dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This work is funded by the Hessian State Chancellery in the Department of Digital Strategy and Development in the Förderprogramm Distr@l (Förderprodukt:  Digitale Innovations-und Technologieförderung , Förderlinie:  2A Digitale Innovationsprojekte ). Acknowledgement. The publication has been supported by  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  856533 , project  ARETE. Acknowledgements. Authors would like to thank  María Rosario Jaén Martín , the teacher that participated in this research, and her students from  I. E. S. Pinar de la Rubia. This research has been partially funded by projects  VA257P18  ( Regional Government of Castile and Leon, ERDF) ,  TIN2017-85179-C3-2-R  ( AEI ,  ERDF ). Acknowledgment. This work was supported in part by the  Institution of Educational Sciences (IES ;  R305A180301 ).
We thank all participating teachers and reviewers. Acknowledgments. This work has been partially funded by the  EU Regional Development Fund  and the  National Research Agency of the Spanish Ministry of Science and Innovation  under project grants  TIN2017-85179-C3-3-R  and  PID2020-112584RB-C33. P. Santos  acknowledges the support by the  Spanish Ministry of Science and Innovation  under the  Ramon y Cajal programme. D. Hernández-Leo  ( Serra Húnter ) acknowledges the support by  ICREA  under the  ICREA Academia program. Acknowledgments. The authors acknowledge that the publication/registration fees were totally covered by the  University of West Attica. The  Universidad de Valladolid  co-authors acknowledge funding of the  European Regional Development Fund  and the  National Research Agency of the Spanish Ministry of Science, Innovation and Universities , under project grant  TIN2017-85179-C3-2-R , and  PID2020-112584RB-C32 , the  European Regional Development Fund  and the  Regional Government of Castile and Leon , under project grant  VA257P18.
Acknowledgements. This research has been partially funded by projects  VA257P18  ( Regional Government of Castile and Leon, ERDF ),  TIN2017-85179-C3-2-R  ( AEI ,  ERDF ),  Cross-Forest  ( CEF 2017-EU-IA-0140 ),  VirtualForests  ( Erasmus+ 2020-1-ES01-KA226-HE-095836 ) and ' Virtualización de aulas forestales ? Marteloscopes virtualization ',  UVA didactic innovation project  ( UVA-PID2020-015 ). Acknowledgement. This work has been partially funded by the  EU Regional Development Fund  and the  National Research Agency of the Spanish Ministry of Science and Innovation  under project grants  TIN2017-85179-C3-3-R ,  PID2020-112584RB-C33. D. Hernández-Leo  ( Serra Húnter ) acknowledges the support by  ICREA  under the  ICREA Academia program. E. Hakami  acknowledges the grant by  Jazan University, Saudi Arabia.
Acknowledgements. This work was supported in part by the  FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación , through the  Smartlet Project  under Grant  TIN2017-85179-C3-1-R , and in part by the  Madrid Regional Government  through the  e-Madrid-CM  Project under Grant  S2018/TCS-4307  and under the  Multiannual Agreement  with UC3M in the line  of Excellence of University Professors  (EPUC3M21), and in the context of the  V PRICIT  ( Regional Programme of   Research and Technological Innovation ), a project which is co-funded by the  European Structural Funds (FSE and FEDER). Partial support has also been received from the  European Commission  through  Erasmus+ Capacity Building in the Field of Higher Education projects , more specifically through projects  LALA ,  InnovaT and PROF-XXI  ( 586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP ), ( 598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP ), ( 609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP ).
This publication reflects the views only of the authors and funders cannot be held responsible for any use which may be made of the information contained therein. Acknowledgements. This work has been partially funded by  FEDER , the  National Research Agency of the Spanish Ministry of Science, Innovations  and Universities  TIN2017-85179-C3-3-R ,  PID2020-112584RB-C33. Davinia Hernández-Leo  ( Serra Húnter ) acknowledges the support by  ICREA  under the  ICREA Academia programme. Patricia Santos  acknowledges the support by the  Spanish Ministry of Science and Innovation  under the  Ramón y Cajal programme. Acknowledgments. This work has been partially funded by the  Volkswagen Foundation  ( COURAGE  project, no. 95567 ). TIDE-UPF also acknowledges he support by  FEDER , the  National Research Agency of the Spanish Ministry ,  TIN2017-85179-C3-3-R  and by  ICREA  under the  ICREA Academia programme  ( D.
