dataesr/scientific-paragraphs-categorization huggingface dataset:
From Washington University, data sets were obtained from 98 subjects referred for a florbetapir examination (3 very mild dementia, 95 MCI or clinical dementia patients). Of a total of 359 PET/MRI brain studies from the two centers, 22 studies were excluded (9 FDG, 3 PiB and 10 florbetapir). Exclusion was due to metal implant-induced artifacts in the brain images (3 subjects) or data errors due to corruption or missing raw-data (19 subjects) leaving 337 studies available for analysis. The local ethics committees had approved the original studies and all patients gave informed consent.
FDG All of the methods presented in this study, except for two, had been made available and were processed in Copenhagen. The MR-AC SEGBONE attenuation maps were created by Siemens, and MR-AC UCL attenuation maps were created in London, as the CT/MRI database could not be shared at the time of this study. 2. 3. 1. Template/Atlas-based methods (Segbone, Ontario, Boston, UCL, MaxProb) • Siemens Healthcare GmbH -Segbone: The prototype method by Siemens Healthcare  (Koesters et al. , 2016; Paulus et al. , 2015)  is a template-based method, where an MR model image of the skull region is non-rigidly registered to an individual subject exclusively on the Dixon input images. A pre-aligned skull mask is then brought to subject space, and attenuation values greater than soft-tissue are superimposed on the original Dixon attenuation map.
• London UCL: The method by Burgos et al. (2014) non-linearly aligns a database of 41 T1-CT pairs to the intensity-normalized T1 image of a patient. At each voxel, a patch is extracted and the local normalized correlation coefficient is calculated between the patient T1 and each of the T1 images in the database. A weighting vector is calculated from the coefficients ranking each T1-CT pair relative to the other pairs, and used to obtain a target CT voxel value. The resulting synthesized CT image is converted to LACs, blurred with a 4 mm Gaussian filter, and denoted MR-AC UCL. • Lyon -MaxProb: The method by Merida et al. (2015) is similar to  Burgos et al. (2014)  by also aligning a database of 27 T1-CT pairs to an intensity normalized T1 MPRAGE image of a patient, but deviates in the method used to combine the database into a target CT image.
Segmentation-based (Munich, CAR-RiDR, RESOLUTE) • Munich: The method by Cabello et al. (2015) uses the individual's UTE TE1 and TE2 images to extract air, soft tissue as well as the bone signal, which is extracted using R 2 * and scaled to LACs by normalizing to the maximum intensity and thresholding using an empirically chosen value. The intensity of the bone voxels from the R 2 * map are equalized by the mean and maximum values from a CTbased database. The resulting image is blurred with a 3 mm Gaussian filter, and denoted MR-AC MUNICH. • University of North Carolina -CAR-RiDR: The method by • Copenhagen -RESOLUTE: The method by Ladefoged et al. (2015) segments brain, CSF, soft tissue, and air from the UTE TE images, and also uses R 2 * to extract a bone signal, which is converted to LACs using a set of R 2 * -CT relationship pairs from 10 training patients.
Global PET performance As a prior step to evaluating the global PET performance, we first created individual brain masks. We moved all patient data to common MNI space using ANTs  (Avants et al. , 2011)  by non-rigidly registering the patients' T1 images to the ICBM 152 2009a template  (Fonov et al. , 2009). The MNI brain mask was back-warped to patient space, and the PET image reconstructed with the CT attenuation map was thresholded at > 20% of the maximum intensity value. Finally, the intersection between the two masks was calculated and used as the final mask of the brain, and used throughout this manuscript when referring to the brain region. Next, the voxel-wise percent difference relative to PET CT , defined as: Rel PET PET PET = - ×100, x C T CT % (1) as well as the absolute percent difference, defined as: Abs PET PET PET = - ×100, x C T CT % (2) were calculated for the PET images corrected with each of the evaluated methods (PET X ).
