dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability statement Type material was deposited in the invertebrate collection of the Royal Museum for Central Africa (Tervuren, Belgium) (RMCA) (RMCA_VERMES_44366-44602), the collection of the Research Group Zoology: Biodiversity and Toxicology of Hasselt University (Diepenbeek, Belgium) (HU 842-853), the Finnish Museum of Natural History (Helsinki, Finland) (MZH) (MZH http://id. luomus. fi/KN. 37258-http://id. luomus. fi/KN. 37274), the Iziko South African Museum (SAMC) (Cape Town, South Africa) (SAMC-A095104-A095122), and the Muséum National d'Histoire Naturelle (Paris, France) (MNHN) (MNHN HEL1906-1922). The morphological data that support the findings of this study are openly available in Morpho Bank at www. morphobank. org, at https://doi. org/10. 7934/ P4626. Phylogenetic trees and data matrices for the analysis in TNT are included as additional data in MorphoBank.
3 , 2 5 , 2 7 , 29 , 2 11 , 213 , 215  A095110, and MNHN HEL1912-1913. Species descriptions and characterisations are presented in the following. Note that the authors of the new species are different from the authors of the article [according to Article 50 of the International Code of Zoological Nomenclature (ICZN)]. Infection parameters can be found in Table 1. Symbiotypes and symbioparatypes are given as follows: RMCA accession number (specimen IDs). Chromidotilapia guntheri (Sauvage, 1882). Additional host: Chromidotilapia linkei Staeck, 1980. Type locality: New Calabar river, Akpor, Nigeria; 4. 87, 6. 90; 01/12/1990 on type host. Additional locality: Lake Barombi-Kotto, Cameroon on Chromidotilapia guntheri and road Yabassi-Yingui, Cameroon on Chromidotilapia linkei. SAMC-A095109-Cichlidogyrus ataikputu Moons, Kmentova, Pariselle, Vanhove & Cruz-Laufer n.
Discrete logic-based qualitative modelling has been increasingly used to model large-scale networks for which kinetic data is scarce  29, 30. For example, in a Boolean model, each node can take only two values, 0 (FALSE) and 1 (TRUE). The next value of each variable is determined by a logical function (using the classical AND, OR, NOT operators) of the current values of its regulators (upstream nodes). The evolution of each node also depends on the updating scheme chosen. The synchronous scheme updates all variables in the model simultaneously; in the asynchronous scheme, the variables are updated individually in a non-synchronous manner  31, 32. In this work, we use the state-of-the-art RA map  19  and the tool CaSQ  33  to build a large-scale, modular Boolean model of RA-FLS focused on inflammation, bone erosion, cartilage destruction, cell proliferation, and apoptosis.
The model consists of five-phenotypespecific submodels (to be mentioned as modules later in the paper) that can be simulated individually and a five-phenotype global model (to be mentioned as the global model later in the paper). Systematic testing of different initial conditions using the modules and the global model showed that both the modules and the global model could reproduce small-scale experimental results from the literature. To search for meaningful steady states, we reduce calculations by propagating and eliminating fixed input components, as introduced in ref. 34  and implemented in the CoLoMoTo notebook  35  in ref. 36. Input propagation consists of assigning fixed values to some of the model's inputs and using Boolean algebra to simplify the rules of their downstream components.
RESULTS Enhancing the cell specificity of the initial network RA-FLS is the most frequent cell type in the RA map, covering a total of 45%, followed by synovial tissue with 36%  19. We used the Fig. 1  Construction and simulation of a large-scale, modular Boolean model of RA-FLS for evaluating novel drug combinations. The RA map was converted into an executable Boolean model using the map-to-model framework described in ref. 33. Using single-cell omic datasets and literature studies, the RA generic model was subsequently enriched in RA-FLS-specific data. The RA-FLS model focuses on five phenotypes (apoptosis, cell proliferation, inflammation, matrix degradation, and bone erosion) characteristic of RA's fibroblasts. Individual phenotypespecific submodels and a five-phenotype global model were created.
