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The cryosphere's surface (snow, sea ice, and water) regulates global climate through several feedback mechanisms. Broadband albedo is a critical parameter determining the radiative energy balance of the complex atmosphere-cryosphere system, but there is currently no reliable, operational albedo retrieval product capable of assessing the global sea-ice albedo with sufficient spatial-temporal resolution for studies of sea-ice dynamics and for use in global climate models. A framework was established for remote sensing of sea ice albedo that integrates sea-ice physics with high computational efficiency, and can be applied to any optical sensor that measures appropriate radiance data. A scientific machine learning (SciML) approach was developed and trained on a large synthetic dataset (SD) constructed using a coupled atmosphere-surface radiative transfer model (RTM). The resulting RTM/SciML framework combines the RTM with a multi-layer artificial neural network SciML model. In comparison to the NASA MODIS MCD43 albedo product, this framework does not depend on observations from multiple days, and can be applied to single angular observations obtained under clear-sky conditions. Compared to the existing melt pond fraction-based approach for albedo retrieval, the RTM/SciML framework has the advantage of being applicable to a wide variety of cryosphere surfaces, both heterogeneous and homogeneous. Validation of the RTM/SciML albedo product using MODIS and SGLI data against measurements obtained from aircraft campaigns revealed excellent agreement, with mean absolute error of 0.047 for above 2000 clear-sky pixels.

The Ocean Color—Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a robust data processing platform utilizing scientific machine learning (SciML) in conjunction with comprehensive radiative transfer computations to provide accurate remote sensing reflectances (Rrs estimates), aerosol optical depths, and inherent optical properties. This paper expands the capability of OC-SMART by quantifying uncertainties in ocean color retrievals. Bayesian inversion is used to relate measured top of atmosphere radiances and a priori data to estimate posterior probability density functions and associated uncertainties. A framework of the methodology and implementation strategy is presented and uncertainty estimates for Rrs retrievals are provided to demonstrate the approach by applying it to MODIS, OLCI Sentinel-3, and VIIRS sensor data.

BACKGROUND Connecting the antegrade wire (AW) and the retrograde wire (RW) is a goal of chronic total occlusion (CTO) treatment, but angiographic guidewire location is sometimes misleading. AIMS The aim of this study was to evaluate the association between intravascular ultrasound (IVUS)-defined AW and RW position and procedural outcomes when treating CTO lesions using the retrograde approach. METHODS Overall, 191 CTO lesions treated using an IVUS-guided retrograde approach at three centres in Japan, China, and the USA were included. RESULTS When the AW and RW angiographically overlapped, four wire positions were seen on IVUS: (i) AW within the plaque (AW-intraplaque) and RW-intraplaque in 34%; (ii) AW-intraplaque and RW in the subintimal space (RW-subintima) in 28%; (iii) AW-subintima and RW-subintima in 22%; or (iv) AW-subintima and RW-intraplaque in 16%. The procedure succeeded without repositioning the wire in 89% of AW-intraplaque/RW-intraplaque, 61% of AW-intraplaque/RW-subintima and 57% of AW-subintima/RW-subintima, but only one (3%) AW-subintima/RW-intraplaque. Lesion and procedure complexity and failure/complications were greatest in AW-subintima/RW-intraplaque. CONCLUSIONS IVUS-identified vascular compartment concordance versus IVUS-identified vascular compartment mismatch leads to higher success rates irrespective of intraplaque or subintimal passage. AW-subintima/RW-intraplaque was associated with the most complex CTO morphology and procedure, and repositioning the wire was almost always necessary. Visual summary. When the antegrade wire is in the subintimal space and the retrograde wire is in the intraplaque, re-wiring is almost always necessary.

