dataesr/scientific-paragraphs-categorization huggingface dataset:
The accuracy of the method has been thoroughly tested using plate and rods as input model and it has delivered excellent results. The 2D time domain simulations are carried out using SPECFEM2D a code that solves the elastic wave equation using finite difference in time and the spectral element method in space. The parallelization is implemented through domain decomposition with MPI. The mesh is made of quadrilateral elements and it is generated using the commercial software CUBIT. Simulations are then run on a parallel cluster (Froggy at University of Grenoble) on 16 CPUs. 2D plots have been generated with Paraview. The dispersion curves in Fig. 3c  have been generated solving the eigenvalues problem on the unit cell using the COMSOL Multiphysics software suite with the Matlab interface. The solution is computed via finite element using Bloch-Floquet conditions on the left and the right boundaries of the unit cell.
Acknowledgements All the computations presented in this paper were performed using the Froggy platform of the CIMENT infrastructure (https://ciment. ujf-grenoble. fr), which is supported by the  Rhone-Alpes region  ( GRANT CPER07_13 CIRA ), the  OSUG2020  labex (reference  ANR10 LABX56 ) and the  EquipMeso project  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d' Avenir supervised by the  Agence Nationale pour la Recherche. A. C and R. C. thanks the  EPSRC  for their support through research grants  EP/I018948/1 ,  EP/ L024926/1 ,  EP/J009636/1  and  Mathematics Platform  grant  EP/I019111/1. S. G. is thankful for an  ERC starting grant (ANAMORPHISM)  which facilitates the collaboration with  Imperial College London. P. R. and P. G. are part of  Labex OSUG@2020  ( ANR10 LABX56 ).
Results RF ablation caused significant changes in the tissue autofluorescence profile. The magnitude of these spectral changes in human left atrium was relatively small (< 10% of peak fluorescence value), yet highly significant. Spectral unmixing of hyperspectral datasets enabled high spatial resolution, in-situ delineation of radiofrequency lesion boundaries without the need for exogenous markers. Lesion dimensions derived from hyperspectral imaging approach strongly correlated with histological outcomes. Presence of blood within the myocardium decreased the amplitude of the autofluorescence spectra while having minimal effect on their overall shapes. As a result, the ability of hyperspectral imaging to delineate ablation lesions in vivo was not affected.
Hyperspectral imaging approach and protocol To compose a three dimensional HSI hypercube with three axes (x,y,λ), an object can be imaged in a number of ways: by either moving the sample across a linear spectral detector (HSI modality called pushbroom-HSI), by altering excitation wavelength (HSI modality called source-HSI), or by using a bandpass or a tunable filter in front of the camera while the illumination source stays the same. The latter modality can be referred to as detector-based HSI and is the method used in this study. Samples were illuminated with a fixed 365nm light source (LED spotlight, Mightex, Pleasanton, CA), while a liquid tunable filter in front of a CCD camera was sequentially tuned from 420nm to 720nm in 10nm steps using Nuance FX hyperspectral imaging system hardware and software (PerkinElmer/Cri, Waltham, MA).
A Nikon Micro-Nikkor 60mm f/2. 8D lens in front of the camera included a pair of aspherical elements to avoid spatial aberrations and an extra-low-dispersion glass element to minimize chromatic aberrations. A 4x3cm field-of-view captured at 1392x1040 pixels resulted in ~30 micron/pixel spatial resolution. After each hyperstack was acquired, Nuance FX analysis software was used to perform linear unmixing in order to derive greyscale HSI component images  [8]. To provide reference spectra for linear unmixing of HSI hyperstacks, a user placed ~10x10 pixel regionof-interest within known areas of ablated and unablated tissue and the extracted spectra were then used to identify the remaining lesions in the field-of-view. A more detailed description of the region-of-interest based linear unmixing approach and its comparison to principal component analysis can be found in our recent publication  [7].
