dataesr/scientific-paragraphs-categorization huggingface dataset:
The  Rome Study  was supported by the  Associazione Italiana per la Ricerca sul Cancro (AIRC)   IG 2011 10491  and  IG2013 14220  to S. B. , and  Fondazione Veronesi  to S. B. The IARC Latin American study was funded by the  European Commission INCO-DC programme  ( IC18-CT97-0222 ), with additional funding from  Fondo para la Investigacion Cientifica y Tecnologica (Argentina)  and the  Fundação de Amparo à Pesquisa do Estado de São Paulo  ( 01/01768-2 ). We thank  Leticia Fernandez ,  Instituto Nacional de Oncologia y Radiobiologia ,  La Habana ,  Cuba and Sergio  and  Rosalina Koifman , for their efforts with the  IARC Latin America study São Paulo center. The  IARC Central Europe study  was supported by  European Commission 's  INCO-COPERNICUS Program  ( IC15-CT98-0332 ),  NIH/National Cancer Institute  grant  CA92039 , and the  World Cancer Research Foundation  grant  WCRF 99A28.
The  IARC Oral Cancer Multicenter study  was funded by grant  S06 96 202489 05F02  from  Europe against Cancer ; grants  FIS 97/0024 ,  FIS 97/0662 , and  BAE 01/5013  from  Fondo de Investigaciones Sanitarias, Spain ; the  UICC Yamagiwa-Yoshida Memorial International Cancer Study ; the  National Cancer Institute of Canada ;  Associazione Italiana per la Ricerca sul Cancro ; and the  Pan-American Health Organization. Coordination of the EPIC study is financially supported by the  European Commission (DG-SANCO)  and the  International Agency for Research on Cancer. The lung cancer genome-wide association analyses:  Transdisciplinary Research for Cancer in Lung (TRICL)  of the  International Lung Cancer Consortium (ILCCO)  was supported by ( U19-CA148127 ,  CA148127S1 ,  U19CA203654 , and  Cancer Prevention Research Institute of Texas   RR170048 ).
The Liverpool Lung project is supported by the  Roy Castle Lung Cancer Foundation. The  Harvard Lung Cancer Study  was supported by the  NIH (National Cancer Institute)  grants  CA092824 ,  CA090578 ,  CA074386. The Multi-ethnic Cohort Study was partially supported by  NIH  Grants  CA164973 ,  CA033619 ,  CA63464 , and  CA148127. The work performed in MSH-PMH study was supported by The  Canadian Cancer Society Research Institute  ( 020214 ),  Ontario Institute of Cancer and Cancer Care Ontario Chair Award  to R. J. H. and G. L. and the  Alan Brown Chair  and  Lusi Wong Programs  at the  Princess Margaret Hospital Foundation. NJLCS was funded by the  State Key Program of National Natural Science of China  ( 81230067 ), the  National Key Basic Research Program  Grant ( 2011CB503805 ), the  Major Program of the National Natural Science Foundation of China  ( 81390543 ).
The Norway study was supported by  Norwegian Cancer Society, Norwegian Research Council. The  Shanghai Cohort Study (SCS)  was supported by  National Institutes of Health   R01 CA144034  (PI: Yuan) and  UM1 CA182876  (PI: Yuan). The  Singapore Chinese Health Study (SCHS)  was supported by  National Institutes of Health   R01 CA144034  (PI: Yuan) and  UM1 CA182876  (PI: Yuan). The work in TLC study has been supported in part the  James & Esther King Biomedical Research Program  ( 09KN-15 ),  National Institutes of Health Specialized Programs of Research Excellence (SPORE)  Grant ( P50 CA119997 ), and by a  Cancer Center Support Grant (CCSG) at the H. Lee Moffitt Cancer Center and Research Institute , an  NCI designated Comprehensive Cancer Center  (grant number  P30-CA76292 ). The  Vanderbilt Lung Cancer Study-BioVU  dataset used for the analyses described was obtained from  Vanderbilt University Medical Center's BioVU , which is supported by institutional funding, the  1S10RR025141-01  instrumentation award, and by the  Vanderbilt CTSA  grant  UL1TR000445  from  NCATS/NIH.
