dataesr/scientific-paragraphs-categorization huggingface dataset:
Histological classi cation of human sarcoma FFPE tissues In the next step, a conceptual databank was created using a cohort of human sarcoma FFPE samples (29 patients; 37 samples). Human sarcomas are known to be a very heterogeneous cancer presenting many challenges for accurate diagnostics  (31, 32). FFPE sarcoma samples were classi ed (Fig. 4 ) based on two 2 different sarcoma types and 2 different classes (malignant and benign) (Fig. 4a ). The samples were collected from patients with different age, sex, and tumor origin as shown in Table  S4. Sarcomas are very rare lesions (1% of malignant tumors); therefore, it is di cult to obtain an equivalent number of samples of the same sub-type. The acquired spectra for each subtype are shown in Fig. S5. The PCA analysis (Fig. S6 ) shows that the rst 3 PCs explain more than 80% of the total variance in the dataset (PC1 45.
Blind screening in real-time of FFPE samples To demonstrate the e ciency of our platform and human sarcoma model in a clinical environment, we simulated the tissue classi cation in real-time. Two FFPE human sarcoma samples of different subtypes were randomly selected by the pathologist and analyzed in blind using SpiderMass accordingly to the optimized analytical work ow. The previously 4-class generated human sarcoma PCA-LDA model was loaded into AMX recognition software  (13). Each acquisition is automatically predicted, and the software gives out the readout of different color-coding and percent of certainty: Leiomyoma (Red), Leiomyosarcoma (Green), Lipoma (Turquoise) and Liposarcoma (Purple). The results of the two blind acquisitions are shown in Fig. 5. The interrogation software correctly predicted in real-time with over 95% probability value of the two subtypes of human sarcoma.
In the rst case, the software predicted the sample to be a benign Lipoma for all three acquisitions (Fig. 5a ). The scan 53-55 was predicted with 99. 25 % probability, scan 62-64 with 99. 28 % probability and scan 71-72 was predicted with 98. 25%. In the second case, the software predicted the sample to be a benign leiomyoma (red) (Fig. 5b ). The prediction was with 96. 21 % certainty for scan 11-12 and 97. 82 % probability for scan 23-24, and nally 95. 38% for scan 33-34. Following the fast screening, the results were compared to the histological annotations of the pathologist con rming the correct classi cation in both cases obtained in real-time with the SpiderMass platform. These results show it is possible to provide correct classi cation accuracy for blind sample, above 95%, already from a rather small training cohort of samples.
Since our platform doesn't necessitate depara nization and rehydration, many phospholipids species such as PAs, PSs, PIs and PCs were observed and identi ed from FFPE rat brain tissues in positive and negative ion mode as well as a mixture of Triglycerides (TGs) in Human Sarcoma FFPE tissue sections in positive ion mode. These lipid species were used as classi ers to build the molecular databases. The IR laser microprobe of the SpiderMass system is tuned to excite the most intense vibrational band of water (O-H) which acts as an endogenous matrix. Since, FFPE tissues do not retain water, an exogenous matrix is necessary to induce the desorption/ionization process. This is clearly observed by the low signal intensities in the mass spectra without sample preparation in both rat brain and sarcoma (Fig.
1 , Fig. 6 ). Glycerol structure contains (O-H) bonds like water which promote desorption/ionization with the IR SpiderMass laser. This effect is observed by signal intensities of some lipid species reaching 100x the intensity of the initial measurement in FFPE brain tissue analysis (Fig. 1 ) similar to what is usually obtained by SpiderMass from fresh-frozen tissues (16). The exact same effect is observed when measuring the FFPE preserved human sarcoma tissues in positive ion mode (Fig. 6 ). Two successive layers of the 20% glycerol/IPA (approx. 10s spray) have a tremendous impact on lipid signal intensity. The improved sensitivity of lipid signals enabled us to construct classi cation databanks from canine and human sarcoma subtypes. Human sarcomas are a rare and complex type of cancer forming more than 100 different histopathological subtypes.
