dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Electron electronic stopping power test In this test the electron electronic stopping power is calculated in a set of target materials and compared to the ESTAR collision stopping power data, that is available online in the NIST Reference Database  [44]. Electronic and collision stopping powers are the same physical quantity, however the ICRU Report No. 85  [45]  recommends the use of the specific term electronic. From this observation, the term electronic was adopted here. This technical test was developed for the regression testing of Geant4 for both low energy and high energy physics applications to demonstrate the level of agreement between available models of electron stopping powers. Here details of the simulation, results and discussion are provided for electrons with an incident energy between 10 keV and 10 GeV.
In the geant-val web interface  [12] , the test has the name ElectDEDX. Assuming small energy losses, the stopping power is calculated as the energy deposited in the first step of the track, divided by the true step length, which takes into account multiple scattering correction. The electronic stopping power is calculated for targets made of Al, Ar, Cu, Au, Pb, in order to represent a range of atomic numbers. Water is considered as a target as well because it is of interest for medical physics. The target materials are modelled from the Geant4 NIST material database, described in the Geant4 Application Developer Manual  [40]. The ESTAR electronic stopping power values are calculated based on Bethe theory, with a density-effect correction evaluated according to Sternheimer, as described in the documentation of the NIST Standard Reference Database  [44].
C. Electron backscattering test Electron backscattering is an important process in several physics applications including medical physics. It occurs when electrons, incident on a target, are scattered in the backward direction. The capability of properly reproducing this complex interaction in Geant4 has considerably improved in the last years, reaching a satisfactory agreement with experimental measurements published in the literature, performed in different periods and using different techniques  (Dondero et al 2018 [17] ). This work considers the wide selection of the available experimental data that is documented in Dondero et al 2018  [17] , where a detailed description of each experimental technique used in the reported experiments can be found. This selection includes all the datasets known to the authors that respects basic quality criteria, as discussed in the reference article.
The results obtained with Opt0 are very close to the ones generated with Opt3 as both constructors use the Urban multiple scattering model (Ivanchenko et al 2010  [39] ) in the electron energy range under study. Penelope and Livermore agree very well with Opt4 as they use the Goudsmit-Saunderson model to simulate multiple scattering (see Section III). In conclusion, SS, GS, Livermore, Penelope and Opt4 EM constructors show the best agreement with experimental results. The agreement above 0. 2 MeV is within 3 σ ref and between 5 keV and 0. 2 MeV is within 15%. The agreement between simulation and experimental data below about 5 keV is, in general, worse than at higher energies due to differences of up to 40% for certain datasets. As discussed above, in this region the simulation results are particularly sensitive to experiment-related parameters, like the measurement thresholds applied to the detection of scattered electrons.
E. Bremsstrahlung from thick targets The measured yield of bremsstrahlung from electrons normally incident on thick targets at radiotherapy energies as a function of target material, electron energy, and angle provides a key benchmark for the modelling of linear accelerator treatment heads since this requires accurate simulation of both electron scatter and bremsstrahlung within the target. The benchmark reference dataset are the measured photon fluence per incident electron and differential in energy: 1) along the axis for 10-30 MV x-ray beams from thick targets of aluminum and lead, and 2) from 0-90 • of 15 MV beams from thick targets of beryllium, aluminum and lead. Photon fluence per unit energy per incident electron, and total photon fluence, integrated over energy, per incident electron was experimentally determined at 1 m from the target.
5, as the best alternative to the G4UrbanMscModel ). This agreement is obtained under the condition that energy loss fluctuations are ignored in Geant4. Indeed, EGSnrc simulation data do not take into account these fluctuations. Such agreement is not observed when energy loss fluctuations are taken into account in the Geant4 simulations (data not shown). This underlines that, under this condition, the GS model used in the three above constructors is able to perform as EGSnrc and can provide more accurate simulation data. It remains however impossible to fully validate such simulations in the absence of experimental data in liquid water in this energy range. At higher incident electron energies, effects of energy loss fluctuations are not important and Geant4 Opt4 with GS model and EGSnrc results agree well, so it is possible to conclude that both simulation tools (on the one hand Geant4 Opt4 constructor with GS model, and EGSnrc on the other hand) can be regarded as a gold standard for such simulations.