The study in Lodz center was partially funded by  Nofer Institute of Occupational Medicine , under task  NIOM 10. 13 : Predictors of mortality from non-small cell lung cancerfield study. Genetic sharing analysis was funded by  NIH  grant  CA194393. The research undertaken by M. D. T. , L. V. W. , and M. S. A. was partly funded by the  National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the Department of Health. M. D. T. holds a  Medical Research Council Senior Clinical Fellowship  ( G0902313 ). The work to assemble the FTND GWAS meta-analysis was supported by the  National Institutes of Health (NIH) ,  National Institute on Drug Abuse (NIDA)  grant number  R01 DA035825  (Principal Investigator [PI]: DBH).
The study populations included COGEND (dbGaP phs000092. v1. p1 and phs000404. v1. p1), COPDGene  (dbGaP phs000179. v3. p2),  deCODE Genetics, EAGLE (dbGaP phs000093. vs. p2), and SAGE. dbGaP phs000092. v1. p1). See Hancock et al. Transl Psychiatry 2015 (PMCID:  PMC4930126 ) for the full listing of funding sources and other acknowledgments. The  Resource for the Study of Lung Cancer Epidemiology in North Trent (ReSoLuCENT)study  was funded by the  Sheffield Hospitals Charity ,  Sheffield Experimental Cancer Medicine Centre and Weston Park Hospital Cancer Charity. The ovarian cancer genome-wide association analysis: The  Ovarian Cancer Association Consortium (OCAC)  is supported by a grant from the  Ovarian Cancer Research Fund  thanks to donations by the family and friends of  Kathryn Sladek Smith  ( PPD/RPCI.
07 ). The scientific development and funding for this project were in part supported by the  US National Cancer Institute GAME-ON Post-GWAS Initiative  ( U19-CA148112 ). This study made use of data generated by the  Wellcome Trust  Case Control consortium that was funded by the  Wellcome Trust  under award  076113. The results published here are in part based upon data generated by The Cancer Genome Atlas Pilot Project established by the  National Cancer Institute and National Human Genome Research Institute  (dbGap accession number phs000178. v8. p7). The OCAC OncoArray genotyping project was funded through grants from the  U. S. National Institutes of Health  ( CA1X01HG007491-01 (C. I. A. ) ,  U19-CA148112 (T. A. S. ) ,  R01-CA149429  (C. M. P. ), and  R01-CA058598  (M. T. G. );  Canadian Institutes of Health Research  ( MOP-86727  (L.
Xerencia de Xestion Integrada de Vigo-SERGAS, Instituto de Salud Carlos III, Spain; Grant 10CSA012E, Consellería de Industria Programa Sectorial de Investigación Aplicada, PEME I + D e I + D Suma del Plan Gallego de Investigación, Desarrollo e Innovación Tecnológica de la Consellería de Industria de la Xunta de Galicia, Spain; Grant EC11-192. Fomento de la Investigación Clínica Independiente, Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain Programme under grant agreement n°223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978), and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065, and 1U19 CA148112.
Statistical analysis Statistical analysis was done with SPSS, version 17 (SPSS Inc. , USA). Mean values and standard deviations are presented for descriptive. To compare mean values of miRNAs in aorta and saphenous vein samples, paired sample t test was used. To compare mean values of miRNAs in relation to patients BMI independent sample t test was used. Exact P value of <0. 05 with 95% confidence interval was considered as statistically significant.
INTRODUCTION Renal cell carcinoma (RCC) represents 3% of all cancers  (1). The incidence of RCC has increased worldwide and in Europe  (2)  due to the use of abdominal imaging  (3). Surgical treatment such as radical and partial nephrectomy is recommended for T1-4 tumours. The improvement of surgical techniques and peri-operative care has diminished peri-operative morbidity  (4). However, nephrectomy remains at risk: studies suggest that the thirty-day mortality (TDM) after nephrectomy ranges between 0. 6% and 3. 6%  (5) (6) (7). It has been suggested that the TDM rate is related to age, disease stage and type of surgery  (7). To our knowledge, there is no data in the literature reporting outcomes and measurements of morbidity and mortality reviews after nephrectomy. Our objective was to determine the TDM rate after nephrectomy for cancer in a large multicentric database as well as identifying causes of and risk factors of death.
METHODS For every deceased patient, we looked at medical and MMR reports in order to find out about the analysis of the case and clinical conclusions. The SPSS®, version 20. 0 software package was used for all statistical analyses. Independentsample t-tests and chi-square tests were used for comparisons of quantitative and qualitative variables, respectively. Univariate and multivariate regression models were used to assess the influence of different variables on post-operative death.
