dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset: The dataset consists of a stack of single particle images of size 360 × 360 pixels and pixel size of 1. 05 Å (normalized so that the average of the image is zero and the standard deviation is 10) and 5 metadata files containing the orientation and translation parameters for 5 image classes obtained in  (Svidritskiy et al. , 2014)  using FREALIGN  (Lyumkis et al. , 2013). Two of these metadata files, with the parameters of 23,726 and 22,369 images, were used in  (Svidritskiy et al. , 2014)  to reconstruct two cryo-EM maps, accessible in EMDB database with the codes EMD-5976 (rotated conformation with 1 tRNA at resolution of 6. 2 Å) and EMD-5977 (nonrotated conformation with 2 tRNA at resolution of 6. 3 Å), respectively. Hamitouche, I. , Jonic, S doi: 10. 3389/fmolb. 2022. 965645 24 Data preprocessing and data splitting for neural network: After inspecting all 5 classes obtained in  (Svidritskiy et al.
, 2014)  (quality and number of images in each class as well as 3D reconstruction reproduced for each class), we decided to run DeepHEMNMA only on images used for reconstructing  095  images in total). The other 3 classes seemed less "clean" (many images seem to contain different objects than ribosomes) and the number of images in these classes was much smaller. Before running DeepHEMNMA, images were CTF-phase flipped and downscaled to the size of 128 × 128 pixels (pixel size: 2. 95 Å). Our preliminary tests with this experimental cryo-EM dataset have shown large angular prediction errors (with respect to HEMNMA estimation) for the network trained using 20,000 images (recall that this is the number of images used to train the network with synthetic data). Therefore, we decided to split the set of 46,095 images as follows: (1) 32,000 images for training; (2) 2,000 images for validation (adjusting hyperparameters of the network); (3) 12,095 images for testing (large test set), out of which 2,000 images for quickly testing and comparing the trained models (small test set).
Reference model and normal mode analysis: The reference model used by HEMNMA to calculate normal modes and to analyze images with these normal modes was a coarse-grain model of the nonrotated conformation, which was made by keeping only Cα and P atoms from the atomic model available in the PDB database under the code PDB:3j78 (the atomic model derived from EMD-5977 map in  (Svidritskiy et al. , 2014) ). The coarse-grain model had 17,082 atoms (Cα and P). Its normal modes were calculated using RTB block size of 20 residues and the interaction radius of 20 Å.
Selection of normal modes for image analysis with HEMNMA: Regarding the selection of normal modes, an option was to only select the mode that describes the rotation between the large and small subunits of the ribosome, which is often informative enough to separate different ribosome states, as shown in our previous work  (Jin et al. , 2014). However, we decided to include more normal modes to demonstrate, using this experimental dataset, the performance of our deep neural network learning and prediction of a larger number of normal-mode amplitudes. Therefore, in this work, we selected normal modes by analyzing the motion field between the conformations obtained in  (Svidritskiy et al. , 2014)  with FREALIGN. More precisely, we performed flexible fitting of the coarsegrain reference model (obtained from PDB:3j78) into EMD-5976 map, using 7 lowest-frequency non-rigid-body normal modes (modes 7-13), by employing our normal-mode-based 3D-to-3D flexible fitting approach of HEMNMA-3D  (Harastani et al.
Furthermore, we found that the additional mass in the map reconstructed using inferred parameters (Figure  8A ) could be better resolved if more images were used for this 3D reconstruction. We illustrate this by using a larger images with HEMNMA-estimated parameters (from 32,000 images used for network training). The 2D PCA space for this set of 22,095 images (Supplementary Figure  5 ) was split along the first principal axis into two groups of images, one with 7,870 images and the other with 6,682 images (Supplementary Material H). The 3D reconstructions from the latter two groups of images (Figure  8E  The need to use more images for 3D reconstruction in order to better resolve the tRNA could be explained by a larger conformational heterogeneity of the dataset. In Figure  9 , we show more extensively the conformational variability using 3D reconstructions from a larger number of groups of images selected along the first principal axis of the 2D PCA space of the 12,095 images used for the inference.
