dataesr/scientific-paragraphs-categorization huggingface dataset:
We acknowledge NHS funding to the  Royal Marsden/ICR NIHR Biomedical Research Centre. The authors thank the WHI investigators and staff for their dedication and the study participants for making the program possible. Institutes of Health , the  National Cancer Institute  (grant number  Z01-CP010119 ), and the  National Institute of Environmental Health Sciences  (grant number  Z01-ES049030 ). The work of the BBCC was partly funded by  ELAN-Fond of the University Hospital of Erlangen. The BCEES was funded by the  National Health and Medical Research Council, Australia  and the  Cancer Council Western Australia  and acknowledges funding from the  National Breast Cancer Foundation (JS). For the  BCFR-NY, BCFR-PA, BCFR-UT  this work was supported by grant  UM1 CA164920  from the  National Cancer Institute.
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 ; and Grant  FEDER-Innterconecta. Ministerio de Economia y Competitividad, Xunta de Galicia, Spain. CBCS is funded by the  Canadian Cancer Society  (grant #  313404 ) and the  Canadian Institutes of Health Research. CCGP is supported by funding from the  University of Crete. The CECILE study was supported by  Fondation de France ,  Institut National du Cancer (INCa) ,  Ligue Nationale contre le Cancer ,  Agence Nationale de Sécurité Sanitaire, de l' Alimentation, de l'Environnement et du Travail (ANSES) ,  Agence Nationale de la Recherche (ANR).
The collection of cancer incidence data used in the California Teachers Study was supported by the  California Department of Public Health  pursuant to California Health and Safety Code Sect. 103885;  Centers for Disease Control and Prevention's National Program of Cancer Registries , under cooperative agreement 5NU58DP006344; the National and the Australian Cancer Database. The MEC was supported by  NIH  Grants  CA63464 ,  CA54281 ,  CA098758 ,  CA132839  and  CA164973. The MISS study was supported by funding from  ERC -2011-294576 Advanced grant,  Swedish Cancer Society   CAN 2018/675 ,  Swedish Research Council, Local hospital funds, Berta Kamprad Foundation   FBKS 2021-19 ,  Gunnar Nilsson. The MMHS study was supported by  NIH  grants  CA97396 ,  CA128931 ,  CA116201 ,  CA140286  and  CA177150.
The  Carolina Breast Cancer Study (NCBCS)  was funded by  Komen Foundation , the  National Cancer Institute  ( P50 CA058223 ,  U54 CA156733 ,  U01 CA179715 ), and the  North Carolina University Cancer Research Fund. The NHS was supported by  NIH  grants  P01 CA87969 ,  UM1 CA186107 , and  U19 CA148065. The NHS2 was supported by  NIH  grants  UM1 CA176726  and  U19 CA148065. The PBCS was funded by  Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Genotyping for PLCO was supported by the  Intramural Research Program  of the  National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the  Intramural Research Program of the Division of Cancer Epidemiology and Genetics  and supported by contracts from the  Division of Cancer Prevention, National Cancer Institute, National Institutes of Health.
The SASBAC study was supported by funding from the  Agency for Science, Technology and Research of Singapore (A*STAR) , the  US National Institute of Health (NIH)  and the  Susan G. Komen Breast Cancer Foundation. The SBCS was supported by  Sheffield Experimental Cancer Medicine Centre and Breast Cancer Now Tissue Bank. SEARCH is funded by  Cancer Research UK  [ C490/A10124 ,  C490/A16561 ] and supported by the  UK National Institute for Health Research Biomedical Research Centre  at the  University of Cambridge. The  University of Cambridge  has received salary support for PDPP from the  NHS in the East of England through the Clinical Academic Reserve. The Sister Study (SISTER) is supported by the  Intramural Research Program  of the  NIH, National Institute of Environmental Health Sciences  ( Z01-ES044005  and  Z01-ES049033 ).
Genotyping of the OncoArray was funded by the  NIH  Grant  U19 CA148065 , and  Cancer Research UK  Grant  C1287/A16563  and the  PERSPECTIVE  project supported by the  Government of Canada through Genome Canada  and the  Canadian Institutes of Health Research  (grant  GPH-129344 ) and, the  Ministère de l'Économie, Science et Innovation du Québec through Genome Québec  and the  PSRSIIRI-701  grant, and the  Quebec Breast Cancer Foundation. Funding for iCOGS came from: the  European Community 's  Seventh Framework 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 -the  GAME-ON initiative ), the  Department of Defence  ( W81XWH-10-1-0341 ), the  Canadian Institutes of Health Research (CIHR)  for the  CIHR Team in Familial Risks of Breast Cancer , and  Komen Foundation  for the Cure, the  Breast Cancer Research Foundation , and the  Ovarian Cancer Research Fund.
