dataesr/scientific-paragraphs-categorization huggingface dataset:
• Enable the Editor Scripting Utilities plugin (Python Editor Script Plugin is enabled by default); • Download the Python scripts and config files shared alongside this article. There are various ways to execute Python scripts: the main ways one can use a Python file or chunk of code are illustrated in Figure  13. The Unreal Python scripting documentation is available at the following address and is an important source of information: https://docs. unrealengine. com/4. 27/en-US/ PythonAPI/, https://docs. unrealengine. com/4. 27/en-US/PythonAPI/, accessed on 22 February 2023. The pieces of script we worked on are partly visible in the explanations below. The whole scripts are in the Jupiter Notebook returned alongside this paper as additional material. Generally useful Unreal classes and functions: People who use Python scripting generally work on the persistent-level actors.
Automatic Loading of the 3D Models with Datasmith For the pipeline, we decided to directly import the 3D models of the TwinMotion prototype with Datasmith. There are other Datasmith-related Unreal classes, as illustrated by Figure  A3  of Appendix B, but those are apparently not relevant to import the models. To do so simply, one only needs to construct a DatasmithScene from the file that contains the model and then import it. The corresponding code can be found in Appendix B. The most-important functions are the one that loads a TM project and the one that removes an element of the loaded scene. For our 3D models, even though the useless elements can be deleted after the import, we chose to do this beforehand, which can save a bit of the memory space when auto-saving. For this purpose, there is the function: unreal.
For each mesh, we can try using wisely all the DataTables or using specific DataTables chosen according to the name of the mesh. How to be more precise in the replacement: Instead of being satisfied with a general DataTable, which would only contain the general ideas of the materials, we wanted to be more specific. For instance, using several DataTables enabled us to be precise if the replacement metallic-like material should be old looking or not. The set of DataTables was created using the references of the assets in TwinMo-tionMaterial plugin. In the plugin, the materials are classified by the following looking characteristics: brick, ceiling, concrete, fabric, glass, grid, ground, leather, marble, metal, modeling, parquet, plastic, roof, stone, tile, wall, which explains the structures of our created DataTables: Method 1: Material replacement with one DataTable; Method 2: Material replacement with succession of DataTables; Method 3: Material replacement with specific DataTables for each mesh; Method 3 is described with its Python code in Appendix D.
This means that one would like to create the mesh replacement table by looking at two keywords in the name of a mesh (the tree species and an age indicator), which leads us to the second method. Method 2: Replace meshes by directly setting the MeshComponent of the actor: There are three StaticMeshes of a tree species: a young (SM_Y), a middle-aged (SM_M), and an aged (SM_A) version. Most of the time, one would want to access the middle-aged trees. To access those meshes, directly loading them with the absolute path will not work. One must enable the TwinMotionContent plugin in the project and then use the path of the plugin that Unreal understands: "/TwinMotionToUnrealContent/Library/Vegetation/Trees/" The Python code for vegetation replacement is given in Appendix A. Nota Bene: To load an asset located somewhere in the content folder of an Unreal project, the path to use begins with "/Game/" and uses the directory from the content, as if the content does not exist.
Results Let us remind that the goal of our immersive visit was to make a platform on which architectural studies can be produced. Therefore, the realism of the obtained visit is essential to the project. Figure  19  illustrates the kind of improvements our method can be bring compared to a standard TwinMotion project. One might argue that such a result could be obtained with TwinMotion, but it would be at the cost of many manual operations. Indeed, the city model of Phase 1 of LaVallée contains 34 buildings, road networks, urban furniture, and landscape vegetation, which would require a modification at least on the shell of the building. Furthermore, the integration of the Phase 2 and Phase 3 models that will be collected from the architectural firms can now be automatically modified: textures, materials, lighting, and vegetation are already set up, just by running the Unreal Engine Python scripting.
To develop the virtual tour, it was necessary to obtain a more realistic model. To do so with Unreal Engine, several tasks can be carried out manually in the case of a small model, but due to the large scale of our model, we proposed several ways of developing the tools to speed up those steps and automate them. During this work, we created various sets of Unreal assets and Python scripts to perform the stated tasks much more simply and quickly. For the research study, we created more realistic scenes extracted from the global model and improved the global model using the pipeline implemented with Unreal Python scripting. For the automation of realism creation process, we built DataTables associated with the assets of the TwinMotionMaterials plugin and devised scripts related to each step of the whole process.
