dataesr/scientific-paragraphs-categorization huggingface dataset:
Roelof Koster, NCI, NIH) were maintained in RPMI 1640 supplemented with 10% FBS. The two melanoma cell lines, UACC1113 and UACC903, were obtained from the University of Arizona Cancer Center and grown in RPMI 1640 supplemented with 10% FBS and 25 mM HEPES. Cell lines were tested for authentication (October 2014) with a panel of short tandem repeats (STR) using the Identifiler kit (Life Technologies) and compared with the ATCC and the DSMZ (German Collection of Microorganisms and Cell Cultures) STR Profile Databases. All cell lines with profiles in either database matched (UACC1113, UACC903, 2102Ep, IMIM-PC-1 and IMIM-PC2 did not have profiles listed). The 2012Ep profile matched a previously published profile  59. The cells were routinely tested for Mycoplasma and and A549 cells. Seventy-two hours after delivery of the siRNA, ZNF148 and TERT expression, and telomerase activity, were assessed as described above.
Figure1| Map of TERT and CLPTM1L region. Recombination hotspots in the CEU population (red line), as well as 1000G combined recombination rate (blue line) across the TERT/CLPTM1L region are shown relative to the CLPTM1L and TERT genes, as well as the grouping of ten highly correlated sequence variants strongly associated with risk of pancreatic, testicular, and lung cancers in the region closest to CLPTM1L. (a) Chromatin interaction analysis paired-end (ChIA-PET) sequencing data from the K562 chronic myeloid leukaemia cell line using an antibody against RNA polymerase II generated by the ENCODE project (https://www. encodeproject. org/) is shown. For each of the ten strongly associated variants, layered H3K4Me1, H3K4Me3, and H3K27Ac chromatin immunoprecipiation (ChIP-seq), DNAse I hypersensitivity sequencing (DNase) and transcription factor ChIP-seq (TF-ChIP-Seq) data from the ENCODE project are shown (b) as displayed by the UCSC Genome Browser (lower panels).
Acknowledgements We acknowledge the contribution of the staff of the  Cancer Genomics Research Laboratory (CGR, NCI) , as well as  Mr Timothy Myers  and  Lea Jessop (LTG, DCEG, NCI)  for their invaluable help throughout the project. We are grateful to  Dr Francisco X. Real , The  Spanish National Cancer Institute (CNIO) in Madrid, Spain  for providing reagents. We are grateful to  Dr Dom Esposito ,  Frederick National Laboratory for Cancer Research, Frederick, MD, USA , for cloning for lentiviral production. We thank  Paul D. P. Pharoah ,  Department of Oncology, University of Cambridge, Cambridge, UK and Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Cambridge, UK  for providing GWAS data. Furthermore, we thank  Dr Stephen Chanock  for his advice, encouragement and critical reading of the manuscript.
This work was supported by the  Intramural Research Program  of the  US National Institutes of Health (NIH) ,  National Cancer Institute , by contract number  HHSN261200800001E , by  Cancer Research UK  for SEARCH ( C490/A10124 ). Funding for M. V. was provided by the  Netherlands Organization for Scientific Research (NWO Gravitation Program Cancer Genomics Netherlands). Funding for M. M. was provided by a grant from the  Marie Curie Initial Training Network (ITN) DevCom  ( FP7 , grant number  607142 ). The content of this publication does not necessarily reflect the views or policies of the  Department of Health and Human Services , nor does mention of trade names, commercial products, or organizations imply endorsement by the  US Government.
Molecular analysis The consensus sequence was used in a BLASTN (basic local alignment search tool) search  (Boratyn et al. 2013 ) to find matching sequences  (Zhang et al. 2000). This sequence was deposited in GenBank with accession number KY212128. This COI sequence (KY212128) matched the Tuta absoluta sequences KX443111 and KX443108  (Sint et al. 2016 ) and JQ749676 from Tunisia  (Bettaïbi et al. 2012 ) with 100% identity. The molecular data support the morphological studies and confirm the present specimens as T. absoluta.
Acknowledgements Jan Hendrik Venter , Manager: Plant Health Early Warning Systems,  Department of Agriculture, Forestry and Fisheries (DAFF) , for valuable discussions and information about the first occurrence of Tuta absoluta in South Africa. Martin Krüger ,  Ditsong National Museum of Natural History, Pretoria , who verified the initial identification of T. absoluta. Michael Stiller ,  Agricultural Research Council, Plant Protection Research Institute, Pretoria  for photographs of the genitalia. Riaan Stals , of the same institution for valuable advice and comments during the preparation of this paper.
