dataesr/scientific-paragraphs-categorization huggingface dataset:
A key-point was that RILA significantly predicted less toxicity with increasing values when taken as a continuous variable, leading to the integration of RILA in a multifactorial nomogram  [23]. Larger datasets using uniform laboratory and clinical protocols are needed to incorporate RILA assay data into statistical models including the known prognostic patient, treatment and genetic factors. A barrier to collection of larger datasets is the lack of information on the transferability of the assay between laboratories. A previous study which compared the results from two different laboratories on 25 head and neck cancer patients found good agreement, but larger studies in more centres and cancer types are needed  [24]. REQUITE is an EU-funded multi-centre observational study that has recruited >4400 patients with breast, lung or prostate cancer and is following them for at least two years to score radiotherapy side effects  [25].
Acknowledgements This work was supported by funding from the  European Union Seventh Framework Programme  for research, technological development and demonstration under grant agreement no. 601826  (REQUITE). We gratefully acknowledge the contribution of the physicians, study nurses and patients enrolled into the REQUITE study. These include in the UK:  Teresa Beaver ,  Kiran Kancherla ,  Sarah Nicholson  and  Tim Rattay ; Germany:  Elena Sperk ,  Anke Keller ,  Annette Kipke ,  Katharina Fleckenstein ,  Martina Ottstadt ,  Michael Ehmann ,  Gritt Welzel ,  Daniel Bürgy ,  Yasser Aboumadian ,  Chrisian Neumaier ,  Katharina Heim ,  Mario Grimm ,  Anna Simeonova ,  Frank Giordano ,  Elisabeth Heesch ,  Georg Lars Hildenbrand ,  Benjamin Gauter-Fleckenstein ,  Tina Reis ,  Sabine Mai ,  Christiane Zimmermann ,  Stefanie Kolb  and  Markus Mihalko , in France:  Jean-Pierre Bleuse ,  Laura Bourillon ,  Roxana Draghici ,  Anne Fenoglietto  and  Julie Grataloup.
Nowadays, monitoring programs including the combination of measurements and simulations seem to represent the best choice in terms of data reliability, spatial/time coverage and costs  [3]. For this reason they continue to receive an increasing attention from coastal scientists and managers, providing benefits for beach management, risk assessment and coastal planning  [4] [5] [6]. In this work, we assess the performance of an operational wave system in the Mediterranean Sea by comparing computed wave data with three measured wave datasets, one of them obtained in the nearshore and the others in intermediate and deep waters. The nearshore data were collected in the Gulf of Cagliari, Southern Sardinia, where this study focuses. In recent decades, the coastal areas of the Gulf of Cagliari have been experiencing an intense urbanization with the development of the city of Cagliari and its hinterland (about 500,000 inhabitants).
Residential development in addition to industrial activities have led to significant human pressure on the two beach systems, Giorgino and Poetto, included in the metropolitan area of Cagliari. A monitoring program, including periodic field surveys and video image acquisition, has been set up and implemented in the last five years at Poetto beach by the Coastal and Marine Geomorphology Group (CMGG) of the University of Cagliari  [7]. As a specific task of the beach monitoring program, a field experiment was carried out in spring 2017 to collect nearshore wave and current data. We use these nearshore data, together with intermediate and deep water buoy data proceeding from the French agencies Cerema and Meteo France, to assess the reliability of an operational wave system running for the entire duration of the experiment.
Field Wave Data Field measurements were carried out over a 6-week period at Poetto beach, from 30 March to 16 May 2017. Presented within this work are data collected by an Acoustic Wave And Current (AWAC) profiler deployed in the nearshore at a water depth of 18 m. The deployment location was chosen by scuba divers in a sandy intramatte region between patches of Posidonia oceanica meadow. The AWAC profiler measured directional spectra and bulk wave parameters with a time interval of two hours with the purpose of synchronizing incoming wave data with video data from the video camera system located on land (see Figure  1 ). The spectral estimates were obtained by applying Fourier transforms of time series of 1024 s sampled at 2 Hz. Besides marine data, a meteorological station located on land in the proximity of the beach recorded wind speed and other atmospheric variables.
