dataesr/scientific-paragraphs-categorization huggingface dataset:
The UniToChest Dataset The UniToChest dataset has been collected within the EU-H2020 DeepHealth  [5, 16]  project and consists of more than 300k lung CT scans of pulmonary lungs from 623 different patients. The scans are in DICOM format and each scan comes with a manually annotated segmentation mask in black and white PNG format, both being 512 × 512 in size. A comparison with similar datasets in Table  1  shows that UniToChest has more nodules with a wider diameter range especially at the top end. The Uni-ToChest contains data collected from a gender-balanced population and spanning across a wide range of ages. Moreover, it includes images acquired using 10 different devices. The demographic details of patients and insights of the data collection process can be found in the original dataset paper (accepted to ICIAP 2022)  [4].
For all the CT scan slices in UniToChest, the radiologist has manually segmented the present lung nodules to provide a segmentation mask. To ensure UE regulation on privacy all the patient identifiers are removed from the CT scan slices and the segmented masks. For the purpose of training a neural network, we split the dataset into training, validation and test set randomly as 80-10-10 of patients. We maintain data consistency across multiple splits by assigning a single split to each patient. The data population with respect to the splits is summarized in Table  2. All the three sets (training, validation and test) have a 60 to 40 ratio between the number of male and female patients. Furthermore, the Table  3  presents an in-depth distribution of different nodule diameters within the three splits done for training of the neural network.
Results and Discussion In this section, we experiment over the UniToChest dataset with the neural network based method described in the previous section for nodule segmentation. All results are relative to UniToChest test set, i. e. images that have not been used at training time. For the experiments Docker image dhealth/pylibs-toolkit:1. 2. 0-1-cudnn with PyECVL 1. 2. 0 and PyEDDL 1. 3. 0 are used.
Conclusion and Future Works This paper proves the feasibility of the Deephealth toolkit and its libraries. In particular, PyECVL and PyEDDL in providing Deep Learning and Computer Vision off-the-shelf services. In this study we proposed a U-Net based architecture using PyEDDL that yields promising results at the segmentation of lung nodules from UniToChest. Future research directions of this work include exploiting similar datasets and perform an efficiency comparison with PyTorch.
of the functionalities needed by Deep Learning in the Health sector, is easy to use and integrate by developers, and becomes the most widely used Deep Learning library for Health. EDDL uses High-Performance Computing (HPC) and a Cloud infrastructure transparently. It provides Neural Network topology components and hardware-independent tensor operations. Using the Neural Network library both the training and inference of a model can be performed, along with a finer control on deeper levels like gradient manipulation or individual batches. The development language of EDDL is also C++ and a Python wrapper class PyEDDL is provided. The latest versions of EDDL and PyEDDL along with documentation are published publicly on GitHub [7] [19].
Statistical Analyses All statistical analyses were conducted using R v3. 5. 3 (R Development Core Team, 2019). We built a dataset that included one line for each parasite species in each faecal sample as the unit of analysis, i. e. , three times (three parasite species) the number of faecal samples analysed. In addition, each sample was associated with an individual and all of its attributes, including its centrality measures (degree, strength, and eigenvector), Elo-rating, sex and age, along with the sample's collection date. EPG was used as the response variable in the statistical models described below. The dataset (SP 1) and scripts are available on Git repository: https://github. com/XuZhihong-cn/Network_Centrality_analysis_and_KOsimulation.
The variable domains (V-DOMAIN) of the antigen receptors, immunoglobulins (IG) or antibodies and T cell receptors (TR), which specifically recognize the antigens show a huge diversity in their sequences. This diversity results from the complex mechanisms involved in the synthesis of these domains at the DNA level (rearrangements of the variable (V), diversity (D), and joining (J) genes; N-diversity; and, for the IG, somatic hypermutations). The recognition of V, D, and J as "genes" and their entry in databases mark the creation of IMGT by Marie-Paule Lefranc, and the origin of immunoinformatics in 1989. For 30 years, IMGT ® , the international ImMunoGeneTics information system ® http://www. imgt. org, has implemented databases and developed tools for IG and TR immunoinformatics, based on the IMGT Scientific chart rules and IMGT-ONTOLOGY concepts and axioms, and more particularly, the princeps ones: IMGT genes and alleles (CLASSIFICATION axiom) and the IMGT unique numbering and IMGT Collier de Perles (NUMEROTATION axiom).
