dataesr/scientific-paragraphs-categorization huggingface dataset:
Results We developed a bioinformatic pipeline that aims to identify novel MGEs and other biologically relevant entities from metagenomic data. The pipeline does not rely on existing databases, but instead detects transfer of candidate MGEs based on sequences newly introduced into evolving communities. As such sequences are of "foreign" nature, we refer to these as "xenotypic sequences", and the pipeline is called xenoseq (https://github. com/bramvandijk88/xenoseq). For a full description of the bioinformatic pipeline see methods and Supplementary Material I and II. Xenoseq was first benchmarked using simulated mock communities (Supplementary Materials II), and then applied to datasets from  Quistad et al. (2020). The data by Quistad et al. is of particular interest, because the experimental design allows for dissemination of MGEs within horizontal communities, without allowing the migration of microbial cells (Figure  1a ).
Movement of xenotypic sequences between communities To investigate the dissemination of xenotypic contigs, xenoseq maps reads from all communities to the xenotypic contigs representing candidate MGEs to track their dynamics. Figure  4  shows the results for seven selected examples: a complete 200 kb phage (Figure  4a ), an incomplete but prolific phage sequence (Figure  4b ), a putative viral sequence exclusively predicted by Seeker (Figure  4c ), an element predicted as both phage and plasmid unanimously (Figure  4d ), a large 313kb plasmid (Figure  4e ), and a stretch of apparent chromosomal DNA adjacent to an IS3 element (Figure  4f ). Finally, xenoseq also revealed transfer of chromosomal DNA fragments lacking MGE predictions, many of which were annotated as Candidatus Saccharibacterium (Figure  4g ), and many other interesting sequences (full interactive dataset available online).
Cellvibrio C1 also emerges in three other mesocosms independently (in HC9 at week 8, and HC4 and HC10 at week 32), and its emergence is always accompanied by a MAG containing only a single 313kb contig (shown in purple in Figure  5a , henceforth called Cp). This contig is of circular nature, and is in fact the same plasmid-like element earlier identified by xenoseq (Figure  4e ). The large mobile element carries important plasmid partitioning proteins(e. g. parB and parM) typically associated with low copy-number plasmids  [78] ,  [79]  (see Figure  5c ), which is consistent with it co-occurring with Cellvibrio C1 in an approximate 1:1 ratio. In addition to features that are akin to large plasmids, Cp also carries many ORFs associated with phages as evident in matches to the viral PHROGs-database 66 (for example, integrase and endolysin).
Funding B. v. D. , A. F. , P. B. and P. B. R. acknowledge support from the  Deutsche Forschungsgemeinschaft (DFG)   Collaborative Research Center 1182 'Origin and Function of Metaorganisms'  (grant no. SFB1182 , Project  C4  to P. B. R. ) and  Agence Nationale de la Recherche  ' Investissements d'avenir' programme  (grant no. ANR-20-PAMR-0004  to P. B. R). P. B. R. acknowledges generous core funding from the  Max-Planck Society. B. E. D. was supported by the  European Research Council (ERC)   Consolidator  grant  865694 : DiversiPHI, the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation)  under Germany's  Excellence Strategy - EXC 2051 -Project-ID 390713860 , and the  Alexander von Humboldt Foundation  in the context of an  Alexander von Humboldt Professorship  funded by the  German Federal Ministry of Education and Research.
Data availability Raw sequencing data are taken from  Quistad et al. (2020) , and are publicly available online (https://www. mg-rast. org/mgmain. html?mgpage=project&project=mgp18485). MAGs, interactive datasets, and R scripts for designing the mock communities are published on Zenodo (https://doi. org/10. 5281/zenodo. 7589193). Any further requests for data can be sent to rainey@evolbio. mpg. de.
Simple Summary: Radiation therapy (RT) is one of the corner stones of the local treatment of breast cancer (BC). Toxicity factors related to RT and their consequences are poorly known because of limited DICOM data and limited analyses on contouring, dose distribution and the RT technique. This manuscript describes the methodology used and provides the first characterization of the study population and RT data in CANTO-RT (CANcer TOxicities RadioTherapy). To our knowledge, our study is the largest available multicenter prospective multicenter cohort of early breast cancer with full DICOM RT data (files (CT, RT Structure, RT Dose, RT Plan)). This study answers to a concern about toxicity factors related to radiotherapy and their consequences and aims to identify predictors of development and the persistence of long-term toxicities in breast cancer patients.