Simulation setup The 192 Ir Flexisource, described in Granero et al 2006  [83] , has been modelled in the center of a water box (modelled as G4 WATER) with 30 cm size. The photon radiation field, detailed in Granero et al 2006  [83] , is emitted from the iridium radioactive core. Opt0, Opt3, Opt4, Livermore and Penelope have been tested. A global production cut was fixed equal to 0. 05 mm. A Geant4 scoring mesh was defined to calculate the energy deposition in the plane containing the source. The plane is subdivided in voxels with size equal to 0. 25 mm along x, y and z. 10 9 histories were simulated to obtain a statistical uncertainty of 1. 5% in the results. The same statistical uncertainty affects the reference data  (Granero et al 2006 [83] ).
Results and Discussion Figure  11  shows the radial dose rate distribution about an 192 Ir brachytherapy source, as a measure of the energy deposition per unit of mass, along the transverse axis of the source, 90 • from the source axis. As it can be observed in Figure  11 , the simulation results obtained with any Geant4 EM constructor agree with the reference within the uncertainty of the simulation data σ ref (1. 5%), for almost all points, up to a distance of 8 cm from the radioactive core. At 10 cm depth the agreement between Geant4 results and the reference data is within 3% (corresponding to 2 σ ref ) for all the EM constructors. No recommendation to use any of the EM constructors can be done at this stage with this test, because the reference data are derived from Geant4 as well (Geant4 7.
A. Test of Nucleus-Nucleus hadronic inelastic scattering cross sections This test calculates the total cross section of hadron-nucleus and nucleus-nucleus collisions. The cross sections are then compared to reference experimental measurements publicly available in the Experimental Nuclear Reaction Data (EXFOR) database (Zerkin et al 2018  [97] ). EXFOR provides libraries containing an extensive compilation of experimental nuclear reaction data. The total inelastic scattering hadronic cross sections are calculated for incident protons and carbon nuclei. The test was named NucNucInelXS in the geant-val web interface  [12].
Results and Discussion Figure  13  shows the total inelastic cross sections calculated by means of the QGSP BIC, as a function of the kinetic energy of the projectile, compared to the experimental data of the EXFOR database. Overall, for both incident protons and carbon ions, an agreement within 10% was observed for most of the incident projectile energies. Significant differences (20%-40%) were found for proton energies below 2 MeV in the case of a carbon target. These differences are caused by the large variations in the experimental inelastic cross sections for p+ 12 C, likely due to resonances that enhance the cross section, particularly near 10. 5 MeV proton energy (e. g. Dyer et al 1981  [101] , Davids et al 1971  [102] ). These variations will tend to arise for low energy proton induced reactions, particularly for light targets where the density of states is low.
B. 62 MeV/u 12 C fragmentation test This test concerns the nuclear interaction models available in Geant4 for low energy carbon ions. The name of the test is LowEC12Frag in the geant-val web interface  [12]. The models were benchmarked against double-differential cross sections of the secondary fragments produced in the 12 C fragmentation at 62 MeV/u on a thin carbon target. This dataset has been acquired from De Napoli et al 2012  [103].
On the contrary, INCL shows a single distribution peaked at lower energy, with respect to the experimental peak. The INCL predictions overestimate by around a factor ten the production of α-particles below 62 MeV/u but are compatible with the experimental data for particles produced with higher kinetic energy. This could be due to its complete-fusion model. Here we show only the plot with the comparison of the α-particle production. All other plots, with the different fragments, can be found at the geant-val web interface  [12]. In conclusion, the results show that both of the tested models, BIC and INCL, exhibit limitations in reproducing doubly differential cross sections of fragments produced in the interaction of 12 C with a thin carbon target. Efforts are underway to interface new models to Geant4 dedicated to nuclear interactions below 100 MeV/u (Mancini et al.
