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We introduce a novel method to retrieve the cloud fraction and the optical thickness of liquid clouds over a water surface based on polarimetry. The approach is well suited for satellite observations providing multi-angle polarization measurements, in particular those of the Hyper-Angular Rainbow Polarimeter #2 (HARP2). Unlike commonly used methods to derive cloud fractions, our method does not depend on the spatial resolution of observations, and it does not require any threshold values for cloud detection. Based on radiative transfer simulations, we show that the cloud fraction and the cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region. In the cloudbow, the degree of polarization mainly depends on the cloud optical thickness. Conversely, for a viewing direction in the sun glint region, the degree of polarization depends on the clear fraction of the pixel, because here the radiation scattered by cloud droplets is almost unpolarized, whereas radiation reflected by the surface is highly polarized. Utilizing these dependencies, we developed a retrieval using a simple lookup table approach. Based on sensitivity studies, we show that prior information about wind speed and aerosol optical thickness improves the accuracy of the cloud fraction retrieval. Prior information about the cloud droplet size distribution can reduce the uncertainty of the cloud optical thickness retrieval. The prior information should be obtained by combining our method with already existing aerosol and cloud retrieval algorithms. We performed 3D radiative transfer simulations and found that the cloud optical thickness is generally underestimated due to neglect of 3D scattering effects. The cloud fraction is overestimated in cloud shadows and underestimated in in-scattering regions. As a demonstration, we apply the methodology to airborne observations from polarization cameras of the Munich Aerosol Cloud Scanner (specMACS) instrument. The high-spatial-resolution data (10–20 m) have been averaged to a spatial resolution of approximately 2.5 km to mimic satellite observations. From the average linear polarization at scattering angles of 140 and 110°, we derive continuous cloud fraction values and the corresponding cloud optical thicknesses. Comparison for cases including low, medium, and high cloud fractions shows that the retrieval, using only reflected polarized radiances at two scattering angles, provides accurate estimates of the cloud fraction for observations with coarse spatial resolution.

Surface albedo is an important parameter in radiative-transfer simulations of the Earth's system as it is fundamental for correctly calculating the energy budget of the planet. The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on NASA's Terra and Aqua satellites continuously monitor daily and yearly changes in reflection at the planetary surface. The MODIS Surface Reflectance Black-Sky Albedo dataset (version 6.1 of MCD43D) provides detailed albedo maps for seven spectral bands in the visible and near-infrared range. These albedo maps allow us to classify different Lambertian surface types and their seasonal and yearly variability and change, albeit only into seven spectral bands. However, a complete set of albedo maps covering the entire wavelength range is required to simulate radiance spectra and correctly retrieve atmospheric and cloud properties from remote sensing observations of the Earth. We use a principal component analysis (PCA) regression algorithm to generate hyperspectral albedo maps of the Earth. By combining different datasets containing laboratory measurements of hyperspectral reflectance for various dry soils, vegetation surfaces, and mixtures of both, we reconstruct albedo maps across the entire wavelength range from 400 to 2500 nm. The PCA method is trained with a 10-year average of MODIS data for each day of the year. We obtain hyperspectral albedo maps with a spatial resolution of 0.05° in latitude and longitude, a spectral resolution of 10 nm, and a temporal resolution of 1 d (day). Using the hyperspectral albedo maps, we estimate the spectral profiles of different land surfaces, such as forests, deserts, cities, and icy surfaces, and study their seasonal variability. These albedo maps will enable us to refine calculations of the Earth's energy budget and its seasonal variability and improve climate simulations.