However, toxicity factors related to RT and their consequences are poorly known because of limited DICOM (Digital Imaging and Communications in Medicine) data with limited analyses on contouring (target and organs at risk volumes), dose distribution, RT technique and quality involving precise calculation and delivery of the planned dose. This understanding is nevertheless essential to characterize radiation-induced toxicities, to better understand treatment related toxicities and to identify the predictive factors for the occurrence of these toxicities. CANcer TOxicities (CANTO) (NCT01993498, UNICANCER 0140/1103, 2011-A01095-36 ('study of chronic toxicity of treatment of patients with localized breast cancer') is a multicenter prospective cohort study with the primary objective of identifying factors predictive of chronic toxicity in patients treated for stage I-III breast cancer  [6].
Within CANTO, detailed RT data were collected for a subset of patients representing CANTO-RT (CANcer TOxicities RadioTherapy), a large multicenter prospective cohort of early breast cancer (BC) patients treated with RT that aims to identify predictors of the development and persistence of long-term toxicities. In this paper, we describe the methodology used to collect RT data (full DICOM) and to ensure RT data quality control to provide a first characterization of the study population and RT data in CANTO-RT.
Study Design CANTO (NCT01993498) is a French prospective longitudinal multicenter cohort study designed to evaluate chronic toxicities in patients treated for non-metastatic BC diagnosed and enrolled between 2012 and 2018, in  26  French centers. The details on the CANTO study procedures have previously been published in accordance with the French national regulatory requirements, good clinical practice guidelines and European General Data Protection Regulation (GDPR) as previously described by Vaz et al. [6]. This study, sponsored by Unicancer, enrolled 12,012 patients. In the database lock of December 2020, data from 2012 to 2017 were obtained corresponding to 9599 patients.
Study Population The subset of patients matching all CANTO inclusion and exclusion criteria, who received RT in one of the 10 top recruiting CANTO centers with a minimum follow up of 3 years, and who were still in follow up at the time of the database lock were selected for CANTO-RT (Figure  1  Flowchart). Patients included were followed for 10 years as part of the study, with a minimum of 36 months follow-up. CANTO-RT patients met the following inclusion criteria: female patients aged 18 years and over covered by the national social security system, with histologically proven non-metastatic invasive BC (cT0-3, cN0-3) without previous cancer treatment. Conventional or hypofractionated RT was prescribed according to local standard-of-care. Eligible patients had breast/chest wall +/-lymph node RT with curative intent.
Data Collection Patients and multimodal treatment characteristics as well as paraclinical parameters including blood chemistries, exams, or toxicity data, etc. , were collected prospectively (Figure  2 ) and were the same as described in  [6]. Patients were assessed at diagnosis (baseline), 3-6 (M0), 12 (M12), 36 (M36) and 60 (M60) months after completion of chemotherapy or RT, whichever came last. In this study, radiotherapy data were exported in standardized Digital Imaging and Communications in Medicine (DICOM) format by each investigating hospital to the UNITRAD online platform hosted by AQUILAB Onco Place™, a company with health-data-hosting authorization. All data were automatically pseudoanonymized and converted to homogeneous naming. We prospectively assessed data at diagnosis (baseline), 3-6 (M0), 12 (M12), 36 (M36) and 60 (M60) months after completion of chemotherapy or RT, whichever came last.
Data Collection Patients and multimodal treatment characteristics as well as paraclinical parameters including blood chemistries, exams, or toxicity data, etc. , were collected prospectively (Figure  2 ) and were the same as described in  [6]. Patients were assessed at diagnosis (baseline), 3-6 (M0), 12 (M12), 36 (M36) and 60 (M60) months after completion of chemotherapy or RT, whichever came last. In this study, radiotherapy data were exported in standardized Digital Imaging and Communications in Medicine (DICOM) format by each investigating hospital to the UNITRAD online platform hosted by AQUILAB Onco Place™, a company with health-data-hosting authorization. All data were automatically pseudo-anonymized and converted to homogeneous naming. We prospectively assessed data at diagnosis (baseline), 3-6 (M0), 12 (M12), 36 (M36) and 60 (M60) months after completion of chemotherapy or RT, whichever came last.
