dataesr/scientific-paragraphs-categorization huggingface dataset:
User workshop To evaluate SILA 3D, we asked nineteen people with different skills to participate in different experiments to evaluate and validate the efficiency of the proposed method and framework. The experiments were performed on both databases described in Section 4. 2. 1. Different tasks were performed: • We asked users to segment images by manually positioning all bounding boxes without using spatial relationships. • We asked users to segment images at their discretion with all available functionalities in SILA 3D (e. g. automatic positioning according to the learned relationships, manual positioning, order of segmentation, etc. ) 3 French Regional project -see acknowledgment. • We asked users to segment an image by choosing only the order of segmentation (e. g. sequential selection of the substructure to segment).
FreeSurfer and JLF were applied to the MICCAI'12 dataset, but only JLF was applied to the NeuroGeoEx database. Fig. 6  shows the Dice ratio for the human brain images. The segmentation quality of FreeSurfer is lower than the quality of the proposed approach; however, the JLF method provides better results. This result is verified for all of the 13 regions studied in this experiment. The difference between the proposed local method and JLF depends on the anatomical structures, and is large for ventricles (+ 10. 8%) but smaller for putamen (+2. 19%). The results of the proposed method with two well positioned regions ( 2 ) are also similar to the results with all regions well positioned (  ). Fig. 7  shows the quantitative results with the MRI test images. This image is relatively different from those in the training database, which consists mainly of elderly brains.
This difference is characterized, among other factors, by ventricles with important sizes. FreeSurfer segmentation suffers from different errors, particularly on the putamen and pallidum, which are well managed in JLF and  segmentation. The thalamus and primarily the ventricle were better localized with JLF than with. Elongated and thin regions, such as the ventricles, seem to be the most problematic segmentation with local atlases. Table  2  shows the global results of this experiment. The average Dice ratio of the proposed approach without using the spatial relationships (  ) is 7. 7% greater than that with FreeSurfer and 5 % less than that with JLF. Fig. 8  and Table  3  show the results of the same experiments applied on the NeuroGeoEx data set. One of the primary differences between the two databases is the number of available images.
Influence of the number of regions positioned with the ground truth Spatial relationships are usable when at least one region has already been segmented with manual positioning, but how many must be positioned before obtaining correct segmentation with the spatial relationships must be studied in more detail. This information is important because it shows the number of user interactions that is necessary to obtain satisfactory results. In our experiments, a well-positioned region is a region that is segmented by considering the ground truth (i. e. the labeled image) to position its bounding box. This ideal case can occur if the user or the spatial relationships do not include any mistakes. In the following experiment, the test database is segmented by positing different numbers of regions perfectly with the ground truth, while the other regions are positioned using the spatial relationships.
Influence of the size of training database We argue that the proposed method can be used when the number of available labeled images in the training database is small. In the following experiment, the quality of the results is evaluated depending on the number of images used to construct the  during the learning step. The first  is built only with the first image of the training dataset, the second with the first two images, etc. Figs. 12 and 13 show the quality for each different graph with   and  2 , respectively. 2 is chosen because the segmentation of  1 is too variable to highlight an evolution depending on the data used to construct the. With   , which uses only 4 images for graph  construction, the Dice ratio is near its maximum. A small number of images is required to attain satisfactory results, and adding more images to build the  does not improve the results.
Intermodal segmentation In previous segmentation, training and testing images from the same modality were acquired, but the proposed approach could also be efficient in cases of intermodal segmentation. An  is learned with six images from the NeuroGeoEx database. This information was then used to segment T1 in vivo images of the sheep brains. In contrast to the ex vivo images, the brain is not extracted, and many signals are present. Seven anatomical structures are localized: left and right caudate nucleus; left and right hippocampus; left and right olfactory bulbs; and periaqueductal gray. The other regions are not visible on this image because the resolution (0. 5 × 0. 5 × 0. 5) and contrast are less than with the NeuroGeoEx database (0. 3 × 0. 3 × 0. 3). The intensities of the local atlas and the image to be segmented are different, so the metric used during the registration process is mutual information.
