dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, efforts toward aggregating information on lipids is ongoing in ChEBI, LM or Swiss lipids. Other ontologies exist to describe lipids, such as LM. However, LM ontology misses some layer of identification/annotation: for instance, it takes into account only the completely described molecular species for complex lipids (such as PC(18:0/18:1)), which cannot always be experimentally accessed, whereas the more generic entities such as PC(36:1), which can be measured, are not described in the ontology. Choosing a specific ontology also has some impact when some metabolites are not available in the database. For instance, in MetaboHUB lipid library, manual curation did not allow to find ChEBI for 259 lipids. This list had been sent to ChEBI administrators. The choice of the database to use for identifier matching should be driven by the annotations present in the network and experimental dataset: in the case of Recon2.
2, more metabolites were annotated with ChEBI identifiers, than with LM identifiers for instance, which is expected as the LM database only covers the lipids species in contrary to ChEBI which is more generic. Therefore, one other advantage of using the ChEBI ontology is that the developed methodology can be alternatively used for other metabolomic data sets beside lipids. Inversely, our ontology-based matching being a generic approach, it can easily be adapted and applied to any other ontology of interest. Similarly, this approach can also be applied to any metabolic network containing a sufficient number of ChEBI annotated metabolites. A specific effort has been made in Recon2. 2 to increase the annotation of metabolites with ChEBI identifiers, but this is not the case for all networks: for instance, although  Recon3D (Brunk et al.
The mapped metabolites are colored depending on their lipid family: yellow for fatty acyls, green for glycerophospholipids, blue for sphingolipids, pink for glycerolipids and grey for sterol lipids. The size of the node corresponds to the number of lipids from the NASH dataset that are mapped onto each network metabolite (between 1 and 6). Subnetwork extraction and visualization were performed with MetExplore One of the possible future directions would be to combine both approaches. Results presented in this article show that using the ontology-based matching often result in mapping several lipids from the experimental dataset onto the same node in the network. This can lead to difficulties in the interpretation because, even if these molecules belong to the same family, the concentrations of these different molecular species can be different.
This points out that metabolic network models, or some parts of them, may not be detailed enough to allow for the interpretation of lipidomics dataset. When variations in specific molecular species are of biological relevance, one way to circumvent the issue would be to enhance the details of the corresponding metabolic pathways in the network to get a more comprehensive description and visualization of all the reactions involved. Some attempts have been made in that sense, but the resulting network can very rapidly increase in size and become very large  (Smallbone 2013). Beyond mapping of data in GSMN, matching identifiers is instrumental in integrating data produced by different analytical platforms in order to go further in metabolome coverage [see the approach implemented in MetaMaks  (Redestig et al.
Conclusion The ontology-based method proposed in this article to map lipids onto metabolic networks is a step forward in the integration of metabolic profiling data in the context of metabolic networks. By using ontology hierarchical classification, we demonstrate that we can improve and facilitate the link between GSMN and a chemical library or a lipidomics dataset. In addition, the approach provides ways of improvements in terms of network curation (which parts of the network need to be expended) and in terms of lipidomics data annotation. It is also important to note that the same ontology-based mapping can be applied to all metabolites since ChEBI aims at describing all small molecules. The method is for now only focused on ChEBI identifiers but it could be extended to other identifiers as long as they are associated with an ontology.
Funding This work was supported by the  French Ministry of Research and National Research Agency  as part of the  French MetaboHUB, the national metabolomics and fluxomics infrastructure  (Grant  ANR-INBS-0010 ) and by  PhenoMeNal  project,  European Commission 's  Horizon 2020 program  (Grant Agreement No. 654241 ). AM was funded by  INRA Human Nutrition division  (project  Halomics coordinated by Nicolas Cabaton ). MC is funded by  MedDay Pharmaceuticals.
