dataesr/scientific-paragraphs-categorization huggingface dataset:
Model Intercomparisons The beginning of the first network of exoplanet model intercomparison is a great time to address organizational challenges, and it should be a top priority for the community. In order to advance inter-model comparisons for exoplanets study, a first-order requirement is a collaboration workshop for the community to discuss key issues, especially regarding data to be shared (and how to share it), which would be very important for establishing standards and best practices for intercomparisons going forward. A formal intercomparison workshop could be organized for roughly one year after the THAI workshop (fall 2021), which would allow time for planning the workshop and getting funding support to encourage participation. We can expect that an intercomparison workshop would produce documentation about best practices for model intercomparisons, and most importantly, produce a community consensus on how to share data so that a common repository (to be identified) would host a comprehensive set of common diagnostic outputs that are most important for addressing the science questions asked (e.
2020). The current version of HEXTOR attempts to improve the accuracy of the radiative transfer in the model by using a lookup table, which conducts a nearest-neighbor interpolation for OLR and albedo using a database containing thousands of 1D radiative-convective climate calculations. This provides an advantage in accuracy at the cost of added computational expense. During the THAI workshop, Dr. Haqq-Misra showed that HEXTOR in a latitudinal configuration either underestimates or overestimates the global average temperature in the THAI simulations, because the hemispheric differences between the day and night sides cannot be represented with a single dimension in latitude; however, he also showed that HEXTOR can also be configured as a longitudinal EBM through a coordinate transformation, which places the substellar point at the north pole and allows the day-to-night side contrast to be represented more accurately  (Fortney et al.
The runtime performance of Python can be improved by using pre-compiled libraries (e. g. , numba or NumPy), but it has not yet been used to develop a GCM. (5) Machine learning Machine learning techniques are on the verge of revolutionizing many fields of science, including astrophysics (e. g. ,  Way et al. 2012; Ivezić et al. 2019 ) and exoplanets (e. g. ,  Shallue & Vanderburg 2018; Armstrong et al. 2020). We thus asked the survey participants if they believe that Machine Learning (ML) /Artificial Intelligence (AI) techniques could also significantly help atmospheric modeling and if so how. The results are presented in Fig. 5. Opinions are again very divided, but with a significant peak for people with no opinions. This is most likely symptomatic of the fact that the use of ML techniques is a topic that has been very little discussed in the (exoplanet) atmospheric modeling community to date.
This workshop report therefore recommends to GCM users to systematically disclose the amount of CO 2 released by running computer simulations and eventually consider a carbon mitigation plan. While it has not been actively discussed during the THAI workshop, it is important to mention here the access of GCM data post publications. Discussions among co-authors generally agree that GCM data should be made available post publication, when possible. However, the amount of GCM data can be very large which may lead to additional fees to store them on disks and/or clouds beyond the limit that is usually allowed for free. It has also been discussed that it is actually quite rare that data from a published study are effectively downloaded and used. Therefore, the ratio benefit to cost of systematically making available GCM data may not always be relevant.
A. 1. 1. ExoCAM ExoCAM is an exoplanet branch of the Community Earth System Model (CESM) version 1. 2. 1. CESM is provided publicly by the National Center for Atmospheric Research in Boulder, CO, (http: //www. cesm. ucar. edu/models/cesm1. 2/) and ExoCAM is freely available on GitHub (https://github. com/ storyofthewolf/ExoCAM). To use ExoCAM, the user must first obtain CESM v1. 2. 1, and then ExoCAM is installed as a patch on top of the core CESM code. Ex-oCAM was developed by E. T. Wolf to facilitate accessible configurations for exoplanet and planetary modeling, and is now used by several different research groups in the community. The ExoCAM code package includes model configurations, initial condition files, source code modifications, and an accompanying flexible correlated-k radiative transfer model, ExoRT https: //github.
A. 1. 2. LMD-G The LMD-G GCM -or the LMD Generic model -is a 3D Global Climate Model historically developed at the Laboratoire de Meteorologie Dynamique (LMD) in Paris, France. The model originally derives from the LMDz three-dimensional Earth  (Hourdin et al. 2006)  and  Mars (Forget et al. 1999 ) Global Climate Models, but it benefits from the lessons learned by developing GCMs for most atmospheres in the solar system, where models can be tested against a wide range of observations  (Forget & Lebonnois 2013). It solve the primitive equations of geophysical fluid dynamics using a finite difference dynamical core on an Arakawa C grid. The LMD-G GCM is equipped with flexible radiative transfer (based on the correlated-k method,  Wordsworth et al. (2011b) ) and thermodynamics/cloud microphysics packages with the objective of being able to simulate any cocktail of atmospheric gases (as long as spectroscopic datasets are available) and aerosols.
