dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016b) , Mitomaster  (Lott et al. , 2013) , or HmtDB  (Clima et al. , 2017)  available on a web-server, or integrated into an all-in-one pipeline as in MToolBox bioinformatics suite  (Calabrese et al. , 2014) , MseqDR mvTool  (Shen et al. , 2018) , mit-o-matic  (Vellarikkal et al. , 2015). Conversely, the Phy-Mer software allows the classification of haplogroups from the FASTQ files, i. e. , without prior alignment, avoiding mistakes caused by artifactual sequencing variants  (Navarro-Gomez et al. , 2015). However, few databases provide information about the prevalence of a variant in a specific haplogroup. Mitomaster allows a quick overview of the variant distribution in the different haplogroups, while HmtDB through multiple queries gives the frequency of a pathogenic variant within the haplogroup.
, 2017). In addition, it was recently shown that, apart from any pathogenic mtDNA variants, the combination of rare non-synonymous polymorphisms could lead to LHON, as exemplified by both combinations of variants m. 14258G>A in the MT-ND6 gene (p. Pro139Leu) and m. 14582A> G (p. Val31Ala); m. 14258G> A, m. 10680G> A in the MT-ND4L gene and m. 12033A> G in the MT-ND4 gene  (Caporali et al. , 2018). Functional studies of cybrid cells carrying both variant combinations revealed that the biochemical deficiency was transferred to mutant cybrids. Unfortunately, currently databases and bioinformatic pipelines do not allow identifying rare co-occurrences of variants, and further developments of these databases are needed to implement a searchable function of possible combinations of mtDNA variants.
, 2014; Giordano et al. , 2015). Unfortunately, major databases such as Mitomap, HmtDB, and HmtVar do not currently allow the search of the cooccurrence of mtDNA variants or in combination with nuclear variants. As the mtDNA data can be extracted from exome or genome sequencing data  (Griffin et al. , 2014) , these information could be integrated in general databases such as GnomAD. The integration of additional information in mitochondrial databases or in the filtering and prioritization process of bioinformatics pipelines, such as haplogroups, co-occurrences of mtDNA or nuclear variants shown to modulate the phenotype should be very helpful to assess the pathogenicity of a given variant for a better interpretation and as a possible explanation for incomplete penetrance or phenotypic variability.
Quantifications of tract size and feather follicles/dots number Tract size: Feather-containing surfaces normalized by that of the whole dorsum were measured using Fiji software on pictures of flat skins at developmental stages corresponding to tract completion (E11 for the domestic chicken, n=2; E10 for the Japanese quail, E12. 5 for common pheasant, n=3, and E26 for the emu), and at hatching for the zebra finch (the relative surface of the completed tract being conserved during development, follicles are best visualized at P0; n=2). Follicle/dot number: To quantify feather follicles or dot number (F) respectively on pictures of flat skins or model simulations in a time efficient and consistent manner (i. e, across species and at different stages), we developed a custom Matlab program. The algorithm follows three steps: first, a Gaussian filter reduces optical noise through image smoothing (Matlab function imfilter; filters were obtained with the function fspecial).
Second, morphological operations are applied (i. e. , closing followed by opening of the image with a disk of radius 1 pixel using functions imopen and imclose; the disk was constructed using the strel function). Third, feather follicles/dots are detected as connected components of the image and their properties (centroid, bounding box, surface, etc…) stored. Because background levels on skin images can fluctuate, the algorithm contains a set of linearly spaced thresholds to (i) produce a binary image through a detection threshold of the pre-treated image, (ii) identify the connected components of the binary image (Matlab function bwconncomp), and (iii) repeat the second step if the maximal value in a given component is above the next threshold. The resulting feather/dot locations are presented in a Matlab interface (Supp.
Fig. 1 ) allowing to adjust thresholds, filter properties, and correct for undetected follicles or false positives (needed for less than 5% of the total follicle number). Locations are processed to identify rows by segmenting the set of feathers/dots according to their location along the x-axis (and corrected by hand in the cases of Japanese quail and zebra finch for which fr# is shifted laterally and in the emu for which counting was performed along six virtual lines). Program code and interface (Dotfinder) are available on github.
