dataesr/scientific-paragraphs-categorization huggingface dataset:
The mock-up TBM on DIII-D has four protective carbon tiles arranged vertically with a thermocouple placed on the back of each 2. 5 cm thick tile (figure  1 ). In a first series of experiments (in 2009) the thermocouple signals were used to deduce fast-ion induced heat loads on the surface of the tiles  [4]. This involved modelling of the fast-ion losses to the TBM tiles in the presence of a highly localized error field near the TBM mock-up (figure  1 ) and the modelling of the heat transport through the tiles. Although good agreement between the measured and modelled thermocouple temperature response was found, there were still questions about possible heat-load contributions from the thermal plasma and of the precise shape of the hot spot on the tiles. In those studies four fast-ion transport codes were used, the ASCOT code  [6] , the DELTA5D Monte Carlo code  [7] , the OFMC code  [8, 9]  and the SPIRAL code  [10].
In this way the heat loads from successive beams on the TBM tiles was measured in a single discharge. The gap between the TBM surface and the last-closed flux surface was kept at 4. 2 ± 0. 4 cm with a steady H-mode density profile. Heat loads on the TBM tiles were obtained from the measured IR temperature data by using the THEODOR 2D analysis code  [12]. The thermal analysis included temperature dependent heat diffusion and conduction coefficients for the ATJ graphite while a loosely adhered surface layer was taken into account with a heat transmission coefficient of 5 MW m -2 K -1. In the heat-load analysis care was taken not to include the increased heat loads due to ELMs. The power that was deposited onto the TBM surface was obtained by integrating the heat load over all the four tiles and is given in the second column of table 1 while the measured heat loads are shown in the left-hand side columns of figures 4 and 5 for the tangential and perpendicular beams, respectively, and for the half beam in figure  6.
Increased heat loads due to ELMs were observed in the IR camera data but omitted from the reported experimental results. It has previously been shown experimentally and from simulations that the heat loads and power depositions are very sensitive to the gap between the last-closed surface and the TBM tiles  [4]. When this gap is decreased in the simulations so that the simulated deposited power agrees with the measured deposited power, it does not solve the problem: decreasing the gap will result in much higher peak heat loads in the simulations than the observed ones. An alternative explanation for the discrepancy between the measured and simulated power deposition might be found in the way the deposited power is obtained. In both the experiment and simulations the deposited power was obtained by integrating the heat loads over the four TBM tiles.
Acknowledgments We thank  J. -W. Ahn  and  Oak Ridge National Laboratory  for loan of the fast IR camera used in the experiments. This work was supported by the  US Department of Energy  under  DE-AC02-09CH11466 ,  SC-G903402 ,  DE-FC02-04ER54698  and  DE-AC05-00OR22725. The supercomputing resources of CSC -IT centre for science were utilized in the studies. This work was partially funded by the  Academy of Finland  projects No 121371 and  134924. The views and opinions expressed herein do not necessarily reflect those of the  ITER Organization.
Measurements Data were obtained from patient interviews together with primary care and hospital clinical records review. Each of the patients was interviewed after diagnosis using a structured questionnaire during the inpatient stage, oncology visit or, if this were not possible, at home by a trained nurse or GP. Informed consent for study participation and access to medical records was obtained during the interview. In cases where the patient was deceased or could not be interviewed for other reasons, permission to review patient clinical records was obtained from a close family member. Details about measurements can be found in Additional file 1. Interview measurements: Each patient was asked how long he/she had been feeling unwell and given a checklist to identify the type of symptom/s noted  [18].
Statistical analysis The graphical analysis of interval times showed skewed distributions confirmed with the Kolmogorov-Smirnov test. Diagnosis or treatment intervals are presented as median and 25-75 percentiles. To assess the relationship between the different types of symptom interval duration and the observed variables, we used the Mann-Whitney U test for dichotomous variables and Kruskal-Wallis test for those variables with more than two categories. In order to avoid symptoms that could not be related to CRC, no more than a 24-month interval between symptom and diagnosis was accepted. Nineteen patients (0. 3%) had a SDI of 24 months and twenty-one (0. 7%) in the case of STI. All statistical analyses were performed using SPSS v. 15. 0. This Study was approved by the Primary Health Care Committee of each health district and by the Ethical and Clinical Research Committee of each participating region.