(1) Background: In spite of the undeniable clinical value of the index of microvascular resistance (IMR) in assessing the status of coronary microcirculation, its use globally remains very low. The aim of this study was to validate the novel single-view, pressure-wire- and adenosine-free angiographic microvascular resistance (AMR) index, having the invasive wire-based IMR as a reference standard. (2) Methods: one hundred and sixty-three patients (257 vessels) were investigated with pressure wire-based IMR. Microvascular dysfunction (CMD) was defined by IMR ≥ 25. AMR was independently computed from the diagnostic coronary angiography in a blinded fashion. (3) Results: AMR demonstrated a good correlation (r = 0.83, p < 0.001) and diagnostic performance (AUC 0.94; 95% CI: 0.91 to 0.97) compared with wire-based IMR. The best cutoff value for AMR in determining IMR ≥ 25 was 2.5 mmHg*s/cm. The overall diagnostic accuracy of AMR was 87.2% (95% CI: 83.0% to 91.3%), with a sensitivity of 93.5% (95% CI: 87.0% to 97.3%), a specificity of 82.7% (95% CI: 75.6% to 88.4%), a positive predictive value of 79.4% (95% CI: 71.2% to 86.1%) and a negative predictive value of 94.7% (95% CI: 89.3% to 97.8%). No difference in terms of CMD rate was described among different clinical presentations. (4) Conclusions: AMR derived solely from a single angiographic view is a feasible computational alternative to pressure wire-based IMR, with good diagnostic accuracy in assessing CMD.

Atherosclerosis is the main cause of cardiovascular disease. Systemic inflammation is one important characteristic in atherosclerosis. Pro-inflammatory macrophages can secrete inflammatory factors and promote the inflammation of atherosclerosis. It has a great value for the treatment of atherosclerosis by inhibiting the release of inflammatory factors in macrophages. However, the detailed mechanism of this process is still unclear. In this study, we constructed an APOE-/- mice model of atherosclerosis to research the molecular mechanism of atherosclerosis. Protein tyrosine phosphatase non-receptor type 2 (PTPN2), an anti-inflammatory gene, was dramatically decreased in inflammatory mice. Deletion of PTPN2 could significantly induce monocytes toward M1 phenotype of macrophages, enhance the secretion of IL-12 and IL-1, and promote cell proliferation, invasion and metastasis. Mechanism research showed that PTPN2-mediated p65/p38/STAT3 de-phosphorylation could block the process of macrophage inflammation. In vivo experiments showed that PTPN2 may effectively inhibit the inflammatory response during atherosclerosis. In conclusion, we uncovered the negative role of PTPN2 in the occurrence of atherosclerosis, and this study provides a new potential target for atherosclerosis treatment.

Hypoxia/reoxygenation (H/R)-induced myocardial cell injury is the main cause of acute myocardial infarction (AMI). Many proofs show that circular RNA plays an important role in the development of AMI. The purpose of this study was to investigate the role of circSAMD4A in H/R-induced myocardial injury. The levels of circular SAMD4A (circSAMD4A) were detected in the heart tissues of AMI mice and H/R-induced H9C2 cells, and the circSAMD4A was suppressed in AMI mice and H/R-induced H9C2 cells to investigate its' function in AMI. The levels of circSAMD4A and miR-138-5p were detected by real-time quantitative PCR, and MTT assay was used to detect cell viability. TUNEL analysis and Annexin V-FITC were used to determine apoptosis. The expression of Bcl-2 and Bax proteins was detected by Western blot. IL-1β, TNF-α and IL-6 were detected by ELISA kits. The study found that the levels of circSAMD4A were up-regulated after H/R induction and inhibition of circSAMD4A expression would reduce the H/R-induced apoptosis and inflammation. MiR-138-5p was down-regulated in H/R-induced H9C2 cells. circSAMD4A was a targeted regulator of miR-138-5p. CircSAMD4A inhibited the expression of miR-138-5p to promote H/R-induced myocardial cell injury in vitro and vivo. In conclusion, CircSAMD4A can sponge miR-138-5p to promote H/R-induced apoptosis and inflammatory response.