Organizational structure was previously described  [6]  and a summary of the data collection is presented Figure  2. In CANTO RT, individual full DICOM RT data (CT, RT Structure, RT Dose, RT Plan) were collected, pseudo-anonymized, structured and analyzed on the CANTO-RT/UNITRAD web platform using AQUILAB Onco Place™ and Analytics Dose module (Figure  3 ). In the Analytics Dose module, RT data were extracted, filtered and grouped according to sets of constraints by volumes (mean dose, median dose, DX%: dose covering X % of the volume expressed in Gy, VX Gy: volume receiving at least X Gy expressed in %, near-min dose, near-max dose). In CANTO RT, individual full DICOM RT data (CT, RT Structure, RT Dose, RT Plan) were collected, pseudo-anonymized, structured and analyzed on the CANTO-RT/UNI-TRAD web platform using AQUILAB Onco Place™ and Analytics Dose module (Figure  3 ).
In the Analytics Dose module, RT data were extracted, filtered and grouped according to sets of constraints by volumes (mean dose, median dose, DX%: dose covering X % of the volume expressed in Gy, VX Gy: volume receiving at least X Gy expressed in %, nearmin dose, near-max dose). In CANTO RT, individual full DICOM RT data (CT, RT Structure, RT Dose, RT Plan) were collected, pseudo-anonymized, structured and analyzed on the CANTO-RT/UNI-TRAD web platform using AQUILAB Onco Place™ and Analytics Dose module (Figure  3 ). In the Analytics Dose module, RT data were extracted, filtered and grouped according to sets of constraints by volumes (mean dose, median dose, DX%: dose covering X % of the volume expressed in Gy, VX Gy: volume receiving at least X Gy expressed in %, nearmin dose, near-max dose).
Data were extracted, filtered and grouped according to sets of constraints by volumes (mean dose, median dose, DX% (dose covering X % of the volume expressed in Gy), VX Gy (volume receiving at least X Gy expressed in %), near-min dose, near-max dose). Characteristics of the patients (age, medical history, clinical examination and concomitant treatments), tumors (including TNM, histology, HER2, estrogen and progesterone receptor), paraclinical examinations (blood/plasma tests, bone densitometry, cardiac echography or myocardial scintigraphy in case of treatment with anthracyclines/trastuzumab/RT to the left breast and/or Internal mammary chain), type of breast (lumpectomy, total mastectomy) and lymph node surgery (sentinel node, axillary dissection), chemotherapy, targeted anti-HER2 therapies and endocrine therapy were recorded from the CANTO data.
Data Management and Quality Control Quality control of clinical data was performed regarding RT data available (laterality, type of mammary and lymph node surgery) on Aquilab Onco Place™ versus December 2020 database lock of CANTO CRF (Case Report Form). All inconsistencies were corrected by the participating centers after reopening the files on the Aquilab Onco Place™ database before dose extractions. Quality control of dosimetric data was performed after a first extraction of Dmean and D95% of the volume CTVp_Breast or Chestwall for all patients with CTV delineated. We highlighted some dose inconsistencies and identified them by manually opening the dosimetry to understand their origin. A low dose away from the usually prescribed 50 Gy could indicate severe hypofractionation (used for partial breast irradiation protocols NCT01024582 and NCT01247233) or a dosimetry offset on the centering scanner (patient error or DICOM error).
CANTO-RT Characteristics A total of 3875 BC patients matching all CANTO-RT inclusion and exclusion criteria, compliant with RT data check and with succeed analysis were selected among the 8708 patients treated with RT out of the 9599 CANTO patients. The CANTO-RT cohort included 1947 (50. 2%) left-side, 1850 (47. 8%) right-side and 78 (2%) bilateral BC patients with a median follow-up of 64 (range: 4 to 102) months. The baseline patient and tumor characteristics and treatment information are summarized in Tables  1  and 2. in 25 fractions in five weeks +/-boost on the tumor bed of 16 Gy in 8 fractions). Moderate hypofractionationated RT was delivered in 166 (4. 3%) patients with mostly 40 Gy in 15 fractions of 2. 67 Gy in three weeks. More severe hypofractionation (accelerated partial breast irradiation 38,5/40 Gy in 10 fractions or 30 Gy in 5 fractions) was used in 1.