Data collection Plant cover of all vascular plant species (expressed as the relative area covered by a species in percentage) was estimated with the Braun-Blanquet 7-grade coverabundance scale  (Braun-Blanquet 1932). In addition, young trees were identified at species level and categorized as seedlings (height ≤ 15 cm) or saplings (15 cm < saplings ≤ 3 m). We also estimated density and mean height for saplings only (by species) in each subplot. These measurements were used to compare establishment and growth for the five target tree species: beech (Fagus sylvatica), sycamore maple (Acer pseudoplatanus), hornbeam (Carpinus betulus), sessile (Quercus petraea) and pedunculate oak (Q. robur). We combined the two oak species into a single category (hereafter "oak") since considerable hybridization between sessile and pedunculate oaks prevented us from distinguishing these two species.
We collected data between May 15 and June 15 every two years from 2006 to 2014 (with one additional survey in 2005 for site 1 only). In addition, we used the Braun-Blanquet 7-grade coverabundance scale to estimate overall plant cover at each subplot for three vegetation layers: tree layer (woody vegetation >2. 0 m), shrub layer (woody vegetation 2. 0-0. 5 m) and herbaceous layer (woody and non woody plant species <0. 5 m).
Acknowledgements We especially thank  Philippe Hum  (ONF),  Jean-Luc Hamann  (ONCFS) and their students for field work. We thank  François Klein  (ONCFS) and  Jean-Pierre Tremblay  for their advice. We also thank  Y. Dumas  ( Irstea ) for his helpful assistance in 2016 data collection (soil). We thank  Yoan Paillet  for re-reading. L. Laurent was funded by a PhD grant from  Irstea  and the  Conseil Régional de Centre-Val de Loire  (grant number  00087494 ) and through funds provided by the  ONCFS  (grant number  2013 /16 / 6171 ).
Optical sections were collected at 0. 3 μ m intervals. Images of oocytes for fluorescence intensity measurements were all acquired using the same technical conditions. Image analysis. Images of entire meiotic spindles or DNA surfaces (for intensity measurements) were deconvolved using Huygens Professional (Scientific Volume Imaging) and then further analysed with Image J (NIH, USA) and Imaris (Bitplane, Switzerland). To determine the distance between two sister chromatid kinetochores, the automated spot detection function in Imaris was used to detect each kinetochore (CREST staining), followed by visual observation and manual correction of software errors in MI oocytes. The spot to spot closest distance function (interface with Matlab) was then used to determine the minimum distance between spots.
Pairings were then controlled visually by locating CREST spots on DNA in 3D images. Measurement data were included in the study only if they belonged clearly to a sister kinetochore pair within a bivalent (70 to 100% of kinetochore pairs). In MII-arrested oocytes, sister chromatids were detected manually and IKT distances were calculated using the Pythagorean Theorem. To determine BUB1/BUBR1 and CREST signal intensity at kinetochores, the automated spot detection function in Imaris was used in the BUB1/BUBR1/CREST detection channels. BUB1/BUBR1 and CREST spots were colocalized and visual observation allowed correcting software errors. The BUB1/BUBR1 mean signal intensities in the BUB1/BUBR1 and CREST spots were exported to Excel files for further analysis. Western blot analysis. Snap-frozen oocytes were lysed and pooled by 20 according to their maturation stage or to women's age directly in Laemmli sample buffer containing DTT as reducing agent.
Acknowledgements We are grateful to the  Prof. S. Hamamah (Unité d' AMP DPI ,  CHU Arnaud de Villeneuve, Montpellier ) and  Dr. G Regnier-Vigouroux  ( Centre Saint Roch, Montpellier ) teams for their assistance in collecting oocytes as well as to the patients who donated their oocytes. We thank  S. Delachambre ,  V. Georget  and  S. De Rossi  from  Montpellier RIO Imaging  and  N. Morin  for the generous gift of the CREST antibodies. We are indebted to  Dr. Jose Manuel Andreu  ( CIB, Spain ) for the gift of the anti-alpha tubulin antibodies and to  Thierry Gostan  for his help with statistical analysis. We are grateful to  K. Wassmann  for critical reading of this manuscript. This work was supported by a grant awarded by "the Appel d' Offres Interne GCS MERRI Montpellier-Nimes Recherche Clinique Translationnelle ".
