dataesr/scientific-paragraphs-categorization huggingface dataset:
3D structure determination from cryoChip samples To validate the suitability of cryoChips for 3D structure determination, we collected datasets for three test specimen (P. furiosus ferritin (ApoFtn, 480 kDa, O), T. acidophilum 20 S proteasome (T20S, 700 kDa, D7) and tobacco mosaic virus (TMV, 37 MDa, helical)) that we selected for differences in size, shape and symmetry. The low magnification maps revealed that most observation squares were suitable for data collection. We occasionally observed partially broken or cracked membranes. Observation squares in such areas were omitted for data collection. Micrographs for all three specimens showed good image contrast (Figure  4a ). To examine the compatibility of cryoChips with the condition to form vitreous ice, we evaluated the fraction of images containing crystalline ice by manual inspection for the presence of ice rings or crystalline reflections in the computed power spectra from all images.
We used 2D and 3D classification to select a final set of 21,238 particles (O symmetry) from 815 micrographs and after 3D refinement obtained a reconstruction at an overall resolution of 3. 0 Å displaying visible side chain densities (Figure  4c/d ). The reference model for 3D alignment was generated from the data itself using the stochastic gradient descent method (Figure  4 -figure supplement 4), suggesting that the quality of data collected from cryoChips is sufficient to allow initial model generation even for samples considered difficult for reference model generation. To demonstrate the versatility of cryoChips for structure determination from a variety of biological specimens, we also processed the datasets for TMV and T20S proteasome (Figure  4 -figure supplement 5, Figure  4 -figure supplement 6).
These datasets were collected on a JEOL JEM3200-FSC with a K2 detector and yielded 3D reconstructions with overall resolutions of 3. 7 Å and 5. 4 Å obtained from 14,238 segments (20 asu/segment) and 5750 particles (D7), respectively (Figure  4f , Supplementary file 1). The quality of a dataset is often quantified from a plot of the inverse-squared resolution as a function of the number of particles used in the 3D reconstruction. The slope of this function is related to a B-factor that describes the width of a Gaussian dampening function in reciprocal space used to model the loss of high-resolution information in particle images owing to a combined effect of performance-reducing factors related to the sample, data collection and  instrumentation, and data processing  (Rosenthal and Henderson, 2003).
We quantified the B-factors for each of the three datasets (Figure  4g ). For comparison, we also acquired datasets for ApoFtn and T20S on conventional holey carbon supports with the same acquisition parameters, yielding reconstructions at an overall resolution of 2. 4 and 3. 2 Å (Figure  4 -figure supplement 7, Supplementary file 2). This leads to B-factors of 217 Å 2 vs. 178 Å 2 for ApoFtn and 490 Å 2 vs. 328 Å 2 for T20S determined for cryoChip and holey carbon datasets, respectively. We also note a consistent upward shift of the offset for holey carbon B-factor plots compared to those from cryoChips, consistent with the differences in overall signal-to-noise ratio caused by additional scattering from the SiN x membrane.
To investigate beam-induced sample movement in cryoChips, we computed and analysed the alignment trajectories for the ApoFtn dataset (Figure  5a ). The calculated traces show that movement is largely unidirectional, but clusters into several distinct directions of motion. To further investigate the trajectories, we separated them either by chip or by the beamimage shift condition corresponding to the shot position in the multishot acquisition scheme for each acquisition point (Figure  5b , Figure  5 -figure supplement 1). For each of the chips analysed, we observe three clusters of directional motion, each associated with one of the three shot positions. While the precise origin for this clustering needs further investigation, we suggest that different thickness and geometry of the confined ice layer in the subregions for each of the multishot positions may lead to preferential directionality of specimen movement, possibly combined with microlensing effects resulting from adjacent exposures that induce the build-up of semi-static charge on the nonconductive SiN x membrane.
To compare average specimen movement in cryoChips with other commonly used specimen supports we also computed the alignment trajectories for four datasets in the EMPIAR database Colour coding is equivalent for panels (c-g). Micrographs in (a) were taken at a defocus of -1. 2 µm for ApoFtn, -1. 8 µm for TMV and -2. 8 µm for T20S. Scale bars are equivalent for images in (a) and class averages in (b). The online version of this article includes the following source data and figure supplement(s) for figure  4 : Source data 1. Raw data for vitrification statistics. Source data 2. Raw data Fourier Shell Correlation (FSC) curve for ApoFtn dataset. Source data 3. Raw data Fourier Shell Correlation (FSC) curve for T20S dataset. Source data 4. Raw data Fourier Shell Correlation (FSC) curve for TMV dataset.
