dataesr/scientific-paragraphs-categorization huggingface dataset:
2020a). Accordingly, epidemiological studies on glyphosate have reported variable results. In Europe, the mean range of glyphosate levels was 0. 16 to 7. 6 ng/ml with detection frequencies in EU-states ranging between 10 and 90%  (Conrad et al. 2017; Gillezeau et al. 2019; Connolly et al. 2020a; Nova et al. 2020). Little data is available concerning glyphosate levels in the French general population. A study conducted in an adult cohort on several pesticides (classified as organophosphorus, pyrethroid and azole compounds) showed lower exposure, based on urine sample levels, in frequent organic food consumers  (Baudry et al. 2019). The present study included 6848 participants recruited between June 2018 and January 2020. The whole of France was covered except the north-east area, and to a lesser extent, a corridor from the north east to the south west.
The Open Microscopy Environment Remote Objects (OMERO) is an open source image manager used by many biologists to store, organize, view and share microscopy images, while the open source software ImageJ/Fiji is a very popular program used to analyse them. However, there is a lack of an easy-to-use generic tool to run a workflow on a batch of images without having to download them to local computers; and to automatically organize the results in OMERO. To offer this functionality, we have built three tools in Java language: "Simple OMERO Client", a library to communicate with an OMERO database from Java softwares ; an ImageJ/Fiji plugin to run a macroprogram on a batch of images from OMERO and "OMERO Macro extensions", a dedicated vocabulary to interact with OMERO in macroprogramming. The latter is intended for developers, with additional possibilities using tag criteria, while the "Batch OMERO plugin" is more geared towards non-IT scientists and has a very easy to use interface.
Introduction Cell biology research is a big provider of multidimensional image data through the use of multimodal microscopy approaches to decipher cellular processes. Photonic microscopes and associated areas of expertise in image analysis are often available in cellular imaging facilities which tend to propose unified tools to manage images and associated projects. Over the last decade many tools emerged which are in constant development: software platforms (QuPath,  1  Cell Profiler,  2  Icy,  3  KNIME, 4 napari 5 ), browser-based and collaborative frameworks (ImJoy,  6  BIAFLOWS,  7  TissUUmaps 8 ), and image databases (BisQUE,  9  Cytomine 10 ). While some of this software is focused essentially on the analysis of images and others on their management, the global trend for image analysis in the machine and deep learning era is to use complementary software or platforms integrating a combination of these tools.
1, 2, 15, 16 Among these, the OMERO plugin of ImageJ/Fiji 17 allow access to the OMERO. insight interface to open and treat an image on local desktops before saving regions of interest (ROIs), results tables and images in the database after a manual or semi-manual sequence of treatment. Although it is easy and common to automate such processing with Fiji via a macro for a folder of images, it is not yet possible to automate all the processes (import/export, saving images, ROIs, results, etc. ) through a unique Macro program in ImageJ/Fiji for images hosted in the OMERO database. The only solution today is to write your own processing program according to the example available in the OMERO guide for threshold segmentation on datasets. 18 ut this is not within the reach of non-IT personnel. The goal of this work is to ease the access to image analysis for all users who are managing their projects and images in OMERO.
Based on an exhaustive library for importing/exporting images and results from and to the OMERO database, named Simple OMERO Client, we propose two ways to interact with the OMERO database using the ImageJ Macro language: (i) through a graphical user interface (GUI) on the same basis as the Batch Process module of ImageJ, that will loop a macro program written for one image on whole datasets, or (ii) using new OMERO buit-in Macro functions to write a macro program that will loop the analysis on datasets. Theses two tools are developed using a new java library, Simple OMERO client, that will also be described in this paper.
OMERO Macro Extensions Once the OMERO Macro Extensions plugin is installed in ImageJ, along with its dependencies, it can be used through the macro language. When writing a macro using these extensions, the first thing to do is to load the plugin with the following command: run("OMERO Extensions"); Connecting to OMERO is done using: Ext. connectToOMERO("host", 4064, "username", "password"); Then, switching group can be performed through: Ext. switchGroup (groupId); Afterwards, interacting with OMERO only takes simple commands, such as: datasets = Ext. list("datasets"); or: imageplusID = Ext. getImage (imageId); When done, you can disconnect with: Ext. disconnect();.
Batch OMERO plugin In the same way, the batch OMERO plugin first needs to be installed in the ImageJ plugins folder along with its dependencies. If this plugin is installed in ImageJ2/Fiji, it will make use of SciJava to run scripts using script parameters, otherwise it will only run ImageJ1 macro files, with arguments specified manually. When the plugin and the Simple OMERO Client are downloaded and installed, ImageJ/Fiji launched, a new "Batch process…" menu is added in the Omero plugin menu. When chosen, the GUI (Figure  1 ) is opened. A connection window to OMERO appears by clicking « Connect » in this window. After connection the drop down menus will be filled by information coming from the default group of the user (Group, user, Project, Dataset). Depending on the saving options (OMERO or local), the window will adapt its output choices.
