dataesr/scientific-paragraphs-categorization huggingface dataset:
aeruginosa to the environment in patients with CF is low, with limited numbers of transposition events occurring during chronic infection  [90]. This finding contrasts with reports on in vitro evolution experiments with E. coli  [91, 92]. A search of the Pseudomonas database  [28]  with the nucleotide sequence of the CLJ1-ISL3 fragment revealed a total of 12 strains bearing 100 % identical fragments. Eight of these strains were isolated at Copenhagen University Hospital  [93] , and the four others in a Seattle-based hospital intensive care units  [94]. The precise positions of CLJ-ISL3 in those genomes and the phenotypes produced by the disrupted gene(s) are currently unknown, but this knowledge could provide insights into whether and how the CLJ-ISL3 sequence contributes to the colonization process and the adaptation of those strains to a particular infectious niche.
Funding information This work was supported by grants from the French National Research Agency (ANR-15-CE11-0018-01), the Laboratory of Excellence GRAL (ANR-10-LABX-49-01) and the Fondation pour la Recherche M edicale (Team FRM 2017, DEQ20170336705). Proteomics experiments were partly supported by the Proteomics French Infrastructure (ANR-10-INBS-08-01). We further acknowledge support from CNRS, INSERM, CEA and University Grenoble Alpes. Work in S. Five reasons to publish your next article with a Microbiology Society journal 1. The Microbiology Society is a not-for-profit organization. 2. We offer fast and rigorous peer reviewaverage time to first decision is 4-6 weeks. 3. Our journals have a global readership with subscriptions held in research institutions around the world. 4. 80% of our authors rate our submission process as 'excellent' or 'very good'.
Acknowledgements The authors thank  Maria Guillermina Casabona  for early proteomics experiments on CLJ strains, and  Yves Vandenbrouck  ( CEA, Grenoble ),  Fr ed eric Boyer  ( UGA, Grenoble ) and  François Lebreton  ( HMS, Boston ) for advice on genome analysis. We express our particular thanks to Myl ene  Robert-Genthon  and  Florian Chenavier  for RT-qPCR and PCR experiments and to  Peter Panchev  for performing the Galleria infection assays. We are grateful to  Tim Tolker-Nielsen  for the gift of the pUCP22-pcdrA-gfp(ASV) c plasmid.
datasets have been uploaded to GEO under identifiers GSE123106 and GSE123107. The MSy proteomics data were deposited with the ProteomeXchange Consortium via the PRIDE partner repository under dataset identifier PXD011105. These data can be accessed on https://www. ebi. ac. uk/pride/archive/. The RNA-Seq experiments were performed in accordance with the ENCODE data files. One supplementary figure and eleven supplementary tables are available with the online version of this article.
In this paper, we present the results of an SNMR monitoring experiment associated with a typical high-intensity Mediterranean rainfall event. Such SNMR monitoring is unique. In this paper, we present only non-inverted SNMR data for several reasons. Firstly, evaluation of non-inverted SNMR is required to discuss the applicability and effectiveness of the method to this particular context. It also provides qualitative insights into karst UZ recharge dynamics. Secondly, in the actual stageof our study, time-lapse inversion code was in the process of being developed and validated  [15]. Additional evaluation tests need to be performed to ensure the validity of their outcomes, particularly for the UZ of karst hydrosystems. Finally, by presenting only the SNMR non-inverted data, we avoid confusion with the technical details of the inversion methodologies and the related uncertainties that exceed the scope of both this Special Issue and the journal.
Signal Processing Data were acquired using a NUMIS POLY SNMR instrument and Prodiviner data acquisition software. A post-processing procedure was completed using the NumisPro 11 × 62 program and consisted of two types of signal filtering: (i) a 15 Hz band-pass and (ii) a 50 Hz notch filter. SNMR signal parameters are derived using the least square-curve-fitting estimator  [23].
SNMR Limitations It is important to highlight the limitations of the SNMR monitoring in this context. The first limitation is related to the ambient electromagnetic noise that can seriously decrease the SNMR data quality. This ambient noise may be either natural or anthropogenic, and it can vary in time and in space. Even though it can be treated with a post-processing procedure, ambient noise can be a severe limitation to the success of time-lapse SNMR measurements. The second limitation is the duration of the SNMR sounding in addition to the integrated nature of the method. With an average of 6 h for a complete sounding, over a large investigated underground volume, SNMR does not have the sensitivity to monitor the rapid circulation that may occur within fine karst features, and the signal could be affected by the lateral inhomogeneity of the medium.
