dataesr/scientific-paragraphs-categorization huggingface dataset:
Whether this enrichment is unrelated to codon age and simply results from amino acid content constraints, whether CDSs favor the usage of old codons for ignored reasons, or whether this observation results from a combination of both remains unclear. IGORFs encode peptides that display a wide diversity of fold potentials, including a substantial amount of foldable peptides We next used the HCA score in order to assess the fold potential of the peptides encoded by IGORFs. As a reference, we calculated the HCA scores for three sequence data sets consisting of 731 disordered regions, 559 globular proteins, and 1269 TM regions extracted from TM proteins, thereby expected to form aggregates in solution while being able to fold in lipidic environments (Methods) (Fig. 2A ; Supplemental Fig. S6 ). Based on their HCA scores, we defined three categories of fold potentials (i.
CDSs and IGORFs The CDSs were extracted from the genome of S. cerevisiae S288C according to the genome annotation of the Saccharomyces Genome Database  (Cherry et al. 2012). All unannotated ORFs of at least 60 nt, no matter if they start with an AUG codon, were extracted from the 16 yeast chromosomes. We only retained ORFs that are free from overlap with another gene or that partially overlap with a gene if the nonoverlapping region is >70% of the IGORFs sequence.
Data sets of reference The disorder data set consists of 731 disordered regions extracted from intrinsically disordered proteins of the DisProt database  (Hatos et al. 2020) , that were used for the calibration of HCAtk  (Bitard-Feildel and Callebaut 2018). The globular data set consists of 559 globular proteins extracted from the Protein Data Bank  (Berman et al. 2000; Burley et al. 2021 ) that were used for the calibration of IUPred  (Dosztányi et al. 2005; Mészáros et al. 2009; Dosztányi 2018; Mészáros et al. 2018). The TM regions data set gathers 1269 TM regions extracted from the transmembrane proteins contained in the Protein Data Bank of Transmembrane Proteins (PDBTM)  (Tusnády et al. 2004 (Tusnády et al. , 2005;; Kozma et al. 2012). We only retained TM segments longer than 20 amino acids corresponding to the minimum size of an IGORF.
WebMaBoSS is an easy-to-use web interface for conversion, storage, simulation and analysis of Boolean models that allows to get insight from these models without any specific knowledge of modeling or coding. It relies on an existing software, MaBoSS, which simulates Boolean models using a stochastic approach: it applies continuous time Markov processes over the Boolean network. It was initially built to fill the gap between Boolean and continuous formalisms, i. e. , providing semi-quantitative results using a simple representation with a minimum number of parameters to fit. The goal of WebMaBoSS is to simplify the use and the analysis of Boolean models coping with two main issues: 1) the simulation of Boolean models of intracellular processes with MaBoSS, or any modeling tool, may appear as non-intuitive for non-experts; 2) the simulation of already-published models available in current model databases (e.
g. , Cell Collective, BioModels) may require some extra steps to ensure compatibility with modeling tools such as MaBoSS. With WebMaBoSS, new models can be created or imported directly from existing databases. They can then be simulated, modified and stored in personal folders. Model simulations are performed easily, results visualized interactively, and figures can be exported in a preferred format. Extensive model analyses such as mutant screening or parameter sensitivity can also be performed. For all these tasks, results are stored and can be subsequently filtered to look for specific outputs. This web interface can be accessed at the address:.
To bridge these two formalisms, we have developed MaBoSS (Markovian Boolean Stochastic Simulator) which relies on a Boolean framework but uses a stochastic simulation approach by applying a Markov process on the Boolean network  (Stoll et al. , 2012 (Stoll et al. , , 2017)). MaBoSS is a C++ software that exists in several formats: as a stand alone version that works on all platforms (MacOS, Linux and Windows), as a python library and as a web interface. WebMaBoSS is a user-friendly tool that simulates Boolean models available in standard format using MaBoSS tool. What makes MaBoSS different from the other tools is that it associates parameter values to each variable of the model representing the speed of activation and inactivation of the corresponding node. With this framework, it is then possible to compute time trajectories over time, and provide probabilities for the model solutions (corresponding to a vector of the state of the variables).
