dataesr/scientific-paragraphs-categorization huggingface dataset:
Monomers discovery As already mentioned, the non-validated entries can be due to a wrong annotation either in the SMILES or in the monomeric graph. In the latter case, peptides may contain monomers not present or wrongly annotated in Norine. Thus, rBAN was run in discovery mode to identify new monomers. The software suggested 61 new building blocks. Some of these predictions could be wrong due to mistakes in the input SMILES or wrong mapping of the software. Hence, a manual inspection was required before their addition into the database. To increase confidence, only the monomers present in more than one compound were evaluated. From the 18 monomers examined, eleven were correct suggestions (Table  1 ). N-Formyl-Lysine was the most commonly found monomer, missing in Norine because CO is currently defined as a monomer in the database and occurs in several NRP graphs.
The peptidic structures (SMILES) of these records contained errors or did not even correspond to the right molecule due to the ambiguous PubChem searches previously performed. The eleven new monomers were added in the Norine database along with the correction of the wrong annotations, either in the monomeric graphs or in the SMILES of the compounds (find examples in Additional file 1: Fig. S1 ). This step was essential for evaluating the increase of the validated data. In the end, 11 added new monomers along with the correction of wrong annotations boosted the count from 492 to 526 validated entries.
rBAN vs s2m Within the retro-biosynthetic tools targeting NRPs, s2m is the closest to rBAN as it produces the same output: a monomeric graph. Yet the two approaches substantially differ in their features and algorithmic approaches set to handle the issues raised by mapping the molecules. These involve among others, the monomer search, the light matching or the heterocycles treatment (see Table  2 ). In order to compare both tools, we analyzed their results, their robustness and their computational performance. The benchmark in the following sections was performed running s2m in the light matching mode to allow tautomer identification and obtain results comparable to those of rBAN. Results comparison s2m was run to validate the same SMILES data previously used in the curation protocol of the Norine database.
S2 ). The three other false positives of s2m show similar problems. The fifth entry is Kermamide K, the only true positive in the set. It was not validated by rBAN because this software does not consider CO as a monomer. The manual evaluation of the 52 peptides uniquely validated by rBAN confirmed their validity and uncovered some structural patterns that were optimally handled by rBAN and not by s2m. The fragmentation model implemented in rBAN properly annotated large peptide structures whose monomeric composition was not revealed by the tiling algorithm of s2m. Similarly, the annotation of NRPs containing thiazoles and oxazole heterocycles was successfully carried out using rBAN, while the results of s2m did not match the monomer graph. Another pattern also observed in the rBAN-validated entries was the presence of monomers whose hydrated and dehydrated forms coexist in the monomer database.
Robustness comparison The existence of several combinations of monomers mapping the same peptide substructure increases the complexity of the problem. Hence, the extension of the monomer database can easily influence the mapping of a molecule and could lead to the appearance of wrong annotations that were previously correct. The robustness of the two software was tested while extending the monomer database and evaluating its impact on the results. An additional set of monomers was obtained using the PubChem Classification browser to retrieve the chemical entities defined as non-proteinogenic amino acids (ChEBI Ontology). Components with a molecular mass higher than 450 g/mol were discarded, as they greatly exceeded the average monomer size. Chemical structures already present in the monomer database were also discarded to avoid repetitions.
A total of 550 monomers were sequentially added in order to test the response of both software to different extensions of the database (Fig. 6c ). rBAN correctly annotated the same amount of entries (492) for all the database sizes. Note that the number of correct annotations could not be improved because the Norine graphs were not modified to include the new monomers so maintaining the same correctness was the best that could be expected, proving the robustness of the software. On the other hand, s2m correct results dropped from 445 to 435 with the addition of 100 new monomers, although the rest of the extensions was steadily handled, only dropping by two in the extension to 450 monomers.
rBAN vs GRAPE As already mentioned, GRAPE is another tool for the retro-biosynthesis of NRPs and polyketides (PKs). However, the annotations provided by this software differ from those of rBAN as (1) they are based on a different monomer library and (2) the modifications are annotated separately from the monomers. These differences make the comparison of correctness difficult and that explains why the benchmark was limited to the analysis of coverage (ratio between annotated atoms and total number of atoms in the molecule). The same set of SMILES without the wrong entries previously identified was used to test GRAPE. Out of 653 peptide structures, GRAPE fully annotated 468, while rBAN reaches 560 annotated entries, 492 of them being correct. In fact, from these results it is possible to indirectly compare the correctness of the two software.