Conclusion To conclude, we have revealed the possibility to create a rapid diagnostic SpiderMass platform to screen clinical FFPE tissues with minimal sample preparation. The speed of the screening procedure is exempli ed in the videos conducted during the unknown sample evaluation (Video 1, Video 2). The creation of canine and human FFPE sarcoma databanks are just one of the examples where the platform could be used for consolidation of histopathological diagnosis. Tissue banks with different types of FFPE cancer could utilize this platform. However, any further implementation into the clinical environment will require further elaboration of the database as well as on-site validation with larger sample cohorts of the technique. Moreover, we determined, that samples up to 6 years old, can be used, to create a databank increasing our availability of large sample cohorts.
Acknowledgments: wish to thank  Marc Le Parquier  from the  "Centre d'Études et de Recherches, Lasers et Applications" (CERLA)  of the  University of Lille  for his technical support. We also thank all the members of  SATT-Nord  involved in SpiderMass, and in particular,  Eglantine Rollet  and  François-Xavier Denimal , for their support to the project.
Genomes and metagenomes contain a considerable percentage of genes of unknown function, which are often excluded from downstream analyses limiting our understanding of the studied biological systems. To address this challenge, we developed AGNOSTOS, a combined database-computational workflow resource that unifies the known and unknown coding sequence space of genomes and metagenomes. Here, we present AGNOSTOS-DB, an extensive database of high-quality gene clusters enriched with functional, ecological and phylogenetic information. Moreover, AGNOSTOS allows integrating new data into existing AGNOSTOS-DBs, maximizing the information retrievable for the genes of unknown function. As a proof of concept, we provide a seed database that integrates the predicted genes from marine and human metagenomes, as well as from Bacteria, Archaea, Eukarya and giant viruses environmental and cultivar genomes.
AGNOSTOS partitions the CDSspace using gene clusters, and unlike the previous GC centered methods  [3] [4] [5] [6]  , performs an extensive validation yielding high-quality biologically and phylogenetically aware functional units. Further, AGNOSTOS provides a thorough characterization of the unknown space, including distant-homology classification methods. Moreover, GCs sharing remote homologies are aggregated in communities (GCCs)  2. Altogether AGNOSTOS allows exploring the CDSspace at different levels: from the gene similarities within a GC to the remote homologies in the GCCs. The AGNOSTOS databases (AGNOSTOS-DBs) are embedded into the AGNOSTOS analytical environment and allow different levels of analysis, as shown in Figure  1. The AGNOSTOS-DB provides GC sequence profiles that can be used to quickly screen new datasets (profile-search module).
Alternatively, a novel DB can be created from any metagenomic and genomic data via the DB_creation module. Finally, a key feature of AGNOSTOS is its scalability, i. e. , the possibility to update existing DBs with new sequences. The AGNOSTOS seed-DB integrated the sequence data from 1,829 marine and human metagenomic assemblies and 28,941 bacterial and archaeal genomes, for a total of 5,287,759 GCs and 335,439,673 genes 2. To show the flexibility and scalability provided by AGNOSTOS, here we expanded the seed-DB by integrating genomes affiliated to Eukarya and their infecting nucleocytoplasmic large DNA viruses (NCLDV) characterized mostly from metagenomic sequence data corresponding to the surface of the oceans (Gaia et al. , personal communication). First, we integrated the predicted genes from 3,243 NCLDV environmental and cultivar genomes  [7] [8] [9]    Subsequently, we integrated the predicted genes from 713 eukaryotic environmental genomes  10  , creating a comprehensive database of 6,572,081 GCs and 341,655,294 genes (Table  2 ) that expand to all domains of life and viruses.
Moreover, after the two integrations, we observed an increase of 23% in unknown GCs (187% for the EU GCs alone) compared to the seed-DB. These novel GCs found in the context of NCLDV and eukaryotic genomes offer a starting point for targeted analyses of the unknown fraction. Ultimately, as shown in Figure  2 , we found most GCs unique to the prokaryotic or eukaryotic plankton fraction where more than half of the GCs in these two sets belong to the unknown fraction (Figure  2 ). We also identified 5,220 GCs shared by all datasets and 2,854 GCs shared between the NCLDVs and the plankton eukaryotes, in both cases mainly members of the known fraction (Figure  2 ). This last set constitutes a valuable resource to better understand the relationship between the NCLDVs and their eukaryotic hosts  11.
