dataesr/scientific-paragraphs-categorization huggingface dataset:
Results A total of 612 WSI slides were obtained from 184 patients (see supplement Table  1 ), representing at least 20 independent patients for each of the main anatomo-pathological categories. With this dataset, we generated a total 533. 461 tiles for further use in this study, labeled for 25 subcategories, including nine major histopathology categories of diagnostic interest and their helper classes (see glossary and supplemental Figure  1 ). Training of the ResNet18 CNN was carried out with a batch size of 16 tiles each and the Adams optimizer (https://doi. org/10. 48550/arXiv. 1412. 6980). The learning process used the cyclic learning rate policy (https://doi. org/10. 1109/WACV. 2017. 58). The maximum learning rate multiplier variable was set to 0. 015, as determined by the fastai learning rate finder.
The training of the model was performed with a case cohort representing approx. 80% of each of the 25 categories was validated with the remaining cases, i. e. , dataset splitting at the patient level. The ResNet18 CNN was fed with tiles from our comprehensive extraction routine and managed by the WSI processing pipeline  [24]. The first run achieved an accuracy_multi of 0. 986, however, which prompted us to stop the training after the tenth run at an accuracy_multi of 98. 8% (F1MultiScore = 82. 2%) within the best epoch (epoch nine, total computing time of approx. 16 h, supplement Table  2 ). Training progress ceased after the seventh training epoch. Any further training epoch did not show notable improvement in the control metrics (data not shown). However, we did not observe overfitting, i.
e. , discrepancies between train_loss and validation_loss, accuracy_multi and F1MultiScore at higher epochs (supplemental Figure  2 ). The advantage of dataset splitting at the patient level vs. random dataset splitting became further evident when challenging the two approaches with entirely new cases of genetically confirmed diagnosis never presented to the CNN (supplement Table  1 ). This analysis revealed a poorer generalization of the random-split model (supplement Table  3 ) and was not considered further, therefore. Our basic model (Table  3 ) included all categories specified (see glossary) and achieved a maximum accu-racy_multi score of 0. 988 after the tenth epoch. The RecallMulti score for this basic model achieved 0. 780 and showed more variation across our categories (Table  3 ).
In a next step, we developed a data class to manage calculated predictions and their graphical representation on the WSI by the trained model and to provide an interpretation aid, i. e. , the histopathology classifier (Fig. 2 ). All digitally available information was extracted from the WSI by OpenSlide including the magnification scale and spatial x and y dimension of each WSI image under study. This information was necessary to extract tiles with the same specification used to train the CNN. Then, the program was able to generate a grid of tiles covering the entire space of the WSI (1000 9 1000 lm and 1024 9 1024 pixels each; see Fig. 2a ). The tiles were fed into the trained model in a png-format, and their prediction score was calculated (Fig. 2b ). The results of this calculation were stored with reference to the respective patches.
A raster image was created from these predicted values in the same dimension as the original patch raster. An algorithm then determined the average prediction probability for all categories recognized on the slide. Using an adjustable threshold value, categories with low predictive values were hidden, e. g. , \ 0. 5 (Fig. 2b ). A color and transparency value was calculated for categories above the prediction threshold, and which were based on a pre-defined RGB color code table depending on the prediction probability as defined by the user (Fig. 2c ). A thumbnail of the original WSI image was assembled from the individual png-formatted tiles, anticipating the large file size of each original WSI. The raster image was scaled up to the size of the generated thumbnail, and the two images were merged using an alpha channel and the pillow software package  [10].
Our first visualization attempts confirmed the benefit of helper categories such as non-tissue background (NTU) and the subarachnoidal space (SUB-PM and SUB-SX) for training the CNN in order to improve the graphical output of the classifier. During each experiment, we applied the visualization program to 20 randomly selected cases of the validation cohort and microscopically reviewed regions of the slides identified by the CNN with an accuracy threshold above 0. 5. This analog review confirmed the presence of the predicted lesion in all our cases, i. e. , BA17, BA4, DG, mMCD, MOGHE, FCD1a, FCD2a and FCD2b. This system was further tested using WSI of genetically defined disease probes of MOGHE-SLC35A2 altered, FCD2a-DEPDC5 altered and FCD2b-MTOR altered. These histopathologically and genetically defined cases were not seen by the CNN before and were correctly assigned to their respective category (Fig.
