dataesr/scientific-paragraphs-categorization huggingface dataset:
Because the resulting correlation with the crossing-over map was slightly lower than when using the complete data set, we use the complete dataset for the further analyses. As little is known about the population structure of Z. ardabiliae, we conducted additional simulations in order to assess the putative impact of structure on the inference of recombination rate. We used a five-islands structure model, with sample sizes equal to 2, 3, 4, 5 and 6 in each deme, respectively, with a total sample size of 20, comparable to the 17 genomes of the Z. ardabiliae data set analyzed here. Migration rates between demes were symmetrical and all equal, and we tested several rates. We find that while ρ is systematically overestimated in the presence of population structure, it is remarkably proportional to the true value, in particular after filtering on the confidence intervals (Figure  S2 ).
Because of the relatively high rates of recombination in exons of Z. tritici and Z. ardabiliae, we sought to determine whether recombination could play a particular role in plantpathogen co-evolution. Plant pathogens interfere with host defenses and manipulate the 20 host metabolism by the secretion of so-called effector proteins produced to target molecules from the host  [73]. Antagonistic co-evolution of these interacting proteins is often reflected in accelerated evolution and signatures of positives selection  [74]. To assess the role of recombination on effector evolution, we first predicted effector proteins computationally in the secretomes of both species using the EffectorP software  [75]. This approach identified 868 putative effector proteins in Z. tritici and 1,122 and Z. ardabiliae.
Frequency and intensity of recombination hotspots is higher in Z. tritici The fine scale Ldhat recombination maps clearly reveal the presence of distinct peaks of recombination in both Z. tritici and Z. ardabiliae (Fig. 3 ). We used the program Ldhot to call positions of statistically significant recombination hotspots  [76]  and applied highly stringent selection criteria (see Materials and Methods) to obtain positions of the most significant hotspots for which the within-hotspot rate was at least ten times higher than the flanking regions (Fig. 8A ). Interestingly, our approach revealed a considerably greater number of recombination hotspots in Z. tritici (2,578 hotspots) than in Z. ardabiliae (862 hotspots). Furthermore, we find a significant difference in the size of the hotspot regions between the two species.
Genome data The lifecycle of Z. tritici is predominantly haploid and the genome analyses conducted here thus rely on haploid genome data. The 40-Mb reference genome of the Z. tritici isolate IPO323 was sequenced at the Joint Genome Institute using Sanger sequencing  [38]. Two Iranian Z. tritici isolates and four Iranian Z. ardabiliae isolates were sequenced in a previous study using Illumina sequencing (Table  S1 )  [37]. We used genome data from an additional ten isolates of Z. tritici that originate from wheat fields in Denmark, France and Germany  [44]. In this study, we report the genome sequences of thirteen isolates of Z. ardabiliae that originate from wild grasses collected in the province of Ardabil in Iran (Table  S1 ). DNA extraction was performed as previously described  [37]. Library preparation and paired end sequencing using an Illumina HiSeq2000 platform were conducted at Aros, Skejby, Denmark.
Sequence data has been deposited under the NCBI BioProject IDs PRJNA277174. The thirteen re-sequenced Z. ardabiliae genomes were assembled from 100 bp paired end reads using the de novo assembly algorithm of the CLC Genomics Workbench version 5. 5 (Qiagen, Aarhus, Denmark). The assemblies were created using standard settings for pairedend reads. We used a previously published RNAseq based annotation to distinguish the parameter estimates for coding and non-coding sequences  [39]. To predict the genes that encode effectors we used the software EffectorP  [75]  with default settings, on genes predicted by SignalP  [84]  to encode a secreted protein.