Computational workflow We implemented the AGNOSTOS using the Snakemake workflow management system  12  to easily process large datasets in a reproducible manner. The workflow is publicly available in a GitHub repository at https://github. com/functional-dark-side/agnostos-wf (version 1. 0, used in this paper, is archived at https://doi. org/10. 5281/zenodo. 4557847). The workflow provides three different modules (Fig. 1 ): The DB-creation module, which creates a de-novo GC dataset, taking as input metagenomic contigs or predicted genes in fastA format. The output is a dataset of characterized GCs, partitioned in four categories (Fig. 1 ). The output of the DBcreation module is already formatted to be updated with new data via the DB-update module. The DB-update module allows the continuous integration of new sequence data into an existing GC database.
The new sequences are integrated into existing clusters if similar enough or clustered in new GCs if not. The final output consists of the updated GC database and related files containing contextual data about the GCs. Moreover, the workflow provides a profile-search module that enables the screening of existing GC databases using sensitive profile-search methods. The sequence-profile search is performed using the search program of the MMseqs2 13 software (parameters: -e 1e-20 -cov-mode 2 -c 0. 6). This module takes in input the GC MMseqs2 profiles, a tabular file with the information about the GC functional categories and the sequences to search against the profiles, in fastA format. The search results are then filtered to keep hits within the 90% of the best log(e-value) and retrieve the queries consensus GC annotation via a quorum majority voting approach.
The required software tools and programs are installed via Conda or via the provided installation script. When the existing database is the seed-DB, or an updated GC database originated from the seed-DB, the DB-update final rule links the new integrated GCs with the ecological, phylogenetic, and experimental metadata obtained from the Vanni et al. analyses  2  (Fig. 1 ). To build the GC databases presented here, we ran the workflow in the de. NBI Cloud (https://www. denbi. de/cloud) 14 , using a cluster setup with 10 nodes of 28 cores and 252G of memory each. The cluster was built using BiBiGrid (https://github. com/BiBiServ/bibigrid) and it is using SLURM  15  for job scheduling and cluster management.
Data collection To build the seed-DB, we combined a set of 1,829 metagenomes from five major metagenomic surveys of the ocean and human microbiome with 28,941 archaea and bacterial genomes 2. The data sources per project are specified in Table  5 -A, and the number of genes predicted from each project in reference genomes collected from GenBank 9 (Table  6 ). The NCLDV dataset was passed to AGNOSTOS in the form of predicted genes (amino acids). Additionally, we added to the seed+NCLDV-DB, 10,207,450 eukaryotic plankton predicted genes, obtained from the combination of 683 environmental genomes and 30 single amplified genomes  10. The plankton eukaryotic genes were predicted by Delmont et al. using a combination of three approaches: a homology-based search against protein reference databases (Uniref90 + METdb), the mapping of the metatranscriptomics assemblies to the environmental and single-amplified genomes, and an ab-initio gene prediction using gmove (http://www.
Update of a gene cluster dataset AGNOSTOS updates an existing GC database using the clusterupdate module of MMseqs2 23 using the same clustering parameters described in Vanni et al. 2. The output of the clustering update is then parsed to retrieve three GC datasets: (1) The "original" GCs, a set containing the original GCs without any similarity to the new sequences. (2) The "shared" set of GCs with mixed, original and new, sequences. (3) The "new" GCs built from the new sequences without any similarity to the existing GCs. The "new" GCs and the shared GCs that were originally singletons, are processed through all the workflow steps until the GCC inference. Results of the integration and gene cluster contextualization. At the end of the DB-update module, the three GC sets, together with their additional information, are re-combined in a single GC dataset.
Data Records The AGNOSTOS seed-DB is available on Figshare at https://doi. org/10. 6084/m9. figshare. 12459056 (Data Citation 1). We base encoded the seed version of the database as agnostosDB_dbf02445-20200519, which resulted from using the crc32 hash algorithm to encode the word "seed", followed by the release date (YYYYMMDD). Future integrations of (meta)genomic data into the seed-DB will be named by adding the new dataset name in the crc32 encoding command followed by the new release date. The seed-DB is a dataset of microbial GCs containing 40,423,549 GCs with 415,971,742 genes predicted from 28,941 bacterial and archaeal genomes and 1,749 metagenomes. The "agnostosDB_dbf02445-20200519_original-data" (https://ndownloader. figshare. com/files/23066858) contains the list of metagenomes, genomes, contigs and predicted genes per metagenomic project and found in GTDB.
