dataesr/scientific-paragraphs-categorization huggingface dataset:
We then sought to investigate the extent to which the matrix resolution and sequencing depth affect the accuracy of Centurion's predictions. As already seen in Supplementary Figures  S2  and S3 , different species give different results: for S. cerevisiae, increasing the matrix resolution to 10 kb results in lowered accuracy of centromere calls, while in P. falciparum the call quality improves slightly. We speculated that our ability to call centromeres in a given species at a given resolution may depend on the choice of restriction enzyme, the sequencing depth and the resolution of the contact map. We next evaluated the effect of depth of sequence coverage on the quality of our centromere predictions. We generated 500 low-coverage datasets by randomly downsampling the raw contact counts. We then ran the optimization process on these downsampled datasets, initializing with perturbed calls as before.
Centromere calls on a metagenomic dataset We next sought to call centromeres in several species simultaneously by combining Centurion with metagenomic Hi-C libraries. We previously  (20)  generated two Hi-C datasets from synthetic mixtures: one containing 16 yeast strains (in-cluding four strains of S. cerevisiae), and one containing a mixture of 8 yeasts and 10 prokaryotic species. The two samples contain a total of 19 yeast species, some of which are much better characterized than others: centromere positions are already known for eight species (K. lactis, L. kluyveri, L. thermotolerans, S. cerevisiae, S. kudriavzevii, S. mikatae, S. pombe, S. rouxii) and partially for one more (S. bayanus)  (12, (42) (43) (44). We aligned the reads from the metagenomic Hi-C datasets to these yeast species' reference genomes (see Materials and Methods).
The quality of the individual species datasets differ greatly because the organisms vary in abundance in the metagenomic samples, and because many sequences are shared nearly identically between organisms, making the number of uniquely mappable reads for each organism range between 109 k for one of the S. cerevisiae strains and 26 M for the bacteria V. fischeri. Consequently, the sparsity of the matrices is variable (Supplementary Tables S5 and S6). Furthermore, some contact count matrices include at least one interaction count for >99% of all possible locus pairs, whereas other matrices are below 5%. Similarly, in the 40 kb matrices, the average number of interchromosomal contact counts per bin varies from <0. 004 to >200. In particular, the matrices for the four S. cerevisiae strains are very sparse: the reference genomes of the four strains are very similar to one another; thus, we are not able to map reads uniquely.
We therefore discarded those strains from our analysis, as well as organisms with incomplete reference genomes. We applied Centurion to the remaining 14 yeasts (E. gossypii, K. lactis, K. wickerhamii, L. kluyveri, L. waltii, S. bayanus, S. kudriavzevii, S. mikatae, S. paradoxus, S. stipitis, P. pastoris, L. thermotolerans, S. pombe, S. rouxii) on both 20 kb and 40 kb contact maps. Across these 14 species Centurion performs well, both on high-coverage datasets (K. lactis, L. kluyveri, S. bayanus) and low-coverage datasets (S. mikatae), at 20 kb and 40 kb, finding centromeres at an average deviation from the ground truth of 10 kbp (Figure  4B  and Supplementary Figure  S6 ). Given this success with yeasts with known centromere positions, we next made de novo centromere calls for the other six yeast species present in the metagenomic samples.
The effect of the choice of restriction enzyme In addition to the resolution of our contact matrices, the underlying resolution of the Hi-C data itself may limit the accuracy of our predictions. Hi-C reads can only occur near the recognition site of the restriction enzyme used in the Hi-C assay; indeed, the best resolution we can hope to achieve is a matrix in which each corresponds to one restriction enzyme fragment. Some restriction enzymes cut much more frequently than others. Thus, we speculated that a Hi-C experiment using enzymes that cut more frequently might yield more accurate results than an experiment using less frequently cutting enzymes. To address this question, we compare the accuracy of centromere calling from two Hi-C libraries created from a single metagenomic sample using different combinations of restriction enzymes.
The first library was created using the two 6 bp-cutters, HindIII and NcoI. The second library uses Sau3AI, which has a 4 bp recognition site, and AflIII, which has a 6 bp recognition site with two degenerate sites, making it effectively a 5 bp cutter. Digestion with HindIII/NcoI yields a total of 8324 restriction fragments, whereas digestion with Sau3AI/AflIII yields 42359 restriction fragments. We corrected for the difference in Hi-C sequencing depth between Sau3AI/AflIII and the NcoI/HindIII libraries by generating downsampled datasets with an equal number of reads from each sequencing library. We then normalized the datasets and applied Centurion. The sample includes three species for which we possess the ground truth centromere locations, only one of which (L. thermotolerans) had enough reads in both the Ncol/HindIII (63 000 reads) and the pooled Sau3AI/AflIII (55 000 reads) datasets to correctly call the centromeres.
