dataesr/scientific-paragraphs-categorization huggingface dataset:
Only 11 (4 from ATLAS and 7 from CMS) were based on early 13 TeV Run 2 data with 2-13 fb -1 of integrated luminosity; most of these were preliminary results from ATLAS conference notes or CMS public analysis summaries. In this note we now present the implementation of the Run 2 SUSY search results from CMS with 36 fb -1 , presented at the Moriond and the summer (LHCP and EPS) conferences of 2017. This extends the SModelS database by 84 new cross section upper limit (UL) maps from 19 different analyses. We give an overview which results have been included, show their validation in SModelS, and demonstrate their constraining power for the phenomenological Minimal Supersymmetric Standard Model (pMSSM) as compared to the 8 TeV data.
Analysis Ref The superscript † denotes SMS with three mass parameters, for which only one mass plane is available; we included them for completeness but note that they apply to the given 2D slice of parameter space only, not to general mass patterns. χ ∼ =y + 20, m t =x, m g m 1 0 χ ∼ c → t , t ~ t → g , g ~ g ~ → T5tctc : pp exclusion (SModelS) 20% (SModelS) ± (upperLimit) CMS-SUS-16-050 x 300 400 500 600 700 800 900 1000 1100 3 =y 1 0 χ ∼ =0. 5*x + 0. 5*y, m ± 1 χ ∼ =x, m t m 1 0 χ ∼ W → ± 1 χ ∼ , ± 1 χ ∼ b → t , t ~ t ~ → T6bbWW : pp exclusion (SModelS) 20% (SModelS) ± (upperLimit) CMS-SUS-17-001 Figure  2 : Examples of validation plots. The coloured histograms show the r-values, defined as the ratio of the theory prediction over the observed upper limit. The full gray lines are the SModelS exclusion contours, r = 1, to be compared to the official CMS exclusion lines in black (±1σ as dashed black lines).
For example, an experimental result for pp → tt , t → t χ0 , but this has a different topology (vertex structure) and corresponds to a distinct simplified model (T6bbWW). For conciseness, the "off" maps are not listed in Table  1 , with the exception of PAS-SUS-16-052, which has only UL maps for compressed spectra where W s are always off-shell. In total, the 84 new results in the v1. 1. 2 database cover 25 distinct topologies (35 when counting on-and off-shell versions separately). As can also be seen in Table  1 , several analyses have SMS interpretations for the same topologies (txnames). For instance, upper limits for pp → gg, g → t t χ0 1 (T1tttt) are provided in seven of the eight searches for gluinos. Likewise, the different stop searches in the 0, 1, and 2 leptons channels all give upper limits for pp → t1 t1 , t1 → t χ0 1 (T2tt) and pp → t1 t1 , t1 → b χ+ 1 → bW χ0 1 (T6bbWW).
In principle it would be possible to compile, for each topology, the limits from different analyses into one single map, using only the strongest constraint in each mass bin. Instead, we have chosen to include all the individual results which are provided by the experimental collaboration. This makes the database larger and the evaluation slightly slower, but has the advantage of more flexibility. For instance it allows to compare the constraining power of different analyses for the same signal. When speed is a limiting factor, knowledgeable users can build a slimmed-down pickle file, applying only the subset of analyses which give the strongest constraints; see the SModelS v1. 1 manual  [8]  for more details. There is a further reason for including all individual SMS results: when using SModelS to constrain non-SUSY scenarios, it is possible that, depending on the selection cuts in the analyses, some SMS results do not apply.
Impact on the pMSSM To assess the impact of these new 13 TeV results in a general manner, we make use of the extensive scan of the pMSSM  [28]  with 19 free parameters from the ATLAS pMSSM study  [29]  (see also  [30] [31] [32] [33] ). The ATLAS collaboration made the whole scan, in total more than 310k parameter points with SUSY masses up to 4 TeV, publicly available on HepData  [34]. These points were classified into three sets according to the nature of the LSP: bino-like (103,410 points), wino-like (80,233 points) and higgsino-like (126,684 points). They all have m h = [124, 128] GeV and satisfy constraints from SUSY searches at LEP and the Tevatron, flavor and electroweak precision measurements, cold dark matter relic density and direct dark matter searches. We remove from this dataset the points which contain long lived charged sparticles (cτ > 1 mm), which cannot be treated in the official SModelS version.
