dataesr/scientific-paragraphs-categorization huggingface dataset:
Notably, images with a higher PSNR and lower NRMSE theoretically exhibit higher image quality. 29 Synthetic T1-weighted images, which were inherently aligned to synthetic FLAIR images, were skull-stripped by the intracranial masks and segmented into GM, WM, and CSF using the FMRIB Automated Segmentation Tool (FAST; http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/fast). No gross error was visually observed in this segmentation step. Each tissue map and intracranial mask were used to extract the PSNR and NRMSE metrics in the GM, WM, CSF, and aggregate intracranial tissues. Conventional FLAIR images were linearly coregistered to synthetic FLAIR images using SPM 12 software (http://www. fil. ion. ucl. ac. uk/spm). The WM lesions on these images were automatically segmented using a lesion-prediction algorithm  30  in the Lesion Segmentation Toolbox, Version 2.
Statistical Analysis Statistics were computed using R (version 3. 2. 1; http://www. r-project. org/). Because not all the datasets were normally distributed when analyzed by the Shapiro-Wilk test, we used the nonparametric Wilcoxon signed rank test to compare the quantitative and qualitative scores between synthetic and DL-FLAIR images. The PSNR and NRMSE values were also compared among the GM, WM, and CSF on synthetic or DL-FLAIR images. Significance was set at P Ͻ. 05 (2-sided). Multiple comparison correction was not performed.
Recently, a pixel-wise neural network translation model was used to approximate the dictionary in MR fingerprinting,  34  and the training method used the same acquired data for the input and target, allowing it to learn the time- consuming conventional reconstruction method of MR fingerprinting by DL. Because we aimed to train the generator to create FLAIR images resembling conventional FLAIR images from raw synthetic MR imaging data, positional differences between the input and target data were unavoidable. The 2 approaches for overcoming positional difference were to use a GAN based on patches and to calibrate the positional deviation and take the absolute error as a loss. In the first approach based on patches, adversarial loss represents the complex reality expressions of generated patches by comparing them with patches of target images (conventional true FLAIR images herein) rather than pixel-wise similarity between generated images and target images.
The present study has some limitations. First, we did not explicitly compare the qualities of DL-FLAIR and conventional FLAIR images. Additional studies are required to investigate whether DL-FLAIR images with further image-quality improvement can serve as substitutes for conventional FLAIR images in clinical settings. Second, data from only 1 scanner were used in this study. Hence, future studies should assess the generalizability of our results using more patients and several scanners. Third, we did not perform a reading study to count the number of MS plaques. This was due to broad diffusely abnormal white matter and/or fused plaques in some of our patient data. Fourth, only 30 cases were used for training the DL algorithm used in this study. Considering the large number of pixels per case, we thought that 30 scans could substantially cover the possible signal distributions of the synthetic MR imaging raw data.
This work was supported by  Japan Society for the Promotion of Science KAKENHI  grant No. 16K19852 ; grant No. 16K10327 ; grant No. JP16H06280 ,  Grant-in-Aid for Scientific Research on Innovative Areas , resource and technical support platforms for promoting research " Advanced Bioimaging Support "; the  Japanese Society for Magnetic Resonance in Medicine ; the  Impulsing Paradigm Change through Disruptive Technologies (ImPACT) Program of the Council for Science, Technology and Innovation (cabinet office, Government of Japan ); the program for Brain Mapping by  Integrated Neurotechnologies for Disease Studies (Brain/MINDS)  from the  Japan Agency for Medical Research and Development (AMED) ; and  AMED  under grant number  JP18lk1010025.
10. 1029/2018JB017288 However, one of the drawbacks of this method is that the data coverage needs to be dense enough for the migration isochrons to stack up constructively. What makes Kirchhoff migration attractive is that only the traveltimes of the scattered phases need to be estimated, instead of the complex scattered wavefield required by other methods (RTM and GRT). The advantage is that the traveltimes can be quickly computed by solving the Eikonal equation. We compute them using the fast marching approach with the FM3D software package developed by de  Kool et al. (2006). Cheng et al. (2016)  showed that a fully 3-D Kirchhoff prestack migration is computationally tractable. Their method was tested using 2. 5-D synthetic data obtained from ray tracing  (Raysum, Frederiksen & Bostock, 2000) , as well as data from the Cascadia93, Mendocino, and USArray experiments.
