dataesr/scientific-paragraphs-categorization huggingface dataset:
Characterization The structural investigations of the prepared samples were carried using a powder X-ray diffractograms (2q = 5°-70°, Phillips powder diffractometer operating with copper Ka radiation source/l = 1. 54060 Å). The obtained XRD data were processed with X'Pert HighScore soware, where the Miller indices (hkl) and their corresponding interplanar distance (d hkl ) were identied. The presence of the metal-oxide groups was studied using a PerkinElmer Fourier Transform Infrared (FT-IR) Spectrometer in transmission mode, in the range 400-4000 cm -1. The morphological properties and the surface elemental composition (energy dispersive X-ray spectrum) of the synthesized samples were obtained using a scanning electron microscope (Model JEOL JSM7100F) coupled with EDX spectrometer using double-faced adhesive carbon pads (AGG3347N from Agar Scientic).
Catalyst characterization 2. 2. 1 Powder X-ray diffraction (PXRD). Wide angle PXRD patterns were collected using a Bruker X-ray AXS D8 Advance diffractometer in the Bragg-Brentano geometry conguration tted with a LynxEye Super Speed detector. XRD patterns were recorded with Cu Ka radiation (l = 1. 54184 Å) at 40 kV and 30 mA. Patterns were recorded over a 2q range of 10-80°, at a step size of 0. 02°per step and a counting time of 0. 5 s per step. Crystal phase identication was carried out by comparison with the ICDD database. 2. 2. 2 Small angle X-ray scattering (SAXS). Small angle X-ray scattering (SAXS) was performed on a Xeuss 2. 0 (Xenocs) instrument operated under vacuum with a GeniX3D microsource (l = 1. 54184 Å) at 0. 6 mA and 50 kV and a 2D Pilatus 3R 200K detector. 2. 2. 3 ICP-OES.
Acknowledgements The authors would like to thank  Pardis Simon  for XPS facilities and  Anne-Marie Blanchenet  for TEM facilities. Laassiri wishes to acknowledge the  Foundation I-SITE ULNE/France  for support through the grant  ERC Generator  and the program MOGPA. The French national research agency is acknowledged for support via the program Make  Our Planet Great Again. Chevreul Institute  ( FR 2638 ),  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation ,  Région Hauts de France  and  FEDER  are acknowledged for supporting and funding partially this work.
Correlative light and volume electron microscopy (vCLEM) is a powerful imaging technique that enables visualisation of fluorescently labelled proteins within their ultrastructural context on a subcellular level. Currently, expert microscopists find the alignment between acquisitions by manually placing landmarks on structures that can be recognised in both imaging modalities. The manual nature of the process severely impacts throughput and may introduce bias. This paper presents CLEM-Reg, a workflow that automates the alignment of vCLEM datasets by leveraging point cloud based registration techniques. Point clouds are obtained by segmenting internal landmarks, such as mitochondria, through a pattern recognition approach that includes machine-learning. CLEM-Reg is a fully automated and reproducible vCLEM alignment workflow that requires no prior expert knowledge.
First, relevant structures are tagged with organic dyes or fluorescent proteins and an image stack through the sample volume is acquired using FM. The sample is immobilised with cross-linkers, either before or after the FM imaging, to conserve structural features. It is then stained with heavy-metal salts to introduce contrast, dehydrated, embedded in resin, and trimmed to the region of interest (ROI)  (7). In volume EM (vEM), layers of the embedded sample are physically removed, and either the face of the block or the sections themselves are imaged to create a stack of serial EM images through the volume of the sample  (5). This results in two corresponding image stacks, one from FM and one from EM, each containing complementary data from the same physical region of the sample, but typically imaged at non-identical orientations.
As well as this orientation mismatch, the sample preparation and imaging processes can introduce both linear and non-linear deformations between the acquired FM and EM image volumes. Thus, to correlate the FM signal to the underlying structures visible in EM, the image volumes need to be brought into alignment. Due to the significant differences in resolution, contrast and FOV between FM and EM, this is a challenging task that cannot be directly approached with intensity-based methods that are routinely used for aligning data from visually similar modalities. There are two general approaches to solving this problem. The first approach is to process one or both images such that they share similar visual appearance, for example by directly converting across modalities  (8, 9, 19)  or by constructing a shared modality-agnostic representation  (10, 28).
