dataesr/scientific-paragraphs-categorization huggingface dataset:
As expected, the performance of both models decreases as the size of the test set increases, though less so for AN2VEC. For Cora and CiteSeer, similarly to Table  1 , higher overlaps and a shallow decoder in AN2VEC give better performance. Notably, the shallow decoder version of AN2VEC with full overlap is still around. 75 for a test size of 90%, whereas both GraphSAGE variants are well below. 65. For PubMed, as in Table  1 , the behaviour is different to the first two datasets, as overlaps 0 and 16 yield the best results. As for Cora and CiteSeer, the approach taken by AN2VEC gives good results: with a test size of 90%, all AN2VEC deep decoder variants are still above. 75 (and shallow decoders above. 70), whereas both GraphSAGE variants are below. 50.
Node classification Since the embeddings produced also to encode feature information, we then evaluate the model's performance on a node classification task. Here the models are trained on a version of the dataset where a portion of the nodes (randomly selected) have been removed; next, a logistic classifier 11  is trained on the embeddings to classify training nodes into their classes; finally, embeddings are produced for the removed nodes, for which we show the F1 scores of the classifier. Figure  6  shows the results for AN2VEC and GraphSAGE on all datasets. The scale of the reduction in performance as the test size increases is similar for both models (and similar to the behaviour for link prediction), though overlap and shallow versus deep decoding seem to have less effect. Still, the deep  decoder is less affected by the change in test size than the shallow decoder; and contrary to the link prediction case, the 0 overlap models perform best (on all datasets).
This is valid for all test sizes, and suggests (i) that at most 8 dimensions are necessary to capture the commonalities between network and features in CiteSeer, and (ii) that having more dimensions to capture either shared or non-shared information is not necessarily useful. In other words, 8 overlapping dimensions seem to capture most of what can be captured by AN2VEC on the CiteSeer dataset, and further increase in dimensions (either overlapping or not) would capture redundant information. Node classification, on the other hand, does not exhibit any consistent behaviour beyond the reduction in performance as the test size increases. Models with less total dimensions seems to perform slightly better at 0 overlap (though this behaviour is reversed on Cora), but neither the ordering of models by total dimensions nor the effect of increasing overlap are consistent across all conditions.
As an example one can take a social network with the task of predicting future social ties, or reconstruct existing but invisible social ties. Solutions to this problem can rely on network similarities in terms of overlapping sets of common friends, or on feature similarities in terms of common interest, professional or cultural background, and so on. While considering these types of information separately would provide us with a clear performance gain in the prediction, these similarities are not independent. For example, common friends may belong to the same community. By exploiting these dependencies our method can provide us with an edge in terms of predictive performance and could indicate which 14 As reported by our scripts and by GNU Parallel. similarities, structural, feature-related, or both, better explain why a social tie exists at all.
Furthermore, we expect that our method will help yield a deeper understanding between node features and structure, to better predict network evolution and ongoing dynamical phenomena. In particular, it should help to identify nodes with special roles in the network by clarifying whether their importance has structural or feature origin. In this paper our aim was to ground our method and demonstrate its usefulness on small but controllable featured networks. Its evaluation on more complex synthetic datasets, in particular with richer generation schemes, as well as its application to larger real datasets, are therefore our immediate goals in the future.
F1-micro score for node classification using AN2VEC and GraphSAGE over all datasets. AN2VEC top row is the shallow decoder variant, and the bottom row is the deep decoder variant; colour and line styles indicate different levels of overlap. GraphSAGE colours and line styles indicate embedding size as described in the main text (colour and style correspond to the comparable variant of AN2VEC). Each point on a curve aggregates 10 independent training runs. Figure7: AUC for link prediction using AN2VEC on CiteSeer, as a function of overlap, with variable total embedding dimensions. Columns correspond to different test set sizes. Top row is with shallow decoder, bottom row with deep decoder. Colours, as well as marker and line styles, indicate the number of embedding dimensions available for adjacency and features.
Availability of data and material The synthetic datasets generated for this work are stochastically created by our implementation, available at github. com/ixxi-dante/an2vec. The datasets used for standard benchmarking (Cora, CiteSeer, and PubMed) are available at linqs. soe. ucsc. edu/data. Our implementation of AN2VEC is made using the Julia programming language, and particularly making heavy use of Flux  [61]. Parallel computations were run using GNU Parallel  [62]. Finally, we used StellarGraph  [63]  for the GraphSAGE implementation.