The ESTHER study was supported by a grant from the  Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the  German Cancer Aid (Deutsche Krebshilfe ). PROCAS thank  NIHR  for funding. The GENICA was funded by the  Federal Ministry of Education and Research (BMBF)   Germany grants   01KW9975/5 ,  01KW9976/8 ,  01KW9977/0  and  01KW0114 , the  Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ) ,  Heidelberg , the  Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA) ,  Bochum , as well as the  Department of Internal Medicine ,  Johanniter GmbH Bonn ,  Johanniter Krankenhaus ,  Bonn, Germany.
The GESBC was supported by the  Deutsche Krebshilfe  e. V. [70492] and the  German Cancer Research Center (DKFZ). The KARMA study was supported by  Märit and Hans Rausings Initiative Against Breast Cancer. The KBCP was financially supported by the special Government Funding (VTR) of  Kuopio University Hospital  grants,  Cancer Fund of North Savo , the  Finnish Cancer Organizations , and by the strategic funding of the  University of Eastern Finland. kConFab is supported by a grant from the  National Breast Cancer Foundation , and previously by the  National Health and Medical Research Council (NHMRC) , the  Queensland Cancer Fund , the  Cancer Councils of New South Wales, Victoria , Tasmania and  South Australia , and the  Cancer Foundation of Western Australia. Financial support for the AOCS was provided by the  United States Army Medical Research and Materiel Command  [ DAMD17-01-1-0729 ],  Cancer Council Victoria, Queensland Cancer Fund, Cancer Council New South Wales, Cancer Council South Australia , The  Cancer Foundation of Western Australia, Cancer Council Tasmania  and the  National Health and Medical Research Council of Australia  (NHMRC; 400413, 400281, 199600).
G. C. T. and P. W. are supported by the  NHMRC. RB was a  Cancer Institute NSW Clinical Research Fellow. LMBC is supported by the 'Stichting tegen Kanker'. DL is supported by the  FWO. The MARIE study was supported by the  Deutsche Krebshilfe e. V. [ 70-2892-BR I, 106332 ,  108253 ,  108419 ,  110826 ,  110828 ], the  Hamburg Cancer Society , the  German Cancer Research Center (DKFZ)  and the  Federal Ministry of Education and Research (BMBF) Germany  [ 01KH0402 ]. The MCBCS was supported by the  NIH  grants  R35CA253187 ,  R01CA192393 ,  R01CA116167 ,  R01CA176785  a  NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer  [ P50CA116201 ], and the  Breast Cancer Research Foundation. The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria.
88 0. 96 0. 62 0. 23 0. 22 0. 87 0. 25 0. 95 0. 10 0. 11 0. 10 1. 00 (0. 97-1. 03) 1. 00 (0. 97-1. 04) 1. 00 (0. 96-1. 03) 1. 00 (0. 97-1. 04) 1. 00 (0. 97-1. 04) 1. 03 (0. 96-1. 10) 1. 00 (0. 98-1. 02) 1. 00 (0. 98-1. 03) 1. 00 (0. 97-1. 03) 1. 01 (0. 98-1. 04) 1. 01 (0. 99-1. 02) 1. 01 (0. 99-1. 02) 0. 98 (0. 92-1. 05) 1. 04 (0. 97-1. 12) 1. 00 (0. 98-1. 02) 1. 02 (1. 00-1. 05) 1. 02 (1. 00-1. 05) 1. 02 (1. 00-1. 05) 0. 08 0. 08 0. 08 0. 08 0. 08 0. 02 0. 29 0. 14 0. 16 0. 91 0. 50 0. 50 0. 02 0. 02 0. 29 0. 86 0. 86 0. 86 A/T AT/A A/G T/G T/C C/T GC/G A/G G/C T/G C/T C/A G/C C/T GC/G C/T ACC TCG T GAT CCG C/A G/T 129,833,111 129,832,988 129,841,483 129,843,663 129,834,483 21,112,413 105,120,195 28,326,896 28,339,154 41,912,024 7,713,860 7,716,250 26,302,665 21,112,413 105,120,195 24,594,430 24,600,947 24,592,857 2 2 2 2 2 7 8 9 9 17 18 18 21 7 8 13 13 13 Overall breast cancer risk Number of full-term pregnancies (per 1 birth) rs10928872 Number of full-term pregnancies (per 1 birth) rs36064687 Number of full-term pregnancies (per 1 birth) rs79929694 Number of full-term pregnancies (per 1 birth) rs77107485 Number of full-term pregnancies (per 1 birth) rs79722231 Age at menarche (per 2 years) rs73277506 Current smoking (yes/no) rs11322161 Adult height (per 5 cm) rs80018847 Adult height (per 5 cm) rs1360506 Adult height (per 5 cm) rs1237669 OC use (yes/no) rs147290549 OC use (yes/no) rs664040 Current smoking rs75489324 (yes/no) ER + breast cancer risk Age at menarche (per 2 years) rs73277506 Current smoking (yes/no) rs11322161 1 receptor positive breast cancer risk Build 37 Position Age at menarche (per 2 