dataesr/scientific-paragraphs-categorization huggingface dataset:
Public DNA methylation 450K-array cohorts The DNA methylation profile quantified by the Illumina HumanMethylation 450K-array platform was downloaded from the TCGA data portal under the project of TCGA-KIRC  34. According to our previous study  35  , a total of 23 cases were removed as they harbored molecular pathological features of MiTF/TFE translocation RCC, chromophobe RCC and clear cell papillary RCC, leading to 301 ccRCC patients' samples and 160 adjacent normal tissues with available RNA-Seq data. Additionally, we collected raw *. IDAT files of DNA methylation 450K-array from 46 ccRCC samples with matched normal kidney tissues from GSE61441  36. We also retrieved DNA methylation 450K-array data from the Beuselinck cohort (E-MTAB-3274) with 102 patients who developed metastatic ccRCC, and who were treated with the first-line TKI of sunitinib; such a methylation profile was preprocessed with a total of 292,317 probes available.
DNA methylation and expression profiles for human renal cancer cell line of CCLE Pre-processed DNA methylation 450K-array data for human cancer cell lines were downloaded from GSE68379, and a total of 28 cell lines with the tumor barcode of KIRC were extracted  37. Specific mutation of PBRM1 was assessed from cBioPortal under the archive of Cancer Cell Line Encyclopedia (Broad 2019). Of these 28 cell lines (CCLE dataset in this study), 12 cases with available gene expression data quantified by transcripts per kilobase million (TPM) value were retrieved from the DepMap portal (https://depmap. org/portal/download/)  38.
Other molecular and clinical profiles of the TCGA-KIRC cohort For the TCGA-KIRC cohort, raw count data of high-throughput sequencing was downloaded by the R package "TCGAbiolinks"  39. We then converted RNA-Seq raw counts to the number of fragments per kilobase million (FPKM) and further into TPM values, the signals of which were more similar to those quantified from microarrays and were more comparable between cases  40. Additionally, copy number segment data were collected from FireBrowse (http://firebrowse. org/) which is a companion portal to the Broad Institute GDAC Firehose analysis pipeline, and was developed to cull and analyze data generated by TCGA. Somatic mutations, clinicopathological features, OS and PFS rate data were downloaded from the cBioPortal (https://www. cbioportal. org/).
Quantification and filtering of DNA methylation profiles To those cohorts with available raw *. IDAT files, we used the R package ChAMP to first extract the raw signal intensities from the *. IDAT files and calculate the β value for each probe as /( + ) , in which  and  respectively refer to the pre-processed mean methylated and unmethylated probe signal intensities  41. By default, ChAMP performed comprehensive filtering procedures and the following filtering criteria were applied for both 450K and EPIC arrays: removal of probes with detection of P value > 0. 01, probes with < 3 beads in at least 5% of samples per probe, all non-CpG probes, all SNP-related probes, all multi-hit probes, and probes located in chromosome X and Y  42.
Unsupervised clustering for loss of DNA methylation in cancer To enrich for sites that lose methylation in cancer and explore the genomic relevance of DNA demethylation, we selected CpG probes found in more than 50% normal samples with a β value ≥ 0. 7 or probes with a median β value ≥ 0. 7 for both DNA methylation 450K and EPIC arrays. Subsequently, probes with high variability measured by standard deviation (> 0. 2) were kept for unsupervised clustering. Specifically, hierarchical clustering was performed using a Euclidean distance measurement with Ward's clustering method. For unsupervised clustering of demethylated probes located in the enhancer, consensus PAM was performed by using R package ConsensusClusterPlus  43. To be specific, the consensus process was set to 80% of features re-sampling with 500 perturbations.
Differential analysis and functional enrichment The a meta-analysis framework using a vote-counting approach was designed. Specifically, each probe was determined as one of the methylation patterns (i. e. , hypermethylated, hypomethylated, and wild), and we considered probes that presented one methylation pattern if it uniquely maintained that pattern in more than three out of five datasets (four cohorts and one cell line dataset) used in this study (i. e. , TCGA-KIRC, GSE61441, CCLE, PSL, BIONIKK). Functional enrichment analysis through GGST was performed enhancers. Specifically, the closest 20 upstream and downstream genes were collected for each probe, and for each candidate probe-gene pair, the Mann-Whitney U test was used to test the null hypothesis that overall gene expression in  + is greater than or equal to that in  -; 4) identify enriched motifs for the enhancers that are significantly hypomethylated and linked to a putative target gene in  +.