We were very surprised to find only two MMR reports for the 35 patients. The first reason for our surprise was that MMRs are suggested by all surgical guidelines. The main aim is to improve patient' care and safety with a multidisciplinary reviewing of all death circumstances  (19). MMR can be seen as a "post-operative black box" similar to a flight recorder with the aim of facilitating the investigation of medical accidents and incidents. MMRs often take place in surgical environments and provides practical advice that participates to medical education  (20). Antonacci et al. (21)  suggested that in surgical departments, MMRs can decrease the mortality by 40%. We believe our results are of interest: data was extracted from a multicentric and prospectively maintained database in a modern era.
After approval from each institutional review board, we performed a retrospective study on a prospective database involving seven centers (Toulouse, Bordeaux, Poitiers, Rennes, Henri Mondor, Reims and Angers University Hospitals) part of the French network for research on kidney cancer UroCCR (ClinicalTrials. gov: NCT03293563). The data of patients who had been treated by surgery for a renal tumor between 2014 and 2017 were prospectively collected in the national registered institutional review board approved multicenter database UroCCR (CNIL agreement DR-2013-206). Upper-tract transitional cell carcinomas and non-cortical renal tumours (melanomas, sarcomas, and lymphomas) were not included. Pre and post-operative parameters such as age, sex, local symptoms (hematuria, lower back pain, abdominal mass) and general (fever, general status, paraneoplastic syndroms), cTNM stage, type of procedure (open, laparoscopic, robotassisted surgery), type of surgery (partial, radical nephrectomy), time to death and death cause were recorded.
Transmission electron microscopy of magnetosomes and by-products of their degradation The samples fixed at day 0 and day 21 were contrasted with Oolong Tea Extract (OTE) 0. 5% in 0. 1 M Na cacodylate buffer, post-fixed with 1% osmium tetroxide containing 1. 5% potassium cyanoferrate, gradually dehydrated in ethanol (30% to 100%) and gradually embedded in epoxy resins. Ultrathin slices (70 nm) were collected onto 200 mesh copper grids and counterstained with lead citrate prior to being observed with a Hitachi HT 7700 TEM operated at 80 kV and images were acquired with a charge-coupled device camera (AMT). The sizes of the nanoparticles at Day 0 (compared with the one of the magnetosomes in solution) and of the magnetosomes and iron deposits in the ferritins at Day 21 were measured by the Image J software (at least 400 diameters measured per condition).
XANES/EXAFS analysis (Synchrotron radiation) XAS measurements were performed on the biodegradation samples that consist in dense cellpellets easily transferable, by contrast with the independent cells of the biosynthesis model. Analyses were carried out at the CRG beamline BM25-SpLine of the European Synchrotron Radiation Facilities (ESRF) in Grenoble (France). The sample spectra were measured at the Fe K-edge (7112 eV) at room temperature in the fluorescence mode. The energy calibrated with the energy edge position of a Fe foil. Magnetite, ferrihydriteand maghemite standards were chosen as references (shown in Figure  S10 ). From 4 to 5 spheroids were used to obtain the sample spectra at different maturation time. Several scans were taken in order to obtain a good signal-to noise ratio. Data analysis was carried out using Athena program  4  identifying the beginning of the absorption edge, E0, and the pre-edge and post-edge backgrounds.
ACKNOWLEDGEMENTS This work was supported by the  European Union  ( ERC-2014 - CoG  project  MaTissE # 648779 ). The authors acknowledge the staff of the MPBT (physical properties -low temperature) plateform of  Sorbonne Université  for their support. We thank  Nathalie Luciani  and  Anouchka Plan Sangnier  for help and fruitful discussions. We acknowledge The  ESRF  for beamtime and the  CRG beamline BM25-SpLine personnel  for technical support, and AE acknowledges support from  Comunidad de Madrid  ( Talento  project  2018-T1/IND-1005 ).
For each image, four types of measurements were obtained using ImageJ, including mantle width (light blue), root diameter (green), Hartig net boundary (red), and root circumference (yellow). These measurements were used to calculate both the ratio of average mantle width to average root diameter and average Hartig net boundary to average root circumference. Bars 5 50 mm. (B) and (C) Summary plots of the ratios of both mantle width to root diameter (B) and Hartig net boundary to root circumference (C). These data revealed that sLCOs (n 5 109 root cross sections), but not nsLCOs (n 5 77), induced an increase (P-value < 0. 05) in ECM colonization compared with the mock treatment (n 5 111). For both graphs, bars represent the mean of the data and error bars represent the SE of the mean. The data were statistically analyzed by one-way ANOVA (see Supplemental File 1) with Tukey pairwise comparison to assign significance groups a and b (P-value < 0.