Only the peptides with a full coverage can have full correctness. Hence, assuming that all the annotations from GRAPE are correct (468), the result is still lower than the number of correctly annotated peptides from rBAN (492). The whole distribution of coverage shows how GRAPE tends to leave less peptides with low coverage (Fig. 7 ). Nevertheless, the annotations of the 18 peptides with zero coverage in rBAN were manually checked for GRAPE. As it turned out, their monomer fragments were categorized as "unknown". Finally, the computational performance was evaluated using the The global distribution of the correctness do not show substantial differences between the two software but it proves that rBAN does not only have more correct peptides, but also less peptides with correctness values close to zero.
c The monomer database was extended with new chemical entities to evaluate its effects on the peptide mapping. The results of rBAN remained unchanged proving its robustness, while the extension of the monomer database affected mapping in s2m. d The computational performance was evaluated with different amounts of input peptides. In all cases rBAN outperformed s2m, being between four and five times faster same data. rBAN analysed the 653 peptides in an average time of 26. 94 s, while the same process with GRAPE resulted in an average time of 81. 34 min. Web implementation A web application interface was designed and integrated into Norine as an additional tool for the database curation. With the aim of providing a simple and user-friendly interface, the online version of rBAN is limited to the analysis of a single peptide.
Conclusions The usage of rBAN for Norine curation ended with the validation of 97% of the entries and the introduction of 242 SMILES and 11 monomers in the database. These results prove the ability of the algorithm to deduce effectively the monomeric graph of an NRP from its SMILES. The comparison with s2m and GRAPE clearly favored rBAN, which annotates more entries and with a higher perfomance. We also demonstrated the efficacy of the monomer discovery mode for the correction/addition of monomers. Furthermore, rBAN automatically produces graphs where the edges are labeled with the bond types linking the monomers. The current monomeric graphs in Norine do not contain this information, which is useful for the development of automatic substructure search. In the end, rBAN was integrated in Norine as a complementary tool for the future curation of the database.
Limitations and perspectives The main limitation of the method is its dependence to the defined fragmentation rules. Hence, it fails mapping natural products following different patterns such as Polyketides (PKs). The introduction of new rules based on PK biosynthesis patterns would solve this issue and would extend the range of secondary metabolites covered. The software currently provides the results in a JSON format but returning the graphs in specific annotation formats such as HELM or SCSR is planned in order to improve the usability of the tool. Finally, the current slow performance of the discovery mode will be addressed by trying alternative programmatic access to PubChem data or by downloading a part of the PubChem database to our local server. In future versions of the software it would also be interesting to include a modification database and implement an optional mapping where the monomers and their modifications are annotated independently.
mapping Based on molecule fragmentation through common monomer linking bonds Based on mapping of monomers and selection of best tiling b) Light matching Positions of double/triple bonds are ignored Implicit hydrogens and bond order are ignored c) Heterocycles treatment Accounts for NRP cyclisation patterns initiating oxazoles and thiasoles formation Does not include any rule/pattern for heterocycles d) Presence of new monomers Unmatched regions left unannotated and potentially identified in discovery mode Matches the most similar monomers in a given database and leaves out uncovered atoms e) Graph serialization Labelled edges with bond type and directed in accordance to functional groups in each side Unlabelled edges.
