dataesr/scientific-paragraphs-categorization huggingface dataset:
Enteritidis fimbriae) operon in the Salmonella pathogenicity island (SPI) SPI-10 (Fig. 1B ), thus confirming the chromosomal location of a partial pefI-srgC operon in some serovars as suggested by Collighan et al. using Southern hybridizations  (18). These data suggest a common evolutionary process of S. Paratyphi A, S. Typhi, S. Dublin, and S. Enteritidis chromosomes in the pefI-srgB region. It is also interesting to note that a srgB allele displaying 81% identity with p14028s srgB was retrieved alone on 11 other serovar chromosomes, including S. Paratyphi B, S. Poona, S. Sendai, and S. Montevideo (data not shown). Even if we did not find any new complete pefI-srgC operon sequence in the reference genomes studied, these results highlighted the presence of partial pefI-srgC operons in several new serovars and prompted us to perform a wider analysis using Enterobase.
Distribution of the pefI-srgC operon in Salmonella species and subspecies using Enterobase. To extend this analysis, we retrieved the whole-genome multilocus sequence typing (wgMLST) sequence types (ST) generated by Enterobase from a total of 188,233 assemblies of Salmonella assigned by the Salmonella in silico typing resource (SISTR) prediction tool to the two Salmonella species and to all subspecies, including 465 S. enterica subsp. enterica different serovars. One should consider a bias in this analysis when interpreting the results: the presence of numerous false-negative hits in the data set. For example, srgC was not detected in any S. Bovismorbificans strain and usually was lacking in S. Enteritidis by wgMLST, while contig analysis clearly indicated its presence downstream of rck (data not shown).
Consequently, the absence of an ORF in the wgMLST scheme corresponds either to a real absence of this ORF or to a false negative. As specified on the database documentation, this phenomenon occurs when the sequence of the allele of interest is not trustworthy, being either fragmented or duplicated (https://enterobase. readthedocs. io/en/latest/pipelines/backend -pipeline-nomenclature. html). In contrast, a positive result always signifies the presence of an ORF. Among the extracted assemblies, 67,596 were associated with at least one ORF of the pefI-srgC operon, most of them belonging to S. Enteritidis and S. Typhimurium serovars, whose genomes are the most represented in the database. The profiles of the pefI-srgC operon in each serovar are described in Table  S2  in the supplemental material.
Interestingly, the diversity of the allelic profile of these serovars was strongly biased toward a dominant profile displaying very high frequencies (i. e. , according to the allelic profiling categories used in Enterobase; S. Dublin: profile 5-4-6-64-0-0, P = 0. 977; S. Paratyphi A: profile 20-2-35-55-0-0, P = 0. 992; S. Typhi: profile 2-2-4-3-0-0, P = 0. 911). Altogether, these results confirm the data obtained with reference genomes.
g. , Infantis, Heidelberg, Blegdam, Onarimon, Moscow, Lexington, Panama, Anatum, and Weltevreden)  (22, (26) (27) (28) (29) (30) (31). Regarding our interesting preliminary results, we intended to investigate further on these serovars except for serovars Blegdam, Onarimon, and Moscow, which were missing in our data set. Only three assemblies were positive for serovars Heidelberg (1/ 4,528) and Infantis (2/7,405). No assemblies from the other serovars were positive for the pefI-srgC operon. rck ORF distribution within the assembled genomes database Enterobase. The Rck invasin, encoded by rck, is the most characterized protein of the pefI-srgC operon. It acts as an invasin through interaction with EGFR to mediate a zipper-like internalization within a variety of mammalian cell lines in vitro.
It also acts as a resistance to complement factor through interaction with multiple inhibitors of this innate defense system. We thus decided to deepen our analysis on the distribution of this virulence protein using the wgMLST data retrieved from Enterobase. We were able to confirm the presence of the ORF in S. Typhimurium (monophasic variant: 1,459/1,560; diphasic variant: 15,814/35,973), S. Enteritidis (31,480/35,996), and S. Bovismorbificans (157/653) assemblies. Additionally, 33 new serovars and two subspecies were associated with the gene encoding this invasin (Fig. 3 ). Among these serovars, some are part (S. Enteritidis and S. Typhimurium) of the most frequently isolated serovars responsible for human salmonellosis in the European Union from 2017 to 2019, such as S. Newport (n = 8/ 9,364), S.