Source data 5. Raw data B-factor plot for T20S dataset. Source data 6. Raw data B-factor plot for TMV dataset. Source data 7. Raw data for COMSOL simulation. (Iudin et al. , 2016)  and ApoFtn on holey carbon (Figure  5c , Figure  4 -figure  supplement 7b, c ). These datasets have been acquired using different specimen supports but resulted in high-resolution 3D reconstructions (Supplementary file 4). We then computed the accumulated movement as the root mean squared deviation (RMSD) of all trajectories from the ApoFtn cryoChip dataset and the EMPIAR datasets and evaluated them over an equivalent exposure range (Figure  5d ). The datasets show small differences in accumulated movement over the whole exposure, ranging from 3. 8 (QF-R2/2) to 5. 1 Å (cryoChip) RMSD over 0-40 e -/Å 2. These movements are in the order of 0.
1 pixel per frame and are therefore amenable to motion correction. These overall motions represent a combination of mechanical (stage) drift and beam-induced motion. Limiting motion in early frames is essential to maximise recovery of high-resolution detail. EMPIAR-10306 has a remarkably low initial movement of 0. 7 Å, while both our dataset and EMPIAR-10389 display a accumulated motion of 1. 8 Å. EMPIAR-10389 led to a 2. 0 Å reconstruction of Y. enterocolitica urease, indicating that initial specimen movement of that order is not limiting for achieving resolution beyond 2. 0 Å. Preferential partitioning to the solid-liquid interface effectively concentrates protein particles Interaction with the air-water interface may cause perturbation of molecular structure in thin film-based cryo-EM sample preparations.
Similarly, we find preferential abundance of side views for T20S particles (Figure  4 -figure supplement 6). We did not observe convincing correlation of preferred orientations with the molecular electrostatic potential (Figure  6 -figure supplement 3). We note that the current analysis is limited to particles with internal symmetry for which preferred orientations are less problematic. Further investigation will be required to conclude whether systematic effects for preferred protein adsorption to the solid-liquid interface exist and need to be addressed. Micrographs from datasets of all three specimen display particle densities exceeding those expected from conventional cryo-EM grids prepared with the same specimens and sample concentrations. To quantify this concentration effect, we estimated the particle concentration in the nanochannels by segmenting and counting ApoFtn particles, and measuring the channel volume from the reconstructed tomogram (Figure  6 -figure supplement 1).
The cantilever-based sample application efficiently drives chip filling via capillary action, thus allowing essentially loss-less cryo-EM sample preparation. Together with the highly reproducible sample thickness we expect these properties to accelerate developments towards full automation of the cryo-EM sample preparation process. Notably, sample preparation using cryoChips does not require humidity control and is compatible with any standard plunger available in cryo-EM laboratories. Using cryoChip supports, we demonstrated ab initio structure determination for three different test specimen resolved at resolutions suitable for mechanistic interpretation even from datasets with a comparatively small set of particles. The diversity in shape, size, and physico-chemical properties of the macromolecular complexes suggests that cryoChip sample preparation should be applicable for routine cryo-EM imaging of a wide variety of different macromolecular targets.
Electron cryo-tomography A cryoChip with ApoFtn at 3. 4 mg/mL concentration was prepared as described above and stored in liquid nitrogen until imaging. Tilt series of cryoChips were acquired on a JEOL JEM3200-FSC with a bidirectional tilt scheme starting from 0° to -60° and subsequently from 2° to 60°, in increments of 2° with a total exposure of 94. 6 e -/Å 2 , a pixel size of 4. 706 Å and a defocus target of 6 µm. Micrographs acquired as dose-fractionated movies of 30 frames were motion-corrected in MotionCor2 v1. 0  (Zheng et al. , 2017)  and dose-weighted according to their accumulated dose  (Grant and Grigorieff, 2015). The tilt series was aligned using patch tracking and reconstructed using back-projection as implemented in Etomo from the IMOD package  (Mastronarde and Held, 2017). Segmentation of silicon nitride membrane and particles was done in the tomoseg program of EMAN2 using a machine learning approach  (Chen et al.