2005; Mayne et al. 2014b ). The UM includes sophisticated physical parameterizations for subgrid-scale turbulence, convection, water cloud and precipitation, as well as radiative transfer which is solved by the open-source, two-stream, correlated-k code SOCRATES, accessible at https://code. metoffice. gov. uk/trac/socrates and surface and sub-surface processes 1. Note that through the SOCRATES radiative transfer code, the UM is capable of generating synthetic spectra for any given 3D simulation (e. g. ,  Lines et al. 2018c; Boutle et al. 2020). Adaptation and application of the UM to exoplanets has been led by the Exeter Exoplanet Theory Group (EETG, exoclimatology. com). The UM was initially benchmarked against a range of standard Earth-like planet tests  (Mayne et al. 2014a) , opening an avenue for studying temperate extraterrestrial atmospheres.
ACKNOWLEDGMENTS The workshop SOC acknowledge funding support from the  Nexus for Exoplanet System Science (NExSS). T. Fauchez ,  R. Kopparapu ,  S. Domagal-Goldman  and  M. J. Way  acknowledge support from the  GSFC Sellers Exoplanet Environments Collaboration (SEEC) , which is funded in part by the  NASA Planetary Science Divisions Internal Scientist Funding Model. M. Turbet received funding from the  European Research Council (ERC)  under the European Unions  Horizon2020 research and innovation program  (grant agreement No. 724427/FOUR ACES ) and from the  European Unions   Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No. 832738/ESCAPE. M. T. thanks the  Gruber Foundation  for its generous support. This work has been carried out within the framework of the  National Centre of Competence in Research PlanetS  supported by the  Swiss National Science Foundation.
However, because of the large amount of data required for calculating differential dependencies across gene networks, such analytics are often not feasible across the limited -omics datasets of rare or emerging diseases. Pulmonary hypertension (PH) represents such an enigmatic vascular disease that consists of five World Symposium of Pulmonary Hypertension (WSPH) groups  (3). In particular, WSPH group 1 (pulmonary arterial hypertension, PAH) and group 3 (due to hypoxic lung disease) PH subtypes are driven by shared triggers of hypoxia and inflammation, and mortality is high. Current medications primarily vasodilate, are mostly used to treat group 1 PAH, and are not curative. Thus, an unmet need exists for new drug discovery. In particular, endothelial pathobiology is a characteristic and pathogenic feature of PH contributing to the inflammation and aberrant vascular remodeling observed in this disease  (4).
However, because of complex spatio-temporal manifestations that balance critical processes such as apoptosis and proliferation  (5, 6)  during disease progression, therapeutic targeting of endothelial dysfunction in PH has been challenging. The advancing appreciation of broad molecular parallels between PH and cancer in general  (7) , as well as the direct link between developing PH in the setting of lung cancer specifically  (8) , have increased enthusiasm for repurposing existing small-molecule inhibitors from cancer to PH  (9). This may be particularly relevant for precise therapeutic targeting of dysregulated endothelial survival-a process also crucial for hypoxic and inflammatory-driven tumorigenesis  (10). To date, however, the broad molecular profiling existing in cancer datasets has yet to be leveraged for such PH drug discovery.
EDDY has been used in the study of human diseases  (11)  and transcriptomic analyses from human PH lung tissue  (12)  to identify DDNs in disease. EDDY was applied to a dataset derived from the Cancer Cell Line Encyclopedia [CCLE; encompassing a catalog of RNA sequencing data from 810 cancer cell lines  (13) ] and the Cancer Therapeutics Response Portal [CTRP; surveying the response of those cell lines to 368 small molecules  (14, 15) ]. In doing so, for each cancer drug surveyed, this EDDY-CTRP identified DDNs that define drug response by virtue of their specific rewiring in sensitive versus resistant cells  (16). Leveraging those principles, we developed a computational platform (EDDY-CTRP-PH) to predict the landscape of cancer drug functions that rely upon rewired DDNs of genes common to cancer and PH as well as have shared links to hypoxia and inflammationthus exerting robust activity in controlling multiple PH subtypes.