Readers are referred to Passarella  [23]  to obtain more details about this experiment. In addition to coastal observations, we present here wave data measured by two wave buoys: one located in intermediate depths and the other moored in deep waters. Since during the period of the experiment there was no availability of measurements from wave buoys located in the vicinity of Sardinian coasts, we used data from the French network. The buoy located in intermediate waters is La Revellata directional buoy (managed by Cerema) that is moored in 130 m depth in the sea North-West of the island of Corsica (42. 57 • N, 8. 65 • E). Since at this water depth the low-frequency band of the wave spectrum ( f < 0. 8 Hz) is affected by the bottom, we refer to this buoy as located in intermediate water depths.
The fine grid extends over a region between the shoreline and 22 km offshore where boundary conditions are imposed accordingly to the wave field computed over the coarse grid. To avoid pronounced grid-boundary concavities located at the middle of large gulfs, the grid width extension is increased in these areas (see the panel (b) of Figure  3  that plots the grid configuration in the Gulf of Cagliari). Both coarse and fine grid simulations run over the bathymetry obtained using the ETOPO1 (Earth topography 1 arc minute) dataset  [33]. The wave model is set up with 24 frequency bins and 24 directions. The frequency range covers from 0. 04 to 0. 74 Hz, whereas wave directions are 15 • spaced. Nonlinear wave interactions in deep waters were computed with the Discrete Interaction Approximation method  [34].
Due to extensive measured dataset, they were able to provide assessment metrics for different Mediterranean Sea subregions. In contrast with the methodology followed by Ravdas et al. [10]  who applied a computational grid with constant spatial resolution of 1/24 • covering the whole Mediterranean basin, in this study we have nested a coastal fine grid (resolution 1/60 • ) into a deep-water coarse grid (resolution 1/6 • ). Our multi-grid approach is intended to improve the characterization of the coastal wave processes relevant for the beach monitoring program. While most of the wave operational systems present in the literature are tested against data (from wave riders or satellite) retrieved in deep or intermediate water  [2, 6, 10] , we include an evaluation of our operational wave system against wave measurements collected at 18 m depth.
Among the few examples of operational wave systems tested in shallow waters, we can compare our results with Ponce de Leon and Orfila  [11]. Ponce de Leon and Orfila  [11]  used three different wind datasets and two phase-averaged spectral wave models with a grid resolution of 1500 m to simulate waves around Mallorca Island (Spain, Mediterranean Sea) under the effect of fast-changing sea breezes. Thus, their wave conditions, characterized by a combination of moderate Mediterranean swells and locally-generated wind waves, were similar to those we have observed in our experiment at Poetto beach. Testing their system with wave data at a water depth of 135 m, they obtained a scatter index (SI = 0. 35) higher than that we have obtained at the same depth (SI = 0. 23 at La Revellata). However, when the evaluation was extended to shallower depths (28 m depth), the performance of their system decreased (SI = 0.
Reducing the error in coastal waters would contribute to the reduction of the uncertainty of the operational wave system applications in coastal hazard and management. Even with the mentioned limits, the information provided by the operational wave system can integrate measurements collected by in situ and remote systems inside a multi-platform monitoring program. By making this environmental dataset available to coastal scientists and managers, the monitoring program is of crucial importance to plan economical activities in coastal areas with applications including flood and erosion risk assessment, beach management and support to stakeholder activities. The dataset produced through the monitoring tasks is especially important in areas subject to considerable anthropological pressure, such as the beach systems of the metropolitan area of Cagliari, where the knowledge of wave forcing is a key factor to understand and predict short and long-term geomorphological processes.
Acknowledgments: The authors would like to thank the  Battellieri di Cagliari  and  Marco Piroddi  for their assistance during the field campaign. Gilbert Emzivat  of  Meteo France  and  Alain Le Berre  of CEREMA are acknowledged for providing the buoy data. The authors acknowledge the organizers of the Coastlab 2018 conference (22-26 May, Santander, Spain) where part of this research was first presented. The suggestions and comments made by the reviewers significantly improved the manuscript.
