dataesr/scientific-paragraphs-categorization huggingface dataset:
GenBank Accession Numbers The complete sequences of p1. 1. 1 (CP047113), p1. 1. 2 (CP047114), and all Tn7-like transposons, Tn6763 (MN641830), Tn6764 (MN628641), Tn6817 (MT469878), Tn6813 (MT469876), Tn6814 (MT469877), and Tn6765 (MT503200), identified in the present study were submitted to NCBI GenBank.
Genetic Characterization of Tn7-Like Transposons Among the Tn7-like transposons carrying strains, six multidrug resistant isolates were randomly selected for whole-genome sequencing, and yielded six completed Tn7-like transposons genetic structures. According to the transposon nomenclature 3 , we designated them Tn6763, Tn6764, Tn6765, Tn6813, Tn6814, and Tn6817. Tn6763 (GenBank accession number MN641830), Tn6764 (GenBank accession number MN628641), and Tn6817 (GenBank accession number MT469878) were located on the chromosomes of P. mirabilis, E. coli, and S. enterica, respectively. 3 http://transposon. lstmed. ac. uk/ They were typical Tn7 transposons comprising the transposition module (tnsA, tnsB, tnsC, tnsD, and tnsE), three gene cassettes (aadA1, sat2, and dfrA1), and an inactive class 2 integrase gene (Figures  2, 3 ).
Tn6813 (GenBank accession number MT469876) and Tn6814 (GenBank accession number MT469877) were novel Tn7-like transposons in chromosomes, acquired from E. coli SFE8 and SCZE5, respectively. Tn6813 was 32,688 bp in size and Tn6814 was 32,874 bp. The structures of the two Tn7-like transposons were respectively surrounded by two IS1 elements, while one IS1 of Tn6813 was incomplete with missing C-terminus. Transposition module (tnsA, tnsB, tnsC, tnsD, and tnsE) of Tn7 in Tn6814 was intact, but tnsD in Tn6813 was truncated by an IS26 element. Compared to canonical Tn7, the area's downstream of transposition modules of Tn6813 and Tn6814 differed by deletions involving class 2 integrons and its associated gene cassettes. Actually, Tn6813 had no intI2 and no gene cassettes (dfrA1-sat2-aadA1), and Tn6814 had only an aadA1 and an incomplete sat2 gene left (Figure  2 ).
Tn6765 (GenBank accession number MT503200) was located on a plasmid of P. mirabilis SCBX1. 1, named p1. 1. 2 (GenBank accession number CP047114). Sequence analysis showed that 19 resistance genes, except cfr and erm(B), were carried by the Tn7-like transposon (Figure  3 ). The novel MDR transposon harbored different resistance genes, including bla DHA-1 (cephalosporin resistance), qnrA1 (fluoroquinolone), aac(6')-Ib-cr (fluoroquinolone and aminoglycosides), floR (chloramphenicol/florfenicol), mphE and msrE (macrolide), and lunF (lincosamide) genes. However, the cfr and erm(B) gene was carried by another 12,795 bp plasmid p1. 1. 1 (Supplementary Figure  S1 ; GenBank accession number CP047113), which existed in the same strain as p1. 1. 2 (Figure  3 ). Tn6765 was 64,752 bp (corresponding to bases 47,309-112,060 in GenBank accession number CP047114) with an average GC content of 52.
94% that differed from that of the rest of the P. mirabilis plasmid (GC content, 44. 35%). It had partial characteristics of the Tn7 transposon, which contained transposase genes tnsA, tnsB, and tnsC. But its transposition module lost tnsE gene and the tnsD was truncated by insertion of the inverted repeat (IR mcp) of Tn1721 (Figure  3 ). The plasmid carrying Tn6765, named as p1. 1. 2, was 138,818 bp in size and had a GC content of 44. 35%. Blastn results showed that, except the Tn6765 region, other regions of plasmid p1. 1. 2. showed 99. 98% similarity and 91% coverage with nucleic acid sequence of P. mirabilis plasmid pPm60 (GenBank accession number MG516911), which also carried the Tn7-like transposon published in NCBI database (Figure  4 ).