The tool can process images retrieved remotely from OMERO or locally from a folder, using Bio-Formats. Conversely, it can save the output on OMERO and/or locally. The main possible outputs are new images, ROIs, results tables, log windows and the user has to specify (Figure  1 ) what should be saved in accordance with the outputs of the macro. ROIs can be loaded from OMERO: if this option is chosen (Figure  1 ), then OMERO ROIs will be exported to the ROI Manager using Simple OMERO Client. 3D/4D ROIs can thus be accessed from macros through two ROI properties: "ROI" and "ROI_ID", which contain, respectively, the local index and the ROI ID on OMERO for each 2D shape. When saving to OMERO, the users have to choose an existing project or a dataset they own in the current group. Furthermore, the following rules apply: • If ROIs are saved but images are not, then they are saved to the input image on OMERO, which should be annotable by the user.
These inputs can be displayed and modified through the GUI of the plugin with the "Set Arguments" button. This macro is the generic one called by the next one and the use case macro of OMERO Macro extensions. • "Macro_to_Batch_onOmero_3D_IJ1_Arguments": this macro is only coded in ImageJ1 macro language to get the parameters and calls the previous one. Its goal is to show another way to get input parameters. Indeed, the batch OMERO plugin calls the macro each time an image is opened from a dataset. If it is an ImageJ1 macro containing a dialog box to get input parameters, this one will be displayed at each execution of the macro, by default. This problem can be circumvented by getting the number of times the macro is called by the plugin with the getArguments() command. The dialog box will be displayed at the first call and parameters stored in a text file.
Simple OMERO Client The expected use case for the library is to use it as a Maven dependency in a Java project, as demonstrated by the two ImageJ plugins presented in this paper. An other use case would be to call it from an ImageJ2 script to access advanced OMERO functions directly from there. It is, for example, short and easy to retrieve the maximum value from images inside a dataset and tag them if the value is between two set thresholds (script available in OMERO toolbox examples).
Conclusion In this paper we provide new tools to fluidize automatic image processing with ImageJ/Fiji on images managed through an OMERO database. One of the main advantages of these tools is to simplify the macro-programming and give the opportunity to quickly analyze multiple images. Among these advantages, avoiding the tedious image format management when coding Macro programs in ImageJ/Fiji, will accelerate Macro development for end-users and help focus on the essential goal: image analysis. The spirit of the plugin could be considered close to the Batch Process plugin integrated in ImageJ1, as people without any knowledge with Macro language could record their image analysis process and apply it after on all their images. Consequently, we should highlight, that our plugin potentiates the original Batch Process by offering the choice to alternatively work with local folders, like the original one, or the files could be imported/exported from local directories or the OMERO database.
Extended data The Dataset of images dedicated to these treatments and processed with the "Macro_to_Batch_onOmero_3D", through the Batch OMERO Plugin and their results obtained are available on the public webpage of the OMERO database from Université Côte d'Azur and EMBRC-France, managed by the "Microscopie Imagerie Côte d'Azur" (MICA) Facility and housed by "Institut Français de Bioinformatique" from: https://bioimage. france-bioinformatique. fr/omero-mica/webclient/?show=project-3006.
Software availability • Simple OMERO Client: -Software and source code available from: https://github. com/GReD-Clermont/simple-omero-client -Archived source code at time of publication: https://doi. org/10. 5281/zenodo. 6320867 -License: GPLv2+ • OMERO Macro Extensions: -Software and source code available from: https://github. com/GReD-Clermont/omero_macro-extensions -Archived source code at time of publication: https://doi. org/10. 5281/zenodo. 6320876 -License: GPLv2+ • OMERO Batch plugin: -Software and source code available from: https://github. com/GReD-Clermont/omero_batch-plugin -Archived source code at time of publication: https://doi. org/10. 5281/zenodo. 6367840 -License: GPLv2+ The explicit goal described by the authors "to ease the access to image analysis for all users who are managing their projects and images in OMERO.
Is the rationale for developing the new software tool clearly explained? PartlyIs the description of the software tool technically sound? YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? YesAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly Competing Interests: No competing interests were disclosed.
Acknowledgements We thank  Carole Rovere  for providing the initial use case of this project in the context of a co-mentored internship. We thank  Christophe Blanchet  from  Institut Français de Bioinformatique  for the  Université Côte d'Azur  and  EMBRC-France  OMERO Database administration. We also thank  Sophie Abélanet  for her expertise in OMERO. Figure, helpful for this manuscript.