Introduction Immunoglobulins (IG) or antibodies  [1, 2]  and T cell receptors (TR)  [3]  are antigen receptors of the adaptive immune responses in vertebrates with jaws (gnathostomata)  [4]. The huge diversity of the variable domains (V-DOMAIN) of the IG and TR chains of the immune repertoires results from several mechanisms that occur during their synthesis  [1] [2] [3] [4]. In particular, the combinatorial diversity depends on the number of variable (V), diversity (D), and joining (J) genes found in the IG and TR loci, which potentially can rearrange to form V-DOMAIN encoded by V-(D)-J regions  [1] [2] [3] [4]. It is the recognition of the V, D, and J as "genes" and their entry in databases that mark the creation of IMGT in 1989 by Marie-Paule Lefranc (Universitede Montpellier, CNRS) at Human Gene Mapping 10 (HGM10) and is at the origin of immunoinformatics, a new science at the interface between immunogenetics and bioinformatics  [4].
Other mechanisms of diversity comprise the junctional diversity with exonuclease trimming at the ends of the V, D, and J genes and the random addition of nontemplated nucleotides, preferably "g" and "c," by the terminal deoxynucleotidyl transferase (TdT) encoded by the DNA nucleotidylexotransferase (DNTT) gene, creating the N-regions [1-3], and for IG, somatic hypermutations  [1, 2]. For 30 years, IMGT ® , the international ImMunoGeneTics information system ® http://www. imgt. org, has implemented databases and developed tools for IG and TR immunoinformatics  [5] , based on the IMGT Scientific chart rules (see Subheading 2) and IMGT-ONTOLOGY concepts and axioms  [6, 7] , and more particularly, the princeps ones: IMGT genes and alleles (CLASSIFICA TION axiom)  [8] [9] [10] [11] [12]  and the IMGT unique numbering  [13] [14] [15] [16] [17]  and IMGT Collier de Perles  [18] [19] [20] [21]  (NUMEROTATION axiom).
The IMGT Collier de Perles  [18] [19] [20]  can be generated by the IMGT/ Collier-de-Perles tool  [21]  (see  Subheading 7). Fig. 1  Protein displays of IG and TR V-DOMAIN based on the IMGT unique numbering for V-DOMAIN  [13]. The V-DOMAIN translations were obtained from the analysis by IMGT/V-QUEST  [22, 23]  (see Subheading 3) of the nucleotide sequences of shown accession numbers in IMGT/LIGM-DB  [36]. The identification of FR-IMGT and CDR-IMGT and of beta strands and loops was performed by IMGT/DomainGapAlign  [30, 31]  (see  Subheading 6) , which provides a standardized delimitation whatever the species, the receptor type, and the chain type. CDR-IMGT lengths are indicated between brackets, separated by dots (column on the right). 1st-CYS 23 and 2nd-CYS 104 are in pink, and W 41, hydrophobic AA 89, and W or F 118 are in blue.
IMGT/V-QUEST provides warnings (not shown) that appear as notes in red to alert the user, if potential insertions or deletions are suspected in the V-REGION (see  Note 15) , or if other possibilities for the J-GENE and allele names are identified. Users are encouraged to check alignments in related detailed result sections. Below the "Result summary," notes in black (not shown) may appear to indicate: Fig. 4  IMGT/V-QUEST "Detailed results"  [22, 23]. The parameters of the analysis are recalled on the top of the page. The first part the "Detailed results" for "seq_1" (IMGT/LIGM-DB  [36]  accession number X81732) includes the sequence in FASTA format (the first 57 nt not underlined in green are not part of the V-DOMAIN) and the "Result summary. " Seq_1 functionality is "productive. " This human IGHV sequence expresses the IGHV3-9*01, IGHD3-3*01, and IGHJ4*02 genes and alleles.
