dataesr/scientific-paragraphs-categorization huggingface dataset:
The cell profile matrix retrieved from the package was generated from the Human Cell Atlas lung small conditional RNA-Seq data set and appended with neutrophil profiles, as described by Desai et al. (5). For correlation analyses, the abundance of cell types is reported for cell types present with greater than 2 relative abundance in more than 2 AOIs. Deconvoluted cell-type output is shown in Supplemental Figures  5  and 6  and Figures  3  and 4. Construction of AOI group-correlation networks. We constructed correlation networks to investigate the relationship among the WGCNA modules, the estimated cell abundances (from SpatialDecon), and the expression levels of immune signaling genes for each of the 5 AOI spatial groups (R igraph library; layout = layout_with_dh). Nodes were scaled in size and/or color according to cell abundance, normalized gene expression, or WGCNA module eigengene expression.
Drawn edges represent significant correlations (P < 0. 05) and were weighted according to the value of the correlation coefficient (Spearman's ρ). WGCNA modules were included in the network if they had a median module eigengene expression greater than zero within the relevant group of AOIs. Immune signaling genes from the KEGG cytokine-cytokine receptor interaction pathway (hsa04060; human) were included if they had a median expression above the expression detection threshold (as defined in the subsection Quality control and preprocessing of GeoMx transcript expression data) in a given AOI group. Cell-type abundance estimates were included if they had a median abundance of more than 2 of the given AOI group. For inclusion in the network, nodes (modules, cell types, and genes) had higher median values than the thresholds stated above and at least 1 positive correlation to another node type.
Mass cytometry imaging. FFPE lung tissue-section slides (5 μm thick) were stained with metal-conjugated Abs (anti-human CD68, CD4, CD8, and CD15; Fluidigm) after antigen retrieval. Intercalator-Ir (Fluidigm) was used to stain DNA. Slides were ablated on the Fluidigm Hyperion Imaging System using CyTOF7 software (Fluidigm) and visualized using an MCD Viewer (Fluidigm). Images were processed for publication using FIJI  (56)  to despeckle and sharpen the images. Data and material availability. The NanoString GeoMx DSP raw sequencing data, the processed expression data, and the metadata are deposited at Gene Expression Omnibus series GSE186213. RBG composite images of the whole immunofluorescence-, IHC-, and H&E-stained tissue sections are available on Zenodo (10. 5281/zenodo. 7269953). Annotated scripts for the computational analysis are available from https:// github.
com/sansomlab/CrossJCIInsight2022; commit ID c604c5ce069f652324e5eeade26a3e23bbfe96d9. Statistics. Statistics were carried out as indicated in the subsections Differential gene expression and over-representation analysis, WGCNA, Cell deconvolution of GeoMx transcript expression data, and Construction of AOI group-correlation networks. Study approval. This study was approved by the ethics committee of the University of Navarra, Spain (approval no. 2020. 192) and the Medical Sciences Interdivisional Research Ethics Committee of the University of Oxford (approval no. R76045/RE001). Tissues were stored at the John Radcliffe Hospital according to Human Tissue Authority regulations (License 12433).
Acknowledgments Thanks to  David Scoville  and  Andy Nam  at  NanoString  for their assistance with the GeoMx workflow. Thanks to  Tiong Tan ,  Kuan-Ying Huang , and  Alain Townsend  for enabling the first immunohistochemical studies for virus detection. ARC is funded by the  Oxford-Bristol Myers Squibb Fellowship  (project number 2034). JH is a  Kidney Research UK Senior Fellow  ( SF1/2014 ). FI is a  Wellcome Trust   Clinical Research Career Development Fellow  ( Wellcome  Trust  211122/Z/18 ). LPH, LD, and GO are supported in part by the  National Institute for Health and Care Research Oxford Biomedical Research Center. The investigators acknowledge the philanthropic support of the donors to the  University of Oxford 's COVID-19 Research  Response Fund. CEA, MVE, and IM are supported by the  Banco Bilbao Vizcaya Foundation  for " Ayudas a Equipos de Investigación Científica   SARS-CoV-2 y COVID-19.
Further details of the numerical design optimization process are provided in Paper I. At the end of the optimization, the performance (contrast, IWA, throughput, PSF field dependence) obtained with the down-selected design is verified with an independent coronagraphic code written in IDL including Fresnel propagation  (Beaulieu et al. 2018 ). This code is used for a tolerance analysis of the PIAACMC components (optics and FPM) in order to define the specification for the manufacturer  (Martinez et al. 2018). A last numerical code is used independently to guarantee that the PIAACMC design is in step with the SPEED optical contrast design; namely the end-to-end SPEED modeling code (SPEED e2e,  Beaulieu et al. 2017) , which incorporates Fresnel diffraction propagation using PROPER  (Krist 2007 ) within a realistic system model (SPEED) with simulated wavefront sensing and control, including as-built optics with measured wavefront errors.
