dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this work, an entire mapping of the celestial sphere has been presented by combining data sets recorded at the Telescope Array and the Pierre Auger Observatory above 10 19 eV. The unavoidable systematic uncertainty in the relative exposures has been treated by designing a cross-calibration procedure relying on the common region of the sky covered by both experiments. This cross-calibration procedure makes it possible to use the powerful multipolar analysis method for characterising the sky map of ultra-high energy cosmic rays. Throughout the series of anisotropy searches performed, no significant deviation from isotropy could be captured at any angular scale. From the multipolar coefficient measurements performed in this work, upper limits can be derived for any kind of pattern.
Acknowledgments The successful installation, commissioning, and operation of the  Pierre Auger Observatory  would not have been possible without the strong commitment and effort from the technical and administrative staff in Malargüe. We are very grateful to the following agencies and organizations for financial support:  Comisión Nacional de Energía Atómica, Fundación Antorchas ,  Gobierno De La Provincia de Mendoza ,  Municipalidad de Malargüe ,  NDM Holdings  and  Valle Las Leñas , in gratitude for their continuing cooperation over land access, Argentina; the  Australian Research Council ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (FINEP) ,  Fundacão de Amparo à Pesquisa do Estado de Rio de Janeiro (FAPERJ) ,  São Paulo Research Foundation (FAPESP)  Grants #  2010/07359-6 , #  1999/05404-3 ,  Ministério de Ciência e Tecnologia (MCT) ,  Brazil ;  MSMT-CR LG13007 ,  7AMB14AR005 ,  CZ.
Multidrug-resistant and highly virulent Klebsiella pneumoniae isolates are emerging, but the clonal groups (CGs) corresponding to these high-risk strains have remained imprecisely defined. We aimed to identify K. pneumoniae CGs on the basis of genome-wide sequence variation and to provide a simple bioinformatics tool to extract virulence and resistance gene data from genomic data. We sequenced 48 K. pneumoniae isolates, mostly of serotypes K1 and K2, and compared the genomes with 119 publicly available genomes. A total of 694 highly conserved genes were included in a core-genome multilocus sequence typing scheme, and cluster analysis of the data enabled precise definition of globally distributed hypervirulent and multidrug-resistant CGs. In addition, we created a freely accessible database, BIGSdb-Kp, to enable rapid extraction of medically and epidemiologically relevant information from genomic sequences of K.
Genomes from Sequence Databases Genomic sequences available as of July 15, 2013, for the entries Klebsiella, K. pneumoniae, and K. variicola, were downloaded from the NCBI (National Center for Biotechnology Information) genome sequence repository (http://www. ncbi. nlm. nih. gov/genome/). The downloaded sequences comprised 7 complete genomes and 115 whole-genome shotgun sequences available as scaffolds or contigs. Of these 115 draft genomes, 2 were discarded because of the poor quality of assembly and 1 was discarded because it corresponded in fact to the more distant species K. oxytoca. Thus, a total of 119 publicly available genome sequences were included (Table  S1 , http://bigsdb. web. pasteur. fr/klebsiella/archives/Bialek_Technical Appendix. pdf).
Definition of MLST and Core Genome MLST (cgMLST) Schemes A set of 634 genes, defined as the strict cgMLST set, was obtained by using stringent conservation and synteny criteria to maximize typeability and to minimize paralogous or xenologous loci (see Supplemental Methods section at http://bigsdb. web. pasteur. fr/klebsiella/archives/ Bialek_TechnicalAppendix. pdf). Two typing schemes, defined as sets of predefined loci, were implemented by using the BIGSdb genome database software  (16)  in a newly created database named BIGSdb-Kp (http://bigsdb. web. pasteur. fr). First, the MLST scheme, with its reference sequences and sequence types (STs), was imported from the international MLST database (http://www. pasteur. fr/mlst) into BIGSdb-Kp, which now acts as the reference. Second, a 694-gene cgMLST scheme was defined as the combination of the 7 MLST genes, the 53 ribosomal MLST genes  (17) , and the 634 strict cgMLST genes.
