dataesr/scientific-paragraphs-categorization huggingface dataset:
A complementary approach is to exploit gravitational lensing offered by massive galaxy clusters  (Kneib & Natarajan 2011) , which magnifies the brightness of intrinsically faint sources. This has been successfully used to detect galaxies over a wide redshift range, taking advantage of the flux magnification (e. g. Kneib et al. 2004; Bouwens et al. 2009; Richard et al. 2011; Alavi et al. 2013 ) and the higher spatial resolution for detailed, small-scale studies of high-redshift galaxies (e. g. ,  Brammer et al. 2012). In this framework, the CLASH 11  program  (Postman et al. 2012 ) yielded important results in a variety of fields using 25 lensing clusters, resulting in a significant progress in cluster mass modeling (e. g. Zitrin et al. 2012; Bradley et al. 2013; Coe et al. 2013; Vanzella et al.
2013). Combining the exquisite capabilities of HST with the power of "gravitational telescopes", the new HST Frontier Fields 12  (HFF) initiative is aiming to peer deeper into the distant Universe by initially devoting a total of 560 orbits to observe four clusters down to a magnitude limit of ∼ 29. Over Cycles 21 and 22, the program will obtain deep optical and NIR imaging in seven filters using ACS and WFC3 for both the clusters and the parallel blank fields. Here we present the first results of the NIR observations of the Abell-2744, where we search for high-redshift candidates at z ∼ 6 -8 behind the galaxy cluster. In Section 2, we present the observational dataset and reduction steps leading to the construction of the source catalog. The lensing model is described in Section 3. The procedure used to select high-redshift candidates is detailed in Section 4 together with the sources of contamination.
Previous HST Data Optical observations were obtained with the Advanced Camera for Survey (ACS) onboard HST in cycle 17 (GO 11689, PI: Dupke) and used in the strong lensing analysis presented in  Merten et al. (2011). Images were taken with the Wide Field Camera (WFC) using three broadband filters F435W, F606W, and F814W. A summary of the dataset is presented in Table  1. For the basic reduction steps, we used the CALACS package v2012. 2 that include Charge Transfer Efficiency (CTE) corrections, which were not available in the publiclyreleased reductions. Then all exposures in each filter were median combined using Astrodrizzle task in the new STSDAS/Drizzlepac package 13. First, the different exposures were corrected for small misalignments using Tweakreg before rejection of cosmic rays and correction for geometrical distortions.
Hubble Frontier Fields Data 2. 3. Spitzer Data Spitzer imaging of Abell 2744 using Infrared Array Camera (IRAC) was obtained on September 2013 as part of the Frontier Field Spitzer program (PI : T. Soifer). The dataset we used here represents 50% of the planned observations for the cluster. The total exposure time is 25 hours on sources in each of the 2 IRAC channels 3. 6 and 4. 5 microns. We used corrected Basic Calibrated Data (cBCD) images, that are provided by the Spitzer Science Center and automatically corrected by pipeline for various artifacts (such as muxbleed, muxstripe, and pulldown). The cBCD frames and associated mask and uncertainty images were processed, drizzled and combined into final mosaics using the standard SSC reduction software MOPEX. The mosaic has a 2-σ point source sensitivity (measured from the noise in a clean area) of 0.
Photometric Catalogs We used a deep (F125W+F140W) image for source detection with SExtractor software  (Bertin & Arnouts 1996). First, all images were PSF-matched to the F160W image with a PSF model derived using Tiny Tim  (Krist et al. 2011 ) and to a pixel scale of 0. 13 ′′. Then we ran Sextractor in dual mode with the deep IR image as detection frame and the image in each filter to perform the photometry within the same aperture. The isopho- The dropout candidates are represented by green circles with 1-σ uncertainties and the selection window by the shaded region. We also show the expected color tracks as a function of redshift for starburst galaxies (solid lines) and elliptical galaxies (dotted lines). The color code indicates an increase of attenuation from A V = 0 (in blue) to A V = 2 (in red).