These results are provided even if IMGT/JunctionAnalysis gives no results. 4. Delimitation of the FR-IMGT and CDR-IMGT in V-REGION: the sections 6, 7 and 8 provide three displays of the V-REGION  [22, 23] : (a) "6. V-REGION alignment according to the IMGT unique numbering" for the nt sequences with the FR-IMGT and CDR-IMGT delimitations according to the IMGT unique numbering  [13]. (b) "7. V-REGION translation" for the nt sequence and its AA translation, aligned with the closest germline V-REGION. Fig. 5  IMGT/V-QUEST "Detailed results"  [22, 23]. Results of IMGT/JunctionAnalysis  [37, 38]  for AB063867 IMGT/LIGM-DB accession number. This human IGH sequence results from the rearrangement of IGHV3-11*05 F, IGHD6-13*01 F, and IGHJ4*02 F. The JUNCTION is in-frame. The length of the CDR3-IMGT is 14 AA (42 nt).
8  IMGT/V-QUEST "Detailed results"  [22, 23]. Top of Detailed results for the "Advanced functionality" "Analysis of single chain fragment variable (scFv)". scFv_1 and scFv_2 sequences correspond to the accession numbers AJ006120 and AF117956 in the IMGT/LIGM-DB database  [36]. The 5 0 V-DOMAIN are VH and 3 0 V-DOMAIN are V-KAPPA for both scFv. The detailed results are then provided for each domain that express the same V gene and alleles: "1. Alignment for V-GENE," "2. V-REGION alignment according to the IMGT unique numbering"  [13] , "3. V-REGION translation," and three different formats for the "V-REGION protein display. " Section "7. V-REGION most frequently occurring AA per position and per FR-IMGT and CDR-IMGT" shows the most frequent AA in sequences expressing the same V genes and alleles per FR-IMGT and CDR-IMGT and per position according to the IMGT unique numbering  [13].
3. 4 IMGT/V-QUEST Output for Excel File "Excel file" allows the users to open and save a spreadsheet including the results of the IMGT/V-QUEST analysis  [22, 23]. The file contains 11 sheets or 12 for the Advanced Functionality "Analysis of single chain Fragment variable (scFv)"  [41]  (see Subheading 4. 3 for the detail of their content in IMGT/HighV-QUEST sequence analysis results). Fig. 9  IMGT/V-QUEST Synthesis view  [22, 23]. (a) The 12 first columns of the "Summary table": the four analyzed sequences are shown in the "V-GENE and allele name" order in the Summary table. The sequences ID are accession numbers of IMGT/LIGM-DB  [36]. The hyperlinks allow to get the corresponding results in "A Detailed view. " The two sequences assigned to IGHV3-9 and the two sequences assigned to IGHV5-51 will be, respectively, aligned together in the detailed result sections 1 to 6.
IMGT/Collier-de-Perles The IMGT/Collier-de-Perles tool  [21]  generates "'IMGT Colliers de Perles"  [18] [19] [20]. For V-DOMAIN, IMGT Colliers de Perles are obtained on one or two layers, provided that the V-DOMAIN Fig. 19  IMGT/DomainGapAlign Results  [30, 31]. Bottom of the result page for VH domain of daclizumab 3nfp_H chain (PDB code 3nfp of IMGT/3Dstructure-DB  [30, 50, 51] ): the CDR-IMGT lengths are [8. 8. 9] with a total of three AA changes. The FR-IMGT lengths are  [25. 17. 38. 11]  with a total of 14 AA changes (AA) sequence is gapped according to the IMGT unique numbering  [13]  (see  Note 32). Resulting IMGT Colliers de Perles show the standardized delimitation of FR-IMGT and CDR-IMGT, and of beta strands with their orientation in the IG and TR V-DOMAIN, allowing the visualization of the amino acids, which are important for a 3D structural configuration and bridging the gap between sequences and structures.