S2A to G ). Polymorphism of the rck ORF. rck 1 sequences were found to harbor a noteworthy diversity of allelic variants. Indeed, 89 different alleles (including those previously described on p14028s [number 1], pSENV [number 3], and pVIRBov [number 15]) harbored by a total of 49,001 strains were retrieved from our data set. Comparison of these sequences revealed that the rck ORF is very well conserved, with 81/88 displaying more than 95% of nucleotide identity with allele number 1. However, the phylogenetic tree based on this alignment revealed the existence of two clades, one related to allele number 1 and the other to allele number 3 (Fig. 3 ). In addition to being the dominant allele retrieved from S. Typhimurium assemblies (monophasic variant: 1,455/ 1,459; diphasic variant: 15,603/15,814), allele number 1 was also retrieved from 60 S.
DISCUSSION The rck ORF, which belongs to the pefI-srgC operon itself carried on the large virulence plasmid of Salmonella, encodes an OMP involved in cell invasion and complement resistance. Little was known about its distribution, and while it was assumed that it was restricted to only a few serovars, several reports have established the presence of virulence plasmid-associated genes in less characterized serovars  (18, 26, 30). Moreover, other ORFs of the operon (pefI, srgD, srgA, and srgB) have been described on the serovar-specific pathogenicity island SPI-10  (35, 36). The constant progress in genomics and the improvement of sequencing techniques has allowed for an increase in the number of sequenced and characterized genomes. We took advantage of the large number of Salmonella genome sequences available to investigate further the distribution of the operon and more specifically of the rck ORF using both complete genomes on ATCC and Enterobase databases.
ACKNOWLEDGMENTS This work was supported by the  ERANET InfectERA  project " Sal host trop " funded by the  Agence Nationale pour la Recherche. M. K. holds a doctoral fellowship from the  University of Tours (France). The authors are grateful to  F. -X. Weill  ( Institut Pasteur, Paris ) for the kind gift of the S. Bovismorbificans 201910217 strain. We thank  C. Clamagirand  and  C. Leprout  for technical We also thank  Florent Di Meo  ( INSERM U1248 IPPRITT, Limoges ) for his advice in structure modeling.
Data availability. All supporting data and protocols have been provided within the article or through supplementary data files. Data from Enterobase as well as those originating from the ATCC database are publicly available. pSUP202-rck-C pSUP202 containing S. Bovismorbificans 201910217 rck ORF (variant no. 15) and its RBS (Cm r , Cb r ) This work pSUP202-rck-D pSUP202 containing rck gene variant no. 7 and its RBS (Cm r , Cb r ) This work pSUP202-rck-E pSUP202 containing rck gene variant no. 8 and its RBS (Cm r , Cb r ) This work pSUP202-rck-F pSUP202 containing rck gene variant no. 16 and its RBS (Cm r , Cb r ) This work pSUP202-rck-G pSUP202 containing rck gene variant no. 19 and its RBS (Cm r , Cb r ) This work pSUP202-rck-H pSUP202 containing rck gene variant no. 22 and its RBS (Cm r , Cb r ) This work pSUP202-rck-I pSUP202 containing rck gene variant no.
Calibration To solve the non-linear stochastic model, we linearized the optimal conditions and the equilibrium conditions around the steady state. We then run simulations by using the Dynare program  (Adjemian et al. , 2014). The benchmark calibration of the model is displayed in Appendix B. For standard parameters, which are related to the behavior of households and firms (preferences and technology), we choose plausible values by drawing on the literature about DSGE models and estimates for the Eurozone (e. g. Smets and Wouters, 2003; Christiano et al. 2005; Coenen et al. , 2008; Jondeau and Sahuc, 2008; Kollmann et al. , 2013; Albonico et al. , 2019). The parameter of home bias in private consumption is set at 0. 75, and the parameter of home bias in public consumption is set at 0. 89. These values are derived from import contents of private consumption and government consumption, which are respectively 29% and 11% on average in eurozone countries  (Bussière et al.