Database search Pest information, on hosts and distribution, was retrieved from the European and Mediterranean Plant Protection Organization (EPPO) Global Database (EPPO, 2018a) and relevant publications. Data about the import of commodity types that could potentially provide a pathway for the pest to enter the EU and about the area of hosts grown in the EU were obtained from EUROSTAT (Statistical Office of the European Communities). The Europhyt database was consulted for pest-specific notifications on interceptions and outbreaks. Europhyt is a web-based network run by the Directorate General for Health and Food Safety (DG SANT E) of the European Commission, and is a subproject of PHYSAN (Phyto-Sanitary Controls) specifically concerned with plant health information. The Europhyt database manages notifications of interceptions of plants or plant products that do not comply with EU legislation, as well as notifications of plant pests detected in the territory of the MS and the phytosanitary measures taken to eradicate or avoid their spread.
4. 1). As of November 2018, there were 18 records of interception of the RSSC in the Europhyt database. Up to now, with two exceptions, interceptions of the pathogen have not distinguished between different races, biovars, phylotypes or species. Nine interceptions were reported in 2018, all on S. tuberosumfour of these interceptions were on intra-EU trade (from Spain to Portugal, from the Czech Republic to Slovakia, from Greece to Poland, and from the Netherlands to the UK) and five interceptions originated in Egypt (to the Czech Republic (two), to Italy, Poland and Romania). Six interceptions were reported in 2017 (four on S. tuberosum and two on Rosa spp. ) -five of these interceptions were made on intra-EU trade (from Spain to Portugal, from the Netherlands to Portugal, from Germany to the Netherlands, from Germany to Poland and from the Netherlands to Poland) and one interception originated from Egypt (to Poland).
, 2007). There have been several recent outbreaks of the RSSC in various EU MS (Bulgaria, France, Germany, Italy, Netherlands, Poland, Portugal, Spain) in 2017 (10) and 2018 (20), mostly on S. tuberosum (14), Rosa spp. (10) and S. lycopersicon (6), but also on S. melongena (1) (some outbreaks were reported without a host; Europhyt database, as of December 2018). Given the widespread cultivation of potato, and the ability of the RSSC to infect host crops in a wide variety of climatic conditions, the losses due to this disease would be extremely high if the pathogen were to become widespread within the EU. This is likely to be the case for all phylotypes/ species.
Abstract. 1. Introduction. 1. 1. Background and Terms of Reference as provided by the requestor. 1. 1. 1. Background. 1. 1. 2. Terms of Reference. 1. 1. 2. 1. Terms of Reference: Appendix 1. 1. 1. 2. 2. Terms of Reference: Appendix 2. 1. 1. 2. 3. Terms of Reference: Appendix 3. 1. 2. Interpretation of the Terms of Reference. 2. Data and methodologies. 2. 1. Data. 2. 1. 1. Literature search. 2. 1. 2. Database search. 2. 2. Methodologies. 3. Pest categorisation. 3. 1. Identity and biology of the pest. 3. 1. 1. Identity and taxonomy. 3. 1. 2. Biology of the pest. 3. 1. 3. Intraspecific diversity. 3. 1. 4. Detection and identification of the pest. 3. 2. Pest distribution. 3. 2. 1. Pest distribution outside the EU. 3. 2. 2. Pest distribution in the EU. 3. 3. Regulatory status. 3. 3. 1. Council Directive 2000/29/EC.
2) Experimental: We manufacture two pseudo-amorphous objects consisting of 1µm diameter glass beads. One specimen is assembled in a glass capillary tube of inner diameter 42µm, the other in a tube of inner diameter 117µm diameter. The two specimens were used to undertake the first tests of X-ray ptycho-tomography at the Diamond Light Source in 2015, but the data can act as a useful model in the current work. We took no particular care in packing the glass beads, but simply dipped the capillary into a water suspension of the beads, which were then naturally drawn up by the capillary force, and then left to dry. The tubes were mounted vertically and illuminated by a focussed beam of 9. 12keV X-rays at the I13 beamline at the Diamond Light Source, as illustrated in Figure  1. A coherent area of the beam was selected by apertures upstream of a Fresnel zone plate (FZP) lens.