Once the processed image stacks are sufficiently similar in visual appearance, traditional intensity-based registration techniques, such as those employed in medical imaging  (12) , can be used to automatically align the two datasets. The second approach is to use a landmark-based method such as those implemented in software tools like BigWarp (13) and ec-CLEM  (14). These tools rely on manual identification of precise spatial regions visible in both modalities, for example small sub-cellular structures or prominent morphological features. By manually placing a landmark at the identical physical position in each modality, spatial correspondences can be established. A set of these landmarks can then be used to compute the transformation between the image volumes (1), bringing them into alignment via an iterative optimisation process  (15, 16).
Methods of automating the selection of corresponding landmarks across modalities have been investigated, for example determining cell centroids in LM and EM  (18) , or using semi-automated feature detection  (14). However, such methods are often restricted to 2D or limited to relatively coarse alignment, requiring subsequent manual refinement. Moreover, deep learning-based methods that convert across modalities  (8)  or construct a shared modality-agnostic representation  (28)  rely on the availability of large amounts of aligned ground truth data. Due to the low throughput and required expertise of manual vCLEM alignment, generating such ground truth data is challenging. A significant advantage of landmark-based approaches is the inherent sparsity of the representation, which substantially reduces memory and computational requirements compared to intensity-based registration techniques that must generally hold both image volumes in working memory.
Various segmentation approaches are widely used in microscopy ranging from classical image processing techniques  (9, 20, 21)  to machine learning  (22) (23) (24). Depending on the complexity and density of structures in the image at hand, different algorithms are appropriate. For instance, segmentation of objects like mitochondria in FM acquisitions can be performed by filtering and thresholding coupled with further downstream image processing  (20) , while automatically segmenting EM data typically requires advanced deep learning techniques. Common deep learning architectures such as the U-Net (25) can be trained locally from scratch to very good effect, but the burden of obtaining sufficient ground truth data presents a huge challenge, often requiring significant amounts of expert effort or crowdsourcing of manual annotations  (24).
Results Benchmark dataset acquisition. To assess the performance of CLEM-Reg against an expert, two benchmark CLEM datasets (EMPIAR-10819 and EMPIAR-11537) of HeLa cells were acquired by microscopists from the Electron Microscopy Science Technology Platform at The Francis Crick Institute. Mitochondria (MitoTracker Deep Red), nucleus (Hoechst 33342), Golgi apparatus protein TGN46 (GFP-TGN46) and lysosomes (LysoTracker Red) in EMPIAR-10819 and cell membrane (WGA) in EMPIAR-11537 were tagged so that the unbiased registration performance could be assessed using target structures. We also observed GFP-TGN46 labelling endosomes, as part of the trafficking process between the plasma membrane and the trans-Golgi network  (30). After imaging the samples with a Zeiss Airyscan LSM900, two corresponding EM volumes with an isotropic voxel-size of 5 nm were acquired using a focused ion beam scanning electron microscope (FIB-SEM).
The acquired images were preprocessed following standard EM protocols (Fig. 1a , Methods). The CLEM-Reg pipeline. CLEM-Reg automatically registers volume CLEM data by segmenting mitochondria in FM and EM that are then used to generate point clouds which are aligned with CPD, a state-of-the-art point cloud registration technique. The FM volume is then warped onto the EM volume using the found transformation (Fig. 1b ). CLEM-Reg can be accessed via a napari plugin that allows users to automatically register vCLEM datasets with a single button click. The plugin includes a range of features, such as the option to delineate a corresponding ROI using the "Shapes" layer in napari, as well as the option to choose between rigid, affine and non-linear registration. Parameters such as the FM segmentation settings, point cloud sampling density and the maximum number of iterations for the point cloud registration can be tuned.