Lastly, we implement a second-root stack that acts as an amplitude coherence filter. In the following sections, we derive our improved 3-D Kirchhoff imaging approach and discuss its ability to resolve complex 2-D and 3-D structures. Here, we only describe the method for use with the P-to-S RF, but one could devise a similar method for use with S-to-P RF. After describing in detail the method in section 2, we test it in section 3 by conducting a series of synthetic tests using the Raysum software  (Frederiksen & Bostock, 2000)  in both artificially challenging and realistic scenarios. We show that a typical subduction zone structure can be retrieved. Finally, we test our method on a field data set from Greece in section 4.
Synthetic Waveforms The synthetic data are generated with a ray-based approach for modeling teleseismic body waves in dipping anisotropic structures (Raysum software;  Frederiksen & Bostock, 2000). This approach computes the arrival times and amplitudes of all converted and reflected (i. e. , first-order free surface multiples) phases in a layered geometry. The advantage of this algorithm is that it is accurate and computationally efficient, as it uses analytical formulae to compute the traveltimes, amplitudes, and phase of the transmitted and scattered waves. The Raysum software can handle a large number of planar, homogeneous anisotropic layers with arbitrary strikes and dips. However, the models cannot contain velocity or anisotropy gradients inside the layers, and the layers themselves cannot intersect in regions that are traversed by rays.
Resolution Test In order to determine how well our method will be able to perform with this data set, we first perform a synthetic resolution test on a 2-D subduction model. We generate data for all 52 events recorded at a 35 stations setup corresponding to the MEDUSA south line. The results will be used to determine the maximum resolution we can achieve given the data coverage, maximum frequency, and station distribution. In order to better compare our image with GRT or RTM migrations, we project the locations of stations on the imaging line as previously stated. 14 ), (  16 ), and (  19 ); see text). The overriding Moho, the subducting crust upper and lower limits are clearly imaged. To perform this test, we design a three-layer reference model described in Table  1  as MRT. The first layer is the 40-km-thick overriding crust, then there is a 10% velocity increase at the Moho and another 10% velocity increase at the slab interface.
Since the amplitude of the velocity jump is similar for the two interfaces, we expect the two interfaces to show the same scattering potential on the final image. The data are generated with the Raysum code and has a dominant frequency of 0. 3 Hz. This corresponds to wavelengths up to 20 km for P waves and 10 km for S waves, so for the resolution test and the real data we use a 2 × 2 × 2 km voxel size. Because the backscattered S wave multiples have shorter wavelengths in the model space, we allow for the data to be migrated in different frequency bands for the various scattering (see, e. g. ,  Tauzin et al. , 2016). With an average station spacing of just under 10 km, a 40-km-deep Moho should be easily retrievable. Results for this synthetic resolution tests (with same frequency range for all modes) are presented in Figure  12.
For waveform inversion methods this is done by solving the wave equation in 3-D, which is expensive. In our method, this is done through the use of scattering patterns. We separate the calculation of the phase (traveltimes), which we obtain by running the FM3D software, from the calculation of the amplitudes, estimated using precise scattering geometry and scattering patterns, to avoid having to solve the wave equation in 3-D. This way of computing scattering intensities is similar to what is done in GRT and is faster than full-wavefield computations used in RTM methods. Also, by using fully 3-D scattering patterns, as opposed to their projection in 2-D, we can treat all scattered phases, including S V -to-S V and S H -to-S H scattering. As shown in section 2, the use of scattering patterns allows us to automatically process data from all slownesses and back azimuth without worrying about polarity and amplitude issues.