However, a practical integrated approach is required. In 2014, on behalf of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA),  6  AIRWAYS-ICPs (integrated care pathways for airway diseases) was initiated. 7, 8 The objective was to launch a collaboration to develop multisectoral care pathways (ICPs) for chronic respiratory diseases in European countries and beyond as a Global Alliance against Chronic Respiratory Diseases (GARD) demonstration project (Figure  1 ). MASK (Mobile Airways Sentinel NetworK) is the mHealth strategy of AIRWAYS-ICPs and ARIA. 9 It was based on the ARIA study group which exists in 92 countries. MASK is deployed in 26 countries and 18 languages. MASK, as a practical ICT integrated approach, was initially developed as an app (MASK-air ® ) and is now an e-platform for allergic diseases and asthma.
| Characteristics MASK-air ® is an ICT (Information and Communication Technology) system centred around the patient. 17 It is operational in 26 countries and 18 languages. It uses a treatment scroll list which includes all of the medications customized for each country. Furthermore, a visual analogue scale (VAS) assesses rhinitis control (global allergy impact, nose, eyes, asthma), sleep and work productivity. 26, 27 SK-air ® is combined with prediction on allergen season and air quality (POLLAR: Impact of POLLution on Asthma and Rhinitis, EIT Health-funded project). 23 MASK is available in 26 countries and 18 languages including some middle-income countries (Table  2 ). Patients' organizations and scientific societies are involved.
| Privacy, General Data Protection Regulation (GDPR) and Medical Device Regulation (MDR) The General Data Protection Regulation (GDPR) regulates the processing of personal data in the European Union (EU). 28, 29 MASK-air ® follows the five main principles of personal data protection to be respected during the development of the app: purpose, proportionality and relevance, limited retention period, security and confidentiality, as well as the rights of the users regarding management of personal data (including withdrawal and modification). 30 Moreover, MASK-air ® uses k-anonymity for geolocation. 31 A double encryption database has been set up. MASK-air ® is currently a Class 1 Medical Device but will be upgraded to Class 2A with the new MDR to be enforced in the EU in May 2021. 32.
| Implementation and communication strategy for patients Without a communication strategy, the app will not be largely used. However, the communication plan will only be put in place in 2020 once the POLLAR module has been added. Documents are available in 18 languages and can be downloaded from the MASK website (https://www. mask-air. com). They include leaflets for patients, physicians and pharmacists as well as other documents. In Mexico, this strategy was found to be effective. It will be deployed to other countries.
TA B L E 3 Maturity level of MASK The communication strategy must involve local patients' organizations. It will be deployed with the patients' organization EFA. The importance of patients' associations has always been recognized in ARIA. For the digital transformation of health and care, they are even more important. The following messages sent by the app need to be reinforced for the patients: 1. Better understanding the symptoms. 2. Sentinel network linking aerobiology data and control. 3. Improved adherence. 4. Self-management. 5. Patient empowerment.
| CON CLUS I ON: TOWARDS A RE VOLUTION IN RHINITIS AND A S THMA MANAG EMENT The MASK strategy represents a proof of concept for other chronic diseases, as asthma-rhinitis multimorbidity plays a key role in understanding asthma and can be used as a general model of multimorbidity. Moreover, asthma and rhinitis have a life-course approach, whereas most chronic diseases start early in life but are only clinically evident in adulthood. The lessons learnt by the MASK strategy are therefore transposable to other chronic diseases. Anamorphosis is a metaphor for reimagining and expanding on appearances and overcoming otherness. MASK digital anamorphosis makes it possible to look at data from a different angle. The data then appear to be different to their familiar, expected and/or generally accepted form.
will include (a) a standardized assessment of severity and control, (b) the development of an upgraded e-platform for severe asthma including screening, assessment by physicians and follow-up, (c) the analysis of MASK-air ® data on file for asthma, (d) a pan-European IT-based alert system for exacerbations, (e) MASK-asthma IT tools for registries and databases, (f) transfer of innovation, (g) a digital network of centres of excellence (ARIACARE-Digital) and (h) the development of next-generation care pathways for severe asthma.