This has only a small effect on the bino-like and higgsino-like LSP sets (99,492 and 123,498 points remaining, respectively) but removes most of the wino-like LSP points (only 8,772 points remaining). For this dataset, we analyse how the SModelS exclusion improves with the new 13 TeV results as compared to the 8 TeV results. As a first overview, we list in Table  2  the total number of points studied, the number of points that can be excluded by SModelS when using only the 8 TeV results in the database, and the number of points that can be excluded when using the full 8 TeV + 13 TeV database. As one can see, the gain is quite substantial, between a factor of 2 for the higgsino-like LSP dataset and a factor of 2. 7 for the bino-like dataset. 2 : Summary of results, listing the total number of points tested from the ATLAS pMSSM scan (without long-lived charged particles), the number of points excluded by SModelS using only the 8 TeV database and the number of points excluded when using the full database with 8 TeV and 13 TeV results.
To address the question which signal topologies are most relevant for the improved constraints, Fig. 5  provides a break-down by txnames as a function of the gluino mass. For each point excluded at 13 TeV, but not at 8 TeV, we take the txname with the highest r-value (r = σ SMS /σ UL ) and then show the (stacked) histograms for each txname normalized by the total number of points in each bin. As we can see, when gluinos are within reach (i. e. , for m g 1. 5 TeV), T1 and T1bbbb are among the most constraining topologies for the bino-like and wino-like datasets; for higgsinolike LSP dataset gluino decays via the 3rd generation are more important and thus T1bbbb and T1tttt are among the most constraining topologies. Overall, however, and increasingly so at high gluino masses, the strongest exclusion comes from squark topologies.
Indeed, T2 (2 jets + E miss T ) is clearly the leading topology with some contribution also from T2bb (2 b-jets + E miss T ). A comment is in order regarding the prominence of the T2cc topology in the bino-like LSP dataset. In principle, T2cc describes stop-pair production followed by stop decays into c + χ0 1. However, from the three analyses  [10, 17, 20]  which provide constraints for this case, only  [20]  includes charm tagging. The other two  [10, 17]  actually constrain 2 jets + E miss T , not 2 c-jets + E miss T , so they also apply to what is normally a T2 topology, i. e. pp → q q, q → q χ0 1 or pp → gg, g → g χ0 1. We note here that the conventional T2 UL maps cover squark-LSP mass differences down to 25 GeV only. The UL maps for T2cc from  [10, 17] , on the other hand, are designed to cover the compressed region and go down to mass differences of 11-12 GeV.
Download and installation The full v. 1. 1. 2 database is now included in the SModelS v1. 1 release available on GitHub at https://github. com/SModelS/ or on the SModelS homepage, http://smodels. hephy. at/. Installation instructions are given in the manual, available as paper  [8]  and online, and the INSTALLATION. rst file in the distribution. For people who have already installed SModelS, smodels-database-v1. 1. 2. tar. gz is also available separately from http://smodels. hephy. at/wiki/CodeReleases. For the standard installation, it suffices to put this tarball into the main smodels folder and explode it there. That is, the following steps need to be performed mv smodels-database-v1. 1. 2. tar. gz <smodels folder> cd <smodels folder> tar -xzvf smodels-database-v1. 1. 2. tar. gz rm smodels-database-v1.
1. 2. tar. gz The v1. 1. 2 database will be unpacked into the smodels-database directory, replacing the previous version and the pickle file will then be automatically rebuilt on the next run of SModelS. For a clean installation, it is recommended to first remove the previous database version. If the tarball is unpacked to another location, one has to correctly set the SModelS database path when running SModelS. If using runSModelS. py, this is done in the parameters. ini file. Alternatively, the database can also be obtained from the https://github. com/SModelS/smodels-database-release repository.
Conclusions and outlook We presented the update of the SModelS database with the simplified model cross section upper limits from 19 CMS SUSY analyses from Run 2 with 36 fb -1 of data. These results significantly improve previously available constraints. Using the pMSSM as a showcase for a realistic model, we demonstrated how the limits on various SUSY masses are pushed to higher values by the 13 TeV results as compared to 8 TeV results. The improved constraints affect not only the masses of colored sparticles-particularly noticeable are the much stronger constraints on LSP masses up to about 600 GeV. All in all, the number of points from the ATLAS pMSSM scan  [29]  which can be excluded by SModelS increases by a factor 2. 3 as compared to the 8 TeV results. The v1. 1. 2 database is publicly available and can readily be used in SModelS to constrain arbitrary BSM models which have a Z 2 symmetry, provided the SMS assumptions  [6, 8]  apply.