V. ELECTROMAGNETIC AND HADRONIC PHYSICS BENCHMARKING TESTS The tests included in this section activate both electromagnetic and hadronic physics processes, using pre-built Geant4 physics lists. QGSP BIC HP, QGSP BERT HP and QGSP BIC EMY were tested for the application of Geant4 in proton therapy. Details of those physics lists are reported below in the energy range of interest for proton therapy and summarised in Table  IX. In HadronElasticPhysics elastic hadronic scattering of protons and neutrons is modelled by means of the CHIPS model  (Kossov et al 2002 [108] ). Below an energy of 20 MeV, the High Precision (HP) data libraries are used to model the neutron elastic scattering. For deuteron, triton and α particles the elastic scattering model is based on the Geant3/GHEISHA approach  (Fesefeldt 1985 [99] ).
D. Fragmentation of a 400 MeV/u 12 C ion beam in water This test, published in Bolst et al 2017  [26] , benchmarks Geant4's modelling of the fragmentation process against experimental measurements of a 12 C ion beam incident upon a water target performed at GSI  (Haettner et al 2013 [120] ). The measured data includes fragment yields from hydrogen to boron for different thicknesses of water irradiated by a mono-energetic 400 MeV/u 12 C ion pencil beam, as well as the fragment's angular and energy distributions. The full results of the test can be downloaded from the geant-val web interface  [12]. The name of the test is FragTest.
VI. SUMMARY AND CONCLUSIONS G4-Med is a group of currently 18 tests, regularly executed on the CERN computing infrastructure via the geant-val platform, to benchmark Geant4 against reference data for medical physics applications. G4-Med can also be used for regression testing purposes of Geant4. The results of the tests are provided to the Geant4 user community on-line via the geant-val web interface  [12]. This work was focused on the description of the tests of G4-Med and on the results of the benchmarking of Geant4 10. 5 against reference data. Table XI is intended to provide a summary of the main results of the electromagnetic physics tests, subject of Section III. We encourage the readers to refer to the specific tests of interest, for a more detailed analysis of the results in specific scenarios of application of Geant4.
The agreement is defined based on the discussion of the results of each individual test in Section III. SS and GS are not included in the Table as they have been used in a very limited set of tests. In the case of the test concerning photon attenuation coefficients in water (PhotonAttenuation), slightly better agreement with respect to reference data was found for Opt4. The test includes one material only at this stage and we will expand it to other materials in the near future. We will also consider to extend the comparison of Geant4 to other reference data. The electronic stopping power test (ElectDEDX ) showed an agreement with ESTAR within the reference data uncertainty (1 σ ref ). This test is useful for regression testing purposes as it monitors a physical quantity important for the calculation of energy deposition in a target.
The electron backscattering test shows an agreement with respect to the available experimental data that clearly depends on the electron energy range and the Geant4 EM physics constructor. In particular below 5 keV the results should be taken with caution for any constructor, including the suggested ones. The best EM physics constructors to model the electron backscattering were found to be Opt4, SS, Livermore and Penelope. For electron forward scatter, the angular distribution of electron fluence at 1. 18 m calculated with Opt4 was significantly underestimated (> 1 standard deviation) in comparison to experimental data for foils of higher Z than Be by 2-5%, with calculated results comparable to or better than the comparison with the other physics constructors. For bremsstrahlung from thick Be, Al and Pb targets at radiotherapy energies, bremsstrahlung yields (≤ 1-2 standard deviations) were in better agreement with experimental data when using Opt3 and Opt4.
It remains impossible to validate Geant4 in terms of DPK in the absence of experimental data in water in the low electron energy range. The results of the microdosimetry test (microyz ) demonstrate that, when using Geant4 condensed history approach in EM simulations of electron transport in microdosimetric applications, it is important to limit the step size. It is shown that 1/10 of the sensitive volume diameter is adequate. The Brachytherapy test (Brachy-Ir ) has a regression testing purpose. Geant4 is compared against previous simulation results in calculating the radial dose rate distribution of a 192 Ir source. This test is of interest to monitor the evolution of Geant4 and will be extended in the near future to other brachytherapy sources. We intend also to compare the results of the simulation to dosimetric experimental measurements available in the literature.