Background Chronic chagasic cardiomyopathy (CCC) is the leading cause of heart failure in Latin America and often causes severe inflammation and fibrosis in the heart. Studies on myocardial function and its molecular mechanisms in patients with Chronic chagasic cardiomyopathy are very limited. In order to understand the development and progression of Chronic chagasic cardiomyopathy and find targets for its diagnosis and treatment, the field needs to better understand the exact molecular mechanisms involved in these processes. Methods The mRNA microarray datasets GSE84796 (human) and GSE24088 (mouse) were obtained from the Gene Expression Omnibus (GEO) database. Homologous genes between the two species were identified using the online database mining tool Biomart, followed by differential expression analysis, gene enrichment analysis and protein–protein interaction (PPI) network construction. Cytohubba plug-in of Cytoscape software was used to identify Hub gene, and miRNet was used to construct the corresponding miRNA–mRNA regulatory network. miRNA-related databases: miRDB, Targetscan and miRWalk were used to further evaluate miRNAs in the miRNA–mRNA network. Furthermore, Comparative Toxicogenomics Database (CTD) and L1000 Platform were used to identify hub gene-related drugs. Results A total of 86 homologous genes were significantly differentially expressed in the two datasets, including 73 genes with high expression and 13 genes with low expression. These differentially expressed genes were mainly enriched in the terms of innate immune response, signal transduction, protein binding, Natural killer cell mediated cytotoxicity, Tuberculosis, Chemokine signaling pathway, Chagas disease and PI3K−Akt signaling pathway. The top 10 hub genes LAPTM5, LCP1, HCLS1, CORO1A, CD48, TYROBP, RAC2, ARHGDIB, FERMT3 and NCF4 were identified from the PPI network. A total of 122 miRNAs were identified to target these hub genes and 30 of them regulated two or more hub genes at the same time. miRDB, Targetscan and miRWalk were further analyzed and screened out hsa-miR-34c-5p, hsa-miR-34a-5p and hsa-miR-16-5p as miRNAs regulating these hub genes. Finally, Progesterone, Flutamide, Nimesulide, Methotrexate and Temozolomide were identified to target these hub genes and might be targeted therapies for Chronic chagasic cardiomyopathy. Conclusions In this study, the potential genes associated with Chronic chagasic cardiomyopathy are identified and a miRNA–mRNA regulatory network is constructed. This study explores the molecular mechanisms of Chronic chagasic cardiomyopathy and provides important clues for finding new therapeutic targets.

International audience; Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA – ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (̂ρ w ). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. This volume of data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errors in ̂ρ w (560) and ̂ρ w (664) were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15–30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concentration of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20–30% uncertainties in ̂ρ w (490 ≤ λ ≤ 743 nm) yielded 25–70% uncertainties in derived Chla and TSS products for top-performing AC processors. We summarize our results using performance matrices guiding the satellite user community through the OWT-specific relative perfor­mance of AC processors. Our analysis stresses the need for better representation of aerosols, particularly absorbing ones, and improvements in corrections for sky- (or sun-) glint and adjacency effects, in order to achieve higher quality downstream products in freshwater and coastal ecosystems.

Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA - ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (??w). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. The volume of our data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errorsin ??w(560) and ??w(664) were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15- 30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concertation of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20- 30% uncertainties in ??w(490