Connectivity Map analysis and therapeutic response prediction The CMap (https://clue. io/query) includes more than 7,000 gene expression profiles for approximately 1,300 compounds, and it is a tool to assist in discovering relationships among diseases, cell physiology, and therapeutics  67. With CMap as a resource, potential therapeutic drugs for  + were considered for those compounds with scores less than -90 (a negative score suggests a potential therapeutic effect). To validate the clinical implications, we employed the R package pRRophetic to estimate the chemotherapeutic response for each ccRCC case based on drug sensitivity and phenotype data from GDSC as the training cohort, and we used the expression profiles of clinical tumors as the testing cohort. The IC50 of each clinical tumor treated with a specific compound was predicted using ridge regression, and 10-fold cross-validation was leveraged to evaluate predictive accuracy  68.
As the evolution of the primary anisotropies is linear, the multipoles of the angular power spectrum are uncorrelated when the full sky is observed. However, any realistic experiment requires masking parts of the sky, either to avoid regions that are highly contaminated by foregrounds (e. g. , galactic emission or point sources) or because the scanning strategy is designed to observe specific regions of the sky. The estimation of the power spectrum on the masked sky, the so-called pseudo-power spectrum, is biased, and different multipoles become correlated  (Hivon et al. 2002 ). An unbiased estimator of the spectra can then be obtained through the MASTER approach  (Hivon et al. 2002) , as implemented in the PolSpice 1  software, for instance  (Szapudi et al. 2001; Chon et al. 2004). A robust inference of cosmological parameters requires accurate covariance matrices that describe the variance of the spectra along their diagonal, as well as the correlations between multipoles in the off-diagonal terms.
Temperature The pseudo-power spectrum estimator can be regularized in real space following the PolSpice approach in  Szapudi et al. (2001). The pseudo-correlation function ξ is smoothed with a scalar apodizing function f apo (θ), which cuts out large θ and then corrects for the bias coming from the weighted mask described in Eq. (  38 ). The scalar apodizing function goes smoothly from f apo (0) = 1 to f apo (θ max ) = 0 to avoid Fourier ringing. θ max should be chosen as the maximum angular size of the weighted mask. A new correlation function estimator ξ(θ) is defined as ξ(θ) ≡ g(θ) ξ(θ), (39) with g(θ) = f apo (θ)/w(θ) ∀θ ∈ [0, θ max ) , 0 ∀θ ∈ [θ max , π]. (40) The function g is well defined and smooth for all angles through the apodization f apo. As a consequence of Eqs. (  38 ) and (  39 ), the 8 https://github.
Acknowledgements. We are grateful to the  SPT collaboration  for discussions and suggestions. We thank  Lennart Balkenhol  for discussions and insightful comments on the manuscript. This work has received funding from the  French Centre National d'Etudes Spatiales (CNES). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  101001897 ).
Comment We have recently read with a lot of interest the work of Xiao et al. about using the FHIR RDF specification to develop structured electronic health records (structured EHRs)  (Xiao, et al. , 2022). It was interesting to see FHIR RDF as a standard ontology that allows the structured representation of clinical information and consequently enables the validation of biomedical data using shape-based methods and logical constraints (Prud  'Hommeaux, et al. , 2021)  and the reasoning over clinical knowledge using machine learning algorithms as well as SPARQL as a query language  (Xiao, et al. , 2022). It was also important to see how external resources, particularly the OMOP Vocabulary concepts, are used to describe clinical information about patients  (Xiao, et al. , 2022). However, there are multiple motivations for the development of electronic health records as knowledge graphs in the Resource Description Framework (RDF) format that have not been highlighted in this research work and that can solve several major challenges related to clinical practice.