) Figure  1 ) (a) Schematic of the experimental set up. The capillary tube containing the glass beads is mounted vertically on an x-y stage that can be rotated on axis. A high brightness X-ray beamline provides a coherent flux of X-rays that are focussed by a Fresnel zone lens onto the specimen. The specimen is mounted a small distance from the focal length of the lens so as to increase the spot size. Scattered photons are recorded in the Fraunhofer diffraction plane. (b) Example of the raw data on the detector. Figure  2  shows the phase image of the transmission function of one projection from the 117µm specimen. We display phase because the modulus of the specimen transmission at this energy is approximately unity (no absorption), and we will use this signal to model the electron bright-field image, where a similar phase specimen approximation applies.
In particular, the higher fascicle Poisson's ratio value of data set D 2 results in considerably reduced E m MAP values with respect to the ones computed for the data set D 1. However, the dependence of E m on f r and θ is analogous to the one obtained for the fiber modulus E f. The a-posteriori, most probable inferred fiber moduli values E f well compare to the ones suggested by the experimental study of  Gentleman et al. (2003) , while the matrix modulus values E m to the analytical modeling predictions of  Ault and Hoffman (1992) , being smaller than 0. 7 MPa in the entire parametric space. For the data sets  E fasc (t = 0)  (Derwin and Soslowsky, 1999; Haraldsson et al. , 2009; Svensson et al. , 2010)  ν fasc (-) Ēfasc (t) (-) D 1 640| 480| 550 1  (Cheng and Screen, 2007)  Figure  3D  (Sc)  (Screen, 2008)  D 2 640| 480| 550 3  (Lynch, 2003)  Figure  3D  (Sc)  (Screen, 2008 )   1  relate to a range of η f values in between 40 and 80 GPa s.
Literature search The reviewing process conducted made use of all elements of the PRISMA statement  [35]  that were relevant for a search for literature on cereal biomarkers. In brief, original research papers and reviews were searched in at least two databases, such as CAB Abstracts, Scopus, and ISI Web of Knowledge using combinations of the grouped search terms (biomarker* OR marker* OR metabolite* OR biokinetics OR biotransformation OR metabolism) AND (trial OR experiment OR study OR intervention) AND (human* OR men OR women OR patient* OR volunteer* OR participant) AND (urine OR plasma OR serum OR blood OR excretion) AND (intake OR meal or diet OR ingestion OR consumption OR eating OR drink* OR administration) AND (wheat* OR rye OR oat* OR barley OR rice OR sorghum OR corn OR maize OR germ OR endosperm OR bran OR wholegrain OR whole-grain OR "whole grain" OR bread* OR cereal* OR flour* OR pasta*).
The research was limited to papers in the English language, while no restriction was applied for the publication date. The research papers with identification or use of potential biomarkers of cereal intake were selected by one or more skilled researchers from the list of retrieved references in a process outlined in Fig. 1. Additional papers were identified from reference lists in these papers and from reviews or book chapters identified through the search. For each potential biomarker identified an additional search was conducted with ("the name and synonyms of the compound" OR "the name and synonyms of any parent compound") AND (biomarker* OR marker* OR metabolite* OR biokinetics OR biotransformation) in order to identify potential other foods containing the biomarker or its precursor. In this second step, PubMed, Scifinder, and Google Scholar were also used as search platforms, along with the databases listed above.
Acknowledgement We would like to acknowledge  Professor Lars Dragsted  for valuable comments on this manuscript and we acknowledge the  JPI-FOODBALL consortium  for setting the framework for this and other reviews on dietary biomarkers. Nutrigenomics Unit,  Department of Food Quality and Nutrition, Research and Innovation Centre ,  Fondazione Edmund Mach ,  San Michele  all'Adige, 38010  Trento, Italy. 4 Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Food Technology Reference  Net (XaRTA),  Nutrition and Food Safety Research Institute (INSA-UB) ,  Faculty of Pharmacy and Food Sciences ,  Campus Torribera ,  University of Barcelona, Barcelona, Spain. 5CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES) ,  Instituto de Salud Carlos III, Barcelona, Spain.