The chosen parameters can be saved and later re-used to ensure reproducibility. Overlays can directly be exported from napari, as well as the initial and transformed point clouds for quality control purposes. Intermediate outputs like segmentation masks and sampled point clouds can directly be outputted to the napari viewer to aid troubleshooting (Fig. 6 ). In this study, the performance and user-friendliness of the CLEM-Reg plugin were assessed by an expert electron microscopist from the Electron Microscopy Science Technology Platform at The Francis Crick Institute on two vCLEM datasets (EMPIAR-10819 and EMPIAR-11537).
Generating modality-agnostic point clouds from internal landmarks and registration. The alignment between the FM and EM segmentations can be approximated by sampling 3D point clouds from the previously obtained segmentation masks. This reduces computational load for large datasets and allows for minor missegmentations to be ignored by using a probabilistic registration algorithm, such as CPD. The number of points in both point clouds can be modified by adjusting a user-defined binning and downsampling factor. Increasing these two parameters drastically speeds up the registration and reduces the required random-access memory (RAM) (Fig. 3a ). After sampling, the point clouds are registered using either rigid CPD, affine CPD or non-linear BCPD (Fig. 3b ). Note that these probabilistic methods are necessary, as opposed to ICP, since the stochastic sampling means there are no explicitly paired points across the modalities.
To obtain overlays from the two benchmark datasets, it was found that rigid alignment was sufficient (Fig. 4 ). Assessing CLEM-Reg performance against experts. The FM volumes were manually registered to the EM acquisitions by an expert microscopist with a global affine registration using BigWarp. The resulting alignments were then compared to a manual alignment obtained from an expert electron microscopist (Fig. 5 ). CLEM-Reg uses landmarks (mitochondria) identified in both modalities for registration. To accurately assess the registration performance across the whole cell, target structures (lysosomes in EMPIAR-10819 and endosomes in EMPIAR-11537) were manually segmented in EM in various regions of the cell. To quantify the overlay, the fluorescent signal was segmented by thresholding and the agreement compared to the manual segmentations (see Materials and Methods).
CLEM-Reg is able to correctly match the fluorescent signal to all the target structures in EM (TPR lysosomes = 0. 48±0. 22 and TPR endosomes = 0. 74±0. 18). However, the fraction of fluorescent signal correctly overlaid to the corresponding manual segmentation is higher for the manually aligned datasets (TPR lysosomes = 0. 66±0. 12 and TPR endosomes = 0. 83±0. 12).
CLEM-Reg plugin for napari. Napari is an open-source multi-dimensional image viewer for Python that allows third-parties to develop plugins which enable direct modification of the image viewer, as well as the ability to read and write files  (33). The napari-clemreg plugin was developed for this platform due to the increasing adoption of Python in the bioimage analysis community and the seamless integration of state-of-the-art image processing packages, as well as deep learning libraries into napari. Furthermore, napari's support for the visualisation of point clouds and segmentation in the form of the "Points" and "Labels" layers respectively made it an ideal choice for illustrating many steps of the CLEM-Reg workflow. The default operation of the plugin is that, once datasets are loaded, it is possible to initiate an alignment via a single click in the GUI (Fig.
Discussion This study introduces CLEM-Reg, an automated and unbiased end-to-end vCLEM registration framework. It can be easily accessed by end-users through a dedicated plugin for napari. The proposed registration approach relies on extracting landmarks with a pattern recognition approach that includes deep learning. After segmenting internal landmarks in FM and EM, point clouds are sampled and preprocessed to obtain a memory efficient modality agnostic representation. By using a combination of linear and non-linear state-of-the-art point cloud registration methods, a mapping between the two imaging volumes is found with which the FM acquisition is warped onto the EM volume to obtain a final CLEM overlay. CLEM-Reg achieves expert-level performance on two benchmark CLEM datasets. Nevertheless, certain limitations remain in regard to the automated organelle segmentation in EM which directly impacts the alignment performance.
While MitoNet was shown to perform well on the two vCLEM datasets shown in this study, this can not be guaranteed for all EM volumes. One approach to address this potential limitation is to make use of transfer learning, a deep learning method in which already trained models are briefly retrained on a smaller dataset to increase performance. While training deep neural networks previously required access to GPUs and fluency in programming languages such as Python, open-source projects like ZeroCostDL4Mic  (34) , DeepImageJ  (35)  and ImJoy  (36)  are rapidly removing these barriers by offering frameworks that leverage freely available computing resources for training and enable GUI-driven interaction with a range of deep learning architectures. Open source and community-driven model zoos such as Bioimage.