Availability of data and materials The interface of the software is available in the following webservers: https ://web. expas y. org/rban, http://bioin fo. crist al. univ-lille. fr/rban. A JAR file with more functionalities than the web application is available in Bitbucket: Project name: rBAN. Project home page: https ://bitbu cket. org/sib-pig/rban/downl oads. Archived version: BitBucket. Operating system(s): Platform independent. Programming language: Java. Other requirements: Java 1. 8 or higher. License: Creative Commons Attribution-NonCommercial-ShareAlike 4. 0 International Public License. The datasets supporting the conclusions of this article are included within the article (and its additional files).
io ), an open-source platform designed to deliver advanced, yet easy-to-use data analysis tools, especially based on deep learning. The core of ImJoy is a browser based application (i. e. a software running in a web browser) that offers a rich and interactive user interface and leverages modern web technologies ( Fig 1a, Supplementary Figure  1  ). ImJoy's data analysis functionalities are provided by a flexible system of plugins that operate independently of each other but can be organized into workspaces and workflows ( Supplementary Figure  2 ). Depending on the need, ImJoy can access various computational resources by performing computations either in the browser itself or using 'plugin engines' that can run either locally or remotely ( Fig  1a,  Supplementary Figure  1  ). Below, we highlight ImJoy's main features, along with illustrative plugins with installation links and descriptions being provided in the Methods.
A detailed documentation on how to use ImJoy and its plugins and how to develop new ones is provided online ( https://imjoy. io/docs ). ImJoy provides self-contained plugin development support with a built-in code editor for developing and testing plugins. It permits the use of plugins written in different programming languages (JavaScript, HTML, and Python) within the same analysis workflow. ImJoy's API enables bidirectional data exchange and allows plugins to use functions from other plugins ( Supplementary Figure  2 ). Plugins are stored as separate files, can be hosted on GitHub, and distributed with a single link, e. g. by email or social media ( Fig  1b  ). This link directly opens ImJoy and, importantly, allows developers to control and automate complex installations, e. g. to set up platform-dependent deep learning libraries (see Methods for more details).
Thanks to sandboxing inherent to browser computing, ImJoy also offers higher levels of security and stability than non-browser based software. ImJoy supports several browser-based libraries that offer advanced data processing capabilities. For example, TensorFlow. js allows to run deep learning models on graphical processing units (GPUs), while WebAssembly allows to run Python code in the browser. Note that browser-based computing does not require an Internet connection. As an example, we implemented a plugin to classify images of potentially cancerous skin lesions  18  using deep convolutional networks ( Methods : Skin-Lesion-Analyzer plugin). Thanks to the interface versatility mentioned above, the plugin can run on a smartphone and directly analyze skin images taken with the camera ( Fig  1b  ).
Therefore, ImJoy can connect to a plugin engine ( Fig 1a, Supplementary Figure  1  ), which can be launched on different hardware, ranging from a local computer to private servers or public cloud services. The plugin engine has access to the entire Python ecosystem, and uses Conda to automatically solve plugin dependencies and set up virtual environments to avoid dependency conflicts. This relieves users from setting up complex software dependencies and provides reproducible analysis workflows, which is crucial for data science  21. Importantly, the unified plugin interface of ImJoy ensures that remote procedure calls to the plugin engine are transparent, i. e. the user will be exposed to the same interface independently of where computations actually occur. As an illustration, we implemented two ImJoy plugins for deep learning based image transformation methods: a plugin implements ANNA-PALM, a recent method to create dense super-resolution images from sparse localization data using neural networks  6  ( Methods : ANNA-PALM plugin), and another implements CARE, which uses deep learning for 3D image restoration  5   to fine-tune the algorithm -for example by adding regularization or perform early stopping to remedy overfitting.
