dataesr/scientific-paragraphs-categorization huggingface dataset:
For these calculations the factorization and renormalization scales are set to the default value of p Born T , the transverse momentum in the 2 → 2 process before the hardest emission. The partonlevel configurations are passed to the PYTHIA generator for fragmentation and underlying event simulation. Both the AUET2B and Perugia 2011  [49]  tunes are considered. The version of the program used includes an explicit calculation of all hard emission diagrams  [39, 50]. This provides improved suppression of rare events, removing fluctuations that arise from the migration of jets with low values of parton-level p T to larger values of particle-level p T. Corrections for electroweak tree-level effects of O(αα S , α 2 ) as well as weak loop effects of O(αα 2 S )  [51]  are applied to both the NLOJet++ and POWHEG predictions.
The NNPDF2. 1 and NNPDF2. 3 PDF sets are exceptions, where uncertainties are expressed in terms of replicas instead of by independent components. These replicas represent a collection of equally likely PDF sets, where the data entering the PDF fit were varied within their experimental uncertainties. For the plots shown in section 12, the uncertainties on the NNPDF PDF sets are propagated using the RMS of the replicas, producing equivalent PDF uncertainties on the theoretical predictions. For the frequentist method described in section 11, these replicas are used to derive a covariance matrix for the theoretical predictions. The eigenvector decomposition of this matrix provides a set of independent uncertainty components, which can be treated in the same way as those in the other PDF sets. In cases where variations of the theoretical parameters are also available, these are treated as additional uncertainty components assuming a Gaussian distribution.
Quantitative comparison with NLOJet++ predictions The ratio of the NLO QCD predictions from NLOJet++, corrected for non-perturbative and electroweak effects, to the data is shown in figures 6-9 for various PDF sets. The CT10, HERAPDF1. 5, epATLJet13, MSTW 2008, NNPDF2. 3, and ABM11 PDF sets are used. As discussed in section 10, the individual experimental and theoretical uncertainty components are fully correlated between m 12 and y * bins. As such, the frequentist method described in section 11 is necessary to make quantitative statements about the agreement of theoretical predictions with data. For this measurement, the NLOJet++ predictions using the MSTW 2008, NNPDF2. 3 and ABM11 PDF sets have smaller theoretical uncertainties than those using the CT10 and HERAPDF1. 5 PDF sets. Due to the use of ATLAS jet data in the PDF fit, the predictions using the epATLJet13 PDF set have smaller uncertainties at high dijet mass compared to those using the HERAPDF1.
5 PDF set, when only considering the uncertainties due to the experimental inputs for both. The frequentist method is employed using the hypothesis that the SM is the underlying theory. Here, the NNPDF2. 1 PDF set is considered instead of NNPDF2. 3 due to the larger number of replicas available, which allows a better determination of the uncertainty components. The epATLJet13 PDF set is not considered since the full set of uncertainties is not available. The resulting observed p-values are also shown in figures  6 7 8 9 , where all m 12 bins are considered in each range of y * separately. The agreement between the data and the various theories is good (observed p-value greater than 5%) in all cases, except the following. For the predictions using HERAPDF1. 5, the smallest observed p-values are seen in the range 1.
This shows that the choice of generalized χ 2 as the test statistic in the frequentist method behaves as expected. Because the data at larger values of y * are increasingly dominated by experimental uncertainties, the sensitivity to the proton PDFs is reduced. For this reason, when considering a combination only the first three ranges of y * < 1. 5 are used. While the low dijet-mass region provides constraints on the global normalization, due to the increased number of degrees of freedom it also introduces a broadening of the χ 2 distribution from the pseudo-experiments that reduces sensitivity to the high dijet-mass region. To focus on the regions where the PDF uncertainties are large while the data still provide a good constraint, the comparison between data and SM predictions is also performed using a high dijet-mass subsample.