Novartis and Roche, and grants from GlaxoSmithKline, all outside the submitted work. VC reports personal fees from ALK, Allergopharma, Allergy Therapeutics, Diater, LETI, Thermo Fisher and Stallergenes. JCS reports other from Boehringer Ingelheim, GSK, personal fees, nonfinancial support and other from AstraZeneca, personal fees and other from Mundipharma. AC reports personal fees from Novartis, Regeneron, Thermo Fisher Scientific, Philips and Sanofi. ME reports personal fees from DBV Technologies and Mylan. JF reports being a partner in a company developing mobile technologies for monitoring airways diseases. EH reports personal fees from AstraZeneca, Novartis, GSK, Sanofi Genzyme, Teva, Circassia and Nestlè Purina. GI is consultant for Amicus Therapeutics and received a research grant from Amicus therapeutics.
To study the macroscopic behavior of the melting fuel material, we develop a model based on recent homogenization techniques that describes its thermal-mechanical behavior. This article focuses on the validation of the modeling principle before its implementation in ALCYONE, a multi-dimensional fuel performance code co-developed in the PLEIADES platform by CEA, EDF and FRAMATOME  [1]. ALCYONE is a complex multi-physics numerical tool, used to simulate the fuel element behavior during normal and off-normal operation conditions. This requires the coupling of the neutronic, thermohydraulic, thermo-mechanical and physicochemical models. A simplified flowchart of ALCYONE is presented in A. To validate the proposed melting model, we implement it in the finite element code Cast3m  [2] , verify its convergency and test its calculated outputs in a simple case.
All the aspects related to material knowledge are treated by means of the code generator MFRONT, which is developed within the PLEIADES platform  [3, 4]. Its future implementation in ALCYONE is meant to evaluate the effect on clad deformation induced by the progressive melting of the fuel and to guide the development of a measurement method adapted to study the melting scenario in a dedicated instrumented device in the JHR material testing reactor  [5]. In this paper, we first present in section 2 a review of the thermal-mechanical behavior of solid fuel material. For this specific work, we use the model proposed by Monerie and Gatt in Ref. [6]. Section 3 is dedicated to the thermal-physical aspects of the melting process. In section 4 we proceed to the description of the analytic model proposed, here stating the methodology and hypothesis used and detailing the effects on the mechanical behavior.
The first two (PDMS-PDMS chips, and Dry Film chips techniques  [23] ) are described only in supplementary data, as they were finally discarded because of their respective limitations (difficulty of reproducible alignment, and adhesion issues). Therefore, we will only concentrate on the details of silicon-glass process that we developed to meet the requirements, and that was finally used to conduct our experiments Masks were designed and drawn using Clewin software. The technique used was etched Silicon channels, bonded on etched glass channels. This technique answered all the disadvantages we faced with PDMS and dry film. First, regarding alignment, glass and silicon are hardly deformable, which means their deformation was very low during the process. We used a glass type with the same thermal expansion coefficient as silicon, which prevented any misalignment upon heating or cooling.
Acknowledgements This work was supported in part by  CNRS  through the  PICS CNRS 2016-2019 program  " Microfluidics for Soft Matter ", and by  INSIS-CNRS. This work was partly supported by  LAAS-CNRS  micro and nanotechnologies platform member of the  French RENATECH network. We thank  Fabien Mesnilgrente ,  Laurent Mazenq , and  Samuel Charlot  for help in the microfabrication process,  Rémi Courson  for the 3D chip-holder fabrication ( Multifab  project),  Sandrine Souleille  for help in microfluidic instrumentation,  Julien Roul  for help in confocal imaging, and  Elouan Ronjon  for the leakage experiments.
ligo. caltech. edu/ stein's theory of gravity in which gravity is described as due to the curvature of the fabric of time and space. The data collected by the different LIGO observatories is about Tera-octets per hour. To analyze this huge amount of data, scientists modeled their workloads using scientific workflows. The LIGO workflow may contain up to 100 sub-workflows and 200,000 jobs  (Juve et al. , 2013)  which requires many hours to be performed in a Grid computing environment. To obtain simulation results within acceptable timeframes, large scientific workloads are executed on distributed computing infrastructures such as grids and clouds  (Brewer, 2000). The Cloud Computing environment offers on-demand access to a pool of computing, storage and network resources through Internet that allows hosting and executing complex applications such as scientific workflows.