years) rs4770552 2 Reference/Alternate alleles in Europeans (forward strand) 3 Effect allele frequency in controls in OncoArray dataset Age at menarche (per 2 years) rs113684695 4 Bayesian False Discovery Probability at prior probability of 1 × 10 -05 Age at menarche (per 2 years) rs9510997 SNP: Single Nucleotide Polymorphism; Chr: Chromosome; Ref: Reference Allele; Alt: Alternative Allele; EAF: Effect Allele Frequency; OR marg : SNP Marginal Odds ratio; CI: Confidence Interval; P marg : Marginal p-value (metaanalyzed); OR int : Interaction p-value (meta-analyzed); P int : Interaction p-value; BFDP: Bayesian False Discovery Probability; ABF: Approximate Bayes Probability; OC use: Ever use of oral contraceptives; ER.
Introduction Fire is a major hazard in many industrial sectors  [1]  and especially in nuclear power plants (NPPs)  [2, 3]. Fire can affect the main safety functions of the NPPs such as the removal of the thermal power, the containment of radioactive substances or the protection measures  [4, 5]. International operating experience has also shown that some of the most challenging precursors to a core damage accident have involved internal fires  [6] [7] [8]. About 550 fire events from NPPs were recorded in the most recent version of the OECD/NEA FIRE (Fire Incidents Records Exchange) Database for the period from the 1980's to the end of 2017  [9]. In addition, Werner et al. [10]  mentioned that transformers and electrical cabinets were the most frequent fire sources with 47 and 45 fire events, respectively, that were recorded in the 2011 version of the above database  [11].
In these areas, electrical cabinet fires could for instance damage nearby cables supporting important mitigating equipment or lead to the failure of a safety train  [10]. The work  [22]  especially mentions that 47 electrical cabinet fire events, recorded in the database collected by EPRI and the U. S. NRC from NPPs across USA over 2000-2014  [23, 24]  were classified as potentially challenging or greater. Challenging fires are specified as fires which have observable and significant repercussions for the environment outside the initiating source. In another nuclear facilities, such as in the most complex particle accelerator facility, operated by CERN, a design fire calculator was recently developed to assess the fire hazard related to the numerous configurations of electrical cabinets and racks found in this facility  [25].
Finally, carbon dioxide (CO 2 ), carbon monoxide (CO) and oxygen (O 2 ) concentrations, gas temperatures, pressures and volume flow rates were measured in the exhaust ventilation duct of the large-scale calorimeter of the SATURNE facility, as well as in the rooms and ventilation network of the DIVA facility. These measurements were especially used in this study to assess the heat release rate (HRR) of the four fire tests. Finally, the data acquisition system for the entire series of measurements had a sampling time set at 1 s.
Acknowledgements This work was performed by  IRSN  in the frame of the  OECD /NEA  PRISME-3  Project with the support of the following international partners:  ENGIE  and  Bel V (Belgium) ,  VTT (Finland) ,  DGA  and  EDF (France) ,  GRS  and  iBMB (Germany) ,  CRIEPI and NRA (Japan) ,  KAERI (Korea) ,.
The TARGET (Tumor characterization to Guide Experimental Targeted therapy) study was one of the first study to capitalize on the use of ctDNA, relative to tissue-based testing, to identify clinically actionable genomic alterations in early phase clinical trial patients  [75]. Actionable mutations were identified in 41% patients (n ¼ 100) of whom 11% received a matched therapy. Analysis of matched tissue samples showed good concordance between the tissue and blood-based assays. Different national program to implement inclusion in early phase clinical trials based on multiplex NGS-based assays are now available. The gusSTave roussy cancer profilING (STING) is a biology driven, multicenter phase I study (NCT04932525) designed to identify actionable molecular alteration in adults with solid malignant tumors or CHIP, Clonal Hematopoiesis of indeterminate potential; MTB, Molecular Tumor Board.