Genes Associated with Virulence and Heavy Metal Resistance or with Drug Resistance Sequences of genes previously associated with virulence or heavy metal resistance were used to define virulence-associated loci, and all currently described variants of major antimicrobial drug resistance determinants (namely, β-lactamases, aminoglycoside resistance-conferring enzymes and fluoroquinolone-resistance loci) were included in the BIGSdb-Kp database. For selected strains, the antimicrobial drug resistance phenotype was analyzed by using conventional methods (see Supplemental Methods section at http://bigsdb. web. pasteur. fr/klebsiella/archives/ Bialek_TechnicalAppendix. pdf).
Data Analysis Phylogenetic networks were constructed by using SplitsTree v4. 13. 1  (18)  based on comparison of allelic profiles using distance matrices corresponding to the percentage of distinct loci (excluding missing alleles), which were obtained using BioNumerics v6. 6 (Applied-Maths, Sint-Martens-Latem, Belgium). Clonal complexes (CCs) were defined as groups for which MLST profiles showed only 1 allelic mismatch with at least 1 other member of the group. Phylogenetic reconstruction based on cgMLST genes was performed by using minimum evolution analysis after discarding putative homologous recombination biases (see Supplemental Methods section at http://bigsdb. web. pasteur. fr/klebsiella/archives/Bialek_TechnicalAppendix. pdf). Selected genomes were annotated by using the Micro-Scope annotation and comparative genomics platform  (19).
Data Availability The annotated genomic sequences of strains BJ1-GA, T69, SA1, and cur15505 (SB2390) were deposited in the European Nucleotide Archive (ENA) and are available under accession nos. CBTU010000000, CBTV010000000, CBTW010000000, and CCBO010000000, respectively. The sequences of CIP 52. 145 chromosome and plasmids are available under accession nos. FO834904 and FO834905 and FO834906, respectively. Sequence reads corresponding to the remaining 43 isolates have been deposited in the ENA Sequence Read Archive under study accession nos. ERS500935-ERS500961, ERS503301-ERS503315, and ERS508075. The 48 assembled genomic sequences generated in this study are accessible from the BIGSdb-Kp database through Institut Pasteur's whole-genome MLST home page (http://bigsdb. web. pasteur. fr).
Diversity of Virulent and MDR K. pneumoniae Isolates as Determined by MLST The average number of contigs and the N50 (i. e. , the length for which half of the bases of a draft genome are situated in contigs of that length or longer) of the 48 genomes generated in the present study were similar to those of the 112 publicly available draft genomes (Table  S2 , Definition of K. pneumoniae Clonal Groups http://bigsdb. web. pasteur. fr/klebsiella/archives/Bialek_ TechnicalAppendix. pdf). These 2 characteristics quantify the level of fragmentation of the sequence assemblies. MLST allele sequences were retrieved from genomic sequences imported into the BIGSdb-Kp database. For 46 of the 48 sequenced strains, MLST alleles were also obtained by the classical Sanger sequencing method, which, in all cases, confirmed the genomic data.
This result corresponds to <1 error in 138,552 nt (i. e. , 0. 00072%), demonstrating the high quality of consensus base calls in the assemblies. Forty-three different STs were found in the entire dataset. Among the isolates we sequenced, the 20 K1 isolates represented 2 STs, among which ST23 (n = 18) predominated, whereas the 22 K2 isolates represented 10 distinct STs, among which ST380 (n = 5) and ST86 (n = 6) predominated (Table  S1 , http://bigsdb. web. pasteur. fr/klebsiella/archives/Bialek_TechnicalAppendix. pdf). Isolates belonging to these 3 predominant STs, as well as ST57, ST65, and ST375, were previously reported from invasive infections  (7) (8) (9) (10) (11) 20). Reference strains NTUH-K2044 and CG43 were found to belong to ST23 and ST86, respectively. Publicly available genomes predominantly included (85/119, 71%) STs previously associated with multidrug resistance: ST11, ST14, ST15, ST258, and ST512  (4, 5, 21, 22).