Genome sequencing Viral genomes were obtained from nasopharyngeal swab fluid using next-generation sequencing (NGS) and the Illumina Nextera XT paired-end strategy on a MiSeq instrument (Illumina Inc. , San Diego, CA, USA), as described previously  (Colson et al. , 2021). Genome consensus sequences were assembled by mapping on the SARS-CoV-2 genome of GenBank accession number NC_045512. 2 (Wuhan-Hu-1 isolate) using CLC Genomics workbench v. 7, with thresholds of 80% for nucleotide sequence coverage and 90% for nucleotide similarity. SARS-CoV-2 sequences obtained in the study institute have been submitted to the GISAID database (https:// www. gisaid. org).
Genome analysis The 1038 SARS-CoV-2 whole genome sequences obtained in our laboratory were analysed using the Nextclade tool (https:// clades. nextstrain. org/) (Hadfield et al. , 2018)  and an in-house script written in Python. Viral clades were defined on the basis of at least five available genomes sharing the same pattern of mutations. Phylogenetic trees were reconstructed using the nextstrain/ncov tool (https://github. com/nextstrain/ncov) and visualized with Auspice software (https://docs. nextstrain. org/ projects/auspice/en/stable/). In addition, the SARS-CoV-2 genomes obtained in our laboratory were integrated into another phylogenetic analysis together with sequences from the GISAID database (https://www. gisaid. org) that were recovered from humans and mink. All of these genomes were aligned using MAFFT v.
7  (Katoh and Standley, 2013). Then, phylogeny reconstruction was performed using IQ-TREE software with the GTR Model and 1000 ultrafast bootstrap repetitions (http://www. iqtree. org) (Minh et al. , 2020) , and the tree was visualized with iTOL (Interactive Tree Of Life) software (https://itol. embl. de/) (Letunic and Bork, 2016). PCR detection of the SARS-CoV-2 Marseille-4 variant A qPCR system was designed that targets the nsp4 gene at nucleotide positions 9460-9543 in reference to genome GenBank accession number NC_045512. 2(Wuhan-Hu-1 isolate). The primers and probe are described in Supplementary material Table  S1. This qPCR was run on an LC480 thermocycler (Roche Diagnostics, Mannheim, Germany). The reaction mixture contained 5 ml of 4X TaqMan Fast Virus 1-Step Master Mix (Thermo Fisher Scientific, Grand Island, NY, USA), 0.
5 ml of forward primer (10 pmol/ml), 0. 5 ml of reverse primer (10 pmol/ml), 0. 4 ml of probe (10 pmol/ml), and 8. 6 ml of water, and it was completed with 5 ml of extracted viral RNA. PCR conditions were as follows: a reverse transcription step for 10 min at 50 C, then 20 s at 95 C followed by 40 cycles comprising a denaturation step at 95 C for 15 s and a hybridization and elongation step at 60 C for 60 s. Full-length genome sequences obtained in this study were compared to those available in the GISAID database (https://www. gisaid. org/). Phylogenetic trees were reconstructed and visualized using the Nextstrain pipeline (https://github. com/nextstrain/ncov/) (Hadfield et al. , 2018).
Identification and circulation of the Marseille-4 variant The highly transmissible SARS-CoV-2 Marseille-4 variant identified in Marseille at the end of July 2020 rapidly became predominant, reaching 100% of identified viral strains in the geographical area on November 2, 2020. Using genome sequences available through the GISAID database (https://www. gisaid. org/), the outbreaks of this variant were traced back in different countries. The first case of infection with the Marseille-4 variant, named 20A. EU2 in the Nextstrain classification (https://clades. nextstrain. org/) (Hodcroft et al. , 2020) , was detected in a German patient on March 24, 2020. Then, two cases were detected on a Balearic island, Spain, on May 29 and June 18, 2020. Additional cases were detected in Southwestern France from July 9, then in Denmark, and from August 1 in other European countries and other regions of France (Figures  1  and 2 ; Supplementary material Figure  S1 ).