These motivations imply clinical data federation, analysis, and interoperability. Structured EHRs can be easily federated with other biomedical knowledge resources, particularly open knowledge graphs like Wikidata  (Turki, et al. , 2022a)  and Open Biomedical Ontologies  (Konopka, 2015). As biomedical knowledge resources include background knowledge for clinical research and practice, they can be easily mirrored and then reused to drive evidence-based clinical decisions for patient management, including disease diagnosis and prognosis, risk factor identification, medical prescription, and drug interaction identification  (Callahan, Tripodi, Pielke-Lombardo, & Hunter, 2020). External identifiers of diseases, symptoms, and drugs in open knowledge graphs like Wikidata can be very useful to identify resources related to the condition of the patients: just like relevant publications can be found in bibliographic databases like PubMed  (Turki, et al.
, 2022b) , records about other concepts resources like datasets, software, cell lines, biomarkers or ontologies can be located in biomedical databases like Orphanet  (Ito, et al. , 2015)  and online encyclopedias like Wikipedia  (Turki, et al. , 2022a). This will permit easier access to medical information by health practitioners and facilitate medical education for patients, medical students and the general public. The use of more specialized systems like QR codes  (Diazgranados & Funk, 2013)  or text-to-speech systems  (Baumann, Köhn, & Hennig, 2019)  can facilitate access to biomedical knowledge for specific demographics, e. g. illiterate or disabled patients 1. Another advantage of reusing concepts from other knowledge resources is that these items can be easily aligned to a multilingual ontology or knowledge graph, particularly Wikidata, and then translated into multiple languages  (Turki, et al.
, 2022a; Rasberry, et al. , 2022) , allowing the easy reuse of electronic health records across countries, especially when important clinical capacities are found abroad. Furthermore, the SPARQL endpoint of structured EHRs is not only used for the identification of patients having a particular characteristic  (Xiao, et al. , 2022)  and the validation of biomedical data  (Turki, et al. , 2022c)  but also for increasing the automation of health management and scholarly research. This is possible thanks to the development of specific SPARQL queries that provide insights from the EHR database and then to the embedding of the results of the queries into a dashboard  (Turki, et al. , 2022a). These dashboards can cover hospital administration, epidemiology, and clinical trial monitoring and are automatically updated as the electronic health records are enriched  (Turki, et al.
, 2022a). The results of the dashboards can be visualized in a variety of layouts including bar charts, graphs, pie charts, and tables, and finally discussed through a federated SPARQL query that compares the outcomes of the real-time analysis with the findings of related scholarly publications as retrieved from the Open Research Knowledge Graph, a large-scale ontological database providing a structured representation of the outputs of research papers  (Jaradeh, et al. , 2019). Moreover, as a standard for the data modeling of patient clinical information, FHIR RDF also enhances system interoperability  (Girardi, De Gennaro, Colizzi, & Convertini, 2020). Such an interaction between FHIR RDF and other systems can be ensured using a distributed architecture, particularly Blockchain, for data security purposes  (Girardi, De Gennaro, Colizzi, & Convertini, 2020).
Funding This output is funded by the  Adapting Wikidata  to support clinical practice using Data Science,  Semantic Web  and  Machine Learning Grant  supported by the  Wikimedia Foundation , the non-profit organization maintaining Wikipedia, Wikidata, and other projects. The contributions of  Houcemeddine Turki, Mohamed Ali Hadj Taieb ,  Mohamed Ben Aouicha , and  Nejmeddine Hentati  are supported by the  Ministry of Higher Education and Scientific Research in Tunisia (MoHESR)  in the framework of Federated Research Project  PRFCOV19-D1-P1.
Clinical image data analysis is an active area of research. Integrating such data in a Clinical Data Warehouse (CDW) implies to unlock the PACS and RIS and to address interoperability and semantics issues. Based on specific functional and technical requirements, our goal was to propose a web service (I4DW) that allows users to query and access pixel data from a CDW by fully integrating and indexing imaging metadata. Here, we present the technical implementation of this workflow as well as the evaluation we carried out using a prostate cancer cohort use case. The query mechanism relies on a Dicom metadata hierarchy dynamically generated during the ETL Process. We evaluated the Dicom data transfer performance of I4DW, and found mean retrieval times of 5. 94 seconds and 0. 9 seconds to retrieve a complete DICOM series from the PACS and all metadata of a series.