Genome alignment and SNP calling Genome alignments were separately created for each population using the MultiZ program from the TBA package  [85]. Default parameters were used, although LastZ was used instead of BlastZ for pairwise alignments. Genome alignments were projected against the two reference genomes of each species: IPO123 for Z. tritici and STO4IR-1. 1. 1 for Z. ardabiliae  [37, 38]. The projected alignments in MAF format were filtered using the MafFilter program  [86]  with the following filters: 1) each syntenic block was realigned using Mafft  [87] , and blocks with more than 10 kb were split for computational efficiency; 2) only blocks where all individuals were present were retained (13 Z. tritici and 17 Z. ardabiliae); 3) a window of 10 bp was slid by 1 bp, and windows containing at least one position with gaps in at least 2 species were discarded and the containing blocks were split; 4) a window of 10 bp was slid by 1 bp, and windows with a total of more than 100 gaps were discarded and the containing blocks were split; and 5) all blocks were merged according to the reference genome with empty positions filled by 'N', which resulted in one masked alignment per chromosome for Z.
Estimating recombination Filtered alignments (1-Mb windows, data set 1) were exported as fasta files for the Ldhat and Ldhelmet packages. The program convert from the Ldhat package was used to convert fasta files into input loci files for the program interval  [41]. Only fully resolved biallelic positions were exported (see Table  1  for the details of SNP numbers). Likelihood tables were generated for θ values of 0. 0005, 0. 005 and 0. 05. The interval program was run with 10,000,000 iterations and sampled every 5,000 iterations with a burn-in of 100,000 iterations. Ldhelmet was run with the parameters suggested in the user manual  [13]  and https://sourceforge. net/projects/ldhelmet/). Comparison of recombination maps on the same set of SNPs was performed using standard principal component analysis, as implemented in the R package ade4  [88].
Pairs of SNPs for which the confidence interval of the recombination estimate was higher than two times the mean were discarded and therefore not used in the average computation. Using the gene annotations available for the two reference species  [39] , we calculated the following information for each gene: 1) the average recombination rate in exons, 2) the average recombination rate in introns, and 3) the average recombination rate in the 500 bp flanking 5' region and 4) in the 500 bp flanking 3' region. We also calculated the average recombination rate for each intergenic region (500 bp from / to genes). GFF3 files from  [39]  were retrieved and processed using the "Genome Tools" package to add intron annotations  [89]. The resulting gene annotations were analyzed in R together with recombination maps  [90].
Because recombination rates are typically exponentially distributed, Pearson's coefficient was measured for the log rates instead of the raw ρ rates. Spearman's coefficient assumes that the variables are continuously distributed; therefore it does not resolve ties. Thus jittering was used to randomly resolve ties in the input variables (R function 'jitter', with default parameters). Conversely, Kendall's coefficient assumes ordinal input variables. Therefore, using the three correlation measures allows assessing the robustness of the correlation signal. A graphical representation was performed using the ggplot2 package for R, which performed local polynomial regression fitting for the curves  [92].
Mapping of hotspots Hotspots were detected using the Ldhot program  [76]. For computational efficiency, Ldhot was run on the 100 kb alignments (data set 2). A background recombination map was first estimated for each alignment using the interval program of Ldhat with a θ value of 0. 005  [41]. The resulting maps were highly correlated with the maps based on 1-Mb alignments and showed little effect of the discretization scheme. The background recombination map was used as input to Ldhot with default parameter values and 1,000 simulations. Significant hotspots were filtered for further analysis. First, only the hotspots with a value of ρ between 5 and 100 across the hotspot coordinates were selected because higher values are most likely artifacts and the performance of Ldhot is low for weak hotspots  [76].
Both methods estimate the local population recombination rates based on the LD between SNPs in a given genome dataset using a composite likelihood method. The methods infer the populationscaled recombination rate ρ across the genome, based on an a priori specified population mutation rate θ. The parameter ρ relates to the actual recombination frequency by the equation ρ = 2N e * r for haploid individuals, where N e is the effective population size and r is the per site rate of recombination across the region. Inferring r from ρ therefore requires knowledge of N e. Furthermore, in Zymoseptoria species, sexual reproduction is not obligatory and may vary from year to year with environmental conditions and the availability of compatible hosts and mating partners, rendering the estimation of r very difficult without any additional knowledge of the amount of clonal reproduction.