The PCA space was split quasi-uniformly in the way to get at least 900 images per group. One can note a variable degree of rotation between the small and large subunits as well as the presence and absence of the additional tRNA over the seven maps reconstructed from  1018, 1148, 1461, 1816, 1771, 975 , and 949 images (Figure  9 ). This experiment shows that DeepHEMNMA can be useful for extensive analyses of conformational variability of biomolecular complexes, where large sets of experimental single particle images can be obtained. At least 30,000 particle images would be required for the neural network training. To take full advantage of the power of the trained network, one should aim at analyzing millions of single particle images. The analysis of such large datasets is not practical with conventional methods, whereas it has a low computational cost with trained neural networks.
The network is a ResNet 34 feature extractor followed by a multilayer layer perceptron. The trained network is then used to predict the parameters from the remaining images (unseen during the training). Finally, the conformational landscape is obtained by mapping the inferred normal-mode amplitudes onto a lower-dimensional space, which allows 3D reconstructions Hamitouche, I. , Jonic, S doi: 10. 3389/fmolb. 2022. 965645 29 using the inferred angles and shifts. Also, this space allows animations of a model displacement and identification of possible hidden conformations. We described this new approach and showed its performance with synthetic and experimental data. Using a synthetic dataset and a publicly available experimental dataset, we demonstrated a good generalization capability of the network (no overfitting against the training data), meaning that the trained network is able to accurately predict the conformation, orientation, and position of the molecule in the images that were not used for the training.
However, in the future, we will add an option to our open-source DeepHEMNMA software to allow a combined training for all three types of parameters, which is expected to be faster than the separate training for each parameter type, for the same size of the training dataset. DeepHEMNMA is a standalone method that can be used independently of other conformational variability methods (such as those of cryoSPARC, RELION, or Scipion) if a 3D model of one conformation of the complex can be provided (e. g. , an atomic model from PDB database or a cryo-EM map from EMDB database). DeepHEMNMA does not use the particle pose information to learn the conformations (i. e. , the normal-mode amplitudes, which together with normal modes describe the conformation). In DeepHEMNMA, the poses are only used for calculating 3D reconstructions from the groups of images selected based on similar conformations in the conformational space but not for obtaining this conformational space.
The poses in DeepHEMNMA are obtained by prediction. Alternatively, after predicting the conformations, the poses could be determined by classical rigidbody 2D-to-3D alignment the images with the density volumes simulated from the predicted conformations. This option could be useful in some cases, such as small number of images, and will be provided in the future. DeepHEMNMA was developed for analyzing large sets of images where pose prediction is more suitable. If the images are processed with other software before using DeepHEMNMA (e. g. , cryoSPARC, RELION, Scipion, etc. ), the poses determined by this software could be used instead, but their accuracy may be lower than the accuracy of the poses predicted by DeepHEMNMA or those determined by the rigid-body 2D-to-3D alignment of the images with the density volumes from DeepHEMNMA predicted conformations.
This option may be provided in the future for the users willing to test it. In this article, we reported the times required for the neural network training from scratch (without pre-training). Retraining a pre-trained model can help sometimes. However, when the datasets vary significantly in terms of conformational heterogeneity, SNR and CTF, and in particular with experimental data, we noticed that retraining a pre-trained model may take approximately the same time to converge as the training from scratch. We tested different batch sizes for training  (2, 8, 10, 16, 64, and 128)  and obtained the best trade-off between processing time and accuracy with the batch size of 2. In particular, training with the batch size of 2 helped to avoid overfitting, together with other types of regularization (dropout and L2 regularization).
31 For running HEMNMA, the smallest recommended image size is 128 × 128 pixels. A reason for this is that HEMNMA uses a 2-level multiresolution data pyramid that includes further data downsampling (processing on the 64 × 64 pixel level first and then refining on the 128 × 128 pixel level). Another reason is that we recommend using HEMNMA with a rigid-body 3D-to-2D image alignment in wavelet domain (for robustness to noise) and the implemented wavelet transform requires the image size that is a power of 2. This was explained in the earlier publications of HEMNMA. We have not performed systematic tests of the neural network training with images smaller than 128 × 128 pixels. As in the case of HEMNMA, DeepHEMNMA software will be publicly available (upon the article publication) as part of ContinuousFlex  (Harastani et al.
for the inferred parameters, 3. 0-3. 1 Å for the ground-truth parameters, and 3. 0-3. 3 Å for HEMNMAestimated parameters. Speed assessment: DeepHEMNMA is faster than HEMNMA alone and it is even faster for larger datasets. The wall-clock times of HEMNMA, network training, and network inference are provided in Supplementary.