Multiple sequence alignment In order to assess the conservation among candidate coronavirus E-proteins, we performed a protein-protein BLAST (BLASTp: Basic Local Alignment Search Tool) with the E protein of SARS-CoV-2 isolated from Shanghai in April (accession number in QII57162. 1). Using a 90-100% sequence identity as filters, we aligned the resulting sequences using the online alignment tool Clustal Omega  [35]  and MegaX  [36]. Both the software enables high fidelity multiple protein sequence alignment. Jalview (www. jalview. org) was used for visualizing the sequence alignment, conservation score, quality of alignment and consensus residue for each position. The same methods were undertaken for spike proteins (100 sequences), M proteins (54 sequences) and protein 7a (30 sequences).
Disparity score and mutability Estimates of evolutionary divergence are shown for the different E-proteins isolated from different sources (bat (HKU3-7), human SARS 2018, bat SARS_RsSHC014, BtRI-BetaCoV, SARS-CoV-1 and SARS-CoV-2 (accession number: QII57162. 1)). Analyses were conducted using the Poisson correction model. All ambiguous positions were removed for each sequence pair (pairwise deletion option). The disparity index is a measure of heterogeneity among sites  [37]  and was calculated from MegaX  [36]. Briefly, the disparity index uses a Monte-Carlo procedure to test homogeneity among the input sequences. Values greater than 0 indicate the larger differences in amino acid composition biases than expected, based on the evolutionary divergence between sequences and by chance alone.
Frequencies of different amino acids were calculated across the aligned sequences. Mutability was defined as the probability of an amino acid to be non-consistent across the sequence and was calculated as follows: pðMÞ ¼ 1 À AAð%Þ=21 where AA: amino acid composition in percentage and 21 indicates the total amino acid residue space. The same analytical methods for spike proteins were performed. All data is represented as mean ± SD.
Homology modeling of the E-protein The structure of the envelope (E) protein of SARS-CoV-2 was modeled with a template-based homology modeling approach using the NMR structure of E protein from SARS-CoV-1 (PDB id: 5X29;  [10] ). The full-length pentameric model of the E protein was generated using the following steps: 1. Backbone and side-chain modeling of the monomeric unit. 2. Loop refining of the monomeric unit. 3. Pentamer modeling from the monomer unit. 4. Model refinement and further structural fine-tuning of unreliable structural regions, using the GalaxyWEB server  [38]. Another pentameric model of the E protein was generated using the same template (PDB id: 5X29) using the SWISS-MODEL  [39, 40]. The structures obtained from these modeling servers were validated by scores obtained from the MolProbity  [41, 42].
Pore volume and topology estimation The pore volume of the protein in its different conformational states is determined using CASTp (Computed Atlas of Surface Topography of proteins;  [44] ). In order to account for the presence and impact of the modeled pentameric E-put protein, we estimated the presence of surface pockets, internal cavities, and cross channels using the CASTp server. Briefly, CASTp uses a computational geometry approach to measure area and volume and to identify topological imprints on the protein. The lining residues are annotated from the UniProt database and mapped along with the cavity. The cavities with the two maximum molecular surface area and volume were selected. The molecular surface is colored by its hydrophobicity index. These values are determined by the Kyte-Doolittle scale of hydrophobicity with appropriate colors ranging from blue for the most hydrophilic to orange-red for the most hydrophobic pockets.
Discussion Structural determination of proteins using NMR spectroscopy provided a set of models of the E protein from SARS-Co-1 (Pdb id: 5X29) having the lower energies (S6A, side view, Fig 6A-ii, top view). Homology modeling of E-put using this template provided us with many structural models of the protein. Some of them upon proper structural refinement, represent functionally relevant structures despite having a higher Clash score or MolProbity score. The higher scores signify that the particular model is in a higher energy state correlative with functional intermediates of the protein during the viral progression. We show that the E protein of novel SARS-CoV-2 might assemble on the ER-Golgi network and ERGIC complex of human cells as pentamers similar to previous reports in other coronavirus mediated diseases like SARS-CoV-1 and MERS  [33].
