dataesr/scientific-paragraphs-categorization huggingface dataset:
DENV-2 RNA was detected by real-time reverse transcription-PCR (RT-PCR) in the serum of both patients  (7). Two DENV-2 clinical strains, called 7869191/BF/2016 and 7754691/BF/2016, respectively, were isolated from sera using C6/36 cells. Both isolates are available to the scientific community via the European Virus Archive goes Global (EVAg) project, a nonprofit organization. After three passages in C6/36 cells, viral RNA was extracted from cell culture supernatant. Overlapping RT-PCR products spanning the complete genome were generated using a specific set of primers previously described (8), purified, pooled, and sequenced using next-generation sequencing (Ion Torrent, Thermo Fisher Scientific). Around 80,000 reads were produced for each sample, from which two contigs with a length of 10,675 nucleotides (nt) were generated with CLC Genomics Workbench software (CLC bio).
91% identical (10,665/10,675 nt; 3388/3391 aa), demonstrating that both patients were infected by the same epidemic strain. Phylogenetic analysis also indicated that these clinical isolates shared a common ancestor with a DENV-2 strain circulating in Burkina Faso in 1983 (GenBank accession no. EU056810; 97. 1% nt and 99. 4% aa identity). This interesting relationship suggests that this strain of DENV-2 virus has been endemic in Burkina Faso for more than 30 years, probably maintained through a sylvatic (nonhuman primate/sylvatic mosquitoes) cycle. These genome sequences might contribute to a better understanding of the molecular epidemiology of DENVs in Burkina Faso. Accession number(s). The virus genome sequences described here have been deposited in GenBank under the accession numbers KY627762 and KY627763 for strains 7869191/BF/2016 and 7754691/BF/2016, respectively.
The primers designed with Primer3 software, not to amplify genomic DNA, are listed in Supplementary Table  S2. Reactions were run on a CFX96 Real-time system (Bio-Rad) with the following cycling conditions: 30 s polymerase activation at 95 °C and 40 cycles at 95 °C for 5 s and 60 °C for 10 s. An additional step from a start at 65 °C to 95 °C (0. 5 °C/0. 5 s) was performed to establish a melting curve in order to verify the specificity of the real-time PCR reaction for each primer pair. The results were normalized using the geometric averaging of the GAPDH as reference gene. Normalized relative quantities were calculated using the ΔΔCT method  37, 38. Data were collected from three independent experiments and expressed as down-or up-regulation fold. The statistical differences were determined by Student's t-test.
Acknowledgements This study was in part supported by the  European Union  ( HEALTH-F3-2008-223585 ) and by the  University Paris-Sud  "Attractivité-2012 and 2013" grants. We sincerely thank the technical assistance of  Claudine Delomenie  ( Region Ile de France, IFR141 IPSIT ) for qRT-PCR analyses, as well as  Valérie Domergue  ( Region Ile de France, IFR141 IPSIT ) for animal facilities. We also thank  Sylvie Lambert-Bordes  and  Sandra Hoys  for technical support.
These conditions were sufficient to remove the native oxide layer naturally present on the steel surface. Then, XPS spectra are recorded once each hour for 120 h to evaluate the kinetics of each segregating element. Figure  2  presents the XPS spectra obtained after a segregation kinetics at 600 o C (top spectrum) as compared with before (bottom spectrum). After 120 h at 600 o C, the XPS spectrum shows that the main segregated elements on the surface are S, As, Sn, Sb, and Cu. At this stage, Phosphorus is not present anymore as its signal appears and finally disappears at the beginning of the segregation kinetics (next part). Data are sequentially recorded in the energy window of each element. Each time, peak intensity of individual elements is determined using Shirley inelastic background subtraction.
Model application From the XPS measurements of the segregation kinetics previously presented in Fig. a , surface concentration of segregated elements are deduced by reversing equation 6 using scilab software. [32] ll segregation kinetic curves of Fig. a  were therefore fitted by polynomial functions and then discretized to obtain intensities at same times. Figure  5  presents the evolution of surface concentrations of the segregating elements as a function of t 1=2 derived from the quantification model. Results show that the segregation monolayer is mainly composed of P briefly with a maximum coverage of around 0. 25 at 600 o C. Phosphorus is the first impurity to reach and to cover the surface before being supplanted by the other elements. The segregation monolayer is finally composed of several impurities.