Acknowledgements We thank the  CMS SUSY group  for providing a vast amount of SMS cross section upper limits in digital format. Moreover, we owe special thanks to  Federico Ambrogi  for his contribution in the early stage of this work. J. D. is partially supported by funding available from the  Department of Atomic Energy, Government of India , for the  Regional Centre for Accelerator-based Particle Physics (RE-CAPP) ,  Harish-Chandra Research Institute. She thanks moreover the  LPSC Grenoble  for hospitality, and the  INFRE-HEPNET (IndoFrench Network on High Energy Physics) of CE-FIPRA/IFCPAR (Indo-French Centre for the Promotion of Advanced Research) , as well as the " Investissements d'avenir, Labex ENIGMASS " for financial support for a research visit in May 2017, during which this work was started.
Monitoring the subtidal extension of mud banks and understanding the hydro-sedimentary processes linked to their migration could help resolve unanswered questions. Satellite data have been used effectively for monitoring surface-suspended particulate matter (SPM)  [18] [19] [20] [21] [22] , and have revealed a strong spatio-temporal coupling with mud banks. Recent studies have allowed a better characterization of the morphodynamics of French Guiana mud banks from remote sensing  [14, 20, 22]. These previous studies were based on high-(SPOT and Landsat) and medium-resolution sensors (MODIS). Despite the high temporal resolution of medium-resolution sensors, which enables a good overview of mud bank dynamics, the details of the subtidal footprint of mud banks in the near coastal waters are lacking.
Landsat 8-OLI data The images used within the framework of this study were collected by Landsat 8. The OLI sensor has eight bands. The first four are in the visible, band 5 is in the near infrared (NIR), and bands 6 and 7 are in the shortwave infrared spectral regions (SWIR); all seven have a spatial resolution of 30 m. In addition, there is a panchromatic band with a resolution of 15 m. The revisit time of Landsat 8-OLI is 16 days. OLI level 1 data (L1T) were downloaded from the Earth Explorer data base (https://earthexplorer. usgs. gov/) for the four scenes covering the study area between April 2013 and December 2017, leading to a total of 151 Landsat 8-OLI images after the elimination of images that were highly affected by clouds. The low latitude of French Guiana (3 • 30-6 • 30 N) and the solar and viewing path geometry of OLI led to the presence of a quasi-permanent glint contamination over the Landsat 8-OLI images used within the framework of this study.
As a consequence, the use of Landsat 8-OLI data is highly limited due to the loss of the pixels located towards the eastern part of the four scenes covering this coastal domain  [32]. In this study, a historical method based on the use of NIR information for estimating the level of sun glint was applied in order to correct for this effect  [33] , taking advantage of the availability of SWIR information which is more adapted for applications related to very turbid waters such as those of French Guiana  [32]. Landsat 8 L1T sun glint-corrected data were processed using ACOLITE software (acolite_win 20170718. 0) developed at RBINS (https://odnature. naturalsciences. be/remsem/software-and-data/ acolite). The ACOLITE atmospheric correction chain allows simple and fast processing (no external measurements are required) of Landsat 8-OLI images for marine and inland water applications.
To avoid sharpening noise, a threshold is given for the minimum contrast required for a pixel to be considered an edge pixel. This allows sharpening in relatively smooth regions of the image. A Laplacian of Gaussian filter is created with a size of 5 × 5 and a standard deviation of 0. 45. The filter is applied to the sharpened image, allowing the detection of wave damping due to the presence of the subtidal part of the mud banks. The wave damping limits were measured manually using SeaDAS 7. 4 (NASA Official-https://seadas. gsfc. nasa. gov/) software for each season (dry and rainy) of each year since 2013.
The application of Han algorithm to high-(Landsat 8-OLI) resolution images (Figure  1b, c ) illustrates the SPM patterns at the most ultra-nearshore marine domain, as expected, for Landsat 8-OLI images (Figure  1b, c ). Nevertheless, the latter higher spatial resolution is potentially limited by the temporal resolution (16 days for Landsat 8-OLI) when compared to medium-resolution data (daily data). The SPM patterns estimated after the two Landsat 8-OLI scenes (Figure  1b, c ) show great spatial variability, even though the time elapsed is one month. Figure  1b  shows higher SPM concentration values in the nearshore waters, ~190 mg/l, corresponding to the intertidal part of the mud bank, while the plume is characterized by low values of SPM, between 40 and 60 mg/L. Conversely, Figure  1c  is characterized by a mean value over the plume area of 115 mg/l and lower concentrations in the intertidal part than for the situation depicted from the Figure  1b , ~135 mg/L.
