dataesr/scientific-paragraphs-categorization huggingface dataset:
Genomic Medicine in the Private Sector The increased integration of genomics into public healthcare systems is mirrored by an explosion in the use of genomics in the private sector, particularly in the US. Geisinger Health System's MyCode project, which began as a partnership with Regeneron Pharmaceuticals to perform exome sequencing in 100,000 Geisinger patients and use the results for drug discovery and clinical care,  42  has recently expanded to all consenting Geisinger patients. Foundation Medicine has developed a number of genomics-based tests in the domain of precision cancer medicine while also contributing to public databases, such as the National Cancer Institute's Genomic Data Commons. [45]  International Collaboration to Accelerate the Implementation of Genomics into Healthcare The above-mentioned implementation approaches and priorities of genomic-medicine initiatives in high-income countries might not necessarily be applicable to low and middle-income countries.
46 Yet, broad implementation will be crucial in building representative population reference datasets that improve variant interpretation globally  47  and in accelerating the discovery of genes associated with rare disease, particularly in populations where consanguinity is common. 48 Implementation in a range of economic and social contexts will also help address health priority areas with a major contribution to global disease burden, including host-pathogen interactions in infectious diseases; common monogenic disorders, such as sickle cell disease and thalassemias; and complex conditions, including hypertension, dyslipidemia, diabetes, stroke, and kidney disease. The Global Genomic Medicine Collaborative is working to identify and share genomic-medicine implementation activities around the globe, including those in under-resourced areas.
Genomic Data Sharing The importance of breaking down data silos to accelerate the development of knowledge databases that directly improve patient outcomes cannot be underestimated. 9 Genomic data generated within healthcare settings are subject to strict national regulatory frameworks that are unlikely to allow large-scale data migration, and innovative solutions are necessary to enable federated data analysis without data movement across geographical borders  58  while maintaining public trust. 59 National genomic-medicine programs have the opportunity to resource and promote best practices in data sharing by structuring data access and consent processes, collecting clinical and genomic data in interoperable formats, committing to global data sharing, and informing public debate and policy development.
Box 1. National Genomic-Medicine Initiatives: Collaborative ''Cross-Country'' Projects Currently Underway d Align research protocols to enable discovery across larger datasets, as well as compare outcome measures such as diagnostic and clinical utility, cost effectiveness, and patient-and family-reported outcomes (Genomics England, Australian Genomics, NHGRI Newborn Sequencing in Genomic Medicine and Public Health) d Evaluate new sequencing and computational methods for clinical use (Genomics England and French Genomic Medicine Plan) d Harmonize collection of clinical and phenotypic data: define the minimum clinical dataset required for interpreting genomic tests and the health informatics infrastructure required for data capture and exchange (Australian Genomics and Genomics England) d Improve understanding of variant-and gene-disease associations by sharing the curation effort, developing common data models to capture evidence, and contributing to public knowledge repositories (NHGRI ClinGen, Genomics England, and Australian Genomics) d Develop an evaluation framework for assessing existing educational resources (Australian Genomics and Genomics England); enable broader access, particularly to early adopters in countries with emerging genomic-medicine programs d Develop strategies and capture experience in engaging culturally and linguistically diverse populations, indigenous populations, the general public, patients, professionals, and funders (Australian Genomics, Genomics England, NHGRI, All of Us, and Japan Agency for Medical Research and Development [AMED]) d Compare national consent procedures: reduce unnecessary heterogeneity, identify common features that represent best practices to allow global data sharing, and explore new models such as dynamic consent platforms (Australian Genomics, Genomics England, Swiss Personalized Medicine Network, and Japan AMED).
Acknowledgments The  Australian Genomics Health Alliance  is supported by the  Australian National Health and Medical Research Council  ( GNT1113531 ). The 100,000  Genomes  Project is managed by  Genomics England  (a wholly owned company of the  Department of Health). It is funded by the  National Institute for Health Research and NHS England. The  Wellcome Trust ,  Cancer Research UK , and  Medical Research Council  have also funded research infrastructure. The 100,000  Genomes  Project uses data provided by patients and collected by the National Health Service as part of their care and support. M. L. is funded by  Health Data Research UK.