Biological scenarios extracted from the literature were used to evaluate and validate the models' behaviour leading to some modifications of the original models. The modified RA-FLS model was then used to test mono and combined RA therapies. Drug repurposing analysis and further drug combination simulations led to a panel of suggestions of drug combinations that are predicted to have a favourable outcome (apoptosis active, cell proliferation, inflammation, bone erosion and matrix degradation inactive). updated fibroblast overlay list provided in refs. 37, 38 to calculate the RA-FLS specificity of the global model. In this study, they used the RA fibroblast list provided in ref. 19  and updated it with the RNA-seq single-cell dataset available in the GEO database  39  by performing Differential Expression Analysis (DEA) using BioTuring software and the Venice method.
Using this list, out of 261 unique model components (proteins, genes, phenotypes), 194 are RA-FLSspecific. Other cell types primarily include synovial tissue with 29 components, peripheral blood mononuclear cells (PBMCs) with 25 components, along with the presence of other sources like blood, synovial fluid, T-helper (th1), macrophages and chondrocytes (Supplementary Table  1 ). Five-phenotype-specific modules and global RA-FLS model The tool bioLQM  40  was used to extract the phenotype-specific modules and the global model using the SBML file produced by CaSQ  33  using RA map (Supplementary Fig. 1 ) XML file as input (files available on GitLab). The size of the models in terms of nodes and reactions can be seen in Fig. 2a. One straightforward observation is that the size of the five-phenotype global model is not the sum of the subparts.
Calculating continuous time phenotypic probabilities The RA-FLS model, in both versions, was used to calculate phenotypic probabilities. We used the software MaBoSS to reproduce representative scenarios with different parameters (discussed in "Materials and methods")  58. One scenario per phenotype from those listed in Supplementary Table  2  was selected for simulations, and results showed that in all cases but apoptosis, the two model versions were able to reproduce the experimental observations. In brief, we performed the following simulations: Inflammation. IL-6, one of the main pathways regulating inflammation, was set active (1), and the Inflammation phenotype was chosen as the output. As a result, the simulation confirmed the activation of inflammation (Fig. 4a ). Cell proliferation.
As a substitute for patients who did not respond to conventional medication or who did not have easy access to biologics treatment, BPs have also been tested in RA patients. More specifically, scientists studied intravenous pamidronate administration for refractory rheumatoid arthritis. Results showed that pamidronate infusions were beneficial for most patients, but the alleviation of symptoms did not last for more than six months  73. More recently, Zoledronic acid (ZA) was tested in combination with Methotrexate (MTX) in 66 RA patients for its efficacy in inhibiting RA disease activity. The combined treatment of ZA and MTX effectively reduced disease activity, fracture risk and bone pain in patients with RA-derived secondary osteoporosis  74. A clinical trial involving 28 patients to assess the effects of ZA on patients with early-stage RA and low disease activity was concluded in 2018, but the results are not yet available (https:// clinicaltrials.
gov/ct2/show/NCT02123264). ZA has also been used to induce cell apoptosis in human and murine osteoclast precursors and mature osteoclast-like cells by triggering ROSand GSK-3β-mediated Mcl-1 downregulation  75. According to our model, BPs could also alleviate RA symptoms and inflammation, as they could inhibit CAV1 and activate apoptosis in RA-FLS. The inhibitory effects of ZA on tumour-related growth factor IL-6 in hormone-resistant prostate cancer cell lines have also been studied. The study showed the inhibitory effect of ZA on IL-6 secretion, which in turn resulted in increased apoptosis  76. Keeping the default model conditions, we performed the simulations targeting CAV1 with Zoledronic acid. The results showed that when CAV1 is inhibited, apoptosis will become activated (Supplementary Fig.
DISCUSSION In this work, we focused mainly the mechanisms regulating inflammation, matrix degradation, bone erosion, apoptosis, and cell proliferation, in RA-FLS. Our framework presents the experimental and clinical data and model results in an approachable manner which links various software applications and data. To build our large-scale model, we used the state-of-the-art RA map  19  and the map-to-model framework proposed in ref. 33. Fivephenotype-specific modules and a global model were extracted and further enriched using RA-FLS-specific literature and singlecell data. The inferred models were further analysed using a variety of software, such as GINsim 81 , bioLQM  40  , MaBoSS  82  and the CoLoMoTo notebook  35. To our knowledge, the RA-FLS model is the first large-scale model that describes these cells' signalling and gene regulation mechanisms in disease-specific settings.