ACKNOWLEDGEMENTS We thank  B. Perthame  for help with project conception and analyses,  K. Painter ,  T. Lecuit , and  F. Bosveld  for helpful discussion,  E. Bouvet ,  M. Ladner ,  N. Haupaix , and  P. Galipot  for help with specimen collection, and  N. Quenech'Du  and the  Collège de France imaging facility  for help with image processing and analysis.
A third fraction consists of rare archaea that are uncommon in public databases, inactive, and alien to the studied ecosystem, representing a non local seed bank of potential colonizers  7. These rare taxa could therefore represent a gene reservoir and might play key roles in biogeochemical cycles  7, 9. In intertidal mudflats, sharp vertical gradients in salinity, oxidative state and nutrients 10 shape microbial establishment (at the DNA level)  11  as well as microbial activity (at the RNA level)  12. While archaea represent a small fraction of the active prokaryotic community compared with their bacterial counterparts, vertical shifts in major archaeal phyla have been recorded  12. The taxonomic affiliation of several of these rare archaeal taxa that have been newly discovered in many environmental samples is still under active debate  13, 14.
Sampling and nucleic acid extraction. Marennes-Oléron Bay is located in the center of the French Atlantic coast. Sampling was performed along ridges at low tide. Triplicate 15-cm-diameter cores were sliced into five layers from 0 to 10 cm below the sediment surface (bsf) then homogenized and stored at -20 °C. DNA was extracted from ~1 g of thawed sediment using the UltraClean Soil kit ® (MOBIO, USA) according to the manu- facturer's recommendations to maximize yields. As described in detail in a previous study  12  , RNA extracts were obtained from five sediment layers of a tidal mesocosm using the Power Soil TM Total RNA Isolation Kit (MOBIO, USA). cDNAs were generated using SuperScript III and random hexamers (Invitrogen, Life Technologies, USA). High-throughput sequencing. In the current study, three sets of metabarcoding data were used.
Archaeal 16S rRNA paired-end reads were merged with a maximum of 10% mismatches in the overlap region using FLASh  19. Denoising procedures consisted of discarding reads outside of the expected length range (i. e. , expected size between 370 and 580 bp) and reads containing ambiguous bases (N). After dereplication, the sequences were clustered into operational taxonomic units (OTUs) using SWARM  20  with a local clustering threshold. In the present work, the aggregation distance was equal to 3. Chimeras were then removed using VSEARCH  21  , and low-abundance sequences were filtered at 0. 005% (i. e. , OTUs with at least 0. 005% of all sequences were retained  22  ), discarding singletons from the datasets. Taxonomic affiliation was evaluated using both RDP Classifier  23  and BLASTN+  24  against the 119 SILVA database  25.
Phylogenetic analysis. From the high-throughput sequencing step using the Illumina HiSeq platform, 82 archaeal 16S rRNA gene sequences associated with the C3 group were obtained. All the sequences were analyzed using the ARB software package  26  (version July 2014) and the corresponding SILVA SSURef 99 database  27  , version 128, released on 07. 09. 2016. All the sequences were automatically aligned using the SINA tool online  28. The aligned sequences were added to the tree of the SILVA database. Tree reconstruction was first performed with 241 full 16S rRNA gene sequences using two methods: neighbor joining (NJ, ARB) and maximum likelihood (ML, RAxML 29 v8. 0. 24 using the CIPRES gateway  30  ) to analyze the topology of the tree. Only the 25 most abundant OTUs (Supplementary Information, Table  S1 ) were selected for the final tree.
e. , 13% of total archaeal 16S rRNA transcript sequences were found from 2 to 5 cm bsf, and up to 70% were in the deeper zone, from 5 to 10 cm bsf; Fig. 1 ). These findings suggest the presence of ecological niches that favor the activity of these archaea, although they remain rare along the vertical gradient. These findings were possible because of the use of archaeal 16S rRNA transcript sequencing, whereas most other related studies have described only microbial diversity at the 16S rRNA gene level or have lacked this specific focus on archaeal communities, thus, potentially overlooking active communities and archaeal diversity. While the C3 group is considered a class of Bathyarchaeota 15 , the SILVA database considers C3 as an emergent group that is more closely related to Thaumarchaeota.
