dataesr/scientific-paragraphs-categorization huggingface dataset:
We included the fnr1:ZmFNR1 plants in our study to examine whether tighter binding to membrane tethers might enhance PSI protection. In most experiments, this was not the case, but intriguingly (1 min each), the white bar indicates continuous illumination at 1,385 mmol photon m -2 s -1 , and the black bar indicates a dark relaxation period. A, tPm was measured over illumination and absolute Pm was determined after 30 min of dark adaptation. B, Effective quantum yield of PSI (Y(I)). C, acceptor limitation at PSI (Y(NA)). Data shown are means ± SEM (n 5 3 individuals per genotype). Significant differences were attributed by post-hoc honestly significant difference Tukey's test and P values under 0. 05 are indicated by are indicated as the following letters: "a" for pgr5 and crr2 6 ¼ WT; "b" for pgr5 and WT 6 ¼ crr2; "c" for pgr5 6 ¼ crr2; "d" for pgr5 6 ¼ WT and crr2; "e" for pgr5 6 ¼ WT.
Funding This work was supported by funding from the  BBSRC  ( BB/ R004838/1 ) to G. T. H. and  CONACYT  to M. R. H, G. F. acknowledges funds from the  INRAE , and the  HFSP  and the support of the  GRAL LabEX GRAL ,  ANR-10-LABX-49-01  financed within the  University Grenoble Alpes graduate school 1 (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ).
Maps are also useful tools in paleoecology and to reconstruct the past relief constraints on human dispersal and societal complexity. The first loess maps at the European scale were produced by  Grahmann (1932)  and  Fink et al. (1977). Later, a digital European Loess Map was published by  Haase et al. (2007). More recently,  Bertran et al. (2016)  generated a map of European Pleistocene aeolian deposits based on topsoil textural data from the Land Use and Cover Area frame Statistical survey database (LUCAS,  Orgiazzi et al. , 2018; Tóth et al. , 2013). Lastly,  Li et al. (2020)  presented global distribution maps of provenance and transport pathways of major loess areas and discussed their genesis. Although several examples of loess maps exist, most mapping approaches encounter difficulties related to scale and availability of geodata.
The loess distribution was aligned to the Pleistocene terraces and other geomorphological features determined via the elevation data. Afterwards, the area between these presumably loess-covered terraces was vectorized as alluvial fill and fluvial deposits. In addition to the national data sets, pan-European data sets for potential Pleistocene source areas for aeolian sediments were evaluated and added to the map to substitute missing and deficient national datasets. This includes inter alia Late Pleistocene and Holocene fluvial deposits, derived from the EUSR5000 soil map with a scale of 1:5,000,000 (BGR [Bundesanstalt für Geowissenschaften und Rohstoffe], 2005). In addition to alluvial fill and fluvial deposits, the dry continental shelf during the LGM (modified after  Willmes, 2015) , which represents a main source for aeolian sediments in northern Central Europe, was added to the map.
Statistics To analyze the distribution of loess in Europe, we also extracted information on the surface and height distribution. For the area statistics, the area of each mapped unit in each (sub-)domain was calculated via the 'calculate geometry'-function in ArcMap 10. 6. 1 (see Table  1 ). This was also done for each country in order to estimate the proportion of the national data sets (see Supplement Table  1 ). The ALOS digital surface model (JAXA EORC, 2016) was clipped by the shapefiles representing 'loess and loess derivates' as well as 'aeolian sand' and 'sandy loess'. The resulting raster data sets were analyzed using the 'Zonal Statistics as Table ' and the 'Zonal Histogram' tool with the vectorized (sub-) domains as feature zone data. The zonal histograms were used to calculate the relative surface percentage of each respective sediment unit at each elevation in meters above sea level (m a.
, 2020; Schaffernicht et al. , 2020; Sima et al. , 2009). In the following subchapters, we compare our map to the most widely used European loess map by  Haase et al. (2007) , which combined several existing data sets and a more recent approach by  Bertran et al. (2016) , where the distribution of aeolian sediments was derived from topsoil data. Finally, we discuss our data with the results of the model-simulated dust deposition by  Schaffernicht et al. (2020). Haase et al. (2007)  One of the most commonly used maps of European loess is the one provided by  Haase et al. (2007). This map has a scale of 1:2,500,000 and is based on data compiled in the 1970s, 1980s, and the 2000s. This collaborative effort was carried out by the INQUA Loess Commission under guidance of J. Fink. Similar to our approach, the Haase et al.