, 2013). 20 As for the value of the parameter of home bias in sovereign bond holdings, it is 0. 70. We computed it by using ECB data on the share of securities issued by eurozone governments in total securities held by eurozone monetary and financial institutions. The elasticity of substitution between domestic and foreign bonds is set at 3. 4. It is taken from  Alpanda and Kabaca (2015). It is higher than the elasticity of substitution between domestic and foreign goods (1. 5). The benchmark calibration of the monetary policy rule is made following estimates from  Kollmann et al. (2013). We allow for a strong degree of inertia in the fiscal policy rule, which is also consistent with estimated parameters in  Kollmann et al. (2013). In the baseline model, we disregard any cyclical response of public consumption in order to focus on the impact of the public consumption shock.
11), with a slightly different value in the case of Limited Eurobonds (0. 13). The steady-state government debt-to-GDP ratio is set at 100%. This value is not far from the average government debt/GDP ratio in the Eurozone, which has been above 90 percent since 2012 (AMECO database of the European Commission). The steady-state government consumption/GDP ratio is set at 0. 20, which is the average in the Eurozone (AMECO). Tax rates are taken from the European Commission (2011): the implicit tax rates on labor income and consumption in the Eurozone are respectively 0. 33 and 0. 20. As regards the elasticity of the risk premium to deviation of the government debt-GDP ratio from its steady-state value, it is different under the three cases of government debt issuance. To have an idea of its size, we use the results of  Corsetti et al.
Satellite Data Acquisition and Preprocessing The study area is covered by two Landsat image tiles obtained through the US Geological Survey (USGS) with referencing paths of 205 for both regions and rows of 50 and 51 for Saloum and Casamance, respectively. The surface reflectance images are from the USGS Collection 2 Level 2 and were produced using the LaSRC and LEDAPS algorithms for the Landsat 8 OLI and Landsat 7 ETM+ data, respectively (Table  1 ). Both image sets were corrected for atmospheric effects. Panchromatic images of the area were acquired on the same dates and were taken from Collection 2 Level 1, because they were not included in Collection 2 Level 2. Two Landsat 8 OLI images were acquired on 18 December 2018 for the end date of the diachronic study, and two Landsat ETM+ images were acquired on 8 December 2000 for the initial date.
Pansharpening The mangrove vegetation formations are patches, each with its own morphological characteristics. The high and dense mangrove, often located on the edge of the channel, is a long and narrow strip of vegetation. It rarely exceeds several dozens of metres in depth on the foreshore. From this point of view, the Landsat data at a 30-m resolution are too coarse to discriminate the patches of high and dense mangrove on the edge of the channel with an OBIA approach. However, an OBIA approach using Sentinel-2 images at 10 m has shown some robustness  [40]. A compromise was therefore found by using pansharpening fusion method to obtain images at a 15-m resolution. The IHS (Intensity-Hue-Saturation) method was chosen for its speed of implementation and the good results that it generates in subsequent calculations of spectral indices, such as the NDVI  [41].
We have chosen to randomly select the objects used for training and validation. The partitioning of the dataset was also done randomly. The purpose of Table  6  is to assess the uncertainty of the results due to the sampling. We followed the recommendations of Olosfon et al. (2014)  [54]  to evaluate the accuracy and confidence intervals of the map classes. The margins of uncertainty are large for the high and dense mangrove class but remain smaller for the low and dense mangrove class and the low and open mangrove class. The smallest margins of uncertainty are for the classes that are most involved in the change maps developed in this study. Since the 2000s, mangrove areas have increased in the two main hydrosystems of Senegal. There has been an increase of 5330 hectares of mangrove area in the Saloum delta and 11,614 hectares in the Casamance estuary.
1. `rbioacc` is an `R`-package dedicated to the analysis of experimental data collected from bioaccumulation tests during which organisms are exposed to a chemical (exposure phase) and then put into a clean media (depuration phase). Internal concentrations are regularly measured over time all along the experiment. 2. `rbioacc` provides ready-to-use functions to visualize and fully analyze such data. Under a Bayesian framework, this package fits a generic one-compartment toxicokinetic (TK) model automatically built from the data. It provides TK parameter estimates (appropriate uptake and elimination rates) and bioaccumulation metrics (e. g. , BCF, BSAF, BMF). All parameter estimates, bioaccumulation metrics as well as predictions of internal concentrations into organisms are delivered with their uncertainty.