PDF set y * ranges mass range P obs (full/high) R = 0. 4 R = 0. 6 y * < 0. < 10 -3 ABM11 y * < 1. 5 high < 10 -3 < 10 -3 y * < 1. 5 full < 10 -3 < 10 -3 Table  1. Sample of observed p-values obtained in the comparison between data and the NLO QCD predictions using the CT10, HERAPDF1. 5, MSTW 2008, NNPDF2. 1 and ABM11 PDF sets, with values of the jet radius parameter R = 0. 4 and R = 0. 6. Results are presented for the range y * < 0. 5, as well as the combination of the first three ranges of y * < 1. 5, performing the test in the full dijet-mass range or restricting it to the high dijet-mass subsample. The full information on uncertainties, including their asymmetries and correlations, is used for both the pseudo-experiments and the χ 2 calculation. It is possible to further study the poor agreement observed at high dijet mass for the combination of the first three ranges of y * < 1.
5, performing the test in the full dijet-mass range or restricting it to the high dijet-mass subsample. The full information on uncertainties, including their asymmetries and correlations, is used for both the pseudo-experiments and the χ 2 calculation. calibration uncertainty components. This is because the impact on the total uncertainty and its correlations is only at the few-percent level for most bins, even though these assumptions lead to a relative change of up to 8% in the experimental uncertainty, and up to 12% in its correlations. Quantitative comparisons of theoretical predictions with data would benefit from alternative assumptions for the correlations of the theoretical uncertainties, in particular the large PDF uncertainty components.
Comparison with POWHEG predictions The ratios of the theoretical predictions from POWHEG to data are shown in figures 11 and 12 for values of the jet radius parameter R = 0. 4 and R = 0. 6. Because of the difficulty in propagating multiple PDF sets through the POWHEG generation, direct calculations of the theoretical uncertainties are not made and only qualitative comparisons are provided. However, the theoretical uncertainties are expected to be similar to those shown for the NLOJet++ calculations using the CT10 PDF set. The POWHEG predictions show no major deviations from data, with the Perugia 2011 tune resulting in slightly larger cross-sections (10-20%) than the AUET2B tune. At high dijet mass, the difference in the theory calculations is less pronounced, decreasing to a few percent at low values of y *.
Exploration and exclusion of contact interactions To illustrate the sensitivity of the measurements to physics beyond the SM, the model of QCD plus contact interactions (CIs)  [69]  with left-left coupling and destructive interference between CIs and QCD as implemented by the CIJET program  [70]  is considered. The use of NLO QCD calculations for the CI portion unifies the treatment of the two predictions. For this study, the measurement is restricted to the high dijet-mass subsample m 12 > 1. 31 TeV in the range y * < 0. 5, where theory predicts the largest effect. Because QCD predicts a more uniform y * distribution compared to CIs, where events are preferentially produced at smaller values of y * , this region provides the highest sensitivity to the CI contribution. Furthermore, the additional QCD plus CIs matrix elements are sensitive to different quarkgluon compositions.
Given that the positive electroweak corrections and the CIs produce similar contributions, the electroweak corrections are not applied to the CI portion. This results in a conservative approach when setting limits for the kinematic region considered here. Non-perturbative corrections are also not applied to the CI portion, but have a negligible size of less than 1% at high dijet mass where CI contributions are most significant. The PDF sets for which the SM predictions in this region provide a good description of the data are considered: CT10, HERAPDF1. 5, MSTW 2008, and NNPDF2. 1. Even when considering the ABM11 PDF set in only the low dijet-mass region m 12 < 1. 31 TeV, where CIs were previously excluded  [71, 72] , the observed p-value is < 0. 1% in the range y * < 0. 5. For this reason, the ABM11 PDF set is not considered in this example.
In both cases, corrections to account for electroweak effects are applied. The statistical uncertainties are smaller than in the previous ATLAS measurement of dijet production using data collected during 2010. In particular, the improved measurement in the high dijet-mass region can be used to constrain the PDF's at high momentum fraction. The correlations between the two measurements, which should be accounted for in a common fit of the PDFs, are non-trivial due to the different treatments of the jet energy calibration uncertainty. Detailed quantitative comparisons between the measured cross-sections and theoretical predictions are presented, using a frequentist method employing a χ 2 definition generalized for asymmetric uncertainties and their correlations. Good agreement is observed when using the CT10, NNPDF2.