Reliable retrieval of atmospheric and surface properties from sensors deployed on satellite platforms rely on accurate simulations of the electromagnetic (EM) signal measured by such sensors. A forward radiative transfer (RT) model of the coupled atmosphere-surface system can be used to simulate how the EM signal responds to changes in atmospheric and surface properties. Realistic RT modeling is a prerequisite for solving the inverse problem, i.e. to infer atmospheric and surface parameters from the EM signals measured at the top of the atmosphere. The surface may consist of a soil-plant canopy, a snow/ice covered surface or an open water body (ocean, lake, river system). An overview will be provided of forward and inverse RT in such coupled atmosphere-surface systems. A coupled system consisting of two adjacent slabs separated by an interface across which the refractive index changes abruptly from its value in air to that in water /ice [1] will be used as an example. Several examples of how to formulate and solve inverse problems involving coupled atmosphere-water systems [2] will be provided to illustrate how solutions to the RT equation can be used as a forward model to solve practical inverse problems. Cloud screening [3], atmospheric correction [4], treatment of two-dimensional surface roughness, Earth curvature effects, and ocean bottom reflection for shallow water in coastal areas will be discussed, and the advantage of using powerful machine learning techniques to solve the inverse problem will be emphasized.References[1] Stamnes, K., and J. J. Stamnes, Radiative Transfer in Coupled Environmental Systems, , 2015.[2] Stamnes, K., B. Hamre, S. Stamnes, N. Chen, Y. Fan, W. Li, Z. Lin, and J. J. Stamnes, Progress in forward-inverse modeling based on radiative transfer tools for coupled atmosphere-snow/ice-ocean systems: A review and description of the AccuRT model, , 8, 2682, 2018.[3] Chen N., W. Li, C. Gatebe, T. Tanikawa, M. Hori, R. Shimada; T. Aoki, and K. Stamnes, New cloud mask algorithm based on machine learning methods and radiative transfer simulations, , 219, 62-71, 2018.[4] Fan, Y., W. Li, C. K. Gatebe, C. Jamet, G. Zibordi, T. Schroeder, and K. Stamnes, Atmospheric correction and aerosol retrieval over coastal waters using multilayer neural networks, , 199, 218-240, 2017.

Background: Increasing numbers of confirmed cases and deaths due to 2019-coronavirus infected disease (COVID-19) have occurred in Wuhan, China since December 20

The treatment of strongly anisotropic scattering phase functions is still a challenge for accurate radiance computations. The new delta- M+ method resolves this problem by introducing a reliable, fast, accurate, and easy-to-use Legendre expansion of the scattering phase function with modified moments. Delta- M+ is an upgrade of the widely used delta- M method that truncates the forward scattering peak with a Dirac delta function, where the “+” symbol indicates that it essentially matches moments beyond the first M terms. Compared with the original delta- M method, delta- M+ has the same computational efficiency, but for radiance computations, the accuracy and stability have been increased dramatically.

The prognostic impact of microvascular status in patients with high fractional flow reserve (FFR) is not clear.The goal of this study was to investigate the implications of coronary flow reserve (CFR) and the index of microcirculatory resistance (IMR) in patients who underwent FFR measurement.Patients with high FFR (0.80) were grouped according to CFR (≤2) and IMR (≥23 U) levels: group A, high CFR with low IMR; group B, high CFR with high IMR; group C, low CFR with low IMR; and group D, low CFR with high IMR. Patient-oriented composite outcome (POCO) of any death, myocardial infarction, and revascularization was assessed. The median follow-up was 658 days (interquartile range: 503.8 to 1,139.3 days).A total of 313 patients (663 vessels) were assessed with FFR, CFR, and IMR. Correlation (r = 0.201; p0.001) and categorical agreement (kappa value = 0.178; p0.001) between FFR and CFR were modest. Low CFR was associated with higher POCO than high CFR (p = 0.034). There were no significant differences in clinical and angiographic characteristics among groups. Patients with high IMR with low CFR had the highest POCO (p = 0.002). Overt microvascular disease (p = 0.008), multivessel disease (p = 0.033), and diabetes mellitus (p = 0.033) were independent predictors of POCO. Inclusion of a physiological index significantly improved the discriminant function of a predictive model (relative integrated discrimination improvement 0.467 [p = 0.037]; category-free net reclassification index 0.648 [p = 0.007]).CFR and IMR improved the risk stratification of patients with high FFR. Low CFR with high IMR was associated with poor prognosis. (Clinical, Physiological and Prognostic Implication of Microvascular Status; NCT02186093).