The IS26 segment in Tn6814 showed high level homology to the segment characterized in E. coli plasmid pV408 (GenBank accession number LN735560), and it harbored different mobile genetic elements (complete or truncated) and two resistance genes (bla CTX-M-65 and fosA3). The bla CTX-M-65 -containing segment (IS26-fipA-IS1380-bla CTX-M- 65 -IS903B) in Tn6814 also showed nucleotide identity to the corresponding genetic structure that habored bla CTX-M-65 gene in Tn6813, with the exception of one IS26 sequence replaced by IS903B sequence in Tn6814 (Figure  2 ). Interestingly, the organisms of Tn6813 and Tn6814 came from the same swine farm, and therefore, the bla CTX-M-65 segment bounded by two ISs in Tn6814 could be derived from Tn6813 or vice versa and likely by ISs-mediated mobilization. Another resistance region bounded by IS26 in Tn6814 was identical to M1 module of pC271 in E.
coli (GenBank accession number LN735561;  Riccobono et al. , 2015) , containing resistance determinants to aminoglycosides [aac(3)-IV and aph(4)-Ia] and florfenicol (floR). Diverse mobile genetic elements in Tn6814 included four intact ISs (three copies of IS26, an IS903B, and an ISEc9) and one incomplete IS1380, while six intact ISs (four copies of IS26, an IS6, and an ISEc57) and five incomplete elements (three ISCR2, an IS1380, and a Tn5393) were inserted in Tn6813 (Figure  2 ). From the findings of various IS inserted sequences in these novel transposons, it is indicated that an increase in the diversity of Tn7-like transposons was prompted by the ISs-mediated homologous recombination. Tn6765 was highly homologous with Tn6450 (GenBank accession number MF805806), which was located on the chromosome detected by  Chen et al.
1. 1 (corresponding to bases 8,925 to 12,726 to 4,624 in GenBank accession number CP047113) harbors a genetic structure, showing homology to the cfr segment characterized in P. cibarius G11 (Genebank accession number CP047287), which is partially differ from the existence of IS26-cfr-▵Tn554 tnpB-▵Tn3 family tnpA-IS26 section in another Tn7-like transposon, Tn6451 (GenBank accession number MG832661; Chen et al. , 2019; Figure 5).
ACKNOWLEDGMENTS The authors are grateful to  Adam P. Roberts  ( University College London ) for helpful suggestionsregarding the nomenclature of  Tn6813 ,  Tn6814 ,  Tn6817 ,  Tn6763 ,  Tn6764 andTn6765. This manuscript has been released as a pre-print at ResearchSquare, (He et al. , 2020).
The presence of light-absorbing particles (LAPs) in snow leads to a decrease in shortwave albedo, affecting the surface energy budget. Precisely quantifying the impacts of LAPs on snowpack evolution is crucial to characterise the spatio-temporal variability of snowmelt and assess snow albedo feedbacks in detail. However, the understanding of the impacts of LAPs is hampered by the lack of dedicated datasets, as well as the scarcity of models able to represent the interactions between LAPs and snow metamorphism. The present study aims to address both these limitations by introducing a survey of LAP concentrations over two snow seasons in the French Alps, as well as an estimation of their impacts based on the Crocus snowpack model that represents the complex interplays between LAP dynamics and snow metamorphism.
First, we present a unique dataset collected at the Col du Lautaret (2058 m a. s. l; French Alps) for the two snow seasons 2016-2017 and 2017-2018. This dataset consists of spectral albedo measurements (manual and automated), vertical profiles of snow specific surface area (SSA), density, and concentrations of refractive Black Carbon (rBC), Elemental Carbon (EC) and mineral dust. Spectral albedo data are processed to estimate near-surface SSA and LAP absorption-equivalent concentrations near the surface of the snowpack. These estimates are then compared to chemical measurements of dust and BC concentrations, as well as to SSA measurements acquired by near-infrared reflectometry. Our dataset highlights large discrepancies between the two measurement techniques of BC concentrations, with EC concentrations being one order of magnitude higher than rBC measurements.
Such measurements are however essential to gather information about the seasonal evolution of LAPs at lower altitudes. Di  Mauro et al. (2015)  report detailed measurements of dust concentrations using both a particle counter and gravimetry techniques, but only for samples taken on a single day. A similar dataset is presented in Di  Mauro et al. (2019)  with one day of dust measurements after a strong deposition event. The longest monitoring program of LAP concentrations in the European Alps is presented by  Dumont et al. (2017) , covering two months at the end of a snow season at a mid-altitude site (1300 m). To our knowledge, no other intensive LAP monitoring survey has been conducted so far on seasonal snow in the Alps, which strongly limits the understandings of their impacts. There are mainly two experimental approaches to determine the radiative impact of LAPs in seasonal snow.