The results presented are qualitative due to a lack of ground truth. Two segmentations are shown in Fig. 16 ; the first image 16(a) comes from an animal also present in the NeuroGeoEx database, and segmentation is performed manually (i. e. users interact when necessary); the second image 16(d) does not come from an animal present in the training database and is segmented automatically, except for the first region (a caudate nucleus), which is manually positioned. When the process is driven by an expert, segmentation is consistent. The caudate nucleus, hippocampus and PAG do not suffer from particular problems with the first images. The olfactory bulbs are more difficult and seem to be too small compared to what they should look like. When the bounding boxes are automatically set, the segmentation is more variable but still coherent.
Heart images 3D MRI and CT scans of the heart are processed with the proposed approach. The  is built from 20 MRI images of the heart and applied to 40 MRI images. This database comes from the Multi-Modality Entire Heart Segmentation Challenge  (Zhuang et al. , 2010; Zhuang & Shen, 2016). The same process is performed with the CT scan images, and the images are composed of 8 anatomical structures: left and right ventricle blood cavities, left and right atrium blood cavities, myocardium of the left ventricle, ascending aorta and pulmonary artery. Mutual information is used as a metric for the registration (training and testing) of the MRI images. The segmentation of the CT scan was performed automatically; only the left ventricle was positioned manually, and the other structures were positioned with spatial relationships.
Each local model can be used independently from the others to enable partial segmentation. The segmentation of an image becomes an incremental process allowing user interaction before and after each local segmentation. We also proposed an algorithm to determine the best order for the local segmentations automatically based on the coherence between spatial relationships. This order provides a good alternative to user interaction and allows the system to be used in nearly automatically if desired. These orders of segmentation have been shown to be efficient compared to user or yo random order of segmentation. The experiments presented in this study enhanced the small training databases that are required to use the proposed method efficiently. Only 4 images were sufficient to reach a level of segmentation quality that equal that of other systems evaluated on the MICCAI'12 database.
Better visualization of the segmentation results and more ergonomic positioning should significantly improve users' experiences. Finally, the user experiment highlighted the ownership of the software and the process; after segmentation a few images, users became more comfortable with the segmentation tools. SILA 3D thus achieves similar or higher accuracies with fewer user interactions in less time than traditional interactive segmentation methods. The proposed method has been integrated into an operational framework that is available online and has been used in several studies and different kinds of projects. It is probably true that this framework cannot compete with a finely tuned method dedicated to a specific dataset/population or a classification task where huge annotated data are available to learn a deep learning architecture fully automatically, but this situation is not so common in real life applications.
Funding and acknowledgments This work was mainly supported by the  Région Centre Val de Loire in France during NeuroGeo  ( 2015-2017 , n • 00091714) and  Neuro2co  ( 2017-2020 , n • 00117257) projects. We thank  Hans Adriansen  for the in vivo MRI acquisitions performed at ( CIRE-INRAE ) and  Cyril Poupon  for the ex vivo MRI acquisitions performed at (NeuroSpin-CEA). These images are included in the sheep brain image datasets used for the experiments. We are also grateful to all the participants to the SILA 3D user workshop and especially to  Ophélie Menant  for her huge help regarding the data annotation.
The PDB ProtVista is a reusable and customisable sequence feature viewer that provides intuitive and detailed 2D visualisation of residue-level annotations while also supporting interactive communication with 3D viewers. The Protein Data Bank in Europe (PDBe) team develops and maintains PDB ProtVista. Several web services use it to display structural and functional annotations such as macromolecular interaction interfaces, intrinsic disorder predictions, sequence variants, and sequence conservation.
Availability and Implementation The PDB ProtVista is a native JavaScript (ES6) application that is freely available from https://github. com/PDBeurope/protvista-pdb. The above URL also provides extensive documentation and step-by-step user guides on integrating PDB ProtVista with existing web applications and 3D molecular viewers. It also offers an example of how to display custom data of the user and functional annotations for PDB and UniProt entries powered by a rich set of PDBe API endpoints.