1 and MSTW 2008 PDF sets for the theoretical predictions with values of the jet radius parameter R = 0. 4 and R = 0. 6. Disagreement is observed in some ranges of dijet mass and y * when using the HERAPDF1. 5 PDF set, for both values of the jet radius parameter. Even stronger disagreement is observed between data and the NLOJet++ predictions using the ABM11 PDF set, indicating the sensitivity of the measurements to the choice of PDF set. The sensitivity of the frequentist method to assumptions about the correlations between the jet energy calibration uncertainties is tested and found to be small. However, tests of the impact of assumptions about the correlations between the theoretical uncertainties, dominant in the comparison between data and the SM, would be desirable. The CLs method is employed using measured cross-sections to explore possible contributions to dijet production from physics beyond the Standard Model.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MIN-ERVA, GIF, DIP and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Background: Immunotherapy offers a promising novel approach for the treatment of cancer and both adoptive T-cell transfer and immune modulation lead to regression of advanced melanoma. However, the potential synergy between these two strategies remains unclear. Methods: We investigated in 12 patients with advanced stage IV melanoma the effect of multiple MART-1 analog peptide vaccinations with (n = 6) or without (n = 6) IMP321 (LAG-3Ig fusion protein) as an adjuvant in combination with lymphodepleting chemotherapy and adoptive transfer of autologous PBMCs at day (D) 0 (Trial registration No: NCT00324623). All patients were selected on the basis of ex vivo detectable MART-1-specific CD8 T-cell responses and immunized at D0, 8, 15, 22, 28, 52, and 74 post-reinfusion. Results: After immunization, a significant expansion of MART-1-specific CD8 T cells was measured in 83% (n = 5/6) and 17% (n = 1/6) of patients from the IMP321 and control groups, respectively (P < 0.
Study groups The present trial was approved by the local IRBs and by the Swiss national regulatory authority (Swissmedic). It has been registered in the NCI Clinical Trials PDQ under the number NCT00324623. Patients with stage IV cutaneous melanoma were accrued between October 2005 and August 2011. Eligibility was based on the following inclusion criteria: progressive disease with no evidence of brain metastases, HLA-A2 positivity; tumor expression of Melan-A by IHC; and a measurable, endogenous anti-MART-1 CD8 T-cell response for patients that had no prior adjuvant vaccine therapy, as described  [10]. MART-1 CD8 T-cell responses were assessed by tetramer staining using ELAGIGILTV-MHC class I multimers with a threshold of 0. 03% of CD8 T cells above background. Patients underwent CT scan of the thorax, abdomen, and pelvis within 4 weeks of therapy.
This spatial investigation involved exhaustive taxonomic identifications and cell counting using the traditional microscopical method. Through the construction of 18S rDNA gene clone libraries for three selected samples along the surface salinity gradient, the genetic diversity of microeukaryotes was explored, providing a first insight into the protistan diversity that may occur in such a transition zone. The data offered an opportunity to compare the parallel use of morphological and molecular approaches for identifying taxa and measuring diversity in transitional waters.
Physicochemical and biological measurements Physicochemical profiles (depth, temperature, salinity) of the water column were obtained with a multi-parameter probe (Hydrolab Data sonde 5 Options, USA), and vertical profiles of irradiance were measured with an underwater quantum sensor (LICOR LI-1400, Nebraska, USA). Inorganic nutrients analysis was performed in the laboratory with a Bran+Luebbe Autoanalyzer AA3 according to Aminot & Kerouel  [21]. Chlorophyll a biomass (Chl a) was estimated by fluorometry (Trilogy 7200-000 -Turner Designs, California, USA) according to the method of Welschmeyer  [22]  as described in Bazin et al. [23].
Taxonomic affiliation and phylogenetic analyses All the sequences were manually checked, trimmed and edited using the SeqAssem software  [31] , and then compared to those available in public databases (GenBank) using the NCBI BLASTn web application  [32]. Potential chimeras were detected with the online softwares Bellerophon  [33]  and KeyDNAtools  [34]. After removal of low-quality sequences, metazoan sequences, and suspected chimeras, the remaining sequences were aligned using the slow and iterative refinement method FFT-NS-I with MAFFT 6. 9 software  [35]. The resulting alignment was checked and corrected manually. Based on this alignment, the sequences were clustered into distinct operational taxonomic units (OTUs) with MOTHUR 1. 13  [36]  using a similarity threshold of 98% that roughly corresponds to the genus/species level  [37].