Big Data scientific workflows modeling Scientific workflows are usually modeled using Directed Acyclic Graphs (DAG). Since our objective is to handle big data workflows, we model a workflow as a DAG graph where tasks are represented by nodes and dependencies between them are represented by links. Dependencies typically represent the dataflow in the workflow, where the output data produced by one task is used as input of one or multiple other tasks. A single workflow task T i can be modeled using the triplet T i = (D, Dt in , Dt out ) with: • Dt in = {Dt in j | j ∈ [0. (i -1)] } represents the amounts of data to be processed by T i with Dt in j is the dataset from the task j. • D = {D k |k = 1. r} is the vector of demands of the T i in terms of resource capacities with D k is the minimum capacity required of the resource type k to process the task and r the total number of resource types.
, 2002). The raw data collected during experiments is a collection of continuous time series at various sample rates. The amount of data that will be acquired and cataloged each year is on the order of tens to hundreds of terabytes  (Deelman et al. , 2002). These data are treated in real time by the LIGO workflow, so, several instances of this workflow should run simultaneously to achieve the objectives of the project. Consequently, the execution environment should provide efficient tools and enough resources for the execution and the data storage of the LIGO workflow. Therefore we have chosen this workflow as an application case for our proposed algorithm BiO-CSO for its excessive use of computing and storage resources.
(  2012 ), which was later used to study the specific case of a single lineage rate shift due to the introduction of a key innovation  (Etienne and Haegeman, 2012). The correction comes down to a division by the number of lineages at the shift time (see Appendix E for details), and has been implemented in version 4. 3 of the DDD package  (Etienne and Haegeman, 2020). Extending the corrected likelihood L corr to multiple shifts The extension of the corrected likelihood (  14 ) to the case with multiple shifts is relatively straightforward. Although the formulas become increasingly cumbersome, the correction is based on the same idea as in the single-shift case. To account correctly for the choice of the species undergoing the rate shift at the rate-shift time, we have to divide by the number of species in which the rate shift can occur.
Discussion We have shown that several methods developed for detecting shifts in diversification rates at particular points in phylogenies are based on an incorrect likelihood that could even yield probabilities larger than 1. Our new likelihood formulaswhich even apply when diversification rates are time-dependent or diversity-dependentcan be used to correct these methods. This was already been done for the KI model in version 4. 1 of the DDD package  (Etienne and Haegeman, 2020) , which involved only a few lines of code (division by the number of species). For a single shift, DDD allows conditioning in three different ways, corresponding to the diversity-dependent extensions of Eqs. (  15 )-(  17 ). For multiple shifts, only the last conditioning is feasible, under the additional assumption that shifts can only occur in the main clade.
We noticed that the results for two different conditionings were similar for a single shift, so we are confident that this restriction is not very severe. For multiple shifts the DDD package only contains the likelihood calculation, not a function to do likelihood optimization. This could be done by integrating this likelihood calculation with BAMM, MEDUSA, or related multi-shift methods. Our framework has identified four ways to ask questions on rate shifts at a given shift time. One can ask whether a rate shift occurred in a particular observed lineage in the phylogeny at the shift time, whether it occurred in any observed lineage present at the shift time, whether it occurred in any unobserved lineage present at the shift time, or whether it occurred at all at the given shift time (either in an observed or unobserved lineage).