The original seed-DB GC MMseqs2 database files are available in "agnostosDB_dbf02445-20200519_mmseqs_clustering" (https://ndownloader. figshare. com/files/23066651). The distribution of the GCs and genes in the four categories can be found in Supplementary Figure  2  of Vanni et al. 2. The list of refined GCs, their categories and their genes are available in the TSV file: "cluster_ids_categ_genes. tsv. gz". The TSV file "HQ-clusters. tsv. gz" contains the IDs of the set of high-quality GCs, containing mostly complete genes. The GC HH-suite database files are contained in the folder "agnostosDB_dbf02445-20200519_hh-suite-db" (https://ndownloader. figshare. com/files/23064476). The HH-suite database can be used to screen the GCs in search for remote-homologies, using HHblits The database agnostosDB_4eab867d-20201104 combining the seed-DB GCs with the NCLDV and the eukaryotic plankton GCs is available on Figshare at https://doi.
Gene cluster database validations The quality control steps in AGNOSTOS thoroughly validate the AGNOSTOS-DB GCs. These steps include the identification and removal of eventual spurious genes, the validation of the GCs internal homogeneity, both in terms of Pfam protein domain annotations and sequence composition, and a remote-homology refinement of the GCs classification in categories, which aimed to avoid an overestimation of the real number of uncharacterized genes 2,25.
Computational workflow validations We followed the FAIR guiding principles 26 to create a reproducible and reusable workflow. We implemented AGNOSTOS using the Snakemake workflow management system  12  and is available as an open source software (https://doi. org/10. 5281/zenodo. 4557847). The majority of the dependencies can be installed within a Conda environment  27, 28  integrated in the workflow. We provide an installation script to install the software not available in Conda. The workflow can be tested using the provided test datasets for the DB-creation and DB-update modules of AGNOSTOS. The test datasets are intended to validate the installation and help the user with an easy example of the workflow functioning and extensive outputs. For the testing dataset, we selected three TARA Ocean surface samples from the Indian Ocean (IO) (TARA_038, TARA_039 and TARA_041), all within a size fraction of 0.
Usage Notes The workflow is accompanied by detailed usage instructions and description of the output files available in the GitHub repository (https://github. com/functional-dark-side/agnostos-wf). The GC profiles and the tables with the GC categories used by the profile-search module can be downloaded from their respective AGNOSTOS-DB Figshare folders. Any other MMseqs2 profile database can be used for the search. For example, to screen only the seed-DB high-quality GCs, we can use their identifiers from the TSV file "HQ_clusters. tsv. gz", to subset the full GC profile database with the MMseqs2 createsubdb module (see the MMseqs2 user guide available at https://mmseqs. com/latest/userguide. pdf). By default, the DB-update module of the workflow will download and use the seed-DB as the existing GC database.
Any other GC database built with the DB-creation module, or containing the same output file structure, can be used by specifying the different database location in the DB-update configuration file. The output of the DB-update and DB-creation modules can be analyzed using anvi'o  29  , by integrating all the information gathered for the GCs as "functions" in the anvi'o contig database. An introduction on how to integrate AGNOSTOS products in the analyses with anvi'o is available in the form of a tutorial at https://merenlab. org/agnostos-tutorial. The tutorial describes the integration of the infant gut dataset (IGD) from Sharon et al. 30  in AGNOSTOS (workflow and databases) and how to use it in combination with anvi'o to explore genes of unknown function. Within anvi'o, the GCs can be explored in the context of assembled contigs allowing the identification of contigs enriched in uncharacterized genes or the exploration of the genomic context of GCs of interest.
Code Availability The workflow is publicly available and maintained on a GitHub repository (https://github. com/functional-dark-side/agnostos-wf). The workflow has been tested in an HPC cluster setup with at least 4 nodes of 28 cores and 252 G of memory each, which uses SLURM  15  as Grid Batch Scheduler. The workflow programs and software contained in the Conda environment are listed in a. yml file at https://github. com/functional-dark-side/agnostoswf/blob/master/envs/workflow. yml. The other programs are listed in the bash installation script at https://github. com/functional-dark-side/agnostos-wf/blob/master/installation_script. sh.