Acknowledgements This research was supported by a PhD grant from the  Charente Maritime department  to C. L. , by the program  CPER of Poitou-Charentes  ( 2006-2013 ), and by the french national program  EC2CO/CNRS-INSU ("CAPABIOC", 2012-2014 ) to H. A. We acknowledge the  Molecular Core Facilities at LIENSs laboratory  and are grateful to  Jean-Christophe Auguet  for his expertise. Authors also thank the editor members and the anonymous reviewers for their highly positive comments and suggestions.
The two C-domains (residues 337-549) of both structures were aligned prior to simulation. U TMD ¼ 1 2 k N ½RMSðtÞ À RMS Ã ðtÞ 2 ðEq 1Þ where RMS(t) is the instantaneous best-fit RMS distance of the current coordinates from the target coordinates, and RMS Ã (t) evolves linearly from the initial RMSD at the first TMD step to the final RMSD at the last TMD step. The elastic constant k is scaled down by the number N of targeted atoms. Identification and characterization of druggable cavities were achieved with Fpocket or MDpocket software  [59]. Selection criteria were the volume and mean local hydrophobic density (ratio of neighboring apolar alpha spheres divided by the total number of apolar alpha spheres in the pocket); this ratio is then normalized in respect to the other binding pockets  [60].
The potential energy function of the five conformers selected from TMD simulation (and further used for ensemble virtual screening) was minimized with 50,000 steps of conjugate gradient using NAMD. The conformers were subjected to a careful structural quality assessment using the ProSA-web server  [61]   [62]  in order to compare the overall quality with the experimental crystal structure (4H2G and 4H1S). A chemical library of 324,400 compounds was generated from the Molport screening compound database gathering 34 suppliers and composed of natural and synthetic molecules with drug-like properties (http://www. molport. com). The library of screening compounds was composed of unique molecules, commercially available from several main suppliers (Asinex, ChemDiv Inc. , Vitas-M laboratory and Enamine).
Before using it for virtual screening, the library was filtered in order to remove duplicates, add explicit hydrogens, generate 3D coordinates and finally to transform in PDBQT (Vina) or Sybyl mol2 (Gold) format using Open Babel 2. 4. 1  [63]. Despite a careful filtering, few compounds escaped to the modified Lipinski's rules of 5 (initial Ro5 with a molecular weight allowed to be greater than 500 Da and a clog P greater than 5) as shown by some low molecular weight fragments found in the library. Virtual screening was performed using the highly parallelized implementation, VinaLC-1. 1. 2 [64]  of the Autodock Vina molecular docking program  [65]. A confirmation of the docking poses was achieved using a second program and scoring function (GOLD 5. 2 program, CCDC Software Limited,  [66] ) in order to increase the prediction accuracy.
Determination of inhibition mode and K i For the most interesting compounds, the inhibition mode was determined by steady state kinetic assays to obtain apparent catalytic (k cat ), Michaelis (K M ) and inhibition (K i ) constants. Recombinant enzyme (2. 5 nM), substrate (AMP, at eight different concentrations: 1, 1. 5, 2. 5, 5, 10, 25, 50 and 100 μM) were mixed in a thermostated beaker at 37˚C in the presence or in the absence of inhibitors and reaction was stopped every 5 s by acid quenching before HPLC analysis (as mentioned above). Quantification of adenosine and AMP was achieved by integrating peaks (Empower software, Waters) and raw data were analyzed using Grafit 7 (Erithacus software) and fitted with four different equations describing either a competitive, uncompetitive, non-competitive or mixed inhibition mode.
In silico mining of a rice diversity panel For in silico mining of OsSWEET13 and OsSWEET14, we analyzed the promoter regions of LOC_Os12g29220 and LOC_Os11g31190, as evidenced in the Nipponbare rice genome annotation project  [26]. Using the Rice SNP-seek database  [27] , we searched for genetic variation in a diversity panel containing 3000 rice genome sequences. Accessions were pre-selected with variations within 1. 5kb region upstream of the transcription initiation site of both OsSWEET genes. The default setting of the program TALVEZ was used for prediction of the EBE of PthXo1, PthXo2, PthXo3, AvrXa7, TalC, and Tal5 in the promoter of pre-selected accessions  [28]. The software uses the repeat variable diresidues (RVD) pattern of each TALE to assess sequence specificity in the rice genome.