Introduction Clinical images data analysis, especially with artificial intelligence methods, is an active area of research that holds promise for disease characterization, precision medicine, and early assessment of treatment response. However, a key challenge needs to be overcome: unlocking hospital imaging software components (i. e. Picture Archiving and Communication Systems, PACS and Radiology Information Systems, RIS) to integrate imaging data with the other patient clinical data into Clinical Data Warehouses (CDW) or data lakes for research reuse  [1, 2]. Researchers have developed several solutions for reusing imaging data, such as Research PACS  [3, 4]  that manages the whole process for imagingoriented clinical trials. Other solutions exist to carry out imaging studies, but they are fully imaging oriented  [5, 6] , or extract data from the PACS for a given predefined cohort and allow crossing imaging data with clinical data a posteriori  [7].
System design: We designed a stand-alone JAVA server component called I4DW to connect the ETL component with the PACS. Figure  1  is an overview of the system architecture. I4DW relies on the DICOM protocol. This server, using the Spring Framework 1 , provides a HTTP REST API (Application Programming Interface) on the CDW side and a Dicom standard interface on the other side, using the PixelMed Java library  2  , to connect to one or more PACS. To avoid unnecessary solicitations of the IT resources, a DataBase cache is dedicated to store image collections (one or more cohorts) loaded from the PACS. I4DW is based on a "data extraction/enrichment" plugin system that facilitates the addition of new functionalities without increasing the application burden and without any impact on the API. The ETL process uses a transitional "pivot" XML file.
Each document to be integrated in the CDW goes through this format before its integration into the target data model. For more flexibility, we chose to create a dedicated data extraction plugin that produces this pivot format as a result. The main parameters of the REST API call performed by the ETL are the list of accession numbers to retrieve, the list of features needed (i. e the list of plugins to call) and a flag indicating the need to store the images in the cache. Periodically, the ETL component calls the I4DW, passing as parameter a list of accession numbers obtained via the already integrated radiology reports data stream (coming from the RIS). This method allows retrieving the link between the targeted Dicom image data and the patient ID. The DICOM protocol allows to make queries on a limited set of fields indexed by the PACS (CFIND query) or queries requiring the retrieval of at least one image to obtain the whole header (CMOVE).
To keep track of the original study, all these documents and the radiology report document are linked by an accession number. This logical view of a Dicom study is computed at the application level and available in the dedicated view on the eHOP interface (Figure  3B ). eHOP allows querying all the patient's data (age, sex, etc. given structured element, e. g. , when defining a constraint on modality the system suggests a set of possible values, "MR", "CT", etc. Figure  2  shows how this terminology is displayed in the query builder GUI.
Evaluation: We evaluated the performance of our system for selecting a cohort and generating a dataset that was compared to an existing cohort of 271 patients with prostate cancer used as "gold standard". The objective was to generate, from the data of 1. 4 million patients available in our CDW, a datamart containing all clinical data including imaging reports and tests for this cohort. Inclusion criteria were (i) patients who underwent prostatectomy between 01/01/2014 and 31/12/2019, (ii) and patients with at least one pre-op MRI. Standard metrics of information retrieval (Precision, Recall, and F-measure) were computed to assess whether our system could retrieve all patients of the existing prostate cancer cohort and their imaging data. We evaluated I4DW data transfer performance both in "routine" mode (i.
Data integration performance: The ETL sends queries to the I4DW server through a list of three accession numbers on each call, specifying whether to store the images in the cache (a CMOVE for all images is performed) or not (a CMOVE is performed for a single image per series). This limit of three accession numbers has been chosen because the I4DW response that contains the list of eHOP pivot files must not exceed a specific size limit. In function of the implied Dicom modalities (e. g. MRI, CT, ultrasonography), each HTTP call will include different numbers of series. Table  1  shows the mean durations (in seconds) of the image retrieval tasks for the whole series and for the first image of a series. For the first retrieval task, on a set of 1200 Dicom series, all images were retrieved and cached in the I4DW component.