Reconstructions We now address the reconstruction performance and applicability of the proposed methods. We first use a known phantom from  [20]  (the "logos" test case) to test the reconstruction fidelity of the refocused objects, for different up-scaling factors (Fig. 3 ). For brevity we only reconstruct the logos example for an ULF camera geometry. We then demonstrate the ability of our approach to reconstruct real-data for both ULF and FLF camera configurations. For the ULF case, we first show reconstructions of two versions of the "Tarot Cards" dataset from the Stanford Light-Field Archive (http://lightfield. stanford. edu/lfs. html), originally acquired by Andrew Adams (data: Fig. 4 ). The two versions are used for different purposes: one is downscaled and blurred to simulate an MLA-based acquisition setup, and assess the PSF correction and super-resolution performance; the other is the original full-resolution version, which is used to show that the developed methods can work with full-resolution gantry setups.
To further test the ULF case, we then show reconstructions of the "Tea" dataset, acquired with the setup in Fig. 5 (a) using the Lytro Illum plenoptic camera (data: Fig. 6 ). For the FLF case, we show the reconstruction of two datasets called "Letters" (micro-images: Fig. 7 (a), sub-aperture images: Fig. 7(b) ) and "Flower" (micro-images: Fig. 8 (b), sub-aperture images: Fig. 8(c )), that we acquired experimentally with the dedicated setup in Fig. 5(b ). The Letters dataset depicts vector art, that is easily recognizable by the human eye, and that is favoured by the TV-min constraint. The Flower dataset depicts natural imagery (https://github. com/ pratulsrinivasan/Local_Light_Field_Synthesis), which is more complex and favoured by the SWT-min constraint. b ) is due to the fact that FLF data mixes spatial information with angular information at the micro-images  [36] , and that the lenslets perform an image space inversion of the images that form on their object space focal plane.
Data description The logos scene is composed of three different objects positioned at 90, 100 and 110 mm respectively from the ML, as displayed in Fig. 3 (a). The dataset is composed of 16 × 16 sub-aperture images, each composed of 512 × 256 pixels. The sub-aperture images for this case are presented in Fig. 3(b ). The diameter d 1 of the main lens is 10 mm, while each lenslet has a diameter d 2 = 16 µm. The distance z 2 is 50 µm, and the detector pixel size 1µm. The ML-to-MLA distance z 1 is 25 mm, so that the focal plane in the object space is at 100 mm (|M | = 4). In the creation of this dataset we applied the two PSFs due to Fraunhofer diffraction for the ML and the lenslets in the MLA. These PSFs were generated by computing the Fraunhofer diffraction pattern predicted for the lens aperture (d 1 and d 2 for ML and MLA respectively), detected light wavelength, and lens-to-target distance (z 1 and z 2 for ML and MLA respectively).
This diffraction pattern was then integrated in steps equal to the underlying pixel size (16 µm and 1µm for ML and MLA respectively). To enable the evaluation of the up-sampling refocusing reconstruction quality for the logos case, we also created two additional versions of such test case, where we binned (down-sampled) the sub-aperture images along the (s, t) coordinates by 2 and 4. The reconstruction of these down-sampled datasets was performed at the original dataset (s, t) resolution, turning it into a super-resolved reconstruction problem with corresponding up-sampling factors of 2 and 4, respectively. The resulting up-sampled refocused images could then be compared to the original (high resolution) phantom. In general, this means that for increasing sampling factors, the available data (s, t) resolution becomes worse, less data is available, and more operations are needed to produce images with the original (s, t) resolution.
This procedure also resulted in the re-scaling of the expected lenslet sizes, detector pixel sizes, and PSF widths, according to the sampling factors. The Tarot cards dataset has been acquired using a gantry setup (analogous to a camera array setup), which was built using Lego Mindstorms, and a Canon Digital Rebel XTi, with a Canon 10-22 mm lens. The cropped and registered dataset from the archive comes as a collection of 17 × 17 sub-aperture images, with a relatively high (s, t) resolution (1024 × 1024). The focal length of the main lens f 1 was set to 20 mm. We binned the sub-aperture images to a resolution of 256 × 256, and blurred the light-field in the (u, v) coordinates using a different PSF per color channel. The three PSFs have been computed as the result of the Fraunhofer diffraction effects due to the lenslets, with the following parameters: lenslet diameter d 2 = 20 µm, distance z 2 = 37 µm and detector pixel size 1.