15 ). It should be noted that for the subjects where the CT include the dental region, the CTs were also affected by metal implants resulting in streak artifacts, which could bias the results further. The template/atlas-based methods generally produced attenuation maps very similar to the CT-based attenuation maps, but they are based on the assumption that any new subject can be represented by a template, a local combination of atlas patients, or by a non-rigid registration to a template. The template/atlas-based methods are therefore also potentially challenged by abnormal anatomy and patient surgery. The template/atlas-based methods are, furthermore, currently only optimized and tested for adults, as the application to children might require a purpose specific MRI/CT database. We did not include children, or any patients who have undergone anatomy-changing surgery in this study.
Most subjects chosen came from dementia studies, as this disease has the potential to be a key clinical application of PET/MRI  (Bailey et al. , 2015). The disease is clinically imaged with FDG, or amyloid binding radiotracers such as PiB or florbetapir, with the majority of patients having relatively widely distributed uptake in the brain, making the tracers well suited for a large-scale evaluation of MR-AC methods. The overall performance was, generally, similar across the three tracers for all methods (Supplemental Figs. 1 2 3 ). When considering patient management, the acquisition time and number of steps required for generating AC maps has also to be taken into account. A potential challenge for the template/atlas-based methods is the long computation time due to multiple registrations for each patient in the database (Table  4 ).
The MR-AC MAXPROB and MR-AC UCL methods currently require at least an hour of processing time for each attenuation map, whereas the MR-AC MUNICH and MR-AC CAR-RiDR methods only take a few seconds. The shorter processing time is usually a key argument for choosing the segmentation-based methods in fast-paced clinical services, whereas accuracy is much more important than speed in research applications. The MR-AC SEGBONE prototype requires less than 2 min of processing time, with further potential for speed-up. The processing time for multi-atlas methods could be shortened by code optimization and by reducing the number of database studies using pre-selection of the candidates more likely to match the patient (e. g. using gender, age, etc. )  (Aljabar et al. , 2009)  but this requires further evaluation.
Limitations The use of a CT as gold standard is debatable. The CT image does not fully cover the FOV of the MR and PET area, and is further subject to streak artifacts from dental implants. Since the CT images are not simultaneously acquired with the PET/MRI, they are also subject to non-rigid movement, e. g. jaw movement and swallowing, not accounted for during the rigid registration. In lack of a true gold standard, such as a transmission scan with a rotating source, we considered the CT to be the reference. The cohort, mainly consisting of patients with dementia and agematched controls, represents an ageing population. This study did not include data to test the performance of these MR-AC methods on brain scans from younger patients. Furthermore, the study cohort did not include patients with focal PET lesions.
Acknowledgements A special thanks to biomedical engineering student  David Nyrnberg  and computer science student  Casper Hansen  for aiding with the large data processing tasks involved in this study. The PET/MR system at Rigshospitalet was kindly provided by the  John and Birthe Meyer Foundation ,  Denmark. At UCL this work was supported by an  IMPACT  studentship funded jointly by  Siemens  and the  UCL Faculty  of.
Resistance reduction was obtained by dividing the KDT 50 obtained from insecticide exposure with no synergist by the KDT 50 obtained from insecticide plus the synergist (KDT 50 Insecticide alone /KDT 50 Insecticide plus Synergist ). The differences in mortality was considered statistically significant when P< 0. 05. For kdr detection assays, the fluorescent signals detected in the experimental reactions were compared to those of the controls, and genotyping of each mosquito was done using the CFX manager software version 2. 1 (Bio-Rad, Hercules, CA, USA).
Acknowledgements We highly appreciate the support received from the community from the study villages during the selection of the breeding sites for sampling mosquito larvae. We thank  Dr. Maria Kaiser  from the  National Institute of Communicable Diseases (NICD)  in Johannesburg, for providing guidance during kdr assays, and  Dr. Sherif Amer  of the  University of Twente in The Netherlands  for assisting in the mapping of insecticide resistance. We extend much appreciation to  Neema Nombo  and  Paulina Kasanga  for their enormous work in larvae collections, rearing and assisting with the WHO and synergist bioassays, and  Salum Mapua  also for assisting with WHO bioassays. We sincerely thank  Prof. George Corliss  of  Marquette University  for his valuable time reviewing earlier versions of this manuscript and providing helpful comments.