To address these issues, Centurion infers a 2D Gaussian fit that best explains the observed trans contact counts, jointly optimizing these 2D fits for all pairs of centromeres. Both of these improvements in the pre-localization and the optimization steps allow Centurion to perform better specifically for the cases with limited sequencing depth. Our approach could be improved in several respects. First, better modeling of zero contact counts may improve inference for organisms with many repeated sequences in the pericentromeric regions, or datasets with low sequencing depth. Second, one could model contact counts as a Gaussian distribution centered on the pairs of centromere locations. Maximizing the log likelihood of such a model might yield improved performance. Last, as described here, our method requires reference genomes for the metagenomic samples.
ACKNOWLEDGEMENTS We thank  Celia Payen  for providing the yeast centromere annotations,  Stéfan van der Walt  for advice on peak detection algorithms,  Fabrice Varoquaux  for help on understanding the specificity of A. thaliana genome and  Chloé Azencott  for helpful comments on the manuscript. FUNDING  European Research Council  [ SMAC-ERC-280032  to J. -P. V. , N. V. ];  European Commission  [ HEALTH-F5-2012-305626  to J. -P. V. , N. V. ];  French National Research Agency  [  ANR-11-BINF-0001  to J. -P. V. , N. V. ];  National Institute of Health/National Human Genome Research Institute  [ HG006283  to J. S. ,  T32HG000035  to J. N. B. ];  National Institute of Health/National Institute of General Medical Sciences  [ P41 GM103533  to I. L. , M. J. D. , W. S. N. ;  R01AI106775  to F. A. , W.
V. EVALUATION In this section, we evaluate our security-aware framework on a 3-tier use case using the information leakage metric previously introduced. A 3-tier application delivers a service (most of the time, a website) that is composed of 3 main parts: a frontend server (e. g. , Apache), an application server (e. g. , Tomcat) and a database (e. g. , MySQL). In this section, we consider a 3-tier application composed of 1 frontend VM, 2 application VMs and 1 database VM. Each of these VMs requires 1 core. Firstly, we present our testbed platform. Then, we detail the scenarios we use in our evaluation. Finally, we demonstrate that a security-oblivious placement algorithm breaks a fair amount of isolation properties but taking them into account impacts the acceptance rate (i. e. , the percentage of successfully placed submissions).
However, both are defined such that the resulting residual CR rate is at or below the extragalactic γ-ray background above 100 MeV. (E'-E)/ E -0. 4 -0. 3 -0. 2 -0. 1 0 0. 1 0. 2 0. 3 We used data from a 5. 8 year period with the P8 CLEAN event selection to search for spectral lines in the energy range 200 MeV to 500 GeV. To allow for sidebands in each fit, we used LAT γ-ray data from 100 MeV to 750 GeV. Details on additional event selections are given in Tab. I. We split the data into Celestial and Earth Limb datasets using cuts on both the instrument rocking angle (θ r ) and the event zenith angle (θ z ); see Fig. 1  of Ref. [19]  for a schematic depicting θ r , θ z , and event incident angle θ. Gamma rays from the Earth's Limb are produced by cosmic-ray (CR) interactions in the Earth's upper atmosphere so it is used as a control region where no true spectral lines are expected from dark matter annihilation or decay (see App.
A). We extract our signal regions of interest (ROIs) and other control regions from the Celestial data set, where we require θ z < 100 • in order to remove emission from the bright Earth's Limb. No point source masking is applied since at lower energies the width of the point spread function (PSF) increases significantly. Using an energy-dependent point source mask, as was done in our 3. 7-year analysis, would result in removing most of the dataset below ∼1 GeV. The initial data reduction and all of the exposure calculations were performed using the LAT ScienceTools 1  version (E'-E)/ E -0. 4 -0.