4 µm. The parameters for the lenslets are the same as the ones in a well known plenoptic camera, commercially available under the name of Lytro ILLUM. The modified Tarots cards dataset is displayed in Fig. 4. The Tea dataset (Fig. 6 ) has been acquired using a Lytro Illum plenoptic camera, with the setup in Fig. 5(a). Being the Lytro Illum a proprietary camera, we do not have the complete acquisition parameters. The raw data and metadata were extracted using the proprietary software "Lytro power tools", property of Lytro. This software already performs the rectification and lenslet re-sampling procedures discussed in section 2. 5. The Letters and Flower datasets (Figs. 7  and 8 ) represent printed vector art and natural imagery, that was then photographed with the dedicated setup in Fig. 5(b) , in the laboratories of Imagine Optic (Bordeaux, France).
This can be observed in particular for the sampling factor equal to 2, where the SIRT and CP-LS-TV reconstructions without PSF correction improve their refocusing performance with respect to the original dataset, because of a reduction in size of the lenslet PSF. The choice of the correct regularization (in this case TV-min) improves the result over non-regularized reconstructions, and it becomes relatively more important at higher sampling factors and with smaller PSFs. Finally, in Fig. 10 (b) we can observe the computational time required for every tested method at each down-sampling factor. Even if the focus of this article is not on the computational performance of the proposed method, we believe that it is worth giving an understanding of the performance penalties associated with each addressed aspect.
These tests were carried out on a machine equipped with an Intel i7-6700K CPU, 64GB of DDR4 memory, and a NVIDIA GeForce GTX 970 GPU. The fastest method is the back-projection because it is performed using the ASTRA toolbox, which uses GPU acceleration. All the iterative methods also use the GPU acceleration offered by ASTRA for the forward and back-projection, but perform the rest of the operations on the CPU. This imposes data transfers across the PCIe bus (from GPU to CPU and vice versa) at each iteration. We expect to see substantial improvements by running these refocusing routines entirely on the GPU, by both avoiding bottlenecks due to the data transfers, and allowing to accelerate every other operation, including the PSF application. For the same number of iterations, the addition of the TV minimization term has a negligible impact on the refocusing performance.
Tarot cards refocusing The Tarot cards test case shows the super-resolution and artifact correction potential of the proposed methods, over real-data, and in particular over a well understood and widely used dataset. We first analyze the results of the downscaled and blurred dataset. In Fig. 11 , we can observe the refocusing performance of the most significant reconstruction methods, for the two different up-sampling parameters of 1 and 4. For this test case, we chose to use: back-projection, Fourier slice theorem, SIRT with PSF, and CP-LS with SWT penalization based on Haar wavelets (CP-LS-SWT) and PSF. Again, the back-projection and Fourier based methods deliver comparable refocusing and feature-resolving performance. In Fig. 11(c ), for up-scaling equal to 4, the SIRT reconstruction presents a better feature resolving performance than the one-step approaches, but also visible artifacts in the reconstruction.
The SWT penalized reconstruction from Fig. 11(d ) visibly preserves the higher feature resolving performance of the SIRT approach, while strongly reducing the said artifacts. The choice of the SWT penalization term over the TV term can be understood through the same reasoning detailed in section 2. 4. The Tarot Cards dataset presents a variety of features, including shaded regions. These regions break the assumption of sharp boundaries associated to the TV minimization. We now consider the reconstruction of the unmodified Tarots Cards dataset. It can be deduced from Fig. 12  that the approaches presented in this article allow to correctly reconstruct high resolution gantry data. By comparing the results of the different methods in Fig. 12 , no difference is visible between the reconstructions.