However, we know that depending on the anatomy, the dose to the heart, especially in cases of irradiation of the internal mammary nodal chain (IMN), is not null set even when treating right-sided BC  [10]. We have also shown a heterogeneity of practice in the delineation rate of clinical target volumes (CTV) treated, which varied from 52% to 91% of the cases. The absence of delineation of a treated CTV didn't allow for the proper appreciation of target volume coverage. As expected, tumor bed CTV had the highest rate of delineation (91%), while it was just the opposite for Chestwall CTV. CANTO-RT has several strengths: It is one of the largest prospective multicenter cohorts in BC with full DICOM RT data ever published with the presence of a centralized database and is available on a single platform (Aquilab™) with innovative tools (Analytics).
Second, CANTO-RT followed standard methodological quality criteria for observational studies  [11, 12]. The patient population has well-described inclusion and exclusion criteria: treatment information and patient-reported outcomes were reported with the use of standardized CRFs, and the length of observation has sufficient duration to apprehend treatment-related toxicity. Third, electronic transfer of DICOM data and quality control methods optimized the quality of RT data available, avoiding manual reporting of complex values to be found in a RT technical file. Thus, CANTO-RT reports on RT data available in one of the largest databases in the world with individual full DICOM RT files (CT, RT Structure, RT Dose, RT Plan) and with contemporary RT techniques. Initiatives to centralize information available on large-scale RT exist in some countries but not for a long duration, due to the technological challenges imposed by the volume of this data.
The guidelines for radiation therapy for early BC stay heterogeneous  [22] [23] [24] [25] [26]. CANTO-RT could be a tool for comparing practices, and such international bases would be desirable in the future. The use of this database will allow for the analysis of the dose-effect relationship of radiation received in the organs of women in the CANTO-RT cohort with a possible correlation to the toxicities graded during their prospective follow-up. There are several ongoing projects, such as heart, skin, lung toxicity analyses. CANTO-RT will try to improve knowledge on the relationship between RT toxicities and systemic treatments and the role of potential modifiers of this dose-response such as chemotherapy and hormonal therapy. Other objectives could be the use of statistics and artificial intelligence (Machine, Deep or/and Reinforcement Learning) combined with dosimetry reconstruction approaches to supplement the dosimetric data of the CANTO-RT database during collaborative projects.
Acknowledgments: We thank the patients and their families as well as all of the investigators and their staff involved in  CANTO (Gustave Roussy (GR), Institut Jean Godinot (IJG), Institut Bergonié, Institut du Cancer de Montpellier-Val d'Aurelle, Centre Oscar Lambret (COL) ,  Centre Georges-François Leclerc  (CGFL),  Institut de cancérologie de Lorraine (ICL), Institut de Cancérologie de L'Ouest (ICO)-Site Paul Papin et Saint-Herblain, Centre Léon Berard (CLB), Institut Curie (IC)-Site Paris et Saint-Cloud, Centre François Baclesse (CFB) ,  Institut Paoli Calmettes ,  Centre Jean Perrin ,  Centre Henri Becquerel ,  Centre Antoine Lacassagne ,  Centre Paul Strauss ,  Centre Eugène Marquis ,  Institut Sainte Catherine ,  Institut Claudius Regaud, Centre Hospitalier Régional d'Orléans-Hôpital de la Source, Centre Hospitalier de Blois, Centre d'Oncologie et de Radiothérapie du Parc de Dijon, Hôpital Saint-Louis-Assistance Publique-Hôpitaux de Paris et Hôpital Universitaire Pitié Salpêtrière-Assistance Publique-Hôpitaux de Paris.
Institutional Review Board Statement: The CANTO cohort (NCT01993498) was conducted in accordance with the  Declaration of Helsinki , and approved by the  Institutional Review Board of UNICANCER  ( UC-0140/1103 ). The CANTO cohort was supported by the  Agence Nationale pour la Recherche  ( ANR-10-COHO-0004 ). The study was approved by  French regulatory authorities Afssaps  under the reference: B111158-20 on 14 September 2011 and French ethics committee ID-RCB: 2011-A01095-36 , 11-039  on  14 October 2011. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Structure of the E-learning platform Fig. 2  presents an overview of the developed training programme, which contains 3 modules. Each module is composed of three key components: Instruction videos, interactive sessions and an assessment session composed of quizzes. The components were designed to be short and dynamic to retain the attention of the learners (to meet requirement R1. 2). For example, an instruction video on a specific functionality of the Digi-NewB system would take no more than 3 min, followed by an interactive section on the same functionality. Module 1 introduces the Digi-NewB system to the learners, showing how the training platform works and teaching the learners how to interact with the simulated interface. In modules 2 and 3, learners interact with the system interface in different scenarios.