2e, f ). A third approach addressed the potential disease differentiating anatomo-pathological signatures recognized by the CNN. We downloaded, therefore, the most accurately recognized tiles from the training and validation cohort of the CNN with a predicted confidence C 0. 99 for microscopy review. It was evident from this analysis that the CNN has captured the specific disease tissue pattern for each category. Prominent examples are depicted in Fig. 3  showing the clusters of increased oligodendroglial cell densities in MOGHE, dysmorphic and balloon cells in FCD2b and excessive microcolumnar cortical architecture in FCD1a. An online accessible reviewing program was programmed and made accessible online to nine internationally renowned colleagues experienced in the classification of epilepsy-associated brain lesions to survey their agreement with our CNN's prediction.
This study disclosed that 62. 47% of the CNN's best tiles would have been classified as the respective brain disorder also by independent external referees, i. e. , MOGHE, FCD1a, FCD2a, FCD2b (ranging from 10 to 96%, and kappa values ranging from poor (-0. 80) to almost perfect (0. 92), respectively; see Table  4 ). Training results as well as the expert panel agreement trial suggested that our model was able to recognize complex tissue patterns within large WSI tiles with reasonable accuracy. To improve transparency of the model's decision-making process, we applied several discrimination software tools and algorithms for this interpretation using open access libraries, e. g. , LIME  [28] , Captum [https://doi. org/10. 48550/arXiv. 2009. 07896] with Guided-Grad-CAM [https://doi. org/10. 48550/arXiv.
1610. 02391], integrated gradients [https://doi. org/10. 48550/arXiv. 1703. 01365], an occlusion procedure [https://doi. org/10. 48550/ arXiv. 1311. 2901] as well as the torchcam library [github. com/frgfm/torch-cam] and the therein included Layer-CAM approach [https://doi. org/10. 1109/TIP. 2021. 3089943] to visualize the common patterns of tissue texture as a heatmap trained in the model and which can help us guiding the microscopic examination of a surgical specimen  [19]  (Fig. 4 ). We further investigated the class activation across convolution levels of our trained ResNet18 model applying the torchcam library and Layer-CAM approach included therein (Fig. 5 ). This analysis showed evidence for feature recognition taking place from layer 2 to layer 4 of the CNN. No more complex features were recognized in earlier or later convolutional layers of the model.
confidence = 0. 924); FCD 2b white matter in yellow (max. confidence = 0. 969); FCD 2b neocortex in light blue (max. confidence = 0. 367); FCD2a (X) and MOGHE (Y) were spotted across the WSI with low confidence (\ 0. 415, 0. 562) and were histopathologically not confirmed. f A validation case example of a WSI obtained from a patient with histopathologically confirmed MOGHE and SLC35A2 mutation. Red color coding for the prediction of MOGHE with a maximum confidence score of 1. 000 and average confidence score of 0. 916. The prediction score of FCD1a (Z) and FCD2a (not visible at upper right) was below 0. 534 and 0. 328, respectively. Other categories were not recognized herein tool. Figures 6, 7, 8 and 9 will help to guide through the application and get acquainted with its functionality. However, this version has a database of readily uploaded WSI images as the model can currently process only ndpiimage formats obtained from 4-5 lm thin and HE-stained FFPE sections.
A more recent approach achieved a kappa score of 0. 65 only after four rounds of microscopic review and when molecular testing was disclosed  [4]. These scores were pretty similar to those obtained in our current study, however. Overall, our inter-rater agreement varied between 10 and 96% across the expert panel with kappa values Fig. 6  Landing page of the online FCD classifier application. 1 First, go to the accordion-tab on the left to select your WSI of interest (see also Fig. 7 ). A set of WSI slides is already stored in its database. Future versions will enable the upload of external WSI. Technical issues to allow different WSI formats and size need to be adjusted, however. This tab also allows to choose among trained classifier models. Currently, the herein described basic ResNet18 and One-vs-All FCD2b models can be selected (supplement Table  2 ), but future applications may envisage different topic-specific models addressing the larger spectrum of lesions associated with epilepsy surgery  [6].