Introduction Identifying the biological function of macromolecules is instrumental in understanding and potentially modulating cellular processes  (Waman et al. , 2021). Structural representations of proteins and nucleic acids help researchers unveil these mechanisms  (D'Amico et al. , 2021; Ravarani et al. , 2020). A wealth of structure-and sequence-derived functional and biophysical annotations can further enhance the inherent value of macromolecular models  (wwPDB consortium, 2018; Varadi et al. , 2019). In 2018, we have established the Protein Data Bank in Europe -Knowledge Base (PDBe-KB) that integrates macromolecular structural data with annotations provided by data resources and research teams from across the globe  (Varadi et al. , 2019). However, the complexity of these data makes them challenging to interpret and gain biological insights unless advanced and yet easy-to-use data visualisation tools are readily available to the scientific community  (Sehnal et al.
, 2020). A powerful and intuitive way of visualising annotations is to use socalled sequence feature viewers to overview all the annotated amino acid or nucleic acid residues for a particular macromolecular sequence  (Watkins et al. , 2017). Here, we present PDB ProtVista, a sequence feature viewer with native support for interactions with 3D molecular graphics viewers, that utilises and expands the Nightingale library (https://github. com/ebi-webcomponents/nightingale), which is a collection of web components co-developed by PDBe  (Armstrong et al. , 2019) , UniProt  (Bateman et al. , 2021)  and InterPro  (Blum et al. , 2021). PDBe and PDBe-KB web pages use the PDB ProtVista to visualise annotations and map them interactively onto the 3D models. PDB ProtVista is open-source and available from https://github.
Materials and methods Based on the first version of ProtVista  (Watkins et al. , 2017) , the PDBe, UniProt, and InterPro teams created a collection of web components called the Nightingale library (https://github. com/ebi-webcomponents/nightingale). PDB ProtVista extends and tailors these web components to create an implementation specifically for displaying structural annotations found at PDBe and PDBe-KB. PDB ProtVista is an easily reusable, frameworkagnostic sequence feature viewer that seamlessly communicates with the 3D-molecular graphics viewer, Mol*  (Sehnal et al. , 2021). The PDB ProtVista provides three data visualisation modes, called "tracks", to support various types of sequence and structure annotations: i. ) a "segment" track; ii. ) a "variants" track, and iii. ) a "sequence logo" track.
The PDBe weekly release process includes these sequence conservation calculations, ensuring up-to-date data are displayed. The weekly process generates sequence conservation information for every PDB entry and corresponding UniProt accessions when sufficient sequence data is available in UniProt. The protein sequence is aligned against the UniProt database to obtain a multiple sequence alignment, and the process generates a Hidden-Markov Model profile using the HMMER suite  (Potter et al. , 2018). The data pipeline then calculates amino acid probabilities and conservation scores from the profile obtained. Users can download the multiple sequence alignment (MSA) by clicking on the "Download MSA" button in PDB ProtVista. An interactive demo that showcases how to display custom data provided by the user is PDBe-KB integrates macromolecular structure data with functional and biophysical annotations in a comprehensive knowledge graph  (Varadi et al.
, 2019). The data in this archive is complex and diverse, necessitating advanced and easy-to-use data visualisations to help the scientific community gain valuable insights regarding the biological role of macromolecules. The PDB ProtVista is a sequence feature viewer consisting of reusable, framework-agnostic web components that can provide an overview of structural and sequence annotations while also supporting seamless communication with the 3D-molecular graphics viewer Mol*  (Sehnal et al. , 2021). We showcase the power of PDB ProtVista through the example of Calpastatin (UniProt accession P27321), an inhibitor of the calcium-dependent, non-lysosomal cysteine protease Calpain (Figure  1 ). Calpastatin is the native inhibitor of the protease Calpain. Using publicly available data, PDB ProtVista can display all the related PDB entries and predicted models from AlphaFold DB and SWISS-MODEL.