This unique dataset, presented in Section 2, consists of 30 measurement days during the 2016-2017 and 2017-2018 seasons. The spectral albedo measurements are first processed to estimate snowpack near-surface specific surface area (SSA) and AEC as described in Section 3. These data are then compared to physico-chemical measurements performed in snowpits. To address the second question, the impact of LAPs on snowpack evolution is then calculated using ensemble simulations with the multiphysics version of Crocus model  (ESCROC; Lafaysse et al. , 2017)  including TARTES. Two ensemble simulations accounting or not for the impact of LAP are run and compared to each other, providing an estimate of the impact of LAPs on snowpack evolution and the associated uncertainty. The results of our analysis are presented (Section 4) and discussed (Section 5) before conclusions are drawn (Section 6).
1 ). This site was chosen due to its high elevation and easy accessibility, even during the snow season. The dataset includes automated measurements and manual measurements, the latter acquired over 30 days during the two seasons. The following variables were collected: 1) Automated and manual spectral albedo measurements, 2) Snow physical properties from a snowpit including: snow depth, snow water equivalent as well as vertical profiles of SSA, density, temperature and snow type, 3) Vertical profiles of EC, rBC and dust chemical concentrations and 4) Meteorological measurements and snow depth from an Automated Weather Station. Manual measurements were performed approximately once a week. The actual measurement days were picked to favour stable illumination conditions (fully overcast or clear-sky) thus minimising the uncertainties associated with radiative measurements in changing conditions.
Snowpit measurements Snowpit measurements were performed on each field day for the uppermost 20 centimetres of the snowpack at least. For each new measurement, the snowpits were dug at least one meter away from the former snowpit's location, at a distance of 5 to 20 meters from the Automated Weather Station. Tuzet et al. (2019)  provide a detailed description of these measurements and the most important features of the dataset are recalled below. Vertical profiles of snow density and snow specific surface area (SSA) were collected at the same position as the manual albedo acquisitions. Density was measured with a 6 cm vertical resolution using a cylindrical cutter with a volume of 0. Libois et al. , 2014). The uncertainty associated with these SSA measurements was estimated to be 10%  (Arnaud et al.
(2004). The measured sizes span a range of 0. 6 to 21 µm and we assume here that insoluble particles above 0. 6 µm are mainly dust particles. The dataset also includes Organic Carbon (OC) and Elemental Carbon (EC) measurements in addition to the dataset described in  Tuzet et al. (2019). Snow was sampled with a vertical resolution of 10 to 20 cm, following  Voisin et al. (2012) , with stainless steel instruments and stored frozen (-30 • C) in pre-cleaned borosilicate glass bottles until further processing. Snow was then melted, and a coagulant (NH 4 H 2 PO 4 , 1. 5g / 100mL;  Torres et al. , 2014)  was added before filtering on precombusted QMA filters. The addition of the coagulant lets BC particles agglomerate and helps increase the filtration efficiency for BC to ≈ 90%. This is a key step, as QMA quartz filters in water have typical cutoff diameters around 500nm  (Lim et al.
, 2014; Torres et al. , 2014). EC/OC was then quantified on the entire filter (21 mm) by a Thermal Optical Transmission method (Sunset Lab instrument) following the EUSAAR-2 protocol  (Cavalli et al. , 2010). In this study, we focus on near-surface SSA and LAP concentrations. To obtain these near-surface properties, all the samples of SSA, dust, rBC and EC collected in the top layer are averaged. Note that the EC dataset has a lower vertical resolution (10 or 20 cm) inducing higher uncertainties in the near-surface concentrations.
On chemical measurements in snow Watson (  2005 ) indicates similar differences of about 1 order of magnitude. In our case, the observed bias between AEC from spectral albedo and chemistry is significantly reduced when using eqEC measurements instead of eqrBC (Figure  3  a). This is not surprising given the significant bias between eqrBC and eqEC concentrations (Table  1 ). When focusing on the two types of BC measurements, it appears that for all valid snow samples of our dataset, EC measurements are 10 times higher than rBC measurements on average but EC/rBC ratios range from 0. 5 to 30 depending on the sample (Figure  4 ). Similar findings have recently been observed for Arctic snow in the dataset of  Mori et al. (2019)  and are not unexpected. This suggests that the value of BC MAE can not be chosen independently of the measurement technique used for BC.
Conclusion This study provides an analysis of a unique dataset collected at Col du Lautaret (2058 m a. s. l. , French Alps) site during two snow seasons featuring contrasted meteorological conditions. This dataset comprises automated measurements of spectral albedo and meteorological variables as well as 30 days of manual measurements of spectral albedo, and vertical profiles of snow physical and chemical properties. Spectral albedo measurements are first processed to estimate near-surface SSA and AEC of the snowpack. Then the estimates are compared to the snowpit measurements. Near-surface SSA retrieved from spectral albedo measurements and measured in the snowpit are overall in good agreement. However, our dataset highlights strong discrepancies between different chemical measurements techniques for BC, with a mean EC/rBC ratio around 10 on average and ranging from 0.