Acknowledgements This study has been financed with grants from the  Ministry of Health , Carlos III Institute; PI: 052273 ,  PI050787 , PI050700 ,  PI052692 ,  PI052141. It also received the support of the  Health Promotion and Preventive Activities-Primary Health Care Network , sustained by the  Ministry of Health   ISCIII-RETCI G03/170  and  RD06/0018. The authors would like to thank surgeons, gastroenterologists and general practitioners for their participation in contact with patients, especially to  Dr. Julio Lago. *Collaborators DECCIRE GROUP Baleares:  Magdalena Esteva ,  María Ramos ,  Alfonso Leiva ,  Amador Ruiz ,  Maria Martín-Rabadán ,  María Teresa Novella ,  Elena Cabeza ,  Joana Ripoll ,  Hermini Manzano ,  Isabel Amengual ,  Aina Forteza ,  Maria Company ,  Maria de Lluch Bennassar ,  Joan Llobera.
Anaplastic oligodendrogliomas (AOD) are rare glial tumors in adults with relative homogeneous clinical, radiological and histological features at the time of diagnosis but dramatically various clinical courses. Studies have identified several molecular abnormalities with clinical or biological relevance to AOD (e. g. t(1;19)(q10;p10), IDH1, IDH2, CIC and FUBP1 mutations). To better characterize the clinical and biological behavior of this tumor type, the creation of a national multicentric network, named ''Prise en charge des OLigodendrogliomes Anaplasiques (POLA),'' has been supported by the Institut National du Cancer (InCA). Newly diagnosed and centrally validated AOD patients and their related biological material (tumor and blood samples) were prospectively included in the POLA clinical database and tissue bank, respectively.
Materials Eighty-three patients with a centrally reviewed diagnosis of brain AOD were prospectively included in the present study. For all of the individuals, frozen and formalin-fixed paraffin-embedded (FFPE) tumor tissues were available for the genetic, pathological and immunohistochemical investigations. A blood sample was collected and stored at 220uC until use for research purposes, before any anti-tumor treatment was started, as recommended by POLA network policy. The patients included prospectively in the POLA network have provided their written consent for the clinical data collection and genetic analysis according to the national and the POLA network policies.
The forward primer was labeled with the Fam (D9S1684 and D9S1121) or Ned (D9S171) dyes (Life Technologies TM ). The primer sequences are available upon request. The samples were run on an automatic sequencer and analyzed with the Gene Scan program (Abi-prism, Perkin Elmer). Statistical analyses. The total and allele-specific copy numbers were computed for each sample using the crlmm algorithm  [18, 19]. The correction of the total copy number waves was based on the GC-content of the probes and targeted DNA regions. The total copy numbers from the tumor samples were normalized using the blood sample from the same patient, when available, or with the median signal of all the blood samples processed using the same Illumina platform. The B-allele frequencies from patients with two samples processed using the same Illumina array type were corrected using the TumorBoost algorithm  [20].
In cancer cells, CNLOH has been shown to regulate gene expression according to the parental gene duplicate (regardless of imprinting)  [28]. Interestingly, a homozygous deletion of FUBP1 was detected in one case. These data combined with the recent work of Bettogowda et al. suggest that FUBP1 has a putative tumor suppressor role in oligodendrogliomagenesis. In addition, the high resolution genome-wide analysis conducted in the present study highlighted multiple novel focal genomic abnormalities containing putative genes involved in AOD oncogenesis. Further investigations are required to specify these candidate genes and their role in the biology of AOD. Our study confirms that despite a rigorously controlled homogeneous pathological aspect, AOD is a heterogeneous subgroup of tumors in terms of its molecular features.
Finally, the limited number of non 1p/19q-co-deleted tumors and the short follow-up do not allow robust prognostic analysis so far. Nonetheless, as expected, patients with 1p/19q co-deleted tumors survive longer than patients whom tumor does not harbor this biomarker. In conclusion, high resolution SNP array analysis was used in a prospective centrally reviewed series of AOD-identified novel copy number abnormalities containing putative candidate genes and identified CNLOH as a novel recurrent genomic abnormality in AOD. In addition to neuropathological examination, integration of the copy number abnormality data with other OMICS data will aid in specifying the genetic portraits of the different entities encompassed in the AOD group, aiding in a more accurate histomolecular diagnosis and a better understanding of AOD oncogenesis.