Funding: This research was funded by the E3S project, a partnership between Eiffage and the I-SITE FUTURE consortium. FUTURE bénéficie d'une aide de l'État gérée par l'Agence Nationale de la Recherche (ANR) au titre du programme d'Investissements d'Avenir (référence ANR-16-IDEX-0003) en complément des apports des établissements et partenaires impliqués.
We present an atmospheric analysis of LTT 9779 b, a rare planet situated in the hot-Neptune desert, that has been observed with Hubble Space Telescope (HST)/WFC3 with G102 and G141. The combined transmission spectrum, which covers 0. 8-1. 6 μm, shows a gradual increase in transit depth with wavelength. Our preferred atmospheric model shows evidence for H 2 O, CO 2 , and FeH with a significance of 3. 1σ, 2. 4σ, and 2. 1σ, respectively. In an attempt to constrain the rate of atmospheric escape for this planet, we search for the 1. 083 μm helium line in the G102 data but find no evidence of excess absorption that would indicate an escaping atmosphere using this tracer. We refine the orbital ephemerides of LTT 9779 b using our HST data and observations from TESS, searching for evidence of orbital decay or apsidal precession, which are not found.
g. ,  Lundkvist et al. 2016; Lopez 2017). LTT 9779 b  (Jenkins et al. 2020 ) is an ultrahot Neptune discovered using data from TESS. With a period of less than a day and a radius of 4. 7 ± 0. 23 R ⊕ , LTT 9779 b lies within the hot-Neptune desert, as shown in Figure  1. Radial velocity measurements have constrained the mass to 29 0. 81 0. 78 -+ M ⊕ , yielding a density of 1. 536 ± 0. 123 g cm -3 , which is suggestive of a gas-rich world. Given the rarity of worlds residing within the hot-Neptune desert, LTT 9779 b quickly became a target for atmospheric studies. Photometric eclipse observations of LTT 9779 b with Spitzer revealed a spectrum which is best fitted by a noninverted atmosphere and evidence was found for the presence of CO  (Dragomir et al. 2020). Phasecurve observations with TESS and Spitzer have also revealed a large (∼1100 K) day-night temperature contrast and have suggested a supersolar atmospheric metallicity  (Crossfield et al.
2014). In the context of HST WFC3, this means the persistence is dependent upon the brightness of the host star, the scanning rate, and the readout scheme employed. We show this effect for the second forward scan of the second orbit (the first image used in the analysis) in Figure  3. The differences are clear when comparing the G102 data sets to those taken with the G141 grism: the persistence effect is greater in the latter case, likely due to the higher flux levels as the scan rate and readout schemes were identical. As the extracted flux using the full scan and splitting methods can be different, and to minimize the impact of persistence, we use the splitting extraction in this work. We fit the light curves using our transit model package PyLightcurve  (Tsiaras et al. 2016b ) with the transit parameters from Table  2.
Nevertheless, no major offset in the transit depth was found between the two visits. points. For these intermediary points, we also fit the pressure at which these points occur. Therefore, the isothermal profile adds only a single free parameter to the retrieval while the NPoint adds six. For the free chemistry retrievals, we explore both chemical profiles which were constant with altitude and more complex, varying with altitude, two-layer profiles  (Changeat et al. 2019). Additionally, we conduct equilibrium chemistry retrievals using the code GGchem  (Woitke et al. 2018 ) via the TauREx plug-in system (Al-Refaie et al. 2022). As with the free chemistry retrievals, we include Rayleigh scattering and CIA as well as simple gray clouds. For these retrievals, the free chemical parameters are the atmospheric metallicity and C/O ratio.
