dataesr/scientific-paragraphs-categorization huggingface dataset:
Qualitative PCR Qualitative PCR amplifications were performed on cDNA samples generated from floral total RNA. Primers pairs used are shown in Supplementary Table  S2  and were designed with Primer3 v. 0. 4. 0 (http://bioinfo. ut. ee/primer3-0. 4. 0/) (accessed on 1 January 2020). To validate splice variants, one of the primers was located on internal sequences of the putative intron and the other one on the adjacent exon. Total RNA was extracted from spikelets at stage VII (i. e. , with highest TGS1 expression) according to the Paspalum reproductive calendar  [33]  and using the SV Total RNA Isolation Kit (PROMEGA, Madison, WI USA). cDNAs were synthetized with Superscript II (INVITROGEN, Carlsbad, CA, USA) following the manufacturers' recommendations. PCR reactions were carried out in a final volume of 25 µL containing: 50 ng cDNA, 0.
qPCR Experiments For qPCR, total RNA was extracted from spikelets at the required developmental stage  [33]  by using the SV Total RNA Isolation Kit (PROMEGA, Madison, WI, USA). Primers pairs used are shown in Table  S2  and were designed with Primer3 v. 0. 4. 0 (http: //bioinfo. ut. ee/primer3-0. 4. 0/) (accessed on 1 January 2020). cDNAs were synthetized with Superscript II (INVITROGEN, Carlsbad, CA, USA) following the manufacturers' recommendations. Quantitative PCR reactions (final volume: 20 µL), were performed in a Rotor-Gene Q thermocycler (QIAGEN, Hilden, Germany) and included: 0. 5 µM genespecific primers (Table  S1 ), 1× Real Mix qPCR (BIODYNAMICS, Buenos Aires, Argentina) and 20 ng of cDNA. In each experiment, two biological replicates were processed, in which each determination was run in three technical replicates.
The constitutive gene β-TUBULIN was used as housekeeping reference, since in previous work this gene had been selected as the best reference for comparisons in this particular floral apomictic-sexual Paspalum system  [70] [71] [72]. Negative controls were processed without template DNA. Amplifications were performed with the following program: 2 min at 94 • C, 45 cycles of 94 • C for 15 min, 57 • C for 30 s, and 72 • C for 17 s, and a final elongation step of 5 min at 72 • C. Relative quantitative expression levels were assessed using the REST-RG 2009 software (QIAGEN, Hilden, Germany).
Data Availability Statement: The RNAseq sequence datasets used in this work are openly available at the NCBI repository under the accession numbers SRX1971037 y SRX1971038 (454/Roche FLX+), SRP099144 (small RNAs) and PRJNA511813 (Illumina sequencing). All plant materials used here are maintained at the IICAR greenhouses or controlled chambers and are available after official request for academic purposes.
Initial Composition of the Outer Crust The initial composition of the outer crust of a magnetar was determined in  [18]  but only for a few selected magnetic field strengths, namely B = 1000, 2000, and 3000. We have extended the calculations to the whole range of magnetic field strengths ranging from B = 0 to B = 10 17 G. To this end, we have used the experimental atomic masses from the 2016 Atomic Mass Evaluation  [41]  supplemented with the same microscopic atomic mass table HFB-24  [14]  from the BRUSLIB database 1  [42]. The functional BSk24 underlying the model HFB-24 was also adopted to calculate the equation of state of the inner crust of a magnetar  [18]. This same functional was also applied to construct a unified equation of state for unmagnetized neutron stars  [15] [16] [17] , and to calculate superfluid properties  [43].
Data Availability Statement: The data analyzed in this paper can be found in the 2016 Atomic Mass Evaluation  [41] , the BRUSLIB database (http://www. astro. ulb. ac. be/bruslib/, accessed on 9 June 2022, see  [42] ), and the Nuclear Data section of the International Atomic Energy Agency website (https://www-nds. iaea. org/relnsd/NdsEnsdf/QueryForm. html, accessed on 9 June 2022). The results presented in this study are openly available on the Zenodo repository  [45].
