethiopia') and Robusta ('Indonesia'). The scale bar represents 2 cm (see Supplemental S4). Average weight of 100 green coffee beans (W100) in grams. Area (cm 2 ) and circularity (a value of 1 equates to a perfect circle) of the green beans, as measured by image analysis equipment (software ImageJ/Fiji). 80 m. 9, 22, 40  According to Burkill,  30  C. brevipes is used as wildsourced, local coffee beverage species in tropical West Africa. To our knowledge, this species has never been the subject of a sensory evaluation; its cup quality has not been recorded. Literature relating to this species is largely limited to botanical characterization. 21, 26, 41 Coffea brevipes is a small tree (often no more than 1-2 m high) with obovate leaves, possessing a distinct leaf tip, and red fruits approximately the same size as C.
Figure2. Seed weight and morphology of three coffee species, C. brevipes, C. congensis and stenophylla compared to (top) Arabica ('Brazil' and 'Ethiopia') and Robusta ('Indonesia'). The scale bar represents 2 cm (see Supplemental S4). Average weight of 100 green coffee beans (W100) in grams. Area (cm 2 ) and circularity (a value of 1 equates to a perfect circle) of the green beans, as measured by image analysis equipment (software ImageJ/Fiji).
ACKNOWLEDGEMENTS The authors thank the  Coffea Biological Resources Center (BRC Coffea , maintained by  IRD  and  CIRAD in Reunion Island)  for providing the plant material used in this study. We are also grateful to the main sensory panel judges from the following companies:  Supremo (Belgium) ,  Nespresso (Switzerland) ,  Jacobs Douwe Egberts (JDE, Netherlands) ,  Belco (France) ,  L'Arbre à Café (France) ,  AST Sensory Skills (France) ,  La Claque Café (France)  and  CIRAD (France) , and  Union Hand-Roasted Coffee (UK)  for undertaking the SCA evaluation of C. brevipes.
CircularityFigure 2. 2 Figure2. Seed weight and morphology of three coffee species, C. brevipes, C. congensis and stenophylla compared to (top) Arabica ('Brazil' and 'Ethiopia') and Robusta ('Indonesia'). The scale bar represents 2 cm (see Supplemental S4). Average weight of 100 green coffee beans (W100) in grams. Area (cm 2 ) and circularity (a value of 1 equates to a perfect circle) of the green beans, as measured by image analysis equipment (software ImageJ/Fiji).