A free access, online application of the FCD classifier We developed a web-based open access application to use our FCD classifier on a daily diagnostic routine basis. This application may also become helpful in view of the first update of the FCD consensus classification scheme of the ILAE  [22]. This update includes for the first time the histopathologically difficult-to-diagnose disease entities of mMCD and MOGHE, and our model can help to train colleagues not yet familiar with these diagnosis online. The application prototype is accessible via https://fcd-classifier. eu. ngrok. io. A user will have to register to the platform to get free access (please send an email with your registration request to the senior authors). Currently, the application has a database of securely stored WSI for testing purpose as our analysis pipeline is able to compute only WSI of the Hamamatsu scanning.
Acknowledgements The present work was performed in fulfillment of the requirements of the  Friedrich-Alexander Universita ¨t Erlangen-Nu ¨rnberg (FAU)  for obtaining the degree ''Dr. rer. biol. hum. '' of  Jo ¨rg Vorndran. We kindly thank our colleagues from  Bielefeld-Bethel, Munich  and  Vogtareuth  for sharing their tissue samples and clinical information (see supplement Table x1). Histopathologists from  Mexico City, Paris, Belgrade , Milano, Akita, Utrecht,  Kuopio  and Campinas participated in our agreement trial. The Erlangen histopathology team was instrumental to retrieve the samples, re-cut and stain missing/damaged slides, as well as digitizing and annotating all specimens. Funding Open Access funding enabled and organized by  Projekt DEAL.
) paraffin embedding, tissue cutting and use of staining dyes (Schmitt et al. 2021; https://doi. org/10. 2196/23436). These issues need to be addressed in future studies when testing the model with datasets from different laboratories. Previous experiments showed also that CNN networks are vulnerable to adversarial attacks, when smallest permutations at the pixel level of an input image can cause a change in the predicted value of a classification model (Su et al. 2017; https://doi. org/10. 48550/arXiv. 1710. 08864). Other approaches have used artificially produced images that are completely unrecognizable to humans, but can force CNN's to predict outcomes with high confidence, (Nguyen et al. 2014; https://doi. org/10. 48550/arXiv. 1412. 1897, Kumano et al. 2020; https://doi. org/10. 48550/arXiv.
Recent developments in remote sensing  (Benveniste, et al. , 2019; Melet et al. , 2020)  provide scope for the generation of global databases, for instance, on the distribution of sandy beaches  (Luijendijk et al. , 2018) , sub-aerial beach slopes  (Vos et al. , 2020) , nearshore slopes  (Athanasiou et al. , 2019) , and shoreline change  (Mentaschi et al. , 2018; Luijendijk et al. , 2018). However, while being part of the way forward to improve science-based coastal management strategies, these environmental databases need to be carefully considered in the light of realistic scenarios and models. The purely data-derived information in these databases needs to be carefully used, while considering physical processes as well. For a holistic consideration, it is necessary to gain insight on how the combination of terrigenous sand supply from rivers, and coastal redistribution of this supply, link with sandy beach evolution at the global scale.
This transport occurs mostly in the surf zone which generally extends offshore for tens of metres to a couple of hundreds of metres. Each of these sediment fluxes relies on different physical mechanisms and thus requires a large amount of data to be estimated. Here, Qriver is calculated at each coastal cell from river watershed mean inputs, in this case for the years 1980-2010, from a calibrated erosion law (BQART or Maffre's formulaes, cf. Sect 2. 3) depending on the catchment area, mean annual catchment discharge, average temperature and local slope  (Syvitski and Milliman, 2007; Maffre et al. 2018 ). To verify the soundness of this calculation, we compared our estimate to  Milliman and Farnsworth's (2011)  database, obtaining a good agreement (Supplementary Section S1, Figure  S1 ). Qwave is calculated from climatological ocean data (over the 1993-2015 period) such as wave height, wave period, and wave orientation relative to the coast  (Kamphuis, 1991 , see Sect 2.
Global coastal cells For the coastline grid points, we use the Global Self-consistent Hierarchical High-resolution Geography Database  (GSHHG version 2. 3. 6 August 17, 2016; Wessel and Smith, 1996). The GSHHG coastline is further segmented into coarser points with a 0. 5° alongshore (~50-km) resolution and called cells  (Almar et al. , 2021). Locations at latitudes above 50° north and south were excluded as they correspond to areas such as Patagonia or Northern Canada where the model fails to properly represent sand transport, probably due to the multitude of closely interspaced islands. Also ERAInterim wave model struggles with ice caps.