Acknowledgments Institut  Hospitalier Universitaire de Neurosciences Translationnelles de Paris. POLA tumor bank. POLA Network CHU Amiens: Henri Sevestre. CHU Angers: Sophie Michalak, Edmond Al Nader. CHU Bicetre: Catherine Lacroix, Clovis Adam. CHU Besancon: Gabriel Viennet, Stephanie Servagi-Vernat. CHU Bordeaux: Hugues Loiseau. CHU Brest: Isabelle Quintin-Roue. CHU Caen: Jean-Sebastien Guillamo, Evelyne Emery. CHU Clermont-Ferrand: Jean-Louis Kemeny. CLCC Dijon: Marie-Helene Aubriot-Lorton. CHU Creteil:-Christov Christo. CHU Lariboisiere: Marc Polivka. CHU Lille: Francois Dubois, Claude-Aliain Maurage. CHU Limoges: Edouard Marcel Gueye. CHU Montpellier: Valerie Rigau. CHU Nancy: Jean-Michel Vignaud. CHU Nantes: Mario Campone, Jean-Sebastien Frenel. CHU Nice: Fanny VandenBos. CHU Reims: Marie-Daniele Diebold.
Here a search for γray line-like signatures conducted with the H. E. S. S. experiment in the energy range E γ ∼ 500 GeV -25 TeV is reported, complementing a recent search at energies between 7 GeV and 200 GeV with the Fermi-LAT instrument  [15]  and studies discussing an indication for a line feature at an energy of about 130 GeV  [16] [17] [18]. The search for a DM-induced spectral signature in the H. E. S. S. data is performed separately for two sky regions of interest. The first is the CGH, a promising region due to its proximity and predicted large DM concentration. Following  [8] , the search region is defined as a circle of 1 • radius centred on the GC, where the Galactic plane is excluded, by requiring |b| > 0. 3 •. The second region is the extragalactic sky covered by H. E. S. S. observations, with regions containing known VHE γ-ray sources being excluded from the analysis.
METHODOLOGY AND RESULTS The CGH data set is composed of 112 h (live time) of GC observations recorded with the H. E. S. S. VHE γ-ray instrument (see  [21]  and references therein) during the years 2004-2008 2. The mean distance between the telescope pointing positions and the GC is 0. 7 • , with a maximum of 1. 5 •  [8]. The extragalactic data set comprises 1153 h of H. E. S. S. observations taken during 2004-2007, targeted at various extragalactic objects. Regions in the field-of-view (FoV) containing known VHE γ-ray sources are excluded by masking out a circular region (of radius 0. 2 • for point sources) around the source position. Observations with zenith angles larger than 30 • are excluded from the analysis to lower the energy threshold, resulting in a mean zenith angle of 14 • (19 • ) for the CGH (extragalactic) observations.
The energy threshold is 310 GeV (500 GeV) for the CGH (the extragalactic) data set. Differential flux spectra are calculated from the reconstructed event energies separately for the CGH and extragalactic data sets using zenith angle-, energy-and offset-dependent effective collection areas from γ-ray simulations. Since sky regions containing known VHE γ-ray sources were excluded from the analysis, the spectra consist mostly of γ-ray-like cosmic-ray background events (and a fraction of ∼ 10% of electrons). These spectra are well described by the empirical parametrization dN dE γ = a 0 E γ 1 TeV -2. 7 [P (x) + βG(x)] , (1) where E γ is the reconstructed energy of the event under γ-ray hypothesis and P (x) = exp (a 1 x + a 2 x 2 + a 3 x 3  ). G(x) is a Gaussian function with mean µ x and rms σ x , and x = log 10 (E γ /1 TeV).
To search for such lines, a Gaussian term with fixed energy E γ and fixed corresponding width σ E was added to the spectrum parametrization given in Eq. 1. The spectrum was refit, and from the normalization of the Gaussian the flux of the putative line was reconstructed. By repeating this procedure, using ten logarithmically equidistant energies E γ per decade of energy, the flux spectrum was scanned for monochromatic γ-ray signatures. Line scans were performed in the energy range 0. 5 TeV-20 TeV and 0. 8 TeV-25 TeV for the CGH and the extragalactic data sets, respectively. No γ-ray line flux was found to exceed the a-priori chosen detection threshold of ∆ ln L = 12. 5, corresponding to a significance of 5 standard deviations above the background level for Gaussian parameters. Thus flux upper limits were calculated by constraining the flux normalization of the Gaussian to be non-negative in the fit and using the MINOS package from the Minuit  [22]  fitting tool to calculate asymmetric errors with error level ∆ ln L = 1.