IO (https://bioimage. io/#/)which allow easy sharing of pre-trained deep learning models are another important resource in this context. Further limitations prevail with regard to the scale of the datasets that can be aligned. More specifically, downsampling of the FM and EM datasets is generally required prior to execution. Despite this, downsampling may still be insufficient for very large datasets. To circumvent this limitation, chunking of the datasets using next generation file formats  (37)  and tools such as Dask  (38)  could enable computation of datasets that exceeds available memory, thus allowing users to run the CLEM-Reg workflow without encountering memory issues. Additionally, the CLEM-Reg workflow relies heavily on two packages Scipy  (39)  and scikit-image  (40) , for tasks such as FM segmentation and point sampling of the FM and EM segmentation.
However, these packages do not natively provide GPU acceleration, thus not benefitting from available compute resources for users in possession of GPUs. As such, future work to incorporate GPU acceleration into the CLEM-Reg workflow using Python libraries such as Cupy  (41)  or clEsperanto  (31)  could reduce processing time. This optimisation has the potential to significantly accelerate multiple steps within the CLEM-Reg workflow. While CLEM-Reg performs expert-level registration, it is important to appreciate that the acquired benchmark CLEM dataset was obtained with high-end microscopes by professional electron microscopists. Other vEM techniques like serial section scanning EM (ssSEM) or serial section transmission EM (ssTEM) introduce more pronounced nonlinear deformations, as the sample is physically cut with a diamond knife and in the case of ssTEM, imaged in ultrathin sections.
In FIB-SEM, which was used to acquire the benchmark EM data, the sample is milled by a focused ion beam and imaged in a resin-block which provides stability  (43). Thus, further studies are necessary to ascertain the robustness of the proposed algorithm across a range of CLEM datasets acquired under varying conditions in different microscopes. CLEM-Reg is a first step towards potentially making fully automated high-throughput CLEM registration a reality. With recent advances in deep learning enabling the use of ambiguous GT data  (44) , automatically obtained CLEM overlays could replace the need for manually labelled structures removing the need for human annotators. This in turn would significantly accelerate the speed at which new deep learning models could be trained for segmenting a range of structures in vEM.
While CLEM-Reg was developed to register vCLEM datasets, the framework can be extended to other multi-modal registration tasks which are not amenable to intensity-based registration algorithms via the split registration functionality. This is due to the fact that aligning common landmarks represented as point clouds is a modality agnostic approach, provided that segmentations for both modalities are available. With the rapid development of novel generalist deep learning models  (45) , it is anticipated that this step will become progressively easier.
Hoechst 33342 1 µg/µl was added in the last 5 min of the tracker incubation. All probes were from Molecular Probes (Thermo Fisher Scientific). Cells were then washed 3 times with 0. 1 M phosphate buffer (PB) pH 7. 4 and fixed with 4% (v/v) formaldehyde (Taab Laboratory Equipment Ltd) in 0. 1 M PB pH 7. 4 for 15 min. Cells were washed twice and imaged in 0. 1 M PB on an AxioObserver 7 LSM900 with Airyscan 2 microscope with Zen 3. 1 software (Carl Zeiss Ltd). Cells were first mapped with a 10x objective (NA 0. 3) using brightfield microscopy to determine their position on the grid and tile scans were generated. The cells of interest were then imaged at high resolution in Airyscan mode with a 63x oil objective (NA 1. 4). Smart setup was used to set up the imaging conditions. A sequential scan of each channel was used to limit crosstalk and z-stacks were acquired throughout the whole volume of the cells.