These probabilities can then be used to compare the behavior of the system in different conditions (e. g. , wild type vs. mutated conditions). Any standard Boolean model, even if it was not meant to be simulated stochastically, can be used as an input and probabilities of reaching any state of the model can be computed. To facilitate the use of our tool, we have already created a python library and Boolean model can be simulated through Jupyter notebooks in which MaBoSS functionalities can be embedded into a more in-depth analysis using other complementary tools from the logical community within the effort provided by CoLoMoTo  (Levy et al. , 2018; Naldi et al. , 2018; Abate, 2020). What we wish to present here is an even simpler version, WebMaBoSS, which offers the possibility to study online, without any prior installation, any Boolean model, provided that they are available in standard formats.
Web Interface We developed a web interface, WebMaBoSS, to target novices in modeling. Boolean models can be imported/exported in multiple formats (MaBoSS, SBML-qual, GINsim, BoolNet). Models are then stored in a database where they can be accessed at any time. They can also be directly imported from Cell Collective  (Helikar et al. , 2012)  and BioModels databases  (Le Novere et al. , 2006). WebMaBoSS can be accessed at the following address: https:// maboss. curie. fr/webmaboss/. First time users can access the two pre-installed examples  (Cohen et al. , 2015; Corral-Jara et al. , 2021)  with a guest account and directly simulate them. For more advanced features, such as having their own private working directory, importing new models from files or databases, modifying models or performing sensitivity analysis, users need to register by providing a username and a password.
Once loaded, the network of a model, corresponding to the graph of interactions of the Boolean model, can be easily visualized via the web interface: the position of the nodes can be modified, exported as a png file, and the layout can be saved to be exported back into other software such as GINsim (by clicking on the disk icon in the Models working folder page). The logical formulas of the model can be edited and checked for errors. The simulation parameters (initial values, output nodes, parameters and integration settings) can also be set and modified easily in the interface. Once the simulation is executed, four types of results are plotted: time trajectories of both the model state probabilities and the node probabilities, a pie chart representing the probability of the last model states of the simulation, and a table of the fixed points computed and found during the simulation.
Compatibility With File Formats and Databases To ensure reproducibility of Boolean models and to facilitate the exchange between the available modeling software of the community, we ensured the compatibility with other common formats: BoolNet and SBML-Qual. BoolNet uses a text-based format, allowing the possibility to write rules by hand in a simple manner  (Müssel et al. , 2010). SBML-Qual  (Chaouiya et al. , 2013)  is a qualitative extension of the SBML core file format  (Keating et al. , 2020) , the most common file format used in systems biology which focuses on quantitative modeling (ODEs). SBML-Qual can encode both Boolean and multi-valued models. To simulate models in MaBoSS, which only allows simulations of Boolean models, we implemented the method described by  (Didier et al. , 2011)  to convert all models with multiple levels of activation into purely Boolean models.
The compatibility with these formats enable users to directly import models, through WebMaBoSS, from public databases of qualitative models such as Cell Collective  (Helikar et al. , 2012)  or BioModels  (Le Novere et al. , 2006). As previously mentioned, an important added value of this functionality is the interoperability: users can easily build a model with software like GINsim  (Gonzalez et al. , 2006) , Boolnet  (Müssel et al. , 2010)  or CaSQ  (Aghamiri et al. , 2020) , and simulate them using MaBoSS, inscribing MaBoSS and WebMaBoSS into a community combined effort to reproduce and exchange models.
MaBoSS Format, Particularities and Server Implementation MaBoSS was initially built as a multi-platform software  (Stoll et al. , 2017)  which can be run on command line. It uses two major files, one with the model description (bnd file), and one with the simulation settings (cfg file). The outputs are produced in a csv format. This usage, while very efficient, prevented a lot of potential users to explore MaBoSS. In 2018, python bindings for MaBoSS were developed and included in the CoLoMoTo jupyter notebook, which enabled to link MaBoSS to other modeling tools and to allow a new group of users to simulate already-built models with a stochastic approach  (Levy et al. , 2018). The CoLoMoTo environment also includes converter between formats, facilitating the import and export of models from different formats, although indirectly.