The polarity of VH depends on the directions of the applied current and on the probe magnetisation orientation  21, 22. Thus, in the present dataset, parameters for the probe-sample separation, oscillation amplitude, bias current and electrical connections have been kept the same throughout the experiment. First, a stable reference map of VH was established using a commercial probe in +/-orientation of the stray field with the peak signal of VH ~+1. 7/-1. 9 µV, respectively (Fig. 6a  and Table  2 ). In the +FM state of the thin and thick ML-MFM probes, peak VH values of ~+3. 0 µV (Fig. 6b ) and ~+5. 1 µV (Fig. 6c ), respectively, was observed within the Hall cross region. The line profiles in Fig. 6d  show a clear bell type response. The increase in VH response is due to the increment in the Co layer thickness, which directly leads to an increase in the probe's stray magnetic field.
4 nm)/Pt(0. 9 nm)]100 multi-layered sample with perpendicular magnetic anisotropy (Fig. 3a ). This reference sample has well-characterised integral magnetic properties (saturation polarisation Ms = 457 kA/m, perpendicular anisotropy constant Ku = 517 kJ/m 3 , domain transition width δ = 20 nm), and a pure qualitative MFM image allows calculating the effective surface charge pattern of the sample and thus determining the object which has been imaged. The magnetic behaviour of the probe was quantified by de-convolving the measured MFM image and the effective surface charge pattern by means of a Fourier-based qMFM code implemented in SigMath (for more details see Ref. 23  ). The resulting RSTTF presents the probe's stray field derivative profile dHz/dz (x,y) at the distance dtot below the apex of the probe (Fig.
Acknowledgements The authors wish to thank  D. Kurt Gaskill  and  Rachael L. Myers-Ward  for providing the epitaxial graphene,  Arseniy Lartsev  for fabricating the Hall bar devices, and  Sergey Vdovichev  for developing the ML-MFM probes. This work was supported by the  UK government's Department for Business, Energy and Industrial Strategy, EMRP/EMPIR  under projects  EXL04  (SpinCal) and S01 (NanoMag),  EC grants Graphene Flagship  (No. CNECT-ICT-604391 ) and  NMS  under the  SC Graphene Project  ( No 119378 ). B. Gribkov acknowledges financial support by the  CRDF  grant # FSCX-14-61077-0 ,  MES of Russia  ( Agreement 02. В. 49. 21. 0003 ) and  RFBR. L. A. Rodriguez  and  E. Snoeck  acknowledge the " Conseil Regional Midi-Pyrénées ", the  European Union   Seventh Framework Program  under a contract for an Integrated Infrastructure Initiative Reference No.
Moreover, the count of this flora is significantly higher than the other groups of microorganisms (P<0. 05) in fresh beef. According yeasts is known to be the main causative agent of spoilage  [22]. In Khliaa Ezir, their count was found to decrease during the marinade period before their disappearance after 10 days of storage (Figure  2C ). The lower yeasts and molds counts is favored by the low water activity values (data not shown) and predominance of LAB (Figure  2D ) that exerts an antagonistic action on contaminating flora in concert with pH  [23, 24]. Re-appearance of yeasts and molds was observed after 30 days of storage to reach 3log cfu g -1. We think that these bacteria groups might • Fresh meat  play an important role in the definition of the organoleptic profile of the final product as reported for several traditional meat products  [24] [25] [26] [27].
Indeed, taking advantages of advances in the revolutionary next generation sequencing (NGS) technology and of basic knowledge in the symbiosis produced through extensive studies on "historical" model organisms, it is now possible for a small group to study any organism at the genome wide level. A JGI initiative to sequence whole genome of Root Nodule Bacteria (RNB) from different biogeographical regions has led to sequencing of more than 100 RNBs. Such structural genomic database is important for comparative and functional genomics of RNBs  [4]. In this context, and by taking advantage of the trapping system which can, through nodule enrichment, rapidly enable isolation of symbiotic bacteria, one can now select rhizobia from any environment of interest and study them at the genome wide level.