3. The nucleotides "n" at 5 0 and/or 3 0 end of the submitted sequences are automatically trimmed before the analysis. The numbers of 5 0 trimmed-n and 3 0 trimmed-n are indicated in the results if any (see "Trimming of nucleotides "n" at 5' and/or 3' end of the submitted sequences" in the IMGT/V-QUEST  [22, 23]  Documentation). 4. The 14 sections are linked to the corresponding IMGT/V-QUEST  [22, 23]  Documentation in order to help the user in choosing of checking or unchecking them (see also Subheading 3. 2. 2). When "Uncheck all" is selected, only the "Result summary" is displayed. ä Fig. 21  (continued) IMGT/V-QUEST  [22, 23]  analysis of a VH (nt) (accession number X81732 of IMGT/LIGM-DB  [36] ). (b) IMGT Collier de Perles on two layers of a VH with hydrogen bonds between the amino acids of the C, C 0 , C 00 , F, and G strands and those of the CDR-IMGT (daclizumab 3nfp_H, PDB code 3nfp of IMGT/3Dstructure-DB  [30, 50, 51] ).
(c, d) IMGT Colliers de Perles with AA changes of a V-LAMBDA domain generated from the IMGT/DomainGapAlign  [30, 31]  analysis (translation of the AF063723 IMGT/LIGM-DB accession number), (c) on one layer, and (d) on two layers. (e, f) IMGT/Collier-de-Perles  [21]  results on one layer for the entry code p00149 from IMGT/2Dstructure-DB (e) with background color "IGH 80% hydrophopathy classes  [46]  and (f) with background color "IGH 80% physicochemical classes"  [46]  deletions in V-REGION," and (4) analysis on reverse sequence with "Search for insertions and deletions in V-REGION" and corrections if any. 13. The score of the alignment for two sequences is calculated by counting +5 for each identical nt at a given position (match) and À4 for position with different nt (mismatch)  [22, 23].
31. The Smith-Waterman algorithm is used for local sequence alignments of the user AA sequences with the AA IMGT reference directories for V-REGION and J-REGION. The highest alignment scores correspond to the highest sequence similarities. 32. IMGT/Collier-de-Perles  [21]  provides also 2D graphical representations of C-DOMAIN of IG and TR  [14] , of V-LIKE-DOMAIN and C-LIKE-DOMAIN of the IgSF other than IG or TR  [13, 14, 16, 17] , of G-DOMAIN of major histocompatibility (MH), and of G-LIKE-DOMAIN of MhSF other than MH  [15] [16] [17]. 33. "IMGT Colliers de Perles"  [18] [19] [20] [21]  are also provided in IMGT/3Dstructure-DB and IMGT/2Dstructure-DB database entries  [30, 50, 51] : the hydrogen bonds within a V-DOMAIN, determined from experimental structural data, are shown as green lines in generated IMGT Collier de Perles on two layers.
Acknowledgements We are very grateful to  Gerard Lefranc , founder of the  Laboratoire d'ImmunoGenetique Moleculaire LIGM (Universitede Montpellier and CNRS ), for his unique contribution in the creation of IMGT® in 1989 and his unwavering support for these 30 years. We thank all members of the IMGT® team for their expertise and constant motivation. IMGT® was funded in part by the  BIOMED1  ( BIOCT930038 ), Biotechnology  BIOTECH2  ( BIO4CT960037 ), 5th  PCRDT Quality of Life and Management of Living Resources  ( QLG2-2000-01287 ), and 6th  PCRDT Information Science and Technology (ImmunoGrid ,  FP6 IST-028069 ) programs of the  European Union (EU). IMGT ® received financial support from the  GIS IBiSA , the  Agence Nationale de la Recherche (ANR)   Labex MabImprove  ( ANR-10-LABX-53-01 ), the  Region Occitanie Languedoc-Roussillon (Grand Plateau Technique pour la Recherche (GPTR) , and  BioCampus Montpellier.