Conclusions and Perspectives Our data provide the first SNMR monitoring of infiltration dynamics in a karst UZ on the event time scale. Eight SNMR soundings were conducted over 79 days. The obtained results demonstrated the applicability and the effectiveness of SNMR for investigating the hydrogeological processes of karst UZs at a time scale of a few days. Non-inverted data do not allow quantification of groundwater properties, but they provide a valuable insight into UZ infiltration dynamics. Five days after the rain event, the infiltrated water led to an increase in the SNMR signal. The draining process was monitored 15 days after the main event. Finally, the SNMR signal returned close to the observed initial state after 42 days. The next steps of this study are to quantify water contents using a time-lapse data inversion code, which is currently under development and validation.
Acknowledgments: The authors would like to express their gratitude to the LSBB team for their technical and logistic help. This work was performed within the framework of the FDV/LSBB observation site, which is part of the  OZCAR Critical Zone network  (https://www. ozcar-ri. org/fr/ozcar-3/), H+ observatory network  (http://hplus. ore. fr/en/), and KARST observatory network (http://sokarst. org/).
, Y , J, and H (see Figure  1 ), and a low-resolution slitless spectrometer  (Schweitzer et al. 2010). The Euclid Wide Survey is expected to cover 15 000 deg 2 down to 10σ depth of 24. 5 mag in the visible filter and down to a 5σ depth of 24. 0 mag at nearinfrared wavelengths. A deep survey two magnitudes deeper than the main survey will also be conducted over 40 deg 2 in the Euclid Deep Fields. In addition to these main Euclid surveys, extensive plans are in place to complement Euclid observations with ground-based data from the ultraviolet to visible light  (Laureijs et al. 2010; Ibata et al. 2017 ) in order to improve the sampling quality of the SED for each galaxy. This is of course very challenging, given that the goal is to observe uniformly almost the entire extra-galactic sky at Euclid depth, using ground-based instruments.
Overall, this extraordinary galaxy survey will be crucial not only for cosmological studies, but also to investigate several Legacy science key questions, especially related to galaxy formation and evolution. Given that quiescent and star-forming galaxies represent the two most common evolutionary phases of galaxies, and considering the large amount of galaxies that will be observed by Euclid , it is essential to obtain a fast and reliable criterion to select quiescent and star-forming galaxies with the Euclid photometric capability, as this will be the first step for many future studies. One of the dominant difficulties for this endeavour is the main Euclid filter, V IS: its uncommonly large wavelength range was especially designed for Euclid and has therefore never been used or tested with real data (see Table  1 ).
The SED data set For the second approach, we derive mock observations from the best theoretical template that describes the SED of each galaxy. For this, we use the observations in 30 filters of the COSMOS2015 catalogue. In particular, we use the public code LePhare  (Arnouts et al. 1999; Ilbert et al. 2006 ) and consider  Bruzual & Charlot (2003)  templates with solar and subsolar (0. 008 Z ) metallicities, exponentially declining star formation histories with timescale τ between 0. 1 and 10 Gyr, ages between 0. 1 and 12 Gyr, the  Calzetti et al. (2000)  reddening law, and 12 values of colour excess between 0 and 1. We did not apply any cut in S/N on the observed COSMOS2015 observations and we considered magnitude errors and upper limits as derived by  Laigle et al. (2016). We only apply a lower limit to the magnitude errors, i.
5] that are within the selection, and FP is the false-positive fraction, i. e. , the fraction of star-forming galaxies [log10(sSFR/ yr -1 ) >-10. 5] in the sample lying within the selection. We decide to maximise the quantity C (1 -FP) because, generally, the criteria that maximises the completeness corresponds to a false-positive fraction higher than the completeness, whereas the criteria that minimise the false-positive fraction corresponds to a very low completeness. The best separation criterion is  We repeat the procedure for the data sets obtained from real galaxy observations (data sets SED and Int). All values derived for each data set are presented in Table  4. We then combine the results by averaging the completeness and falsepositive fraction of all data sets in the considered parameter space and we derive the best line of separation for quiescent galaxies by maximising again the quantity C (1 -FP).