Updated Ephemeris and Search for Orbital Decay or Precession Maintaining exoplanet ephemerides is crucial for ensuring that atmospheric studies of exoplanets can be undertaken. Therefore, we utilize the midtimes from our HST light-curve fits, as well as literature observations, to refine the period of LTT 9779 b. Data from TESS Sector 2 led to the discovery of LTT 9779 b and it has since reobserved the host star in Sector 29. 32  These TESS data were analyzed by  Kokori et al. (2023)  as part of the ExoClock project  (Kokori et al. 2021 (Kokori et al. , 2022)) , which aims to refine the ephemerides of planets which will be studied by the Ariel mission  (Tinetti et al. 2018 (Tinetti et al. , 2021;; Edwards et al. 2019 ). In our work, we took the TESS midtimes from this previous study and incorporated new unpublished TESS eclipses.
As with  Kokori et al. (2023) , we utilize the 2 minute cadence Presearch Data Conditioning (PDC) light curves  (Smith et al. 2012; Stumpe et al. 2012 Stumpe et al. , 2014)). Due to the poor S/Ns of the TESS eclipses, we fit them in groups of four eclipses to achieve eclipse midtime uncertainties that were of a similar precision to the transit measurements. Furthermore, before the end of operations, Spitzer observed eclipses and phase curves of LTT 9779 b  (Crossfield et al. 2020; Dragomir et al. 2020). We combine the midtimes from these studies with our data to extend the number of epochs during which LTT 9779 b has been observed. A full list of the midtimes utilized here are given in the Appendix in Tables  4  and 5. To constrain the orbital period further, we utilize the radial velocity data taken of LTT 9779 by CORALIE and HARPS, which were presented in the detection paper of the planet  (Jenkins et al.
2020). To model the radial velocity profile, we use RadVel  (Fulton et al. 2018 ). The radial velocity data and the transit midtimes are fitted under one likelihood using Multinest  (Feroz et al. 2009; Buchner et al. 2014 ) to explore the parameter space. For the linear ephemeris model, we assume a circular orbit (e = 0) following the conclusions of  Jenkins et al. (2020). Given the short period of LTT 9779 b, one might expect that the planet's orbit should be shrinking gradually over time due to the transfer of angular momentum from the planet to the host star, eventually leading to planetary engulfment. This may occur when the orbital period of a planet is shorter than the rotational period of its host star, as is the case for LTT 9779 b  (Jenkins et al. 2020). In this scenario the star's tidal bulge lags behind the tidal bulge of the planet, generating a net torque that spins up the star and causes the planet to spiral inwards  (Levrard et al.
2009; Matsumura et al. 2010). For a given arrangement, the rate of the decay of the orbital period is dependent on the efficiency of tidal energy dissipation within the star, parameterized by the modified stellar tidal quality factor * Q ¢  (Penev et al. 2018). Directly measuring the orbital decay rate via long-term transit timing measurements provides an estimate of * Q ¢ , with a larger value being associated with less efficient dissipation and therefore a slower decay rate. While many studies have searched for evidence of orbital decay (e. g. ,  Patra et al. 2020; Hagey et al. 2022 ), the only highly convincing detection so far has been for WASP-12 b (e. g. ,  Maciejewski et al. 2016; Patra et al. 2017; Yee et al. 2019; Turner et al. 2020; Wong et al. 2022). Therefore, in addition to fitting the timing data with a constant-period model, we fit an orbital decay model to the midtimes and radial velocity data of LTT 9779 b.
Search for the 1. 083 μm Helium Line As well as the low-resolution spectrum used for our atmospheric retrievals, we also extract a higher-resolution spectrum using the G102 data with the aim of searching for the helium line at 1. 083 μm that would be indicative of ongoing Figure  8. Spectrum from the chemical equilibrium retrieval which included an offset for the HST WFC3 G141 data. The retrieval preferred to reduce the depth of the G141 data by 60 ppm but the solution is not preferred over a retrieval without an offset. mass loss. However, despite sampling this spectral range with overlapping spectral bins which had a spacing of only 0. 1 A ånd a bandwidth of 3 nm (30 A ̊), we find no obvious evidence of excess absorption at, or around, this wavelength (see the top panel of Figure  10 ). The variations that are seen in this spectrum are likely due to correlated noise.