, 2018a). This data was used in prior mapping approaches. Our new map includes the newest data from the Croatian Geological Survey (2009), which have not been available during data acquisition for the map compiled by  Haase et al. (2007). This might explain the differences between the two data sets. Minor differences are found in the southern Lower Danube Basin, as well as the western parts of Ukraine and Crimea. Areas that are mapped in our loess map that are not present in the map by  Haase et al. (2007)  are a consequence of different source data in our map. This includes areas in Spain, southern France, Italy, and coastal Croatia, which were not mapped before due to their small extent  (Haase et al. , 2007). Aeolian sediments in Great Britain and the Netherlands have not been mapped by  Haase et al.
Comparison with the mapping approach of The differences between the two approaches are due to manifold reasons. One of them is the differing mapping approaches. While the LUCAS database is based on data from top soil samples  (Orgiazzi et al. , 2018; Tóth et al. , 2013) , this study is based inter alia on geological maps. Geological maps usually exclude the uppermost one to two meters below the surface. Therefore, this approach can be expected to miss some of the thin loess and sand covers thinner than one or two meters. This is the case in subdomains Ia and IIa, especially in northern Germany (cf. Lehmkuhl et al. , 2018b). Vast covers of colluviated loess are mapped in some areas, such as basins within the Central European low mountain ranges (Fig. 19 ). Colluviated loess is also mapped in, e.
Data availability Our work highlights the value of the compiled geodata, which can be accessed freely at the CRC806 database (https://crc806db. uni-koeln. de/ start/) at https://doi. org/10. 5880/SFB806. 56, https://doi. org/ 10. 5880/SFB806. 57, https://doi. org/10. 5880/SFB806. 58, https://doi. org/10. 5880/SFB806. 59, https://doi. org/10. 5880/SFB806. 60, http s://doi. org/10. 5880/SFB806. 61 (Lehmkuhl et al. , 2020a (Lehmkuhl et al. , , 2020b (Lehmkuhl et al. , , 2020c (Lehmkuhl et al. , , 2020d, 2020e, 2020f), 2020e, 2020f).
Acknowledgements The investigations were carried out in the frame of the  CRC 806 "Our way to Europe", subproject B1 "The Eastern Trajectory ": " Last Glacial Paleogeography and Archaeology of the Eastern Mediterranean and of the Balkan Peninsula ", funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -Projektnummer57444011 -SFB 806). We thank  D. Haase  for sharing shapefiles of the loess distribution map,  P. Bertran  for providing the shapefiles of the distribution of aeolian sediments modeled by his team,  P. Ludwig  for providing the data of the LGM regional dust model, and  A. Weber  for help with compiling the supplementary material. We thank  G. Újvári  and two anonymous reviewers for their suggestions that helped to improve the manuscript.
Due to the known pattern of disease response in other species (e. g. , cattle)  [18] , tests of CMI are typically able to detect infection at an earlier stage than culture or antibody tests. This study investigated the performance of the IPRA, the DPP, and mycobacterial culture to determine M. suricattae infection status both with and without a reference test. We asked how these test results relate to true infection status by investigating associations between tests, individual animal survival, and the likelihood of clinical disease being present. Finally, we determined whether an individual's age influences its diagnostic test result, and whether this has implications for test reliability. These approaches made use of the same test dataset, but in different ways, giving multiple insights into how the tests may be used and the limitations of the available methods.
Data Collection Nine meerkat social groups at the KMP were selected at random for longitudinal sampling (Groups B, F, J, N, P, Q, R, U, and Z). From September 2014 to September 2016, 268 meerkats were sampled in five sampling periods (Blocks 1-5), each block lasting for three months. Within each block, all animals within the sample groups were scheduled to be sampled once. In block 1, samples from a further 44 individuals were available for testing from a parallel study  [16]  involving an additional 6 groups, and samples previously collected (n = 10) from a captive population described by Clarke et al. [16] , were utilised. Ethical approval for the study was granted by the University of Pretoria's ethics committee in May 2014 (project reference ECO20-14). Throughout this two-year study, each group was visited a minimum of three occasions per week.
The analysis was implemented in a Bayesian framework using the WinBUGS software (MRC, Cambridge, UK,  [28] ) embedded within the R package (R Core Team 2014) using the R2WinBugs library  [29]. Uniform distributions (0,1) were assumed for all sensitivity and specificity priors. A prior for the wild population prevalence was given by the beta (1. 5, 2. 3) distribution, to fit with a prior estimation of approximately 30% prevalence. Diagnostic tests were assumed to be independent due to their differing biological mechanisms of action. Two simulation chains of 1,000,000 iterations were run, with the first 100,000 iterations discarded to allow for burn-in of the chain. The chains were then thinned, taking every hundredth sample to reduce autocorrelation among the samples. Convergence was assessed by checking the trace plots for all monitored parameters  [30].