, 2020). It is worth noting that the first genome available in the GISAID database (EPI_ISL_7079562020-03-24), originating from Germany on March 24, 2020, does not harbour this S477N substitution, which may explain why it did not apparently spread further. Other critical mutations may be substitution Q57H in ORF3a, a viroporin that forms ion channels and was reported as required for viral replication, virulence, and release, and is also predicted to be a proapoptotic protein  (Bianchi et al. , 2021; Law et al. , 2005) , and substitutions A176S in the RdRP and K1141R and E1184D in the NTPase/helicase.
Interestingly, among the 10 516 sequences from the Marseille-4 variant in the GISAID database (on January 24, 2021), the 272 genomes from our laboratory had close relatives with those originating from Northern Europe, mostly Denmark (n = 3366), the UK (n = 2652), and Switzerland (n = 1147) (Supplementary material Figure  S1 ). A phylogenetic tree was constructed that included genomes from mink and human SARS-CoV-2 strains. Mink strains were divided into four and six main groups for the samples from the Netherlands and Denmark, respectively (Figure  5 ). A common phylogenetic node between mink strains, the Marseille-4, The structure was predicted using the Phyre2 web portal (http://www. sbg. bio. ic. ac. uk/$phyre2/html/page. cgi?id=index)  (Kelley et al. , 2015)  and visualized using the Pymol tool v.
SARS-CoV-2 is an epizootic agent that caused an outbreak in humans before being transferred to mink in which it spread rapidly through densely caged animals and subsequently became a source for human infection. To date, more than 800 human infections from mink have been reported (Oude  Munnink et al. , 2021). One hypothesis could be that a human SARS-CoV-2 from infected caregivers infected mink, then the frequency of viral mutations changed in the mink due to a different host selection pressure, and this mink-adapted virus (with multiple mutations) became a new viral source to infect humans. The genome obtained from a German patient sampled on March 24, 2020 (EPI_ISL_7079562020-03-24) is atypical as it is devoid of the S477N substitution, one of the Marseille-4 hallmark mutations, but harbours more mutations (n = 31) than the other Marseille-4 strains, including in the Nsp2, Nsp3, S, and N proteins, and in ORF1b, particularly the Nsp14 exonuclease, which has proofreading activity  (Shannon et al.
Finally, the role of the treatments of COVID-19 with remdesivir or hyperimmune plasma  (Choi et al. , 2020; Kemp et al. , 2020)  in generating and selecting variants should be considered as they may also have contributed to the new outbreaks observed in the most developed countries. Since the final acceptance of this article, the sequence of the SARS-CoV-2 genome obtained from a farm mink sampled the 15 th of November, 2020 in Eure-et-Loire was eventually released the 29 th of March, 2021 (EPI_ISL_1392906). As we suspected and stated in the present article, this genome is strictly identical to the genome of a Marseille-4 variant confirming our hypothesis of a common source of this variant between French minks and humans.
Acknowledgements We are grateful to  Olivia Ardizzoni ,  Vincent Bossi ,  Madeleine Carrera ,  Vera Esteves-Vieira ,  Laurence Thomas ,  Priscilla Jardot , and  Raphael Tola  for their technical help, and to  Audrey Giraud-Gatineau  and  Léa Luciani  for their help with the data analysis. The manuscript text has been edited by a native English speaker.
Data collection Inclusion criteria for this analysis were the following: 1) histologically-confirmed thoracic malignancy, including lung cancer, mesothelioma, and thymic tumors, 2) with scheduled appointment for a consultation and/or administration of cancer treatment and/or hospitalized in the inpatient clinic, 3) from March 23 rd to April 17 th 2020 in the month following the publication of the clinical practice recommendations for the management of patients with thoracic malignancies in the context of the pandemia. This retrospective study was approved by the Institutional Data Review Committee of Institute Curie; all patients gave consent at the initiation of their care for the retrieving of healthcare information from their electronic medical record. Clinical data were then retrospectively collected, including patients baseline characteristics, histology, stage, treatment regimen, dates of treatment, and treatment modification, as well as COVID-19 status and death.