Statement of need `rbioacc` is an `R`-package compatible with `R` version > 4. 1. 0 (R Core Team, 2021) and with all standard operating systems (macOS, Linux, Windows). All `rbioacc` outputs have been compared with previously published results considering other TK model implementations under different software platforms. Giving very similar results, `rbioacc` was thus confirmed as fit-for-purpose to fit TK models on bioaccumulation test data  (Charles, Ratier, & Lopes, 2021; Charles, Ratier, Baudrot, et al. , 2021; Ratier & Charles, 2021; Ratier, Lopes, Geffard, & Babut, 2021). All functions in `rbioacc` can be used without a deep knowledge neither of the underlying probabilistic modelling or of the inference methodology. Indeed, they were designed to behave easily as possible, without requiring the user to provide prior values for input parameters.
Meanwhile, models implemented in `rbioacc` can be used as a first step to explore new models which could appear as more appropriate for some situations. Note that `rbioacc` benefits from a user-friendly and freely available web interface, MOSAICbioacc, from which the same analyzes can be reproduced directly on-line without having to implement them in `R` programming. MOSAICbioacc is directly accessible from the MOSAIC platform at https://mosaic. univ-lyon1. fr (Charles, Veber, & Delignette-Muller, 2018)  or directly from https://mosaic. univ-lyon1. fr/bioacc (Ratier, Lopes, Multari, et al. , 2021). requirements are documented to help users with their installation issues. Besides, `rbioacc` is available on the GitHub repository https://github. com/aursiber/rbioacc/, especially for advanced users encountering troubles or having suggestions regarding the features or the hidden content of therbioacc` functions.
Installation `rbioacc` is linked to Stan, a Bayesian sampler used to perform Bayesian inference with already implemented models, methods, and algorithms  (Carpenter et al. , 2017). Stan is fully integrated toR` via therstan`  (Stan Development Team, 2021)  and `rstantools`  (Gabry, Goodrich, & Lysy, 2020)  packages. In additional, to userbioacc`, you need to install all the other `R`-packages dependencies: `ggplot2`, `Rcpp` (>= 0. 12. 0), `RcppParallel` (>= 5. 0. 1), `testthat, `ggmcmc`, `GGally`, `loo`, `stringr`, `zoo` (>= 1. 8-9), `BH` (>= 1. 66. 0), `RcppEigen` (>= 0. 3. 3. 3. 0), `StanHeaders` (>= 2. 18. 0)`, `knitr`, and `rmarkdown`. For this purpose, you can use the classical `R` command that is provided in theR`-script (Supplementary Information, SI). `rbioacc` is also linked to the programming language C++ that speeds up calculations and runs simulations delivering the TK predictions.
01); the Widely Applicable Information Criterion (WAIC, with thewaic()` function) for model comparisons on a same data set; and the Monte Carlo Markov Chain (MCMC) chains (n = 3 by default) with themcmcTraces()` function. Additionally, thepredict()` and `predict_manual()` functions allow to perform predictions of internal concentrations within organisms, with or without previously observed data. `rbioacc` currently handles constant and time-variable exposure concentration data according to a generic workflow: 1. Upload and format a data set; Those steps are described in full details in aTutorial` available at https://doi. org/10. 5281/zenodo. 5092316, including the description on how to use all `rbioacc` features. More information on the model and the inference process used in `rbioacc` are given in an on-lineUser Guide` available at http://lbbe-shiny.
univ-lyon1. fr/mosaicbioacc/data/user_guide. pdf but also in our companion paper of the MOSAICbioacc web interface  (Ratier et al. , 2021). Please refer to this documentation for further introduction to the use of `rbioacc`. The data, however, must be properly formatted beforehand, in order to be uploaded in theR` software (R Core Team, 2021) as a data frame from a. txt or a. csv file (that is comma, semicolon, or tabular separator). Each line of the data frame must correspond to a unique time point for a given replicate and a given exposure concentration of the contaminant. The data frame must contain at least four columns with predefined headings (Table  1 ): `time`, the time point of the measurement; `expw`, `exps`, `expf`, `exppw`: the exposure concentration, from water, sediment, food, and pore water, respectively; `replicate`, a unique integer or string for each replicate; `conc`, the measured internal concentrations.