For example, during the rainy season, due to the presence of clouds, the number of available images is restricted, and thus constitutes a limitation of the method developed. The recently-launched Sentinel 2 mission (A-23 in June 2015, B-7 in March 2017), with a spatial resolution of 10 m and a time resolution of 5 days, will allow greater insights into the morphology and dynamics of mud banks to be gained, as its resolution is much higher than that of Landsat 8-OLI. The development of this new generation of satellite data for ocean colour is significantly increasing the amount of information available on different spatial and temporal scales, and satellite big data processing is thus becoming a challenging task  [44]. Under these conditions, the application of the wave-damping limit method will become more difficult compared to the implementation of an automatic procedure based on the SPM method for monitoring this mud bank system.
The complete genome of Candidatus Midichloria mitochondrii IricVA was sequenced (GenBank accession no. NC_015722. 1) from the ovarian tissue of a female Ixodes ricinus tick collected in nature (Varese, Italy)  [17]. Bioinformatic analysis enabled studying the genotypic characteristics and modelling the phenotypic characteristics of this uncultivated microorganism from the order Rickettsiales. We believe that sequencing the genome of an uncultivated microorganism directly from the organs of a naturally infected female tick or using a experimentally infected tick model will develop. Then, after genome sequence annotation, the genotypic characteristics may be studied and the phenotypic characteristics modelled using bioinformatics analysis. To classify these uncultivated bacteria taxonomically, taxonogenomics may be used.
4 1. 43268429687864 43 19. R. massiliae MTU5 NC_009900 1 360 898 32. 5 1. 43314583756584 44 20. R. philipii str. 364D NC_016930 1 287 740 32. 5 1. 43325070488125 45 21. Candidatus R. amblyommii str. GAT-30V NC_017028 1 407 796 32. 45 1. 43334598270974 46 22. R. peacockii str. Rustic CP001227 1 288 492 32. 6 1. 43514665884247 47 23. R. akari str. Hartford NC_009881 1 231 060 32. 3 1. 43747339509228 48 24. O. tsutsugamushi str. Boryong NC_009488 2 127 051 30. 5 1. 44599460730303 49 O. tsutsugamushi str. Ikeda NC_010793 2 008 987 30. 5 1. 44642319193296 All genomes were imported from GenBank NCBI (USA): http://www. ncbi. nlm. nih. gov/genome/. *Numberon Fig. 1.
2016). Musa breeding is complicated by the fact that cultivated bananas are generally triploid and have very low fertility levels, a condition that ensures edibility. Enhanced knowledge of the chromosome constitution of interspecific banana cultivars and its consequence on chromosome segregation would help gain greater insight into Musa evolution and domestication and more generally genome evolution in allopolyploids, while also ultimately benefitting banana breeding programs. The present study aimed to characterize the genome structure of diploid, triploid, and tetraploid A/B interspecific banana accessions and to study the impact of these genome structures on chromosome segregation and recombination. We thus developed methods to exploit resequencing data and used them to: 1) characterize the A/B genome structure of nine interspecific banana accessions; 2) compare the global chromosome organization of M.
Conclusion We developed a method that exploits sequencing data to help precisely unravel the interspecific mosaic genome structure of banana accessions. This new access to the mosaic structure of banana cultivar genomes should considerably facilitate the interpretation of phylogenetic, genetic, and functional studies and will help in refining banana subgroup composition and origin. Accordingly, we have presented data that supports the implication of BB genotypes already introgressed through several generations with A genome segments in the origin of some interspecific cultivars. Compared with most other crops for which seed production is crucial, the banana domestication process includes sterility as one major criterion for cultivar selection. Current hypothesis was that partially fertile edible cultivars were selected from the initial interspecific or intersubspecific primary hybrid pool.