SUMMARY Oncoplastic surgery allows an increase in the number of indications for conservative breast cancer treatments. However, uncertainty as to whether it can be performed still exists in certain situations such as with multicentric or multifocal lesions, even when the breast volume can accommodate it. With the aid of a virtual reality software, DIVA, allowing the precise visualisation of tumours and breast volumes based entirely on the patient's MRI, we report the ability to rapidly confirm and secure an indication for partial surgery of multiple lesions in a 31-year-old patient. With the described approach, the patient did not have to suffer significant disfigurement from cancerous breast surgery without compromising safety.
INVESTIGATIONS DIVA is a previously reported software platform that allows visualisation and interaction of to-scale patient volumetric representations, or 'avatars', based on the patient's CT or MRI medical image (ie, native Digital Imaging and Communications in Medicine or DICOM image format) in an immersive virtual reality (VR) context. 1 2  VR immerses users in an entirely computergenerated environment where three-dimensional (3D) perception is made natural through the integration of stereoscopy and motion tracking. The DIVA software includes two modes of use. The first is a desktop mode where a volumetric representation of the patient can be visualised and modified to the user's preference in real time using a 3D look-up table, or transfer function, interface. Volumetric patient representations are generated by a volume ray casting approach that maps each voxel to an opacity and colour based on a rendering equation.
Informed consent was obtained from the patient to undergo a partial mastectomy of the superior pedicle mammoplasty. The patient was aware of the benefits and risks of this procedure, particularly the risk of secondary revision in the case of unhealthy margins and risk of aesthetic sequelae. Preoperatively, we placed two wires in the breast: one in the residual lesion detected by ultrasonography, and another along the coil initially placed, which showed the upper limit of the excision zone. Additionally, the DIVA software was again used to visualise the lesions in the immediate preoperative period in order to allow the surgeon to precisely determine the resection area (see video 1).
TREATMENT Under general anaesthesia, partial mastectomy and superior pedicle mammoplasty were performed guided by the preoperative VR visualisation using the DIVA software platform. We were able to excise the breast tissue that included the cancerous lesions detected prior and after chemotherapy with adequate safety margin. Such limited excision allowed a successful superior pedicle mammoplasty. There were no intraoperative complications. Recovery from anaesthesia was uneventful.
Visualisation of the patient's MRI by common radiological two-dimensional visualisation in complex cases with multiple lesions does not allow a good understanding of actual breast volumes and the precise localisation of lesions for many surgeons. The patient's avatars generated with the DIVA software provided access to a full volumetric understanding of the tumour position within the breast and 3D geometries in relation to the mammary tissue and allowed, through the interactive VR cutting tools, navigation within the tumour to better understand its boundaries. The generation of patient representation from volumetric ray casting does not require tissular boundaries to be predefined. Hence, when handling tumours with ill-defined boundaries, such is often the case with a breast MRI, the VR representation allows boundary regions to be analysed in an unbiased fashion.
Learning points ► Virtual reality-based visualisation of the native MRI grants surgeons an enhanced understanding of tumour localisation and breast volumes. ► Virtual reality visualisation of tumour and breast volumes can increase the incidence of breast-conserving surgeries allowing successful oncoplastic procedures. ► Virtual reality visualisation of breast lesions with the DIVA software platform can enhance the safety of breastconserving surgery. ► The DIVA software platform is a user-friendly means to immerse the breast surgeon in the anatomical pathology of the breast lesion. Twitter Mohamed El Beheiry @MoElbeheiry.
Over the last 10 years, the number of approved disease modifying drugs acting on the focal inflammatory process in Multiple Sclerosis (MS) has increased from 3 to 10. This wide choice offers the opportunity of a personalized medicine with the objective of no clinical and radiological activity for each patient. This new paradigm requires the optimization of the detection of new FLAIR lesions on longitudinal MRI. In this paper, we describe a complete workflow-that we developed, implemented, deployed, and evaluated-to facilitate the monitoring of new FLAIR lesions on longitudinal MRI of MS patients. This workflow has been designed to be usable by both hospital and private neurologists and radiologists in France. It consists of three main components: (i) a software component that allows for automated and secured anonymization and transfer of MRI data from the clinical Picture Archive and Communication System (PACS) to a processing server (and vice-versa); (ii) a fully automated segmentation core that enables detection of focal longitudinal changes in patients from T1-weighted, T2-weighted and FLAIR brain MRI scans, and (iii) a dedicated web viewer that provides an intuitive visualization of new lesions to radiologists and neurologists.