Together with appropriate computational resources (typically GPUs), ImJoy thus enables users to train these powerful methods on their own data or on other public data sets, which was not easily possible before. Finally, we note that ImJoy is ideally suited to integrate with online databases and model repositories, providing a versatile front-end to efficiently leverage large numbers of data samples and methods. In addition to the HPA-UMAP plugin mentioned above, which already illustrated interactive exploration of a large image database, we also provide a plugin that implements DeepBind 10 , a deep learning method to predict the sequence-specific binding affinities of proteins ( Methods : DeepBindScan plugin, Fig  2b  ). This plugin is based on Kipoi. org -a collaborative initiative that provides ready-to-use trained models for genomics  22.
ImJoy enables flexible hybrid computation modes, where analysis tasks can be distributed to a combination of local and remote computational environments. An interesting application is the processing of confidential medical data: raw images could be pre-processed ImJoy -Ouyang et al. - May 29, 2019   and anonymised locally, for example to calculate feature vectors which could then be sent to a remote server for further analysis, e. g for comparison against a database. Although ImJoy was developed with a focus on deep learning, it can be used to implement and deploy a much broader range of computational methods. We foresee several extensions of ImJoy beyond its current implementation and plugins. Among these are the possibility to incorporate scripts already developed for other widely used data processing platforms, for example ImageJ/Fiji  22  , allowing ImJoy to benefit from their functionalities and, conversely, confer new capabilities to these tools, such as easy deployment in the cloud.
ImJoy is a browser application developed with modern web technologies (top). Its functionalities are implemented with plugins that can run either directly in the browser or on a dedicated plugin engine (bottom). Browser plugins allow interacting with the user, visualizing data with a large variety of web libraries, performing computational tasks in webworkers, which enable computational tasks to run in a dedicated thread, or using Python through WebAssembly. Native Python plugins are supported through plugin engines implemented in Python and have access to the entire Python ecosystem, allowing them to perform demanding computational tasks with powerful scientific computing libraries such as Numpy, Scipy, or Scikit-image and to leverage deep learning tools such as Tensorflow and Pytorch. Virtual environments and dependent packages are managed with Conda.
ImJoy web application The core of ImJoy is a set of static HTML/Javascript/CSS code which runs completely in the browser and can be served with any static web server. The official ImJoy website ( https://imjoy. io ) is hosted on GitHub and served through GitHub pages. When the user opens the ImJoy app with the browser, these static web pages will be loaded to the browser. No local data will be sent to the imJoy. io server. All data will remain local or exchanged only with the connected plugin engine. This so-called serverless design makes ImJoy suitable to work with sensitive data and offers maximum scalability. Lastly, this design guarantees that the user always obtains the latest ImJoy version upon relaunching the app. The Desktop App ( https://github. com/oeway/ImJoy-App ) allows to use ImJoy locally without Internet connection, which is achieved by running a web server locally.
The user can then access this locally served version of ImJoy (instead of https://imjoy. io ). While the ImJoy core can run offline entirely, some plugins may still require the Internet to fetch remote resources, e. g. javascript libraries or trained models for inference. To keep the ImJoy application and plugins up to date, users are encouraged to use the online version. Note that this does not imply that the data will be sent to the server, since the ImJoy. io static web server does not accept any user data, and plugins process data either in the browser itself or through the local plugin engine.
ImJoy plugin system Different plugin types are available to implement user interface, data display, or data processing in the browser or on the plugin engine. Developers can use a consistent API (application program interface) across different programming languages (JavaScript, Python), and `Asynchronous programming`to coordinate plugin processes that run in parallel. Plugins are individual html files, and can be directly developed in ImJoy with the built-in code editor. ImJoy provides isolated workspaces, where plugins for a given data processing task can be grouped. Dependencies can be defined for each plugin, and will be automatically resolved by ImJoy. A plugin can run in an isolated virtual environment to avoid conflicts with other plugins. Further, plugin tags provide configurable modes for their dependency installation and execution, e.