BackgroundCerebral venous thrombosis (CVT) is a rare but serious subtype of stroke. Several studies have reported an increased incidence of CVT after either COVID-19 (CoV19) infection or vaccination; however, data on clinical characteristics, the radiological profiles, and the outcomes of these patients with CVT as the only severe symptom of a CoV19 infection or vaccination compared to patients with non-CoV19-related CVT are still scarce.MethodsWe performed a retrospective monocentric study over 10 years (January 2013–December 2022) that included consecutive patients with a confirmed diagnosis of CVT based on imaging of the cerebral venous system. Patients were categorized as CoV19 CVT (either due to infection or post-vaccination) or non-CoV19 CVT and compared regarding demographics, risk factors, clinical characteristics, and imaging findings as well as outcome (at discharge, at 6 months, and last follow-up). Furthermore, sub-analyses were performed to compare CoV19-infection-related-CVT and CoV19-vaccination-related-CVT patients.ResultsOverall, 122 patients with suspected CVT were identified. After excluding patients with missing data (n = 20) or missing imaging of the cerebral venous system (n = 31), 71 patients with confirmed CVT remained for the final analyses. Of those, 11 patients had CoV19 CVT (infection n = 3, vaccination n = 8), and 60 patients had non-CoV19-CVT. There were no differences regarding median age (CoV19: 40 [IQR: 22–70] vs. non-CoV19: 41 [IQR:27–64]) or percentage of female sex among both groups. A lower rate of CVT risk factors was observed in the CoV19 group but without significant differences. No patient with CoV19 CVT displayed impaired consciousness on presentation, and only 30% had focal neurological deficits compared to 51.7% in the control group. The rate of CVT-related intracranial hemorrhage and venous infarcts were 27.3% and 9.1%, respectively, in the CoV19 group and 30% and 16.7%, respectively, in the non-CoV19 group. The mortality rate at discharge was 9.1% in the CoV19-CVT group vs. 3.3% in the non-CoV19-CVT group, without differences in functional outcomes during the follow-up period. Sub-analyses comparing CoV19-infection-related CVT vs. CoV19-vaccination-related CVT patients revealed no significant differences in clinical, imaging, or treatment characteristics.ConclusionIn this monocentric study, there was no signal for a worse severity of CoV19 CVT compared to non-CoV19 CVT regarding clinical characteristics, imaging profile, or outcomes in patients with CVT only. Larger observational data with sophisticated workups of CVT patients are needed to confirm our results.

Summary: The majority of human genomic research studies have been conducted in European-ancestry cohorts, reducing the likelihood of detecting potentially novel and globally impactful findings. Here, we present mid-pass whole-genome sequencing data and a genome-wide association study in a cohort of 264 self-reported Malagasy individuals from three locations on the island of Madagascar. We describe genetic variation in this Malagasy cohort, providing insight into the shared and unique patterns of genetic variation across the island. We observe phenotypic variation by location and find high rates of hypertension, particularly in the Southern Highlands sampling site, as well as elevated self-reported malaria prevalence in the West Coast site relative to other sites. After filtering to a subset of 214 minimally related individuals, we find a number of genetic associations with body composition traits, including many variants that are only observed in African populations or populations with admixed African ancestry from the 1000 Genomes Project. This study highlights the importance of including diverse populations in genomic research for the potential to gain novel insights, even with small cohort sizes. This project was conducted in partnership and consultation with local stakeholders in Madagascar and serves as an example of genomic research that prioritizes community engagement and potentially impacts our understanding of human health and disease.

The increasing resolution of numerical weather prediction models makes inter-column three-dimensional (3D) radiative transport more and more important. However, 3D radiative-transfer solvers are still computationally expensive, largely preventing their use in operational weather forecasting. To address this issue, Jakub and Mayer (2015) developed the TenStream solver. It extends the well-established two-stream method to three dimensions by using 10 instead of 2 streams to describe the transport of radiative energy through Earth's atmosphere. Building upon this method, this paper presents the dynamic TenStream solver, which provides a further acceleration of the original TenStream model. Compared to traditional solvers, this speedup is achieved by utilizing two main concepts. First, radiation is not calculated from scratch every time the model is called. Instead, a time-stepping scheme is introduced to update the radiation field, based on the result from the previous radiation time step. Secondly, the model is based on incomplete solves, with just the first few steps of an iterative scheme towards convergence performed every time it is called. Essentially, the model thereby just uses the ingoing fluxes of a grid box to update its outgoing fluxes. Combined, these two approaches move radiative transfer much closer to the way advection is handled in the dynamical core of a numerical weather prediction (NWP) model, as both use previously calculated results to update their variables and thereby just require access to the neighboring values of an individual grid box, facilitating model parallelization. To demonstrate the feasibility of this new solver, we apply it to a precomputed shallow-cumulus-cloud time series and test its performance in terms of both speed and accuracy. In terms of speed, the dynamic TenStream solver is shown to be about 3 times slower than a traditional 1D δ-Eddington approximation but noticeably faster than currently available 3D solvers. To evaluate the accuracy of the dynamic TenStream solver, we compare its results as well as calculations carried out using a 1D δ-Eddington approximation and the original TenStream solver, to benchmark calculations performed with the 3D Monte Carlo solver MYSTIC. We demonstrate that at the grid box level, dynamic TenStream is able to calculate heating rates and net irradiances at domain boundaries that are very close to those obtained by the original TenStream solver, thus offering a much better representation of the MYSTIC benchmark than the 1D δ-Eddington results. By calling the dynamic TenStream solver less frequently than the δ-Eddington approximation, we furthermore show that our new solver produces significantly better results than a 1D δ-Eddington approximation carried out with a similar computational demand. At these lower calling frequencies, however, the incomplete solves in the dynamic TenStream solver also lead to a buildup of bias with time, which becomes larger the lower the calling frequency is.