ACKNOWLEDGEMENTS The authors are deeply thankful to  Marc Godinot ,  Eric Herbomel  and the rest of the staff of the  Muséum National d'Histoire Naturelle, Paris , for their help and assistance when visiting their collections; to  Joan Rosell  ( Universitat Autònoma de Barcelona ) for providing very useful data about the Àger area; to  Bernard Marandat  ( Université Montpellier ) and  Thierry Smith  ( Royal Belgian Institute of Natural Sciences ) for their comments about the studied material, which have been very useful for the development of this work; to  Víctor Fondevilla  for his help on the elaboration of some figures; to  Marta Roigé  and  Salvador Boya  for their participation in the fieldwork for the correlation between the studied stratigraphic sections; and to  Arnau Bolet  and  Erik Seiffert  for helpful aid and comments regarding the phylogenetic analyses.
Funding This work has been supported by project  Ministerio de Economía y Competitividad (MINECO) ,  Spanish Government  ( CGL2014-54373-P ) and by  Generalitat de Catalunya  ( 2014 SGR 416 GRC, 2014/ 100604  and  CERCA Programme). Joan Femenias-Gual  has also been funded by  MINECO  ( BES-2012-052951  and  EEBB-I-14-08986 ) and  Judit Marigó  has also been funded by  MINECO  ( IJCI-2015-26392 ) and by  Fondation Fyssen. The funders had no role in design, data collection and analysis, decision to publish, or preparation of the manuscript.
Composition In the course of the development of this opinion, the applicant provided several data sets on the composition of rum ether upon EFSA requests. The submission of February 2016 was the first that was considered suitable for assessment. It provided information on the volatile constituents in a total of 22 batches (Table  1 ). The volatile constituents were analysed using gas chromatography/flame ionisation detector (GC/FID) and GC/mass spectrometry (MS). The contents of the volatile constituents were determined on the basis of GC-peak area percentages relative to the total peak area in the chromatogram. No information on the consideration of individual, substance-specific GC-response factors has been provided. A total of 83 constituents were reported; on average 0. 53% of the total peak area detected in the chromatograms remained unidentified.
It has been stated that it may be possible to reduce the concentration of furan in a 'rum ether 10-fold', however not below 0. 01% if the typical flavour is to be maintained. Because the submission of February 2016 is based on a higher GC sensitivity (down to 0. 001 peak area %) than the submission of September 2016, the compositional data for the first submission were considered to provide a more complete insight into the presence of substances occurring at low concentrations. Therefore, these data will be used as basis for the subsequent risk assessment. However, there was also one constituent (i. e. acetaldehyde dimethylacetal, grey-shaded in Table  2 ) that has been reported in the submission of September 2016 which has not been listed in the submission of February 2016 (Table  1 ). This substance will also be included in the assessment.
f) Subsequently, each congeneric group is evaluated as if it were a single substance. The exposure to the congeneric group does not raise a safety concern at the intended levels of use if the exposure to the group is below the Threshold of Toxicological Concern (TTC), for the respective Cramer structural class assigned to the congeneric group (i. e. 1,800 lg/person per day for Cramer structural class I, 540 lg/person per day for Cramer structural class II and 90 lg/person per day for Cramer structural class III). If the exposure is above the TTC, a margin of safety is calculated based on available toxicity data. This margin of safety should be sufficiently large to conclude that there is no safety concern for this congeneric group. If specific data are available that contraindicate the use of the TTC concept for the group (e.
Safety assessment for long-term exposure per congeneric group Classifications into congeneric groups for rum ether and intake data for the congeneric groups are presented in Tables  3  and 4 , and in Sections 3. 2 and 3. 4. Within each congeneric group, metabolic data for one or more members of the group or for structurally related substances have been reported (Appendix D). For more details, reference is made to the JECFA or EFSA evaluations indicated in Appendix A. The established TTC for each congeneric group is compared to total intake for that congeneric group in rum ether, where applicable (Table  7 ).