To do so, we identified the direct regulators of the five different phenotypes and used drug repurposing analysis to identify drug candidates for these targets. More specifically, we used the logical formulae of the Boolean model to identify the direct regulators of each phenotype that could change the phenotypic state if targeted. We used several databases and dedicated sites to infer information about drugs and small molecules that target the action of these regulators. Some of these drugs had already been tested in RA or were administered in the context of other diseases. We had to tag discontinued drugs due to side effects, even when administered in a different disease context. For some drugs, information on toxicity or trial reports was limited. For example, CTS-1027, an MMP inhibitor, had been in phase II clinical trials by Conatus Pharmaceuticals (licensed from Roche) to treat Hepatitis C Virus (HCV) infection.
The availability of more quality datasets could help to analyse the models under different sets of initial parameters and study their relation to the phenotypic outcomes. Furthermore, topological analysis of the model's structural characteristics could reveal hubs and nodes with high centrality measures  89  , which could guide the combination of in silico knockouts (KOs) and knock-ins (KIs) for simulating the models. Our approach supports using systems modelling in preclinical and clinical settings to provide insights into the possible outcomes of drug combinations. Further optimisation of the RA-FLS model using patient-specific omic data integrated with other clinical parameters could help towards personalised treatments.
MATERIALS AND METHODS Generation of RA-FLS phenotype-specific modules and the global model Analysing complex biological networks often requires a workflow that includes several different tools with different specifications and requires many additional steps to harmonise the whole procedure. The CoLoMoTo Interactive Notebook provides an integrated environment to execute, share, and reproduce analyses of qualitative models of biological networks  35. The framework is available as a Docker image on Linux, MacOS and Microsoft Windows with the tools already installed ready to be run with a Jupyter web interface executing the codes and visualising the results. The resulting notebook files can be shared and reexecuted in the same environment. After installing the docker and accessing the latest colomoto-docker-V 2022-10-01 image, the notebook interface with Python modules for different tools was available to execute the analysis.
A global, comprehensive and fully annotated RA-specific map was published recently  19, 90. This map features components and interactions implicated in RA coming from various cell types. The RA-map CellDesigner XML file was first imported into the notebook using BioLQM and then converted into an executable RA-map model using CaSQ (CellDesigner as SBML-Qual), a tool for automated inference of large-scale, parameter-free Boolean models from molecular interaction maps based on network topology and semantics  33. The executable RA model, in standard SBML-qual format  91  , can be further simulated and analysed using different modelling tools supporting the SBML-qual format. First, the RA model was sanitised using bioLQM to replace the model node Ids with node names. Then, the model was further used to extract five-phenotype-specific modules, apoptosis, cell proliferation, inflammation, matrix degradation, bone erosion, and a global model.
Model annotation and use of standards Besides building and analysing large-scale biological models, one of the main challenges in systems modelling is the use of standards and proper annotation of such models to enhance a model's reusability  92, 93. The models obtained in our pipeline are in a standard format (SBML-Qual) and thoroughly annotated using the MIRIAM scheme  94  , including PubMed IDs and pathway identifiers to annotate components and reactions. However, these elements are maintained and visualised only when the models are imported into the Cell Collective platform  62  and lost when the models are further analysed with our CoLoMoTo notebooks due to the sanitisation process. Literature survey to find RA-FLS-specific biological scenarios As our modelling efforts are based on RA-FLS, we did exhaustive literature mining to find biological scenarios to be tested for each phenotype with the individual modules and the global model.
Identification of trap spaces We used terminal trap spaces to evaluate the asymptotic behaviour of our RA-FLS model. A trap space (also known as a stable motif or symbolic steady state) is a subspace from which the system cannot escape  83, 84. In particular, all possible successors of every state of a trap space belong to the same subspace. A trap space is terminal if it does not contain any smaller trap space. Terminal trap spaces accurately represent the Boolean model's asymptotic behaviour and provide a good approximation of all attractors in practice. Trap spaces can be efficiently identified using constraint-solving techniques without performing the simulation. Here we used the implementation provided by the bioLQM software for the identification of terminal trap spaces in reduced models obtained after input propagation, as explained below.