Requirements and technical details In order to use Resummino, several external libraries and header files are required and must be installed on the system. First, information on the benchmark supersymmetric scenario under consideration is passed to the program by means of files compliant with the Supersymmetry Les Houches Accord (SLHA) conventions  [51, 52]. We have adopted the choice to internally handle such files by making use of SLHAea [53], a C++ header-only library dedicated to input, output and manipulation of SLHA data. While SLHAea is fully included in Resummino and hence does not need to be downloaded by the user, this tool relies on some headers of the Boost C++ libraries [54] that are in contrast not provided with Resummino. Therefore, their presence on the system is a necessary prerequisite and the two packages boost and boost-devel must be available.
Next, both the fixed order and resummed components of the hadronic cross section require the evaluation of parton distribution functions, either in the physical x-space or in the conjugate Mellin N -space. The Resummino package does not come with any built-in parton density fit and entirely relies on the external LhaPdf library  [55]  which must therefore be installed by the user. On run-time, the Mellin moment of the parton densities parameterization under consideration are obtained by a numerical fit performed by means of the Levenberg-Marquardt algorithm dedicated to multidimensional fits of non-linear functions  [56, 57] , as implemented in the Gnu Scientific C++ Libraries (GSL). This algorithm consists of an iterative procedure using the method of least squares after a linearization of the fitting curves.
All these integrations, together with the usual two-body and three-body phase space integration relevant for the types of computations performed in Resummino, are achieved by means of an adaptive multi-dimensional integration technique based on the importance sampling of the integration domain  [58]. To this aim, we again make use of the GSL C++ libraries provided with the adaptive multi-dimensional integration Vegas algorithm  [59]. Concerning the fixed order partonic cross sections, Resummino is based on the leading order results of Ref. [19]  and Ref. [20, 21]  for slepton-pair and gaugino-pair production, respectively. Next-to-leading order corrections including both the QCD and supersymmetric QCD virtual diagrams are implemented as given in Ref. [17]  and Ref. [18] , the associated finite pieces of the virtual loops being computed by means of the QcdLoop package  [60].
Installation We recommend the user to always use the latest stable version of Resummino that can be downloaded from the webpage http://www. resummino. org Once downloaded, the package consists of a compressed tar file (resummino-x. x. x. tar. bz2 where x-x-x stands for the version number) that must be unpacked, tar xf resummino-x. x. x. tar. bz2 In the case all the prerequisite dependencies of Resummino are present on the system (see Section 3. 1), it is then necessary to generate a Makefile appropriate to the system configuration. This is done by issuing in a shell the commands cd resummino-x. x. x cmake.
[options] The cmake program checks, in a first stage, that all the dependencies mandatory for Resummino are correctly installed. It subsequently creates a series of Makefile scripts allowing for the compilation of the Resummino source files and their linking with the dependencies. Two optional arguments can be passed to the cmake script. The first of these is related to the LhaPdf libraries. In the case they have not been installed in the directories referred to by the environment variable LD_LIBRARY_PATH (or DYLD_LIBRARY_PATH for MacOS systems), the LhaPdf installation directory must be specified by means of -DLHAPDF=/path/to/lhapdf This instructs cmake that the LhaPdf libraries are stored in the directory /path/to/lhapdf/lib and the header files in the directory /path/to/lhapdf/include. Equivalently, these two directories can be provided separately through the cmake options -DLHAPDF_LIB_DIR=/path/to/lhapdf/lib -DLHAPDF_INCLUDE_DIR=/path/to/lhapdf/include The second optional argument of the cmake script consists of information on the directory where the Resummino executable has to be created (/path/to/install in the example below).
Electrochemical environment The electrolyte used to perform steel corrosion was obtained by dissolving 250g of ammonium thiocyanate, NH 4 SCN, in 1 litter of distilled water at room temperature. A potentiostat using the VOLTAMASTER software was used for electrochemical experiments. Working electrode (WE) was made following the test, either central wire, or strand. Reference electrode, is a saturated calomel electrode (SCE). A platinum grid is used as counter electrode.