5 mL of RiboLock RNase inhibitor, and 1 mL of RevertAid reverse transcriptase (Thermo Fisher Scientific). After incubating for 1 h at 42°C, the transcriptase was inactivated at 70°C for 15 min, and the resulting cDNA was stored at 220°C. RT-qPCR was performed using the Bio-Rad CFX96 real-time PCR system with the CFX Manager v3. 0 software (Bio-Rad). Reactions were performed in 96-well plates using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad), 10 nM of each gene-specific primer (Supplemental Table  3 ), and 1:20 (v/v) cDNA:water. PCR cycling conditions were as follows: 95°C for 3 min, followed by 40 cycles at 95°C for 10 s, 58°C for 25 s, and 72°C for 30 s. The specificity and efficiency of primer pairs were confirmed by analysis of dissociation curves (65 to 95°C) and serial dilution, respectively.
ACKNOWLEDGMENTS We thank  Sarah Swanson  for training and support with confocal microscopy techniques,  Francis Martin  for providing L. bicolor strain  S238N ,  Giles Oldroyd  for providing the binary vector pEC11579 carrying  G-GECO ,  Fabienne Maillet  for providing purified sLCOs and nsLCOs, and  Hugues Driguez  for providing synthetic LCO standards for mass spectrometry. Financial support for this project was primarily provided by the  USDA  (grant  WIS01695 ) and  National Science Foundation (NSF ; grant  DGE-1256259 ). Additional financial support was provided by the  U. S. Department of Energy (DOE ; grant  DE-SC0018247 ) and  NSF  (grants  1546742  and  1331098 ) for partial analysis of Ca 21 spiking; the  French Agence Nationale de la Recherche  (contract  ANR-14-CE18-0008-01 ) and the  Laboratoire d'Excellence entitled TULIP  (grant  ANR-10-LABX-41 ) for the mass spectrometry analyses with additional support from the  ICT-Mass Spectrometry  and MetaToul facilities, and from the  MetaboHUB - ANR-11-INBS-0010  network; the  Tree Genomics and Biosafety Cooperative at Oregon State University  and the  NSF Center for Advanced Forestry Systems  (grant  1238305 ) for partial support of RNAi line development; the  Plant-Microbe Interfaces Scientific Focus Area in the Genomic Science Program , the  Office of Biological and Environmental Research in the DOE Office of Science (Oak Ridge National Laboratory  is managed by  UT-Battelle, LLC , the  DOE  (contract  DE-AC05-00OR22725 ) for the AM colonization assays; and from the  USDA  (grant  2017-67014-26530  to H.
2017 ). To avoid bias, these 500 markers were then removed from all downstream analysis. Linkage disequilibrium was calculated as squared allele frequency correlations (r 2 ) in TASSEL V 5. 0 software  (Bradbury et al. 2007 ), using the Mb position of the markers along the bread wheat reference genome. Linkage disequilibrium (LD) decay was estimated and plotted using the BNeanderthal^method (Jujumaan 2017). The LD decay was measured at 51. 3 Mb for r 2 < 0. 2 (Fig. S1 ). F 7 seeds of the Faraj/Gidara 2 mapping population were grown under greenhouse conditions at the University of Jordan. Fresh leaves were collected and used for CTAB DNA extraction, and the DNA was then provided to TraitGenetics (Germany) for genotyping with a proprietary 20K SNP array. This array includes the most polymorphic Illumina 90K and Axiom 35K sequences.
Data analysis Seedling screening for HF relied only on scores that showed consistent results (resistant or susceptible) across all the 20 tested seedlings. Any genotype for which the score deviated from the others was repeated. Hence, perfect fit statistics was deemed unnecessary for the HF seedling screening. For field performances of the mapping population, best linear unbiased estimators (BLUEs) were obtained for each individual environment using the DAU. test option of the agricolae package (De Mendiburu 2016) on RStudio v1. 0. 136. Significance of the resistant vs. susceptible classes was obtained by grouping genotypes on the basis of their HF response in seedling, and considering the BLUE of the genotypes as replicates. The grain yield performances were then investigated among classes within environment by ANOVA using the lm and anova options of RStudio v1.
