dataesr/scientific-paragraphs-categorization huggingface dataset:
We have to define the exposure times, that is when each patient has been treated according to the randomization or not. First, among the only patients who have been treated at randomization, those who switched are to be censored at the time of switch. Note that from this point, we work with duration times that are obtained subtracting the starting date of the follow-up from the date we are interested in. This is illustrated in the toy example, where patients are censored when initiating the other arm treatment, that is, at time swtrtdt; a treatment censoring indicator cens is thus added to the previous dataset to indicate such a switch. We thus developed the cens. ipw function that should be used as follows: R> toy. long2 <-cens. ipw(toy. long, id = "id", tstart = "tstart", tstop = "tstop", + event = "event", arm = "arm", + realtrt = FALSE, censTime ="swtrtdt") resulting in the following formatted data: Then, only times related to either the treatment switches or the occurrences of death were retained in the dataset.
Nevertheless, weights need to be computed at all the observed event or treatment censoring times in the sample  [20]. Thus, each patient's follow-up must be split according to all these dates. We finally obtained the terminal dataframe using the replicRows function: Step 3: Computation of stabilized weights Once data have been adequately formatted, weights are to be estimated in each arm as explained in Section 2. To this end, the survfit function from the survival package can be fitted to the result from the coxph function for treatment censoring adjusted on baseline and time-dependent confounders. This is the standard approach when events and censoring occur simultaneously given we considered that the event comes first. More precisely, in case of ties occurring at a given time t, the treatment censoring at t does not contribute to the likelihood at the current event time but contributes to the likelihood at the following event time.
To this end, it is possible to use the ipwplot function from the ipw package  [20]. The plots of un-truncated and truncated weights are obtained by: Please, note that the arguments of the ipcw function are explained in Appendix, Section Appendix A, whereas those of the ipwplot function are given in the accompanying paper of the ipw package  [20]. Since the ipcw function allows the calculation of un-truncated weights, the user has the possibility to compare the weights before and after truncation and to better fit the model by including different potential confounding factors. Also, giving less importance to some patients by truncating the weights can lead to biased estimates. Thus, a choice has to be made between the size of the bias and the strength of the instability of the weights. In addition, let us mention that the ipwtm function in the ipw package  [20]  can be used to assess the IPCW from adequately formatted data.
Illustration of the IPCW implementation on the case study We now illustrate the IPCW implementation on the case study described above, following the algorithm introduced in Section 3. Note that we used anonymized data for reproducibility purpose, since both code and data are available in the ipcwswitch package. For each observation, the follow-up started on the date of the randomization visit, that is before patients were administered the study treatment. Applying the IPCW method, patients who never initiated their assigned treatment had to be excluded. Therefore, while there were 197 patients in the ITT analysis, only 193 patients were analysed in the IPCW method described below. Baseline time-fixed covariates were considered, namely age at randomization, gender, number of previous lines of treatment, the dichotomized Royal Marsden Hospital score (0 or 1 vs.
2 or 3) and the altered molecular pathway (distinguishing 3 pathways, namely hormone receptors pathway, PI3K/ AKT/mTOR pathway, and RAF/MEK pathway). Time-varying confounders, measured at each visit scheduled every two months up to study completion, were the Eastern Cooperative Oncology Group (ECOG) performance status, the presence of concomitant treatments and the need of platelet transfusions. All confounders were selected with the physicians. To deal with missing values, the last observation carried forward method was used to impute values after available measurements. First, a long format dataset was obtained. As reported in Figure  1 , 68 patients from the CT arm and 25 patients from the MTA arm, who initiated the other-arm treatment, defined treatment censoring observations. Then, the weights were computed, taking into account the potential confounders.
Acknowledgements We thank the patients who volunteered to participate in this study for their dedication and the study-site staff who cared for them. This work is supported by grant  ANR-10-EQPX-03  from the  Agence Nationale de la Recherche  (Investissements d'avenir) and  Site de Recherche Intégré contre le Cancer (SiRIC). High-throughput sequencing was done by the  NGS platform of the Institut Curie , supported by grants  ANR-10-EQPX-03  and  ANR-10-INBS-09-08  from the  Agence Nationale de la Recherche  (Investissements d'avenir) and the  Canceropôle Ile-de-France.
In the digital age, a genetics cohort has become much more than a simple means of determining the cause of a disease. Two-sided markets, of which 23andMe, Ancestry DNA and MyHeritage are the best known, have showed this perfectly over the last few years: a cohort has become a means of producing massive amounts of data for medical, scientific and commercial exploitation, and for genetic use in particular. French law does not currently allow these foreign private companies to develop on French national territory and also forbids the creation of similar entities in France. However, at least in theory, this same law does not preclude the creation of new types of cohorts in France inspired by the success of two-sided markets but retaining features specific to the French healthcare management system.