As of yet, no studies have linked measured or theoretical HRR values to specific fish stranding risk for any fish species in any actual rivers  (Moreira et al. , 2019). The ability to calculate this indicator will allow for eco-hydraulicians to test its utility as an indicator of fish stranding risk. It is possible for the ecohydraulics community to test the HRR approach, as explained above, by using a specific HRR module added to the HABBY software (https://habby. wiki. inrae. fr/en:start) which In HABBY, the indices of the original mesh common to all simulations are kept as information for every cell. And this is true whatever the operations of cutting or partitioning the cells. Moreover, for a given mesh, whether it is the original mesh or the mesh of a hydraulic unit, we can build a cell connectivity table.
That is, the information for each cell in the mesh, of the indices of at most three cells that share common segments/node pairs with it. For the calculation of Δzsurf i or for the extraction of dewatered cells, these are the elements that allow the efficiency of the algorithms for finding neighboring cells in space and time. Defining minimum depth is a necessary step to exclude cells or parts of cells that are biologically incoherent (because even the smallest fish would not be present in these areas). Once this minimum depth is decided (based on target species and life stages at risk and their behavior), it is necessary to split cells containing depths that are shallower than this threshold. To search for the node position (with depth equal to the threshold) to determine the new geometries, it is recommended to perform linear interpolation, which can most easily be done with a software solution (such as HydroSignature) but specific algorithms or code could also be developed and used.
The HRR module of the HABBY software would enable a rapid first assessment of the HRR concept for the ecohydraulic community but also to conduct a spatial evaluation of stranding risk along the river reach map by mapping the local HRR versus time. This module should also be used as a reference for HRR computation to all who would compute HRR from the formulas presented in this manuscript. The HRR module accepts several 2D hydrodynamic models as inputs and further compatible models will be available in the future. Additionally, work is underway to improve the transition from finite volumes to finite elements. Depending on the hydrodynamic model outputs (e. g. , its resolution), before computing HRR, it is necessary to thoughtfully define minimum water depths which determine the wet/dry transition (1 cm in this example).
is a free and open-source program. HABBY was developed to estimate ecological flows by hydraulic habitat suitability calculations, from external hydrodynamic model outputs. The Python code of the algorithms developed is available on GitHub (https://github. com/ YannIrstea/habby) and a quick tutorial for HRR calculations is available on HABBY webpage. The only necessary data is the outputs of hydraulic models (Telemac 2D in the tutorial). The HRR module of the HABBY software should be used either to compute automatically HRR because no other solutions are currently available or either as reference for everyone who would want to develop their own solutions (to compare the outputs). HRR computation is completely independent of habitat model computations.
By a factor of the inverse product of Coriolis parameter and density, the OSC is then proportional to Ekman pumping. In the southern hemisphere, a positive OSC thus induces Ekman downwelling, and a negative curl Ekman upwelling. Note that the OSC does not include coastal downwelling due to onshore Ekman transport, since the ice shelf as a physical boundary is not included in the calculation. Time series of basal melt rates for FIS are calculated using data from Adusumilli et al. (2020) according to their Eqn. 7 w b (t) = M s ρ i - ρ w ρ w -ρ i dh dt - dh air dt (6) where M s is the surface mass balance, ρ i = 917 kg m -3 is the density of ice, ρ w = 1028 kg m -3 is the density of seawater, h is the ice shelf surface height relative to the ocean surface, and h air is the őrn air content. These data are available at a 3-monthly resolution and taken from December 2009 to November 2018.
Acknowledgements This research was supported by the  Norwegian Research Council  through the programs " KLIMAFORSK" and  " POLARPROG " with grant number  295075. The moorings were maintained by  NPI / Norwegian Antarctic Research Expedition  through the " IceRises " project with grant number  3801-103. The installation of the under ice shelf moorings through the project " ICE Fimbulisen ś  top to bottom" was initiated and őnanced by the  Centre for Ice, Climate and Ecosystems, Norwegian Polar Institute. The authors are.
Data Availability The FIS mooring data will be provided at https://data. npolar. no. Surface pressure, 10 m wind, and 2 m air temperature are available from ERA5 at https://doi. org/10. 24381/cds. f17050d7. Sea ice concentration and sea ice velocity are available from NSIDC at https://doi. org/10. 5067/8GQ8LZQVL0VL and https://doi. org/10.