Conclusions In this study, we designed a new method to migrate teleseismic RFs and recover the three-dimensional distribution of scattering structure in the subsurface. In order to overcome the drawbacks of both fast CCP methods (which rely on the assumption that the underlying discontinuities are horizontal) and complex RTM or GRT migrations (which are too computationally expensive to be run in fully 3-D settings), we designed a new, computationally efficient and fully 3D multimode Kirchhoff migration approach. We adapted the Kirchhoff method from reflection to transmission scattering and applied it to passive seismic data. We expanded the work done by  Cheng et al. (2016)  to include three component data and free-surface multiples into an efficient multimode migration by computing the traveltimes for all scattered phases using the FM3D software.
This contact area is estimated by multiplying the length of the contact region measured in real time with S/TEM images and the specimen cross-sectional thickness measured by the ratio of zero loss/total low-loss EELS. Total error of contact stress is calculated by a quadrature of the errors from the contact length measured by human vision (±10%), the specimen thickness estimated by EELS image (±11%), and the load reported by the nanoindentation software (±5%). The contact area changes through subsequent indentations (Fig. 3 ) due to stage drift (typically 20-60 nm). Toughness (J/m 2 ) is defined as the absorbed mechanical energy, which can be bounded by integrating the stress-displacement curve to find the energy absorbed per unit area (Fig. 4j ). The tablet engineering strain along the c-axis is defined as the ratio of the reduction of the tablet width (that is, compressive deformation directly under the region of loading) to its initial width (Supplementary Fig.
F or decades, the weight of atomic-resolution structure elucidation, a requirement for understanding biomolecular mechanisms in detail, has been almost exclusively borne by X-ray crystallography, as highlighted by the fact that about 90% of all entries in the Protein Data Bank (PDB) are crystal structures. Notwithstanding this success of crystallography, many supra-molecular edifices, self-assembling systems, membrane proteins, and proteins with extended dynamic domains are difficult to crystallize, or the crystals do not diffract to high resolution. Single-particle cryo electron microscopy (cryo-EM) and nuclear magnetic resonance spectroscopy (NMR) are not bound to obtaining well-ordered crystals, and are applicable even in the presence of significant motions. Enabled by decisive instrumental and methodological progress, cryo-EM has recently made a leap in resolution  1, 2  , which opened avenues for determining protein structures at atomic detail.
This shows that the sample states are structurally comparable, which allows combining the data from solution-NMR and MAS NMR in a straightforward manner (see Supplementary Fig. 1 ). To obtain atom-specific assignments of these spectra we collected a series of   20  , shown as a function of the residue number. Residues shown with shorter bars were not assigned, and the secondary structure assignment results from a database approach in TALOS-N. e Experimentally detected intra-subunit long-range distance restraints from solution-NMR and MAS NMR, displayed through lines connecting residues in close spatial proximity. Note that part of these distance restraints were spectrally ambiguous, i. e. , could be assigned to several atom-atom pairs, and was rendered unambiguous throughout the structure calculation approach (displayed in red).
Even though TET2 is larger than any protein assigned to date to a similar extent by MAS NMR, the approach is sufficiently robust to obtain the near-complete assignment even in a fully automatic manner with the program FLYA  19  (Supplementary Fig. 3 ), showing that reliable assignments can be obtained without time-consuming manual spectra analysis. The assigned chemical shifts from MAS NMR allow the determination of ϕ and ψ backbone dihedral angles, and thus the secondary structure from a database approach, TALOS-N  20. NMR-detected secondary structure elements are displayed in Supplementary Fig. 2E. In order to gain tertiary structure information, we measured short inter-atomic contacts through (i) solution-NMR 3D nuclear Overhauser effect spectroscopy (NOESY) spectra and (ii) a 3D MAS NMR radio-frequency driven recoupling (RFDR) experiment (Supplementary Fig.