Following signature of the electronic consent form, the investigating doctor or qualified staff can take the first biological sample (in this case a blood sample) for high-quality DNA extraction (Figure  1A ). The diverse samples are stored in a biobank specific to the protocol. It is therefore essential to add to such a protocol the possibility and explanation of additional visits for the participant, with his or her agreement, for the collection of other samples (saliva, skin biopsy, stools, sweat, tears, etc. ), depending on the scientific questions addressed and according to the expected or unexpected nature of the results of future research collaborations. e-CE would make it possible to create an online computer interface (website) "connecting" participants in research studies, doctors, investigating researchers and study coordinators for the storage and use of data (responses to questions, test results, database) and biological samples (Figure  2 ).
Samples (blood) Participant Doctor e-CE @ @ @ F I G U R E 2 Modeling of interactions between participants, doctors and researchers (1). At the end of the consultation, following the signing of dynamic electronic consent and the issuing of an identifier for logging on to the interface, a biological sample is collected from the participant. This sample is rendered anonymous and sent to the coordinating researcher for storage in the biobank. The responses to questionnaires, also It should be noted that these health-related data are not medical diagnoses, but could constitute information potentially useful to doctors who could use them to decide whether to perform appropriate genetic tests or to obtain validation of the results by Sanger sequencing at an accredited center, resulting in a recognized medical diagnosis, with a proposed care/prevention pathway.
Information (3) (ancestry results) F I G U R E 3 Modeling of interactions between participants, doctors and researchers (2). The genetic data obtained from a genomics platform are analyzed and combined with the responses to the questionnaires present in the database via a computer platform, which returns the data directly to the participant without passing by the doctor. The data delivered are the overall results of the research (information (1)). Some information relating to the health of the participant (information (2)) may also be returned to the participant, if the participant so wishes. However, this information is subject to prior expert evaluation and validation by an accredited geneticist and a multidisciplinary committee before its transmission to the doctor and then to the participant during a consultation.
Other information relating to the ancestry of the participant may be delivered directly to the participant online (information (  3 )) [Colour figure can be viewed at wileyonlinelibrary. com] Informatics Genomics @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ Database Biobank F I G U R E 4 Modeling of the interactions between participants, doctors and researchers. Densification of the participantdoctor-researcher "network. " As the cohort is both connected and dynamic, the objective is to increase, progressively, the numbers of participants and doctors over time and space (avoiding limitation to a single hospital or town, for example) [Colour figure can be viewed at wileyonlinelibrary. com] Informatics Genomics @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ Biobank @ € Database F I G U R E 5 Modeling of interactions between participants, doctors and researchers, plus external researchers.
| Perspectives Once the cohort (biobank, database) and its network of participants, doctors, investigators, researchers and coordinators have been constituted via the RIPH (anything from 1 to an infinite number of participants [Figure  4 ]), another researcher from a different academic laboratory with his or her own research funding can collaborate with the e-CE and pose "his or her" own question to "our" cohort (Figure  5 ). Indeed, this would represent both a saving of time and of funding for the researcher concerned. The researcher would thus have at his or her disposition a ready-built infrastructure that is already functioning, and would therefore need to find funding only for the generation of the genetic data of interest at cost price (in the case of collaboration with a genomics platform), provided that the participants respond to the new question online and thus give express consent via the interface.
A private company (pharmaceutical or biotech company) can, therefore, pose "its" question to "our" cohort, in the same manner as an academic laboratory (again, subject to consent and to the maintenance of participant confidentiality, thanks to the traceability of questions) (Figure  6 ). The participant sees simply that the question comes Informatics Genomics @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ € Biobank @ € € Database F I G U R E 6 Modeling of interactions between participants, doctors and researchers, plus external researchers and companies. A private company can also pose "its" questions to "our" cohort, in the same way as an academic laboratory would do. On each occasion, the participants are informed of the source of the question [Colour figure can be viewed at wileyonlinelibrary.
For phase I, II or III clinical trials, which do not generally include genetic data (which would make the set-up of the protocol much more cumbersome and slow), e-CE could be used for parallel recruitment. Furthermore, e-CE could also be used for the pre-recruitment of future participants at the time of creation of the protocol, making it possible to obtain more accurate evaluations of the real number of participants meeting the inclusion criteria. In any case, these individuals would also be invited to share their genetic data, to maximize sequencing efficiency. The financial surplus generated by this exchange would be used as explained in the previous figure. Institutes, hospitals or research centers acting as study sponsors with this particular goal within the e-CE model would derive considerable benefit from this system over collaborating with the different two-sided markets, which are strictly illegal in France, under the legitimate and real argument of needing to access cohorts of several thousand connected individuals.