A BOOLEAN MODEL ANALYSIS WITH WEBMABOSS WebMaBoSS can be accessed through a browser at https:// maboss. curie. fr/webmaboss/. Registered users can organize and store the models inside a Project folder, where it is possible to upload a model in SBML-Qual or MaBoSS formats, or import a model from public databases such as BioModels and Cell Collective. We illustrate the use of WebMaBoSS with an example of a published model of the regulation of the interleukins IL17  (Corral-Jara et al. , 2021) , which is accessible to guest users and already loaded into the default project of new accounts. Other model analyses and step-by-step tutorials can be found on the GitHub at the following address: https://github. com/sysbiocurie/WebMaBoSS.
IL-17A/IL-17F Differential Expression Model To showcase a full study with WebMaBoSS, we studied a published model of T-helper cells for the regulation of IL-17A/IL-17F  (Corral-Jara et al. , 2021). The step-by-step tutorial showing all the functionalities of the web tool along with the interpretation of the results can be found in Supplementary File S1 and on the GitHub of WebMaBoSS (see above). The model recapitulates the activation of the two cytokines IL-17A and IL-17F that are required for anti-microbial immunity and involved in auto-immune pathologies that can be linked to a subset of T-helper cells, Th1 and Th17. The model encompasses different pathways organized in four modules: the TCR central module, that includes three different signals for the activation of naive T-cells, the Th7 module, that includes the cytokines leading to the Th17 phenotype, the IL-1β and IL-12 modules.
The regulatory graph contains a total of 82 nodes and 136 interactions. The model analysis showed that NFAT2A, STAT5A and SMAD2 are key regulators of the differential expression of IL-17A and IL-17F. As a support for the publication and with the goal to reproduce the results easily, several tasks, including the visualization of the stable states, the model analyses and the comparative studies were performed using the CoLoMoTo notebook which includes the python version of MaBoSS along with other modeling tools and can be found in the tutorial but also on the web repository of GINsim (http://ginsim. org/model/ThIL17diff). The model can be imported directly from the Cell Collective database by selecting the Import button (Figure  1B ). The content of the file that contains the parameters (. cfg file) can be viewed in the Editing tab, where the user can modify the values of Rates, Initial Values, Output, Parameters and Settings.
CONCLUSION We present a web interface of the modeling software, MaBoSS, where Boolean models of various formats can be directly imported and simulated. This web tool offers the possibility to apply alterations to a model, in order to mimic mutations, disease conditions, etc. Automatic perturbations can also be performed, for parameters sensitivity analyses or model optimization for instance. Therefore, Boolean formalism, within this web tool, provides a framework to test in silico hypotheses about the importance of some genes or pathways in cellular phenotypes, to suggest potential treatments with combinations of candidate drugs, etc. All these tasks do not require any prior knowledge of modeling or of the modeling tools. If biologists or nonmodelers wish to play with a model, to try alterations that would mimic a disease or a patient profile, or to search for points of intervention to revert a phenotype (e.
g. , what to mutate to suppress the proliferative characteristic of a cell), they can easily test some ideas. We provide some tutorials in the GitHub repository and several examples on possible analyses and on the type of predictions that can be formulated from these simulations. We are planning to extend some other functionalities of MaBoSS such as the integration of omics data to personalize the Boolean models of signaling pathways  (Béal et al. , 2019). The idea is to build individual models for patients in order to account for the data available [mutations, RNAseq, (phospho)proteomics data, etc. ,] and modify the logical rules or the transition rates accordingly. A personalized model of a patient can already be simulated by hand by forcing the values of the nodes to the mutation profile of that patient.
FUNDING This work received funding from the  European Union   Horizon 2020 research and innovation program  under grant agreement No. 668858  ( PrECISE  project). This work was also partially funded by the  PerMedCoE  project which is part of the  European Union 's  Horizon 2020 research and innovation program  under the grant agreement No. 951773 , and by  Agence Nationale de la Recherche  in the program Investissements d' Avenir  (project No. ANR-19-P3IA-0001 ;  PRAIRIE 3IA Institute ).
Fascin-1 is a marker of embryonal contingent in human HB In order to analyze fascin-1 expression in human HBs we first made use of public datasets  3, 7. We found that fascin-1 mRNA is specifically expressed in the C2-subtype of HBs that corresponds to poor-prognosis tumors, with no apparent difference between C2A and C2B subtypes (Fig. 7A ). As observed in mice, we found that FSCN1 mRNA expression correlates negatively with markers of differentiated hepatocytes such as HNF4a, APOC3, GJB1 and CLDN1 (Fig. S8A ). We then used immunohistochemistry to confirm expression of the protein product in HB samples. Whereas fascin1 is not expressed in human normal hepatocytes and restricted to sinusoidal cells in normal and peri-tumoral tissues (Fig. S8B ), we found that fascin-1 is expressed in a specific contingent of tumor cells in HBs (Fig.