E-proteins are conserved and have lower mutability compared to spike proteins A BLASTp search run resulted in 54 sequences of E-proteins from different origins (Fig 1A). E protein of SARS-CoV-2 isolated in April (Accession number: QII57162. 1) was used as the search reference. Some E-proteins specific to bat (HKU3-7), human SARS 2018, bat SARS_RsSHC014, bat BtRI-BetaCoV, human SARS-CoV-1 and human SARS-CoV-2 (Accession number: QII57162. 1) show a high degree of conservation in their amino acid composition (Fig 1B). In comparison, we also looked at the conservation of amino acids for the spike proteins (S1A Fig), M-proteins (S1B Fig) and protein 7a (S1C Fig). The candidate E-proteins from different origins were closely associated when we plotted the evolutionary distance (S1D Fig). All the selected sequences of Spike proteins, M proteins, E-proteins, and protein 7a were selected based on a 90-100% sequence identity.
designed a template-based pentameric structural model of the E protein of SARS-CoV-2 with the NMR structure of E protein from SARS-CoV-1 (PDB ID: 5X29; (10)) as the template (Fig 2A, side view; Fig 2C, top view). The putative pentameric models of the E-protein have been obtained from GalaxyWEB server (Fig 2B-i, side view, Fig 2D-i, top view) and SWISS-MODEL (Fig 2B-ii-side view, Fig 2D-ii-top view). Based on homology modeling, we obtained 16 different structural models from both the GalaxyWEB (S2A-S2P Fig) and SWISS-MODEL (S3A-S3P Fig). The model obtained from GalaxyWEB spanned the entire protein (1-75) but the five residues (PDLLV) in the C-terminal end of the protein were removed which reduces the structural flexibility of that region improving the MolProbity parameters. The model obtained from SWISS-MODEL spanned from 8-65 residues similar to the NMR structure.
Acknowledgments We thank  Mr. Jaydeep Paul  for insightful discussions on the research idea,  Prof. Felix Rey ,  Dr. Aleksandra Polosukhina  and  Dr. Georgios Agoranos  for reading the manuscript, suggesting additions and changes,  Ms. Madhuparna Chakraborty  for proof-reading the manuscript. We earnestly thank  Prof. Pierre-Marie Lledo  and  Prof. Pinak Chakrabarti  for providing encouragement to conduct the work and reading the finished manuscript.
Immunofluorescence Assay A549 cells grown on glass coverslips were fixed with 3. 7% formaldehyde at room temperature for 10 min. Fixed cells were permeabilized with 0. 1% Triton X-100 in PBS for 4 min. Coverslips were incubated with primary antibodies (1:1000 dilution, 1µg. mL -1 ) in 1% BSA-PBS. Antigen-antibody binding was visualized with Alexa 594 Fluor-conjugated secondary antibodies (1:1000, Invitrogen). Nucleus morphology was revealed by 4′,6-Diamidino-2-phenylindole dihydrochloride (DAPI) staining. The coverslips were mounted with VECTASHIELD® (Clinisciences, Nanterre, France) and fluorescence was observed using a Nikon Eclipse E2000-U microscope. Images were captured and processed using a Hamamatsu ORCA2 ER camera and the imaging software NIS-Element AR (Nikon, Tokyo, Japan).
Funding: This work was supported by the  ZIKAlert and Biomarqueurs projects (European Union - Région Réunion program  under grant agreement n° SYNERGY:  RE0001902  and  RE0001897  respectively), by  Federation BioST from Reunion Island University. E. F. holds a fellowship from the  Regional Council of Reunion Island (European Union - Region Reunion program  under grant agreement n° SYNERGY:  RE0012406 ). J. T. has a PhD degree scholarship from  La Reunion Island University (Ecole Doctorale STS) , funded by the  French ministry MEESR.