Phylogenetic trees including additional selected sequences from both GenBank and ARB databases were reconstructed using both neighbor joining (NJ, Jukes-Cantor distance) and maximum likelihood method (ML) with MEGA 5. Bootstrap support values (BP) were calculated from 1000 replicates for NJ tree and were reported on the ML tree, for which BP were from 100 replicates. The sequences reported in this paper have been deposited in the GenBank database under accession numbers JX645081-JX645156.
Diversity and statistical analyses For the morphological approach, taxa richness and abundance were determined from floristic lists and cell counts. Taxa richness for the molecular data was estimated with MOTHUR, based on OTUs defined at 98% sequence similarity level. Rarefaction curves (Sobs) were generated for the three clone libraries and OTU richness was estimated by the non-parametric Chao 1 diversity estimator  [38]. The comparison of taxa diversity between the samples was carried out through the Jaccard similarity index, by considering taxa (and OTUs) composition only. Principal Component Analysis (PCA) was performed to group samples according to the environmental variables (standardized and ln(x+ 1) transformed), and Correspondence Analysis (CA) was applied to a matrix of the relative contribution of phytoplankton in order to establish relationships between taxonomic composition and sampling stations.
Acknowledgments The authors wish to thank  Frederic Guyot  ( Centre de Recherche en Environnement Cotier, UCBN, Luc-sur-mer ) for logistical support during the cruise;  Antoine Mabire ,  Sophie Picot ,  Florien Mauduit , and  Hugo Lebredonchel  for their help in the field. We are very grateful to IFREMER Port-en-Bessin (particularly  Olivier Pierre-Duplessix ) for the loan of equipment and the nutrient measurements,  Pierre Bazin  for his contribution to the construction of clone libraries,  Gabriele Curdt-Hollmann  ( EPSAG, Georg-August-Universita ¨t Go ¨ttingen, Germany ) for DNA sequencing, and  Didier Goux  ( CMABio, UCBN ) for the high-quality analyses using SEM. We also thank  Drs. Christophe Lelong  and  Guillaume Rivie  `re for their help and advice on the molecular approach; and  Pr.
The libraries were subjected to deep sequencing on the Illumina GAII platform. In combination, the bioinformatic tool miRCat  [29]  was used for de novo miRNA prediction from the whole genome sequence. At first, the predicted miRNAs with differential read numbers in the different samples were investigated. Thirty-two such potential miRNAs were checked by sRNA Northern blotting with their corresponding positive controls, but none of them were detectable. As the read numbers of these sRNAs were relatively low, we also tested another set of potential miRNAs that had high read numbers but were not differentially expressed during abiotic stress (19 sRNAs checked). However, these sequences were not detectable either (data not shown). To rule out the possibility that the concentration of sRNAs in diatoms were so low that it was below the limit of detection, the sRNA fraction was purified from 780 μg total RNA, but even after using 150 times more RNA than necessary to detect miRNAs in other organisms  [30]  we were unable to detect any of these predicted miRNAs.
We used both miRCat  [32]  and miRDeep2  [33]  to predict potential miRNA candidates in both diatoms. We ran miRCat once using default animal-based parameters and once using default plant-based parameters to broaden the search space that diatom miRNAs may be residing in. The data sets supporting the results of this article are available in the GEO repository at NCBI under the accession name GSE57987.
Also, throughout the study period, each time a wolverine den was found, its coordinates were included in a national database (www. rovbase. no, www. rovbase. se), and the site was checked for possible new reproductions in subsequent years. Thus, the monitoring system has been each year taking advantage of the information acquired during previous winters, leading to a potential increase ability to detect the study species. Between 1996 and 2013, the number of known denning sites has increased from zero to 120. We used data from the Scandinavian wolverine monitoring programme to parameterize our simulation model and assess the effects of knowledge accumulation on the performance of the monitoring system. As a parallel programme of scat collection and DNA-based individual identification had estimated population growth rate during the study period to be 1.