III. REGIONS OF INTEREST Within our Celestial dataset, we define our signal ROIs to be the same as those considered in Ref. [19]. These ROIs were optimized for either annihilating or decaying DM and for different profiles of the spacial distribution of the DM distribution in the Galaxy. We used four models of the DM distribution: Navarro-Frenk-White (NFW)  [25] , an adiabatically contracted NFW (NFWc), Einasto  [26] , and a cored isothermal profile  [27]. A generalized NFW profile  [28]  is given by ρ(r) = ρ 0 (r/r s ) γ (1 + r/r s ) 3-γ (1) with r s = 20 kpc. The NFW and NFWc correspond, respectively, to the cases where γ = 1 and γ = 1. 3. We also use an Einasto profile defined as: ρ(r) = ρ 0 exp{-(2/α)[(r/r s ) α -1]}, (2) where r s = 20 kpc and α = 0. 17  [26]. Finally, we consider a cored, isothermal profile given by: ρ(r) = ρ 0 1 + (r/r s ) 2 (3) with r s = 5 kpc.
For details on the ROI-optimization procedure, see App. B of ref  [19]. Figure  4  shows the counts map of the Celestial dataset in R180 with the outlines of the other signal ROIs, and the exclusion of the GP. We note that these ROIs are different from those in Ref. [22] , which optimized the ROIs only at lower energies where the fits are dominated by systematic uncertainties. For the smallest ROI, R3, the effects of leakage both in and out of the ROI from the PSF dominate the search region. The 68% containment radius of PSF3 is ∼ 2 • at 200 MeV and improves to ∼ 0. 4 • at 1 GeV. Therefore, we only use PSF3 events for fits in R3 below 1 GeV. For all other fits, events from every PSF type are used.
IV. ENERGY DISPERSION MODELING The energy dispersion is the probability density of reconstructing a true energy (E) as E. The LAT Collaboration parametrizes the energy dispersion probability density function of E in cosθ and true energy. In this work, we define an effective energy dispersion (D eff ), where we have averaged over cosθ assuming an isotropic source  4. As in Ref. [19] , we find D eff for a given E is well-described as a sum of three Gaussians. We create separate D eff models for each of the EDISP types. Specifically, this is done as follows: Counts map for the Celestial dataset binned in 1 • × 1 • spatial bins in the R180 ROI, and plotted in Galactic coordinates using the Hammer-Aitoff projection. The energy range is 1-750 GeV. Also shown are the outlines of the other ROIs (R3, R16, R41, and R90) used in this search.
To further investigate the energy-dependent variations in the exposure, we analyzed two other control data sets: the Vela Pulsar and the Earth Limb (see App. A). Note that the general energy-dependent behavior of the observed fractional signals in each control region differs (see Fig. 14 ). This is due to differences in the deficiencies of our background models for each control region compared to the observed emission. Figure  6  shows that the fits start to become dominated by statistical uncertainties (δf stat > δf syst ) around E γ = 6 GeV. Therefore, we set δf GP = 0. 015 as the systematic level from the GP since it is the 68% containment of f GP for E γ < 5 GeV. We note that the transition to dominance by statistical uncertainties is dependent on the total number of events in the fit, not E γ.
To obtain the appropriate δf CR value to use in our P8 CLEAN signal dataset, we scaled δf CR,dirty using the γ-ray acceptance ratio between P8 CLEAN and P8 SOURCE and the observed number of events in both event selections. See App. D5 in Ref  [19]  for more details. We estimate δf CR ∼ 0. 01 in R180 and R90. In R41, R16, and R3, δf CR is negligible (< 0. 003). Adding δf CR in quadrature with δf GP gives the total systematic uncertainty in each ROI: δf syst = 0. 016 for R180 and R90 and δf syst = 0. 015 for R41, R16, and R3.
B. Feature in R3 To understand the impact of Pass 8 on R3, we first considered the same time and energy range as our previous 3. 7-year search  [19]. The feature in Pass 7REP, which was narrower than the energy resolution of the LAT and had a local significance of 3. 3σ. With Pass 8 the excess present in Pass 7REP data is reduced to a local significance of 2σ as is shown in Fig. 11. We then considered the data for the full 5. 8-year time range. Figure  12  shows the fit for a γ-ray line at 133 GeV in the 5. 8 year Pass 7REP and Pass 8. The Pass 8 data are fit using the method described in Sec. V and the Pass 7REP data are fit similarly, but using the '2D' D eff model described in Sec. IV of Ref. [19]. The Pass 7REP curve shows a clear decrease in local significance (from 3. 3σ to 2σ) with respect to the previous line analysis over a shorter time interval  [19].