Our analysis is based on numerical simulations, where the M1/M2 error patterns presented in Fig. 4  (middle row images) are used to model a representative error map of 1 nm RMS that can be added to the theoretical M1/M2 maps to emulate scalable manufacturing errors. From these M1/M2 error maps, which are scaled down to a wavefront error of 1 nm RMS over the pupil area, we can assess the impact of errors of various amplitudes, namely from 1 nm RMS to 100 nm RMS, when applied independently to M1 and M2. Our simulations use a code written in IDL that includes Fresnel propagation using PROPER  (Krist 2007) , and a matrix Fourier transform  (Soummer et al. 2007)  for the FPM and the SPEED pupil. While it is difficult to translate RMS to PV error, we can empirically estimate from our modeled error maps of M1/M2 that the PV error is higher than the RMS error by a factor of 7 (M1) and 5 (M2).
2017 (Beaulieu et al. , 2020)). It incorporates Fresnel diffraction propagation using PROPER  (Krist 2007 ) combined with a realistic system model, and is up to date with the bench optical design. It uses simulated wavefront sensing and control, including as-built optics with measured wavefront errors, and guarantees that the emulator is in agree-A6, page 13 of 16 ment with the SPEED bench optical contrast design. The SPEED e2e reproduces the reality of optics positioning and wavefront errors on optical surfaces far from the pupil plane (considering Talbot length), which translates through Fresnel propagation into chromatic phase and amplitude perturbations. The code also includes a dark hole algorithm using and DM2, our two out-of-pupil deformable mirrors. The algorithm generating the zone of high contrast in the science image is based on a linear approach and minimizes the energy inside the dark hole.
BET and FW-H code The blade element theory (BET) represents the most simplified and the least computationally expensive of the applied methods. In the BET, steady lift and drag values are calculated for spanwise blade sections at different azimuthal positions and then integrated for the whole blade and one full rotation. A value of the inflow angle and the corresponding angle of attack can be determined iteratively from a loop with an inflow model. A dedicated Python code used in this study included a linear inflow model developed by Pitt and Peters  [25, 26]  (Fig. 7 ). Aerodynamic characteristics for airfoils identified for each spanwise cut were prepared using XFOIL and considered variation of Reynolds and Mach numbers. Lift and drag coefficients were then extrapolated for high angles of attack with the post-stall Viterna model  [27]  (Fig.
An iterative scheme to ensure pressure equality at the trailing edge and satisfy the Kutta condition was applied in the calculations. The panel generation code PANGEN served in this study as a tool to prepare a computational blade model with finite thickness and sharp trailing edge. The model consisted of 15 spanwise and 95 chordwise panels (Fig. 9 ). The full span free wake of the blades mostly used a vortex lattice, yet additional calculations were carried out using particle wake method, in which the vortex filaments are replaced by point vortices (Fig. 10 ). The particle model solves the vorticity transport equation derived from the incompressible Navier-Stokes equations  [32]. As there are no connections between the neighbouring particles, this approach enables an improved solution for cases where the wake interferes with solid bodies.
PUMA and KIM PUMA (potential unsteady methods for aerodynamics) is an unsteady lifting-line/free-wake solver developed at ONERA since 2013. It includes a coupling between an aerodynamic module and a kinematic module. The aerodynamic module uses the lifting-line method with a free-wake model based on Mudry theory  [34] , which describes the unsteady evolution of a wake modelled by a potential discontinuity surface. It can handle some 3D corrections for blade sweep through local Mach number and angle of attack corrections, and 2D unsteady nonlinear aerodynamics effects through dynamic stall models  [35, 36]. Moreover, different time discretizations are available to balance between accuracy, numerical stability and computational time. The kinematic module employs a rigid multi-body system approach using a treelike structure with links and articulations.