Grant information This work was supported by the  Wellcome Trust  [102350], Intermediate Research Fellowship awarded to FOO, and [104029], Masters Fellowship in Public Health and Tropical Medicine awarded to the lead author NSM under the mentorship of MC, LLK, and FOO. MC is funded by the  DST/NRF South African Research Chairs Initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The impact of the current nuclear data library covariances such as in ENDF/B-VII. 1, JEFF-3. 2, JENDL-4. 0, SCALE and TENDL, for relevant current reactors is presented in this work. The uncertainties due to nuclear data are calculated for existing P\. VR and BWR fuel assemblies (with burn-up up to 40 GWd/tHM, followed by 10 years of cooling time) and for a simplified PWR full core model (without burn-up) for quantities such as k∞, macroscopic cross sections, pin power or isotope inventory. In this work, the method of propagation of uncertainties is based on random sampling of nuclear data, either from covariance files or directly from basic parameters. Additionally, possible biases on calculated quantities are investigated such as the self-shielding treatment. Different calculation schemes are used, based on CASMO, SCALE, DRAGON, MCNP or FISPACT-11, thus simulating real-life assignments for technical-support organizations.
The improvement of predictions in the above fields does not automatically imply a nuclear data improvement. If the propagation of nuclear data uncertainty certainly indicates the need of better basic nuclear data measurements and theories, two other aspects can be improved: (1) better handling of nuclear data with improved processing codes, and (2) improvement of simulations by using more exact transport and solver systems. If these two points are not the subject of this paper, they should still be mentioned for completeness: 1. So-called "processing" of nuclear data allows to provide ready-to-use sets of data for simulations. The fact that the vast majority (with the exception of the AMPX code  [7] ) of such processing systems rely on (parts of) a single code such as NJOY  [8]  does not follow the principle of redundancy, often mandatory in nuclear environments.
This is the first goal of this paper: presenting in a single study calculated uncertainties due to nuclear data for relevant systems in nuclear energy production. To fulfill this goal, different institutes agreed to participate, with their own method and database (see Sec. II A for the different definitions of methods). The only common points in these studies are (a) the systems (see Sec. II B) and (b) the quantities to calculate (see Sec. II C). Such an approach is definitively different compared to the Expert Group on Uncertainty Analysis in Modelling (UAM  [9] ). Here, because the libraries (both for nominal values and for covariances) are different, the calculated uncertainties can not be the same (see Sec. IV for results). The use of recommended covariance libraries is very convenient for the comparison and understanding of the various results, but it also restricts the possible spread of calculated responses due to the differences in the various covariance data libraries available.
[32]  for cross section evaluations and in Ref. [33]  for fission yields. The succinct description is as follows and presented in Fig. 2  Some details for the above steps are described below. There is fundamentally no difference with Ref. [32], except in the step (3) for the choice of the reference data. The set of reference data is chosen here to be the evaluated fission yields as given in the ENDF/B-VII. 1 library. A list of the 21 most-important parameters for the GEF code is considered in this work, as obtained from Ref. [34]  (see Table  7  in this reference) and is presented in Table  II. The adjustment of parameters and their updates is performed with a simplified χ 2 goodness of fit-estimator. After n iterations, the calculated GEF fission yields and uncertainties are close to the reference data (ENDF/B-VII.
GRS-XSUSA The XSUSA ("Cross Section Uncertainty and Sensitivity Analysis") method is based on the random sampling GRS method (Gesellschaft für Anlagen-und Reaktorsicherheit) implemented in the code package SUSA "Software for Uncertainty and Sensitivity Analysis")  [35]. The probability distributions of the uncertain input parameters are used to generate random variations of these input quantities. When applying this method with neutron cross section uncertainties, this means that many nuclear data libraries are generated, where all quantities with available uncertainties, namely inelastic and elastic scattering, (n,2n) and capture cross sections, in the case of fissionable nuclides additionally the fission cross section, the number of neutrons per fission, and the fission neutron spectrum, are varied at the same time for a large number of nuclides.