In the bottom panel of Figure  10  we show the magnitude of this plateau when our 10/90 He/H model is convolved with these spectral bins. Even in the 10/90 case, the expected signature strength is far smaller than the uncertainties on the G102 spectral data (∼20 ppm versus ∼135 ppm). Therefore, we conclude the data are not sufficient to detect this tracer as even though we expect LTT 9779 b has a significant evaporation rate, its evaporation is barely seen in the helium triplet.
Due to the shape of the decay and precession models within an O -C plot (i. e. , a parabola and a sine curve), we surmise that these Spitzer transit midtimes could be driving the model toward a solution that favors precession, with the large gaps in the transit coverage also enabling such a solution. Therefore, we also explore fits to the data without the Spitzer transit midtimes. In this case, the linear model is preferred with ln(E) = -181. 0 versus ln(E) = -185. 8 and ln(E) = -181. 7 for the orbital decay and apsidal precession models, respectively. Though the evidence against apsidal precession versus a linear ephemeris is only marginal, the best-fit solution for the precession model is linear within the uncertainties (see Table  6 ). The inclusion or exclusion of the Spitzer transit midtimes has a minimal impact on the best-fit linear period but, given the potentially spurious nature of the midtimes, we report the recovered period without these points in Table  2.
Acknowledgments B. E. is grateful to  Wilson Joy Skipper  for coordinating the  HST program   GO-16457 , under which these HST WFC3 observations were acquired. Q. C. is funded by the  European Space Agency  under the  2022 ESA Research Fellowship Program. We acknowledge support from  ESA  through the  Science Faculty -Funding reference  ESA-SCI-SC-LE-117. N. S. acknowledges support from the  PSL Iris-OCAV  project, and from  NASA  (Grant # 80NSSC19K0336 ). This project has also received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreements No. 758892 , ExoAI, and No. 817540 ,  ASTROFLOW ) and from the  Science and Technology Funding Council (STFC)  grant  ST/S002634/1  and  ST/T001836/1  and from the  UK Space Agency  grant  ST/ W00254X/1.
, HITRAN (Gordon et al. 2016), and HITEMP (Rothman & Gordon 2014) databases. Based on the expected chemical species of such a hot planet, we include the opacities of H 2 O (Polyansky et al. 2018), CO (Li et al. 2015), CO 2 (Rothman et al. 2010), HCN (Barber et al. 2013), TiO (McKemmish et al. 2019), VO (McKemmish et al. 2016), FeH (Wende et al. 2010), and H -.
Gaia DR2 For Gaia DR2, the CU6 pipeline has processed 280 million spectra and produced radial velocities for 9. 8 million stars without pre-selection on spectral type or colour indices and for a very broad range of signal to noise ratios. The pipeline includes validation functionalities, described in  Sartoretti et al. (2018) , which can identify autonomously and reject problematic data: e. g. negative total spectrum flux or nearby duplicated transits. These diagnostics are usually meant to detect issues at spectrum or transit level. They do not consider the global properties of the data (known a-posteriori) and the potential outliers. Therefore, following the completion of the processing, an off-line validation campaign was conducted on the full 9. 8 million stars sample to assess its characteristics and identify the stars which did not had the quality to be published in Gaia DR2.
1B ; the detailed description of optical paths and lasers of our 8-year-old Leica SP8 system is provided in the Materials and Methods section). This setting enabled the acquisition of 8 to 10 fluorescent channels, on a system primarily designed for 4 colors, over a skin section of the following 3 dimensions, 0. 6 mm (x) x 0. 4 mm (y) x 20 μm (z) within 25 minutes. The obtained 3-D images were then deconvoluted and compensated to correct 3-D fluorescent spectral spillovers (Fig. 1C, D ) using the Huygens software (Scientific Volume Imaging), a strategy routinely applied in flow cytometry to combine multiple fluorochromes simultaneously  6, 15. Compared to classical segmentation strategies based on nucleus expansion  16  , which often lead to under or overestimation of cellular cluster composition, we used the general immune biomarker CD45 as a robust immune staining visualized in most skin-resident immune cells to constitute the core of our cell segmentation strategy for future single immune cell statistics extraction (Fig.