Similarly for the Pass 8 data, the local significance also decreases (2σ to <1σ) using the full 5. 8 year dataset. 12. Fit at 133 GeV for a γ-ray in a 5. 8-year Pass 7REP (left) and Pass 8 (right) data sets using the 2D energy dispersion model in R3. The solid curve shows signal and background fitting procedure described in Sec. V A. The blue dotted line is the signal that best fits the data. The gray line, which is mostly hidden by the solid curve, is the best fit background. The bin size is such that the energy resolution is sampled with 3 bins.
C. Feature in the Earth Limb The γ-ray spectrum of the Earth Limb (see Tab. I) is expected to be featureless; however, in the Pass 7REP data a 2σ feature was found at the same energy as the feature in R3  [19, 36]. This was a strong indication that the feature seen in R3 could have been, in part, a systematic effect. We carried out additional studies with Pass 8 event reconstruction and the full dataset to further understand this feature in the Limb. Figure  13  shows a fit to a γ-ray line at 133 GeV using the full 5. 8 year Pass 7REP and Pass 8. We find a slight detection of a line-like feature in both Pass 7REP and Pass 8 with a similar fractional size. With Pass 8 the significance increases slightly due mainly to the increase in the number of events from the greater acceptance of Pass 8.
IX. SUMMARY In this work, we have presented an updated search for γ-ray spectral lines using techniques developed in our previous line searches  [17] [18] [19] 22]  across more than three decades of energy using data reprocessed and selected with Pass 8. We searched for spectral lines in the energy range from 200 MeV to 500 GeV in five ROIs optimized for signals originating from Galactic DM annihilation or decay. We do not find any significant spectral lines and therefore set monoenergetic flux upper limits in each of our ROIs. Our search improves on our most recent 3. 7-year analysis  [19]  and 5. 2-year analysis  [22]  with increased exposure and a broader energy range. This results in a corresponding increase in the exposure for the entire 5. 8-year time range. Additionally, the exposure toward our smallest ROIs (R3 and R16) benefited from Fermi operating in a modified observing mode that roughly doubled the rate of accumulation of exposure in the GC region for the last five months of the dataset relative to normal survey mode.
61 in the 3. 7-year Pass 7REP data set to f (133 GeV) R3 = 0. 07 in the 5. 8-year Pass 8 dataset. This fractional signal in Pass 8 is consistent with what is also seen in the Earth Limb control region. With the entire 5. 8-year Pass 8 data set, the local significance has dropped to 0. 72σ (from 2. 0σ in the 3. 7-year data set), which is consistent with most of the original feature originating from a statistical fluctuation. The fact that the feature at 133 GeV is still marginally significant in the Earth Limb suggests a small systematic effect at this energy; however, no such feature is present in the GP control data set. The sensitivity of future line searches with the LAT will increase with continued exposure. Additional improvements require a more sophisticated modeling of the standard astrophysical backgrounds beyond our simple power-law approximation.
The Earth's Limb emission is composed of γ rays produced via CR interactions in the Earth's upper atmosphere. The Limb dataset is composed of events which pass the selection |θ r | > 52 • & 111 • < θ z < 113 • (see Tab. I). Above a few GeV, the energy spectrum of the Limb is well modeled as a power law  [36]. Therefore we expect the power-law approximation of background spectra to be better than in the GP. Below a few GeV, modeling the Limb spectrum becomes complicated due to the geomagnetic cutoff, so we only fit for line-like features in the Limb with E γ > 5 GeV. We select a 20 • ROI around the Vela pulsar using the 'Celestial' data selections outlined in Tab. I. In our Vela analysis, we use pulsar phases calculated with the Tempo2 package 9   [37]  and the standard ephemeris 10. For our line fits, we select the on-pulse data (γ rays with phases in the ranges [0.
We used data from a 5. 8 year period with the P8 CLEAN event selection to search for spectral lines in the energy range 200 MeV to 500 GeV. To allow for sidebands in each fit, we used LAT γ-ray data from 100 MeV to 750 GeV. Details on additional event selections are given in Tab. I. We split the data into Celestial and Earth Limb datasets using cuts on both the instrument rocking angle (θ r ) and the event zenith angle (θ z ); see Fig. 1of Ref. [19]  for a schematic depicting θ r , θ z , and event incident angle θ. Gamma rays from the Earth's Limb are produced by cosmic-ray (CR) interactions in the Earth's upper atmosphere so it is used as a control region where no true spectral lines are expected from dark matter annihilation or decay (see App. A). We extract our signal regions of interest (ROIs) and other control regions from the Celestial data set, where we require θ z < 100 • in order to remove emission from the bright Earth's Limb.