e. , of which the cross-peak frequencies pointed unambiguously to one atom pair and 45 spectrally ambiguous ones, i. e. , which could not be assigned to a single atom pair (but which could be unambiguously assigned along the structure calculation procedure, see below). The majority of these restraints (all 544 backbone dihedral restraints and ca. 80% of the distance restraints) was obtained from MAS NMR. Attempts to determine the TET2 subunit structure from these NMR data using a standard structure-determination protocol based on restrained MD, CYANA  22  , failed in achieving convergence to the correct structure (backbone RMSD to the mean of the ten lowest-energy structures and to the crystal structure above 10 Å, see Supplementary Fig. 5 ). In these NMRonly structure calculations, many local structural elements, e.
g. , the local packing of two α-helices or β-strands, are formed, but the positions of these elements relative to each other in the tertiary fold remained poorly defined, thus calling for additional data. Figure  1c  shows the cryo-EM map of TET2 at 4. 1 Å resolution, calculated from 27,130 single particles with the software RELION (see Methods section for details and Supplementary Fig. 6 ). We have attempted to build the structure of TET2 from this cryo-EM map using phenix. model_to_map  23 , in the crystallographic software suite Phenix  24. The program was able to trace the map, with better success when the symmetry of the particle was calculated from the cryo-EM map rather than inferred from the biological unit derived from the X-ray structure. However, the map quality was too low for Phenix to assign the sequence and thus build a model (Supplementary Fig.
7 ). It may have been possible to solve the structure at 4. 1 Å by careful manual building, or by using other computational tools. Nonetheless, the fact that Phenix, an advanced and widely used program for model building, fails in our case illustrates that even with 4. 1 Å resolution, de novo model building is far from trivial. This finding also highlights that the likelihood of succeeding with model building rapidly decays with decreasing resolution. The collection of a larger data set may have allowed obtaining EM data at near-atomic resolution. Nonetheless, at 4. 1 Å resolution, significantly better than the average of EM maps deposited over the last years, our EM data set is insufficient for de novo structure determination, highlighting the challenges that many EM data sets encounter. Despite the inability to trace the backbone chain in the density, a number of features, in particular α-helices, are readily recognized in the cryo-EM map, even at lower resolution and in a fully automatic lightly-supervised manner  25.
It also shows a uniform distribution of views with some accumulation of views along the symmetry axes as it is expected for objects with flat faces. The FSC plots in Supplementary Fig. 10B  show the EM map-to-model agreement for the various resolutions for both the X-ray structure (PDBid: 1y0r) and the atomic model resulting from proposed procedure (PDBid: 6f3k). Structure calculation. The protocol described in this work employed standard programs routinely used in NMR structure determination and EM fitting, namely CYANA (version 3. 97)  22  , XPLOR-NIH (version 2. 44. 8)  27  , iModFit (version 1. 44)  26  , helixhunter2  25, 50  , Phenix as well as in-house written scripts in python language (available from the authors upon request). Step 1a consists of the detection and assignment of α-helices.
We clustered these 12 × 5 helical densities into groups corresponding to individual subunits by an automatic approach based on the "density-based spatial clustering of application with noise (DBSCAN)" algorithm that was implemented in the Sklearn python package (https://pypi. org/project/sklearn/#history), and the outcome is shown in Supplementary Fig. 8. In essence, the underlying assumption is that the α-helices that are closest to each other belong to the same subunit. For the case of the five helices we considered in TET2, this assumption turns out to be correct. Note, however, that our approach does not rely on the validity of the assumption that the closest helices belong to one subunit; any other combinations, including (erroneous) assignments of one chain to densities belonging to different subunits can equally be considered in the CYANA calculations.
2b. Note that out of the 40 CYANA runs, 20 were stopped due to divergent target function energy, showing that these helix-to-density assignments were incompatible with the other NMR data. The 20 remaining calculations are shown in Fig. 2b. The second criterion for selecting the correct assignment was the goodness of the fit between the EM map and the structure, evaluated only for the five helices considered in a given helix-to-density assignment. Basically, the idea is to evaluate whether all residues of the helix reside within the experimental map; incorrect helix-to-density assignment would lead to a distorted structure and therefore the helices would not fit in the EM map. To implement this criterion in practice, electron-density maps were generated in silico from the lowest-energy CYANA model, using the backbone N, Cα, C, and O atoms of each of the residues in the five helices which were used in the given helix-to-density assignment (as identified by TALOS-N), using the module "molmap" in UCSF Chimera  52.