Acknowledgements We thank  Dr D. Vignjevic  ( Curie Institute, Paris, France ) for Fascin-1 DNA constructs. We thank the plateforme  GenomEast of Strasbourg (Strasbourg, France ). Microscopy was done in the  Bordeaux Imaging Center , a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure  France Bio Imaging , with the help of  Dr. Philippe Legros  for STED analysis. We thank  Anne-Aurélie Raymond  ( Inserm U1053, Bordeaux, France ) for her help in transcriptomic analysis.
Finally, the global score was represented as the mean score AE standard deviation (SD) of the four scores given by each clinician independently. Second, a quantitative analysis of bone growth was performed using the software InVesalius 3 (Centro de Tecnologia da Informação Renato Archer, CTI). The volume of interest (VOI) was defined as the total volume of newly formed bone inside and around the bone defect. BG was used as a positive control. Based on the visual observations, we decided to segment and define two types of bones: poorly and highly mineralized. To quantify these two fractions, the VOI was segmented using a global threshold, namely between 230 and 629 HU for poorly mineralized bone and values >630 HU for highly mineralized bone. The volumes of the newly formed bone were expressed in cm 3.
Two negative controls were added: empty defect (no scaffold) and film-coated implant without BMP-2. (A, B) CT-scan scores (mean of the global scores (S) given by the four clinicians AE SD) calculated from the CT scans as a function of time, and corresponding exponential fits to the data (red and blue lines) for the (A) EDC30 and (B) EDC70 groups. The plateau value (B max ), characteristic time (τ) deduced from the fits, and fit quality R 2 are given in the corresponding tables. (C) Total bone volume as a function of time (D29, D50, and D90) and as a function of the BMP-2 dose expressed in total dose per implant (in μg), for the two crosslinked films EDC30 and EDC70. Lines have been added to guide the eye. films with a BA/TA ratio of 1. 5 AE 1% for BMP20 and 40. 8 AE 1. 4% for BMP110 (Fig. SI 6F ).
Acknowledgments We thank the following for their contribution:  Isabelle Paintrand  (CNRS) and  Jie Liu  ( Grenoble Institute of Technology ) for help in the preparation of the samples,  Jean-Luc Coll  ( Institute of Advanced Biosciences ) and  Remy Gerez  ( Institut Claude Bourgelat ) for fruitful discussions,  Heiko Richter  and  Birgitta Stolze  ( LLS Rowiak ) for technical discussions,  R Lartizien  ( Annecy Genenevois hospital ) for help in the clinical score,  Sebastien Schoumacker  for help in 3D printing,  Doroth ee Palluy (Stryker)  for providing the reconstruction plates and screws, and  Sylvie Berthier  ( CHU-Grenoble Alpes ) for her help in the quantitative histological analysis.
Funding The work was supported by the  European Commission  under the  PF7 program  ( European Research Council  grant  BIOMIM GA259370  and Proof of Concept REGENERBONE 790435 to CP), by the  'Association Gueules Cass  ees' (contract n  21-2016  and  10-2018 ) and by the  French National Research Agency  ( ANR-18-CE17-0016 , OBOE). Bone imaging systems were acquired thanks to  France Life Imaging  ( FLI ,  ANR-11-INBS-44 0006 ). CP is a senior member of the  Institut Universitaire de France  whose support is acknowledged.
Ground-based photometry Since both transiting events are deep and have a short period, they are easily amenable to a detection from small groundbased observatories. Scheduled photometry has thus confirmed for both candidates that the transit was occurring on the main target of the TESS aperture, rejecting the background eclipsing binary astrophysical false positive scenario. We used the TESS Transit Finder, which is a customised version of the Tapir software package  (Jensen 2013) , to schedule the observations. The photometric data were extracted using the AstroImage (AIJ) software package  (Collins et al. 2017). For TOI-1296b, two full transits at the expected ephemeris were observed with 40-cm telescopes and four partial transits targeting the ingress were observed with 40 and 61-cm telescopes.