FLOWer and ACCO For high fidelity simulation, the numerical process chain used at the IAG was applied. CFD results are generated with the structured code FLOWer, originally developed by DLR  [42]  and further developed at the Institute of Aerodynamics and Gas Dynamics at the University of Stuttgart  [43]. Acoustic coupling was provided by IAG's FW-H solver ACCO  [44]  with usage of the data output of FLOWer. A second-order dual time stepping for temporal evolution was used with a time step of 1 • to resolve the acoustic waves. Furthermore, the Menter-SST turbulence model was applied to close the URANS equations. For spatial discretization, the surface of the propeller was meshed with 0. 1% of the chord length in streamwise direction. To recognise tip effects, mesh refinement up to 1% of the radius in spanwise direction was set.
RAMSYS and ACO suite The CIRA aerodynamic simulations were carried out using the medium-fidelity code RAMSYS  [47] , which is an unsteady, inviscid and incompressible free-wake vortex lattice boundary element methodology (BEM) solver for multi-rotor, multi-body configurations developed at CIRA. It is based on Morino's boundary integral formulation  [48]  for the solution of Laplace's equation for the velocity potential , whilst the wake is modelled according to the novel formulation proposed by Gennaretti and Bernardini  [49]. The surface pressure distributions are evaluated by applying the unsteady version of Bernoulli equation, which is then integrated to provide the forces and moments on the configuration and the surrounding obstacles. A computational acceleration is obtained by applying the module for symmetrical flows and geometries implemented in the solver and the parallel execution via the OpenMP API.
In particular, the coupling of the FW-H code with the boundary element method is enabled through a boundary surface defined in terms of pressure and normal derivative of the pressure. The FW-H code automatically extrapolates a secondary boundary surface along the normal direction to each panel of the primary boundary surface where the acoustic pressure is computed and the corresponding derivative is computed by finite difference techniques amongst the two surfaces. The quadrupole contribution due to the nonlinear terms distributed in the perturbed field around the blade is neglected. The computational acceleration is obtained by a parallel execution via the MPI API. The simulation of the aeroacoustic free-field was carried out using the aerodynamic database evaluated by RAMSYS, and consisting of the rotor blade pressure distributions.
The ACO-FAM solver was used for the simulation of the acoustic scattering field. It is based on the numerical solution of the convected Helmholtz equation which can be handled with an integral boundary element method  [54]. In particular, ACO-FAM implements a combined Helmholtz integral equation formulation (CHIEF) which is solved with a collocation boundary element method. The ACO-ENV solver implements the acoustic propagation of a generic noise source through a ray approximation as described in  [55]. This solver allows for the computation of several noise metrics starting from the definition of the flight trajectory and the noise source database encompassing the flight envelope. Moreover, it is also able to account for single reflections and attenuation by barriers or buildings. Single reflections are managed through an analytical mirroring of the source whereas the attenuation is managed through semi-analytical formulations  [56, 57].
16. The accuracy of these solvers becomes worse for low advance ratios (Fig. 15 ), which can be explained by the limitations of XFOIL for calculating cases with very small Reynolds numbers and omission of rotational effects in the airfoil data. For most of the analysed points, underprediction of torque values is expected from potential flow solvers, like UPM or RAMSYS caused by neglection of viscosity effects. A post-processing correction in UPM estimating the contribution of profile drag considerably improved the agreement between calculated and measured torque values. The assumption of inviscid flow leads to   With the rotor tilted backwards, the interaction with vortices becomes dominant and the lines from BET and PUMA diverge, as the BET code did not include a wake model in the solution (Fig.
Conclusions and outlook Aeroacoustic and aerodynamic calculations were performed, indicating that measurement results for most cases were reproducible with satisfactory agreement by all computational methods, regardless of their fidelity level. The most basic and time-efficient BET shows compatibility with other tools for moderate flight velocities and propeller tilt angles, when it comes to time-averaged results, yet the code is not a reliable tool for reproducing transient loading. The BET approach loses credibility for cases where a three-dimensional wake influence becomes dominant, like flow with backward inclination of the propeller. An accurate result for such conditions can be achieved with methods including wake in the solution, from which codes incorporating viscous effects (like FLOWer, UPM with particle wake) offer more stability.
This is a 13% increase from the 2023 GHGI estimate for CONUS in 2019. We find livestock emissions of 10. 4 (10. 0 -10. 7) Tg a -1 , oil and gas of 10. 4 (10. 1 -10. 7) Tg a -1 , coal of 1. 5 (1. 2 -1. 9) Tg a -1 , landfills of 6. 9 (6. 4 -7. 5) Tg a -1 , wastewater of 0. 6 (0. 5 -0. 7), and other anthropogenic sources of 1. 1 (1. 0 -1. 2) Tg a -1. The largest increase relative to the GHGI occurs for landfills (51%), with smaller increases for oil and gas (12%) and livestock (11%). These three sectors are responsible for 89% of posterior anthropogenic emissions in CONUS. The largest decrease (28%) is for coal. We exploit the high resolution of our inversion to quantify emissions from 73 individual landfills, where we find emissions are on median 77% larger than the values reported to the EPA's Greenhouse Gas Reporting Program (GHGRP), a key data source for the GHGI.