River sediment supply 2. 3. 1 BQART The annual river sediment discharge, represented by the variable Qriver, was calculated over the 1979-2015 period from river watershed coastal points and interpolated at each coastal cell using the widely used BQART formula  (Syvitski and Milliman, 2007 , see Equation  3 ). We used it for every watershed connected to the ocean, as defined in the HydroBASIN database  (Lehner et al. , 2013). Regarding the size of watersheds for which BQART is valid, recent work indicates that BQART may not be applicable to small watersheds more exposed to singular behavior such as extremes  (Warwick et al. , 2022 (Warwick et al. , , 2023)). Of note,  Syvitski et al. (2022b)  indicate that small basins contribute only very little fluvial sediments. In the HydroBASIN database, small streams that drain directly to the coast are aggregated into larger entities of the order of 100 km 2 (max 500 km 2 ).
e. dams); it ranges between 0 and 1, with 1 representing complete retention. This parameter is poorly constrained. Due to missing regional information, here the same value was used for all the catchments and corresponds to a worldwide mean of 0. 2  (Syvitski and Milliman, 2007). Last, EH is an anthropic factor, and can have one of three possible values  (Syvitski and Milliman, 2007)  : EH = 0. 5 for areas with conservative human footprints (density < 200 inh. /km 2 , and gross domestic product per capita> 15000 $/yr) ; EH = 2 for areas with high human footprints (density > 200 inh. /km 2 , and gross domestic product per capita< 1000 $/yr); or EH = 1, for areas with low human footprints. In order to calculate Qriver at the coast for all catchments with the BQART formula, we extracted the values of Q, A, R, T, Ag, population density, and gross domestic product per capita from the HydroBASIN database  (Lehner et al.
Dam impact To quantify the impact of dams in Qriver, we used the GOODD dam database  (Mulligan et al. , 2020) , which provides the location of the dams as well as the associated upstream watersheds. We calculated Qriver,dam by masking the area upstream from the dams, so that it mimics total sediment retention in dam reservoirs assuming that 100% of the sediment is trapped. Overall, Qriver,dam fits Qriver calculated by using the BQART with a correlation coefficient of R = 0. 76. To investigate the influence of potential impact on river catchments, we also calculated an unrealistic pristine potential Qriver,p assuming a world without artificial river catchments (i. e. dams) and using Equation  3 , so that we can compare the two situations with respect to the sandy coast evolution. Because our model focuses on sand, we consider that only a fraction (e.
A positive/negative ΔQwave across a cell is indicative of accretion/erosion. When Islands are considered as isolated coastal systems, there is no sand flux from one island to another. The model provides results in one iteration starting from a hypothetical initial situation where sandy beaches are infinite reservoirs of detached sand and non-sandy stretches of coast are empty reservoirs. Here, the sandy cells were delineated as in the database of  Luijendijk et al. (2018).
Numerous articles document this feedback by waves in reshaping the delta coastline  (Anthony, 2015; Nienhuis et al. , 2015) , for instance at the  Mekong (Marchesiello et al. , 2019)  and the Volta river deltas  (Anthony et al. , 2016). We believe this aspect, which involves coastline evolution and its feedback on waves, is currently out of the reach of our methodology, especially at global scale. Although the sand budget plays a primary role in the evolution of the coastline on a global scale, other phenomena, natural or not, can influence this evolution on different spatial and temporal scales. The contribution of these phenomena is difficult to quantify because global databases (e. g. high-resolution offshore bathymetric data or a subsidence map) are still lacking. Among these various phenomena, the global rise in sea level over the last 100 years has had a visible impact on beaches, especially along gently sloping low-lying coasts.
Ranwez, Céline Scornavacca To cite this version: Rémi Allio, Frédéric Delsuc, Khalid Belkhir, Emmanuel J P Douzery, Vincent Ranwez, et al. Or-thoMaM v12: a database of curated single-copy ortholog alignments and trees to study mammalian evolutionary genomics. Nucleic Acids Research, 2024, 52 (D1), pp. D529-D535. 10. 1093/nar/gkad834. hal-04266876v2.