0. 136. Since significance at 0. 01 level was identified for the classes, the F-protected option LSD test of the package agricolae (De Mendiburu 2016) on RStudio v1. 0. 136 was used to determine actual LSD differences between classes in each environment. Genome-wide association analysis was conducted for the HF seedling score among the set of 159 modern lines, using the kinship matrix developed by  Kabbaj et al. (2017)  for this panel, but including only two subgroups (clusters 7 and 9). The PROC-GLM function including the kinship matrix indicated above (Q + K) was used on the software TASSEL  (Bradbury et al. 2007 ) to identify marker-traits associations. The value calculated for the LD decay of 51. 3 Mb (Fig. S1 ) indicated that this association panel interrogated the 12,000 Mb of the durum wheat genome via 248 marker-traits hypothesis, and hence this panel has a Boneforroni correction as suggested by  Duggal et al.
2005)  at LOD of 3. 0 were used to remove any marker that did not fit the order. Then flips with a window size of 9 and LOD of 3. 0 was used to adjust the short distance errors in marker order. Thus, a map containing 1267 markers assigned to the 14 durum chromosomes was considered as the anchor on the software QTL IcIMapping v4. 1  (Wang et al. 2016 ). The remaining markers were grouped at LOD of 5. 0 and mapped using the By Anchor Order option. The resulting genetic map is used here even though, due to physical anchoring, the centromeric recombination distortions were not accounted for (see result section for details). The associations between markers and two phenotypes were analyzed in the same software using SMA option for single marker analysis and ICIM option for composite interval mapping with a step size of 0.
The plasma-facing surfaces of the first wall (FW) and divertor will be made of beryllium (Be) and tungsten (W) respectively. Tritium retention in ITER will mainly be driven by co-deposition with Be eroded from the first wall as observed experimentally in JET operating with the ITER material mix  [5]. Predictions of retention rates during nuclear operations have been performed in recent years using the WallDYN code  [6, 7]. However, previous simulations used SOLPS solutions that had carbon as a divertor radiator when calculating the plasma background, which is now no longer the case, since ITER operations will commence with a full W divertor  [8]. During initial pre-fusion power operations (PFPO), hydrogen and helium will be used in order to demonstrate the technical performance of ITER before fusion power operation (FPO).
In the original ITER Research Plan (IRP), there was limited information on plasma-wall interactions (PWI), especially during the non-active operation phases. As part of the recent update of the IRP  [9] , the WallDYN code is used in order to produce data on co-deposition and fuel retention for hydrogen plasmas in non-active ITER operations. For the purpose of this study, three SOLPS-4. 3 simulations for hydrogen L-mode plasmas, q 95 =3, P SOL =20 MW, and different SOL densities are considered. During FPO, neon (Ne) is a candidate seeding impurity for divertor power flux control  [10]. To investigate the T retention and Be deposition profiles for Ne seeded plasmas, a series of plasma backgrounds generated by the SOLPS-4. 3 code with all-metal plasma-facing surfaces  [10, 11]  are used as the basis for WallDYN simulations.
WallDYN is a 2D code and flat surfaces at the apex of the wall elements are assumed. The SOLPS-4. 3 solutions do not include classical drifts and correspond to a fueling scan for high power Q DT = 10 operation with power into the inner core boundary of the computational grid of P SOL = 100 MW. Varying strengths of Ne gas injection provide concentrations in the range c Ne = 0. 4-1. 3%, which are the average concentration on a poloidal ring defined by the first flux surface inside the separatrix  [7]. The fuel throughput scan yields divertor states ranging from high recycling through to partial detachment, corresponding to a range of sub-divertor neutral pressures. In this study, high recycling and partially detached cases with a constant c Ne = 0. 8% are considered. Since the physics basis is still too weak to know with confidence what the ITER burning plasma far-SOL will look like, various scenarios and their effect on the erosion and deposition profiles are considered.
3  [15] , which consists of the B2 2D Braginski plasma fluid code coupled to the Eirene Monte Carlo neutral kinetic code. The SOLPS solutions do not extend to the main wall, so extrapolations are made based on a range of different considerations from the ITER Heat and Nuclear Load Specifications (HNLS)  [16]. The extrapolation to the wall is made in 2D, with the last point being the apex of the wall element. Flat surfaces at the apex of the wall elements are assumed. Note that the divertor solution from SOLPS is used directly since the OSM model cannot capture the complexities of the high recycling or detached plasma  [17]. For more detailed information about how OSM extrapolations, please see  [1]. Given that the far-SOL plasma conditions in ITER are subject to large uncertainty and because WallDYN studies are used to evaluate fuel retention (most importantly, T) and Be migration rates in support of design activities and preparation for operations, it is important that a range of assumptions are considered (Table  1 ), so that envelope estimates can be provided.