dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, we used scRNA-seq data and CoRegNet to calculate the influence of the transcription factors present in this dataset on the gene expression of FLS from RA and OA patients. To validate the results we got from this dataset, we applied ISMARA and DoRothEA for calculating the TFs activity. Proliferation, apoptosis, migration/ invasion and inflammation markers identification :. First, we retrieved hallmark gene sets from MSig database  26. Two hundred genes were retrieved for the inflammatory pathway, 171 plus 3 fibroblast specific genes for the apoptosis pathway, 40 fibroblast specific genes for the migration/invasion pathway and 85 fibroblast specific genes for the proliferation pathway. The expression profiles of these markers come from: Genotype-Tissue expression (GTEx) project data  54  , Human Tissue compendium data  55  , The NCI-60 Human Tumor Cell Lines Screen data  56  and the Global Cancer Map from Broad Institute  57.
The fibroblast specific gene sets were contributed by the Gene Ontology consortium  58. We performed a differential expression analysis using the Limma package  59  to the scRNA-seq data (GSE109449) to see which of the corresponding markers have differential expression between RA and OA patients. We kept only the genes having a p-value lower than 0. 05 and a log fold change higher than 1. 5 in absolute value. Then, for the apoptosis pathway, we kept all the overexpressed antiapoptotic markers and all the underexpressed proapoptotic ones. For the inflammation, proliferation and migration, we only kept the overexpressed markers having a positive regulation and the underexpressed markers having a negative regulation on these pathways. Finally, we searched to see which of these genes are under the control of the five identified master regulators based on the co-regulatory network inferred by CoRegNet.
Correction for the age and sex of the patients. The distribution of the sex between RA and OA patients is similar in the synovial tissue samples (p-value Fisher test = 0. 52) and in the fibroblasts microarray samples (p-value Fisher test = 1). In the fibroblast single cell samples, all individuals were females. Therefore, we did not adjust for sex. Concerning the age, we did not have information for each individual in the synovial tissue samples (only the mean age at onset for RA and OA). Therefore, we did not adjust for age. A pairwise comparison was also performed for the sex and age between the OA patients of the 3 datasets and the RA patients of the 3 datasets (for age, the synovial tissue samples were not considered as explained above). The sex and age were homogeneously distributed between OA patients (p-value Fisher test > 0.
Surprisingly, in existing software, this scatterplot representation is rarely interactive with the image itself, although this would allow to locate selection results back into the image but also to filter or correct results and fine-tune the gates defining cell populations to obtain in return a better visualization of them into the image. To this end, we developed the Shiny Analytical Plot of Histological Image Results (SAPHIR), an R/Shiny application for the quantitative analysis of tissue section images. Since image segmentation is a complex task in continuous development and highly dependent on image quality and information, the integration of a single type of segmentation method in an image analysis application is not necessarily recommended. Therefore, we decided to separate segmentation from SAPHIR, but we provided two Fiji macro examples with associated images, which can be used to perform this task before running SAPHIR.
Data requirement The segmentation required to use the application SAPHIR has been carried out under Fiji  9  , with custom-made macros, which are available on GitHub in the Demonstration Files. Cell nucleus detection was used for segmentation and fluorescence intensity signal of each channel within a circular ring around nuclei was determined to define cell of interest (COI) phenotype. In addition, regions of interest were defined using DBSCAN (optional)  10. The segmentation process of the image to be analyzed should create a csv result file containing the channel intensity values (ranging from 0 to 255) and, if required, the positioning (x, y, z) linked to each COI. Another file, termed ROI. zip, should contain the image contour of each COI. Importantly, many other morphological measurements like area, roundness, or solidity as well as other information (COI belonging to a region of interest, COI interaction with other cells, COI centroid position) can be added to the segmentation result file.