, 2021) , PDB ProtVista can list all the known structures of Calpastatin in the PDB and predicted models from AlphaFold DB and SWISS-MODEL  (Tunyasuvunakool et al. , 2021; Waterhouse et al. , 2018). It also displays information on the observed secondary structure elements, complemented by consensus disorder predictions from MobiDB and backbone flexibility predictions from DynaMine. PDB ProtVista can also highlight the interaction interfaces between Calpastatin and Calpain and display sequence conservation and sequence logo data. Together, these data visualisation tracks show that Calpastatin is an intrinsically disordered protein, according to MobiDB, and it has regions that may adopt context-dependent secondary structure, according to DynaMine. These regions adopt short helices in the Calpain/Calpastatin complex (PDB structure: 3bow)  (Hanna et al.
, 2021). For example, users can compare intrinsic disorder predictions with the observed interaction interfaces and secondary structure elements in 2D and 3D. The MobiDB data resource provides these up-to-date consensus disorder annotations that PDBe-KB integrates weekly with the core PDB data  (Varadi et al. , 2019). Two-way communication between Mol* and PDB ProtVista makes it easy to interactively identify regions that are likely to adapt semi-stable secondary structures when bound to their interaction partners, such as Calpastatin's case described above. In conclusion, PDB ProtVista is a robust, reusable and intuitive sequence feature viewer that specialises in displaying structural and functional annotations in 2D, with built-in support for mapping these annotations onto 3D molecular representations.
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is bioRxiv preprint doi: ; https://doi. org/10. 1101/2022. 07. 22. 500790 this version posted July 22, 2022. The copyright holder for this preprint. CC-BY 4. 0 International license made available under a available at https://embed. plnkr. co/plunk/nPNvqHhyamzHGrCT. Comprehensive documentation, user guides and further examples of displaying data provided by the PDBe Aggregated API endpoints and linking PDB ProtVista to the 3D-viewer Mol* are publicly available from the PDB ProtVista GitHub repository (https://github. com/PDBeurope/protvista-pdb/wiki). 3 Discussion and conclusion.
[1]. The experimental part was done at the ICube laboratory, Illkirch, France. A RedPower SPI SP-100C continuous wave fiber laser of 100 W with a Gaussian profile, at wavelength of 1090 nm, and with diameter spot of 0. 7 mm was used. A Newport motion controller was used for the table displacement in X direction, while the laser power and time exposure were done with LabVIEW software. Displacements in the Y direction were done manually using a micrometer stage installed directly on top of the X stage underneath the laser focusing set up. In this AM procedure only 2D manufacturing was possible. In parallel a COMSOL Multiphysics model was developed to understand and optimize the experimental process. The multiphysics model was implemented with the thermal conductivity, density, heat capacity and latent heat of fusion of the regolith simulants.
Data sampling and data input for each compartment Biological fish data (e. g. , abundance, length, and weight) were obtained monthly, from October 2013 to September 2014, with a seine net (67. 5 m in length with a mesh size of 10 mm). Three replicates were carried out for each sample. Fish were identified and weighed. The stomach contents were analyzed for some species and used as input for the diet matrix (Supplementary Table  S1 ). The sampled area was obtained by GPS tracking using the open-source image processing software ImageJ  (Schneider et al. , 2012). Landing data for this area, considering 2000 to 2007, were obtained from official Brazilian statistics (IBAMA, 2008) (See Supplementary Table  S2 ). Biomass values for fish groups were estimated by the sum of the individual weights of each group divided by the total trawled area (t.
Comparison of estuarine EcoTroph models We constructed an EcoTroph model based on the Ecopath model from the Santa Cruz Channel estuary (SCC model) and compared it with two other Ecopath models on Brazilian estuaries (Sirinhaém -SIR and Mamanguape -MAM)  (Lira et al. , 2018; Xavier, 2013). These estuaries are different in type, size, fishing intensity, and anthropogenic stressors (see details in Supplementary Table  S9 ). Each model was calibrated using EcoTroph R package 1. 6 developed by  Colléter et al. (2013). Eco-Troph is based on trophic level, biomass, catch, production, and Omnivory Index for each group from the balanced Ecopath models. Sensitivity analyses conducted by  Halouani et al. (2015)  showed that some of these parameters (mainly the α parameter) changed the magnitude of the result but not the observed trend.