We finally identified 52 images coming from 17 multiply-imaged background sources. Five of them (corresponding to two background sources) are located close to sub-clumps (group scale) located to the North and North West limits of the ACS coverage (outside the WFC3 field of view). We targeted the core of this cluster with FORS2 on the ESO Very Large Telescope (VLT) and measured spectroscopic redshifts for two systems, allowing to reliably calibrate the mass model. The optimization is performed in the image plane, using the Lenstool software  (Jullo et al. 2007). We found that a five-component mass model is able to reproduce the observational constraints with an RMS in the image plane equal to 0. 7 ′′. Three large scale mass clumps are located in the core of the cluster and are associated with the brightest galaxies.
2012; Brammer et al. 2012). The HFF observations will extend this study to the most magnified high-redshift galaxies. Candidate 561 for instance shows an arc-like extended morphology. The source reconstruction of such objects will enable us to analyze the clumps and morphology of the building blocks of present day galaxies and compare them to other results at intermediate redshifts  (Zitrin et al. 2011; Bradley et al. 2012). Because the optical ACS data do not match the depth of the new IR observations, we restricted the search to relatively bright candidates. Specifically, in the case of objects undetected in the I 814 filter, we measured the 2-σ limit in the same aperture as in the IR. Then we retained only candidates that satisfy the selection criteria 1, replacing the I 814 magnitude by its limit.
Since the infrared data have been taken much later than the ACS data, transient sources such as supernovae that exploded recently, can be detected only in the infrared satisfying the continuum break criterion. We first compared the four epochs of IR observations spanning a period of five weeks and verified the consistency between the photometry in all the bands. furthermore, such sources would have appeared as point sources. The inspection of the high-resolution images of the HST shows that the candidates are all spatially resolved. Possible sources of contamination also include lowredshift galaxies that enter the selection space. This can occur when strong nebular emission lines contribute to the total flux in one filter, mimicking a continuum break  (Atek et al. 2011 ). However, the use of multiple and contiguous filters greatly mitigates the contamination from such sources, since the emission line should be isolated and the flux enhancement restricted to only one filter (or two in the case of a combination of lines such as Hα and [OIII]).
Photometric Redshifts Additionally, we computed photometric redshifts for our candidates by fitting the photometric data with spectral energy distribution (SED) templates using the Hyperz software  (Bolzonella et al. 2000). We used standard stellar population libraries of  Bruzual & Charlot (2003)  and a  Salpeter (1955)  initial mass function (IMF). We find that the probability distributions of the photometric redshifts all indicate a high-z solution in agreement with the color-color selection. The photometric and color information of the candidates are presented in Table  2. We also include in the same table the best-fit redshift given by the probability distribution function for each galaxy, together with the magnification factors. Figures  4  and 5  show the image cutouts of the z ∼ 6 -7 and 8 candidates in the ACS, WFC3, and IRAC filters.
Then we created artificial galaxies using ARTDATA package in IRAF, exploring the parameter space of observed magnitude, color, shape, and position in the image. We generated 10,000 galaxies randomly added to the actual optical and IR images, using 100 objects at a time. We applied a distribution of absolute magnitudes between -23 and -14 and applied the colors derived from the spectral templates. We used a log-normal distribution of half light radii derived from the observed sizes of spectroscopically confirmed z ∼ 4 LBGs of  Vanzella et al. (2009). We applied a factor of (1+z) -1 to account for redshift-evolution in size (see  Grazian et al. 2011). The sizes are computed in the source plane and the galaxy positions chosen randomly in the image plane. Then we used Lenstool to simulate the size and flux of the magnified galaxies in the image plane using our A2744 mass model.
The sliding HyperLogLog algorithm The sliding HyperLogLog algorithm is mainly based on the HyperLogLog algorithm  [11]  designed by  Flajolet et al. in 2007. The HyperLogLog algorithm is a very efficient probabilistic algorithm for cardinality estimation. It performs a single pass on data and gives an accurate estimation of the number of distinct elements, using a very small memory. The HyperLogLog algorithm is nowadays used in many fields such as databases and networks, where an exact counting is impractical because of memory consumption and high latency. An example of an application of HyperLogLog for the improvement of database queries in Google systems is provided in  [12]. A new release of the well-known database management system, PostgreSQL, has been recently developed in 2013 to add HyperLogLog data structures as a native data type (see  [13]  for more details).