The 2D PCA space obtained for the set of 12,095 test images (Supplementary Figure4) was split along the first principal axis into two groups of images, one with 4,741 images and the other with 4,219 images (Supplementary Material H). The two 3D reconstructions obtained from these two groups (Figure8A-D) indicate two different average conformations, with an additional mass in one reconstruction where the additional tRNA is expected (the region indicated by a red ellipse in Figure8A) and without this additional mass in the other reconstruction (Figure8B). The reconstructions obtained using FREALIGN metadata files from EMPIAR-10016 (Svidritskiy et al. , 2014)  (Figure8I-L) show similarity with those obtained with DeepHEMNMA (Figure8A-D and Figure8M-P). Note however that the two reconstructions from FREALIGN metadata files were obtained using 22,369 and 23,726 images (related to EMD-5977 and EMD-5976 maps, respectively).
plugin (https://github. com/scipion-em/scipion-em-continuousflex) for Scipion V3 (de la Rosa-Trevín et al. , 2016), including graphical user interface giving the user the opportunity to easily use DeepHEMNMA on hybrid CPU-GPU architectures. Ilyes Hamitouche and Slavica Jonic IMPMC -7590 CNRS, Sorbonne Université, MNHN, 4 place Jussieu, 75005 Paris, France.
ACKNOWLEDGMENTS We acknowledge the support of the  French National Research Agency -ANR  ( ANR-19-CE11-0008-01 ) and the access to HPC resources of CINES and IDRIS granted by  GENCI  ( A0100710998R ,  A0100710998 ,  A0070710998 ,  AP010712190 ,  AD011012188 ). We thank  Dr. Bruno Klaholz  ( IGBMC, Illkirch ) for discussions regarding the experiments with ribosome data.
is the Supplementary Material of an article accepted for publication in Front. Mol. Biosci. Sec. Structural Biology, https://doi. org/ 10. 3389/fmolb. 2022. 965645 SUPPLEMENTARY MATERIAL TO: DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images plugin (https://github. com/scipion-em/scipion-em-continuousflex) for Scipion V3 (de la Rosa-Trevín et al. , 2016), including graphical user interface giving the user the opportunity to easily use DeepHEMNMA on hybrid CPU-GPU architectures. Ilyes Hamitouche and Slavica Jonic IMPMC -7590 CNRS, Sorbonne Université, MNHN, 4 place Jussieu, 75005 Paris, France This Corresponding author: Slavica Jonic IMPMC -UMR 7590 CNRS Sorbonne Université, CC 115 4 place Jussieu, 75005 Paris, France Phone : +33 1 44 27 72 05 Fax : +33 1 44 27 37 85 E-mail : slavica.
g. ,  [37] [38] [39] [40]. A worst-case scenario is that one might claim a wrong cosmology, e. g. a time-varying dark energy model, from a given dataset due to inaccurate theoretical templates. In this paper we estimate cosmological parameters by comparing halo model predictions to the clustering observables, galaxy-galaxy weak lensing and the projected auto-correlation function of galaxies, measured from the Subaru HSC Year 1 datasets [hereafter HSC-Y1; 3] and the spectroscopic LOWZ and CMASS galaxy samples of the Sloan Digital Sky Survey (SDSS; York et al. [41] ). In this paper we use luminosity-limited, rather than fluxlimited, samples of LOWZ and CMASS  [42]  galaxies because those samples are nearly volume-limited in each of the redshift bins we use, and display weaker redshift evolution of the clustering properties in each bin than do the flux-limited samples, allowing us to use redshiftindependent model predictions in the parameter inference  [23, 24].