IMGT ® is currently supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Ministere de l'Enseignement Superieur, de la Recherche et de l'Innovation (MESRI) , the  University of Montpellier , and the  French Infrastructure Institut Francais de Bioinformatique (IFB)   ANR-11-INBS-0013. IMGT ® is a registered trademark of CNRS. IMGT ® is member of the  International Medical Informatics Association (IMIA)  and a member of the  Global Alliance for Genomics and Health  ( GA4GH ). This work was granted access to the  High Performance Computing (HPC) resources of Meso@LR and of Centre Informatique National de l'Enseignement Superieur (CINES) , to Tres  Grand Centre de Calcul (TGCC) of the Commissariat al'Energie Atomique et aux Energies Alternatives (CEA)  and  to Institut du developpement et des ressources en informatique scientifique (IDRIS)  under the allocation  036029  ( 2010-2022 ) made by  GENCI  ( Grand Equipement National de Calcul Intensif ).
While unsaturated zone thickness is not considered during Step 1 (potential recharge equals recharge), it is expected that Step 2 reveals its importance. As developed below, we focus on water table and recharge anomalies, here called "fluctuations". These fluctuations are obtained by removing the mean value from input data (potential recharge and pumping rates used during Step 1) over the study period. Consequently, simulated WTF obtained during Step 1 will be compared to observed fluctuations. Finally, RF are obtained from observed WTF (Step 2) and will be compared to potential RF. In the next sections, we describe (1) the analytical GW flow model deployed to estimate recharge from observed WTF; (2) the soil models used to estimate potential recharge as input to the GW model; (3) the GW parameters calibration strategy and (4) the analytically inverted GW model.
The transfer function H(ω) describes the amplitude ratio between output and input in the frequency domain as: Ȳ = H X (8) Thus, the transfer function quantifies P -P ET efficiency to recharge GW, ie. a proxi of rainfall efficiency. These functions allow to infer the role of the soil and more generally of the unsaturated zone. As a comparison, we also computed coherence and transfer functions between P -P ET and potential recharge. Here, we used 'mscohere' and 'tfestimate' Matlab func- consists of fields and meadows with slight topography (average slope around 3 %). GW is hosted in highly fractured crystalline rocks  (Ruelleu et al. , 2010; Touchard, 1999; Jimenez-Martinez et al. , 2013). Ploemeur site has been pumped since 1991 while Guidel site has not been perturbed by pumping and hosts large GW upflowing zones creating groundwater-dependent ecosystems.
Groundwater level data While first GW level data in Ploemeur dates back to 1991, we focus our analysis on the 1996-2017 period to avoid potential transient responses to the pumping setup. On the Guidel site, data is available from 2009 to 2017. Water levels are recorded at minute to daily time steps and decimated to daily time scales for our analysis. Hydraulic heads measured in boreholes are corrected from atmospheric pressure variations. In Ploemeur, GW levels are relatively deep due to the pumping (depth ∼ 7 to 30 m, see Figure  C1 ) but still contain seasonal and interannual variability (Fig. 3 ) due to both pumping and climate variations. Note that WTF in Ploemeur boreholes have quite similar patterns. Seasonal variations decrease with the distance to the pump: amplitude is 5 m at well F11, 2.
Therefore, the impact of local heterogeneities is smoothed out so that aquifer-scale equivalent characteristic time and storage coefficient are reachable with limited dependence to prescribed potential recharge. The developed GW models are also invertible analytically to recover boundary conditions. Indeed, we estimated groundwater recharge fluctuations from observed GW level fluctuations time-series. A key novelty of this approach is developing the WTF method in the frequency domain  Townley (1995). Note the method can be used to infer mean annual recharge value. However, we argue this is less relevant in heterogeneous aquifer. The approach was tested on two neighboring sites, one being pumped for 25 years. First, estimated recharge from borehole data analysis is coherent among each borehole, on each site.