EDDY-CTRP-PH: In silico mapping of small molecules that depend upon rewired PH-specific DDNs for activity To identify PH-specific DDNs that mediate crucial cellular responses to specific small molecules, a catalog of gene networks integral to PH pathogenesis was necessary for initial input. Building upon our prior methodology  (25) , 55 PH-relevant gene clusters were identified (table  S1 ) and analyzed by EDDY in the context of the CCLE and CTRP datasets (EDDY-CTRP-PH workflow; Fig. 1A ) to discover PH gene clusters strongly associated with a cancer cell's response to drugs and mediators for each PH gene cluster. Namely, for each cancer drug tested, cell lines were categorized into two groups: drug-sensitive and drug-resistant, as described  (16). For each drug, transcriptomic profiles were analyzed by EDDY to define PH-relevant gene clusters that displayed significant rewiring of DDNs between sensitive versus resistant cell lines.
10 ). Drug repurposing has been viewed as an attractive method for reducing the cost and time of drug development, particularly for rare diseases like PH where investment is lower than other prevalent diseases  (42). Relevant to the COVID-19 pandemic, repurposing has also been pursued for emerging diseases where prior knowledge of pathogenic target genes or pathways can be used as a linchpin for system-wide predictions of therapeutic drug activity  (43). Here, the computational strengths in gene dependency analyses of EDDY-CTRP-PH coupled with the vast cancer cell data answer an unmet need for a rapid and system-wide method to identify therapies for rare diseases, such as PH, without prior knowledge of the molecular drug target of interest. As deep sequencing projects mature for PH and are continually applied to EDDY-CTRP-PH, broader predictions of drug-pathway interactions will be possible, extending beyond the existing PH gene clusters derived from curated literature searches and the known gene interactome.
Moreover, with the statistical power to map DDNs within a single blood or tissue sample via single-cell sequencing, future applications of EDDY-CTRP-PH to precision medicine could be envisioned via identification of individuals and/or disease subtypes with specific DDN profiles who are most likely to respond to repurposed cancer drugs. Our experimental validation of EDDY-CTRP-PH also advances our understanding of complex cellular pathways in PH and offers guidance for translation of both I-BET762 and BRD2889 to PH. First, for both drugs, their therapeutic roles emphasized the importance of endothelial pathobiology in PH, with both long-term and immediate translational implications, as early human clinical trials are underway for BET inhibitors in PAH (NCT03655704). Second, EDDY-CTRP-PH identified multiple rewiring events for these molecules, particularly in the hotspot and extensively altered clusters C15 and C43.
Experimental design The goal of this study was to generate a computational-to-empirical pipeline for identifying and ranking the most robust actions of specific cancer therapeutics and revealing their disease-relevant downstream targets in an example of a rare noncancerous disease such as PH. Data sources for EDDY-CTRP-PH included CCLE (13), CTRP  (14, 15) , and a specific PH gene network (table  S1 ). Following identification of I-BET151/762 with convergent actions on PH gene cluster 15 (including the gene LGALS8), gene expression, mitochondrial redox levels, and cellular apoptosis were measured in primary human PAECs. To determine the effect of this drug on PH in vivo, C57BL/6 mice suffering from hypoxia-induced group 3 PH and two models of Sprague-Dawley rats suffering from group 1 PH (MCT exposure and SU5416-hypoxia) were treated with I-BET762.
Data and code availability All the microarray data have been submitted to GEO (accession nos. GSE125508 and GSE160255 for I-BET and BRD2889, respectively). EDDY software is available at GitHub repository (https:// github. com/dolchan/eddy-gpu). The EDDY-CTRP-PH analysis for all the clusters and small molecule is available at https://chan. vmi. pitt. edu/eddy-ctrp-ph/.
Acknowledgments: We acknowledge the  Center for Organ Recovery & Education , the organ donors, and families for human lung and heart tissue samples used in this study. Funding: This study was supported by  National Institutes of Health  grants  R01 HL124021 ,  HL 122596 ,  HL 138437 , and  UH2/UH3 TR002073 (S. Y. C. ) ; American Heart Association  grant  18EIA33900027  (S. Y. C. );  University of Pittsburgh Center for Precision and Translational Pharmacology (S. Y. C. ); University of Pittsburgh   CTSI Biomedical Modeling Pilot Award  (S. Y. C. );  National Institutes of Health  grant  R01 HL148712 (I. A. G. );American Heart Association grants15SDG24910003  and  18IPA34170257  (I. A. G. ); American Lung Association  grant  RG-515656  (I. A. G. );  Gilead   RSP in PAH grant  (I. A. G. ); Texas A&M System Chancellor's Research Initiative for the Center for Computational Systems Biology at the Prairie View A&M University  (S.