Calibration step To first illustrate the calibration step, we built two basic examples using standard bioaccumulation data sets available within `rbioacc` thanks to thedata()` function. The first data set was collected from a laboratory bioaccumulation test with the rainbow trout (Oncorhynchus mykiss) exposed to 2 different concentrations of a highly hydrophobic compound in spiked water for 49 days. There was 1 replicate per concentration. The duration of the depuration phase was 97 days. Internal concentrations were monitored at regular time points  (Crookes & Brooke, 2011). The second example concerned the species Chironomus tentans, a freshwater invertebrate, exposed to benzo[a]pyrene in spiked sediment for 3 days. Only 1 exposure concentration was tested with 2 replicates. The duration of the depuration phase was 3 days.
Last updates Based on the clear observation that it is difficult to find enough raw data sets to validate generic modelling approaches, especially for TK models, we have recently developed a database with a collection of more than 200 accumulation-depuration data sets. They were extracted from the scientific literature and are now freely available on-line at http://lbbeshiny. univ-lyon1. fr/mosaic-bioacc/data/database/TK_database. html. These data sets are associated with several outputs: the text file with raw data under the expected 'rbioacc' input format; the full report with the fitting results and the GoF criteria as provided either by MOSAICbioacc or `rbioacc`; and a link to the original paper  (Ratier & Charles, 2021). MOSAICbioacc recently shifted from an all-embarked web application to be a user interface of the newly developed `rbioacc` package.
Aesthetic The previous version of MOSAICbioacc was based on a scroll of only one web page. The new version works with several tabs unlocked once the user performed the actions required from the previous tab, except for the prediction and validation tabs that are always available. Globally, the same visual elements were kept, but they got reorganised in a tab structure with 5 different levels (Figure  7 ): `Data upload`, `Model and parameters`, `Results`, `Downloads`, `Prediction tool`. The results of the fitting process were also compacted in a tab and column structure to better suit the new no-scroll policy. This tremendously reduced the length of the previously displayed unique web page. This revamp in a tab based interface, as well as other minor changes, were partly decided upon by analysing the behaviour of the potential users of MOSAICbioacc.
Conclusion `rbioacc` was designed to help stakeholders and researchers to analyze TK data collected from bioaccumulation tests. This turnkey package automatically builds and fits the appropriate TK model, providing TK parameter estimates, GoF criteria and bioaccumulation metrics (namely, BCF/BSAF/BMF). In particular, it is designed to fulfil all requirements of regulators when examining marketing applications for chemical approval. In this perspective, the associated MOSAICbioacc web interface may reveals of particular help if supported by supervisory bodies.
Fundings The authors are thankful to  ANSES  for providing the financial support for the development of the MOSAICbioacc web tool (CNRS contract number  208483 ). This work was also made with the financial support of the  Graduate School   H2O ' Lyon  ( ANR-17-EURE-0018 ) and " Université de Lyon" (UdL) ,  as part of the program  " Investissements d'Avenir" run by  " Agence Nationale de la Recherche" (ANR).
4c, d ). Further, over the last 20 years at the Central Channel collection site, the temperature of water below the thermocline at 245 m, has varied annually by 0. 55-1. 94 °C, (from an average low temperature of 4. 60 °C to an average high temperature of 5. 78 °C) while the temperature at 15 m (a similar depth to the shallow-fjord site in Tracy Arm) has varied by 2. 75-4. 54 °C (from an average low temperature of 4. 32 °C to an average high temperature of 7. 99 °C)  16  (Supplemental Information Fig. S1 ). Thus, at the Central Channel collection site, the annual range of temperatures in shallow water has been more than twice that of the deep water below every year since 1999. While full water column profile data are not available for the deep-ocean and shallow-fjord sampling locations, long-term temperature loggers deployed at these sites measured annual temperature ranges at Shutter Ridge and Tracy Arm that fit the pattern of greater variability in shallow waters (Fig.
Unlike immature or wholly non-reproductive colonies characterized by a complete lack of gametes, these affected colonies produce gametes that progress through some of the gametogenic program but are not fully formed and lack key functional features, likely reducing or inhibiting the production of larvae. An important aspect of this dysfunction unique to deep-sea organisms is how easily it might be overlooked in standard deep-sea collections. The deep-sea environment is remote, requiring resource intensive, logistically complex sampling efforts. In many cases, sampling is inadequate and rarely can the same individual or population be resampled, an important aspect of any scientific study. Often, descriptions of reproductive seasonality in deep-sea organisms occur at the same time as the initial description of gametes in that species, resulting in a limited data set and a lack of temporal perspective.