, unpublished data) and Merzouga in Morocco  [11]  that all share the same type of biome. Surprisingly, these strains were isolated from sandy soils in Baja California where wild bean had been observed (Mexican isolates) or from desert sand dune upon Acacia root nodule trapping (Moroccan isolates). Despite the great number of sequences deposited in GenBank databases that were produced from rhizobia diversity studies performed on Phaseolus and Acacia species, no housekeeping gene sequence identity was found outside this apparent new group of desert rhizobia. Given the presence of isolates sharing the same clade on 3 continents that appear restricted to alkaline hot desert type of biome and the variable hosts with whom they develop effective symbioses, we decided to test whether these strains were indeed belonging to the same and yet undefined species by means of comparative genomic analyzes.
Using NGS as well as recent releases of comparative genomic tools and pipelines now enabling the delineation of species from genomic datasets, and rapid prediction of genes and associated functions, we sequenced the genomes of 2 desert rhizobial strains isolated from Acacia in Morocco, 2 strains isolated from wild bean in Mexico as well as one Tephrosia strain originating from the Indian Thar desert. These 5 genome sequences were combined with the sequence of another Indian Thar desert isolate (TW10) already released  [12]  and compared to available genomic data from E. meliloti, E. medicae and E. fredii.
Image analysis and base calling were performed using the HiSeq control software and real-time analysis component. Demultiplexing was performed using Illumina's sequencing analysis software (CASAVA 1. 8. 2). The quality of the data was assessed using FastQC from the Babraham Institute and the Illumina software SAV (Sequence Analysis Viewer). Potential contaminants were investigated with the FastQ screen software from the Babraham Institute. Genome Assembly was performed with the CLC Genomics workbench software (version 5. 5. 1, CLC bio) after trimming of adaptor sequences and filtering on low quality scores. Scaffolds above 500 bp with an average coverage above 700×, except for LEM457 for which a significant fraction of the scaffolds which contained notably symbiotic genes showed a mean coverage of 15×, were kept for further analyzes.
For each genomic dataset, scaffolds were then ordered using the "Move contigs" tool from the MAUVE aligner program  [25]  (version 2. 3. 1) as follows. First scaffolds resulting from the strain LMR001 genome assembly were ordered according to the complete genome of E. meliloti 1021 with replicons in the order chromosome, pSymA and pSymB. The reordered scaffolds of LMR001 were subsequently used to order and move scaffolds of the remaining strains LMR013/LEM451/LEM457/TP6 and TW10.
Using these primer pairs, the amplified products were respectively 163, 112, 100 and 138 bp. The qPCR assays were performed as described above. The primer efficiencies were calculated as described for the 16S copy number estimations (Additional file 1: Table  S2 ). For each genomic DNA extract, the copy numbers of the chromosome and of the putative symbiotic plasmids were estimated by averaging data from their respective markers and replicates.
To estimate the core genome at the Ensifer genus level, the LMR001 Bidirectional Best Hits (BBH) of translated proteins showing more than 70% identity for all 28 strains belonging to 8 Ensifer species (Table  2 ) were retained and retrieved. To identify genes and/or functions that were present in all 6 strains in an Ensifer species but not conserved at the genus level, the fraction of PEGs corresponding to the core genus level were subtracted from each core species genome. For each one of the 4 compared Ensifer species, a pangenome analysis was also performed using Blast analyzes as implemented in the GView package  [38]  with as cutoffs, 1e-10 e-value, 80% identity and 80% of minimal alignment length.
Functional description and comparison of 4 Ensifer species To get insights in functions associated to desert symbiotic bacteria, Clusters of Orthologous Groups of proteins (COG) numbers were identified by Reverse Position   Specific Blast (RPS Blast) against the COG database [39] using the WebMGA server  [40]. COG number(s) with e-values below 0. 001 and their respective functional class and category were retrieved for all predicted PEGs and the 4 core species genomes. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) resources  [41]  were also used to assign KEGG orthology (KO) identifiers from core proteomes, reconstruct pathways and modules to gain insights on species conserved functional units. Finally, prophage and insertions sequences (IS) were predicted from pseudo-assembled genomes using the PHAST  [42]  and ISsaga  [43].