For TOI-1298, three TRES measurements were obtained within 55 days, with an rms of 32 m/s and individual errors of 25 m/s. These few data points are not included in the global fit. The Stellar Parameter Classification (SPC;  Buchhave et al. 2012)  tool was used to derive stellar parameters from the weighted average spectrum of S/N 35 and 55 for TOI-1296 and TOI-1298, respectively. SPC cross correlates an observed spectrum against a grid of synthetic spectra based on Kurucz atmospheric models  (Kurucz 1992 ). The weighted average results derived with the effective temperature (T eff ), metallicity ([M/H]), and surface gravity (log g) and vsini as free parameters are, for TOI-1296, T eff = 5646 ± 50 K, log(g) = 4. 19 ± 0. 10, [M/H] = 0. 50 ± 0. 08, and vsini = 4. 0 ± 0. 5 km/s. For TOI-1298, the values are as follows: T eff = 5830 ± 50 K, log(g) = 4.
2010). With regular spectral calibrations of the spectrograph through the night using the Fabry-Pérot etalon, the instrumental drift was also corrected at a level smaller than 1 m/s, which is much better than the photon noise RV errors for these faint stars. Exposure times were about 1200 s and 1500 s per visit for TOI-1296 and TOI-1298, respectively. Signal-to-noise ratios of typically 20-30 per pixel were obtained at 550 nm, corresponding to photon noise uncertainties of about 3 m/s for both stars. One outlier measurement was removed from the data set for each star, corresponding to RV errors of 1. 5 to 2 times the average error bars. The RVs were obtained by cross-correlating the extracted spectra with a binary mask corresponding to a G2 spectral type, which contains 3645 lines. There are 14 RV measurements for each time series.
Data modelling We performed a global modelling including all available data. The fitted parameters are as follows: q 1 and q 2 (Kipping 2013), the quadratic limb darkening coefficients for all light curves, the radius to semi-major axis ratio, the radius ratio, the orbit inclination, the mid-transit time, the orbital period, the semi-amplitude of RV variations, the systemic velocity, the stellar density, and a jitter term for the RV time series. Eccentricity was let free to vary, adding the fitted parameters √ e cos ω and √ e sin ω. For TOI-1296b, Gaussian process regression was used for TESS data and all ground-based light curves, which were modelled simultaneously with SOPHIE data. For TOI-1298b, only TESS and SOPHIE data were used in the modelling. The TESS data included in the analysis are centred on the transit events, with a width of three times their duration.
The priors used in the analysis are uniform for most of the parameters, except the stellar density for which a normal prior is used from the stellar mass and radius derived from the spectroscopic analysis and SED modelling, as discussed in section 3. 1. The priors we imposed on the stellar density are the following: 0. 358 ± 0. 052 g cm -3 for TOI-1296 and 0. 721 ± 0. 074 g cm -3 for TOI-1298. For this global modelling, we used the Juliet package (Espinoza et al. 2019). Within Juliet, we used the transit model batman  (Kreidberg 2015 ) and the RV model radvel  (Fulton et al. 2018). We used the approximate Matern kernel included in celerite  (Foreman-Mackey et al. 2017)  for the photometric datasets, including both TESS data and ground-based light curves. Quadratic limb darkening was let free for each filter.
198 ± 0. 031 g/cm 3. The derived eccentricity is compatible with a circular orbit. TOI-1298 was modelled in the same way. We obtain a posterior value of 0. 670 ± 0. 062 g cm -3 , showing good agreement with the prior value of 0. 721±0. 074g cm -3. The derived planet's mass is 0. 356 ± 0. 032 M Jup and the radius is 0. 841 ± 0. 021 R Jup , implying a density of 0. 743 ± 0. 091 g/cm 3. The eccentricity is not significantly different from 0. The posterior results are listed in Table  2  for TOI-1296 and TOI-1298 systems, while Figures  6  and 7  show the best fit to the data in both cases. We then fixed the transit parameters to the posterior maximum and let the time of individual transits vary to search for transit time variations. We used a normal prior with a width of 0. 03 days with respect to the linear ephemeris of the fit, obtained in the global modelling.