Acknowledgements. This work is part of the  ANR  project  EQUIPEX CRITEX  (grant  ANR-11-EQPX-0011 ) and set on the  Ploemeur Critical Zone Observatory H + (SNO H+ , http://hplus. ore. fr/en/ploemeur). We are greatful to the OZCAR research infrastructure (http://www. ozcarri. org/). We thank  M. Smilovic  for revising the manuscript for grammar and syntax.
Materials The Clinical Data Warehouse at the European Hospital Georges Pompidou The European Hospital Georges Pompidou (or HEGP) is a 700 beds teaching hospital located in Paris. It is specialized in oncology, cardiovascular diseases and emergency medicine. The hospital has deployed since 2008 a clinical data warehouse based on i2b2  [7]  integrating virtually all the data generated during everyday care. Among all the data sources, patients in oncology are associated with narrative reports, such as discharge summaries or histology reports, which include free text sections and semi structured tables and clinical questionnaires. Such questionnaires are well structured and some of them collect chemotherapy toxicities associated with their grades. Answers for such questionnaires are collected by caregivers the day before chemotherapy administrations.
Cohort definition We queried the data warehouse using ICD code C34 ("Malignant neoplasm of bronchus and lung"), and its descendants to identify 3,239 patients treated for lung cancers. Out of the 3,239, we identified 470 patients with at least one narrative report and one questionnaire about chemotherapy toxicity. Out of 470, we randomly selected 330 patients to constitute our studied cohort. We left out 140 for a future evaluation (out of the scope of this work). For each patient, all narrative reports and toxicity questionnaires were extracted, representing 11,819 narrative reports and 71,140 questionnaire items.
Conception, implementation and import of external ontologies The ontology was implemented in OWL using Owlready  [8] , a module for ontology management in Python and Protégé, the ontology editor  [9]. OntoTox is composed of 11 classes which are organized around the central class ChemotherapyToxicity. OntoTox also includes 8 object and data properties that serve to qualify toxicities. To this aim, the ChemotherapyToxicity class can be linked to Grade, StartDate and Patient classes through object properties. Grade class has 7 subclasses that are Grade0, Grade1, Grade2, Grade3, Grade4, Grade5 and GradeNull. GradeNull corresponds to the absence of a detected grade, whereas Grade0 denotes the explicit report of a grade 0, i. e. the absence of this toxicity, which is commonly found in questionnaires and tables.
ChemotherapyToxicity instances can be associated with different data properties to characterize the context of the extraction of the toxicity (e. g. , isNeg, isHyp qualify the fact that the toxicity may be extracted as a negated or a hypothetical fact). StartDate and Patient classes are instantiated using documents metadata. To identify the set of classes and properties necessary to represent toxicity information of various provenance, we selected randomly a small set of EHR (n=20) that we reviewed to instantiate manually the ontology. We instantiate UMLS and MedDRA concepts with OntoTox toxicities leveraging the PymedTermino library  [10]. An example of the instantiation of OntoTox is illustrated in Figure  1. OntoTox is available at https://github. com/TeamHeka/OntoTox. git.
Recognition of entities of interest In this initial stage of our work, we only identified two types of entities of interest: toxicities and grades. Both are recognized by the same method independently from the considered source. We created a toxicities dictionary based on our toxicities sources terminologies (see Materials), and enriched it with synonyms to 4,038 terms mapped on 835 UMLS concepts thanks to PymedTermino. We relied on QuickUMLS  [12] , to extract toxicity entities using this dictionary. QuickUMLS is a Python tool leveraging Simstring  [13]  for approximate string matching. It was applied with a length overlapping criteria, jaccard distance similarity and a 0. 9 threshold parameter. In addition to the identification of the toxicity, the dictionary approach also provided us with UMLS Concept Unique Identifier, enabling some normalization of the toxicities.