Finally, the minimal requirements to run the application are the image in TIFF with multiple channels and optionally slices, the segmentation result file with COI identity in csv and the roi. zip file containing contours of each COI. A legend file with all channel information can also be provided (optional). Operation SAPHIR is built in the R programming language, version 4. 0. 2, and uses many packages available on CRAN (e. g. shinydashboard, ijtiff, magick, ggplot2, plotly) and one package (EBImage) available on Bioconductor. It has been tested on macOS and Windows 10. A user flowchart from image acquisition to obtention of quantitative data of COI with SAPHIR is shown in Figure  1.
Implementation The first step of SAPHIR is either to run a segmentation program to obtain appropriate files or to load the three required files (image. tif, roi. zip, results. csv) and if needed the legend (csv file) in the "select your results" menu of the application. Here, result, region of interest (ROI) and legend files were obtained with an in house-made segmentation macro developed under Fiji. The first tab of the "Plot to Image" menu is shown in Figure  2  and allows the users to filter their data depending on one or two parameters displayed on histogram or scatterplot, respectively. This can be used to remove cells that have been badly segmented. Indeed, such cells display an aberrant area or volume (doublets or aggregates in red in Figure  2 ). Here, only cells within the Gaussian curve were retained (in blue in Figure  2 ).
Data and availability SAPHIR is provided with segmentation data from two demo pictures (https://github. com/elodiegermani/SAPHIR/tree/master/ Demonstration%20files): one with only two intensity channels to test the application in a very easy and quickly way, and a more complex one with 6 intensity channels, 12 slices and two additional parameters (belonging to a ROI and cell-cell interaction) for advanced testing of the application. These data are parts of several projects conducted in the Center of Immunology of Marseille-Luminy (France). Acquisition of the images was made with spectral confocal microscopy and analyzed with Fiji with the provided macro.
Grant information: This work was supported by institutional funding from  Centre National de la Recherche Scientifique  and  Institut National de la Santé et de la Recherche Médicale , by the  Agence Nationale de la Recherche  grant  ANR-10-INBS-04-01  France Bio Imaging, by «  Investissements d'Avenir » French Government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ), by  Excellence Initiative of Aix-Marseille University  -A*MIDEX and by the  Fondation pour la Recherche Médicale (FRM)  grant  DEQ20170336745.
We report a method to model, simulate and predict high-level nuclear waste (HLW) thermal behavior over short and long time-scales. The approach relies on an inverse method using homogenized thermal fluxes at the HLW package surfaces during the vitrification process. The thermal simulation can then be used as input in subsequent structural analyses. This allows us to compare for two different vitrification processes the developed thermal stress build-up investigated inside the glass block. The first process is the original experimental vitrification process, without the presence of the radionuclides. The second one is a simulation made possible by the proposed inverse method, where the cooling and solidification for a HLW integrate the long-term heat source of the radionuclides.
(2008) ,  Li et al. (2009) ,  Sizgek (2005) ,  Li et al. (2011) ,  Moncouyoux and Jacquet-Francillon (1993) ,  Avila et al. (2000) ). As a result, a complete dimensional and temporal multi-scale approach of thermal, mechanical and leaching phenomena would help in the prediction of the capacity of any HLW repositories to comply with given environmental standards. A combining effort towards such aim was proposed by the computational approaches of Bouyer and coworkers  (Crevoisier et al. (2011) ,  Repina et al. (2019) ,  Repina et al. (2020) ). However, some of the aforementioned thermal studies only deal with the HLW as a thermal source term, depending on the radionuclides mix. This is often the case as a point source or linked to the geometry approximation of the package  (Acar and Zabunoglu (2013) ,  Finsterle et al.
(2019) ). In such configurations, the HLW packages with thermal power to dissipate due to the presence of radioactive waste are in thermal balance with their direct environment. However, the thermal history of each HLW package has an effect on the ultimate internal structure of the HLW glass block. The track of this thermal history stems from the cast and cooling process conditions, to the temporary storing conditions, and finally to the ongoing repository characteristics (its design and the neighboring rock formation). Hence, to better predict the p. 4 ultimate internal structure of the HLW glass blocks, better tools and knowledge of the HLW packages' thermal history need to be exploited. The aforementioned reported studies cover multi-physics approaches contributing by part to address the ultimate metric of the confinement of radionuclides.