For all accessions except the PKW self-population, reads were processed as follows: 1) reads were aligned to the Musa reference A genome version 2  (Martin et al. 2016 ) using BWA v0. 7. 15 with the mem algorithm  (Li 2013). Reads aligning at several positions were removed using samtools v1. 3  (Li et al. 2009). 2) For WGS libraries, redundant reads were removed using MarkDuplicate from Picard Tools v2. 7. 0. 3) Reads were locally realigned around indels using the IndelRealigner tool of GATK v3. 3 package  (McKenna et al. 2010). 4) For each accession, at each covered position, all mapping bases with a mapping quality !10 were counted with the bam-readcount program (https://github. com/genome/bam-readcount). The complete process described here was performed using the process_reseq_1. 0. py custom python script available at (https://github.
Variant Calling Variant sites were then filtered according to the following criteria: 1) only data with a coverage between 10 and 100,000 reads were considered; 2) for each accession, alleles supported by at least three reads and with a frequency !0. 05 were kept as variant; 3) sites with at least one variant were kept for variant calling; and 4) for each selected site and each accession, a genotype was called based on the maximum likelihood of all possible genotypes calculated based on a binomial distribution assuming a sequencing error rate of 0. 005. The resulting variant calling file was generated in VCF format. The complete process described here was performed using the VcfPreFilter. 1. 0 custom python script available at (https://github. com/SouthGreenPlatform/vcfHunter).
SNP Filtering The VCF file was sequentially filtered according to the following parameters: 1) indel variant sites were removed; 2) genotype calls based on <10-fold coverage and <3-fold coverage for the minor allele were converted to missing data; 3) variant sites with >5% missing genotype calls were filtered out; 4) only biallelic sites were kept; and 5) SNP clusters identified using GATK v3. 3 and defined as at least two SNPs in a 2-base window were removed. A total of 148,329 SNPs were obtained in the final VCF data set. Data filtering was performed using the vcfFilter. 1. 0. py custom python script available at (https://github. com/SouthGreenPlatform/vcfHunter).
A/B genome ratios plotted along chromosomes which identify the A/B mosaic landscape were visually scanned to detect breakpoints in the A and B ratio distribution. Breakpoints at the boundaries of T1 and T3 fragments corresponding to reciprocal translocation were not recorded as recombination points. A hotspot of recombination breakpoints located at the beginning of chromosome 3 probably resulting from an  Baurens et al. doi:10. 1093/molbev/msy199 MBE A/B recombination point already present in the CRBP39 genome was not recorded (see Results section). The approximate coordinates of the breakpoints were graphically recorded. The complete process described here was performed using vcf2allPropAndCov. py and vcf2allPropAndCovByChr. py custom python scripts available at (https://github. com/ SouthGreenPlatform/vcfHunter).
Genetic Mapping Musa balbisiana Genetic Map Construction After the filtering steps (no missing data in the progeny, at least two genotypes in the progeny with at least five individuals in the minor class), a total of 10,074 SNPs were retained to build a Musa balbisiana genetic map. The genetic map was constructed using the ML mapping algorithm in JoinMap 4. 1  (Stam 1993; Van Ooijen 2006)  with standard parameters. To compare the A/B genomes of Musa, markers from the M. balbisiana genetic map were aligned along the M. acuminata reference sequence V2  (Martin et al. 2016)  using BWA  (Li 2013). Marker locations in the M. acuminata reference sequence were compared with their locations in the M. balbisiana genetic map using SpiderMap software (JF Rami, unpublished).
e. , a measure of the observed recombination frequency between pairs of markers) and marker segregation. These statistics were used to generate a dotplot showing the marker pairwise distance along chromosomes associated with a linear plot showing marker segregation distortion. The process described here was performed using vcf2popNew. 1. 0. py, RecombCalculatorDDose. py, and Draw_dot_plot. py custom python scripts available at (https://github. com/ SouthGreenPlatform/vcfHunter).
Acknowledgments We thank  CRB Plantes Tropicales Antilles CIRAD-INRA Guadeloupe  for providing the plant material. This work was supported by the  Centre de coop eration Internationale en Recherche Agronomique pour le D eveloppement (CIRAD). The authors are grateful for financial support from the  CGIAR Research Program on Roots, Tubers, and Bananas (RTB) , the  French Government  " Investissement d'Avenir "  FRANCE GENOMIQUE  ( ANR-10-INBS-09 ) and the  Agropolis Fondation  " GenomeHarvest project " (ID  1504-006 ) through the  "Investissements d'avenir" programme (Labex Agro :  ANR-10-LABX-0001-01 ). This work was supported by the  CIRAD-UMR AGAP HPC Data Center of the South Green Bioinformatics platform  (http://www. southgreen. fr/).