Quizzes A quiz is presented at the end of each training module (formative assessment based on feature F2. 8). Learners are asked several multiplechoice questions related to the topics covered in each session (Fig. 6 (a) ). At the end of the quiz, the learners are provided with the results of the quiz (Fig. 6 (b) ). If a learner answered one or more questions incorrectly, they are required to watch a recap video on these specific topics and are then provided with the opportunity to answer the question(s) again. Learners are only able to complete the training session after answering all questions correctly. The training platform was developed using ReactJS, an open-source JavaScript library used for building web user interfaces. This library was chosen since it allowed the building of rich user interfaces that can run fast in commonly used web browsers.
For instance, a well-designed and simple-to-use interface may require minimal training while a poorly designed interface may require extensive training to be used effectively. Therefore, in addition to the simulation analysis, it was important to seek feedback from the participants on their perceptions of the effectiveness, efficiency, and user experience with the e-learning training platform itself. A multi-modal qualitative research approach was used, including the analysis of three qualitative data sources: 1. Audio and video recordings of the summative usability testing sessions, conducted in a high-fidelity simulation setting. 2. Interviews with the simulation participants and with a NICU KOL who observed all simulation sessions. 3. A survey was distributed to the simulation participants after the simulation session.
Study Procedures and Data Collected Enrolled patients underwent double simultaneous swab vaginal secretion sampling at the level of the posterior vaginal fornix under speculum. They were assessed for temperature, hemogram, CRP level, and urine and vaginal bacteriological culture. The usual management for TPL was unchanged (±admission, ±corticosteroids for lung maturation, ±tocolysis, and ±etiological treatment). Antenatal corticosteroids for fetal maturation were administered according to the national guidelines: betamethasone given as two 12 mg intramuscular injections 24 h apart. Data were collected and managed using the REDCap (Research Electronic Data Capture) tool hosted at the University Hospital of Clermont-Ferrand  [18]. REDCap is a secure, web-based software platform designed to support data capture for research studies.
Comparisons of continuous variables (such as age, gestational age at inclusion, BMI) between Premaquick ® and QuikCheck fFN™ results were made using Student's t-test or the Mann-Whitney test if the criteria for applying the t-test were not met. The equality of variances was analyzed with the Fisher-Snedecor test. For categorical variables (such as marital status, gestity, parity, smoking), chi-squared or Fisher's exact tests were used. Statistical analyses were performed with Stata software (version 15; StataCorp, College Station, TX, USA) for a two-sided type I error of 5%.
| Biological sample To conduct the statistical analyses in this study, we compiled a set of microanatomical data measured on bone cubes extracted from the femoral head of a large number of amniote taxa. We retrieved the list of taxa from  Doube et al. (2011)  and enriched it with new taxa, notably squamates and turtles, which were not previously included. However, we did not retain all mammal taxa, as they were overrepresented in the study by  Doube et al. (2011). Indeed, if we consider extant species diversity, there are about 5000 extant species of mammals  (Upham et al. , 2019) , compared to about 15 000 extant species of reptiles: 10 000 species of birds  (Jetz et al. , 2012) , about 5000 species of limbed squamates  (Brandley et al. , 2008) , about 350 species of turtles  (Thomson et al.
org/10. 5061/dryad. 83bk3 j9x2. Ketcham and Ryan (2004)  noted that texture orientations could be over-represented towards the edges and corners of a cubic volume. However, our study is not affected by this "edge and corner bias. " Indeed, in BoneJ 1. 4. 3, anisotropy is calculated using the mean intercept length (MIL) method from sampling spheres randomly distributed inside the image stack (see  Doube et al. , 2010). These spheres are never closer to the sides of the image than their radius (M. Doube, personal communication).