Increasing numbers of confirmed cases and mortality rates of coronavirus disease 2019 (COVID-19) are occurring in several countries and continents. Information regarding the impact of cardiovascular complication on fatal outcome is scarce.To evaluate the association of underlying cardiovascular disease (CVD) and myocardial injury with fatal outcomes in patients with COVID-19.This retrospective single-center case series analyzed patients with COVID-19 at the Seventh Hospital of Wuhan City, China, from January 23, 2020, to February 23, 2020. Analysis began February 25, 2020.Demographic data, laboratory findings, comorbidities, and treatments were collected and analyzed in patients with and without elevation of troponin T (TnT) levels.Among 187 patients with confirmed COVID-19, 144 patients (77%) were discharged and 43 patients (23%) died. The mean (SD) age was 58.50 (14.66) years. Overall, 66 (35.3%) had underlying CVD including hypertension, coronary heart disease, and cardiomyopathy, and 52 (27.8%) exhibited myocardial injury as indicated by elevated TnT levels. The mortality during hospitalization was 7.62% (8 of 105) for patients without underlying CVD and normal TnT levels, 13.33% (4 of 30) for those with underlying CVD and normal TnT levels, 37.50% (6 of 16) for those without underlying CVD but elevated TnT levels, and 69.44% (25 of 36) for those with underlying CVD and elevated TnTs. Patients with underlying CVD were more likely to exhibit elevation of TnT levels compared with the patients without CVD (36 [54.5%] vs 16 [13.2%]). Plasma TnT levels demonstrated a high and significantly positive linear correlation with plasma high-sensitivity C-reactive protein levels (β = 0.530, P .001) and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels (β = 0.613, P .001). Plasma TnT and NT-proBNP levels during hospitalization (median [interquartile range (IQR)], 0.307 [0.094-0.600]; 1902.00 [728.35-8100.00]) and impending death (median [IQR], 0.141 [0.058-0.860]; 5375 [1179.50-25695.25]) increased significantly compared with admission values (median [IQR], 0.0355 [0.015-0.102]; 796.90 [401.93-1742.25]) in patients who died (P = .001; P .001), while no significant dynamic changes of TnT (median [IQR], 0.010 [0.007-0.019]; 0.013 [0.007-0.022]; 0.011 [0.007-0.016]) and NT-proBNP (median [IQR], 352.20 [174.70-636.70]; 433.80 [155.80-1272.60]; 145.40 [63.4-526.50]) was observed in survivors (P = .96; P = .16). During hospitalization, patients with elevated TnT levels had more frequent malignant arrhythmias, and the use of glucocorticoid therapy (37 [71.2%] vs 69 [51.1%]) and mechanical ventilation (31 [59.6%] vs 14 [10.4%]) were higher compared with patients with normal TnT levels. The mortality rates of patients with and without use of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers was 36.8% (7 of 19) and 21.4% (36 of 168) (P = .13).Myocardial injury is significantly associated with fatal outcome of COVID-19, while the prognosis of patients with underlying CVD but without myocardial injury is relatively favorable. Myocardial injury is associated with cardiac dysfunction and arrhythmias. Inflammation may be a potential mechanism for myocardial injury. Aggressive treatment may be considered for patients at high risk of myocardial injury.