Single-particle imaging of ApoFtn Three cryoChips with ApoFtn at 3. 4 mg/mL were sent to EMBL Heidelberg for data acquisition. A grid map of the whole observation membrane was acquired for each chip. Acquisition points were set up to automatically acquire three images in a circle by beam-image shift in SerialEM  (Mastronarde, 2005)  using the 'multiple Record Setup' setting. Six acquisition points were placed in the nanochannel section of each observation window, which leads to 18 images per nanochannel section. Using py-EM  (Schorb et al. , 2019) , reference maps were generated from the grid map to find back and align to the acquisition points with high precision. Coma-free alignment was performed on the collapsed silicon nitride membrane adjacent to the nanochannels. Movies of 90 frames were acquired during a 9 second exposure on a Krios (ThermoFisher Scientific) microscope with a Quantum-K2 detector.
The total exposure of the image was 63 e -/Å 2 , the pixel size 0. 8127 Å/pix and the dose rate ~2 e -/pixel/s. The defocus range was set to -1 to -2 µm. In total 945 movies were acquired from three cryoChips. A reference dataset of 276 movies were acquired for ApoFtn on QF1. 2/1. 3 holey carbon grids using the same data acquisition settings.
Single-particle imaging of T20S and TMV Datasets comprising 121 movies of the T20S proteasome at 1. 4 mg/mL and 82 movies of TMV at 1. 1 mg/mL were acquired on a JEOL JEM 3200-FSC microscope operated at 300 kV. For T20S, we used cryoChips with a slightly different design featuring orthogonal nanochannels arranged in a cloverlike pattern instead of straight nanochannels (Figure  6 -figure supplement 1). Both TMV and T20S single-particle datasets were acquired on a Quantum-K2 detector at a magnification of 30,000x (1. 288 Å/pixel) and an exposure time of 10. 95 seconds with a total exposure of 59. 68 e -/Å 2 over 73 frames. A reference dataset of 879 movies were acquired for T20 on QF1. 2/1. 3 holey carbon grids using the same data acquisition settings.
Retained particles were used for initial model generation by cryoSPARC's stochastic gradient descent method while imposing O symmetry. The resulting reference volume was low-pass filtered to 15 Å and used in homogeneous refinement, leading to a 3D reconstruction at 3. 7 Å resolution. Per-particle defocus refinement and beam-tilt refinement improved the resolution to 3. 0 Å. Heterogenous refinement with three classes was used to further prune particles and lead to a 3. 0 Å resolution reconstruction from 21,238 particles. In all cases, O symmetry was imposed during reconstruction. 276 movies of a reference dataset of ApoFtn on QF1. 2/1. 3 holey carbon grids were processed in the same way and led to a reconstruction at 2. 4 Å resolution. Eighty-two movies from two cryoChips filled with TMV were imported in cryoSPARC 3.
The initial model thus obtained was low-pass filtered to 30 Å and used for homogenous refinement, yielding a reconstruction at 7. 9 Å resolution. After heterogenous refinement with three classes, particles in the class with highest resolution (5,750 particles) were selected and subjected to another round of homogenous refinement yielding the final reconstruction at 6. 0 Å resolution. Per-particle defocus and global CTF refinement (tilt & trefoil) further improved the resolution to 5. 4 Å. All reconstructions had D7 symmetry imposed. 879 movies of a reference dataset of T20S on QF1. 2/1. 3 holey carbon grids were processed in the same way and led to a final reconstruction at 3. 2 Å resolution. For all final reconstructions, five independent runs of ResLog  (Stagg et al. , 2014)  analysis were performed and the results averaged for ResLog curves.
Atomic model building Atomic models for TMV (PDB ID 4udv) and T20S (PDB ID 3j9i) were rigid body-fitted into the density maps and not further refined. The atomic model from an available crystal structure of ApoFtn (PDB ID 2x17)  (Kasyutich et al. , 2010)  was rigid body-fitted into the cryo-EM density, and real-space refinement in Phenix 1. 13  (Adams et al. , 2010)  was iterated with manual model building in Coot 0. 8. 9. 2  (Emsley et al. , 2010). Model validation statistics were retrieved from MOLPROBITY  (Chen et al. , 2017)  as implemented in Phenix and overfitting was probed by half-map refinement.
Beam-induced motion trajectory analysis Four datasets (10200  [Zivanov et al. , 2018] , 10272  [Naydenova et al. , 2019] , 10306  [Weis et al. , 2019] , 10389  [Righetto et al. , 2020] ) from the Electron Microscopy Public Image Archive (EMPIAR  [Iudin et al. , 2016] ) were subjected to full-frame motion correction in cryoSPARC  (Punjani et al. , 2017)  to calculate motion trajectories and were compared with equivalently derived trajectories from the cryoChip ApoFtn dataset.