Computational resources. The algorithms described here were primarily developed and tested on a portable machine running on a Linux distribution with the following specifications: 64 GB RAM, Intel Core i7-11850H 2. 50GHz × 16 CPU and GeForce RTX 3080 GPU. An additional workstation with the following specs was also used for testing: 256 GB RAM, Dual Intel Xeon (14 CPU each) 2. 6GHz, Quadro M600 16GB GPU. FM segmentation with thresholded Laplacian of Gaussian. Firstly, the FM volume was resampled to obtain 20 nm isotropic voxels. Then, to ensure the FM pixel values were in the range of 0 and 1, min-max normalisation of the following formula was used, where is the FM volume, is the lowest pixel value in 𝑥 𝑥 A dynamic threshold based on a relative σ 1 = 3. 0 σ 2 = 4. 8 user-defined threshold and the mean intensity of a given slice was then applied to each image in the resulting volume to account for inconsistencies arising due to attenuation of the signal at deeper imaging levels.
The default relative threshold was set to. Lastly, to reduce the number of spurious segmentations, a 𝑇 = 1. 2 size-based filter was then applied. This was achieved by using a 3D connected components algorithm from the Python package cc3d 3. 2. 1 (50) which removes components below a user-defined size threshold with a default value set to remove all segmentations with a size below the 50th percentile. EM segmentation with MitoNet. The mitochondria within the EM volume were segmented using the pre-trained deep learning model, MitoNet  (31). The MitoNet model using its default parameters was applied to the EM volume, resulting in instance segmentation of the mitochondria. Point cloud sampling of the EM and FM segmentation. The method employed for producing point clouds from the EM and FM volumes uses the same standardised procedure.
For registration, the Python package probreg 0. 3. 6  (52)  was used, as it implements both the CPD  (17)  and BCPD  (53)  algorithm with a unified API. After applying either CPD or BCPD the output was a globally registered point cloud of the FM to the EM which is then used for the warping of the image volumes. Warping of image volumes. The transformation matrix found from the global point cloud registration was applied to the source image volume using the affine transform implementation provided by SciPy 1. 10. 1  (39) , a powerful open-source Python library that provides a range of functions for mathematics, science, and engineering. This implementation makes use of inverse projection to interpolate the intensity values of the warped image thus requiring an inverted transformation matrix which is found with NumPy's 1.
24. 2 (54) linear algebra module. Interpolation of the warped image was achieved with higher-order spline interpolation. Non-linear image warping was achieved with thin-plate spline deformation which finds a mapping between a set of control-points such that the deformed source points match the target points as 𝑓: (𝑥, 𝑦, 𝑧) → (𝑥', 𝑦', 𝑧') closely as possible while ensuring that the image between these control-points remains smooth. As there are no open-source implementations for volumetric thin-plate spline warping available for Python, a custom script was used based on  (32). To reduce computational overhead, the FM volume was nonlinearly warped as eight individual chunks. Benchmarking of CLEM-Reg against experts. Due to the inherent differences in appearance between the two imaging modalities, direct intensity-based quantification of the registration performance is not possible.
Therefore, a metric measuring the target structure agreement was used to assess the resulting overlays. Firstly, target structures (lysosomes in EMPIAR-10819 and endosomes in EMPIAR-11537) were manually segmented in 3D in the EM volume. These lysosomes and endosomes were then cropped with a bounding box from the corresponding warped FM volumes and then segmented using the Otsu thresholding method (55) to generate the correlating FM segmentation. The intersection between the FM and EM segmentations is computed and divided by the number of pixels in the EM segmentation. This measure is also known as the true positive rate (TPR). This was used in preference to familiar measures such as IoU due to the very different imaging resolutions of the two modalities. To capture volume-wide effects between the imaging volumes, target structures in different areas of the cell were chosen.
Plugin development and deployment. The plugin was developed for Python 3. 9 and is compatible with napari 0. 4. 17  (33). It integrates the CLEM-Reg workflow in the form of a single widget running it end-to-end and as a set of individual widgets each carrying out an individual step (split registration functionality). To distribute the plugin and make it accessible from the napari interface, the project is packaged using the cookiecutter plugin template provided by napari. The plugin is available for installation via the package installer for Python (pip) in the command line under the project name napari-clemreg. The plugin can also be installed via the built-in plugin menu which lists all plugins currently available on napari-hub The source-code is available as a GitHub repository alongside a detailed project description, installation instructions and user guide.