Note that we do not average the best lines of each data set; we average the completeness and false positive fraction of each possible line in the four data sets and then derive the best line. Moreover, we do not apply any weight on the different data sets, as each of them has different drawbacks and strong points. For example, the SED data sets have photometric errors similar to what is expected for Euclid , but the Int data do not apriori assume a shape for the SED. In order to provide galaxy selection criteria at different redshifts, we derive the redshift evolution of each parameter in Equation  1. This is done from the average completeness and false-positive fraction to ensure the stability of the final results compared to the method used to obtain mock observations. Because the errors of the parameters are correlated, we cannot perform an independent fit to the evolution of the parameters that describe the selection area.
From top to bottom: the slope, the intercept, the lower limit in (u -V IS) colours and the upper limits in the (V IS-J) colours. Mock observations are obtained from the best fitting SED template describing the COSMOS2015 observations (orange squares) and from the interpolation of the COSMOS2015 observations (green triangles). We consider the observational depth planned for both the Euclid Wide Survey (filled symbols) and the Euclid Deep Survey (empty symbols). Black crosses correspond to the best-line derived considering the average completeness and false-positive fraction for the four data sets. Coloured data points are slightly shifted horizontally for clarity, while black crosses mark the centre of each bin. The red solid line shows the best fit for each parameter (see Equation  2 ), as derived from the average completeness and false-positive fraction.
Marginalised error bars correspond to the parameters values for which the quantity C (1 -FP) varies by less than 10% in each different data set. ure 9). The average fraction of false-positives is below 15% at z 1. 25, with a maximum of ∼ 20% at the highest redshifts. We find that the average completeness is above 55% at all redshifts. However, the selection is particularly effective at 0. 25 < z ≤ 1, where the completeness is greater than ∼ 70%. Note that the completeness of the Int Deep data set is quite low. This is due to some galaxies with intermediate colours that are particularly faint and have large photometric errors in the Euclid Deep Survey and are too faint to be detected in the Euclid Wide Survey. In general, false-positive fractions are higher for galaxies in the Int Wide data set.
It is important to consider that both of these data sets are affected by the photometric errors given by the COS-MOS2015 catalogues that are typically larger than the errors expected for Euclid. These inflated photometric errors may have negatively affected the recovered false-positive fraction and completeness. In Figure  9  we also show how the completeness and false-positive fraction vary with the observed V IS magnitude for galaxies at z ≤ 1. 5. The average false-positive fraction remains almost constant (between 11% and 16%) for V IS magnitudes between 18 and 25 mag, with lower false positive fractions for both brighter and fainter objects. On the other hand, a clear trend is visible between the completeness and the V IS observed magnitude, with an average completeness above 80% at magnitudes brighter than 22 mag and a steady drop at fainter magnitudes.
For both Deep Surveys the drop in completeness happens at around 23 mag for both the Int and SED data set. The difference between the completeness in the Wide and Deep Surveys are due to the different uncertainties associated to each galaxy, but also to the different depths in the u-band, i. e. the Deep Survey is two magnitudes deeper. At V IS > 22 mag, only the bluest quiescent galaxies are detected in the uband. This selection is more important in the Wide Surveys than in the Deep surveys (see also Figure  5 ). These are galaxies with relatively higher sSFR and are generally the most difficult to disentangle from star-forming galaxies. To give a more quantitative example, galaxies in the SED Wide data set at z≤1. 5 and detected in the H, J, and u filters have a median log 10(sSF R/yr -1 ) = -12.
2. The subsample of galaxies that have the same redshift and detection selection, and also have V IS >22 mag have median log 10(sSF R/yr -1 ) = -11. 1. On the other hand, the same selections in the SED Deep data set produces less of a difference between the two subsamples that have median log 10(sSF R/yr -1 ) = -11. 8 and -11. 7, respectively. We conclude that the (u -V IS) vs. (V IS -J) colours can be used to isolate quiescent galaxies using the selection described in Equation  2 , with a generally low contamination by star-forming galaxies and a completeness above 60%, at least up to z ∼ 1. For comparison, the U V J diagram has been tested and used up to z ∼ 3. 5, but, as we previously mentioned, the U , V , and J rest-frame magnitudes are challenging to derive with only the four Euclid filters.