Reproducibility and Epidemiologic Relevance of cgMLST One pair of public genomes in fact corresponded to the same strain: BAA-2146. This isolate, an MDR NDM-1producing isolate belonging to ST11, was independently sequenced twice and was assembled by using 2 distinct methods (23) (GenBank accession no. APNN01). The 2 genomic assemblies did not show a single allelic difference. This observation indicates that cgMLST is highly reproducible. To explore the ability of cgMLST to cluster epidemiologically associated isolates and distinguish them from other genetically closely related isolates, we included in our analysis 20 published genomes of isolates from a 2011 outbreak at the National Institutes of Health Clinical Center (Bethesda, Maryland, USA)  (24). The outbreak isolates formed a unique cluster nested within the diversity of CG258 (Figure  3 ), suggesting that cgMLST may be useful for short-term epidemiologic questions and outbreak investigation.
Discussion K. pneumoniae clinical isolates are evolving toward increasing levels of antimicrobial drug resistance, placing this species among the infectious bacterial pathogens that are most challenging to control (3). In parallel, even though most infections still occur opportunistically in debilitated patients, K. pneumoniae is emerging as a cause of severe community-acquired invasive infections  (2). Recognizing the resistant or hypervirulent CGs is a prerequisite to better understand and control their global emergence. We implemented a genome database for K. pneumoniae, BIGSdb-Kp, and defined 694 genomic loci suitable for genotyping as well as loci associated with virulence and antimicrobial drug resistance. A genome-wide gene-by-gene approach based on the BIGSdb system  (16)  was previously applied to Neisseria meningitidis  (37)  and Campylobacter species (38).
Our findings show that the discriminatory power of cgMLST, based on 694 genes, makes it possible to distinguish a subgroup of isolates involved in the 2011 K. pneumoniae outbreak in Bethesda  (24)  from epidemiologically unrelated CG258 isolates. However, local epidemiologic investigations will require more resolution to decipher recent transmission events. Because the typical K. pneumoniae genome harbors ≈5,300 genes, there is ample room for defining additional loci to improve discriminatory power. The BIGSdb-Kp database can host additional loci and schemes curated at a distance by distinct laboratories  (16). Our findings demonstrate the existence of clearly distinguishable K. pneumoniae CGs and show that MDR and hypervirulent populations of this species are largely nonoverlapping. However, hypervirulent K.
Acknowledgments We are grateful to  K. Jolley  for advice on BIGSdb installation. We thank  L. Lery  and  R. Tournebize  for providing the complete genome sequence of strain CIP 52. 145,  J. -M. Thiberge  for help in the initial setup of the BIGSdb-Kp database, and  N. Nihotte  for contributing to the  Institut Pasteur BIGSdb home pages. The following colleagues are acknowledged for providing K. pneumoniae isolates or reference strains:  B. De Barbeyrac  ( BD-DU ),  F. Randrianirina  ( BG94  and  BG141 ),  C. De Champs  ( CH137 ),  E. Carbonelle  ( Zaire1 ),  H. Courtade  ( 100519185 ),  E. van Duikeren  ( V9902406 ),  C. Forestier  ( LM21  and  CH1031 ),  F. Jauréguy  ( BP1011625 ),  P. A. D. Grimont  ( CDC 4241-71 ),  A. Mérens  ( 610356538 ),  A. Merlet  ( 20479 ),  H. -L. Peng  ( CG43 ),  D.
Tainturier  ( MET1_63/88063 ), and  J. -T. Wang  ( A3021  and  A5011 ). We thank  E. Rocha  for helpful comments. A. C. and genomic sequencing were supported financially by a grant from  Region Île-de-France. S. B. -D. was supported by a postdoc grant from  Assistance Publique-Hôpitaux de Paris and Institut Pasteur. This work was supported by the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  (grant no. ANR-10-LABX-62-IBEID ). Dr Bialek-Davenet  is a microbiologist working as a postdoctoral fellow in the  Microbial Evolutionary Genomics Unit, Institut Pasteur. Her research interests include genomic epidemiology and resistance and virulence determinants of K. pneumoniae.
Historical photographs become widely used in geographical and environmental applications. Their enhancement involves converting them into georeferenced data, such as orthoimages or digital models. However no ground control points are available unlike in current image processing, and many problems such as image noise, landscape modifications, perspective distortion and unknown sensor calibration prevent automatic tie-point retrieval with current orthoimages. That is why photograph georeferencing remains a manual and time-consuming task. A novel method is presented in this paper to register photographs with current topographic database using line feature matching. Indeed, geometrical considerations only let avoid high radiometric difference issues when dealing with current orthoimages. Besides topographic database use lets selecting stable through time features, such as road network and historical buildings.