Calibration of HyLands A main challenge when applying HyLands in real settings is the calibration of both the river incision and landslide parameters. The power of any LEM lies in its capacity to integrate data over multiple spatial and temporal timescales. Therefore, a range of datasets can be used to constrain model parameters. We identify three main categories of potential calibration data. 1. Topographic parameters that can be derived from DEMs. These include a range of metrics describing river characteristics (drainage density, river steepness, river stream power), hillslope properties (slope distribution, mean and median slope angels, aspect), and landslide scaling relationships (magnitude-frequency and area-volume distributions). All these metrics can be derived from topographic data and subsequently used to constrain HyLands erosion parameters (cfr.
Fig. 7 ). 2. Data directly constraining the the integrated effects of river incision or landslide erosion. Ongoing efforts to map mass movements in landslide prone areas now enable estimates of erosion rates over decal timescales  (Hovius et al. , 1997; Larsen and Montgomery, 2012). Data on sediment redistribution following landslide events is however more difficult to collect. Although initial compilations now exist of global landslide sediment mobilization rates  (Broeckx et al. , 2020) , such inventories remain incomplete. Data on landslide mobilization rates can be used to train HyLands while in turn, HyLands can be used to further extend datasets on landslide mobilisation rates and predict landslide sediment production rates in regions which are otherwise difficult to access. 3. Catchment-averaged cosmogenic radionuclide (CRN) derived erosion rates.
Code availability. The HyLands 1. 0 TTLEM component as well as all other TTLEM components used in this paper are part of TopoToolbox version 2. The source code and future updates are available in the GIT repository: https://github. com/BCampforts/topotoolbox. The exact version of the software code used to produce the results presented in this paper is archived on Zenodo (https://zenodo. org/badge/ latestdoi/78645261). Upon publication we will submit a pull request to the master repository where TopoToolbox is housed: (wschwanghart/topotoolbox). Documentation, installation instructions, and software dependencies for the HyLands project can be found at https://github. Data availability. Digital elevation models used in this paper are derived from the 30 m SRTM v3 DEM (NASA JPL, 2013). The resampled DEM is available through https://github.
As life scientists collect microscopy images of increasing size and complexity  [1] , the use of computational methods has become inescapable in order to extract quantitative information from these datasets. Unfortunately, most published image analysis methods are at best shared as source code, often requiring expert setup and configuration, and seldom as user friendly modules for mainstream BioImage Analysis (BIA) platforms  [2] [3]  [4]. Additionally, test images are not consistently provided with the software and it can be difficult to identify the critical parameters to adjust in order to optimize the quality of the results. Overall, this does not only limit the reusability of the methods and impedes reproducing published results  [5] , but it also makes it difficult to adapt these methods to process similar image datasets.
To improve this situation, scientific datasets are now increasingly made publicly available through web-based software  [6] [7]  [8]  and open data initiatives  [9] , but existing web platforms do not systematically offer advanced features such as the ability to remotely view multidimensional images, launch image analysis workflows (a software implementation of a method), and compare the results between methods or to a ground-truth reference. Having all these features integrated in the same web-based platform would offer a number of advantages and enable a fair comparison of BIA workflows on publicly available annotated image datasets (a. k. a. benchmarking). Benchmarking is known to stimulate the continuous improvements of BIA methods, simplify their deployment by specifying standard input and output data formats, and promote their wider diffusion  [10].
Numerous biomedical image analysis benchmarking challenges have already been organized to compare existing methods, however all these competitions have limitations  [11] : their focus is on a single bioimage analysis problem and they often rely on poorly reusable, ad-hoc, data access protocols and scripts to compute performance metrics of the workflows. Both challenge organizers and participants are therefore duplicating efforts from one challenge to the next, whereas competing workflows are rarely available in a sustainable and reusable fashion. Finally, the vast majority of annotated images released in these challenges come from medical imaging, not from research in biology: as of January 2019, only 15 out of 168 Grand-challenges datasets  [12]  were collected from fluorescence microscopy, arguably the most common imaging technique in the field.
Results Within the Network of EUropean BioImage AnalystS (NEUBIAS 1  ), an important body of work focuses on channelling the efforts of bioimaging stakeholders to address these issues and ensure a better access to and assessment of existing bioimage analysis software. Together, we have envisioned and implemented BIAFLOWS (Fig. 1 ), a web platform to benchmark bioimage analysis workflows on publicly shared annotated multidimensional imaging data. Whereas some emerging bioinformatics web platforms  [13] [14] rely on reproducible dockerized environments and interactive Python or R notebooks to analyse scientific data accessed from a database, BIAFLOWS offers a complete framework to 1) handle multidimensional annotated images, 2) encapsulate and version BIA workflows running on most major BIA platforms, 3) remotely visualize images and workflow results and 4) assess workflows performance from widely accepted benchmark (performance) metrics.