Acknowledgements EHS is supported by intramural funding from the  Max Planck Society, Germany , a personal grant from the  State of Schleswig-Holstein, Germany  and a grant from the  German Research Council ,  DFG , grant number  HO 4435/1-1. JYD is supported by intramural funding from the  Max Planck Society, Germany. The authors thank  Nicolas Galtier  for helpful discussions on the gBGC and  Daniel Croll  for providing genetic data from experimental crosses of Z. tritici.
Cardiac magnetic resonance analysis and diagnostic yield Two readers with 5 and 15 years of experience in CMR reviewed all CMR studies. RV and LV volumes and EF were quantified using Argus software (Siemens Medical Systems, Erlangen, Germany). Ventricular dilatation and systolic dysfunction were defined based on previously reported normal values in men and women on the left ventricle and right ventricle. 14 Cine images were visually assessed to look for LV or RV wall motion abnormalities. End-diastolic LV myocardial thickness was measured on cine short-axis images. LGE was categorized as subendocardial, midwall, subepicardial, or transmural. Subendocardial LGE with coronary distribution was considered to be of ischaemic origin, whereas midwall and subepicardial LGE were considered to be of non-ischaemic origin.
Cardiac magnetic resonance data on cardiac anatomy and myocardial scar were integrated in three-dimensional mapping systems and used to provide guidance during the ablation procedure in 19 of these patients. LGE was positive in 18 of 19 patients and colocalized with abnormal findings at electroanatomical mapping in all cases. Patient outcome was analysed over a median follow-up period of 9 months (Q1-Q3: 7-24 months). To document arrhythmia recurrence, Holter ECG was performed during follow-up in 21 patients (most often motivated by clinical symptoms), and ICD reports were available for all implanted patients (n = 97). Ventricular arrhythmia recurrence was detected in 13 of 21 (62%) patients on follow-up Holter and 14 of 97 (14%) patients with ICDs received appropriate therapy during follow-up.
Measurements of strange hadron (K 0 S , Λ+Λ, and Ξ -+Ξ + ) transverse momentum spectra in pp, pPb, and PbPb collisions are presented over a wide range of rapidity and event charged-particle multiplicity. The data were collected with the CMS detector at the CERN LHC in pp collisions at √ s = 7 TeV, pPb collisions at √ s NN = 5. 02 TeV, and PbPb collisions at √ s NN = 2. 76 TeV. The average transverse kinetic energy is found to increase with multiplicity, at a faster rate for heavier strange particle species in all systems. At similar multiplicities, the difference in average transverse kinetic energy between different particle species is observed to be larger for pp and pPb events than for PbPb events. In pPb collisions, the average transverse kinetic energy is found to be slightly larger in the Pb-going direction than in the p-going direction for events with large multiplicity.
Hydrodynamical models predict that the average p T (or, equivalently, the average transverse kinetic energy KE T , where KE T ≡ m T -m, with m T = m 2 + p 2 T and m the particle mass) of produced particles is larger in the Pb-going direction than in the p-going direction, while this trend could be reversed in models based on gluon saturation  [37]. Measurement of identified particle p T spectra as a function of η could thus help to constrain theoretical models. This Letter presents measurements of strange-particle p T spectra in pp, pPb, and PbPb collisions as a function of the multiplicity in the events. Specifically, we examine the spectra of K 0 S , Λ, and Ξ -particles, where the inclusion of the charge-conjugate states is implied for Λ and Ξ - particles. The data were collected with the CMS detector at the LHC.
With the implementation of a dedicated high-multiplicity trigger, the pp and pPb data samples exhibit multiplicities comparable to that observed in peripheral PbPb collisions, where "peripheral" refers to ∼50-100% centrality, with centrality defined as the fraction of the total inelastic cross section. The most central collisions have 0% centrality. This overlap in mean multiplicity allows the three systems, with drastically different collision geometries, to be compared. The large solid-angle coverage of the CMS detector permits the strange-particle p T spectra to be studied in different rapidity ranges, and thus the study of possible asymmetries with respect to the p-and Pb-going directions in pPb collisions.