Moreover,  Bergmann et al. (2019)  have recently shown the presence of microplastics in snow in remote snow-covered areas. We thus recommend further work to determine if this new type of particle in snow 1) has a significant radiative impact; and 2) affects uncertainties of other LAP concentration measurements. This dataset is also compared to ensemble snowpack simulations performed with the Crocus detailed snowpack model. Two ensemble simulations are performed, one assuming that no LAPs are deposited on the snowpack and another one using BC and dust deposition fluxes from the ALADIN-Climate atmospheric model. This simulation framework makes it possible to isolate the impact of LAPs on snowpack evolution while accounting for modelling uncertainties. Near-surface properties of Crocus simulations are in good agreement with measured values, except for a marked SSA bias for high SSA values.
Acknowledgements CNRM/CEN and IGE are part of  Labex OSUG@2020  (investissement d'avenir - ANR10 LABX56 ). This study was supported by the  ANR  programs  1-JS56-005-01   MONISNOW  and  ANR-16-CE01-0006  EBONI; the  INSU/LEFE  projects  BON  and ASSURANCE; the  Ecole Doctorale SDU2E of Toulouse  and the  CNES   APR grant MYOSOTIS. This research was at least partially supported by  Lautaret Garden-UMS 3370 ( Univ. Grenoble Alpes, CNRS, SAJF, 38000 Grenoble, France ), member of  AnaEE-France  ( ANR-11-INBS-0001AnaEE -Services, Investissements d'Avenir frame) and of the eLTER-Europe network (Univ. Grenoble Alpes, CNRS ,  LTSER Zone Atelier Alpes, 38000 Grenoble, France). The authors are grateful to LISA and  PSI  for chemical analysis of snow samples presented in this study.
Live microscopy. Oocytes were placed on glass-bottom dishes (MatTek, Ashland, MA) in a small drop of medium covered with mineral oil, and imaged with a Leica SP5 or SP8 confocal microscope, using a 63x or 20x oilimmersion objective. For 63x live imaging, a single confocal Z-plane was acquired every 1 min. For 20x live imaging, 20 confocal Z-planes separated by 3. 2 µm were acquired every 2 min. In both cases, the temperature was maintained at 37°C using a stage top incubator (INUBG2E-GSI, Tokai Hit, Shizuoka-ken, Japan) fitted on the microscope stage. Image processing and data analysis. Software. All image processing and data analyses were performed using ImageJ 1. 52t or Matlab 2015a either separately or together using the MIJ plugin (D. Sage, D. Prodanov, C. Ortiz and J. Y. Tivenez, retrieved from http://bigwww.
epfl. ch/sage/soft/mij). The graphics were produced using OriginPro 9. 0 software and exported to Adobe Illustrator CS6 for final processing. Live 3D segmentation and tracking procedure. To monitor the position of the chromatids clusters within the shell of the oocyte, we developed an automatic 3D segmentation and tracking procedure coded in Matlab and ImageJ. This method, based on grey levels thresholding, enabled us to extract the DNA clusters and the cell outlines in H2b-mCherry injected oocytes. Briefly, we filtered the images using Gaussian blurs to smoothen the edges and applied adaptive thresholding to account for the overall brightness reduction in the deeper part of the Z-stacks. We then used different threshold parameters to separate the DNA clusters from the cytoplasmic signal and finally tracked the DNA clusters over time using a proximity criterion.
The spindle mid-point distance to the oocyte centroid which we obtained by measuring the 3D Euclidian distance between the spindle mid-point (equidistant from the two clusters) and the oocyte centroid. The initial time of rotation (ti rotation) which we defined as the time when the spindle reaches 5% of its total fitted rotation. To fit the rotation, we manually selected the time-points corresponding to the actual rotation and fitted the results using a 5 parameters logistic model implemented in Matlab (G. Cardillo, retrieved from https://github. com/dnafinder/logistic5).
Particle image velocimetry (PIV). To measure the flow of cytoplasmic materials in activated oocytes, we devised a Matlab routine built on the PIVlab implementation of particle image velocimetry  (55). To avoid any interference with the analysis, we eliminated the vectors outside the oocyte using a mask. We smoothed the vector field in space and time with custom filters based on running averages. Finally, for display purposes, we color-coded the vector norm using the "quiverc" function (B. Dano, retrieved from https://www. mathworks. com/matlabcentral/fileexchange/3225-quiverc). In S1A Fig, we used an interrogation window of 80 pixels for the first pass and 40 pixels for the second pass. We smoothed data using a 5x5 vectors kernel in space and a 9 time-points kernel in time. In S1B Fig,  Fig 2G  and 5G , we used an interrogation window of 80 pixels for the first pass and 40 pixels for the second pass.