The input parameters of all these algorithms are set to their optimal values, and the mean detection delays are compared. The main result is that the CUSUM algorithm outperforms the other algorithms when the observed process has small shifts. Recall that in the context of on-line port scan detection, we focus on change-point detection in a high-speed data stream. Sequential data is provided on the fly from the counting process, and our purpose is to identify as quickly as possible the change point using a very small memory. According to Basseville and Nikiforov  [15] , all the approaches described above can be used in the context of on-line analysis. But, in our particular context of port-scan detection, no assumption about the distribution of the attack time can be made. So, the Bayesian algorithms are not adapted for such applications.
Compared to PHT and CUSUM, EWMA has the advantage to closely relate the importance of the observed data to its age which is more meaningful in the context of data stream. In PHT and CUSUM algorithms, all data history is equivalent and is considered in the same manner. Our objective in this is to adapt the EWMA algorithm to the context of port scan detection and to experiment and evaluate the so obtained version. EWMA is an advanced control chart which calculates, over a sliding window, a weighted average of the data, taking into account the past observations. The older the observation, the less weight it has in the computation of the average. The sliding weighted average is updated as follows: EWMA(0) := the average of all the observations EWMA(t) = λ * Y (t) + (1 -λ) * EWMA(t -1), for t > 0, where Y (t) is the observed value at time t.
Dataset The so-obtained algorithm with the two complementary parts (the counting and the decisional mechanisms) is tested against real IP traffic containing some port scan attacks. The considered traffic trace was captured in December 2007, by Orange Labs, in the context of an ANR-RNRT project on network security called 'OSCAR'. The traffic capture was performed on a router belonging to the IP backbone network of Orange Labs. Some global characteristics of this traffic trace are given in Table  1. The flow is defined as the set of those packets with the same source and destination addresses, the same source and destination port numbers, and the same protocol type.
EWMA versus CUSUM algorithm The CUSUM algorithm was first introduced by Page in 1954  [20]. It aims to detect small process shifts and is based on the cumulative sum C t of the observed process Y (i): CUSUM(t) = t i=1 (Y (i) -μ 0 ) = CUSUM(t -1) + (Y (t) -μ 0 ), where μ 0 is the target of the process given by its mean value. A process shift is declared if CUSUM(t) exceeds the upper bound k * σ , σ being the standard deviation of the process Y (i). Notice that in this original version of the CUSUM algorithm, σ and μ 0 are calculated on the whole process. Therefore, in our case, two passes on the dataset are required to detect the port scan attacks using the original version of CUSUM. This version is clearly not adapted to an online analysis. Figure  11  shows the result of the CUSUM algorithm on the dataset containing some port scan attacks.
This can be explained by the fact that the CUSUM algorithm accumulates the effect of the attack over several sliding windows as it is based on a sum and gives the same weight to all the observed values. X(i) = Y (i) -μ 0 σ , http Just like the EWMA algorithm, we introduced a learning step to adapt the CUSUM algorithm to the online analysis context. Thus, μ 0 and σ computation is only based on the first 10 min. Moreover, CUSUM(t) is not updated in case of attack. The results of the so-obtained version of CUSUM are given by Figure  12. The attack that happens at the end of the traffic trace (around the second 3,450) is not detected. In addition, between the second 2,000 and 3,000, the mean of the input process is slightly higher, which is considered by the CUSUM algorithm as an attack. One can easily check that it is a false positive using Figure  6  which displays the aggregated input data over larger time window.
Conclusion In this paper, a new method identifying online port scan attacks in IP traffic is proposed. First, some relevant statistics are extracted from the data stream using the sliding HyperLogLog algorithm. A good choice of the parameters of the this algorithm has to be done in order to ensure an acceptable accuracy of the statistics. Second, a change point detection method based on the EWMA algorithm is used to identify suspicious traffic. It is mainly an adaption of the EWMA to the data stream context. For this purpose, a learning phase is added at the beginning of the algorithm in order to initialize its parameters. Then, a new constraint is added to the moving average EWMA(t) update to overcome the false-positive problem when this latter exceeds the UCL detection threshold. Finally, we run experiments on a real traffic trace captured in the IP backbone network of Orange Labs in December 2007 in the context of the ANR-RNRT OSCAR project.