The test on monoenergetic x-ray internal breast dosimetry (Mammo) showed an agreement between Geant4 and experimental data within the experimental uncertainty. It was observed that Opt0 provided the worst performance among the EM constructors under study. In summary, the EM tests showed that Opt0 in general is the constructor with the least agreement with respect to the reference data. For the other physics constructors, the results of the tests should support users to select the appropriate EM physics constructor for their medical physics application. It was observed that Opt4 had good agreement with the reference data for all the tests. This result was expected as this constructor was developed to provide the most accurate Geant4 physics models, irrespective of CPU performance. Table  XII  summarises the results obtained for the hadronic tests subject of section IV.
The test of Nucleus-Nucleus hadronic inelastic scattering cross sections (NucNucInelXS ) considered a limited number of projectiles of interest for hadron therapy (protons and 12 C ions), incident on a limited number of targets. The choice of targets was performed based on the availability of experimental measurements in the EXFOR data library (Zerkin et al 2018  [97] ). In general, an agreement within approximately 10% was found between Geant4 and the experimental data for the cases under study. The next step of this test is to find more reference experimental measurements to be able to benchmark the Geant4 inelastic scattering cross sections for more targets of interest for medical physics applications. The 62 MeV/u 12 C fragmentation test (LowEC12Frag) showed that both the Geant4 Ion Binary Cascade and the INCL models have limitations in reproducing doubly differential cross sections of fragments produced in the interaction of 12 C with a thin carbon target.
ACKNOWLEDGMENTS The authors would like to thank  Krzysztof Genser  and  Daren Sawkey  for their feedback aimed to improve the quality of the paper. D. Bolst  acknowledges the support of the  Australian   Government Research Training Program Scholarship. M. A. Cortés-Giraldo ,  A. Perales  and  J. M. Quesada  acknowledge financial support from the  Spanish Ministry of Economy and Competitiveness  under grant  FPA2016-77689-C2-1-R. B. Faddegon  and  J. Ramos-Méndez  acknowledge partial financial support from the  NIH  grant  U24CA215123. D. Bolst ,  S. Guatelli ,  D. Sakata ,  S. Incerti  and  I. Kyriakou  acknowledge financial support from the  Australian Research Council ,  ARC DP170100967. S. Incerti acknowledges the financial support of  CNRS  through the  IN2P3/MOVI Master Project  and through the  France-Greece PICS 8235 funding scheme.
I. Kyriakou  acknowledges additional financial support from the  European Space Agency  (Contract No. 4000126645/19/NL/BW ). E. C. Simpson  acknowledges financial support from the  Australian Research Council  under grant  DP170102423. I. Sechopoulos  and  C. Fedon  acknowledge financial support from the  Susan G Komen Foundation  for the Cure grant IIR13262248.
Various action buttons were inserted to systematically interlink the slides as part of the tool design. Image and video buttons are available at various stages, offering an opportunity to learn the anatomy of the spinal pathways through interactive images, with on / off interactive labeling (Figure  1A ) and short videobased descriptions, respectively. Javaid M, Schellekens H, Cryan J, Toulouse A MedEdPublish https://doi. org/10. 15694/mep. 2020. 000088. 1 The module begins with a tutorial window, describing various regions / sections of the screen layout and explaining the functions of navigation buttons. A Menu button provides a drop-down list of the various learning stations of the CNS (Figure  1B ). Each learning station in the e-resource corresponds to a specific axial section of the brain or the spinal cord along the course of a spinal tract.
The user learns the course of the entire neuronal pathway by actively studying its anatomy at each of these learning stations. Following the image and video-based descriptions, the user is presented with a quiz section comprising multiple-choice questions. An immediate feedback is provided to the learner for correct or incorrect responses accompanied by a detailed explanation. An interactive exercise follows the quiz section, in which the user uploads the relevant axial sections of the CNS into the Interactive Sketch Pad (right side) and is prompted to click over the location of the spinal tract in that uploaded image (Figure  1B ). A correct selection automatically draws and connects the location of the spinal tract in the adjacent axial section. At the end, the learning module offers an opportunity to revise the spinal tract neuroanatomy using cadaver-/ prosection-based images (Figure  1C ).