Indeed, if we consider their small number and their mixing in colour space, we realise that the separation criteria would be poorly constrained at higher redshifts. Colours are shown for the data set SED Wide and they are overall similar to the colours of the other five data sets. We overall find that the star-forming and quiescent galaxies show similar (V IS -Y ) and (J -H) colours at low redshift and their separation becomes clearer and cleaner with increasing redshift. This is mainly due to the absence of filters tracing the λ = 4000 Å-break at z < 1, which is the most prominent feature of an old stellar population. 3 This is not surprising given that the science goals of the Euclid mission focus their attention at z > 1. At z > 1, the V IS band starts to trace near-UV to optical light, while all other bands still trace wavelengths redward of the 4000 Åbreak and, indeed, quiescent galaxies have redder (V IS -Y ) colours than star-forming objects.
5] and quiescent galaxies [log 10 (sSFR/ yr -1 ) < -10. 5], respectively. The black lines show the separation between quiescent and star-forming galaxies that maximises the quantity C (1 -FP). On the top left of each panel we report the completeness (C) and false-positive fraction (FP) of the quiescent galaxy selection with the corresponding Poisson errors. The selection using Euclid filters works best in the redshift range 1 < z < 2, where we find a completeness above 65%. (V IS -Y ) > m (J -H) + q , (V IS -Y ) > L low We average the results of the mock galaxies of all the six data sets to obtain the evolution of the line separating star-forming and quiescent galaxies with redshift (Figure  12 ). In the Euclid Flagship mock galaxy catalogue used for the Flag Wide and Flag Deep data sets, there are almost no quiescent galaxies at z > 1.
We do not find substantial differences between the Wide and Deep Surveys. Most differences arise from a variation in the data sets, particularly between the data sets derived from real galaxy observations (SED and Int data sets) and those from the simulated galaxies (F lag data sets). In particular, as investigated in more details in Appendix A, the F lag data sets have on average galaxies with lower sSFR and fainter VIS magnitudes than the other two data sets. Star-forming galaxies with relatively low sSFR are generally more difficult to separate from the quiescent galaxies and this influences the recovered completeness and false-positive fraction. Overall, we conclude that (V IS-Y ) and (J -H) colours can be used to select quiescent galaxies at 1 < z < 2 (0. 75 < z < 2) with an average completeness above 65% (55%) and with false-positive fractions typically below ∼ 20%.
SUMMARY Colour-colour selections are widely used and well accepted methods in extragalactic astronomy to separate different galaxy population, such as quiescent and star-forming galaxies. Given the limited number of filters in general and the unusually wide visual filter in particular designed for the Euclid telescope, it is vital to determine a framework astronomers can use for this purpose with the extensive imaging data that will arise from Euclid. In this paper, we show that Euclid filters alone are not sufficient to pin down a best fit template to determine the rest-frame colours based on U , V , and J bands used in standard selections, nor are they adequate to derive specific star-formation rates. We therefore derive Euclid specific selection criteria for the separation of quiescent and star-forming galaxies using Euclid observed colours.
To do so, we define three different sets of mock Euclid observations: i) the first interpolates the multi-wavelength observations of galaxies in the COSMOS2015 catalogue; ii) the second uses the best theoretical template describing the multi-wavelength observations of galaxies in the COS-MOS2015 catalogue; iii) the third takes galaxy parameters from the Euclid Flagship mock galaxy catalogue. Each data set contains mock observations for Euclid 's visible V IS filter, and the near-infrared filters NISP Y , J, and H. Data sets i) and ii) also include CFIS/u band observations. Similar u-band data will be available with other overlapping surveys such as LSST. By selecting galaxy types in the commonly accepted U V J plane derived from these mock observations, we only recover ∼ 20% of the original quiescent galaxy population up to redshifts z = 3.
ACKNOWLEDGEMENTS UK acknowledges support from  STFC. CC acknowledges the support of the  STFC Cosmic Vision funding. CT  acknowledges funding from the  INAF   PRIN-SKA 2017 program   1. 05. 01. 88. 04. The Euclid Consortium acknowledges the  European Space Agency  and the support of a number of agencies and institutes that have supported the development of Euclid , in particular the  Academy of Finland , the  Agenzia Spaziale Italiana , the  Belgian Science Policy , the  Canadian Euclid Consortium , the  Centre National d'Etudes Spatiales , the  Deutsches Zentrum für Luft-und Raumfahrt , the  Danish Space Research Institute , the  Fundação para a Ciência e a Tecnologia , the  Ministerio de Economia y Competitividad , the  National Aeronautics and Space Administration , the  Netherlandse Onderzoekschool Voor Astronomie , the  Norwegian Space Agency , the  Romanian Space Agency , the  State Secretariat for Education, Research and Innovation (SERI)  at the  Swiss Space Office (SSO) , and the  United Kingdom Space Agency.