As basis for generating the data variations, the SCALE 6. 1 covariance data library is currently being used. This library contains uncertainties for relevant nuclides on the basis of various sources, including high-fidelity evaluations from ENDF/B-VII, ENDF/B-VI, and JENDL-3. 3, as well as approximate uncertainties obtained from a collaborative project performed by Brookhaven National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory  [36]. These covariance matrices are processed in a multi-group structure with 44 energy groups. When performing depletion calculations with the TRITON sequence from the SCALE 6. 1 package [37], the default 94 nuclides in the fuel are taken into account. Thus, while the "traditional" SUSA method is predominantly being applied to problems with a limited number of parameters and only few correlations between them, the application to depletion calculations with nuclear data uncertainties leads to a large amount of uncertain parameters (94 nuclides, typically 2-8 reactions per nuclide, 44 energy groups per reaction), with a large amount of correlations between the energy group data of each nuclide/reaction combination, and also cross correlations between data of different reactions or even data for different nuclides.
Decay calculations were performed by independent ORIGEN-S calculations after the assembly burnup using the requested benchmark steps. In summary, to propagate uncertainties in nuclear data along depletion, the applied calculation scheme used random pre-computed perturbation factors to generate, for each sample, a perturbed infinitely-dilute multigroup cross-section library, perturbed self-shielding factors and a perturbed yield library for TRITON sequence. Those data were used in an individual coupled cross-section processing, transport and depletion calculation to obtain a sample of the output quantities of interest along burnup. A total of 200 samples were obtained, and it is worth noting that all samples were independent, being the set of perturbed data propagated to downstream codes independently on other runs.
One can vary one parameter at a time, or many parameters at the same time. The beauty of Monte Carlo is that many parameters can be varied at the same time, while preserving the same convergence rate: in this case, the variance converges as 1/M (the standard deviation as 1/ √ M ), irrespective of the number of input parameters varied. Since the amount of nuclear data used in a reactor simulation is very large, the Fast TMC method is well suited for this situation: each nuclear data file contains many parameters that are needed for simulations, such as cross section for various reaction channels, energies and angular distributions of outgoing particles, etc. Therefore this method is applied here to the calculation of local power densities in a full core reactor simulation: with the amount of computer time needed to calculate local power densities in the Martin-Hoogenboom benchmark, it is possible to simultaneously calculate the uncertainties in these local power densities due to the uncertainties in nuclear data.
UU-TMC This section describes the method used at the Uppsala University with DRAGON. The deterministic modeling of the transport of neutrons in a fuel assembly utilizes as material parameters the so-called multi-group microscopic cross-sections as presented earlier. The term multigroup refers to the discretization of the energy variable of such cross-sections and therefore, they are presented as being averaged along different energy bins. One of the most utilized code to compute multi-group microscopic cross-sections is NJOY. It will not only process point-wise information from the different nuclear databases that exist around the world in order to obtain energy averaged parameters, but it also has the capability of arranging the information in different multi-group library formats that can be read by different lattice codes (e.
g. WIMS, DRAGON, APOLLO, etc). The NJOY code, developed at Los Alamos National Laboratory, is modular and sequential. The module that averages the cross-sections in energy and represents them in ENDF format is known as "GROUPR". The ENDF format allows understanding in an easy way the different multi-group information required for an assembly lattice calculation (i. e. microscopic cross-sections, different Legendre orders of the scattering matrices, energy spectrum, released neutrons per fission, etc. ). Afterwards, other NJOY modules, such as WIMSR, processes the multigroup ENDF file and arrange it into an ASCII library that some lattice codes understand and may utilize for transport calculations. This final library format is known as WIMS library, and codes such as DRAGON and the ones belonging to the WIMS-family are able to run with the WIMS library format.