The InVisionFirst-Lung test, an amplicon-based NGS panel spanning 37-NSCLC related genes, was used to perform a LB in Arm A. When LB results were useful, treatment was started based on: InVisionFirst-Lung alone for patients with EGFR, BRAF V600E mutation, ALK or ROS1 rearrangement (category 1 alterations); InVisionFirst-Lung and pathological results for patients with ERBB2, MET exon 14, KRAS, BRAF non V600 and/or LKB1 mutation, RET or NTRK rearrangement (category 2 alterations). The time from randomization to the start of the proper treatment, as determined by relevant genomic and pathological data, served as the main outcome. Compared to tissue-matched targeted therapy, LB-matched targeted therapy reduced the time to treatment beginning in this study (NCT03721120). Recently, Bayle et al. highlighted the importance of MTB and the issue of clinical trials requiring molecular aberrations to only be identified through tissue sequencing  [80].
This led to a significant proportion (13%) of untreated patients in the study, encouraging for higher flexibility of inclusion criteria or, even more important, emphasizing the need of trials evaluating the clinical benefit of patients treated by targeted treatment based on an alteration identified in ctDNA but not in tumor tissue. The Blood First Assay Screening Trial (BFAST) was the first bloodbased NGS genomic survey of a large cohort of treatment-naive advanced or metastatic NSCLC designed to prospectively evaluate the relationship between blood-based biomarkers and clinical activity of targeted therapies or immunotherapy in patients with screened for actionable genetic alterations. Data from the ALK-positive and ROS1positive cohort have been reported, supporting clinical value of bloodbased testing to inform clinical decision making in oncogene-addicted NSCLC (NCT03178553).
20% of patients had MRD after the surgery and 62% of patients achieved a clearance of ctDNA after adjuvant chemotherapy which correlated with a longer DFS, primary endpoint of the study  [87]. Overall, the detection of ctDNA after surgical intervention for various localized cancers holds significant value in identifying patients at a high risk of relapse and guiding decisions regarding adjuvant treatment. In such cases, ctDNA measurement can provide valuable insights for implementing multimodal oncological treatment strategies. An exhaustive list of ongoing studies assessing MRD in NSCLC is reported in Table  3. The Observation of ResiduAl Cancer with Liquid Biopsy Evaluation (ORACLE) prospective trial (NCT0505944) is a large surveillance MRD study including around 1000 patients with different solid tumors.
In this context, the Gustave Roussy Cancer Profiling (STING) study (NCT04932525) was designed to identify actionable molecular alterations in cancer patients and to explore mechanisms of sensitivity and resistance to anticancer by the means of high throughput molecular analysis. 1772 patients with metastatic diseases underwent a liquid biopsy as part of their therapeutic management in 2021, with only a low rate of sample failure (6%) and a median turnaround time of 12 days between request and ctDNA results. Most of the patients had digestive (n ¼ 505, 28. 5%), thoracic (n ¼ 326, 18. 4%), genitourinary (n ¼ 313, 17. 7%) or breast tumors (n ¼ 171, 9. 7%). 72% had already received at least one line of systemic treatment. At least one actionable genetic aberration was found in the 64% of the patients; in the 31% of the cases, it was classified es ESCAT I or II.
Data collection Maternal characteristics included maternal age, parity, body mass index (BMI) before pregnancy and the presence of comorbidities (preexistent diabetes or arterial hypertension). We also reported informations about previous cesarean (term, cesarean indication), previous vaginal deliveries and current pregnancy pathologies (diabetes, preeclampsia, suspected macrosomia, cholestasis of pregnancy, intra uterine growth restriction, praevia placenta, fetal pathology, maternal pathology). Indication of labor induction (previously detailed), methods of labor induction (double-balloon catheter, osmotic cervical dilatators, amniotomy, oxytocin), used of intravenous oxytocin (introduced before or after a dilatation of 5 cm), presence of maternal fever (superior to 38 • C) or meconial amniotic liquid and mode of delivery (vaginal spontaneous birth, vaginal instrumental birth or cesarean section) were collected.
Here, we consider turbulent time series from fully developed turbulence and apply, among the different methods that can be found in the literature, a method involving triple correlations. This has been first proposed by Pomeau  [17]  in statistical physics, and a similar approach has been later published also in the field of econometrics by Ramsey and Rothamn  [19]. Below, in the next section, we first present these methods and propose a straightforward generalization; we further present the two databases of measured fully developed turbulence to which these methods are applied. The next sections present the results and discussions.