Acknowledgments The present study could not have been realized without the work of all the participants from the  BIOIMPACT Laboratory. This research was financially supported by  Conselho Nacional de Desenvolvimento Científico e Tecnológico  ( CNPq 407125/2013-2 ) and  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)  in providing student scholarships to  Valdimere Ferreira ,  Leandro Nolé Eduardo  and  Alex Souza Lira  and a research grant to  Flávia Lucena Frédou  ( 308554/2019-1 ) and  Thierry Frédou  ( 307422/2020-8 ). This study is also a contribution to  INCT -Ambientes Marinhos Tropicais  ( CNPq Process 565054 ) and the LMI TAPIOCA, program  CAPES/COFECUB  ( 88881. 142689/2017-01 ).
Video processing and intraneuronal transport quantification Two programs written in python were developed to extract quantitative parameters from videos automatically. The first one relies on Trackpy 0. 4. 2 package (Trackpy 2019), from which it uses two functions: locate to identify isolated spots in each fluorescence frame and fit them with gaussians, and link which connects the spots between frames to form trajectories using Crocker-Grier algorithm  (Crocker & Grier 1996). Transport parameters are then calculated with a second program which first parses each trajectory into "go" and "stop" phases based on the confinement ratio calculation as described in  (Haziza et al. , 2017). Four main transport parameters are extracted for each trajectory: velocity, which is the average speed of all go phases; run length: average distance traveled during all go phases; pausing time: average duration of the stop phases, and pausing frequency (events/min).
Quantification of fluorescence intensity of ATTO 488-labeled -synF in cortical neuron branches We quantify the fluorescence intensity from the first frame of the green channel videos, using Fiji Image J software  (Schindelin et al, 2012). We first identify from the DIC image some well-separated and mainly straight neuronal branches, that we surround with the region-of-interest (ROI) polygonal selection tool as close as possible to the branch to include all the fluorescence signal, over a length of 30 μm. We then used the Analyze function to measure the average intensity counts per pixel in the defined ROI, to which we subtract the average background counts, measured after having moved the ROI in a region without branches.
This work was supported by the  Joint Program on Neurodegenerative Disease Research and Agence National de la Recherche  (contract TransPathND  ANR-17-JPCD-0002 ) to M. S. and R. M. ;  Fondation pour la Recherche Médicale  (contract  ALZ201912009776 ) to R. M. ; Q. L. C. was supported by a PhD scholarship from  Taiwan Ministry for Education and University Paris-Saclay  and  JPND TransPathND  ( ANR-17-JPCD-0002 ). Conflicts of Interest: Authors report no conflict of interest Funding sources:  Joint Program on Neurodegenerative Disease Research  and  Agence National de la Recherche  (contract TransPathND  ANR-17-JPCD-0002 );  Fondation pour la Recherche Médicale  (contract  ALZ201912009776 ) ; PhD scholarship from  Taiwan Ministry for Education and University Paris-Saclay.
Given the uncertainty in determining CFUs, we arbitrary set a threshold of 10 colonies per plate (10 2 CFU/ml) for significance. Results were calculated from five independent experiments and expressed as the log 10 of the frequency. ScanLag assay and data analysis. ScanLag assays were performed as described previously  [24] , but adapted to our purposes: approximately 1-3 x 10 6 cells from mid-EP or SP cultures were spread onto TSA plates supplemented with IMP using the EasySpiral plater. Colony tracking was carried out every 20 minutes for 48-hours at 37 °C. Time of appearance (ToA) and colony growth rates were estimated by running MATLAB scripts publicly available at https://github. com/iosonofabio/scanlag [55]. Colony ToA distributions were analyzed by using the R package mixtools  [56].