Materials and methods In order to validate the predictive numerical model, full-scale HLW package vitrification experiments were conducted by casting about 400 kg of borosilicate glass within a container of stainless steel alloy 309S. In this experiment, the HLW package is equipped with 7 thermocouples. An inactive glass is used to represent the nuclear glass. This glass without radionuclide, "SON68", is a borosilicate glass corresponding to the industrial "R7T7" nuclear glass. The experimental setup and the measured temperatures on the thermocouples as a function of time are presented in Fig. 1  (CEA (2005) ). The heat pipe effect of the setup of 7 thermocouples inside the package is neglected, due to their small overall diameter. Standard Inconel® sheathed thermocouples were used, inside thermowells made of stainless steel of 5 mm diameter that are soldered to the HLW package envelope.
Purpose of the inverse thermal method In usual thermal models of HLW packages, Dirichlet-type thermal boundary conditions (BC) can be directly prescribed as external temperatures, using for instance collected data from thermocouples at different heights on the envelope of the HLW package. With appropriate locations to collect the temperature values, these may be enough to obtain the internal thermal state of a glass block for that experiment. However, this leads to an oversimplified model where the thermal fluxes are of the conduction type only. This approach was used in the case of the aforementioned variant vitrification process studies (hot crucible process)  (Barth et al. (2012) ,  Barth et al. (2014) ). Dirichlet-type BCs enable only the study of the experimentally driven scenarios and do not allow to properly investigate process parameters for more general cases.
The proposed inverse method consists in implementing Neumann-type BC, i. e. thermal fluxes, for the modeling of the thermal behavior. The objective is then to compute a simulated temperature field that is coherent with the thermocouple measurements. The approach leads to more complexity, since there is no unique way to define the expressions of the thermal fluxes over the relevant interfaces of the HLW package (such problem is mathematically ill-posed). In the proposed approach, many corrections to the conduction-convection-radiation model of the HLW package are grouped into a high number of fluxes superimposed to the thermal model properties and boundary conditions. This may create heat at some point of the transient analysis to correct the thermal response toward the observed temperature dynamics, which may then only be removed later by a flux of opposite sign.
We made this choice to avoid multiplying the thermal sub-models to account for specific effects expected in the HLW package thermal balance as for example the ones provided in the Refs. (Gogoi and Das (2013) ,  Das et al. (2017) ,  Das (2014) ,  Das et al. (2011) ,  Das et al. (2008) ). In these works, the thermal inverse analysis modeling approaches are conducted on many kinds of thermal parameters and BC. It can be noted that these approaches include the use of optimization techniques, such as nature-inspired evolutionary p. 7 algorithms  (Barth et al. (2016) ), to assist with exploring the search space and finding good solutions. In the current work, we rely on fixed thermal model parameters and idealized convection and radiation fluxes, all from experiments, using the inverse analysis' Neumann-type BC to optimize the thermal response.
Although the proposed inverse method is more complex than prescribing temperature in a deterministic numerical approach, the thermal modeling gains significant extrapolation capabilities for the vitrification process parameters. For example, a thermal forcing (or perturbation) in the thermal energy balance, due to the radioactive power source term, will directly shift the temperature field of the whole glass block with a physically relevant extrapolated behavior. This includes the shifting of the temperatures at the HLW package envelope, which would otherwise remain constant under Dirichlet-type BC. In this Dirichlet-type case, fixed temperatures at the envelope would lead to an overestimation of the thermal dissipation fluxes, then inappropriate for the prediction of the thermal gradients and thermal stresses occurring within the vitrification process.