Acoustic emission monitoring system In order to detect AE signals generated by chemical degradation and damage evolution, four sensors are used  [15] ; two PICO type sensors (S3, S4) witch have a very good sensitivity and two 150 kHz resonance ultrasonic emission sensors, type VS150-M (S1, S5). PICO sensors have an operating Frequency Range 200-750 kHz and resonant Frequency 250 kHz (Ref V/(m/s)). The sensors are connected to VALLEN AEP4 preamplifiers, with a programable gain fixed at 34 dB; the frequency bande extends from 20 kHz with 2 MHz. the AE acquisition device is the commercial VALLEN AMSY 4 ® system. The acoustic data processing is carried out using the VISUAL AE ® software and the analysis of the waveforms is carried out with the VISUAL TR ® software. To eliminate most of unwanted signals due to the external noise, the detection threshold of the AE signals was fixed at 30.
Features of the organism A representative genomic 16S rRNA sequence of S. caldaria H1 T was compared using NCBI BLAST  [8, 9]  under default settings (e. g. , considering only the high-scoring segment pairs (HSPs) from the best 250 hits) with the most recent release of the Greengenes database  [10]  and the relative frequencies of taxa and keywords (reduced to their stem  [11] ) were determined, weighted by BLAST scores. The most frequently occurring genera were Spirochaeta (79. 9%) and Treponema (20. 1%) (17 hits in total). Regarding the two hits to sequences from members of the species, the average identity within HSPs was 99. 4%, whereas the average coverage by HSPs was 98. 4%. Regarding the five hits to sequences from other members of the genus, the average identity within HSPs was 94.
3%, whereas the average coverage by HSPs was 96. 3%. Among all other species, the one yielding the highest score was "Spirochaeta taiwanensis" AY35103, which corresponded to an identity of 95. 2% and an HSP coverage of 94. 4%. (Note that the Greengenes database uses the INSDC (= EMBL/NCBI/DDBJ) annotation, which is not an authoritative source for nomenclature or classification. ) The highest-scoring environmental sequence was FJ462015 ('Microbial ecology industrial digester mesophilic anaerobic reactor fed effluent chemical industry clone 71a'), which showed an identity of 97. 9% and an HSP coverage of 98. 1%. The most frequently occurring keywords within the labels of all environmental samples which yielded hits were 'termit' (26. 5%), 'hindgut' (17. 8%), 'gut' (8. 6%), 'homogen' (5. 5%) and 'flagel' (2.
Genome project history This organism was selected for sequencing on the basis of its phylogenetic position  [39] , and is part of the Genomic Encyclopedia of Bacteria and Archaea project  [40]. The genome project is deposited in the Genomes On Line Database  [18]  and the complete genome sequence is deposited in GenBank. Sequencing, finishing and annotation were performed by the DOE Joint Genome Institute (JGI) using state of the art sequencing technology  [41]. A summary of the project information is shown in Table  2. Phylogenetic tree highlighting the position of S. caldaria relative to the type strains of the other species within the family Spirochaetaceae. The tree was inferred from 1,362 aligned characters  [12, 13]  of the 16S rRNA gene sequence under the maximum likelihood (ML) criterion  [14].
Genome sequencing and assembly The genome was sequenced using a combination of Illumina and 454 sequencing platforms. All general aspects of library construction and sequencing can be found at the JGI website  [44]. Pyrosequencing reads were assembled using the Newbler assembler (Roche). The initial Newbler assembly, consisting of 60 contigs in one scaffold, was converted into a phrap  [45]  assembly by making fake reads from the consensus, to collect the read pairs in the 454 paired end library. Illumina GAii sequencing data (899. 9 Mb) was assembled with Velvet  [46]  and the consensus sequences were shredded into 2. 0 kb overlapped fake reads and assembled together with the 454 data.
Genome annotation Genes were identified using Prodigal  [48]  as part of the DOE-JGI genome annotation pipeline  [24] , followed by a round of manual curation using the JGI GenePRIMP pipeline  [49]. The predicted CDSs were translated and used to search the National Center for Biotechnology Information (NCBI) nonredundant database, UniProt, TIGR-Fam, Pfam, PRIAM, KEGG, COG, and InterPro databases. Additional gene prediction analysis and functional annotation was performed within the Integrated Microbial Genomes -Expert Review (IMG-ER) platform  [50].