Thus, even in the case of a single-topic e-CE, it remains of interest for both the participants and the sponsor to engage in such dynamic research. It is possible to collaborate just for one or several questionnaires, to increase independently the sizes of the different cohorts and/or even to fuse them, subject to the consent of the participants, to constitute a participatory Informatics Genomics @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ € Biobank @ € € Database F I G U R E 7 Modeling of interactions between participants, doctors and researchers plus external researchers and companies. In the case of collaboration in an interventional study with a private or public sector partner, or of a collaboration between public and private sector partners, the participants of the cohort with profiles corresponding to that sought are contacted via the computer interface.
All this is important because, in the world of data, nothing @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ P1 P2 Px @ @ € € @ € Informatics Genomics Biobank Database F I G U R E 8 Modeling of interactions between participants, doctors and researchers, plus external researchers, companies and various sponsors. If other sponsors develop studies based on our e-CE model (genetic data associated with dynamic questionnaires), they can organize themselves to collaborate for one or several questionnaires or to pose their questions to the other cohorts. The initial consent is unmodified and the participants can see the source of this question, like all the others.
In any case, even at this theoretical stage, it is clear that e-CE offers a relatively simple and obvious methodology, with various indirect arguments of different natures in its favor. The MFG-2025 should make use of a single cohort, such as e-CE, rather than multiple small protocols with very restricted goals. However, only a real implementation of this system would make it possible to test its real efficacy, as proposed by the CCNE (expert opinion 129 ****** ), with the testing of genetic information collection from the general population, in particular. e-CE would constitute a real French-style "Genetics version 2. 0," essential for France to remain competitive, both scientifically and economically, in terms of genetic research, in the face of these new foreign two-sided markets, which, every day, extend their influence a little further, thereby creating a continuous drain of our genetic data abroad, to the detriment of France.
rendered anonymous, are stored directly in the database [Colour figure can be viewed at wileyonlinelibrary. com] U R E 1 Modeling of interactions between the participants and doctors. A, Each participant is informed, by a doctor, of the objectives of the study and the means of achieving its ends. The participant provides electronic dynamic consent and answers a series of questions in the presence of the doctor. B, The participant receives an identifier for connection to the computer interface of the study at home or elsewhere, to enable him/her to answer other questions online and to receive more detailed information [Colour figure can be viewed at wileyonlinelibrary. com] The participant can, of course, at any time, accept or refuse to receive such information (in the initial consent and subsequently online if the participant changes his/her mind).
Discussion and modeling This discussion will be divided into two parts. In the first part, the combined findings of Hillert and Pandit  [26, 47]  are rationalized in order to predict thermodynamically the composition of the pearlite's constituents and thus explain the measured compositions inside cementite in the as-transformed state (Sample 1). To our knowledge, we are the first to bring experimental evidence of the validity of such a thermodynamical prediction for hypoeutectoid steels in anisothermal condition. In the second part, the composition evolution in cementite during coiling and annealing will be modeled with DICTRA software, in order to explain composition gradients in as-received and annealed states (Samples 2 and 3, respectively). This work has required reassessing Mn diffusion coefficient in the commercial MOBFE2 database.
[43]. It thus means that the substitutional solute composition of untransformed austenite remains constant during ferritic pro-eutectoid transformation. Only its carbon concentration increases progressively. It is generally admitted that the pearlitic transformation then occurs when the carbon concentration is high enough, i. e. when the local composition in untransformed austenite reaches the extrapolated Acm boundary below the eutectoid point. This criterion is called Hultgren criterion. In house software TACSI developed by ArcelorMittal has been used to calculate this transformation condition in industrial conditions. TACSI software is a simulation tool for intelligent manufacturing able to simulate the phase transformations occurring during hot-rolling of conventional high strength steels, considering complex thermomechanical schedules.
The predicted microstructures (phases, size, precipitation state…) then serve to predict the mechanical properties of the steels using micromechanical approaches. When linked to sensors on industrial plants, TACSI is used to drive the industrial production and is able to predict the local mechanical properties on coils. The model is thus calibrated on large industrial and laboratory databases. The module used to calculate the ferrite/pearlite transformations from austenite (deformed or not) has been extensively described in previous papers  [44, 45]. In this model, the Classical Nucleation Theory (CNT) developed by Aaronson et al. [51] [52] [53] [54]  is applied. Ferrite growth is modeled by a mixed-mode approach. Pearlite transformation is triggered according to a modified Hultgren criterion calibrated on a large database.