The totality of excreted urine was collected from the metabolism cage at time 0 (before the administration), then 0. 5,  1, 1. 5, 2, 4, 6, 10, 24, 28, 34, 48, 56, 72, 80  and 96 h after each oral or intravenous administration. The volume of urine collected at time 0 corresponded to the 24-h urinary output before administration. The stability of torasemide in urine at ambient temperature for 24 h has previously been demonstrated (less than 6% degradation, data not shown). Right after collection, urine samples were weighed, filtered and divided into three aliquots of about 0. 6 mL which were stored at about À75 °C until analysis. Study 2. The aim of study 2 (dose-response study) was to determine the dose-effect relationship of torasemide diuresis in dogs. The ten dogs were randomly allocated into five groups of two animals.
Statistical analyses of drug-induced diuresis (study 1 and study 2) Statistical analyses were performed using the SAS software (version 9. 2) to compare the 24-h drug-induced diuresis (postdose 24-h urine volume minus basal 24-h diuresis) after the different tested torasemide doses. In the study 1 (0. 2 mg/kg intravenous and 0. 2, 0. 8 and 1. 6 mg/kg per os), the increase in urine volume was compared by the use of an analysis of variance (ANOVA) with 4 factors: dose, group, period and dog (group). In the study 2 (0. 15, 0. 4, 0. 75, 1. 5 or 4. 5 mg/kg/day per os for 5 days), comparisons were carried out by the use of a repeated-measures ANOVA with the followings factors: dose, group, period, day, dose x day and dog(group). A P-value of 0. 05 was considered significant.
as low. In the present experiment, renal clearance, Clr, was computed from plasma and urine data according to equation 1 presented previously. For determination of renal clearance, care must be taken to ensure that all urine is collected for a sufficient period of time to gain a good estimation of the total amount of drug eliminated in urine. In practice, the period of time must be at least six elimination half-lives of the drug  (Rowland & Tozer, 2011). A mean terminal half-life of 10. 4 h (CV = 11. 6%) was estimated from simultaneous modelling of plasma and urine data (study 1). As urine was collected until 96 h after administration, this duration was sufficient to provide an accurate estimation of the fraction of the dose excreted in urine. The mean renal clearance of torasemide, Cl R , was 9.
A mean TERU 50, TERU producing 50% of I max , of 1. 45 lg/kg/h was estimated in the present study. This TERU 50 value in healthy dogs is lower than corresponding values obtained by Wakelkamp et al. for furosemide in healthy volunteers: 22 lg/min  (Wakelkamp et al. , 1996) , 19 lg/min  (Wakelkamp et al. , 1998a)  and 7 lg/min  (Wakelkamp et al. , 1998b) , averaging about 14 lg/kg/h in a 70-kg man. Despite comparison being made between different species, this observation is consistent with previous data obtained by  Ghys et al. , 1985; Uechi et al. , 2003  who reported torasemide to be more potent than furosemide in healthy dogs. Tolerance may be defined as diminished response upon repeated administration of the same drug. The choice of a tolerance model in study 1 was supported by observation of a proteresis loop between TERU vs.
As expected, NAC inhibited Lebein-induced ROS generation (data not shown). Interestingly, on Lebein treatment, NAC partially diminished the inhibitory effect of Lebein on LU1205 cell viability (Figure  3B ). At a concentration of 100 nM of Lebein, the percentage of viable cells increased from 35. 1% in Lebein treated cells to 53. 3% in the presence of NAC showing that ROS generation was in part required for cell viability inhibition in LU-1205 cells. To determine the role of caspase activation in Lebein-induced apoptosis, SK-MEL-28 and LU-1205 melanoma cell lines were treated with the pan-caspase inhibitor, z-VAD-fmk (20 μM), 2 h before adding Lebein at different concentrations (0. 1 nM to 100 nM for a further 24 h). The percentage of apoptotic cells was quantified by flow cytometry after Annexin-V staining.