Simulation studies have generally found that BAMM appears conservative with respect to the inference of rate heterogeneity  (Maliet et al. , 2019) , and rate estimates have been shown to be reasonable across a broad range of parameter space  (Title and Rabosky, 2019). However, the fact that the likelihood expression is incorrect implies that BAMM and other approaches may behave unexpectedly in some areas of parameter space or on some datasets. We expect the errors to be largest when there are many species at the time of the shift which do not survive to the present, but the specific conditions under which this situation arises may be hard to identify. Researchers who use these methods should be vigilant in assessing whether results are sensible and should strive to cross-check inferences with alternative methods wherever possible.
(  15 ); • the likelihood for multiple rate shifts, e. g. , eqs. (C10) and (C11) for the case of two rate shifts in the main clade. In fact, while the explicit formulas are cumbersome for multiple rate shifts, the framework of  Etienne et al. (2012)  with the vector Q can easily incorporate an arbitrary number of rate shifts and can be used to numerically evaluate the multiple-shifts likelihood. This has been implemented in version 4. 3 in the R package DDD  (Etienne and Haegeman, 2020).
At the CTPJ-TB, an independent psychiatrist researcher (AAD) asked the patients if they would like to participate in the study and explained the aims of the study. If the patient agreed to participate, an informed consent form was handed out and signed by both the participant and the psychiatrist investigator (EM) who was blinded to the smartphone data. No rewards or incentives were offered to the patients for participating in the study, and all interviews and follow-up assessments were carried out by the same investigator. The Research Ethics Committee (Comité de Protection des Personnes, Ile-de-France VII) approved the study protocol ID-RCB: 2017-A02450-53, which is also registered at www. clinicaltrials. gov (Identifier: NCT03508427).
Clinical Assessments Data on sociodemographic and clinical assessments were performed at baseline and after 2 weeks, 1 month, 2 months, and 3 months. These are the time points at which clinical evaluation is routinely performed at the CTPJ-TB. Hence, the study assessments did not add any extra burden on the participants or extra contact time with the staff. The severity of the depressive and manic symptoms was evaluated using the Montgomery-Åsberg Depression Rating Scale (MADRS)  [43] , a 10-item questionnaire, ranging from 0 to 60, with higher scores indicating more severe depression, and the Young Mania Rating Scale (YMRS)  [44] , a 11-item questionnaire, ranging from 0 to 60, with higher scores indicating severity of (hypo)manic symptoms. Overall functioning was assessed using the Functioning Assessment Short Test (FAST), which encompasses 24 items to evaluate 6 functional domains: autonomy, occupational functioning, financial issues, interpersonal relationships, leisure time, and cognitive functioning.
Levels of activation were measured using the Multidimensional Assessment of Thymic States, a 20-item self-report instrument that assesses the levels of activation uncoupled from mood during the preceding week  [48]. It quantitatively evaluates 5 dimensions, namely, emotional reactivity, sensory perception, psychomotor activity, motivation, and cognition, each of which can vary from hypoactivation to hyperactivation  [7]. At the end of each evaluation, the investigators asked patients to give feedback regarding the overall satisfaction, ease of use, and perceived helpfulness of the Toi Même app (ranging from 1=not at all to 5=extremely). The clinical data were collected using the Research Electronic Data Capture (REDCap) software  [49] , which is a widely used data collection platform. REDCap follows the international rules for validation, qualification, and security gold standards, which are also aligned with the Pasteur Institute's privacy/security rules for data collection.
Moreover, this app is special because Toi Même is the first app developed in France without commercial purposes, and it has been developed specifically for monitoring bipolar illness activity. The principles, content, and the specifications of the Toi Même app were conceived by AAD. After a year of collaborative work among psychiatrists, neuroscientists, software engineers, and designers, the Toi Même app 1. 0 version used in this study was available in French and was free of charge for the study participants. The information technology department of Pasteur Institute provided all the technical support for coding the app and the platform's back office. The Toi Même app functionalities are intended to be minimally invasive to the users' daily routine and normal smartphone usage. After the patients were included in the study, patients received an email with a link to install the Toi Même app in their own smartphones.