ACKNOWLEDGMENTS The  Fermi -LAT Collaboration  acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the  National Aeronautics and Space Administration  and the  Department of Energy in the United States , the  Commissariat à l'Energie Atomique  and the  Centre National de la Recherche Scientifique / Institut National de Physique Nucléaire et de Physique des Particules in France , the  Agenzia Spaziale Italiana  and the  Istituto Nazionale di Fisica Nucleare in Italy , the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) ,  High Energy Accelerator Research Organization (KEK)  and  Japan Aerospace Exploration Agency (JAXA) in Japan , and the  K.
, 2011). The population structure of vibrios has been increasingly studied by the means of multilocus genetics. MLST schemes and databases are available for V. cholerae  (Teh et al. , 2011; Islam et al. , 2013; Octavia et al. , 2013)  and V. parahaemolyticus  (Chowdhury et al. , 2004a,b; Gonzalez-Escalona et al. , 2008; Yan et al. , 2011). Most studies on V. cholerae and V. parahaemolyticus populations are centered on human pathogenicity and/or shellfish contamination. Consequently, research efforts are concentrated on geographic regions of cholera endemicity or vibriosis outbreaks, and on pandemic serotypes or strains harboring virulence traits. It is now obvious that the ecology of potentially pathogenic vibrios extends beyond the human body  (Haley et al. , 2012 (Haley et al. , , 2014;; Islam et al.
(2013)  and  Gonzalez-Escalona et al. (2008) , respectively. PCR products and molecular weight marker (BenchTop 100 bp DNA Ladder, Promega) were separated in 1. 5% (w/v) agarose gel in 0. 5X Tris Borate EDTA (TBE) buffer. Amplification products were sequenced using forward primers on an ABI 3730xl automatic sequencer (LGC Genomics, Germany). Sequencing using reverse primer was performed when the sequences obtained with forward primers were not found in databases. The ssDNA sequences were deposited in the GenBank database with accession numbers from KJ656184 to KJ657569.
Data Analysis Phylogeny Gene sequences were codon-aligned using ClustalX after translation with TRANSLATE (http://www. expasy. org). The length of sequences used for further analyses is indicated in Table  1. Phylogenetic trees were reconstructed for each individual locus and for the 7 loci concatenated nucleotide sequences. Phylogenetic reconstructions were performed on IQ Tree server  (Minh et al. , 2013). The best-fit model was chosen for each dataset according to Akaike information criterion and used to reconstruct Maximum likelihood (ML) trees. Ultrafast bootstrap approximation approach (UFBoot) was used to compute the support of ML phylogenetic groups  (Minh et al. , 2013).
Multi Locus Sequence Typing (MLST) Analysis The alignment obtained for phylogenetic treeing was used for assignation of each isolate to a Sequence Type (ST) according to their allelic profiles and compared to accessible sequences on pubmlst databases (http://www. pubmlst. org). Allele profiles were also analyzed using goeBURST v3 software (http://goeburst. phyloviz. net/) to determine clonal complexes (CC) defined as sets of related strains that share at least five identical alleles on the 7 loci. The singleton (S) STs corresponded to STs differing from all the others by 3 or more of the 7 loci. The Phyloviz program employing eBURST algorithm was used to determine Minimum Spanning Tree that showed the relationships between STs (http://goeburst. phyloviz. net/). Dataset for each species are constituted of strains of this study and pubMLST isolates with complete MLST information: i.
Population Genetics and Recombination Analyses The number of polymorphic sites was calculated using DnaSP version 5. 10 (http://www. ub. edu/dnasp/). The number of synonymous (dS) and non-synonymous (dN) substitutions per site was determined on codon-aligned sequences using SNAP software (http://www. hiv. lanl. gov/content/sequence/SNAP/ SNAP. html). Program LIAN 3. 5 (http://pubmlst. org/) was used to determine the h score of genetic diversity at each locus. The mean genetic diversity (H) was then calculated as the arithmetic mean of the h values for all loci. Rarefaction analysis was carried out using the freeware program aRarefactWin (http:// www. uga. edu/strata/software/Software. html). Program LIAN 3. 5 was also used to evaluate the recombination by calculation of the standardized index of association I A (sI A ) and to test the null hypothesis of linkage disequilibrium.