1  in Ref. 50 ]. The HSC Wide survey footprint overlaps fully with the SDSS sky coverage. Mandelbaum et al. [50]  and Oguri et al. [54]  carried out various null tests to show that the shear catalog satisfies the requirements of HSC-Y1 science for both cosmic shear and galaxy-galaxy weak lensing analyses  [144]. In Appendix B, we give further null tests that are specific for cosmology analysis with the galaxy-galaxy lensing measurements based on the HSC-Y1 and SDSS datasets. The shape catalog includes the following quantities relevant to our weak lensing analysis. The shape catalog has the PSF-corrected galaxy ellipticity (e 1 , e 2 ) = (e cos 2φ, e sin 2φ), where φ is the position angle. Since the ellipticity is defined in terms of distortion, i. e. , e = (a 2b 2 )/(a 2 + b 2 ), where a or b is the major or minor axis, one needs to apply the appropriate responsivity factor when estimating weak lensing shear from galaxy shapes (see Section III A 1 for details).
Source galaxy catalog for galaxy-galaxy weak lensing Thanks to the depth of the HSC-Y1 data, we can define a secure sample of source galaxies behind lens galaxies, for galaxy-galaxy weak lensing measurements. In this paper we select three distinct samples of lens galaxies from the database of spectroscopic SDSS galaxies up to z = 0. 7. To select background galaxies, we use photometric redshift (hereafter photo-z) estimates of each HSC galaxy. Multiple photo-z catalogs using different algorithms are available [see 55, for details]. For our fiducial analysis, we use the MLZ method  [56]  as for our fiducial catalog; in Section VI A we will discuss how the choice of different photo-z algorithms affects the cosmological results. In this paper, following the method in Oguri and Takada  [21] , we use a single sample of source galaxies for the galaxy-galaxy weak lensing measurements for all three samples of lens galaxies in different redshift bins (see below).
As in the ∆Σ measurement, the conversion of angular separation and redshift difference between paired galaxies to the three-dimensional separation (R, Π) requires the use of a reference cosmology, which will in general differ from the true cosmology. We use the method in Ref. [91]  to correct for the conversion with varying Ω m for the flat ΛCDM cosmology. We estimate the covariance matrix of w p (R) using the jackknife method of the actual SDSS data for each of the LOWZ, CMASS1 and CMASS2 samples. We utilize 192 jackknife regions of the SDSS survey footprint [see 23, for details], measure w p (R) from each jackknife region and then estimate the covariance matrix from the measured w p (R) from all the jackknife realizations. In the bottom panels of Fig. 3  we show the measured signals of w p (R) for the LOWZ, CMASS1 and CMASS2 samples.
A. ΛCDM Constraints We show the cosmological parameter constraints for the flat ΛCDM model, which is the minimum theoretical framework that fairly well reproduces a broad range of cosmological observations. In particular, we will focus on the cosmological parameters Ω m , σ 8 and S 8 ≡ σ 8 (Ω m /0. 3) 0. 5 , which are well-constrained by our measurements. Fig. 6  shows the posterior distributions for (Ω m , σ 8 , S 8 ), obtained from the baseline setup in Table  IV Thus the HSC-Y1 data, combined with the SDSS dataset, can constrain S 8 to about 6% fractional precision. Note that we show the posterior distribution in the full parameter space in Fig. 21  in Appendix E. We check the convergence of our nested sampling results as described in detail in Appendix D. We have confirmed that our run terminates at the point where the relative posterior mass is sufficiently small, and the standard deviation of the mode values of the 1-d projected S 8 posterior distributions is 0.
The S 8 parameter was originally introduced to describe the combination of σ 8 and Ω m that cosmic shear can best constrain. However, this combination is not generally optimal for other observables. Thus, we extract the "optimal" constraints for each survey by defining the parameter S 8 (α) = σ 8 (Ω m /0. 3) α and varying α to find the tightest constraint. For each survey, we find the tightest 68% credible interval: S 8 (α = 0. 17) = 0. 745 +0. 039 -0. 031 for HSC-Y1, S 8 (α = 0. 61) = 0. 764 +0. 030 -0. 030 for DES-Y1, and S 8 (α = 0. 58) = 0. 758 +0. 017 -0. 019 for KiDS-1000. Therefore, our HSC-Y1 analysis has comparable constraining power to DES-Y1. We expect the cosmological parameter constraints to tighten significantly due to the larger area coverage of subsequent HSC datasets  [123]  and by further combining the cosmic shear information with the joint-probe measurements in this paper, i.