The aim of the inverse method, over the whole experimental process, is to make the temperatures converge to the measured values at the thermocouple locations. The net total thermal fluxes at the HLW package external surface (convection and radiation fluxes, with the additional virtual fluxes) is hereafter called "homogenized". It will then encompass different complex thermal phenomena. These include, but not limited to: the heat exchanges between the lower and upper parts of the HLW package (due to external or internal thermal fluxes from the glass that has just been casted); a correction of the empirical relations used for convection as well as the thermal radiation; and less important process design characteristics unaccounted for with the model itself, such as the environing facility's sink temperatures map.
The homogenized flux also depends on the internal cracking of the glass block during the thermal experiment, since the conduction is hindered by this phenomenon during the final period of the cooling. However, with the conduction not modeled to account explicitly for such behavior, this effect is directly integrated within the homogenized thermal flux, not the thermal conduction in glass. Altogether, the different thermal fluxes effectively implemented at different locations of the model of the HLW package are further described later in the current section. Throughout the HLW vitrification and cooling process time, all thermal fluxes are fine-tuned to correctly address the main thermal phenomena we expected. The inverse method (IM) steps of the proposed algorithm are presented in the following resolution diagram, see Flowchart, where IM-i represents the inverse method step i.
The glass temperature is in K with the radius R in m, with +1. 1 m in the z-axis in m being the top of the glass block, and with z = 0 m at HLW package bottom (steel envelope included): 0 30 0. 21 1462. 67 23. 78 250 1 0. 21 0. 02 g R R T z δ × -   = + × - -× × +     (1) with δ = 0 if R < 0. 19 m, or δ = 1 otherwise. This prescribed temperature is applied at the beginning of the filling of the glass casting's corresponding layer body which corresponds to p. 11 the estimated process time (the flow of glass from the crucible being tracked by a scale measure, then idealized linearly in this model). This corresponded to filling times at 0 s for the 1 st glass layer, 49 s for the 2 nd , 90 s for the 3 rd , 133 s for the 4 th , 190 s for the 5 th , 270 s for the 6 th , 390 s for the 7 th , 476 s for the 8 th , 590 s for the 9 th , 690 s for the 10 th , 750 s for the 11 th , and at 819 s for the last layer filling corresponding to the first casting.
III. Vitrification and viscoelasticity: the elastic constants (shear modulus) glassy transition is also time-dependent, with possibly different relaxation dynamics compared with point II. In the previous sections, we only reported the boundary conditions linked to the thermal modeling and simulation of the studied experimental HLW vitrification process (I). For this, we implement the whole approach within a 3D finite element method (FEM), using the ABAQUS code (Abaqus  (2013) ). All the defined thermal boundary conditions were scripted into ABAQUS FORTRAN 77 p. 15 subroutines named: HETVAL, DFLUX, and FILM. This way, it facilitates the change of values for the virtual thermal fluxes throughout the iterations of the proposed inverse method. The geometry of the real HLW package is reported by  (Moncouyoux et al.
(2012) ). It can be noted that the implicit time scheme is used (ABAQUS/Standard), so that the forward simulation time steps are varying and will be much larger than the experimental sampling rate (10 s). Consequently, comparisons with experiments will induce small differences when linearly interpolating simulated values and comparing them to experiments. It is then recommended to compare the values at each simulation time steps only, with a weight inversely proportional to the simulation step. The thermomechanical modeling of the HLW package is uncoupled from the thermal modeling. It follows the viscous constitutive model described by  (Barth et al. (2014) ) and reported for different vitrification processes and for a different design where the stainless steel container was not considered in the reported mechanical response.
The thermomechanical constitutive relation was implemented in the subroutine called UMAT for the used FEM code. The mechanical BC were kept similar to the ones in the approach by  (Barth et al. (2014) ), although less constraining in the current work with a designed canister that is fully 3D without any symmetry (for the purpose of applying non-axisymmetric BC in future works). We report in Table  1  the main required properties for the thermal and thermomechanical modeling of the HLW package materials. The other parameters related to viscoelasticity p. 18 relaxations can be found in our previous work  (Barth et al. (2014) ). The material properties are extracted from the following references:  (Moncouyoux et al. (1991) ); Internal CEA note (Langlois (1998)  [unpublished] );  (Barthelemy (2003) ); Internal CEA report  (Godon et al.