The induction of apoptosis in each melanoma cell line after 7 h of treatment with 2 µM staurosporin (100% apoptotic cells) was used to calculate the number of apoptotic cells. Apoptosis was also assessed by the PE Annexin-V apoptosis detection kit (BD Biosciences, San Jose, CA, USA) according to the manufacturer's protocol. For caspase inhibitor activity assay, cells were pre-incubated with a pan-caspase inhibitor, Z-VAD-FMK (20 µM) (BD Biosciences, San Jose, CA, USA) for 2 h before Lebein-treatment. Stained cells were analyzed on a BD FACScanto II flow cytometer (BD Biosciences, San Jose, CA, USA) and further analyzed with BD FACSDiva 6 software (BD Biosciences, San Jose, CA, USA). Cell death was quantitatively evaluated by measuring the proportion of annexin positive cells, regardless of their staining for 7-AAD in order to include both apoptotic and necrotic cell death.
Acknowledgments: This work was supported by grants from the  Ministerio de Economia y Competitividad  (MINECO; Co-financing with  Fondos FEDER)  ( SAF2013-48375-C2-1-R ) and the  Fundación Séneca , the  Región de Murcia (FS-RM)  ( 19304/PI/14 ) to  José Neptuno Rodríguez-López  and by the  Tunisian Ministry of Higher Education and Scientific Research  ( LR11IPT04 ) and the  Institut Pasteur de Tunis  to  Khadija Essafi-Benkhadir. María F. Montenegro  and  Luis Sánchez-del-Campo  were contracted by  Fundación de la Asociación Española contra el Cáncer (FAECC). Manel B. Hammouda  was supported by a grant of  Erasmus mundus EU Mare Nostrum.
The monitoring of emerging diseases in natural populations is also important for preserving biodiversity, because pathogens carried by invasive species may cause the decline of endemic species  (3). There is, therefore, a need to develop screening tools for identifying a broad range of pathogens in samples consisting of large numbers of individual hosts or vectors. High-throughput sequencing (HTS) approaches require no prior assumptions about the bacterial communities present in samples that are diverse in nature, including noncultivable bacteria. Such HTS microbial identification approaches are based on the sequencing of all (WGS [whole-genome sequencing]) or some (RNA-seq [whole-RNA sequencing] or 16S rRNA amplicon sequencing) of the bacterial DNA or RNA in a sample, followed by comparison to a reference sequence database  (4).
The 16S rRNA amplicon sequencing technique is based on the amplification of small fragments of one or two hypervariable regions of the 16S rRNA gene. The sequences of these fragments are then obtained and compared with reference sequences in curated databases for taxonomic identification  (4, 19). Multiplexed approaches of this kind include short indices (or tags) that are linked to the PCR products and specific to a given sample. This makes it possible to assign the sequences generated by the HTS run to a particular sample following bioinformatic analysis of the data set  (18). Razzauti et al. (8)  demonstrated the considerable potential of this approach for determining the prevalence of bacteria within populations and for analyzing bacterial interactions within hosts and vectors, based on the accurate characterization of bacterial diversity within each of the individual samples that it provides.
Use of standardized protocols and reproducible workflows Incomplete reference sequence databases  (39)  False-negative samples Selection of an appropriate database for the selected target region and testing of the database for bacteria of particular interest Error of taxonomic classification  (40)  False-positive samples Positive controls for PCRs (extraction from infected tissue and/ or bacterial isolates and/or mock communities) Checking of taxonomic assignments by other methods (e. g. , blast analyses using different databases) mizing the impact of these sources of error on HTS data interpretation have yet been reported. We describe here a rigorous experimental design for the direct estimation of biases from the data produced by 16S rRNA amplicon sequencing. We used these bias estimates to control and filter out potential false-positive and false-negative samples during screening for bacterial pathogens.