Kymograph generation Movies of the filling process for each cryoChip were acquired with a Toolcraft USB-microscope (Conrad Electronics GmbH, Germany) at 10 frames per second and a pixel size of 1. 6 µm. A 10 pixelwide section along the supply or exit microchannels was averaged and plotted over time. For each time point, coordinates of the liquid front were manually picked and individually plotted for each chip. A square root function of the form x(t) = √ a(tb) + c was fitted to each filling trajectory using the curve_fit function from the scipy package  (Virtanen et al. , 2020). For error estimation of the fitting parameters, a bootstrapping approach was used, performing the fit 100 times with a random set of n out of n coordinates with replacement. The parameter and its uncertainty was estimated as mean and standard deviation over the 100 fits.
The specific heat capacity c p for silicon nitride was assumed as 700 J/(kg K) and its density as 3100 kg/m 3. For thermal conductivity of SiN, we averaged values from  Ftouni et al. , 2015  for thin membranes over the relevant temperature range to obtain 2. 3 W/(m K). For silicon, heat capacity was assumed as 700 J/(kg K) , thermal conductivity as 130 W/(m K) and density as 2329 kg/m 3. The following previously published datasets were used:.
Acknowledgements We thank  Yifan Cheng  for the expression plasmid of T20S proteasome and  Carsten Sachse  for the expression plasmid of ApoFtn. We acknowledge the EMBL cryo-EM imaging platform for access to their facilities, the staff from the  Kavli Nanolab Delft  for cleanroom support and the  Department of Bionanoscience  for generously supporting our cryo-EM facility. We thank  Cecilia de Agrela Pinto  and  Wim Hagen  for critically reading the manuscript and helpful suggestions; and  Jacob Hoogenboom  for insightful discussion. This work was supported by the  European Research Council  ( ERC-StG-852880  to AJ), the  Dutch Research Council  ( NWO. STU. 018-2. 007  to AJ) and the  Kavli Institute of Nanoscience Delft.
Data availability Atomic coordinates have been deposited in the Protein Data Bank (PDB) under accession code 7ohf. Cryo-EM densities have been deposited in the Electron Microscopy Data Bank (EMDB) under accession codes EMD-12901, EMD-12903 and EMD-12915. Tomographic reconstructions have been deposited in the EMDB under accession codes EMD-12902, EMD-12914 and EMD-12917. Raw micrographs for all cryo-EM data have been deposited in the Electron Microscopy Public Image Archive (EMPIAR) under accession code 10708. Numerical data supporting graphs in Figures 1, 4, 5, Figure 2-Figure supplement 1, Figure 4-Figure supplement 2 and Figure 5-Figure supplement 1 are provided in Source Data files. The following dataset was generated: Author(s).
Numerical experiments The performances of the proposed methods in this paper have been examined by several numerical instances inspired from the literature  [27]. For this purpose, the heuristic algorithm, the genetic algorithm and the memetic algorithm have been coded by C++ language in Visual Studio 2015, and ILOG CPLEX 12. 5 is used to solve the instances with the exact method (Branch and Cut) on a computer with a 2. 6 GHz Intel Core i5 processor and 8 GB of RAM. Through some examination, we find that the computational performances of the presented methods are mainly influenced by the following parameters: the number of jobs, speeds, and periods (n, v, T ); the processing times (p j,i ); the energy consumption of the jobs (q j,i ); the electricity prices (c t ). For each size of the instances (n, v, T ) ten different examples are randomly generated.
1% for HA, and 18. 4% for the GA. These large gap between HA to MA, and GA to MA, demonstrate that the efficiency of the HA and GA will decrease when we increase the size of the instance. On the other hand, these results show the high performance of the MA comparing to two other methods. The average computation time of these problems for heuristic, genetic, and memetic algorithms are equals to 7. 82 s, 34. 37 s, and 61. 59 s, respectively. Moreover, an analysis of the variance (ANOVA) with a confidence level of 95% was taken using the Minitab. 17 software to check the statistical validity of the results (Figure  12 ). As can be seen, the minimum variation range of gaps belongs to GA for instance 1  (2, 3, 15) , and the maximum variation range of gaps belongs to HA for instance 8 with a problem size of  (5, 5, 40).