The Whole Genome Shotgun (WGS) projects for strains LMR001, LMR013, LEM451, LEM457 and TP6 have been deposited at DDBJ/ENA/GenBank under the accessions LUAV00000000, LUFU00000000-LUFX00000000 respectively (Table  2 ) and the corresponding versions described in this paper are versions LUAV01000000, LUFU01000000-LUFX01000000. The number of scaffolds in the newly sequenced strains varied from 79 to 193. Surprisingly, for the strain LEM457, a set of 111 scaffolds corresponding to approximately 573 kilo base pairs (Kbp) in length showed a significantly lower sequencing coverage mean of 15. 8 with a Standard Deviation (SD) of 3. 4 compared to the remaining scaffolds which presented a mean coverage 2 log higher 2467. 3 (SD of 1842. 8). Moreover, these low coverage scaffolds were containing many known symbiotic genes that were missing in the remaining scaffolds and were therefore kept for downstream analyzes.
showing at least 70% identity over 1 Kbp) ranged from 24,801 for the most divergent strains (TW10 vs LEM451) to 30,794 for the closest strains (TW10 vs TP6). More than 75% of the 1000 bp fragments were at least 99% identical in all comparisons suggesting high sequence conservation among strains, regardless of their origin. In line with these results, using the TETRA analysis tool from JSpecies, the regression coefficients from pair-wise comparisons of tetranucleotide signatures were very high (above 0. 9996, data not shown). Intra-and inter-species ANI calculations were also performed using available genome sequences confirming that the new taxon described here corresponds to a new species with ANI values comparable to that of E. meliloti, E. medicae or E. fredii intra-species comparisons and which differ greatly from inter-species ANI values that were all below 95% which corresponds to the current cutoff value used for species delineation (data not shown).
There were 114,534 aligned bases in the final dataset after removal of ambiguous positions. Evolutionary distances were computed using the K2P method and are in the units of the number of base substitutions per site. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches (red). The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. Moroccan, Mexican and Indian strains are highlighted in green, purple and blue respectively Bradyrhizobium species  [49]. Despite the lack of complete genome sequences, our results further support such observation. To explore the synteny level and the evolutionary relationships of genes conserved among the 6 desert strains, the 103 housekeeping genes used for the phylogenetic Fig.
Funding This study was financially supported by the  Institute of Research for the Development (IRD)  through the  LMI LBMV and Programme d'Urgence  of the University Mohammed V, Rabat. This work was also supported by the project  SEP-CONACYT-ANUIES-ECOS NORD  France  M08-A02. Guadalupe Rocha  is grateful for the  CONACyT grant. We would like to thank  L'OREAL -UNESCO  for the promotion of Women in Science that awarded  Ilham Sakrouhi  a prize for her PhD project. Indian Authors (HSG, NT and SR) acknowledge the partial financial assistance obtained from  Department of Biotechnology (DBT) ,  University Grant Commission-Special Assistance Program (UGC-SAP) ,  DST-FIST, DST-SERB  ( SB/YS/LS-369/2013 ),  Govt of India.
Availability of data and materials The datasets supporting the conclusions of this article are available in the NCBI's WGS repository under accession numbers LUAV00000000, LUFU00000000, LUFV00000000, LUFW00000000 and LUFX00000000 for LMR001, LMR013, LEM451, LEM457 and TP6 respectively. Furthermore, annotated draft genomes can be browsed and predicted gene/protein sequences retrieved from the RAST website [50] using login and password ' guest'. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Field data collection Field surveys were conducted on 10-14 March, 22-24 May, 10-13 July, 17-22 August and 23-26 September in 2013. A total of 604 ground-truth samples were collected for open water and aquatic vegetation (100 samples in March, 102 samples in May, 112 samples in July, 143 samples in August and 179 samples in September) in macrophyte-dominated zone of Taihu Lake (Figure  1 ), including 405 submerged vegetation samples and 231 floating-leaved vegetation samples. The aquatic vegetation sampling plots were limited to areas measuring at least 60 × 60 m (i. e. , four pixels of an HJ-CCD image) and that had a relatively uniform distribution of vegetation. We used a portable GPS receiver with an accuracy of 3 m to record the centre coordinates of each sample and recorded the type and percent coverage of aquatic vegetation.