Extraction of whole strain-specific SNPs among B. anthracis strains Comparative analysis of the genomic sequences was next carried out. Available data for the B. anthracis Ames Ancestor, Sterne and A1055 strains were also included. The B. cereus AH820 genome was used as outgroup. Figure  1  illustrates the minimum spanning tree (MST) generated by the chromosomal whole-genome SNPs data. A total of 3987 chromosomal SNPs were discovered that precisely positioned French strains within the B. anthracis SNP tree. Additional SNPs (128 in pXO1 and 92 in pXO2) were also identified in both plasmids (Analysis of the 122 French strains, including the African strains IEMVT 89, data not shown). Consistent with previous MLVA and canSNP analyses  [11] , all French isolates were clustered into three distinct groups, i.
French specific SNP discrimination genotyping assays We designed eight novel canSNPs discrimination assays based on High-Resolution Melting (HRM)-PCR technology (Table  1 ). Assays targeted five SNPs identified among the French B. Br. CNEVA genomes, one SNP specific to the A. Br. 001/002 strains isolated in Doubs, and two SNPs defining the two large sub-branches 1 and 2 of the A. Br. 011/ 009 sub-group (Figure  1 ). These diagnostic assays were then successfully validated across the 138 B. anthracis strains of our collection. The two expected alternate alleles exhibited distinct melting curves and melting temperatures (Tms), allowing unambiguous grouping of each allele. On average, differences in Tm ranging from 0. 5 to 0. 8°C were observed between the both allelic states (Table  2 ). The seventeen non-sequenced genomes of the collection fall on the correct geographical groups (data not shown).
Discussion Comparative whole-genome sequencing offers a powerful way for in-depth characterization of any bacterial pathogen. It also provides an unbiased approach for informative SNPs discovery  [13]. In this study, an ultimate picture of the genetic diversity found within the B. anthracis population in France has been established. An extensive NGS dataset of 122 autochthonous strains have been created that allowed the phylogenetic linkage of geographically diverse isolates and the identification of novel SNPs signatures useful to rapidly determine the geographic origin of any strain ecologically established in France. (6 strains). The diameter of each circle varies according to the number of isolates having the same genotype. The length of each branch is proportional (logarithmic scale) to the number of SNPs identified between strains.
Draft whole genome sequencing (WGS) and data analysis In this study, 122 French strains belonging to 3 sublineages (A. Br 001/002 (n = 24), A. Br 011/009 (n = 31) and B. Br CNEVA (n = 67)) were sequenced at the IMAGIF sequencing platform (Imagif, Gif sur Yvette, France)  [17]. Isolates were subjected to paired-end whole genome sequencing on either the Illumina Genome Analyzer IIx instrument (16 samples, paired-end data of 2x75pb) or the Illumina HiSeq2000 platform (107 samples, paired-end data of 2x100pb) (Illumina Inc. , San Diego, CA, USA). Genome coverage of at least 57× was obtained (Additional file 1: Table  S1 , average depth ranging from 57× to 433×). The number of reads that passed Illumina quality filters varied from 4. 5 to 26. 8 million. Ames Ancestor [GenBank:AE017334. 2] was used as the reference genome for assembly.
Ames Ancestor plasmid pXO1 [GenBank:AE017336. 2] and pXO2 [GenBank: AE017335. 3] were used as references for plasmids assembly. Short reads data sets were exported on the FastQ format and mapped to the Ames Ancestor genome and both pXO1 and pXO2 plasmidic sequences using BioNumerics version 6. 6 (Applied Maths, Belgium) and Power assembler module asking for a similarity of at least 90%. A set of SNPs was deduced for each genome sequence data using Bio-Numerics Chromosome Comparisons module. Individual lists were compiled by using an in-house Python script, and data filtered to remove SNP positions at which one or more isolate displayed an ambiguous residue call or missing data. Ribosomal operons and VNTR loci were also excluded from the analysis, so as contiguous SNPs (in a 10pb window). The list of canSNP positions is provided in Table  1.
Whole genome phylogenetic analysis A minimum spanning tree was drawn in BioNumerics by using the filtered whole genome sequencing SNP data as input. The tree was rooted by using the B. cereus AH820 strain as outgroup. B. cereus AH820 resides very close to the B. anthracis cluster using multilocus sequence typing  [18]. Nodes were numbered by BioNumerics. The canSNPs along the three branches leading to the French strains were identified from whole genome sequencing data by searching for SNPs with allelic states shared only by these different subgroups.