Comparison with different analysis methods of HSC-Y1 In Fig. 13  we compare the cosmological parameters obtained from the different analysis methods and/or observables using the same HSC-Y1 dataset. The orange contours, denoted by the "minimal bias" model, are from our companion paper  [45] , obtained using the same signals as used in this paper, but with even more restrictive scale cuts, because they used a perturbation theoryinspired model as the theoretical template to interpret the large-scale information in the ∆Σ and w p signals. Hence, the results of the minimal bias model can be considered a conservative estimate of cosmological parameters. From the comparison, it is clear that interpreting the small-scale information using Dark Emulator improves our ability to constrain cosmological parameters, even after marginalizing over nuisance parameters.
However, there is a shift in the central values of cosmological parameters between our results and the minimal bias method, despite using the same dataset. As discussed above, we used noisy mock data vectors to test whether such shifts in cosmological parameter constraints for these two methods can occur due to sample variance. We found that 3 out of 10 noisy mock realizations exhibit shifts in cosmological parameters similar to those found in the real data. Hence we conclude that the difference between the central parameter values for our method and Sugiyama et al. [45]  are likely due to sample variance. 12: Similar to Fig. 6 , but this figure compares our results with those from other cosmological experiments assuming a flat ΛCDM cosmology. The blue curves (contours) are the main results of this paper, the same as those in Fig.
6. The orange curves are from the DES-Y1 analysis using galaxy-galaxy lensing ("GGL") and clustering signals ("GGC")  [6]. The green curves are constraints from the KiDS1000 analysis with cosmic shear ("CS") and GGL  [12]. The red curves are the Planck 2018 results using the primary CMB anisotropy information ("TT,TE,EE+lowE")  [15]. The HSC-Y1 cosmic shear result  [4] , which does not use any information from the SDSS galaxies, is consistent with our results to within the credible intervals, but the central values display a offset, especially in the Ω m direction. While our constraints are dominated by the SDSS clustering information, we note that Hamana et al. [5] , in their Section 6. 7, showed that apparently large scatters in the central values of the cosmological parameters between different analysis methods could occur due to the sample variance and the use of different ranges of scales even for cosmology inference using the same HSC-Y1 dataset.
To perform a robust analysis, we have defined the luminosity-limited, rather than the flux-limited, samples from the SDSS galaxies to serve as the tracers of w p in the three redshift bins in the range 0. 15 < z < 0. 7, and as the lens samples of ∆Σ. For the ∆Σ measurements, we have opted to use a single sample of background galaxies selected from the weak lensing HSC source catalog with photo-z information greater than 0. 75. The HSC-Y1 dataset, despite the relatively small area (about 140 deg 2 ), allows for a significant detection of the galaxy-galaxy weak lensing signals thanks to both the depth and the high imaging-quality of the data (see Fig. 3 ). On the theory side, we have employed the public code Dark Emulator to accurately model the clustering observables down to small scales. The validation of our cosmological analysis was demonstrated in previous work  [1].
Our results are generally consistent with both Planck and other weak lensing constraints (DES and KiDS) to within the statistical errors (Fig. 12 ). However, if we take the intersection of the (Ω m , S 8 )-posterior distributions at Ω m = 0. 3, our result indicates a possible tension for σ 8 compared to the Planck 2018 result, similarly to those indicated by other weak lensing constraints [e. g. 4]. We performed various tests using the different scale cuts, the extended theoretical template, and the different combinations of datasets, but did not identify any significant signature of residual systematics in our results (Fig. 11 ). There are several ways in which the constraints from this paper could be improved. As can be seen from Fig. 12 , the degeneracy direction in the Ω m and σ 8 space for our method is different compared to that in the cosmic shear constraint, which is usually characterized by the direction of σ 8 (Ω m /0.
In fact, such a shift in the cosmological parameters for the different samples were also seen from the cosmological analysis of redshift-space galaxy clustering [e. g. see Fig. 2  in  36, 138]. Since the shift in the parameters are not large compared to the current statistical errors, we leave this to our future work using a larger dataset from the ongoing HSC survey. One notable feature of this paper is a conservative treatment of the nuisance parameters, i. e. the photo-z biases and the multiplicative shear biases, which are among the most important systematic errors in weak lensing. Throughout this paper, as a conservative approach, we employed rather broad priors for these nuisance parameters, ∆z ph and ∆m γ , and derive cosmological constraints after marginalization over the nuisance parameters.