Introduction OrthoMaM is a comprehensive curated database that relies on an expert phylogenetic framework to describe the evolutionary dynamics of orthologous genes in mammalian genomes. Since its first release  ( 1 ) , OrthoMaM has regularly evolved to include newly available genomes and incorporate up-todate software in its automated analytic pipeline. The initial release contained a set of nucleotide exon alignments of 3,170 single-copy orthologous genes for the 12 mammalian genomes available in Ensembl ( 2 ) at the time. Since then, each new OrthoMaM version sequentially incorporated more mammalian genomes as they became available, while implementing new features  ( 3 ). From the v10 release ( 4 ), we drastically changed our pipeline to include the ever-increasing number of mammalian genomes released in NCBI  ( 5 ).
In the present version, sequence alignments and phylogenies were computed with state-of-the-art tools. Nucleotide and amino acid alignments were obtained using our codon-aware multiple sequence alignment tool MACSE  ( 6 )  together with efficient filtering methods such as HMMCleaner  ( 7 )  and PhylteR  ( 8 ). These high-quality alignments were then used as input for maximum likelihood phylogenetic inference performed with IQ-TREE  ( 9 ). Importantly, the web interface has also been extensively redesigned to improve user experience. Previous versions of our database have been widely used in the evolutionary and comparative genomics community. In particular, the inclusion of NCBI genomes generated much interest from users. Our database has been used, for instance, in studies aimed at reconstructing the evolutionary history of functionally important genes  ( 10 ,11 ) , reconstructing the phylogeny of numerous mammalian clades  ( 12 ,13 ) , studying the process governing the evolution of genome-wide base composition  ( 14 ,15 ) , inferring patterns of natural selection acting on protein-coding genes  ( 16 ,17 ) , and as a benchmark dataset for evaluating various bioinformatic methods  (18) (19) (20) , and testing the impact of different potential sources of phylogenetic incongruence  ( 21 ).
Database content In contrast to orthology databases such as OrthoDB  ( 22 ) , In-ParanoiDB  ( 23 ) , PhylomeDB  ( 24 )  or EggNOG  ( 25 ) , which are solely based on protein sequences, the purpose of our Or-thoMaM database is not to infer orthology relationships, as we rely on NCBI orthology predictions. The uniqueness of our database lies in providing a comprehensive, curated set of high-quality codon and amino acid alignments, together with corresponding phylogenetic trees, for all single-copy proteincoding genes annotated in mammalian genomes. The search facility permits querying the database either by sequence, gene ID, species, taxonomic level or evolutionary parameter values to download subsets of genes of interest. This allows a range of evolutionary genomic analyses to be performed at both the nucleotide and amino acid levels.
The database can also be searched by sequence similarity using user-provided sequences. To our knowledge, it is the only DNA, protein and tree database to provide such a valuable resource for mam-mals, a group of organisms of biomedical, agronomic, and ecological importance. To date, the OrthoMaM database includes 15,868 nucleotide and amino acid alignments for 190 species. The length of the orthologous genes ranges from ∼100 to ∼56k nucleotides (19,417 sites ± 2102), leading to a total of ∼31M of nucleotides (with more than 18M variable sites). The alignments are very complete with 177 / 190 species on average. Gene trees and statistics are provided for every marker and a supertree inferred from all the individual gene trees is also available for download.
Bioinformatic pipeline The aim of the OrthoMaM database is to provide ready-touse curated mammalian orthologous gene alignments to anyone interested in mammal evolution and focusing either on a specific gene or larger sets of orthologs. The two main efforts done to construct such a database have been: (i) to develop a pipeline comprising state-of-the-art approaches for pre-filtering, aligning, post-filtering and analysing gene alignments and (ii) to create a user-friendly website interface allowing users to easily collect genes and trees, but also precomputed associated statistics. The OrthoMaM (OMM) database takes advantage of the publicly available NCBI database, which provides access to a large amount of raw genetic data, provided by scientists from all over the world. As of January 2023, a subset of 190 annotated assemblies (one assembly per mammalian species) was selected based on several assembly statistics (number of scaffolds, N50, etc.