We applied this strategy to 711 commensal rodents collected from 24 villages in Senegal, Western Africa: 208 Mus musculus domesticus, 189 Rattus rattus, 93 Mastomys natalensis, and 221 Mastomys erythroleucus. Pathogenic bacteria associated with the rodents were analyzed using a protocol based on Illumina MiSeq sequencing of the V4 hypervariable region of the 16S rRNA gene  (18). We considered the common pitfalls listed in Table  1  during the various stages of the experiment (see details in the workflow procedure in Fig. 1 ). Biases in assessments of the presence or absence of bacteria in rodents were estimated directly from the data set by including and analyzing negative controls (NC) and positive controls (PC) at various stages of the experiment (see details in Materials and Methods) and by systematically using sample replicates.
RESULTS AND DISCUSSION Raw sequencing results. The sequencing of 1,569 PCR products in two MiSeq runs generated a total of 23,698,561 raw paired-end sequence reads (251 bp) of the V4 region of the 16S rRNA gene. Because we made PCR replicates for each rodent sample, and because we included several controls in each sequencing run, we had more PCR products (N ϭ 1,569) than rodent samples (N ϭ 711) (see summary in Table  S1  in the supplemental material and complete information by sample and run in Table  S2 ). Overall, 99% of the PCRs generated more than 3,000 raw reads (mean, 11,908 reads; standard deviation, 6,062 reads). The raw sequence files have been deposited in FASTQ format in the Dryad Digital Repository (http://dx. doi. org/10. 5061/dryad. m3p7d) (41). Using mothur v1. 34  (42)  and the MiSeq standard operating procedure (http:// www.
mothur. org/wiki/MiSeq_SOP), we removed 20. 1% of the paired-end reads because they were misassembled, 1. 5% of sequences because they were misaligned, 2. 6% because they were chimeric, and 0. 2% because they were nonbacterial. The remaining reads were grouped into operational taxonomic units (OTUs) with a divergence threshold of 3%. Bioinformatics analysis identified 13,296 OTUs, corresponding to totals of 7,960,533 sequences in run 1 and 6,687,060 sequences in run 2. Taxonomic assignment of sequences. We used the Bayesian classifier (bootstrap cutoff ϭ 80%) implemented in mothur with Silva SSU Ref database v119 (42) as a reference for the taxonomic assignment of OTUs. The 50 most abundant OTUs accounted for 89% (minimum, 15,284 sequences; maximum, 2,206,731 sequences) of the total sequence data set (see Table  S3  in the supplemental material).
c Mycoplasma mycoides and Borrelia burgdorferi bacterial isolates were added as positive controls for PCR and indexing (i. e. , PC alien ) (see Fig. 1 ). d Data are given for the two MiSeq runs separately. NC PCR , negative controls for PCR; NC ext , negative controls for extraction; NC mus , negative controls for dissection; PC Bartonella_t , positive controls for PCR; PC Borrelia_b and PC Mycoplsma_m , positive controls for PCR and positive controls for indexing; T CC and T FA , thresholds for positivity for a particular bacterium according to bacterial OTU and MiSeq run (see also Fig. 1 ). frequent taxonomic error reflected in most databases, including Silva  (43). This problem might also affect other taxa. We therefore recommend systematically carrying out a blast analysis against the sequences of taxa of interest in GenBank to confirm the taxonomic assignment obtained with the 16S databases.
2 ). These genera are Bartonella, Borrelia, Ehrlichia, Mycoplasma, Orientia, Rickettsia, and Streptobacillus. Six different OTUs were obtained for Mycoplasma (Mycoplasma_OTU_1 to Mycoplasma_OTU_6) and one OTU each for the other genera (Table  2 ). Finally, the precise significance of the remaining 34% of sequences, which potentially corresponded to commensal bacteria (Bacteroidales, Bacteroides, Enterobacteriaceae, Helicobacter, Lactobacillus), unknown pathogens, undetected contaminants, or undetected sequencing errors, was undetermined. Filtering for false-positive results. mothur analysis produced a table of abundance, giving the number of sequences for each OTU in each PCR product (data have been deposited in the Dryad Digital Repository [http://dx. doi. org/10. 5061/ dryad. m3p7d]) (41).