4 are measured with gas-ionization detectors embedded in the steel flux-return yoke outside the solenoid. A more detailed description of the CMS detector, together with a definition of the coordinate system and the relevant kinematic variables, can be found in Ref. [39]. The Monte Carlo (MC) simulation of the particle propagation and detector response is based on the GEANT4  [40]  program. The data samples used in this analysis are as follows: pp collisions collected in 2010 at √ s = 7 TeV, pPb collisions collected in 2013 at √ s = 5. 02 TeV, and PbPb collisions collected in 2011 at √ s NN = 2. 76 TeV, with integrated luminosities of 6. 2 pb -1 , 35 nb -1 , and 2. 3 µb -1 , respectively. For the pPb data, the beam energies are 4 TeV for the protons and 1. 58 TeV per nucleon for the lead nuclei.
The data were collected in two different run periods: one with the protons circulating in the clockwise direction in the LHC ring, and one with them circulating in the counterclockwise direction. By convention, the proton beam rapidity is taken to be positive when combining the data from the two run periods. Because of the asymmetric beam conditions, the nucleon-nucleon center-of-mass in the pPb collisions moves with speed β = 0. 434 in the laboratory frame, corresponding to a rapidity of 0. 465. As a consequence, the rapidity of a particle in the nucleon-nucleon center-of-mass frame (y cm ) is detected in the laboratory frame (y lab ) with a shift, y lab = y cm + 0. 465. The pPb particle yields reported in this Letter are presented in terms of y cm , rather than y lab , for better correspondence with the results from the pp and PbPb collisions.
Selection of events and tracks The triggers, event reconstruction, and event selection are the same as those discussed for pp, pPb, and PbPb collisions in Refs. [8, 41]. They are briefly outlined in the following paragraphs for pp and pPb collisions, which are the main focus of this Letter. A subset of peripheral PbPb data collected in 2011 with a minimum-bias trigger is reprocessed using the same event selection and track reconstruction algorithm as for the present pPb and pp analyses, in order to more directly compare the three systems at the same multiplicity. Details of the 2011 PbPb analysis can be found in Refs. [41, 42]. Minimum-bias pPb events are triggered by requiring at least one track with p T > 0. 4 GeV to be found in the pixel tracker. Because of hardware limitations in the data acquisition rate, only a small fraction (∼10 -3 ) of triggered minimum-bias events are recorded.
Only tracks with a distance of closest approach 0. 4 cm or less to one of the vertices are included. The HLT selection requires N online trk for the vertex with the largest number of tracks to exceed a specific value. Data are collected in pPb collisions with thresholds N online trk > 100 and 130 for events with an L1 transverse energy threshold of 20 GeV, and N online trk > 160 and 190 for events with an L1 threshold of 40 GeV. While all events with N online trk > 190 are accepted, only a fraction of the events from the other thresholds are retained. This fraction is dependent on the instantaneous luminosity. Data from both the minimumbias trigger and the high-multiplicity trigger are retained for offline analysis. Similar highmultiplicity triggers, with different thresholds, were developed for pp collisions, with details given in Ref.
[8]. In the subsequent analysis of all collision systems, hadronic events are selected by requiring the presence of at least one energy deposit larger than 3 GeV in each of the two HF calorimeters. Events are also required to contain a primary vertex within 15 cm of the nominal interaction point along the beam axis and 0. 15 cm in the transverse direction, where the primary vertex is the reconstructed vertex with the largest track multiplicity. At least two reconstructed tracks are required to be associated with this primary vertex, a condition that is important only for minimum-bias events. Beam-related background is suppressed by rejecting events in which less than 25% of all reconstructed tracks satisfy the high-purity selection defined in Ref. [38]. In the pPb data sample, there is a 3% probability to have at least one additional interaction in the same bunch crossing (pileup).