The  Université Côte d'Azur and EMBRC-France  OMERO Database deployment was financially supported by the  "Complexity and diversity of living systems" Academie of Excellence  established within the framework of the  UCAJEDI IdEx program. This work has been supported by the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01. The work of PP and SD was supported by  CNRS ,  INSERM ,  Université Clermont Auvergne (UCA) ,  16-IDEX-0001 CAP 20-25 challenge 1 , and the  COST-Action INDEPTH  ( CA16212 ), the work of RV was supported by  Pack Ambition Recherche  project  Noyau-HD  from the  Region Auvergne Rhone Alpes. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Participants A pirori, the sample size was determined by using the "pwr" package in R  [27]  with the parameters found in a study testing vagal activity as a predictor of decision-making  [17]  from which an effect size of Cohen's f 2 = 0. 20 was computed. With 0. 80 power, the analysis revealed a sample size of 55 participants. Due to the possible exclusion of some participants (see below) and to ensure sufficient statistical power, sixty-six undergraduate psychology students were enrolled. Inclusion and exclusion criteria were controlled through an interview on their sociodemographic data (age, sex, etc. ) and medical histories. Conditions that may affect cognition and cardiovascular functioning, such as neurological (head trauma, epilepsy, etc. ), physiological (hypo-or hyperthyroidism, type 2 diabetes…), or neurodevelopmental (autism, dyslexia…) conditions were defined as exclusion criteria.
Procedure This study was part of a larger project also testing the association between HRV and visual perception. Some administered tests were thus not considered in this study. Each participant was tested individually in a session that lasted approximately 45 min. After consent, the experimenter gave instructions so that participants could install the Polar H10 heart rate monitor by themselves. To ensure recording quality after the installation of the chest strap, a smartphone application (Polar Sensor Logger) displaying the ECG recording in real time was used. During a 10minute rest period, health and sociodemographic data were collected. Then, participants completed the heartbeat counting task (i. e. asking participants to count their perceived heartbeats without taking their pulse to measure cardioception  [37] ) followed by 5 min of resting time before the IGT.
Then frequentist multiple regression analysis was computed with resting HF, HF reactivity, and HF recovery as predictors and the rationality ratio as the dependent variable. To go further, the same model was conducted with a Bayesian approach using the BayesFactor R package  [44]  to test whether a submodel including HF reactivity and recovery is preferred over other possible models. Bayes factors were computed as the ratio of the likelihood of one particular hypothesis (H 1 ) to the likelihood of another (H 0 ). A Bayes factor BF 10 of 1-3, 3-10, 10-30, 30-100, > 100 can be respectively interpreted as anecdotal, moderate, strong, very strong, and extreme evidence for the alternative H1 relative to the null hypothesis H0  [45] [46] [47]. We have tested sex ratio and age as potential covariables.
Interviews will be recorded, transcribed synthetically and content analysis will be performed, with the aid of NVivo software. Ethics and dissemination This study will be performed in accordance with the declaration of Helsinki. The protocol was approved in October 2020 by the Committee for the Protection of Persons North-West III (20. 07. 16. 44445) and by the French national data protection authority (CNIL-MR003 No1989764-v0). The results of this project will be communicated to the scientific community through publications in international scientific peer-reviewed journals and communications to national and international congresses. Popularised results will also be provided to patient associations. The results of Candy project will also be published on the website of the sponsor, www. cgfl.
STRENGTHS AND LIMITATIONS OF THIS STUDY ⇒ This is a nationwide mixed-methods study using the databases of all French cancer registries, which are representative of regionally treated patients, enabling assessment of long-term outcomes. A mixed-methods combines quantitative and qualitative approaches, to take advantage of the complementarity of these two approaches. ⇒ In this study, sexuality, health-related quality of life (HRQoL), social support, social deprivation and psychological outcomes will be assessed using validated instruments. ⇒ This study will encompass most aspects of postcancer life in young women after breast cancer (sexuality, fertility, HRQoL, return to work, psychological distress, social support, right to be forgotten, access to mortgage insurance and difficulties obtaining a loan).
Open access Form 12 (SF-12); European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30; EORTC QLQ-BR23; EORTC QLQ-INFO25; Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire; Hospital Anxiety and Depression Scale (HADS) questionnaire; the French 'Evaluation de la Précarité et des Inégalités de santé pour les Centres d'Examen de Santé' (EPICES) questionnaire; Sarason's Social Support Questionnaire (SSQ6); additional questionnaires evaluating professional reintegration and collecting fertility data, sociodemographic data (age, weight, height, level of education and place of residence), COVID-19 data (care organisation, medical monitoring and impact of the health crisis), medical data, diagnostic data and data on tobacco consumption. For patients who respond, the data collected from the booklets will be supplemented by confidential medical data from the Registries.