/INAWUWO7QH7B, respectively. Sea surface height is available upon request from Armitage et al. (2018) and at https://doi. org/10. 17882/81032. The Southern Annular Mode index is available at https://legacy. bas. ac. uk/met/gjma/sam. html. Data from mooring AWI253 are available at https://doi. pangaea. de/10. 1594/PANGAEA. 875932 and https://doi. pangaea. de/10. 1594/PANGAEA. 903315. Data for calculating basal melt rates from Eqn. 6 are available at https://doi. org/10. 6075/J04Q7SHT. 5067 and colormaps from Thyng et al. (2016). Bathymetry shown is taken from Eisermann et al. (2020a) (see Eisermann et al. (2020b)   for details) and grounding lines from Mouginot et al. (2017). Coastlines in Fig. 1a and 5c-e are from Mouginot et al. (2017) , all other ones are from Wessel and Smith (1996). The satellite images of Antarctica shown in several őgures are taken from Haran et al.
έ was then scaled to growth (ε = έ/μ), to represent the antibiotic-induced death rate relative to bacterial growth rather than an absolute value. This was necessary to ensure that these antibiotic-induced death rates would work with our previous estimates for bacterial growth, as these were slightly lower than the baseline growth rates estimated here. Since we obtained growth curve time series data for 8 concentrations for each strain, we generated 8 estimates of antibiotic-induced death rate for each strain. Secondly, the three parameters of the Hill equation  [39, 42]  (maximum death rate ε max , Hill coefficient H, and half maximum effective concentration EC50), which calculates the antibiotic-induced death rate as a function of concentration, were estimated by fitting Eq 3 to the 8 antibiotic effects for each strain.
Funding: Q. J. L was supported by a studentship from the  Medical Research Council Intercollegiate   Doctoral Training Program  ( MR/N013638/1 , https://mrc. ukri. org/). G. M. K was supported by a  Skills Development Fellowship  from the  Medical Research Council  ( MR/P014658/1 , https://mrc. ukri. org/). G. M. K and J. A. L were supported by a  Research Grant  from the  Medical Research Council  ( MR/P028322/1 , https://mrc. ukri. org/). The funders played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DNA extraction, Salmonella count by Droplet Digital PCR and choice of low and high carriers As described previously  [17] , individual caecal DNA was extracted from an average of 200 mg of frozen caecal contents and DNA samples were stored at -20˚C. Individual abundances of Salmonella Enteritidis in caecal contents were obtained by Droplet Digital PCR (ddPCR) using the QX200 Droplet Digital PCR system (Bio-Rad) at the @bridge platform (INRAE, Jouy-en-Josas, France). The ddPCR method has been proven to reliably quantify the amount of salmonella spp. in a sample  [71]. We targeted and amplified a region of the InvA gene specific to SE  [72]. Data were analysed with a log transformation of the copies of Salmonella. Analyses of variance (ANOVA) were performed to test the significance of differences of the copies of Salmonella according to different factors (line, sex, experiment or isolator) using the anova function from base R (Type I sum of squares).
RNA extraction and sequencing Caecal tonsils from the 48 low/high carrier chicks were first grinded using ULTRA-TURRAX T25 (IKA). RNAs were then extracted using the NucleoSpin RNA Kit (MACHEREY-NAGEL) according to the manufacturer's protocol. The quantity of RNA was measured using a Nanodrop spectrophotometer (Thermo Scientific) and its quality was assessed using a 2100 Bioanalyzer Expert system using a total RNA nano Kit (Agilent), all samples displayed an RNA integrity number (RIN) > 7. RNAs were sent to the genomic platform GeT-Plage (Toulouse, France) for the cDNA library preparation (TruSeq Stranded mRNA kit, Illumina) and the sequencing (NovaSeq 6000 Sequencing System, Illumina). The sequencing data analysed during the current study are available in the NCBI Sequence Read Archive (SRA) database under the Bioproject accession number PRJNA649900.