Heritage atmospheric correction (AC) and ocean inherent optical property (IOP) retrieval algorithms, such as those implemented in NASA’s SeaDAS platform, often produce questionable results in complex environments, such as in turbid coastal and inland waters and for heavy aerosol loadings. We present new AC and ocean IOP retrieval algorithms for the Medium Resolution Spectral Imager - II (MERSI-II) onboard the FengYun-3D satellite that solve these problems. The algorithm development is based on extensive radiative transfer simulations for a coupled atmosphere-ocean system in conjunction with machine learning techniques (i.e. multi-layer neural networks) to retrieve ocean color products from MERSI-II sensor data. The final ocean color products include spectral remote sensing reflectances (Rrs(λ) values), chlorophyll_a concentration (CHL), and IOPs, i.e. absorption by phytoplankton (aph(λ)), absorption by detritus and gelbstoff (adg(λ)), and particulate backscattering (bbp(λ)). Spectral aerosol optical depths (AODs) and cloud mask results are also provided. The new machine learning based algorithms are first tested using independent synthetic datasets and show very good performance. The average percentage error (APE) is less than 7% for the Rrs retrievals and less than 5.2% for the AOD retrievals with a 1% uncertainty added to the TOA reflectances. The ocean color products retrieved from MERSI-II sensor data are validated against AERONET-OC field measurements and show good quality. For Rrs retrievals, the APE is around 30% for blue and red bands and 22% for green band. For AOD retrievals, the APE is also around 23% - 30%, and for CHL retrievals, the APE is around 32%. The ocean color products retrieved from MERSI-II sensor data are also cross-validated with the corresponding ones retrieved from Aqua/MODIS data using both NASA SeaDAS package and our machine learning based OC-SMART algorithms. The results show that these machine learning algorithms completely resolve the negative Rrs(λ) issue that persists in heritage AC algorithms and significantly improve the quality of retrieved ocean color products, especially in coastal regions. The histograms of the retrieved Rrs, CHL, and ocean IOP products also show good agreement between MERSI-II and Aqua/MODIS ocean color retrievals.

In this study, we used an artificial neural network method to estimate the fractional snow cover area (fSCA), which is fast and accurate, and that can be easily adapted to different remote sensing instruments. We tested our approach using SnowEx data from NASA's Cloud Absorption Radiometer (CAR) over Grand Mesa; one of the largest flat-topped mountains in the world, which features sufficient forested stands with a range of density and height (and a variety of other forest conditions); a spread of snow depth/snow water equivalent conditions over sufficiently flat snow-covered terrain. The retrieved fractional snow-covered area from CAR compares reasonably with a Sentinel-2 image over the same location and demonstrates CAR's unique capability to improve the retrieval of snow properties using machine learning. The retrieved snow fraction parameter from our method is expected to minimize the error associated with the traditional binary snow detection scheme, and improve the retrieval quality of key parameters such as surface albedo.

A simple but novel study was conducted to investigate whether an imager-type spectroradiometer instrument like MODIS, currently flying on board the Aqua and Terra satellites, or MERIS, which flew on board Envisat, could detect absorbing aerosols if they could measure the $Q$ Stokes parameter in addition to the total radiance $I$, that is if they could also measure the linear polarization of the light. Accurate radiative transfer calculations were used to train a fast neural network forward model, which together with a simple statistical optimal estimation scheme was used to retrieve three aerosol parameters: aerosol optical depth at 869 nm, optical depth fraction of fine mode (absorbing) aerosols at 869 nm, and aerosol vertical location. The aerosols were assumed to be bimodal, each with a lognormal size distribution, located either between 0 and 2 km or between 2 and 4 km in the Earth's atmosphere. From simulated data with 3\% random Gaussian measurement noise added for each Stokes parameter, it was found that by itself the total radiance $I$ at the nine MODIS VIS channels was generally insufficient to accurately retrieve all three aerosol parameters ($\sim$ 15\% to 37\% successful), but that together with the $Q$ Stokes component it was possible to retrieve values of aerosol optical depth at 869 nm to $\pm$ 0.03, single-scattering albedo at 869 nm to $\pm$ 0.04, and vertical location in $\sim$ 65\% of the cases. This proof-of-concept retrieval algorithm uses neural networks to overcome the computational burdens of using vector radiative transfer to accurately simulate top-of-atmosphere (TOA) total and polarized radiances, enabling optimal estimation techniques to exploit information from multiple channels. Therefore such an algorithm could, in concept, be readily implemented for operational retrieval of aerosol and ocean products from moderate or hyperspectral spectroradiometers.