The ability of these primers to hybridize to the DNA of bacterial zoonotic pathogens was assessed by checking that there were low numbers of mismatched bases over an alignment of 41,113 sequences from 79 zoonotic genera inventoried by Taylor et al. (1) , extracted from Silva SSU database v119 (89). The FASTA file has been deposited in the Dryad Digital Repository (http://dx. doi. org/10. 5061/dryad. m3p7d) (41). We used a slightly modified version of the dual-index method of Kozich et al. (18)  to multiplex our samples. The V4 primers included different 8-bp indices (called the i5 index in the forward primer and the i7 index in the reverse primer) and Illumina adapters (called the P5 adapter in the forward primer and the P7 adapter in the reverse primer) in the 5= position. The combinations of 24 i5-indexed primers and 36 i7-indexed primers made it possible to identify 864 different PCR products loaded onto the same MiSeq flow cell.
25%) agarose gel to discard nonspecific amplicons and primer dimers. A PCR cleanup gel extraction kit (Macherey-Nagel) was used to purify the excised bands. DNA was quantified by the use of a Kapa library quantification kit (Kapa Biosystems) for the final library before loading on a MiSeq (Illumina) flow cell (expected cluster density, 700,000 to 800,000/mm 2 ) was performed with reagent kit v2 (Illumina) (500 cycles). We performed two runs of 251-bp paired-end sequencing, which yielded high-quality sequencing through the reading of each nucleotide of the V4 fragments twice after the assembly of read 1 and read 2. The raw sequence reads (. fastq format) have been deposited in the Dryad Digital Repository http://dx. doi. org/10. 5061/dryad. m3p7d (41). Bioinformatic and taxonomic classification.
MiSeq datasets were processed with mothur v1. 34  (42)  and with the MiSeq standard operating procedure (SOP)  (18). Briefly, the MiSeq SOP (http:// www. mothur. org/wiki/MiSeq_SOP) allowed us to (i) construct contigs of paired-end read 1 and read 2 using the make. Contig command; (ii) remove the reads with poor quality of assembly (Ͼ275 bp); (iii) align unique sequences using Silva SSU Reference alignment v119 (89); (iv) remove the misaligned, nonspecific (eukaryotic), and chimeric reads (uchime program); (v) regroup the reads into operational taxonomic units (OTUs) with a 3% divergence threshold; and (vi) classify the OTUs using the Bayesian classifier included in mothur (bootstrap cutoff, 80%) and the Silva taxonomic file. At the end of the process, we obtained a table giving the number of reads for each OTU in each line and each PCR product in each column.
For each OTU, the taxonomic classification (up to genus level) was provided. The abundance table generated by mothur for each PCR product and each OTU was filtered as described in Results and Discussion. The most abundant sequence for each OTU in each sample was extracted from the sequence data set with a custom-written Perl script (deposited in the Dryad Digital Repository [http://dx. doi. org/10. 5061/dryad. m3p7d]) (41). The most abundant sequences for the 12 OTUs were deposited in GenBank (see below). The sequences were aligned with reference sequences from bacteria of the same genus available from Silva SSU Ref NR database v119 using SeaView v4  (91). We used a neighbor-joining method (bioNJ) to produce phylogenetic trees with a Kimura 2-parameter model using SeaView software, and species were identified on the basis of the "closest phylogenetic species.
" We also used our sequences for blast analyses of GenBank (BLASTn against nucleotide collection [nr/nt] performed in January 2016) to identify the reference sequences to which they displayed the highest percentage of identity. The raw abundance table, the mothur command lines, the mothur output files, the Perl script, and the FASTA files used for the phylogenetic analyses have been deposited in the Dryad Digital Repository (http://dx. doi. org/10. 5061/dryad. m3p7d) (41). Accession numbers. The most abundant sequences for the 12 OTUs are available in GenBank (accession numbers KU697337 to KU697350).