004 Flavouring Group Evaluation Butan-1-ol Class I - No safety concern at MSDI approach MSDI approach MSDI approach MSDI approach MSDI approach MSDI approach MSDI approach MSDI approach. 85 Category 1 (SCF, 1992) (JECFA, 2000a) -Category A (CoE, Category A (CoE, (JECFA, 1997) Category B (CoE, Category B (CoE, Evaluated by JECFA before consideration required FID FLAVIS (FL) Flavour Information System (database) flame ionisation detector GC -OH 06. 057 03 1,1-Diethoxy-2-methylbutane O O 07. 052 Diacetyl O 07. 169 10 1-Hydroxypropan-2-one O OH 09. 002 -Propyl acetate O 09. 072 -Ethyl formate O 09. 248 Ethyl trans-2-O 09. 442 64 Ethyl pyruvate O O gas chromatography GI gastrointestinal GRAS Generally Recognised As Safe IOFI The International Organization of the Flavor Industry Class I Class II Class I Class I Class I Class I Class I JECFA The Joint FAO/WHO Expert Committee on Food Additives -------MS mass spectrometry MSDI maximised survey-derived daily intake NOAEL no observed adverse effect level OECD Organisation for Economic Co-operation and Development PoD Point of Departure SCF Scientific Committee on Food SPET single portion exposure technique TCA tricarboxylic acid cycles TTC threshold of toxicological concern WHO World Health Organization -O O 05 butenoate O 13.
Material and Methods The True Beam linear accelerator STx equipped with a HD120 MLC was accurately modelled with Geant4 application for emission tomography (GATE) platform using the confidential data package provided by Varian 1. Its corresponding validation was carried out using measurement of depth dose profile (PDD), lateral dose profiles and output factors for 6FF and 6FFF static fields ranging from 5x5cm 2 to 20x20cm 2. Small MLC fields ranging from 0. 5x0. 5 cm 2 to 3x3 cm 2 were used for this part of study. The jaws were positioned at 3x3 cm 2 for MLC fields less than 2x2 cm 2 and 5x5 cm 2 for the rest. Measurements, corresponding to these configurations, were performed in a water phantom at a source surface distance of 95 cm using microdiamond and E diode detectors. The dosimetric accuracy of the detectors and the dosimetric algorithms were compared against MC calculations that were considered as a benchmark.
G rapevine satellite virus (GV-Sat) is a recently discovered agent with a reported prevalence of about 3% in two grapevine collections in the United States  (1). Its genome (KC149510) harbors two open reading frames (ORFs) that may be translationally coupled through frameshifting. The largest protein shows weak homologies  (1)  with the P20 of Bamboo mosaic virus satellite RNA (2) and with the capsid protein of St. Augustine decline satellite virus (3) and Panicum mosaic satellite virus  (4). Double-stranded RNAs from grapevine cv. Askeri in Iran held in the collection of INRA (Domaine de Vassal, Marseillan, France) were purified, randomly amplified, and Illumina sequenced according to Candresse et al. (5). After quality trimming, contigs were assembled and annotated by BlastX and BlastN comparison with GenBank using the CLC Genomics Workbench 8 and in-house developed pipelines.
Analysis of multiple reads spanning the junction and of nonviral nucleotides found in that junction in some of the reads (6, 7) indicates that the correct genome ends correspond to nucleotides 32 and 1,049 of KC149510. In conclusion, the sequence reported here shows the existence of significant sequence variation in this agent and the demonstration of the existence of multimeric forms of GV-Sat in infected grapevines allows the proposal of revised genome ends of this poorly characterized virus. Accession number(s). The complete genomic sequence of GV-Sat reported here has been deposited in GenBank under accession number KY211673.
Rock glaciers were digitized as points located at the rock glacier toe using an on-screen map scale of 1 : 7000. Due to the lack of ground observation data about the dynamic status of rock glaciers, a validation process cannot be accomplished in this research. Nevertheless, the authors have performed measurements of rock glacier dynamics and active layer status within and near the study region (UGP UC, 2010) and have knowledge of additional geophysical evidence of several rock glaciers in the dry Andes  (Janke et al. , 2015). Although these observations cannot be used for independent validation since they were previously known to the authors, their direct evidence of rock glacier dynamic status and ground ice presence is generally consistent with our assessment based on remote-sensing imagery.
Response and predictor variables To build a response variable representing air temperature conditions, AAT was calculated using data from eleven weather stations for a time period of between 1 and 30 years. Temperature data for eight weather stations were provided by Chile's water administration, Dirección General de Aguas (DGA), and additional data were obtained from mining projects distributed throughout the study area. AAT for a particular year was calculated as the arithmetic average of that year's mean monthly temperatures. Since the consistency of elevation references (e. g. above sea level) of available weather station elevations was in doubt, consistent elevation values were extracted from ASTER GDEM. Only weather stations located above 2000 m a. s. l. and at least 100 km inland from the coast were selected in order to reduce oceanic influence  (Hiebl et al.