This is due to the fact that, in this case, the back-projection method already returns a correct reconstruction. In fact, the blur visible in the reconstructions is also present in the sub-aperture images (Fig. 12(a) ). These high resolution gantry data reconstructions can be used to quantify the quality of the super-resolved refocusing reconstructions that used the modified Tarots Cards dataset. The measured RMSE values for the reconstructions in Fig. 11 , against Fig. 12 (b) are the following: 0. 0363353, 0. 103504, 0. 0382393, 0. 0169782 for the back-projection, Fourier, SIRT with PSF, and CP-LS-SWT with PSF respectively. These results confirm the visual interpretation carried out earlier in this section.
Tea refocusing The Lytro Illum plenoptic camera is a popular commercially available compound camera, that allows to easily acquire and extract single-shot light-fields, while maintaining a portable format. In Fig. 13 , we show the reconstruction of the Lytro Illum data acquired with the setup in Fig. 5(a). As it was done for the Tarots Cards dataset, we performed reconstructions with up-sampling of 1 and 4, for the following methods: back-projection, Fourier slice theorem, SIRT with PSF, and CP-LS with SWT penalization based on Haar wavelets (CP-LS-SWT) and PSF. This example shows that the methods developed in this article can be applied to the data acquired with this popular camera. It can be seen from the leaf and castle insets that once again both iterative approaches present a better feature resolving performance than the one-step approaches.
Letters and Flower refocusing The Letters and Flower test cases assess the ability of our approach to handle real-data produced with the FLF camera geometry. We show that every aspect of our approach, including superresolution and regularization, can be carried over to this case. In Fig. 14 , we present the reconstruction of the FLF data from Fig. 7  for the following approaches: back-projection, SIRT without PSF and CP-LS-TV with PSF. For this case, the choice of the TV term is justified by the properties of the imaged objects: the printed letters, with sharp boundaries over a plain background, explicitly satisfy the prior knowledge of the TV minimization. We present both reconstructions with up-sampling equal to 1, and 2. From Fig. 14 , it is clearly visible that the use of super-sampling and regularization techniques can improve the reconstruction of the smallest features.
In Fig. 15 , we present the reconstruction of the FLF data from Fig. 8. Similarly to what was done for the Letters test case, we used the following: back-projection, SIRT without PSF and CP-LS-SWT with PSF. The notable difference from the Letters case is the choice of the SWT regularization term, over the TV term. As previously mentioned, natural imagery generally breaks the assumption of flat and sharp regions in the reconstructed images. In Fig. 15 (a), we can see the expected reconstruction with an ideal camera (with infinite bandwidth). However, in practice, as stated in  [16] , the acquired light-field is convolved with a low pass filter, which has the form of a 4D sinc function. This results in lower than ideal performance for each reconstruction method, but an increasing similarity to Fig.
Abstract. Estimates of the direct radiative effect (DRE) from absorbing smoke aerosols over the southeast Atlantic Ocean (SAO) require simulation of the microphysical and optical properties of stratocumulus clouds as well as of the altitude and shortwave (SW) optical properties of biomass burning aerosols  (BBAs). In this study, we take advantage of the large number of observations acquired during the Ob-seRvations of Aerosols above Clouds and their intEractionS  (ORACLES-2016)  and Layered Atlantic Smoke Interactions with Clouds (LASIC) projects during September 2016 and compare them with datasets from the ALADIN-Climate (Aire Limitée Adaptation dynamique Développement Inter-National) regional model. The model provides a good representation of the liquid water path but the low cloud fraction is underestimated compared to satellite data.
Introduction Southern Africa is one of the main sources of biomass burning aerosols (BBAs) at the global scale. When the intense smoke plumes are transported over the southeast Atlantic Ocean (SAO), they are able to produce a significant positive (warming) direct radiative effect (DRE, which represents the instantaneous radiative impact of aerosols on the energy balance;  Heald et al. , 2014)  at the top of the atmosphere (TOA) in the shortwave (SW) spectral range and in all-sky conditions  (De Graaf et al. , 2012 , 2014; Feng and Christopher, 2015; Zuidema et al. , 2016). Over this specific region, the sign of the DRE is found to be opposite to the cooling effect generally exerted by scattering aerosols at TOA. Based on the combination of satellite observations from A-Train datasets -Moderate Resolution Imaging Spectroradiometer (MODIS), Clouds and the Earth's Radiant Energy System (CERES) and Ozone Monitoring Instrument (OMI) -  Feng and Christopher (2015)  indicate a regional-averaged instantaneous (i.