As uncertainty analysis has become nowadays part of any physical modeling and code development endeavor, methodologies to propagate (in this particular study) cross-sections uncertainties through lattice codes are needed to be developed in accordance to the library format that a certain code utilizes. Due to the fact that the ENDF multi-group format given by GROUPR is easy to read, understand and manipulate, a new methodology was developed to statistically perturb the GROUPR ASCII file in order to create posterior perturbed WIMS libraries. Thus, as many different WIMS libraries are created, as many times the lattice code will be run in order to create a deterministic output into a stochastic one. This is exemplified in Fig. 4. The module WIMSR reads and manipulates only certain information contained on the ENDF file.
3. From the MF=6 and MT=50-91, WIMSR will add them per energy group in order to form inelastic scattering and thus, after energy integration, update the total cross-section. 4. From MF=6 and MT=18, the Nu-Fission crosssection is taken. 5. Even though the MF=5 data contained in the ENDF file gives the fission spectrum, the WIMS library format fixes the fission spectrum and thus, does not take into account these info from the ENDF file. Now, the uncertainty analysis would perturb ALL terms of the parameters that are required for the transport calculation. This is how the perturbation methodology works in order to conserve any further transport calculation: 1. Covariance data is given to the total energy integrated terms of the multi-group cross-sections. From this information (which can be obtained from the ERRORJ module of NJOY) the fission and capture cross-sections can be perturbed.
It was realized that if the fission cross-section (MF=3, MT=18) is perturbed in the ENDF ASCII file, the module WIMSR will change not only the total cross-section but also Nu-fission. This is a good thing since perturbations to the fission cross-section are being conserved by WIMSR. Also, any perturbation to the capture cross-section will immediately update the total cross-section as well. 2. MF=6, MT=2 and 221 should be statistically perturbed in order to change the elastic scattering matrices and also the total cross-section. Since only the P 0 covariances are given, then only the P 0 matrix can be perturbed. 3. For the inelastic scattering term, the perturbation was done only at the MT=91 data. This is good enough since WIMSR adds up all the MT=51-91 terms and thus, the propagation of the perturbation is carried out correctly.
4. The Nu-fission term (MF=6, MT=18) was modified in a way that only the ν uncertainty was propagated, since the fission perturbation is carried out at the MF=3, MT=18 data. 5. For the WIMS library, even if MF=5 data could be perturbed in order to change the fission spectrum, no effect whatsoever would have on the spectrum of the final WIMS library. This is the only term that cannot be perturbed in the ENDF file and thus, such perturbation should be carried out at the final WIMS library.
FISPACT-II employs a sophisticated reaction rate uncertainty quantification method using available cross section covariances and deep reaction/decay pathway analysis  [52]. All independent fission yields, open reaction channels and decays are considered, following from hundreds to a few thousand nuclides. FISPACT-II can process ENDF-6 NI-type covariance data with LB=1, 5 or 6, using a projection operation to match covariance and cross-section energy grids, which are stored in multigroup format (586-group for CASMO-5 based simulation). Once individual reaction rate uncertainties are calculated for a given incident particle spectrum, the reaction/decay production pathway for each nuclide must be determined. In the Takahama case a typical production route for 137 Xe, a precursor to 137 Cs, the path is broken 10 3 10 2 10 1 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 0.
Summary In a request from the European Commission, the Panel on Contaminants in the Food Chain (CONTAM Panel) was asked to assess the risk to animal and human health related to the presence of the mycotoxins deoxynivalenol (DON), metabolites of DON and masked DON in food and feed. Taking into account the availability of data suitable for the risk assessment of DON and its acetylated and modified forms, the CONTAM Panel decided to assess the risk of DON, 3-acetyl-DON (3-Ac-DON), 15-acetyl-DON (15-Ac-DON) and DON-3-glucoside. The CONTAM Panel was asked to consider all relevant adverse acute and chronic health effects, addressing, in particular, co-occurrence of DON with its acetylated and modified forms. Furthermore, to estimate the dietary (chronic and acute) exposure of the European population addressing, in particular, the consumption patterns of specific (vulnerable) groups of the population.