Thermomechanical results From our previous works  (Barth et al. (2014) ) a damage mechanics approach coupled to the viscoelastic constitutive modeling was used (see also  (Barth (2013) ,  Barth et al. (2015) ,  Barth et al. (2018) )). We showed that the cooling rates were of the first importance on the thermal stress build-up inside the glass block, and this remains true within the current thermomechanical study with a more realistic HLW package design. Moreover, for the vitrification process studied in  (Barth et al. (2014) ), the inverse method was not applied: the experimental temperatures for SON68 were directly applied to the edges of the HLW package -by Dirichlet boundary conditions. This did not allow us at that time to extrapolate the case of R7T7 glass. In the current work, this is in contrary accounted for, and the thermal stress build-up in the glass block was calculated for these two kinds of HLW processes using the inverse thermal method (Neumann boundary conditions).
(2014) )). For this work model with canister, the maximum stress of inactive SON68 borosilicate glass, at the centerline, was 152 MPa (tension). For p. 25 the extrapolation of the R7T7 nuclear glass, with an initial thermal power of 2500 W/HLW package (and extending the cooling time for the R7T7 nuclear glass to reach the same ambient temperature at the end of the cooling period), the resulting thermal stress build-up was rather similar, with a value of 145 MPa (tension) at the core of the glass block at ambient temperature. We observe for thermally active R7T7 that, even if the glassy transition is lower and induces more thermal strain compared to the inactive SON68, the stress build-up predictions are then lower than in the case of the experimental vitrification process with inactive glass.
Acknowledgements The authors wish to acknowledge the  CEA (LCLT) -French Alternative Energies and Atomic Energy Commission  -for its financial support as well as providing experimental data (under  VESTALE research program  of  CEA , internal program  DEN/2815-C15190 ). The  Andra -French National Radioactive Waste Management Agency -and AREVA NC  are also gratefully acknowledged for supporting this study. The funding sources were involved for providing the study designs (not released), the collection of data, and the release of the paper content, but had no involvement in the analysis and interpretation of data, the writing of the paper, or the decision to submit the article for publication.
The lack of detections from these searches has allowed to set progressively stronger upper limits, thus limiting also the possible contribution of these sources to the observed astrophysical diffuse neutrino flux. Nonetheless, current limits do not yet provide significant constraints on the validity of the internal shock model  (Piran, 1999) , once the many uncertainties on parameters that affect the predictions are taken into account. The results of previous searches of high energy neutrinos emitted by GRBs with ANTARES data can be found in  Adrián-Martínez et al. (2013 , 2017a,b) , while for IceCube in  Aartsen et al. (2015 Aartsen et al. ( , 2016 Aartsen et al. ( , 2017)). In the present paper, the search for astrophysical neutrinos from GRBs is extended, including almost 10 years of ANTARES data.
In Sec. 4 the neutrino spectra predicted by the internal shock model are discussed, focusing on the uncertainties due to the poor knowledge of some parameters. In Sec. 5 the analysis chain is described, explaining the Monte Carlo (MC) simulations of GRB neutrino events that provide the detector response to the signal. Then, in Sec. 6 the estimation of the background that characterises ANTARES data is presented. In Sec. 7 the analysis optimisation is discussed, through the set up of MC pseudo-experiments generated with the aim of obtaining the highest discovery potential for the neutrino flux, by exploiting an extended maximum-likelihood ratio statistical method. In Sec. 8 the diffuse search performed through the stacking technique, investigating whether the discovery potential can be improved by limiting the analysis to an optimised sub-sample of bursts, is presented.