Continuous time stochastic simulations In this type of simulation, continuous time phenotypic probabilities are being calculated. The probabilities associated with each phenotype represent the number of stochastic simulations leading to each phenotype from predefined initial conditions (based on experimental evidence that mimic the disease state). MaBoSS is a C + + software for simulating continuous/discretetime Markov processes applied to Boolean networks  58, 82. MaBoSS uses a specific language for associating transition rates with each node. Given some initial conditions, MaBoSS applies Monte-Carlo kinetic algorithm to the network to produce time trajectories and thus can associate probabilities with asymptotic solutions. The MaBoSS simulations were used to recreate at least one situation from each phenotype in the form of time-dependent (max time 40) trajectories using chosen parameters, more details of which could be found in the provided Python notebooks.
2021) , it is quite realistic to believe that the GJ 367 hosts more than one planet. As part of the RV follow-up program carried out by the KESPRINT consortium, 36 we here present the results of an intensive RV campaign conducted with the HARPS spectrograph to refine the mass determination of the transiting USP planet and search for external planetary companions, while probing the architecture of the GJ 367 planetary system. The paper is organized as follows: we provide a summary of the TESS data and describe our HARPS spectroscopic followup in Section 2. Stellar fundamental parameters are presented in Section 3. We report on the RV and transit analysis in Sections 4 and 5, along with the frequency analysis of our HARPS time series. Discussion and conclusions are given in Sections 6 and 7, respectively.
2012 (Stumpe et al. , 2014;; Smith et al. 2012). We retrieved TESS Sector 9, 35, and 36 data from from the Mikulski Archive for Space Telescopes (MAST) 37 and performed our data analyses using the PDCSAP light curve. We ran the Détection Spécialisée de Transits (DST;  Cabrera et al. 2012)  algorithm to search for additional transit signals and found no significant detection besides the 7. 7 h signal associated to GJ 367 b, suggesting that there are no other transiting planets in the system observed in TESS Sector 9, 35, and 36, consistent with the SPOC multitransiting planet search.
HARPS High-precision Doppler Follow-up GJ 367 was observed with the High Accuracy Radial velocity Planet Searcher (HARPS) spectrograph  (Mayor et al. 2003) , mounted at the ESO-3. 6 m telescope of La Silla Observatory in Chile. We collected 295 high-resolution (R ≈ 115,000, λ ä378-691 nm) spectra between 2020 November 9 and 2022 April 18 (UT), as part of our large observing program 106. 21TJ. 001 (PI: Gandolfi) to follow-up TESS transiting planets. When added to the 77 HARPS spectra published in  Lam et al. (2021) , our data includes 371 HARPS spectra. The exposure time varied between 600 and 1200 s, depending on weather conditions and observing schedule constraints, leading to a signal-to-noise ratio (S/N) per pixel at 550 nm ranging between 20 and 90, with a median of ∼55. We used the second fiber of the instrument to simultaneously observe a Fabry-Perot interferometer and trace possible nightly instrumental drifts  (Wildi et al.
2010 (Wildi et al. , 2011)). The HARPS data were reduced using the Data Reduction Software (DRS; Lovis & Pepe 2007) available at the telescope. The RV measurements, as well as the Hα, Hβ, Hγ, Na D activity indicators, log R¢ HK , the differential line width (DLW), and the chromaticity index (CRX), were extracted using the codes NAIRA (Astudillo-Defru et al. 2017b) and SERVAL  (Zechmeister et al. 2017). NAIRA and SERVAL feature template matching algorithms that are suitable to derive precise RVs for M-dwarf stars, when compared to the cross-correlation function technique implemented in the DRS. We tested both the NAIRA and SERVAL RV time series and found no significant difference in the fitted parameters. While we have no reason to prefer one code over the other, we used the RV data extracted with NAIRA for the analyses described in the following sections.
HARPS Spectropolarimetric Observations With the aim of measuring the magnetic field of GJ 367, we performed a single circular polarization observation with the HARPSpol polarimeter  (Piskunov et al. 2011; Snik et al. 2011)  on 2022 November 16 (UT), as part of the our ESO HARPS program 1102. C-0923 (PI: Gandolfi). We used an exposure time of 3600 s split in four T exp = 900 s subexposures obtained with different configuration of polarization optics to ensure cancellation of the spurious instrumental signals (see  Donati et al. 1997; Bagnulo et al. 2009). The data reduction was carried out with the REDUCE code  (Piskunov & Valenti 2002)  following the steps described in  Rusomarov et al. (2013). The resulting Stokes I (intensity) and Stokes V (circular polarization) spectra cover approximately the same wavelength interval as the usual HARPS observations at a slightly reduced resolving power (R ≈ 110,000).