The module ends with a clinical interpretation of the pathway information in the context of localization of neurological lesions. The e-resource was uploaded to the Google drive™ folder of a dedicated Gmail™ account and could be downloaded using a link provided to users via email. The corticospinal tract resource can be accessed here and the dorsal colum medial lemniscal pathway tool can be accessed here. The three areas of origin of corticospinal tract are color-coded and matched with interactive buttons above (pink for primary motor cortex, orange for pre-motor cortex, blue for primary sensory cortex). Clicking these buttons provides on and off labelling for the highlighted regions providing immediate feedback to the user. B, Menu list for various levels of the CNS. The interactive sketch pad displays sectional images from the selected levels and interconnect the loci of the spinal tract in each section to trace the entire neural pathway.
Statistical Analysis Study data were coded, anonymized and entered into Microsoft Excel™ 2016 spreadsheets (Microsoft Corp. , Redmond, WA). The percentage correct response (PCR) for easy and difficult questions as well as the total PCR were calculated for each participant-for both quizzes, and used as the dependent variable to compare students' performances between groups. Individual learning gains, G i , were tabulated (G i = Quiz 2 PCR -Quiz 1 PCR). As individual students perform differently in Quiz 1, the potential learning gain will vary greatly between each individual. Learning gain measurements were therefore normalized by dividing individual learning gain by the maximal potential learning gain for each participant (G n = G i / [100% -Quiz 1 PCR])  (Hake, 1998; Colt et al. , 2011; Pickering, 2017).
The data for all participants was exported to the Statistical Package for Social Sciences™ (SPSS), version 22 (IBM Corp. , Armonk, NY). Sample data was tested for normality and homogeneity of variance separately for easy, difficult and total categories (using histogram, normal probability plots, Shapiro-Wilk test), for six possible groups; quiz 1 and quiz 2, for control, experimental and NU groups. Descriptive statistics (median, interquartile range) are used to present the data for neuroanatomy quizzes and Likert scale questionnaires, for each group, across a skewed sample distribution. Performance-improvement in total, easy and difficult categories was gauged both between groups (control v. experimental, experimental v. NU, control v. NU) for both quizzes as well as within each group (quiz 1 v.
Nowinski and Chua, on the other hand, created a neurological localization software, which was geared towards clinical neurology but lacked Javaid M, Schellekens H, Cryan J, Toulouse A MedEdPublish https://doi. org/10. 15694/mep. 2020. 000088. 1 explanation of the underlying basic neuroanatomical details  (Nowinski and Chua, 2013). To date a resource which could explain the spinal pathways' neuroanatomy in adequate detail and in an interactive fashion and at the same time, streamline the thinking process of novice learners towards practical application of this knowledge for localization of neurological lesions, is missing. The "contextualized learning principle" helped frame the neuroanatomical facts around clinical case scenarios in our e-learning tool. In addition, a clinical reasoning was also offered at the end to explain the localization of lesion using the basic neuroanatomical facts taught as part of the spinal pathway modules.
Overall, the relationships between gene expression and methylation level are complex  [24, 28, 50, 51]. Correlation may switch from positive to negative values according to environmental variations such as recently described in palm roots exposed to high salinity  [52]. In our dataset, we found a significant and negative correlation between methylation in the CG context and gene expression at both DMC and DMR levels (Figure  3C ,D; see, for example, CG in introns, Figure  S3B ). A similar observation has been made in other plant species such as in tea  [53]. DMCs/DMRs in the CHH context were globally less frequent in gene bodies and no clear cut relationship emerged between expression and methylation in the upstream or downstream regions. The strong positive correlations observed in Figure  3C  within the CHH context are due to few occurrences (e.