1E, F ). Using the Isosurface algorithm of the Imaris software (Bitplane) we next modeled the 3D fluorescence signal of CD45 for individual immune cells and exported a corresponding singlecell database composed of the mean fluorescence intensity (MFI) of all individual biomarkers and precise x, y, z tissue coordinates obtained with a resolution of 299 x 299 x 999 nm/voxel (Fig. 1F ). We found that CD45-based segmentation enabled an efficient isolation of single immune cell characteristics, even when those were found aggregated around dermal structural elements. Overall, we demonstrate that it is possible to extract a 10 parameter-single-cell database using regular confocal equipment coupled to basic computational imaging steps.
The activation status of DC, Langerin + DC and LC was investigated using levels of HLA-DR expression. A detailed list of excitation/emission/detection strategies is provided in Table  2. We next aimed to develop an adaptable analytical system that could integrate and batch-process extracted single-cell databases and enable an unsupervised phenotyping of immune subsets. To address this latter challenge, we developed MANTIS, an interactive digital tool based on phenotype attribution matrices inspired by the analytical logic of single-cell RNA sequencing that identifies correlations between the single-cell database and the expression profiles of different cell types (Fig. 2A ). Such an analysis is possible by computing Spearman's Rho correlation, which accommodates non-linear relationships in the expression values (i.
We validated the quantitative performance of MANTIS to annotate immune cells by calculations of statistical correlations with a supervised approach of histo-cytometry  15, 32  applied on the same datasets for each antibody panel in all skin samples. This last method consists in a manual gating of immune subsets on the same principle used in traditional flow cytometry. A total of 20,464 single CD45 + immune cells were identified with the following distribution per condition: 1,670 immune cells in 5 healthy-looking skin samples (i. e. , with 895 myeloid and 775 lymphoid cells), 1,703 in 2 Kawasaki syndrome patients (i. e. , with 932 myeloid and 775 lymphoid cells), 5,076 in 3 SLE chilblain patients (i. e. , with 1,560 myeloid and 3,516 lymphoid cells) and 12,015 in 5 COVID-toes patients (i. e.
, as exemplified here with the epidermis) into virtual subspaces and enables to calculate the nearest neighbor to each immune cell (Supp Fig. 5A ). A batch calculation of the distance of each individual cell can then be visualized under the format of a heatmap, providing a quick overview of the dataset (Supp Fig. 5B ). We found that HLA high dDCs (Supp Fig. 5C ), NK cells (Supp Fig. 5D ) and CD8 + CD57 high T cells (Supp Fig. 5E ) were all significantly enriched near the epidermal layer in cases of COVID-toes. Importantly, CD8 + CD57 low T cells were not found enriched in the epidermis (Fig. 3 H-K ), suggesting a biological link between the expression levels of CD57 and epidermal migration in CD8 T cells. Even though the number of patients studied is limited, these findings strongly suggest the potential formation of tissue damaging subepidermal inflammatory clusters composed of cytotoxic T cells and NK cells in COVID-toes.
Discussion Here we propose a general framework for 3-D quantitative and spatial analysis of skin immune cells at the cellular level. We first describe a simple method to perform a fast 3-D acquisition of up to 10 biomarkers simultaneously and extract a single-cell database containing the biological identity (including spatial coordinates) of skin lymphoid and myeloid cells. We then analyze the extracted databases using an automated and interactive analytic pipeline composed of phenotype attribution matrices coupled with cell-to-structure distance calculations and αshape algorithm-based detection of major inflammatory clusters. Our analysis was focused on FFPE samples as it is still the most easily-available source of pathological tissues and can enable analysis of patients' skin-sampled in routine clinical practice.
However, the use of cryopreserved samples is compatible with the approach we describe here, and enables the analysis of thicker tissue sections (unpublished data). We identified that the first steps of the process, which consists in the generation of good quality 3-D multiplexed images with a significantly high ratio signal over autofluorescent background and no spectral spillover, were not necessarily obvious, while being critical for the rest of the study. This is why we emphasized the capability of a conventional 8-year-old (non-custombuilt) confocal laser-scanning system to acquire 10 different fluorochromes simultaneously. This method of acquisition can be democratized to most academic/clinical facilities since they involve a conventional equipment coupled to basic spectral spillover compensation and singlecell data extraction strategies, via the use of commercially-available softwares (Huygens and Imaris; described in detail in the Methods section).