Therefore, the registered historical data can directly be integrated into the geographic information systems (GIS) in order to compare to the current data. This requires a set of ground control points (GCP), which are discriminative points in the photographs and known as terrain coordinates. Ground control point database are usually available in the present aerial photographs process. However, due to the unavailability of the GCP database, the operators have to visually search for the homologous objects in the old scanned photographs and present orthoimages. They also use current digital terrain model (DTM) to get altitude coordinates, as no historical DSM or DTM are available. This method is time-consuming as well as unreliable. Indeed a first concern is that DTM use involves getting only onground GCP.
Therefore our goal is to find an automatic method to register old scanned photographs onto current data with weak initial georeferencing assumptions, such as a place name is usually indicated on photograph border or box. Off-ground object registration should be taken into account. Target dataset is limited to aerial subvertical images of humanly-modified landscapes that contain roads and/or buildings. This excludes entirely natural landscapes and slanted shootings but applies to the majority of heritage photographs. The following part describes related work on the subject. The proposed method is presented in Section 3. In Section 4, it is tested on different data set to evaluate its reliability and result precision. Finally interest and limitations of the method are discussed and perspectives are put forward.
In the context of different date image matching,  (Vassilaki, 2012)  selects the lines that are matched by closest centroid and line ends. Then line break points are automatically extracted and used as GCP. An iterative approach enables to match points and estimate projection parameters. (Li, 1995)  registers multi-sensor (optical or even SAR) data. For that purpose, contours are detected and coded by their internal successive orientations, then contours are matched by principal axis length coherence, and contour points by maximization of cross-correlation of contour coding strings. (Oh, 2012)  registers satellite images on LIDAR data. In this context, contours are extracted with Canny detector, then a grid of GCP is computed by a cross-correlation-like measure on binary data (contour or not contour pixel) and filtered by distance between measure maximum and following values.
However, contour-based approaches suppose that separated full contour lines can be extracted and that break points or contour points are stable features. This is rarely the case, especially in urban areas or with images of different scale and noise level. Line features are also fully compatible with image registration on topographic database or map. (Barsi, 2004)  retrieves a line network in an image from a graph model, a point set extracted by image threshold and a fine initial position. Each graph node is iteratively moved to the closest extracted point and each nonmatched node is moved accordingly to the connected node displacement. (Roux, 1993)  automatically registers coarsely georeferenced satellite images on a map using urban conglomeration and cross-roads as GCP. In this case, detection is made by morphological filtering and linear features detection.
Then registration incorporates Random sample consensus (RANSAC, see  Fischler, 1981) , which is a voting approach, followed by least square minimization. (David, 2003)  registers iteratively an image onto a topographic line database. For that purpose, topographic line ends are projected on the image and matched with the nearest linear feature detected in image with Canny detector. Then an orthography is estimated by weighted least square method in order to minimize point-to-line distances. Weight values are the sum of squared point-to-line distances and line orientation difference. (Frueh, 2004 ) registers slanted aerial images in a similar fashion, except that initial matches are exhaustive and not only closest neighbour, and that weights include line lengths. Initial parameters are obtained either with manual GCP, or by an exhaustive research over all parameters possible value interval.
(Chen, 2006 ) registers a topographic database on a high-resolution orthoimage using cross-roads as GCP. In this case, roadsides are extracted with Canny detector and road area by image threshold. Then crossroad centre position is precisely set by binary cross-correlation with a template made from the database node form (connected road orientations and widths). The major shortcoming of all these methods is the need of a quite fine initial georeferencing. Another issue is the extraction of already well-filtered features of interest for data matching, with radiometric criteria for  (Barsi, 2004) ,  (Chen, 2006 ) and  (Roux, 1993)  that depend much on photograph features. Finally  (Stoica, 2004 ) extract road network with Gibbs point process while  (Ruskoné, 1996)  uses road following from seeds with decision tree (selection of the path of lowest radiometric variance) and object supervised classification to identify and pass over obstacles.