SNP discrimination assays by HRM We designed High Resolution Melting (HRM) assays for eight French-specific SNPs using Primer 3 + software  [19]. The positions of these SNPs in the Ames Ancestor genome [GenBank: AE017334. 2] and the primers sequences used are listed in Table  1. Amplification was performed on the ViiA7™ Real-Time PCR System (Life Technologies) using the LightCycler® 480 High Resolution Melting Master Mix (Roche Diagnostics). The reaction mixture consisted of 0. 2 μM of each primer, 1 × LightCycler® 480 HRM master mix and 2. 5 mM MgCl2 in a 10-μl final volume. The following parameters were used: 10 min at 95°C were followed by 40 cycles consisting of 10 s at 95°C, 10 s at 58°C and 20 s at 72°C. Samples were next heated to 95°C for 30 s, cooled down to 65°C for 1 min and heated from 65°C to 88°C at a rate of 1°C/s with 25 acquisitions/°C.
Br. 001/002 (08-8_20, Doubs) [NCBI: JHCB00000000], A. Br. 011/009 (99-100, Branch 3) [NCBI: JHDR00000000] and B. Br. CNEVA (00-82, Alps) [NCBI: JHDS00000000] have been deposited in the NCBI database. The whole genome sequences for B. anthracis strains CNEVA-9066 (B. Br. CNEVA, Massif Central) [NCBI: NZ_AAEN00000000. 1], A0465 (B. Br. CNEVA, Pyrenees) [NCBI: NZ_ ABLH00000000. 1], Ames ancestor (A. Br. Ames) [NCBI: NC_ 007530. 2], Sterne (A. Br. 001/002) [NCBI: NC_005945. 1] and B. cereus AH820 (outgroup) [NCBI: NC_011773. 1] can be found in the NCBI microbial genome website at http://www. ncbi. nlm. nih. gov/.
Acknowledgements We wish to acknowledge  Nora Madani  ( Anses, France ) for access to the strains collection of the national anthrax reference. This work was supported by the  French National Research Agency (ANR)  and  Direction Générale de l'Armement (DGA)  (project number  ANR11ASTR0007 ). GG is a PhD student co-supported by  Anses  and  DGA  grants. This work has benefited from the facilities and expertise of the high throughput sequencing platform of  IMAGIF (Centre de Recherche de Gif  [http://www. imagif. cnrs. fr]).
Availability of supporting data The ANSES Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the BioProject number PRJNA242332, with accession numbers JHCB00000000 (A. Br 001/002, 08-8_20, Doubs), JHDR00000000 (A. Br 011/009, 99-100, Branch 3) and JHDS00000000 (B. Br. CNEVA, 00-82, Alps). Phylogenetic trees data are available from the Dryad Digital Repository (http://datadryad. org/) with the following identifier: http://doi. org/10. 5061/dryad. rc6m9.
MatrixDB (http://matrixdb. ibcp. fr) is a freely available database focused on interactions established by extracellular proteins and polysaccharides. It is an active member of the International Molecular Exchange (IMEx) consortium and has adopted the PSI-MI standards for annotating and exchanging interaction data, either at the MIMIx or IMEx level. MatrixDB content has been updated by curation and by importing extracellular interaction data from other IMEx databases. Other major changes include the creation of a new website and the development of a novel graphical navigator, iNavigator, to build and expand interaction networks. Filters may be applied to build sub-networks based on a list of biomolecules, a specified interaction detection method and/or an expression level by tissue, developmental stage, and health state (UniGene data).
INTRODUCTION MatrixDB (http://matrixdb. ibcp. fr) is a freely available interaction database, first released in 2009, which focuses on interactions established by extracellular proteins and polysaccharides  (1, 2). MatrixDB is an active member of the International Molecular Exchange (IMEx) consortium, an international collaboration between a group of major public interaction data providers who have agreed to share curation effort and to capture full details of an interaction in a 'deep' curation model. The members of the IMEx consortium use a single set of curation rules when capturing interaction data from publications in peer-reviewed journals  (3). MatrixDB is in charge since January 2009 of the curation of interaction data from articles published in Matrix Biology, a journal affiliated with the American Society for Matrix Biology and with the International Society for Matrix Biology.