We compared these in silico maps to an experimental map, which comprised only the five α-helices considered (colored and labeled A-E in Fig. 1g , map truncated to 8 Å resolution). The module "measure correlation" in UCSF chimera was used to compute the correlation between the in silico maps (of each residue) and this experimental "helix-density" map. This correlation coefficient, obtained residue by residue, reflects the goodness of fit to the map. Examples are shown in Supplementary Fig. 9. As a global measure, we computed the percentage of residues that had a good overlap (>0. 7, in which 1. 0 and 0. 0 means good and bad overlap between the maps, respectively). This percentage is shown in Fig. 2b  as vertical axis. In step 2, an iterative assignment step, we performed rounds of flexible fitting using a common tool used in EM (iModFit  26  ) and an NMR-type structure calculation with CYANA.
For the latter we used the information about the match to the EM map, similarly as the five helix centers were restrained in step 1. This procedure is described as follows. The structure resulting from step 1 was taken as a seed to perform a normalmode based flexible fitting into the density map with the software iModFit v1. 44  26. After this procedure, we identified those protein regions which resided well within the experimental Coulombic potential, in order to be able to fix those residues in the following CYANA calculations. This identification how well the residues correspond to intensity in the experimental Coulombic potential map was done in a similar manner as the goodness-of-fit criterion described in the preceding paragraph, using UCSF chimera modules molmap (allowing to generate density maps from a PDB file) and "measure correlation" (allowing the computation with an experimental map), as follows.
This list of inter-atomic distances was generated using an in-house written python script, and was used in the same manner as the NMR distance restraints, using a tolerance of 0. 5 Å. NMR distance restraints with ambiguous assignments, i. e. , resulting from crosspeaks with frequencies that could be assigned to more than one atom-pair, can be disambiguated with the help of the intermediate structure obtained after the flexible fitting. Briefly, for cases with multiple possible assignments (based on NMR chemical-shift positions) we measured the corresponding distances in the intermediate structures for all those atom pairs that were possible assignment candidates based on their frequency. In cases where out of the possible candidates only one atom pair had short a (<8 Å) distance, this assignment was retained in the subsequent CYANA calculation.
Also in this region, restraints were added from residue 323 to residues 92 and 93 based on a subsequent reanalysis of unassigned RFDR cross peaks. The final XPLOR-NIH calculation was performed in implicit solvent with the 4. 1 Å resolution map, all NMR restraints and the Zn-site restraints. For the calculation with the lower-resolution maps (6, 8 Å), this refinement in implicit solvent has been omitted. The structure deposited as 6f3k in the PDB corresponds to the result of this refinement. In addition, we also investigated whether the result can be further improved with Phenix, a commonly used program in crystallography. The lowest-energy structures from the XPLOR-NIH runs, employing EM data at different resolution, were refined against the cryo-EM maps using phenix. real_space_refine  29.
1 Å, to allow a fair comparison of the low resolution models with that determined by performing step 3 at 4. 1 Å. Correlation coefficients between the cryo-EM map and the model were calculated using phenix. real_space_refine, and correspond to the CC mask described by Jiang and Brunger 56 based on map values inside a mask calculated around the macromolecule. RMSD between the various models and the biological unit derived from the X-ray structure (1y0r) were calculated using the "super" function in PyMOL. We also attempted to rebuild the TET2 structure de novo, by use of the 4. 1 Å cryo-EM map only, using phenix. model_to_map  23 , in the Phenix suite of crystallographic software  24. The program was able to trace the map, with better success when the symmetry of the particle was calculated from the cryo-EM map rather than inferred from the biological unit derived from the X-ray structure.