Transcriptome analysis The quality control of raw reads was performed using the FastQC program. Trimming was performed using Sickle (version 1. 33). Reads were then mapped on the Gallus reference genome (Gallus_gallus. GRCg6a. 98. gtf) with STAR (version 2. 5. 3a) [73]. Genes were counted with htseq-count (version 0. 12. 4)  [74]. The metadata and table of gene counts have been included as Additional files 6 and 7, respectively. PCA and ANOVA tests on PCA components were performed with the stats package (3. 6. 1). DESeq2 (version 1. 26. 0) was used with R version 3. 6. 1 to perform differential gene expression analyses  [75]. In our model, the sex and the isolator effects were fixed. Genes with an adjusted p-value < 0. 05 were considered to be differentially expressed genes (DEGs). The ggplot2 packages (3.
Such ignorance might be a consequence of their distribution across a wide range of publications and the distributions of data among many orders and families. It is even more surprising to see that soil biologists, specialists in other groups (microflora, microfauna or mesofauna), choose to ignore macrofauna in their attempts to propose ecological models of soil functioning  (Guerra et al. , 2021; Nielsen et al. , 2015). To overcome these limitations and propose this necessary comprehensive view of the importance and diversity of soil macroinvertebrate communities, a simple method for sampling macrofauna was developed to quantify the structure and diversity of their communities  (ISO, 2011). Data collected over 30 years using this standard method have been accumulated world-wide and stored in a database.
This paper provides a general overview of the macroinvertebrate communities studied and their major environmental determinants. The study is based on 3,694 sites distributed among 41 countries, collected in the Macrofauna database  (Lavelle et al. , 2022). We first provide a general description of macroinvertebrate communities, the frequency of occurrence of the 16 main broad taxa, average and median densities world-wide. This should clearly inform on the abundance and diversity of this fauna at a global scale. We then searched for a general typology of the communities, looking for general patterns that could be taken into consideration in general conceptual and simulation models, and multivariate analyses were used for this purpose. Network analyses inside communities grouped according to the cluster analysis or land-cover groups completed these analyses.
K E Y W O R D S communities, land cover, Macrofauna database, network analysis, soil ecology, soil macroinvertebrates launched, with the aim to "improve understanding of the role of biological and organic resources and their management for improved tropical soil fertility and the sustainability of land-use systems". Breaking with the taxonomic specialization that has led researchers to use taxon-specific methodologies, we proposed for this programme a macrofauna sampling method that involves hand sorting all the invertebrates recovered from 25 cm × 25 cm samples of the litter layer and the predominantly mineral soil, in 10 cm slices down to a depth of 30 cm  (ISO, 2011; Velasquez, 2020). The definition of macrofauna was extended from the original ">2 mm size" to include all the invertebrates that could be seen with the naked eye.
| The Macrofauna database All the data collected from sites sampled using the ISO/TSBF method have been stored in the Macrofauna database in an opportunistic approach. The ISO/TSBF method has been applied independently in a large number of different projects with different purposes since the earliest publication of the method, in 1989, and their data were also stored in the database. As a result of these dynamics, the data set has not been generated in an organized way, as designed from the beginning. In 1999, however, the Macrofauna project developed within the framework of the International Biodiversity Year programme (IBOY; www2. nrel. colos tate. edu/proje cts/iboy/whata ndwhe re. html), which brought together c. 40 people who had used the TSBF method, in an effort to optimize the method and share data within the framework of the Macrofauna database.
A very detailed format was proposed, and the available data were stored. The data set used in the present study has been derived from the general database created at this time. Sites were all sampled only once, and comparisons among types of plant cover were made according to a space-for-time sampling strategy. Access to the database was provided on the DRYAD platform  (Lavelle et al. , 2022). For each "site", data from one to five or more replicated blocks were pooled and densities expressed as individuals per square metre. Where the purpose of the study was to compare soils submitted to different types of land use in the same place, it was not considered advantageous to measure biomasses because they usually exhibit greater variances than equivalent population density data  (Lavelle et al.