Currently 14 annotated datasets and 16 workflows are available and they correspond to 8 classes of common BIA problems: object detection/counting, object segmentation, pixel classification, particle tracking, object tracking, filament tree tracing, filament network tracing, and landmark detection (Fig. 4  and Fig. 5 , as well as Supplementary Methods section 1, Table  1 ). To enable the automatic execution and benchmarking of the workflows, some standard object annotations formats were specified for each class of problem (see Supplementary Methods, section 5). The code to compute benchmark metrics was adapted from challenges [12] and scientific publications  [20]. The annotated datasets were imported from existing challenges (see section "Methods") or created from synthetic image generators  [19].
Discussion End users running a bioimage analysis workflow from a specific tool (e. g. ImageJ) on their own computer might have very hard time assessing the quality of the results, knowing whether the parameters are well adjusted for their own images, or finding whether there would be a better achieving workflow to tackle the same problem. Hence, it is critical to have an open source, web-based, framework where several parameter optimized workflows from any tool can be run, and where their results can be visualized conjointly and objectively compared to a ground truth reference. Algorithm developers also benefit from using common annotated datasets to compare their workflows to other existing solutions, and hence complement the performance they could assess on project specific data. Advertising the performance of bioimage analysis workflows through a public portal is probably the best way to increase their wide diffusion.
BIAFLOWS is a web-based platform enabling the testing and benchmarking of automated bioimage analysis workflows producing object annotations from raw images, a critical step when extracting quantitative information from scientific images. The online instance managed by NEUBIAS is available at https://biaflows. neubias. org and is populated with a starting set of annotated bioimage datasets and associated workflows corresponding to common bioimage analysis problems; these workflows can be run and benchmarked from a simple web browser. BIAFLOWS provides a framework to integrate new workflows and it is fairly simple to add new content (images, annotations, workflows) or to migrate existing content between different BIAFLOWS instances. We demonstrate BIAFLOWS use cases for 15 annotated datasets and 15 workflows spanning over 8 classes of bioimage analysis problems.
The image datasets have been selected to recapitulate common BIA analysis tasks: spot detection (2D/3D), nuclei segmentation (2D/3D), nuclei tracking (2D), landmark detection (2D), filament tracing (3D), and tissue detection (2D) in whole-slide histology images. All image datasets are imported from previously organized challenges (DIADEM [21], ISBI Cell Tracking Challenge [22], ISBI Particle Tracking Challenge [23], Kaggle Data Science Bowl 2018 [24]), created from synthetic data generators (CytoPacq [25], TREES toolbox [26], Vascusynth [27], SIMCEP [28]), or contributed by NEUBIAS members [37]. To showcase the versatility of the platform, the image analysis workflows available to process these images are running on different BIA platforms: ImageJ macros [29], Icy protocols [30], CellProfiler pipelines [31], Vaa3D plugins [32], ilastik pipelines [33], Python scripts leveraging Scikitlearn [34] for supervised learning algorithms, and Keras/PyTorch [35][36] for deep learning.
Acknowledgments This project is funded by  COST   CA15124  (NEUBIAS). BIAFLOWS is developed by  NEUBIAS  (http://neubias. org)work group 5 and it would not have been possible without the great support from NEUBIAS vibrant community of bioimage analysts, and the dedication of  Julien Colombelli  and  Kota Miura. Local organizers of the hackathons who have fostered the development of BIAFLOWS are also greatly acknowledged:  Chong Zhang ,  Gabriel Martins ,  Julia Fernandez ,  Peter Horvath ,  Bertrand Vernay ,  Aymeric Fouquier d'Herouel ,  Andreas Girod. UR and RV were supported by  ADRIC Pôle Mecatech Wallonia grant. RaM was supported by  IDEES  grant with the help of the Wallonia and the  European Regional Development Fund (ERDF). LP was supported by  Academy of Finland  (grant  310552 ).