4 and p T > 0. 4 GeV. Based on simulated samples generated with the HIJING program, the efficiency for primary track reconstruction is found to be greater than 80% for charged particles with p T > 0. 6 GeV and |η| < 2. 4. For the multiplicity range studied in this Letter, no dependence of the tracking efficiency on multiplicity is found and the rate of misreconstructed tracks is 1-2%. The pp, pPb, and PbPb data are divided into classes based on N offline trk. The quantity N corrected trk is the corresponding multiplicity corrected for detector and algorithm inefficiencies in the same kinematic region (|η| < 2. 4 and p T > 0. 4 GeV). The fraction of the total multiplicity found in each interval and the average number of tracks both before and after accounting for the corrections are listed in Table  1  for the pp data and in Ref.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEP-Center, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Pt L 3 -and Cu K-edge XAS data collections, EXAFS data analysis Pt L 3 -edge (11 560 eV) and Cu K-edge (8980 eV) XAS data were collected at the I811 beamline of the Max Lab II source (Lund, Sweden). 96 Max Lab II was operated at 1. 5 GeV with a uniform current between 250 and 100 mA. The white beam produced by a liquid He-cooled superconducting wiggler was monochromatized by a horizontally sagittally focused double-crystal Si(111) monochromator. At the Cu K-edge the monochromator was detuned to 20% to minimize the third harmonic, while this procedure is not needed at the Pt L 3 -edge. Spectra were collected in transmission mode using 30 cm ionization chambers for I 0 and I 1 ; the intensity I 2 transmitted by a Pt (or Cu) reference foil located aer the sample was measured using a photodiode, and the resulting spectrum was employed for energy alignment purposes.
All XAS spectra were measured employing a home-made cell allowing sample activation in temperature and gas dosage under in situ or operando conditions,  97  monitoring the evolution of the XAS features while controlling the temperature and gas feed. The XAS data reduction and EXAFS extraction procedure was performed using the Athena codes. 98 The parametric EXAFS data analysis was performed with IFEFFIT 99 code that employs phases and amplitudes computed by FEFF6 code, 100,101 as detailed elsewhere. 59.
EXAFS analysis of the data collected during operando H 2 -TPR was performed using the Einstein model to predict the temperature dependence of the Debye-Waller factors and to reduce the correlation among the optimized parameters. In a 10% H 2 /He ow, EXAFS clearly shows the loss of both Cl and N scattering contributions, while the rising of an intense signal at around 2. 7 Å, associated with the Pt-Pt contributions, unambiguously indicates Pt NPs formation. Conversely, in a pure He ow, the only signicant change observed during TPR is the progressive decrease of the Pt-Cl single scattering contribution, leading to the conclusion that the Pt graed to the bpydc-linkers remains naked. In general, Pt sites in UiO-67-Pt MOFs tend to form Pt NPs in the presence of H 2 and remain naked, but still graed to the bpydc-linkers, in an inert gas ow.
Acknowledgements LB, ALB,  AAG ,  AVS  and CL acknowledge the  Megagrant of the Russian Federation Government  to support scientic research at the  Southern Federal University , no. 14. Y26. 31. 0001. KAL acknowledges the support from the  Russian Foundation for Basic Research , Project no. 16-32-00572  mol_a. The authors are grateful to MAXlab for the allocation of the beam time (proposal 20140449) and to  Dr Stefan Carlson  for the technical support during the experiments at the I811 beamline.
Natural Images All 4167 images from the van Hateren natural image database  [15]  (van Hateren and van der Schaaf, 1998) were chosen for analysis. Each image is 1536 by 1024 pixels, with each pixel's intensity represented by a 16-bit unsigned integer, reflecting an effective bit depth of 12. The images mainly contain landscapes and plants, but occasionally, manmade objects such as houses, appear.