ANNA-PALM (Engine) Plugin installation link: https://imjoy. io/#/app?w=imjoy-examples&plugin=oeway/ImJoy-Plugins:ANNA-PALM This plugin implements the ANNA-PALM method  6  , which uses deep learning to reconstruct super-resolution views from sparse, rapidly acquired localization images and/or widefield images. The user can choose between training or prediction from a central launch pad. Training/prediction can be performed either on provided images of microtubules, or on user-supplied data in the csv format of ThunderSTORM. The launch pad also provides access to a detailed user manual, which is the recommended starting point for a new user. More information on ANNA-PALM can be found at https://annapalm. pasteur. fr/#/. Code is ported from https://github. com/imodpasteur/ANNA-PALM.
structures of interest in images, a key requirement for defining training targets in image segmentation ( Methods : Image Annotator plugin). Another example plugin uses uses high dimensional features previously extracted from deep learning models to analyze the localization of proteins based on data from the Human Protein Atlas 17 (HPA, https://proteinatlas. org , Methods : HPA-UMAP plugin, Fig 2a ). Using this plugin, researchers can visualize this entire data set as scatter plots organized by feature-similarity (using dimensionality reduction techniques) and interactively inspect the raw images associated to individual data points. Such interactive visualization capabilities will be instrumental to explore large image databases.
Acknowledgements This work was funded by  Institut Pasteur. W. O. was a scholar in the  Pasteur-Paris University (PPU ) International PhD program and was partly funded by a  Fondation de la Recherche Médicale  grant to C. Z. (DEQ 20150331762). W. O. is a postdoctoral researcher supported by the  Knut and Alice Wallenberg Foundation  ( 2016. 0204 )and  Swedish Research Council  ( 2017-05327 ). We also acknowledge  Investissement d'Avenir grant   ANR-16-CONV-0005  for 8 ImJoy -Ouyang et al. -May 29, 2019 funding a GPU farm used for testing ImJoy. We thank the  IT department of Institut Pasteur , in particular  Stéphane Fournier  and  Thomas Menard , for providing access to the kubernetes cluster and DGX-1 server for running and testing the ImJoy plugin engine and for technical support. We thank  Quang tru Huynh  for maintaining the GPU farm and for advice and assistance during the development of ImJoy.
We explored a technology-supported educational program focused on PA in a Pacific Island rural school environment. Our program (called iEngage) was designed to improve adolescents' PA knowledge and skills and to help them understand their activity tracker data, which provided objective feedback on their PA. Data mining enabled us to develop a comprehensive approach to analyzing PA trends throughout the program. First, clustering took into account all the components of the international recommendations (number of steps, time spent at each intensity, and days over 11,000 steps). Second, a sliding 7-day window captured the frequency and regularity of the PA behaviors.
Study Design and Participants This exploratory study was conducted in a rural school in New Caledonia on Lifou Island. A total of 24 adolescents aged 12 to 14 years participated in this pilot. Parents gave informed written consent before their child's participation in the study, which met all legal requirements and the criteria of the Declaration of Helsinki. The protocol was approved by the ethics committee of the University of New Caledonia and the consultative ethics committee of New Caledonia (CEC-NC03-2016). Our study design is illustrated in Figure  1. Before the study, we communicated extensively with the school principal, the teaching team, and the wider community (including families) to explain the framework of the iEngage educational program. Moreover, 3 weeks before the program, anthropometric and physical fitness data including aerobic capacity, speed, and agility were assessed during scheduled physical education classes as described below.
Once anthropometry and physical fitness testing were completed, participants were equipped with research-grade activity sensors (GENEActiv) for 5 consecutive school days before the iEngage program to measure baseline PA behavior. They then started the iEngage program, which lasted for 4 weeks, with 2 1-hour modules per week. Throughout the program, they continuously wore a commercial activity tracker (Misfit Shine 2, United States), which served as the educational support tool. The 2 activity trackers served very different purposes and collected PA data in different formats. To measure baseline PA, we needed a device meeting the research-grade criterion, and to support learning during the program, we needed a device that was commercially and easily available for future wide-scale deployment.