Photospheric and Fundamental Parameters We derived the spectroscopic parameters using the new coadded HARPS spectrum for GJ 367. Following the prescription in  Hirano et al. (2018) , we first estimate the stellar effective temperature T eff , metallicity [Fe/H], and radius R å using SpecMatch-Emp  (Yee et al. 2017). The code attempts to find a subset of best-matching template spectra from the library to the input spectrum and derives the best empirical values for the above parameters. SpecMatch-Emp returned T eff = 3522 ± 70 K, R å = 0. 452 ± 0. 045 R e , and [Fe/H] = -0. 01 ± 0. 12. We then used those parameters to estimate the other stellar parameters as well as refine R å. As described in  Hirano et al. (2021) , we implemented a Markov Chain Monte Carlo (MCMC) simulation and derived the parameters in a selfconsistent manner, making use of the empirical formulae by  Mann et al.
(2015)  and  Mann et al. (2019)  for the derivations of the stellar mass M å and radius R å. We found that GJ 367 has a mass of M å = 0. 455 ± 0. 011 M e and a radius of R å = 0. 458 ± 0. 013 R e , with the latter in very good agreement with the value derived using SpecMatch-Emp. In the MCMC implementation, we also derived the stellar density ρ å , surface gravity  g log , and luminosity L å of the star. Results of our analysis are listed in Table  1. As the quoted uncertainties of the stellar parameters do not account for possible unknown systematic errors-which in turn might affect the estimates of the planetary parameters-we performed a sanity check and determined the stellar mass and radius using the code BASTA (Aguirre  Børsen-Koch et al. 2022). We fitted the derived stellar parameters to the BaSTI isochrones  (Hidalgo et al.
Magnetic Field Our spectropolarimetric observation of GJ 367 achieved a median S/N of about 90 over the red HARPS chip. This is insufficient for detecting Zeeman polarization signatures in individual lines even for the most active M dwarfs. To boost the signal, we made use of the least-squares deconvolution procedure (LSD;  Donati et al. 1997)  as implemented by  Kochukhov et al. (2010). The line mask required for LSD was obtained from the VALD database  (Ryabchikova et al. 2015)  using the atmospheric parameters of GJ 367 and assuming solar abundances (Section 3. 1). We used about 5000 lines deeper than 20% of the continuum for LSD, reaching an S/N of 7250 per 1 km s -1 velocity bin. The resulting Stokes V profile has a shape compatible with a Zeeman polarization signature with an amplitude of ≈0.
With an orbital period of only 7. 7 hr, the USP planet GJ 367 b is suitable to the FCO method (see, e. g. ,  Gandolfi et al. 2017; Barragán et al. 2018 ). GJ 367 is accessible for up to 8 hr at an airmass < 1. 5 (i. e. , altitude > 40°) from La Silla Observatory, allowing one to cover one full orbit in one single night by acquiring multiple HARPS spectra per night. Within the nightly visibility window of GJ 367, the phase of the long-term signals does not change significantly, the variation being 0. 029, 0. 010, and 0. 006 for the 11. 5, 34, and 52 day signals, respectively. We simultaneously modeled the TESS transit light curves and HARPS RV measurements using the open source software suite pyaneti  (Barragán et al. 2019 (Barragán et al. , 2022)). The code utilizes a Bayesian approach in combination with MCMC sampling to infer the parameters of planetary systems.
Sinusoidal Activity Signal Modeling Using the FCO method, we cannot determine the semiamplitude of the Doppler signals induced by the two outer planets and by stellar activity (Section 4). In the analysis described in this section, we treated the RV signals associated with stellar activity as coherent sinusoidal signals. Once again, we used the code pyaneti and performed an MCMC analysis similar to the one described in Section 5. 1. The RV model includes three Keplerians, to account for the Doppler reflex motion induced by the three planets GJ 367 b, c, and d, and two additional sine functions, to account for the activity-induced signals at the rotation period of the star (∼52 days) and at the evolution timescale of active regions (∼115 days), as described in Section 4. We used Gaussian priors for the orbital period and time of first transit of GJ 367 b as derived in Section 5.