At this stage of the study, each patient will be represented by a unique confidentiality code both for the data in the booklet and for the data from the Registries. The booklet has been tested among a small sample of patients from the 'Jeune&Rose' association; a French national network of young patients who provide mutual support, and share and relay prevention messages. The data from the booklet for the quantitative component will be entered into a database created using Clinsight software, a software package designed for the management of clinical studies. A data validation plan will be developed and will describe in detail the checks to be performed for each variable. Entries will be checked using error messages from validation programmes. The database will be frozen after a final quality control according to an automated and validated procedure.
Qualitative component Subsequently, presence-based semistructured interviews lasting approximately 45-60 min will be carried out by a sociologist trained in qualitative methods in a sample of approximately 30 women who participated in the quantitative component, until data saturation is reached. This sample will be drawn at random from among eligible women who answered the questionnaires in the booklet and who expressed difficulties relating to sexual function (women with sexual dysfunction according to the global FSFI score) or fertility (women having difficulty getting pregnant since BC diagnosis among those who wanted to become pregnant). The selection will be stratified by department, age and deprivation score. On the day of the interview, an information leaflet specific to the qualitative component will be given to each participant and their consent will be obtained.
Quantitative component The data that will be collected in the booklet are shown in tables 1-3. The data collected from the booklet will be supplemented by data extracted from the cancer registries participating in the study, namely age at diagnosis, tumour stage, tumour grade, hormone status, Human Epidermal Growth Factor Receptor 2 (HER2) status, KI67 index and type of treatment (surgery, radiotherapy, chemotherapy, endocrine therapy, ovarian function suppression and targeted therapies). Tumour stage will be categorised and the analyses will be performed on the American Joint Commission of Cancer (AJCC) condensed stage according to the 8th edition of Tumour Nodes Metastasis (TNM)-AJCC classification. 30 alitative component Data for the qualitative component will be generated during semistructured interviews performed using an interview guide that will be finalised on the basis of the results of the quantitative component.
Sample size The approximate number of women aged 40 and younger at the time of diagnosis of BC from 1 January 2009 to 31 December 2016, according to data from the centralised database at the 'Hospices Civils de Lyon' of cancer registries participating in the project is 5119 cases (figure  2 ). After this first selection, each Registry will perform a second round of selection by excluding patients presenting the following criteria: relapse, metastasis, death or other cancers occurring after the diagnosis of primary BC as of 31 December 2020. Each Registry will The FSFI questionnaire is a self-report questionnaire specific to sexual function in women. It was developed by Raymond Rosen  37  and a French version has been validated. 38 Through its 19 items, it optimally explores six scales (desire, excitement, lubrication, orgasm, satisfaction and pain) of sexual function.
Fertility data module Fertility concerns will be assessed using a study-specific questionnaire developed in conjunction with oncologists, clinicians and surgeons. The items of this module were created for the needs of the study on the basis of clinical routine and diverse French population-based surveys. 26 27 39 40 This study does not plan to validate the use of this fertility data module. The purpose of this questionnaire is to assess postcancer fertility in young women who have had BC. Fertility will be quantified by the number of pregnancies that have occurred since the diagnosis of BC. Other data will also be collected, that is: menstrual cycles before and after treatments, parity before and after diagnosis of BC, pregnancy plans at diagnosis and at the end of treatment, information on treatment effects and fertility preservation before treatment, fertility preservation techniques, adoption and current menopausal status.
Open access perform a final update just before sending the questionnaire booklets to the patients. Then, 2500 women will be drawn at random from among all eligible women after these two rounds of selection. Assuming a participation rate in the study of 50% (rate obtained from previous questionnaire surveys carried out within the Côte d'Or Breast and Gynaecologic Cancer Registry), approximately 1250 participants are expected. 27 31-33 Bonferroni correction will be used to take into account of the multiplicity of tests, due to the use of a composite endpoint (sexual function and fertility) and the adjusted α-risk will be set at 2. 5%. The sample size was calculated to make it possible to demonstrate an OR of 2. 5 for women treated with chemotherapy not to have a pregnancy after BC (7%)  34 35  Table  2  Lifestyle habits, anxiety, depression, deprivation, social and professional data collected in the questionnaire booklet sent to patients during the quantitative component.
Module on lifestyle habits Consumption of alcohol and tobacco will be recorded. Data on alcohol consumption will be collected using the validated French version of AUDIT-C questionnaire. AUDIT-C is a 3-question screening test to reveal problematic alcohol use. 47 A score ≥3 in women should suggest misuse, while a score ≥10 suggests dependence. Anxiety and depression module (HADS questionnaire) The HADS questionnaire validated and adapted in French by Lepine et al  48  will be used to detect anxiety and depressive disorders. This scale has 14 items rated from 0 to 3 and describes two dimensions, anxiety and depression, with subscale scores ranging from 0 to 21. A subscale score of 11 or more indicates the presence of anxiety or depression.