Funding The work published here was part of the  FoodBAll  project which is a project funded by the  BioNH call  (grant number  529051002 ) under the  Joint Programming Initiative  " A Healthy Diet for a Healthy Life. " The  project is funded nationally by the respective research councils; the work was funded in part by a grant from the  Spanish National Grants  from the  Ministry of Economy and Competitiveness (MINECO)  ( PCIN-2014-133 ), an award of  2014SGR1566 644  from the  Generalitat de Catalunya's Agency AGAUR , and funds from  CIBERFES  (co-funded by the  FEDER program  from the  EU)  to MGA. Project grants from the  Swedish Research Council  and FORMAS supported the salary of RL. KH was supported by  Academy of Finland and Lantmännen Research Foundation. The source of funding has not affected the way data was collected, analyzed, interpreted, or reported.
Air Pollution Exposure The current study focused on modeled annual average nitrogen dioxide (NO 2 ) at each participant's place of residence as a marker of traffic-related air pollution since this variable was available in all study areas. ESCAPE methods to estimate the NO 2 annual concentrations are described in detail elsewhere  [18]. Briefly, between 2008 and 2011, two-week integrated NO 2 passive sampler measurements at approximately 40 sites in each study area were conducted in three different seasons over a one-year period. Area-specific land use regression (LUR) models were developed to explain the spatial variation of NO 2 using a variety of geographical data including traffic, population and land use variables. The LUR models were applied to estimate NO 2 annual concentrations at each participant's geocoded residential address at the first follow-up.
Statistical Analysis All analyses are based on the pooled data from the three cohorts. We applied multiple mixed linear models to analyze the role of SES in the associations between NO 2 and lung function and used the study area as a random intercept. Regarding SES, we included the following variables: Educational level (three categories), occupation (manual compared to non-manual), and the neighborhood-level variable tertile of unemployment rate. Models were adjusted for the basic variables sex, age, age squared, height, height squared and additionally for the lifestyle variables smoking status (i. e. , current, former, or never smoker), pack-years and pack-years squared (each with separate terms for current and former smokers), BMI (body mass index), and BMI squared. We systematically ran analyses for the full sample (n = 6502) and for a reduced sample (n = 4766) since the unemployment rate was not available for three study areas.
Analysis methods The assembly and core sizing calculations and the reactor physics calculational routes used in these studies, using the WIMS-10 lattice physics code  (Newton et al. , 2008)  and the PANTHER whole-core nodal code  (Hutt, 1992). For our study, the assembly design analysis relied on the WIMS-10 software package using the JEF 2. 2 nuclear database available from the IAEA  (Newton et al. , 2008). For each burnup step, WIMS completes a 172 "fine" group solution to the transport equation in a smeared geometry. It then refines this solution using a few-group calculation in a precise geometry. For our calculation we have considered 6 energy group structure as shown in Table  1. Furthermore, we have used PANTHER  (Hutt, 1992)  for whole-core design which is an advanced 3D nodal code for reactor core analysis.
PANTHER imports few-group nuclear data from the lattice code WIMS. The neutronic powers are calculated using the PANTHER using a nuclear data library prepared with WIMS. WIMS produces "Flat file" for subassemblies with specific neutronic properties. By using an output module "LED", WIMS homogenizes neutronic data for subassembly and exports the "flat file" that is readable by PANTHER. This allows user to load subassemblies prepared in WIMS into PANTHER in order to perform whole-core calculations. Therefore, WIMS and PANTHER are interconnected in this study to perform the subassembly design and whole-core analysis. The detail calculational route is described in the Part I companion paper  (Alam et al. , 2019c  19. 64×10 6 820. 85×10 3 9. 12×10 3 4. 00 625×10 -3 140×10 -3 Lower (eV) 820. 85×10 3 9.
Background. The causal role of high-density lipoproteins (HDL) in coronary artery disease (CAD) has been questioned. Our aim was to analyze whether genetically determined quantitative and qualitative HDL characteristics were independently associated with CAD. Methods. We designed a two-sample multivariate Mendelian randomization study with available genome-wide association summary data. We identified genetic variants associated with HDL cholesterol and apolipoprotein A-I quantity, HDL size, particle levels, and lipid content to define our genetic instrumental variables in one sample (Kettunen et al study, N=24,925) and analyzed their association with CAD risk in a different study (CARDIoGRAMplusC4D, N=184,305). We validated these results by defining our genetic variables in another database (METSIM, N=8,372) and studied their relationship with CAD in the CARDIoGRAMplusC4D dataset.