24(see Usage notes). The GC MMseqs2 profiles used by the profile-search module are stored in the folder "agnostosDB_dbf02445-20200519_mmseqs_profiles" (https://ndownloader. figshare. com/files/23066963). All other files needed by the DB-update and the profile-search modules are also available in the database Figshare folder. The GC database expanded with the NCLDV MAGs was encoded as agnostosDB_a42ac58a- 20200715 and it is available on Figshare at https://doi. org/10. 6084/m9. figshare. 13251743 (Data citation 2).
Eq. 3 Where : -F is a maximum strain criterion 1 = + 2 + 3 for a 1D example criterion, -is the total strain at failure determined experimentally is the elastic strain at failure -is the damaged Young's modulus for strain at failure -G is the critical energy release rate (J/m2) determined experimentally -lc is a characteristic length for a non-local approach (m) with FE use. Lc value is computed by the software. We have modified a user routine in Abaqus in order to compute this length in the perpendicular direction of the yarns direction in order to be coherent with fracture mechanic theory.
Model validation and experimental comparison Once identified on 2x2 twill flax woven laminates, parameters and properties of the material model are introduced into a developed user material subroutine (UMAT) developed for implicit Abaqus® Software. For the purpose of validation of the model, some preliminary tests have been done with a single fully integrated solid element (8 nodes, 3 degrees of freedom per node and 8 Gauss integration points -C3D8 reference in Abaqus software). The static general solver (Quasi static implicit algorithm, Newton-Rahpson) is used in Abaqus. Displacements are prescribed at the boundaries for tensile and shear tests. For the numerical/experimental tensile test comparison, Figure  16  shows the FE model used. Tabs are not meshed. Solid elements used are the same as prescribed before (C3D8).
For improved lesion localization, foreground/background separation is trained through self-supervision, in such a way that occluding foreground pixels transforms the input image into a healthy-looking image. The advantage of such an architecture is that automatic diagnoses can be explained simply by an image and/or a few sentences. ExplAIn is evaluated at the image level and at the pixel level on various CFP image datasets. We expect this new framework, which jointly offers high classification performance and explainability, to facilitate AI deployment.
Image Datasets A model was developed using training and validation datasets with image-level labels. Next, this model was evaluated on multiple datasets with image-level labels and on one dataset with pixel-level labels. In all datasets, images were graded according to the ICDR scale. In practice, image-level labels were not assigned to single images but, more generally, to small sets of images (CFPs) from the same eye. These datasets have various origins: USA, France, India and China.
OPHDIAT Image-Level Evaluation Dataset The first image-level evaluation dataset originates from the OPHDIAT DR screening network, which consists of 40 screening centers located in 22 diabetic wards of hospitals, 15 primary health-care centers and 3 prisons in the Ile-de-France area  (Massin et al. , 2008). Let OPHDIAT eval denote this dataset. This dataset consists of 21,576 CFPs from 9,734 eyes of 4,996 patients (∼ 2 CFPs per eye). As part of the quality-assurance program of OPHDIAT, DR severity in these randomly-selected patients was graded by two ophthalmologists. In case of disagreement, images were read a third time by a senior ophthalmologist.
Training and Validation Datasets The training and validation datasets originate from two DR screening programs: OPHDIAT and EyePACS  (Cuadros and Bresnick, 2009). Images from EyePACS were acquired in multiple primary care sites throughout California and elsewhere. A total of 88,702 CFPs from 88,702 eyes of 44,351 patients (one CFP per eye), released for Kaggle's Diabetic Retinopathy Detection challenge 1  , was used. Training and validation images from OPHDIAT consist of all images from patients that were not included in the image-level evaluation dataset: a total of 610,748 CFPs from 275,236 eyes of 142,145 patients (∼ 2 CFPs per eye) were included. In both subsets, DR severity in each eye was graded by a single human reader. Images were distributed as follows: 90% were used for training and 10% were used for validation.