We consider available current data which include aerial photographs, orthoimages, digital terrain models, maps and topographic database (TopoDB). Starting from that, it can be noted that linear feature detection onto current images (photographs or orthoimages) creates inevitably over-and under-detection issues in addition to those from old photographs. An example is shown in Figure  2. In this case, LSD algorithm (Grompone, 2012) detects field furrows in the current orthoimage and many less in the old photograph due to image noise. Moreover, some main roads which are detected in the old photograph do not match any line of the current orthoimage due to new tree rows all along these roads. On the contrary, linear features extracted from TopoDB are exhaustive and precisely positioned (they are extracted from georeferenced aerial photographs by stereoscopy).
Other photograph positions are then interpolated. This is the case for our data. The second option is to compute tie-points between photographs of the set, followed by estimating a global relative orientation of camera poses, and finally to get an absolute global orientation using only three ground control points. Besides this option enables the computation of the camera calibration while estimating relative orientations. Therefore it takes an eventual distortion into account in the following processing. The third option is to use a previously georeferenced photograph of the same area, which was shot at a date close to the query photograph date. Hence homologous points can be selected automatically. In this case, the query photograph should be georeferenced using current database in order to avoid georeferencing error propagation.
M , where P 1, j and P 2, j are line L j ends, is assumed to be selected from topographic database by considering the approximate footprint of the photograph. Our method to estimate the projection function of Equation 1 is based on  (Frueh, 2004). More precisely, L j is projected on the image by an initial solution. Then every projected line is matched with image lines. The three criteria presented in Figure  3  are computed: relative overlap (further referred as r), mean distance in pixels (referred as d) and orientation difference in rad (referred as or) between the two segments. The match is selected if relative overlap is above a threshold s r , mean distance is above a threshold s d and orientation difference is below a threshold s or. The three threshold initial values depend on the expected precision of the initial solution.
Moreover, whenever the line detection algorithm used, a high number of over-detections is obtained due to the presence of building alignments, building shadows or field furrows that look like road sides. An example in a urban area is presented in Figure  4. A few field furrow over-detections are visible on the old photograph of Figure  2. That is why a voting method such as Random sample consensus  (Fischler, 1981)  is preferred. In this way, topographic database lines are projected on the image and matched with neighbouring detected lines. Then an iterative random selection of a minimum number of matches enables computation of projection parameters. The solution is finally selected by counting the number of the matched inliers explained by estimated projection. Another issue is risen by the unreliable road side detection position.
In the end, topographic database roads are projected on the photograph inside their image area. However, road axis is not centred in road area due to line detection method, which does not extract exactly road pavements. Furthermore, off-ground object projections are shifted as depicted in Figure  6  bottom left, so next step consists in introducing off-ground features to refine the result. Left : initial projection of TopoDB road network on the image Right : meaningful alignments detected in image Left : TopoDB projection on the image with an affinity estimated by RANSAC algorithm (main roads are in red) Right : TopoDB projection with an homography estimated by line-based SoftPOSIT algorithm Figure  5. Registration of subsampled image from Agde dataset (presented in Figure  1  and Table  1 ).
Hence the number of inliers among neighbouring (both planimetrically and altimetrically) building line matches qualifies robustness of local correction. Thus only the inliers of best translation are kept in each area, which decreases algorithm complexity. Left : Initial projection of TopoDB road network on the image (with solution obtained at previous step) Right : Detected lines in image by LSD algorithm Left : TopoDB projection with model defined in Equation  1 estimated from roads only. Building projections on the top of the image do not well match roof shapes. Right : TopoDB projection with model defined in Equation  1 estimated from both roads and buildings Figure  6. Registration of full-resolution image (crop) from Agde dataset (presented in Figure  1  and Table  1 ). ALGORITHM 1 Data : I input grayscale aerial photograph, Π 0 coarse initial projection, L R a set of road segments, L B a set of building limit segments, both from a topographic database Result : Π final projection begin I dz = Subsampling ( I ) l dz = MeaningulAlignments ( I dz ) → (Desolneux, 2000) M dz = ComputeMatches ( Π 0 (L R ) , l dz ) → Sec.