S. (CONUS) for 2019, allowing for detailed analysis of sectoral, state, and urban emissions. Satellite observations of atmospheric methane column concentrations inferred from measurement of backscattered sunlight in the shortwave infrared have been used extensively in inverse analyses of methane emissions  (Streets et al. , 2013; Jacob et al. , 2022). Previous satellite instruments were limited by large pixel sizes  (SCIAMACHY, 2003 (SCIAMACHY, -2012) )  or sparse observations  (GOSAT, 2009 -present). TROPOMI provides daily, global observations of atmospheric methane columns at 5. 5 km × 7 km nadir pixel resolution  (Hu et al. , 2018)  with a ~3% success rate limited by cloud cover, optically dark surfaces, and heterogeneous terrain  (Hasekamp et al. , 2019). Inversions of TROPOMI data allow for high-resolution quantification of methane emissions but require understanding the information content of the observations.
The basis is given by the eigendecomposition of the prior-preconditioned Hessian of the cost function, 𝐇 9 ' = 𝐒 ! $ ( ⁄ 𝐊 " 𝐒 & #$ 𝐊𝐒 ! $ ( ⁄ = 𝐕𝚲𝐕 " , (2) where the columns of 𝐕 are the eigenvectors and 𝚲 is a diagonal matrix with entries equal to the eigenvalues. The calculation of 𝐇 9 ' requires substantial memory for large 𝑚 and 𝑛, for which we use Dask, a Python parallelization package  (Dask Development Team, 2016). The reduced-rank posterior approximation is then generated using the largest 𝑘 eigenvalues 𝚲 * and the associated eigenvectors 𝐕 *  (Bousserez and Henze, 2018) : 𝐀 𝐊 = 𝛾𝐒 ! 𝐕 * 𝚲 * (𝐈 * + 𝛾𝚲 * ) #$ 𝐕 * " 𝐒 ! , (3) 𝐒 4 𝐊 = (𝐈 , -𝐀 𝐊 )𝐒 ! , and (4) 𝐱 3 -. = 𝐱 ! + 𝛾𝐒 4 𝐊 𝐊 " 𝐒 & #$ C𝐲 -𝐅(𝐱 ! )E. (5) Here, 𝐱 3 -. approximates the full-rank (FR) posterior 𝐱 3 by minimizing the difference between the two, and 𝐒 4 𝐊 and 𝐀 𝐊 are the optimal posterior error covariance and averaging kernel matrices, respectively, for an inversion solved with a reduced-rank forward model.
Forward model We use the nested version of the GEOS-Chem CTM 12. 7. 1 (DOI: 10. 5281/zenodo. 3676008) at 0. 25° × 0. 3125° resolution over North America as the forward model for the inversion. Earlier versions of the methane simulation were described by  Wecht et al. (2014a)  and  Turner et al. (2015). The model is driven by GEOS-FP meteorological fields from the NASA Global Modeling and Assimilation Office  (Lucchesi, 2017). Methane sinks from OH, Cl, soil uptake, and stratospheric oxidation are as described in  Maasakkers et al. (2019). Initial conditions for January 1, 2019 and 3-hourly boundary conditions for the year are specified by methane concentration fields from a global GEOS-Chem simulation at 2° × 2. 5° resolution using optimized emissions from a global inversion of TROPOMI observations  (Qu et al.
CONUS sectoral emissions We find posterior anthropogenic methane emissions of 30. 9 (30. 0 -31. 8) Tg a -1 for CONUS in 2019, a 13% increase from the GHGI23 estimate of 27. 3 (25. 1 -30. 6) Tg a -1 , where the values in parentheses represent the GHGI23 95% confidence interval (EPA, 2023). Lu et al. (2022)  found larger anthropogenic emissions of 36. 2 (32. 1 -37. 6) Tg a -1 over the same domain for 2017 by optimizing emissions and trends in a joint inversion of GOSAT and in situ observations for 2010 to 2017. Worden et al. (2022)  found lower anthropogenic emissions of 27. 6 (22. 6 -23. 9) Tg a -1 over the U. S. for 2019 by regridding global inversions of GOSAT data that optimized emissions at 2° × 2. 5° resolution using uncertainties for the prior and posterior estimates. Deng et al. (2022)  reviewed an ensemble of global inversions and found median U.