Presentation of the Databases For the test of the above-mentioned indicators, two fully developed turbulence databases were chosen. The first one is recorded using the IFREMER wave and current flume tank (Boulogne-sur-mer, France). The flow turbulent velocity is measured using a Laser Doppler Velocimeter during three hours with an irregular sampling rate that goes up to 900 Hz. The working section of the system is 4 m in width by 2 m in depth and 18 m in length. The streamwise flow velocity has a mean value of < U >= 0. 8 m/s, a turbulence intensity (ratio of the standard deviation of the streamwise component to its mean value) of 20%, a dissipation rate value of = 1. 79 × 10 -2 m 2 s -3 and a Taylor-based Reynolds number of R λ = 487  [24]. The data are freely available through the SEANOE depository  [25].
We consider here the data resampled at a fixed resolution of 100 Hz and a number of datapoints of N = 2 19. The second database is a wind tunnel database of turbulence velocity time series obtained from an experimental homogeneous and nearly isotropic turbulent flow from Johns Hopkins University  [26]. We consider here time series recorded at downstream x/M = 20, where M is the mesh size. The sampling frequency is f 0 = 40 kHz, the mean velocity < U >= 12 m/s, the rms velocity is 1. 85 m/s and the Taylor-based Reynolds number R λ = 720. The total duration analyzed is 30 s, and the number of data points per channel is 1. 2 × 10 6. Since there are 120 realizations and four channels, the total number of data points at this location is 5. 76 × 10 8. The scaling properties of this time series have already been studied using different methods and found to be visible over two decades  [27, 28].
As a first analysis of both these datasets, Fourier spectral analysis is shown in the log-log plot in Figure  1 , where f 0 in both cases is the sampling frequency (100 Hz for the flume tank and 40 kHz for the wind tunnel databases). A dotted line of slope -5/3 is shown for reference: in both cases, a scaling property is visible over more than two decades. Let us introduce here the classical structure functions of order 2: < (∆u) 2 > where ∆u = u(t + τ)u(t) (structure functions in time and we are still using Taylor's frozen turbulence hypothesis). Direct calculation shows that 1 -C u (τ) =< (∆u) 2 > /(2σ 2 u ). In the framework of intermittent multifractal turbulence, < (∆u) 2 >∼ τ α with α 2/3. Hence, the plot of 1 -C u (τ) shows, in real space, the scaling properties of the structure functions and also their scaling range.
This is displayed in Figure  2  for both databases: the scaling range is still clearly visible, with an amplitude which is slightly smaller in the real space compared to the spectral space (this has been studied and explained in other works  [5] ). The scale range from r = 10r 0 to 400r 0 (where r 0 is the time resolution of the measurements) is here the visible scaling inertial range, which is used further for triple correlation analyses.
Results: Triple Correlation Analyses and Time Reversal Symmetries We first display the two indicators G + and G -corresponding to Equations (  1 ) and (  2 ), normalized by the third-order moment < u 3 >, in Figure  3a, b. It is visible that these two functions do not coincide in both cases, indicating a time-reversal asymmetry. They join at a large scale. Since there are more data for the wind tunnel case, the curves are smoother for this database. In order to emphasize the scaling properties, their difference (still with the normalization of the third-order moment) Po = G + -G -is represented in the log-log plot in Figure  4. It is seen in both cases that a scaling behaviour is found with slopes of approximately 0. 9 for the wind tunnel and 1. 1 for the flume tank databases. The scaling ranges seem to be slightly different from the structure functions.
We perform this by representing Ψ(r, ρr) vs. r for different values of ρ from 1 to 100. This is illustrated in Figure  7a ,b for both databases displayed for four different values of s: s = 3, 5, 10 and 20. A common feature is found for both curves: their scaling slope is the same (the curves are parallel); there is a maximum value after which the curve is decreasing, and this maximal value is found for smaller values of r as s increases; finally, the maximum value of the indicator is increasing when s is increasing. In each case, we record the maximum value Ψ max = max r {Ψ(r, ρr)}, the location of this maximal value r m such that Ψ(r m , ρr m ) = Ψ max and also the location for which the indicator is first reaching 0: Ψ(r e , ρr e ) = 0. These variables are shown in Figure  8a, b  (only for the wind tunnel database, but the results are the same for the flume tank database).