In the second phase of mining, only accessions with SNP/Indels in the predicted EBE were retained. In addition, selected lines with desirable SNP/ Indels from the Pakistani aromatic rice breeding program were also analyzed  [29]. The Rice SNP-seek database was used to assess the presence of Xa4, xa5, Xa7, xa13, and Xa21 in selected accessions  [30] [31] [32] [33].
Phylogenetic reconstruction In order to reconstruct the phylogenetic relationships of the selected accessions, three family members (Os03g37490, Os0629950, and Os10g20450) of the multidrug and toxic compound extrusion (MATE) cluster were selected. MATE gene family has been previously reported for explaining different evolutionary trends in Arabidopsis and rice  [34]. Information regarding start and end position of these MATE genes was obtained from rice genome annotation project  [26]  and used to extract SNPs from a subset of 366 accessions. An unrooted neighbor-joining tree was constructed with DARwin 6. 0. 14 software with a bootstrap value of 1000  [35] , and the matrix of genetic distances was calculated using the Euclidian distance.
2 (OD 600 ) and concentration of 1x10 -8 -1x10 -9 CFU/mL. For pathotyping, fully expanded, 3-5 leaves of 21 days and 45 days old plants were clip inoculated  [36]. The experiment was replicated three times independently. Data regarding lesion length for each inoculated strain on selected accessions and susceptible check (IR24) was recorded at 14 days post-inoculation (dpi) when lesions were stable. Disease incidence was scored according to IRRI Standard Evaluation System (SES) for rice  [37].
Mining of OsSWEET13 and OsSWEET14 promoter polymorphisms in a large database identified potential new sources of resistance Mining for variations in the promoter sequence of OsSWEET13 and OsSWEET14 in the 3k SNP-seek database resulted in 631 accessions with SNPs/InDels within the region 1. 5kb upstream of both genes. Based on the phylogenetic tree, different incidence of SNP/InDels was found in the four rice subspecies. Variations in the promoter region from O. sativa subspecies indica, japonica, aus, and aromatic accessions showed relative frequencies (expressed in percentage) of 10. 75, 2. 65, 0. 12, 0. 64 for OsSWEET13 and 10. 91, 0. 12, 0. 08 and 0. 04 for OsSWEET14, respectively (Fig  1 ). Selected accessions from initial mining were further screened to filter out those with SNPs/InDels specifically at the EBE regions.
Discussion Plants utilize a number of mechanisms to protect themselves against pathogens. Triggering immunity or disrupting pathogen access to nutrient supply are common strategies that plants evolved to restrict the invading microbes  [22]. Understanding how rice limits sugar effluxes will provide a good opportunity to elevate disease resistance against Xoo. Since members of the SWEET sucrose-efflux transporter family appear to be hijacked by different Xoo populations, pyramiding allelic variants that block Xoo-induced sugar release might have an impact on pathogen fitness. In the current study, we accelerated the discovery of rice accessions with naturally mutated EBEs in the promoter sequence of two major susceptibility genes i. e. OsS-WEET13 and OsSWEET14 by mining a vast diversity dataset.
P43 Knowledge regarding human immune deficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) in a French hospital Introduction: On World AIDS day 2017, a survey regarding HIV and AIDS was conducted in our hospital. The principal aim of the study was to assess HIV knowledge in order to drive awareness of HIV transmission, prevention and treatment. Materials and methods: The survey was conducted by healthcare professionals. The questionnaire consisted of eleven items. Data included respondents' demography (sex, age, occupation i. e. healthcare professional), knowledge of HIV epidemiology, transmission, prevention and treatment. They were collected in the multiple-choice questionnaire and analyzed with Microsoft Excel. Results: We analyzed 107 responses. Among these, 66 came from healthcare professionals.
Materials and methods: We conducted a voluntary survey in French with 40 young people living in Beni city, North Kivu, DR Congo. Data was entered into an Excel database. Results: All 40 persons completed the questionnaire in full. Average age of respondents was 31 years old (range 18-46 years). Males represented 50% of the sample. Marital status was 55% single and 45% married. The majority (97. 5%) was employed, 60% were health workers, and 35% were students (secondary school or university). Over three-quarters (77. 5%) of the respondents agreed to enroll for the self-test, and 72. 5% were able to pay the test fees if required. 27. 5% noted that the self-test is less expensive than the test at the dedicated test facilities. The self-test was perceived as very advantageous for the following reasons: Discretion (58%), rapid result (90%).