We tested DSF on public EEG data encompassing ∼4,000 recordings with simulated channel corruption and on a private dataset of ∼100 at-home recordings of mobile EEG with natural corruption. Our proposed approach achieves the same performance as baseline models when no noise is applied, but outperforms baselines by as much as 29. 4% accuracy when significant channel corruption is present. Moreover, DSF outputs are interpretable, making it possible to monitor channel importance in real-time. This approach has the potential to enable the analysis of EEG in challenging settings where channel corruption hampers the reading of brain signals.
g. , neurologists, sleep technicians, etc. ) to analyze and manually annotate every single recording, as is traditionally done in research and clinical settings. Novel methods facilitating clinical and research applications in real-world settings, especially with sparse EEG montages, are therefore needed. The use of machine learning for automating EEG analysis has been the subject of much research in recent decades  [7, 8]. However, state-of-the-art EEG prediction pipelines are generally benchmarked on datasets recorded in well-controlled conditions that are relatively clean when compared to data from mobile EEG. As a result, it is unclear how models designed for laboratory data will cope with signals encountered in real-world contexts. This is especially critical for mobile EEG recordings that may contain a varying number of usable channels as well as overall noisier signals, in contrast to most research-and clinical-grade recordings.
This module can be inserted before the first layer of any convolutional neural network architecture in which activations have a spatial dimension  [12, 13, 14] , and then be trained end-to-end for the prediction task at hand. The rest of the paper is structured as follows. Section 2 presents an overview of the EEG noise handling literature, then describes the attention module and denoising procedure proposed in this study. The neural architectures, baseline methods and data used in our experiments are introduced in Section 3. Next, Section 4 reports the results of our experiments on sleep and pathology EEG datasets. Lastly, we examine related work in Section 5 and discuss the results in Section 6.
Computational considerations We set the following hyperparameters when training deep neural networks: optimizer, learning rate schedule, batch size, regularization strength (number of training epochs, weight decay, dropout) and parameter initialization scheme. In all experiments, we used the AdamW optimizer  [61]  with β 1 = 0. 9, β 2 = 0. 999, a learning rate of 10 - 3  and cosine annealing. The parameters of all neural networks were randomly initialized using uniform He initialization  [62]. Dropout  [63]  was applied to f Θ 's fully connected layer at a rate of 50% and weight decay was applied to the trainable parameters of all layers of both f Θ and h Θ DSF. Moreover, during training, the loss was weighted to optimize balanced accuracy. Some hyperparameters were tuned on a dataset-specific basis and are described along with the datasets (i.
e. , weight decay and batch size). Deep learning and baseline models were trained using a combination of the braindecode  [12] , MNE-Python  [64] , PyTorch  [65] , pyRiemann  [66] , mne-features  [67]  and scikit-learn  [68]  packages. 3 Finally, deep learning models were trained on 1 or 2 Nvidia Tesla V100 or P4 GPUs for anywhere from a few minutes to 7 hours, depending on the amount of data, early stopping and GPU configuration.
In pathology detection experiments, we allowed Autoreject to reject bad epochs, as classification was performed recording-wise. For sleep staging experiments however, we did not reject epochs as one prediction per epoch was needed, but still used Autoreject to automatically identify and interpolate bad channels. In both cases, we used default values for all parameters as provided in the Python implementation  5  , except for the number of cross-validation folds, which we set to 5. Finally, data augmentation consists of artificially corrupting channels during training to promote invariance to missing channels. When training neural networks, the data augmentation transform was applied on-the-fly to each batch. For feature-based methods, we instead precomputed augmented datasets by applying the augmentation multiple times to each window (10 for pathology detection, 5 for sleep staging), and then extracting features from the augmented windows.
TUH Abnormal EEG dataset (TUAB) The TUH Abnormal EEG dataset v2. 0. 0 (TUAB)  [84, 74]  contains 2,993 recordings of 15 minutes or more from 2,329 different patients who underwent a clinical EEG exam in a hospital setting. Each recording was labeled as "normal" (1,385 recordings) or "abnormal" (998 recordings) based on detailed physician reports. Most recordings were sampled at 250 Hz and comprised between 27 and 36 electrodes. The corpus is already divided into a training and an evaluation set with 2,130 and 253 recordings each. The mean age across all recordings is 49. 3 years (min: 1, max: 96) and 53. 5% of recordings are of female patients. The TUAB data was preprocessed in the following manner. The first minute of each recording was cropped to remove noisy data that occurs at the beginning of recordings  [44].