China's fossil-fuel CO 2 (FFCO 2 ) emissions accounted for approximately 28 % of the global total FFCO 2 in 2016. An accurate estimate of China's FFCO 2 emissions is a prerequisite for global and regional carbon budget analyses and the monitoring of carbon emission reduction efforts. However, significant uncertainties and discrepancies exist in estimations of China's FFCO 2 emissions due to a lack of detailed traceable emission factors (EFs) and multiple statistical data sources. Here, we evaluated China's FFCO 2 emissions from nine published global and regional emis-sion datasets. These datasets show that the total emissions increased from 3. 4 (3. 0-3. 7) in 2000 to 9. 8 (9. 2-10. 4) Gt CO 2 yr -1 in 2016. The variations in these estimates were largely due to the different EF (0. 491-0. 746 t C per t of coal) and activity data.
The large-scale patterns of gridded emissions showed a reasonable agreement, with high emissions being concentrated in major city clusters, and the standard deviation mostly ranged from 10 % to 40 % at the provincial level. However, patterns beyond the provincial scale varied significantly, with the top 5 % of the grid level accounting for Published by Copernicus Publications on behalf of the European Geosciences Union. 11372 P. Han et al. : Evaluating China's fossil-fuel CO 2 emissions from a comprehensive dataset of nine inventories 50 %-90 % of total emissions in these datasets. Our findings highlight the significance of using locally measured EF for Chinese coal. To reduce uncertainty, we recommend using physical CO 2 measurements and use these values for dataset validation, key input data sharing (e.
Thus, an accurate quantification of China's CO 2 emissions is the first step toward understanding its carbon budget and making carbon control policy. The total emission estimates for China are thought to be uncertain or biased due to the lack of reliable statistical data and/or the use of generic emission factors (EFs) (e. g. ,  Guan et al. , 2012; Liu et al. , 2015b). National and provincial data-based inventories use activity data from different sources. The Carbon Dioxide Information Analysis Center (CDIAC) uses national energy statistics from the United Nations (UN)  (Andres et al. , 2012) , and both the Open-Data Inventory for Anthropogenic Carbon Dioxide (ODIAC) and Global Carbon Project (GCP) mainly use CDIAC total estimates, and thus they are identical in time series  (Le Quéré et al.
, 2018; Oda et al. , 2018). The Emissions Database for Global Atmospheric Research (EDGAR) and Peking University CO 2 (PKU-CO 2 , hereafter named PKU) derive emissions from the energy balance statistics of the International Energy Agency (IEA)  (Janssens-Maenhout et al. , 2019; Wang et al. , 2013). In contrast, provincial data-based inventories developed within China all use the provincial energy balance sheet from the China Energy Statistics Yearbook (CESY), National Bureau of Statistics of China (NBS)  (Cai et al. , 2018; Liu et al. , 2013 Liu et al. , , 2015a;; Shan et al. , 2018). There are generally four sources of EFs, i. e. , (1) the Intergovernmental Panel on Climate Change (IPCC) default values, which have been adopted by ODIAC and EDGAR  (Andres et al. , 2012; Janssens-Maenhout et al.
, 2019; Oda et al. , 2018) ; (2) National Development and Reform Commission (NDRC) (NDRC, 2012b); (3) China's National Communication, which reports to the United Nations Framework Convention on Climate Change (UNFCCC)  (NDRC, 2012a) ; and (4) the China Emission Accounts and Datasets (CEADs) EF, which are locally optimized through large sample measurements  (Liu et al. , 2015b). The existing estimates of global total FFCO 2 emissions are comparable in magnitude, with an uncertainty that is generally within ±10 % (Le  Quéré et al. , 2018; Oda et al. , 2018). However, there are significant differences in these values at the national scale  (Marland et al. , 2010; Olivier et al. , 2014) , with the uncertainty ranging from a few percent to more than 50 % in the estimated emissions for individual countries  (Andres et al.