138), Ki67 (probe 224713_at p = 0. 229), all other EMT markers listed are significant at p < 0. 05 for CD24 À CD44 + versus others. Except for ZEB1 (probe 212764_at p = 0. 106), ZEB2 (probe 228333_at p = 0. 336), CDH1 (probe 201131_s_at p = 0. 209), OCLN (probe 209925_at p = 0. 203, probe 231022_at p = 0. 203), CLDN8 (probe 214598_at p = 0. 15), and DSP (probe 200606_at p = 0. 46), all other EMT markers listed are significant with p < 0. 05 for ALDH + versus ALDH À. (B) For CD24 À CD44 + versus others flow-sorted profile data sets, heatmap of EMT markers (jfold changej > 1. 5). (C) As in (A), but for ALDH + versus ALDH À profile data sets. (D) Gene-expression levels measured by qRT-PCR confirm the results obtained with Affymetrix array HU133 Plus 2. 0. Error bars represent mean ± SD. The fold changes are significant with p < 0.
Patients and Clinical Samples We made the following comparisons on flow-sorted samples from breast cancer patients or breast cancer cell lines: (1) CD24 À CD44 + versus all other flow-sorted cells obtained from GSE7513 in the Gene Expression Omnibus (GEO) (patients 32-56 years old), (2) ALDH + versus ALDH À obtained from 16 breast cancer patients (35-60 years old), and (3) CD24 À CD44 + versus all other flow-sorted cells and ALDH + versus ALDH À from breast cell lines MCF10A, SUM149, and primary xenograft MC1, and normal breast epithelial cells from patients (18-40 years old).
ACKNOWLEDGMENTS We thank  Dr. Stephen Ethier  for generously providing the breast cancer cell lines SUM149 and SUM159. We also thank the  University of Michigan Flow Cytometry Core ,  Microarray Core  ( Joe Washburn  and  Craig Johnson ), Vector Core, Molecular Imaging Core, and  Pathology Core  for invaluable assistance. This work was supported by  CAS Stem Cell  grant  XDA01040410  and  NSFC  grant  81322033  (S. L. ),  NIH  grants  CA66233  and  CA101860  (M. S. W. ), and  NIH  grant  U54CA149169  (S. T. W. ). M. S. W. holds equity in OncoMed Pharmaceuticals, receives research support from  MedImmune and Dompe , and is a scientific advisor to  MedImmune, Verastem, Cerulean, and Paganini.
Command Line Implementation A command line implementation of the method in C++ working with PGM gray-level images is available in IPOL web part of this article (ctseg_1. zip)  3. The program ctseg takes three arguments: 1. The source image (PGM file, 8 bits); 2. The marker image (PGM file, 8 bits). This image must have the same size as the source image. All non-zero pixels belong to the marker.
Accession Numbers Sequence data from this article can be found in the GenBank/EMBL databases under the following accession numbers: AOX1 (EMBL EDP02600. 1), AOX2 (EMBL EDP06011. 1), FLVA (EMBL EDP03485. 1), FLVB (EMBL EDO98775. 1), LCIB (EMBL ABG38184. 1), LHCSR3 (EMBL EDP01087. 1), NDA2 (EMBL EDO96450. 1), PGRL1 (GenBank XP_001700905), PsaC (EMBL AAB17714. 1), and PsbD (EMBL P06007. 1).
ACKNOWLEDGMENTS This work was supported by the  French "Agence Nationale pour la Recherche" (ALGOMICS  and  ALGOH2  projects) and by the  CNRS-JST joined program  " Structure and Function of Biomolecules. "Support was also provided by the HélioBiotec platform, funded by the  European Union  ( European Regional Development Fund) , the  Région Provence Alpes Côte d'Azur , the  French Ministry of Research , and the " Commissariat à l'Energie Atomique et aux Energies Alternatives. "Y. A. and M. J. acknowledge the  Academy of Finland  (project number  271832 ). We thank  H. Fukuzawa  ( Kyoto University, Japan ) for the generous gift of the LCIB antibody.