, 1981). Data sites were unevenly distributed globally, with substantial concentrations of sites sampled in Western Europe and tropical America, whereas large gaps were evident in Russia and North America (Figure  1 ). Latitude ranged from 55° S (Ushuaia, Argentina) to 57° N in Finland, elevation from 0 to 4,482 m in the Peruvian Andes, and mean annual temperatures from 5°C (Chuñuchuñuni in the Bolivian Andes) to >30°C in several sites of NE Brazil and Burkina Faso. Annual rainfall ranged from <1,000 mm in Queensland (Australia), Cordoba (Argentina) and Bolivia to >3,000 mm in Karnataka (India), Caqueta (Colombia), Sarawak (Indonesia) and Costa Rica. For each site, the database recorded six geographical data: elevation, latitude, longitude, mean annual temperature and rainfall, and land cover.
Eleven types of plant covers were identified after optimization of an initial set of 21 using preliminary statistical treatments. Tests were made of the evidence of co-occurrence among taxonomic groups forming ecological networks in communities and their changes according to environmental conditions  (Morueta-Holme et al. , 2016). Each pairwise association of taxonomic units was tested against a null model considering local and regional scales in order to avoid spurious correlations. The null model was constructed by resampling the observed data separately within each relevant stratum (the "COVER8" and "CLUSTER" classification of sites according to their plant cover or grouping generated by the general cluster analysis performed on the dataset). We used a resampling scheme that preserves row and column sums.
This procedure identifies significant positive and negative associations among taxa, which are used to define the local networks. The procedure was realized with the function "make netassoc network" from the netassoc (v. 6. 3) R package  (Morueta-Holme et al. , 2016). Clusters of taxonomic groups were identified using the igraph package  (Csardi & Nepusz, 2006). Networks were then characterized by traditional metrics, such as connectance (total number of links divided by the total potential number of links), nestedness and modularity. Nestedness is a property of species assemblages where similar patterns of species occurrence are observed in low-and high-density communities  (Atmar & Patterson, 1993). Modularity measures the division of a network into independent subcommunities. This parameter is often assimilated with community resilience  (Thebault & Fontaine, 2010).
| Methods Macroinvertebrate communities have been assessed across a wide range of sites using the simple low-cost ISO/TSBF method described here. Comparing data obtained from so many places must inevitably have been associated with differences in the quality of  to be expected from sampling such a large number of different taxonomic groups, with their often-aggregated distributions, and using relatively small sampling units  (Ettema & Wardle, 2002; Jimenez et al. , 2001). Patchy distributions frequently observed in soil macroinvertebrate populations probably explain the pattern observed in most taxonomic groups that have a large number of zero values and medians equal to (Table  1 ). Despite these potential limitations, analysis of the data set has revealed clear patterns. Both natural environmental determinants and human impacts on plant cover were assessed globally, and their significances and hierarchical arrangements were tested.
Success in this area is probably attributable to the particular sensitivity of macroinvertebrate communities to environmental drivers and the robustness of this simple sampling method. Other authors have analysed subsets of this and other databases, using levels of taxonomic resolution from orders to families and species. They concluded that orders and families have nearly the same capacity as species or genera to discriminate among the effects of the major environmental general drivers  (Nahmani et al. , 2006; Ruiz et al. , 2011). This underlines the need to consider equivalent levels of scale resolution in comparing sites and taxonomic groups. Sites separated at the moderately broad spatial resolutions (a forest, a cropping plot) generally used in these studies are often better discriminated using coarse taxonomic units (orders and families) than by using lower levels, such as genera and species.
All the environmental factors tested (soil texture, temperature, rainfall, latitude and plant cover) impact soil communities and account for significant but variable percentages of overall variance, thus verifying our first hypothesis. The data set, however, did not allow us to test for their possible organization into the hierarchical suite of determinants, from climate to soil and organic matter quality, proposed by  Lavelle et al. , 1993 ). An extended data set incorporating sites with larger ranges of climatic variables and more precisely defined definitions of soil conditions and litter quality (both surface and root litter) might result in a greater discrimination between macroinvertebrate communities. The present database lacks data from cold and hot, semi-desert and desert areas. We also need consistent definitions of soil taxonomic groupings to test the effect of soil age and a possible trade-off between young soils, rich in nutrients but shallow, and old soils that are deep and well structured, but are generally depleted of nutrients and organic matter.