In addition, the induced SW DRE at TOA and the possible impact of BBA on the regional (continental) climate are also analyzed. This work has been conducted in the context of several international field campaigns over the SAO region, including the ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES)  (Zuidema et al. , 2016) , the Layered Atlantic Smoke Interactions with Clouds (LASIC;  Zuidema et al. , 2018) , the AErosol RAdiation and CLOuds in southern Africa (AEROCLO-sA) and the Cloud-Aerosol-Radiation Interactions and Forcing: Year 2017 (CLARIFY-2017) projects. More specifically, this study takes advantage of the large number of in situ observations acquired from aircraft and surface measurements during September 2016 for the ORACLES-1 and LASIC projects. This unique dataset is combined with satellite aerosol and cloud retrievals -MODIS, OMI and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) -and reanalysis -the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), Copernicus Atmosphere Monitoring Service (CAMS) and Monitoring Atmospheric Composition and Climate (MACC) -data.
2, along with the design of the ALADIN-Climate simulations. Section 3 reports the complete dataset (satellites, reanalysis, in situ surface and aircraft observations). The analyses of the comparisons between simulated and observed Sc clouds and aerosol properties are presented in Sect. 4. Based on the comparisons, we analyze more specifically the concentration of smoke aerosols over biomass burning sources and during the transport and the altitude of BBA, as well as absorbing properties and induced SW heating rate due to smoke. In addition, the impact of the elevated relative humidity within smoke plumes on optical properties is also investigated in Sect. 4. Finally, Sect. 5 focuses on the analyses of the SW DRE exerted by smoke aerosols at TOA during September 2016, as well as their impact on the continental climate in terms of the surface energy budget (temperature, sensible heat fluxes) and lower troposphere dynamics (planetary boundary layer (PBL), notably).
Finally, it should be mentioned that the possible impact of BBA reducing the sea-surface temperature (SST) is not treated here and the ALADIN-Climate model is used in a forced mode configuration (with fixed SST). This possible impact of BBA is outside the scope of the present study. As mentioned previously, the biomass burning emissions from the CMIP6 inventory have been used. These emissions are averaged monthly, reconstructed for the 1997-2015 period from the Global Fire Emissions Database version 4 with Here, r 0 and σ are the median radius (in µm) and geometric standard deviation of the lognormal distribution. Mass density is reported in g cm -3 , m is the complex refractive index, and MEE and SSA are the mass extinction efficiency (m 2 g -1 ) and single scattering albedo in dry state and reported at 550 nm.
MODIS and MISR datasets The Deep Blue ACAOD retrieved from the MODIS instruments is described in  Sayer et al. (2019) , which is a slightly updated version of the demonstration algorithm presented in  Sayer et al. (2016). In brief, this algorithm performs a multispectral weighted least-squares fit of measured reflectance in four bands across the visible spectral region (centered near 470, 550, 650 and 870 nm) to simultaneously retrieve ACAOD and the COD. The minimization is performed using optimal estimation. This provides estimates of the uncer-tainty on retrieved parameters, as well as an indicator of how well the retrieval solution is able to fit the measurements. Retrievals where the forward model is expected to be inappropriate, or where the measurements do not constrain the retrieved quantities, are filtered out.
The main updates since  Sayer et al. (2016)  are twofold. First, the radiative transfer lookup tables have been updated to include dimensions for surface pressure and surface albedo, which improves the realism of the forward model over land. Surface pressure is estimated using terrain altitude, while the surface albedo is taken from a climatology based on the MODIS gap-filled snow-free land albedo dataset  (Sun et al. , 2017). The second update is that, rather than applying the retrieval to each individual pixel, the pixels are aggregated into 10 km × 10 km (effective nadir resolution) boxes, chosen to match the resolution of the level 2 MODIS aerosol products. Then, the median reflectance of water cloud pixels within each box is used for the retrieval. Use of median reflectance decreases sensitivity to factors such as 3-D effects or cloud detection errors.