LMEMs are appropriate models when data are organized in hierarchical levels and observations are therefore grouped  (Pinheiro and Bates, 2000). Unlike ordinary linear regression models, LMEMs are able to account for the dependency among observations that arises from grouping  (Twisk, 2006). In a LMEM, the predictors can contain random and fixed effects. Random effects can be thought of as additional error terms or variation in coefficients that are tied to different grouping levels. In a climatic context, this may include a year's overall departure from long-term mean temperature, for example. Fixed effects are comparable to ordinary linear regression predictors whose coefficients do not vary by group. In this study, fixed and random effects were estimated by maximum likelihood (ML). AAT data were considered as being grouped by year (30 groups, one for each year).
(3) The overall fit of the LMEM was evaluated by examining the residual standard error (RSE) in • C. The LMEM implementation of the "nlme" package in R was used  (Pinheiro and Bates, 2000; R Core Team, 2012). The PISR, defined as the annual sum of direct and diffuse incoming solar radiation, was calculated using SAGA GIS version 2. 1. 0 (Conrad et al. , 2015)  and ASTER GDEM data. PISR was calculated at 10-day intervals with a half-hour time resolution between 04:00 and 22:00; latitudinal effects were accounted for. To simulate the extremely clear and dry skies, a lumped atmospheric transmittance of 0. 9 was used  (Gates, 1980).
Cross validation was repeated 100 times. The model was implemented using R and its package "gam" for generalized additive models  (Hastie, 2013). Performance was assessed using the "verification" package for ROC curves  (Gilleland, 2012)  and "sperrorest" for spatial cross validation  (Brenning, 2012). PFI predictions were created using the "RSAGA" package  (Brenning, 2011).
3 ). Of the 3575 rock glaciers in the inventory, 51 were removed from the data set due to their isolated or unusual location in order to prevent these from becoming influential points. Of the remaining 3524 rock glacier observations, 1909 were used as indicators of permafrost and 1615 as indicators of the absence of permafrost. In general, MAAT and PISR values at sites with permafrost were lower than the sites without permafrost. Exploratory analysis showed a clear trend towards a greater presence of intact rock glaciers at lower MAAT and lower PISR.
Additional research, in particular ground-truthing using a meaningful sampling design, is necessary in order to refine the present model and eliminate possible biases. Data availability. The PFI and MAAT prediction maps are available for downloading and visualizing at www. andespermafrost. com. Moreover, the rock glaciers inventory is accessible for downloading. Alternatively, the data can be downloaded through Pangaea server (doi:10. 1594/PANGAEA. 859332). The Supplement related to this article is available online at doi:10. 5194/tc-11-877-2017-supplement.
Acknowledgements. We thank the  Dirección General de Aguas (DGA) of Chile  for providing rock glacier inventories and weather data and for funding the compilation of rock glacier inventories by the authors in an earlier project. We acknowledge funding received from  CONICYT Becas Chile, University of Waterloo , and  NSERC  through scholarships awarded to  G. Azócar  and a  Discovery Grant  awarded to  A. Brenning. Edited by:  C. Hauck  Reviewed by: three anonymous referees.
Moreover, associated uncertainties generally exceed 10 m yr -1 (e. g. Seehaus et al. , 2015; Schwaizer, 2016) , which limits the reliability in slow-moving areas. The SMB field is another prerequisite for mass conservation. It is not directly measurable by remote sensing techniques. Sparse SMB records can be used to determine elevation gradients that are then extrapolated according to a regional DEM  (Farinotti et al. , 2009b). Otherwise, SMB records are exploited to validate parametric SMB approaches  (Möller et al. , 2016)  or more complex regional climate mod-els  (Lang et al. , 2015; Aas et al. , 2016). For ice-thickness measurements, a standardised, open-access database has recently been launched  (Gärtner-Roer et al. , 2014) , and its gradual growth already justified an updated release  (Gärtner-Roer et al.
, 2016). Despite this international effort, many thickness measurements still remain unpublished. In light of the continuously growing body of information, it becomes easier to gather the input fields for sophisticated thickness-reconstruction approaches. In this regard, we present a two-step approach that provides a physically based thickness field over entire glacier basins, ice fields or ice caps. The first step has limited input requirements (Sect. 2. 2). In the second step, additional velocity information is exploited to update and improve the thickness reconstruction in specific areas (Sect. 2. 3). A final interpolation of the basal topography is not required. For a set of three test geometries on Svalbard, the necessary input data were gathered (Sect. 3) and thickness maps are inferred (Sect.