Simultaneous retrieval of aerosol and surface properties by means of inverse techniques based on a coupled atmosphere-surface radiative transfer model and optimal estimation can yield a considerable improvement in retrieval accuracy based on radiances measured by MERIS, MODIS, and similar instruments compared with traditional methods. There are uniqueness problems associated with radiometric remote sensing measurements (like MERIS/MODIS) that ignore polarization effects, and rely on measuring only the radiance. Use of polarization measurements is particularly important for absorbing aerosols over coastal waters as well as over bright targets such as snow-covered and bare sea ice, where it has proved difficult to retrieve aerosol single-scattering albedo from radiance-only spectrometers such as MERIS and MODIS. We use a vector radiative transfer model for the coupled atmosphere-surface system (C-VRTM) in conjunction with an Optimal Estimation/Levenberg-Marquardt (OE/LM) method to quantify how polarization ...

The standard method to convert the measured water-leaving radiances from the observation direction to the nadir direction developed by Morel and coworkers requires knowledge of the chlorophyll concentration (CHL). Also, the standard method was developed for open ocean water, which makes it unsuitable for turbid coastal waters. We introduce a neural network method to convert the water-leaving radiance (or the corresponding remote sensing reflectance) from the observation direction to the nadir direction. This method does not require any prior knowledge of the water constituents or the inherent optical properties (IOPs). This method is fast, accurate and can be easily adapted to different remote sensing instruments. Validation using NuRADS measurements in different types of water shows that this method is suitable for both open ocean and coastal waters. In open ocean or chlorophyll-dominated waters, our neural network method produces corrections similar to those of the standard method. In turbid coastal wat...

During retrograde chronic total occlusion (CTO) percutaneous coronary intervention (PCI), antegrade wire (AW) and retrograde wire (RW) locations are important. A total of 182 CTO lesions were treated with an intravascular ultrasound (IVUS)–guided retrograde approach at 3 centers in Japan, China

A tutorial review is provided of forward and inverse radiative transfer in coupled atmosphere-snow/ice-water systems. The coupled system is assumed to consist of two adjacent horizontal slabs separated by an interface across which the refractive index changes abruptly from its value in air to that in ice/water. A comprehensive review is provided of the inherent optical properties of air and water (including snow and ice). The radiative transfer equation for unpolarized as well as polarized radiation is described and solutions are outlined. Several examples of how to formulate and solve inverse problems encountered in environmental optics involving coupled atmosphere-water systems are discussed in some detail to illustrate how the solutions to the radiative transfer equation can be used as a forward model to solve practical inverse problems.

Patient initials or identifier number 26866428 ### Relevant clinical history and physical exam 79 years old, female Chief compliant: Dyspnea on exertion Risk factors: Hypertension / DM / Dyslipidemia (+/+/+) H/o CVA: (-) Smoking/ alcohol: nonsmoker, no alcohol recently FHx. of

Ocean color algorithms that rely on "atmospherically corrected" nadir water-leaving radiances to infer information about marine constituents such as the chlorophyll concentration depend on a reliable method to convert the angle-dependent measured radiances from the observation direction to the nadir direction. It is also important to convert the measured radiances to the nadir direction when comparing and merging products from different satellite missions. The standard correction method developed by Morel and coworkers requires knowledge of the chlorophyll concentration. Also, the standard method was developed based on the Case 1 (open ocean) assumption, which makes it unsuitable for Case 2 situations such as turbid coastal waters. We introduce a neural network method to convert the angle-dependent water-leaving radiance (or the corresponding remote sensing reflectance) from the observation direction to the nadir direction. This method relies on neither an "atmospheric correction" nor prior knowledge of the water constituents or the inherent optical properties. It directly converts the remote sensing reflectance from an arbitrary slanted viewing direction to the nadir direction by using a trained neural network. This method is fast and accurate, and it can be easily adapted to different remote sensing instruments. Validation using NuRADS measurements in different types of water shows that this method is suitable for both Case 1 and Case 2 waters. In Case 1 or chlorophyll-dominated waters, our neural network method produces corrections similar to those of the standard method. In Case 2 waters, especially sediment-dominated waters, a significant improvement was obtained compared to the standard method.