Acknowledgements We are grateful to the staff of the  ARCHE-Biosit animal facility and MRIC-Biosit microscopy facility  for technical assistance and expert advice. This work was supported by a  CNRS ATIP  fellowship to GH and the Ligue contre le cancer Grand Ouest (committees 35 and  56 ). G. H. wishes to thank the  Society for Reproduction and Fertility (SRF)  for the award of an  Academic Scholarship. B. D. received a PhD scholarship from the  French Ministry of Research and Higher Education , and complementary funding from the  Fondation pour la Recherche Médicale. The authors declare no competing financial interests.
Apart from a limited number of papers, such as the whole-lake exposure study with ethinylestradiol by  Kidd et al. (2007)  and the impact of pulp mill effluents by  Miller et al. (2015) , the AOP concept has not been widely applied to field-based datasets, particularly in relation to the environmental impact of complex mixtures. The effects of "real-life" chemical mixtures are a challenge for risk assessors. In this context, AOP networks could prove to be a vital tool to explore mixture effects in order to focus attention on chemicals and AOPs that really matter and thus to prioritise remediation efforts  (Villeneuve et al. , 2014). In environmental toxicology, the term 'biomarker' is generally used broadly to include almost any measurement reflecting an interaction between a biological system and a potential hazard, which may be chemical, physical or biological  (WHO, 1993).
In cooperation with the Amsterdam Angling Association (AHV), seine net (40 mm mesh) and electro-aggregate fishing were applied to collect fish at the open water and less accessible spots close to the waterfront, respectively. Length and weight of the fish was determined in the field. Apart from some selected bream with comparable length that were collected for biomarker and bioaccumulation studies, all fish were returned to the water at the sites where they were caught. Macro-invertebrate and fish data were used to determine biodiversity  (Shannon & Weaver, 1949)  indices. Procedures for the classification of the ecological status of macroinvertebrates and fish  (2006) (2007) (2008) (2009) (2010) (2011) (2012) , according to the Water Framework Directive (WFD), are described in the updated "Guideline WFD monitoring of surface water and protocol for assessment and classification" of the Dutch water authorities  (Ohm et al.
Statistical analyses Statistical tests were carried out with SPSS 10. 0 for windows (Statistical Product Service Solutions, Chicago, Il, USA). Biomarker data were analysed for statistical significance using the nonparametric Mann-Whitney rank sum test. Significant differences were determined at a 0. 05 level of probability, using a 2-tailed test. The difference between swimming performance of the 3 exposed groups was tested with one-way analysis of variance (ANOVA). Prior to ANOVA analysis, homogeneity of variance was tested with Levene test. If ANOVA showed difference between groups, the Bonferroni multiple comparison was used to determine the difference between means. Paired T-tests were performed to compare differences within the same group between swim tests 1 and 2. Again, significant differences were determined at a 0.
J o u r n a l P r e -p r o o f According to the SIMONI data there is a low risk of adverse ecological effects due to micropollutants at the A1 site (SRI <0. 5), despite slight EBT exceedances of PXR CALUX. The risks at both A2 and A3 sites were moderate, but acceptable (SRI 0. 5-1. 0). At the A2 site EBT exceedances of the in-situ Daphnia assay, the anti-AR and PAH CALUX assays were observed, while at the A3 site only the EBT of the anti-AR CALUX was exceeded. Although not all bioassays were applied in all sampling programs, SRI corrections were made to compensate for lacking bioassays (Van der  Oost et al, 2017a). A striking result is that, despite the remediation in 2002, increased risks of PAH pollution were still detectable at the A2 site.
Identifying potential adverse outcome pathways Despite the extensive range of field data that were collected in the present study, many relevant variables are still lacking to verify all interactions among pollutant exposure, molecular initiating events (MIE), key events (KE) and adverse outcomes. First, complete datasets could only be obtained for two of the five sites (A1 and A3). Second, important molecular markers (endocrine disruption) and key events (histopathology and embryo toxicity) were not investigated. Establishing J o u r n a l P r e -p r o o f the link between individual-based AOs and population-level responses is complicated by the fact that population outcomes are highly context-specific and depend on both internal (organismspecific) and external (site-specific) factors  (Groh et al.