Remote sensing data collections and processing HJ-CCD images recorded from the HJ-1A/1B CCD cameras were acquired from the China Centre for Resources Satellite Data and Application (CRESDA). These cameras were onboard the HJ-1A and HJ-1B satellites, which were launched by CRESDA on September 6, 2008. Their spectral ranges and spatial resolutions are similar to those of the first four bands of Landsat TM. The single CCD imagery width is 360 km, and the two satellites constellation provides a wider swath width (700 km) and a re-visit time of 48 h (two days). Its high re-visit cycle was of great importance for mapping the dominant SAV species in this study. In this study, eight cloud-free and sun glint free HJ-CCD images covering Taihu Lake and acquired on February 20, March 12, April 25, May 22, July 11, August 16, September 26 and October 28, 2013 were used, respectively.
The ENVI software package was used to pre-process the remote sensing images. Radiometric corrections were made using coefficients from the metadata accompanying the images (e. g. , gains and offsets). FLAASH uses a robust procedure to correct for atmospheric attenuation and adjacency effects  (Module, 2009). Four key input parameters for the FLAASH module included: the mid-latitude atmosphere model, urban aerosol model, atmosphere water vapour and visibility. Based on the location of the study area covered by the scenes and the satellite transit time, the first two parameters were easily determined. However, water vapour and visibility values may vary between the images, and these were determined by trial-anderror until a typical spectral pattern of plants was observed  (Pu et al. , 2012).
The result showed that it is difficult to classify seven SAV species only using multispectral image. Meanwhile, we also acquired their corresponding situ spectral measurements on July 13, 2013 (Figure  14 ). It is indicated that there are large differences between the SAV species. Thus, it is possible to classify some species by hyperspectral data. Meanwhile, the studies also suggested that there are tiny spectral differences between SAV species  (Han and Rundquist, 2003; Yuan and Zhang, 2006) , and only hyperspectral remote sensing data could capture the differences and to then identify SAV species. Therefore, to reduce and eliminate these uncertainties, the resolution of remote sensing data, including spatial resolution and spectral resolutions, must be improved. In future, with the constantly emerging of the hyperspectral sensors, combining our approach, classification accuracies of SAV species would be expected to be further improved.
Population analysis One-and two-compartment disposition models with first-order absorption were fitted to the data using the first-order conditional estimation (FOCE) procedure by means of NONMEM software  [15]  using a Fortran compiler (version 6). The tested models were described in terms of volume of distribution of the central compartment (Vc/F); volume of distribution of the peripheral compartment (Vp/F); total body clearance (CL/F); intercompartmental clearance (CLd/F); and the absorption rate constant (ka). In addition, the inclusion of absorption lag time (Tlag) was evaluated in order to help adjust the model to the data in the absorption phase. Interindividual variability of the parameters was estimated assuming a proportional variance model, with exponential errors following a log-normal distribution, as shown in the following equation: CLi ¼ CLpop Á expðZiÞ where CLi is the true PK parameter of the i th individual, CLpop the population value for the typical individual, ηi is the interpatient random effect with a mean of 0 and variance ω 2.
Optimal design of the next study in 2-5 year olds. The final model was also used to explore the optimal design for a PK study in 2-5 year olds. It was assumed that PK in this age group would be similar to the PK observed in 6-11 year olds. Based on the simulations a dose of 2. 5 mg was selected if weight was lower than 25 kg, and 5 mg if equal or greater to 25 kg. The optimal number of sampling time-points was assessed using WinPOPT software (Release 1. 2. 1)  [18]. The selection of the best design was based on statistical and child factors including the length of time the children were admitted to the hospital's research facilities on the days required to carry out the study. WinPOPT provides the following statistics: the determinant, the relative optimality criterion between two models (efficiency), and the standard errors for the fixed parameters.
Dataset A dataset with concentration and time data from the eleven 6-11 year olds along with sex, age, weight, height and BMI was used to develop the model. The age distribution was as follows: 7 years (9. 1%), 8 years (9. 1%), 9 years (27. 3%), 10 years (9. 1%), and 11 years (45. 4%). A total of 84 plasma concentrations of rupatadine were used in the analysis. Concentrations below the limit of quantification in the elimination phase (amounting to only 12% of observations) were not considered in the calculations  [19]. The observed concentration range of rupatadine was 0. 1 to 4. 9 ng/mL. Most children received a 5-mg dose since all but 2 children weighed more than 25 kg. These two children received a 2. 5 mg dose. Table  2  summarises the demographic characteristics of the included patients.