Acknowledgments: We thank  Marta Manser  of the  University of Zurich , and  Dave Gaynor , site manager for the  Kalahari Research Trust  for providing access to habituated animals and field facilities, and  Tim Vink  for assistance with the database. We are grateful to the  Northern Cape Department of Environment and Nature Conservation  for permission to carry out research and to the owners of farms neighbouring the  Kuruman River Reserve  for allowing us to work on their land. Thanks to the field managers at the time of the study:  Sky Bischoff-Mattson ,  Lyndsey Marris ,  Chris Duncan , and  Laura Meldrum , as well as to  Helen Spence-Jones , research assistant, and all the volunteers at the KMP who contributed towards collection of observational data.
Southern blot analyses For each Southern blot, 3-5 g of genomic DNA digested with Eco RV and Ssp I were loaded on a 1% agarose gel and electrophoresis was performed overnight at 1V/cm. The gel was manually transferred overnight in 20X SSC, on a Hybond-XL nylon membrane (GE Healthcare), according to manufacturer recommendations. Hybridization was performed with a 302 bp 32 P-randomly labeled CAN1 probe amplified from primers CAN133 and CAN135 (Supplemental Table  S2 )  (22). Each probe was purified on a G50 column (ProbeQuant G50 microcolumn, GE Healthcare) and specific activities were verified to be above 2. 4 × 10 8 cpm/g. The membrane was exposed 3 days on a phosphor screen and quantifications were performed on a FujiFilm FLA-9000 phosphorimager, using the Multi Gauge (v. 3. 0) software.
Computer analysis of off-target mutations In a first step, all fastq originating from the different libraries were scanned in order to identify reads coming from the dsODN specific amplification. The test was carried out with the standard unix command grep and the result was used to split each former fastq file in two: with or without the dsODN tag. In a second step, all the resulting fastq files were mapped against the S288C reference genome obtained from the SGD database (release R64-2-1 20150113, https: //www. yeastgenome. org/). Mapping was carried out by min-imap2 (25) using '-ax sr -secondary = no' parameters. Sam files resulting from mappings were then all sorted and in-dexed by the samtools software suite  (26). Subsequently, for dsODN-containing sequences, double strand break positions were identified by searching coverage peaks.
Statistical analysis All statistical tests were performed with R3. 5. 1. Linear regression model was performed to test the correlation between DSB value and the percentage of GFP-positive cells at different time points. Linear regression was performed to determine statistical significance of proteins levels and sgRNA levels over the percentage of GFP-positive cells. For each linear regression, R 2 and P-value were calculated. Oneway analysis of variance (ANOVA) was used to determine the impact of crRNA, sgRNA or DNA free energy over the percentage of GFP-positive cells at 36 h. P-values less than 0. 05 were considered significant. Figures were plotted using the package ggplot2.
The 16S amplicons were sequenced on MiSeq Illumina technology (paired end 2 Â 250 bp) using MiSeq reagent kit v3 (Illumina-MS-102-3003) and PhiX Sequencing Control V3 (Illumina FC-110-3001). 16S-rRNA gene and transcript raw read sequences are deposited in public database GenBank (accession numbers KEXZ01000001 to KEXZ01004520). Bioinformatic analysis. Raw sequences were resolved into amplicon sequence variants (ASVs) with the DADA2 pipeline (version 1. 16) using default settings to correct sequence errors in the R software version 3. 4. 3 (92). This method does not impose the arbitrary dissimilarity threshold defining operational taxonomic units (OTUs) and enables to distinguish sequence variants differing by as little as one nucleotide. Sequences (without primers and adapters) were filtered and further trimmed (removing 15 nucleotides [nt] at both 59 and 39 ends and setting a minimum length of 150 bp), quality checked, and denoised (learnError).
ACKNOWLEDGMENTS The project was funded by the  ANRT  ( Association Nationale de la Recherche et de la Technologie )  CIFRE 2017/1521  project. The authors want to thank  Sylvain Davidson  and  Fabrice Armougom  at the  MIO  for support in HPLC analysis and Dada2 analysis, respectively, as well as the OMICS platform at the MIO for access to molecular and analytics facilities. The authors are highly grateful to  Anne-Sophie Lepeuple  ( Veolia ) for her contribution to the project management.