We also removed cosmic rays from the final reconstructed object cube with a 3D version of L. A. Cosmic (van Dokkum 2001). We extracted spectra from 173 data cubes using kmos_extract_spec recipe with the ESO Recipe Execution Tool (EsoRex). kmos_extract_spec extracts a spectrum from a data cube with the option of defining a mask manually or automatically by fitting a normalised profile to the image of the data cube. We identify multiple sources in 53 data cubes and extracted their spectra by defining the mask manually. Finally, we extracted nearly 250 spectra and used IRAF median filtering with 7 pixels as filter size to smoothen the spectra. After discarding spectra with a signal-to-noise ratio (S/N) below 20 or having negative flux values, there were 91 spectra left in our sample with good S/N.
(2015). The remaining eight sources do not have any counterparts in the SIM-BAD database within 2. 0. In Figure  2 , we show the sources classified in the literature as X-ray source, OB/blue supergiants and non-YSOs using separate symbols. Regarding the classification of five sources as non-YSOs by An11, the large pixel sizes of Spitzer/IRS spectra can lead to mis-identification of sources in high stellar density regions like in the CMZ. Also one non-YSO (#405235) shows Brγ in absorption and CO in emission, indicating the presence of a dense circumstellar disk and considered to be a massive YSO feature though rarely seen  (Bik et al. 2006; Cooper et al. 2013. Thus we stick with our classification scheme for them. Since no clear periodicity was found for the two sources classified as long period variable stars, we assume them to be YSOs as well.
We define a rough criterion to separate them, taking into account the H-K S cut at 1. 5 estimated to remove foreground sources (assuming A V = 30 mag and using extinction laws of  Nishiyama et al. (2009) ). A similar H-K S cut was also suggested by  Schödel et al. (2010)  to remove foreground sources based on their study toward the central parsec of the Galaxy. Here again, we estimate the extinction vector as mentioned before and show that it is almost parallel to the line separating YSOs from late-type stars. Thus the extinction does not greatly affect our criterion. We want to stress that our sample is small and this criterion needs to be confirmed by a larger sample: Table  2. Details of the search for previously identified sources within 2. 0 of our spectroscopically identified YSOs in the SIMBAD database.
acknowledges the Programme National de Cosmologie et Galaxies (PNCG) of CNRS/INSU, France, for financial support. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement n o [614922]. This research made use of the SIMBAD database (operated at CDS, Strasbourg, France).
5 : foundations, brick-faced concrete of the cylindrical wall, lower, intermediate and upper zone of the dome. In addition, we have considered apart the material of the columns in the interior of the Rotonda, made of granite. The concrete densities have been fixed upon the indications given in  [Mark and Hutchinson, 1986]. About the other quantities, the choice is much more problematic. Though we know today rather well the behavior of light concretes, according to historians and researchers in mechanics of ancient monuments, the physical properties of concretes and mortars of the past have quite different characteristics. Moreover, the data that can be found in the literature about ancient concretes are often quite different and most of all fragmentary. The only reliable data about the Pantheon were the densities.
Clinical features of myelodysplastic syndromes (MDS) could be influenced by many factors, such as disease intrinsic factors (e. g. , morphologic, cytogenetic, molecular), extrinsic factors (e. g, management, environment), and ethnicity. Several previous studies have suggested such differences between Asian and European/USA countries. In this study, to elucidate potential differences in primary untreated MDS between Japanese (JPN) and Caucasians (CAUC), we analyzed the data from a large international database collected by the International.
The IPSS-R was developed using data from more than 7000 MDS patients, including CAUC and JPN. These two systems have been widely utilized and validated to predict overall survival (OS) and risk of AML transformation by many groups for ethnically different populations  [18] [19] [20] [21] [22] [23]. In this study, we analyzed this large International Working Group for Prognosis of MDS (IWG-PM) database, which generated the IPSS-R, to address the question of whether ethnic or other differences between JPN and CAUC MDS patients influenced their clinical features and outcomes. In contrast to previous reports, we compared clinical factors in more detail, with particular focus on cytogenetic abnormalities and clinical outcomes.