The objective of this project was to develop, deploy and evaluate a fully-integrated clinical workflow allowing to improve detection of new brain lesions in MS patients. The system has been designed to be usable by both hospital and private radiologists and neurologists in Brittany. The MUSIC project also included centralized storage of MS patients' MRI data so that their data could be accessed and compared even if they moved from one center to another for their MRI or neurological follow-up. The first "proof of concept" phase of the project reported in this article was deployed in 5 centers (2 university hospitals, 2 local hospitals, 1 private radiology center). The MUSIC workflow consists of three main components: (i) a software component that allows for automated and secured anonymization and transfer of MRI data from the clinical Picture Archive and Communication System (PACS) to a processing server (and vice versa); (ii) a fully automated MR image segmentation core that enables detection of new lesions from patients T1 weighted, T2 weighted and FLAIR brain acquisitions, and (iii) a dedicated web viewer that provides an intuitive visualization of new lesions to the clinical staff, easy to show to the patients.
These elements allow clinicians to access and visualize enhanced patient data scanned in any connected clinical center, even without being linked to a clinical PACS. In the present paper, we first illustrate the MUSIC project workflow. Second, we assess the performance of three clinicians, with different levels of expertise, in identifying new MS lesions on follow-up MRI scans, with and without the proposed workflow. For this evaluation, we used longitudinal pairs of scans from 54 MS patients.
Workflow Description The Transfer and Storage Modules: Servers Interoperability and Data Access In order to process the images outside the hospitals, a set of tools to pseudonymize, securely transfer and reidentify data has been set up. Overall, this module is composed of five main components: the hospital PACS, the centralized PACS, a telemedicine platform, the NodeJS transfer server and the research PACS. These elements and their interconnections are presented in Figure  2. The telemedicine platform is responsible for transferring the medical images from the hospital PACS to the centralized PACS. The latter gathers images coming from different hospitals and is hosted in a certified health data hosting provider. At this stage, patient data are still identified. From here, a clinical research assistant initiates the pseudonymization procedure.
An HTTP request is sent to a NodeJS server, also deployed in the same location, with the Universally Unique IDentifier (UUID) of the study to be transferred and the new patient identifier. The server, developed using NodeJS, a JavaScript runtime to develop modular network applications, is a simple server which listens to incoming HTTP requests. It can answer two specific requests: "transfer data" and "import results. " Once it receives a HTTP "transfer data" request, it retrieves the images from the PACS using a DICOMweb TM WADO-RS (Web Access to DICOM Objects Retrieved Study) request, de-identifies the images according to DICOM recommendations (DICOM Supplement 142), and sends the de-identified images to the Research PACS over an HTTPS connection to prevent any attack, using DICOMweb TM STOW-RS (STore Over the Web Retrieved Study).
Thanks to this procedure, the pseudonymization is performed inside the health data hosting provider and no identifying data goes out. To process a set of patient acquisitions, a web application has been developed. This application lists the patients available and is able to run a segmentation over one or multiple selected patients. It also communicates with the research PACS using DICOMweb TM , locally downloads the data temporarily and runs the segmentation algorithms from a Python script. Once the images have been processed and segmented, the NodeJS server is notified that results are available by an "import result" HTTP request. It retrieves the images from the research PACS using a DICOMweb TM WADO-RS request, reidentifies the new images using a patented method (ID EP3756123), and stores them in the centralized PACS.
-Second, we accept to segment both growing and new lesions without distinction and let the image readers assess the relevance of including each of them into their radiological analysis. A first natural solution to detection and segmentation of new lesions consists in first, independently segmenting the lesions for each of the two time-points of interest using a dedicated algorithm and second, comparing the resulting segmentation masks (or their associated probability maps) to infer a mask associated to the presence of new lesions. The main advantages of such an approach consist in its ability to stand on the numerous methods developed in the last decades to segment lesions from brain MRI  (12)  and on the availability of the associated annotated databases. However, by splitting the original problem into two subtasks, this approach disregards the temporal correlation in the images, which may lead to inaccurate segmentations for small lesions or subtle changes.