53 and 0. 65 m, respectively  [12]. The addition of the topographic band to the spectral information has been shown to significantly improve spaceborne sub-metre LULC mapping  [12]. Even if the novelty of the latter work relied on the sole use of a spaceborne stereo-imagery, the bathymetry and the SUSC mapping were not examined. An integration of the terrestrial and marine DSM into the spaceborne sub-metre spectral dataset was elsewhere useful in mapping the seamless coral reefscape in Japan using Google Earth imagery  [23] , but it was not derived from a sole spaceborne by-product. To our knowledge, a unique study has focused on the land-sea coral reefscape mapping using a sole spaceborne sub-metre stereo-imagery  [24]. Despite the use of the WorldView-3 stereo-imagery to produce land-sea DSM, the authors had not previously investigated the added value of the 16-band superspectral dataset to map LULC and SUSC, simultaneously.
A series of 90 photoquadrats and 90 aerial photographs were orthorectified  [8]  to distinguish 10 classes, representative of an average land-sea coral reefscape (Table  1 ). Five land and five sea classes were each constituted of 30 and 30 seed pixels neighbourly and evenly grown to 3000 calibration and 3000 validation pixels, respectively. The three remaining classes, namely forest, roof and shadow, were straightforwardly characterized by 3000 calibration and 3000 validation pixels visually selected on the satellite imagery. Provided with a daily revisit, this sensor leverages a swath width of 13. 1 km and length of 112 km. The WorldView-3 dataset used here is a stereo-imagery acquired on 18 July 2018 at 20:35:38 UTC (Figure  3a , Table  2 ) and 20:36:39 UTC, respectively (Figure  3b , Table  2 ).
Spaceborne Topographic DSM The building of the satellite-based topographic DSM requires both panchromatic imageries to be radiometrically converted from digital number to top-of-atmosphere radiance, then to bottom-of-atmosphere reflectance values by considering the calibration factors (. IMD file), the atmosphere composition and sun irradiance (see  [5]  for details). The reflectance imageries were used to retrieve a 3D point cloud using a dense point matching algorithm  [28]. The matching algorithm seeks for the pairwise pixels of two imageries by shortening the epipolar 2D to 1D, based on the rational polynomial coefficients (RPCs), then by reducing the length of the epipolar line with the global multi-resolution terrain elevation data 2010 dataset. The point cloud was then gridded at 0.
Spaceborne Bathymetric DSM The creation of the satellite-based bathymetric DSM relies on the use of a single multispectral imagery that needs to be both radiometrically and geometrically corrected, as well as pansharpened. The imagery #2 was selected for this purpose since it displayed the lowest off-nadir viewing angle (Table  2 ). Following the radiometric correction (see 2. 4), the VIS+NIR multispectral reflectance imagery was orthorectified using the RPCs and the 20 ground control points, as well as the corresponding panchromatic reflectance imagery. Among seven sharpening methods, the Gram-Schmidt pansharpening procedure yielded the best visual results, and it was then implemented so as to produce a VIS+NIR dataset at 0. 3 m pixel size (see  [5]  for details). The resulting sub-metre eight-band dataset was subjected to the radiative transfer model, called the ratio transform  [29].
WorldView-3 Superspectral Land-Sea Habitat Mapping The classification performance of the BGR basic dataset provided a satisfactory OA of 89. 15% (Figure  5a ). On the one hand, the addition of the coastal and yellow predictors, yielding the VIS combination, increased the basic OA by 2. 69% (Figure  5b ). On the other hand, the addition of the RE, NIR1 and NIR2 to the VIS combination, so as to produce the optical dataset, augmented the basic OA by 8. 79% (Figure  5c ). The addition of the eight MIR spectral bands, individually, to the optical dataset showed a high mean contribution of 9. 52% (Figure  6 ). However, some subtle patterns could be highlighted: the MIR8, MIR7, MIR2, MIR5 contributed to a gain of the basic OA (9. 58%, 9. 57%, 9. 53% and 9. 51%, respectively); the MIR3, MIR4 and MIR6 contributed to the mean increase of 9.