Complementary DNA was amplified using a linear amplification system and used for sequencing libraries. Qualitative analysis of the RNA-Seq data after alignment to the Galgal4 genome assembly (17,108 genes) from the two biological replicates for the four conditions showed a high Pearson correlation coefficient (>0. 99) indicative of experimental reproducibility, confirmed by principal component analysis (Figure  S4 ). Comparison of the differential gene expression (DGE) for FDR1 (False discovery rate ≤ 0. 01) between "MEK1ca + HOXB8 versus MEK1ca" (Figure  2B , Supplementary Table  S1 ) and "MEK1ca versus pCIG" (Supplementary Table  S2  for FDR1; FDR5 (False Discovery Rate ≤ 0. 05) is available in Wilmerding et al. , 2021b) identified 759 commonly deregulated genes (Figure  2C ). Surprisingly, more than 90% (689 genes) of the commonly deregulated genes were inversely correlated, as illustrated by the expression heatmap (Figure  2D ).
Bioinformatics analyses were performed using the galgal4. 0 chicken genome. Qualitative analysis of the RNA-Seq data from the four biological replicates showed a high Pearson correlation coefficient (>0. 99) indicative of experimental reproducibility (Figure  S4 ). The RNA-seq data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [67] and are accessible through GEO Series accession numbers GSE162665, GSE182072 and GSE182076.
Acknowledgments: We sincerely thank  Andrew Saurin  for his help in the RNA-seq analysis. We would like to thank  Sophie Gournet  (IBPS,  CNRS UMR7622 ) for the drawings of Figure 2, and the  Optical Imaging Platform  of the IBDM. We sincerely thank  Heather Etchevers  for her critical reading of the manuscript. We thank  Olivier Pourquié  for its generous gifts of pCIG-HOXB8 expression vector. We thank  Bianca Habermann  for her advice on bioinformatics and  Laure Lo Re  for their technical support at the beginning of this study. We also would like to thank  Emilie Bonzom ,  Nomenjanahary Solotahiana  and  Lalla-Kamilya Gaouzi  for their implication in this work during their Master internship. FACS experiments were done at the  CRCM (Marseille, France)  and transcriptome sequencing at the GATC.
Funding: This research was funded by grants from  Canceropôle PACA  (grant " Emergence " n •  2015-06 ), and  A*MIDEX  project (n •  ANR-11-IDEX-0001-02 ), funded by the  «Investissements d'Avenir» French Government program , managed by the  French National Research Agency (ANR) , and by PhD fellowships from  La Ligue contre le Cancer and IBDM  to A. W. and from  A*MIDEX  (n •  ANR-11-IDEX-0001-02 ) to L. R. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Data Availability Statement: The RNA-seq data discussed in this publication have been deposited in NCBI's Gene Ex-pression Omnibus [67] and are accessible through GEO Series accession numbers GSE162665, GSE182072 and GSE182076. https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE162665. https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE182072. https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE182076 accessed on 10 June 2021.
The PCR products were analyzed with the QIAxcel DNA Screening Kit (Qiagen) to confirm successful amplification. Amplified DNA was purified by adding 1. 2 µL of ExoProStar (GE Healthcare, Piscataway, USA) to each well before a single thermal cycle of 37°C for 60 min, 85°C for 15 min, and a hold at 4°C until sequencing. PCR samples were then sent for Sanger sequencing at the KAUST Bioscience Core Lab. The sequencing reaction was done in each direction, using either Fish F1 or Fish R1 as the sequencing primer. Sanger sequencing of purified target DNA was conducted using the 3730xl DNA Analyzer (Applied Biosystems, Foster City, USA). Reticulate whipray CO1 gene sequences from the Red Sea were compared to all homologous Himantura spp. sequences available including 201 sequences downloaded from the GenBank (www.
ncbi. nlm. nih. gov/genbank/; Clark et al. 2016)  and BOLD (http://www. boldsystems. org/; Ratnasingham and Hebert 2007) public repositories, and two unpublished sequences (Supplementary Table  S2 ). Reticulate whipray, Himantura spp. CO1 gene sequences from GenBank and BOLD were labelled 'H. astra' (N = 1), 'H. australis' (N = 1), 'H. fava' (N = 1), 'H. leoparda' (N = 85), 'H. tutul' (N = 30), 'H. uarnak' (N = 62), 'H. uarnak i' (N = 2), 'H. uarnak ii' (N = 3), 'H. uarnak iii' (N = 3), 'H. undulata' (N = 11), ''Himantura sp. ' (N = 1) and ''Urogymnus asperrimus' (N = 1) (Supplementary Table  S2 ). Partial nucleotide sequences of the CO1 gene were aligned and trimmed to a core length of 613 bp using the BioEdit sequence editing software  (Hall 1999). The nucleotide sequences were sorted by species and nucleotide synapomorphies were then assessed visually.