S. posterior anthropogenic emissions for 2019 of 26. 5 (20. 8 -38. 7) Tg a -1 with GOSAT data and 31. 9 (23. 9 -43. 1) Tg a -1 with in situ data. We allocate our national total to individual emission sectors using the attribution method described in Sect. 2. 8. From the off-diagonal structure of 𝐒 4 𝐊,6;< (Eq. (  8 )), we find very low posterior error correlation between the sectors (mean error correlation coefficients less than 0. 2), indicating that we can accurately separate sectoral emissions. Figure  4  and Table  2  summarize the results compared to the GHGI23. Livestock, oil and gas, and landfills account for 89% of posterior anthropogenic emissions and all increase relative to the GHGI23. We find a significant decrease from the GHGI23 only for coal. For these four sectors, we find sectoral averaging kernel sensitivities between 0.
We validate our posterior landfill results by comparison to aircraft-derived estimates for nine facilities as shown in Fig. 5. The top 10 landfills with the largest posterior methane emissions from the TROPOMI inversion for 2019. Numbers correspond to the labels in Fig. 5. enhancement method with data from 2016 to 2018. We find agreement within error bounds at the Seneca Meadows Landfill in New York (landfill c in Fig. 5 ;  Catena et al. , 2022)  and at the Kiefer (d), Frank R. Bowerman (f), Altamont (g), Newby Island (h), and Keller Canyon (i) Landfills in California  (Smith, 2021; Duren et al. , 2019). We find much larger emissions than previous studies at the South Side Landfill (a) in Indiana  (Cambaliza et al. , 2015)  and at the West Miramar Sanitary (b) and Puente Hills (e) Landfills in California  (Smith, 2021; Duren et al.
The only study that used aircraft data to estimate emissions within a U. S. Census Urban Area found 314 ± 96 Gg a -1 in New York City  (Pitt et al. , 2022) , which is very similar to our estimate of 309 (241 -417) Gg a -1. Plant et al. (2022)  used U. S. Census Urban Areas but relied on TROPOMI methane to CO ratios. They found slightly larger emissions in Atlanta and Philadelphia and much larger emissions in New York City, but their error bars spanned ranges almost twice as large as the derived emissions, limiting the utility of the comparison. Plant et al. (2019)  found larger emissions in New York City and Philadelphia but used larger definitions of urban areas and produced similarly wide error ranges. We find much lower emissions than these studies only in Los Angeles, a difference that decreases but remains significant when we use the same extent as these studies.
Acknowledgments This work was supported by the  NASA Carbon Monitoring System (CMS) ,  ExxonMobil Technology and Engineering Company , and the  Harvard Climate Change Solutions Fund. Part of this work was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration (NASA). We thank  Bryan Mignone ,  Felipe J. Cardoso-Saldaña ,  Robert Stowe ,  Lauren Aepli , and  Melissa Weitz  for helpful discussions.
Data availability The TROPOMI v14 data are available from SRON at https://ftp. sron. nl/open-access-data-2/TROPOMI/tropomi/ch4/14_14_Lorente_et_al_2020_AMTD/ (last access: 19 March 2021). The GLOBALVIEWplus CH4 ObsPack v3. 0 database is available from NOAA's Global Monitoring Laboratory at http://dx. doi. org/10. 15138/G3CW4Q. The prior and observational inputs for the inversion and the posterior emissions and averaging kernel sensitivities are available at https://github. com/hannahnesser/TROPOMI_inversion. Additional data related to this paper may be requested from the authors.
@ xi /@x i https://doi. org/10. 5194/egusphere-2023-946 Preprint. Discussion started: 13 June 2023 c Author(s) 2023. CC BY 4. 0 License. We evaluate the inversion results by comparing simulated observations from GEOS-Chem driven by either the prior or the mean posterior emissions to TROPOMI observations and to independent in situ surface and tower observations from NOAA's GLOBALVIEWplus CH4 ObsPack v3. 0 database (Cooperative Global Atmospheric Data Integration Project, 2019). We followLu et al. (2021)  and use only daytime ObsPack observations with outliers excluded. We use monthly average ObsPack observations over CONUS to increase consistency with the annual temporal resolution of our inversion and its distribution of information content. Compared to TROPOMI, both the prior and posterior GEOS-Chem simulations produce similar coefficients of determination (R 2 ) and root mean squared errors (RMSEs).