WorldView-3 Topobathymetry into Land-Sea Habitat Mapping The evaluation of the influence of the WorldView-3-derived topobathymetry followed the same procedure as in 3. 1. The addition of the DSM to the BGR basic dataset increased the OA by 2. 74% (Figure  7a ). The addition of the DSM to the VIS combination augmented the basic OA by 5. 44% (Figure  7b ). The addition of the DSM to the optical dataset enhanced the basic OA by 9. 15% (Figure  7c ). The addition of the DSM to the eight MIR spectral bands (individually added to the optical dataset) showed a high mean contribution of 9. 55% (Figure  8 ), slightly better than the MIR bands with no DSM (Figure  6 ). Some variations still appeared: the MIR1, MIR8, and MIR7 contributed to the best gains of the basic OA (9. 73%, 9. 69%, and 9. 60%, respectively); the MIR5 and MIR6 contributed to the mean boost of 9.
WorldView-3 Land-Sea Habitat Mapping at the Class Scale The best OA for the WorldView-3 multispectral and superspectral, deprived of and provided with the inner topobathymetry, were further studied at the class level by comparing their confusion matrices with that for the basic BGR (Figure  9 ). Concerning the multispectral level, the optical dataset strongly improved the discrimination of the grass (27. 7%) and wood (19. 27%), as well as the road (15. 93%). The coral reefs also benefited from a better differentiation (1. 2%), partly due to the decline in omission misclassification with the offshore (-0. 93%). The addition of the topobathymetric DSM confirmed the greater distinction between previous classes (grass, 27. 5%; wood, 26. 27%; and road, 19. 27%), but also improved the ability to detect the roof (23.
87%) and soil (8. 53%) classes. The coral reefs' separability was also heightened (1. 87%) owing to the decrease in omission misclassification with the nearshore (-0. 57%). Regarding the superspectral degree, the optical dataset enhanced with the MIR8 empowered to distinguish grass (27. 6%), wood (25. 53%), roof (22. 87%), road (20. 03%), and soil (14. 6%) classes. The coral reefs were also better discerned (2. 07%) given the reduction in omission misclassification with the nearshore (-0. 93%). The integration of the DSM information to the optical+MIR1 dataset still upgraded the classification of the grass (27. 33%), wood (27. 1%), roof (24. 5%), road (20. 67%), and soil (12. 97%). Nevertheless, the coral reefs' classification remained rigorously constant (2. 07%).
Land-Sea Coral Reefscape Mapping with a Multispectral WorldView-3 Stereo-Imagery Together with its predecessor the 2009 WorldView-2, the 2014 WorldView-3 remains the state-of-the-science sub-metre spaceborne optical sensor, leveraging two extra spectral bands in the VIS and two supplementary spectral bands in the NIR gamut, compared to all other sub-metre competitors (see Introduction). The use of the five-band VIS and eight-band optical data showed a greater classification performance of the land-sea coral reefscape than did the basic BGR, namely a gain of 2. 69% and 8. 79%. These results are in strong agreement with the WorldView-2 multispectral Moorea land-sea mapping project  [11]. Even if the satellite-based topography, on the one hand, and bathymetry, on the other hand, are commonly studied for sub-metre optical sensors  [12, 15] , it is still innovative to create satellite-based merged topobathymetry: Pleiades-1  [25] , WorldView-3  [24].
Land-Sea Coral Reefscape Mapping with a Superspectral WorldView-3 Stereo-Imagery The greatest novelty of the WorldView-3 resides in the collection of the eight-band MIR spectral bands, doubling the spectral bands of the WorldView-2 and extending the spectrum to 2365 nm  [27]. The individual contributions of the eight MIR bands all contributed to the gain in classification accuracy of the land-sea coral reefscape compared to the basic mapping performance, ranging from 9. 47% (MIR1) to 9. 58% (MIR8), through 9. 57% (MIR7). These significant inputs corresponded to a boosting of the optical (VIS+NIR) prediction, ranging from 0. 68% to 0. 79%. The topobathymetric DSM reinforced the classification accuracy derived from the WorldView-3 optical datasets provided with the eight MIR bands. The best performances were here ensured by the MIR1 (9.