A maximum-likelihood (ML; Felsenstein 1981) tree of partial CO1 gene sequences was constructed using the MEGA X package  (Kumar et al. 2018). All three codon positions were included. Of all models tested under MEGA X, the HKY model  (Hasegawa et al. 1985)  had the lowest Bayesian information score. A discrete Gamma distribution was used to model evolutionary rate differences among sites (G = 1. 19) and some sites were allowed to be evolutionarily invariable. GenBank sequences nos. EU398838, EU398867, EU398869, JF493649, KF899470, KF899491, KM073002, KM073006 and KP641389 (Maculabatis spp. ), EU398849, KF899499, KM072993, KM073009 and KM073010 (Pateobatis spp. ), EU398860, FJ384709, KF899521, KF899528, KM072995 and KR003772 (Brevitrygon spp. ) and KF965292 were included as an outgroup to root the tree.
Mean nucleotide distances between and within lineages were estimated using the Maximum Composite Likelihood model under MEGA X. The Assemble Species by Automatic Partitioning (ASAP;  Puillandre et al. 2021 ) algorithm was used to partition the DNA sequence dataset into species hypotheses. The alignment of ingroup sequences (N = 216) in FASTA format was uploaded to the ASAP website at the Museum national d'histoire naturelle, Paris (https://bioinfo. mnhn. fr/abi/public/asap/; accessed 06 February 2021). We used the default options of ASAP and the genetic distances were estimated using the Kimura-2-parameter substitution model. The general mixed Yule coalescent (GMYC) algorithm  (Pons et al. 2006 ) was also used to the same purpose. The GMYC algorithm implemented in the program SPLITS under R (R Development Core Team 2011; Fujisawa & Barraclough 2013) compares the branching patterns that are consistent with coalescent and speciation processes.
We used BEAST v. 2. 6. 1  (Bouckaert et al. 2019)  to construct an ultrametric tree, assuming a non-calibrated relaxed lognormal clock, a Yule tree prior, and the HKY model of nucleotide substitution. The run included a total of 10 6 Markov chain-Monte Carlo generations, which were sampled to obtain 10,000 genealogies and model parameter values. Inspection of the output in TRACER v. 1. 5  (Drummond and Rambaut 2007)  helped confirm that the sampling scheme was correct. The sampled trees were then summarised using TREEANNOTATOR v. 2. 6. 1  (Bouckaert et al. 2019)  to produce the final ultrametric tree, based on maximum clade credibility and mean node height. The SPLITS program was then run to obtain genetic assignment of each sequence to a cluster.
For example, the name 'H. uarnak' is currently assigned to no less than five distinct Himantura lineages in the GenBank and BOLD sequence databases. For the present study, we collated the largest CO1 gene sequence dataset to date in the genus Himantura. A total of seven distinct lineages or haplogroups were identified from the phylogenetic tree of the reticulate whipray species complex (Fig. 3 ). The strongly-supported, highly distinctive lineage IV2 was the only one that included haplotypes from the Red Sea (from at least two separate locations north and south of Jeddah), including that of a specimen preserved whole and here chosen as H. uarnak's neotype (see next section). As a consequence, lineage IV2 is here designated as P. Forsskål's H. uarnak. This lineage was distinct from lineage (IV1) which included all leopard whipray H.
leoparda specimens from the Coral Triangle and adjacent regions. Himantura leoparda, whose type locality is the Gulf of Carpentaria turns out to be the sister-species of H. uarnak. The other lineages were identified as H. australis (III2), H. tutul (I) and H. undulata (II) based on this and previous genetic work employing nuclear, cyt b, and CO1 markers  (Arlyza et al. 2013; Borsa et al. 2013; Borsa 2017; Appleyard 2020 ) and on geographic consistency with the type locality of H. australis (southern New Guinea;  Last et al. 2016c). Under the present seven-species hypothesis, two lineages here labelled Himantura sp. 1 (III2) and Himantura sp. 2 (III1) may represent undescribed species. Thus, the fixation of a neotype for H. uarnak strengthens the current nomenclature for species in the genus Himantura, clarifies the identity of over 200 specimens whose CO1 gene sequences have been deposited in public databases and opens the way to the possible description or re-description of up to two other yet-unnamed species in the genus Himantura.