OTU of interest (genus level) Closest species a (% identity in GenBank) No. of positive wild rodents (n ‫؍‬ 704) b Biology and epidemiology Mastomys erythroleucus (n ‫؍‬ 219) Mastomys natalensis (n ‫؍‬ 93) Mus musculus (n ‫؍‬ 203) Rattus rattus (n ‫؍‬ 189) Bartonella Undetermined 60 73 1 6 Bartonella spp. are intracellular fastidious hemotropic Gram-negative organisms identified in a wide range of domestic and wild mammals and transmitted by arthropods. Several rodent-borne Bartonella species have emerged as zoonotic agents, and various clinical manifestations are reported, including fever, bacteremia and neurological symptoms (82). Borrelia B. crocidurae (100) 21 0 8 6 Borrelia is a genus of spiral Gram-negative B. duttonii (100) bacteria of the spirochete phylum. These B. recurrentis (100) bacteria are obligate parasites of animals and are responsible for relapsing fever borreliosis, a zoonotic disease transmitted by arthropods (ticks and lice).
haemomuris (96) 28 42 30 1 Mycoplasma is a genus that includes over Mycoplasma_Otu_2 M. sp. nov. (100) 0 0 0 9 0 100 species of bacteria that lack of a cell (GenBank accession wall around their cell membrane. no. AB752303) Mycoplasma coccoides and Mycoplasma Mycoplasma_Otu_3 M. haemomuris (93) 93 40 1 1 haemomuris are blood parasites of wild Mycoplasma_Otu_4 M. coccoides (96) 0 0 0 18 and laboratory rodents. A new closely Mycoplasma_Otu_5 M. coccoides (95) 3 8 0 0 related species (AB752303) was recently Mycoplasma_Otu_6 M. coccoides (97) 3 13 0 0 isolated from brown rats (45). These species are commonly referred as "hemoplasmas. " Hemoplasmas have been detected within the erythrocytes of cats, dogs, pigs, rodents, and cattle, in which they may cause anemia. There have been sporadic reports of similar infections in humans, but these infections have been poorly characterized (49).
OTU of interest (genus level) Closest species a (% identity in GenBank) No. of positive wild rodents (n ‫؍‬ 704) b Biology and epidemiology Mastomys erythroleucus (n ‫؍‬ 219) Mastomys natalensis (n ‫؍‬ 93) Mus musculus (n ‫؍‬ 203) (9)entia chuto, was recently characterized in sick patients from the Arabian Peninsula, and new Orientia haplotypes have been identified in France and Senegal(9). Rattus rattus (n ‫؍‬ 189) Orientia O. chuto (100) 0 2 46 0 Orientia is a genus of obligate intracellular Gram-negative bacteria found in mites and rodents. Orientia tsutsugamushi is the agent of scrub typhus in humans. This disease, one of the most underdiagnosed and underreported febrile illnesses requiring hospitalization, has an estimated 10% fatality rate unless treated appropriately. A new species, O.
e. , severe vascular shadowing). Therefore, a total of 559 horizontal scans and 557 vertical scans were included to the analysis. To enhance the visibility of the peripheral LC, adaptive compensation was applied to all scan images according to the previously published protocols. [21] [22] [23]  Measurement of ALID, mLCD, and LC curvature index All of the measurements were performed by using ImageJ software (developed by Wayne Rasband, National Institutes of Health, Bethesda, MD; available at http://imagej. nih. gov/ij/). The ALID was defined as the vertical distance between the anterior laminar insertion (ALI) and the reference plane connecting the BMO. The mean values of temporal and nasal ALID and that of the superior and inferior ALID were defined as the horizontal and vertical ALID, respectively.