Query builder and data visualization: Users can query the integrated imaging data with all other data available in eHOP. The structured searching mode allows querying any attribute from the Dicom metadata. Figure  2  shows the query builder with the hierarchy of data elements organized according to the generated Dicom terminology. Each node is associated with the number of patients and documents available in the CDW or in the current datamart. According to the data element type, users can query using numerical or textual criteria. Relevant Dicom attributes ("Study Description", "Series Description" and "Body Part Examined") are indexed as text during the integration phase, and this allows the eHOP text searching mode to easily retrieve imaging tests. After query completion, results are sorted and displayed by patient and hospital stay (Figure  3A ).
As a limitation, our prototype was evaluated using a specific prostate cancer cohort. However, the system was designed independently of a specific clinical domain and can be used for many use cases. The fine-grained integration of metadata generates a very rich collection of technical and clinical data elements. However, some are not useful to clinicians (e. g. , Manufacturer), and some can be confusing (e. g. elements in depth in the hierarchy of a structured report). An improvement would be to customize the DCMEHOP terminology from a technical or clinical point of view. Currently, metadata integration only covers the Dicom public attributes. The management of Dicom private attributes (vendor-defined attributes) is an important problem, as mentioned by Langer  [5]  and by Doran et al  [6].
The aim of this work was, therefore, giving an insight into the technical and cultural features of the flat bread types produced in each one of the Mediterranean countries involved in the FlatBreadMine project (namely Croatia, Egypt, France, Greece, Italy, Jordan, Lebanon, Malta and Spain). The steps to achieve this goal are:  (1)  To identify all the flat bread types produced in the selected countries;  (2)  to collect information on their main technical characteristics, from the starting ingredients to the end-product, including the cultural features; (3) to set up a database containing all the information; (4) to examine and interpret the information collected  [15]  in order to highlight the diversity of flat breads across the considered countries and to define the selection criteria.
Subject of the Survey A survey was carried out to collect information on flat breads, including traditional and artisanal ones. The surveyed flat breads had to be original and native of each surveyed country. Therefore pizza, for example, which has an Italian origin, was listed as an Italian flat bread and surveyed only in Italy, although prepared also in the other countries object of the study. For each flat bread, the following data were collected: (i) The regional area or town of origin, and the area of marketing and diffusion; (ii) the ingredients used in bread preparation (flour, yeast, additional ingredients, and their ratio); (iii) the raw material characteristics (type of flour and its quality features; type of yeast; information on any additional ingredient); (iv) the production process, step by step (kneading time and temperature; conditions of the first leavening step; shaping specifications in terms of average diameter and thickness; conditions of the second leavening step; time and temperature of baking; oven type); (v) the characteristics of bread (type and size; optimal quality features; artisanal or 35-industrial); (vi) the main references and sources of information.
Data Collection Data were collected between October 2021 and May 2022. The first step was the identification of all flat breads produced in each country, which was done by accessing the websites of bakers' associations, food blogs, and scientific literature; browsing the official lists of traditional food products uploaded onto the websites of the EU, the Italian Ministry of Agriculture, and Slow Food; consulting local experts. The latter were scholars involved in the protection and rediscovery of traditional food products including bread, who helped to uncover rare breads not regularly available in the market. They were selected via convenience sampling, based on direct knowledge with the researchers involved in the study, and were contacted by phone for advice. The second step consisted in collecting the information referred to in Section 2.
Database Structure A database was set up by the Excel software (Microsoft Office, Version 2018 for Windows, Microsoft Corporation, Redmond, Washington, DC, USA) to gather information on the flat breads of each surveyed country. The database structure included one row per each bread type and 27 columns for the above reported information. In addition, a representative picture was included for each type of bread. The database was uploaded onto the FlatBreadMine project website and is publicly accessible at the link: https://flatbreadmine. eu/resources/ (accessed on 7 July 2022).