For studied steels and cooling conditions, it has been established that pearlite transformation starts at 570°C. For studied steels and cooling conditions, it has been established with the TACSI software that pearlite transformation starts at 570°C. This value has been confirmed by analyzing the interlamellar spacing in the as-received state (cf. Figure  1a ). The mean interlamellar spacing, measured in cold-rolled pearlite by the planar intercept method is 48 nm. The stereological correction proposed by Ridley  [49]  for pearlite was applied. The true interlamellar spacing before cold-rolling is thus calculated using equation (  2 )  [57] : =. exp ( -) (2) where is the interlamellar spacing after cold-rolling. ε is the plastic equivalent deformation by rolling and can be calculated with Equation (3)  [58] : = √.
s -1 of a 0. 37C-2. 53Mn wt. % steel (Mn content close to the studied steel). They measured Mn concentration in cementite by atom probe tomography close to 11 %wt (U Mn = 12%) in very good agreement with our own observations. As in the studied case, Cr does not reach equilibrium after heating. Mn diffusion lengths in cementite for both cooling from 570°C down to room temperature at 20°C h -1 and heating up to 700°C at 3°C. s -1 were estimated using the sole diffusion coefficient of Mn in cementite found in literature. D Mn θ has been extracted the MOBFE2 database of DICTRA (with TCFE9). For the sake of simplicity, isothermal holdings at 570°C and 700°C, during t = 24 h and t= 200 s were considered to estimate an upper value of √Dt. In both cases, diffusion lengths did not exceed 2 nm. This is far lower than our observations (about 15-30 nm in the first case and larger than cementite half width in the second case).
Since D Mn θ provided by DICTRA software is obviously too weak, it must be reassessed, as did e. g. by  Song [31]. This author proposes to multiply D Mn θ by 5 in order to better calibrate its own experiments. More recently, Luo  [36]  needed to multiply it by 72 to make its DICTRA simulations consistent with their own experimental data. He noticed that this coefficient is still too low. In the present study, we propose to reassess this diffusion coefficient D Mn θ on the basis of our own experimental results. As done by Luo, simulations of Mn diffusion in pearlite will be conducted using the DICTRA software.
DICTRA Simulations A model ternary Fe-C-Mn system is considered for the phase transformation calculations with DICTRA software (2017a version), using TCFE9 and MOBFE2 databases. Our equilibrium calculations have shown that other alloying element (Si and Cr) do not affect significantly the chemical compositions of the phases and transformation temperatures. For this reason, the following DICTRA simulations do not deal with the latter species. A planar configuration representing a lamellar pearlite structure (half ferrite lamella in contact with a half cementite lamella) has been considered for the sake of simplicity, even if our observations are not all obtained in such simple geometrical configurations. Half-lamellar spacing and lamella thickness have been set respectively to 340 and 20 nm in accordance with our observations.
To obtain these values, the composition of the austenite grains have been considered (X c = 1. 78% at , U MN = 1. 94%). In the case of ferrite, both parameters vary significantly with the temperature. An Arrhenius plot was drawn to deduce the values shown in Table  2. Pre-exponential factor Q (kJ. mol - Mn diffusion coefficient in cementite has been tuned to best reproduce our experimental observations. In this procedure, only pre-exponential term has been modified so far (not the activation energy) and multiplied by 2000. This value is consistent with the respective remarks of Song and Luo  [31, 36]. Mn diffusion coefficient in ferrite has been taken from the database and has not been modified, since it is considered well validated.
Numerical ocean models, like other geoscientific models, are a strongly simplified representation of real oceans. They are used as tools to answer research questions about the real-world systems. Therefore, their thorough validation is essential to ensure that the conclusions drawn from the model experiment are valid in reality. We demonstrate a software which allows an interactive model validation through a web interface based on the R Shiny framework. At pre-defined stations, different kinds of plots can be rendered within a few seconds, according to the user's choice, allowing a live validation of different model parameters even in model simulations which are still running. This makes it different from validation approaches which generate a pre-defined set of plots after the calculations have finished and make it particularly useful for model tuning purposes.
Observation data can be read in from text files or can be extracted from a database. Once set up, the validation tool requires no technical skills to use. It can be used for single-or multi-model validation and allows saving the generated plots as high-resolution images suitable for use in scientific publications. A Linux operating system is required for the Validator app, but via a virtual machine, the software can run on Windows or MacOS hosts as well. A Dockerfile is supplied which allows to test the software with example data without installation.