Most of the dust models underestimate the load of the large dust particles, consider spherical shapes instead of irregular ones, and have to deal with a wide range of the dust refractive index (RI) to be used. This leads to an incomplete assessment of the dust radiative effects and dust-related impacts on climate and weather. The current work aims to provide an assessment, through a sensitivity study, of the limitations of models to calculate the dust direct radiative effect (DRE) due to the underrepresentation of its size, RI, and shape. We show that the main limitations stem from the size and RI, while using a more realistic shape plays only a minor role, with our results agreeing with recent findings in the literature. At the top of the atmosphere (TOA) close to dust sources, the underestimation of size issues an underestimation of the direct warming effect of dust of ∼ 18–25 W m−2, for DOD = 1 (dust optical depth) at 0.5 µm, depending on the solar zenith angle (SZA) and RI. The underestimation of the dust size in models is less above the ocean than above dust sources, resulting in an underestimation of the direct cooling effect of dust above the ocean by up to 3 W m−2, for aerosol optical depth (AOD) of 1 at 0.5 µm. We also show that the RI of dust may change its DRE by 80 W m−2 above the dust sources and by 50 W m−2 at downwind oceanic areas for DOD = 1 at 0.5 µm at TOA. These results indicate the necessity of including more realistic sizes and RIs for dust particles in dust models, in order to derive better estimations of the dust DRE, especially near the dust sources and mostly for studies dealing with local radiation effects of dust aerosols.

Objective Lesions of incomplete retinal pigment epithelium and outer retinal atrophy (iRORA) are associated with disease progression in intermediate age-related macular degeneration (AMD). However, the corresponding functional impact of these precursor lesions is unknown and needs to be established.Methods and analysis We present a cross-sectional study of four patients with intermediate AMD employing clinical-grade MAIA (stimulus size: 0.43°, ~125 µm) and adaptive optics scanning light ophthalmoscope (AOSLO, stimulus size 0.07°, ~20 µm) based microperimetry (MP) to assess the specific impact of iRORA lesions on retinal sensitivity.Results AOSLO imaging showed overall reduced photoreceptor reflectivity and patches of hyporeflective regions at drusen with interspersed hyperreflective foci in iRORA regions. MAIA-MP yielded an average retinal sensitivity loss of -7.3 ±3.1 dB at iRORA lesions compared to the in-eye control. With AOSLO-MP, the corresponding sensitivity loss was -20.1 ±4.8 dB.Conclusion We demonstrated that iRORA lesions are associated with a severe impairment in retinal sensitivity. Larger cohort studies will be necessary to validate our findings.

Background Retinal imaging, including fundus autofluorescence (FAF), strongly depends on the clearness of the optical media. Lens status is crucial since the ageing lens has both light-blocking and autofluorescence (AF) properties that distort image analysis. Here, we report both lens opacification and AF metrics and the effect on automated image quality assessment.Methods 227 subjects (range: 19–89 years old) received quantitative AF of the lens (LQAF), Scheimpflug, anterior chamber optical coherence tomography as well as blue/green FAF (BAF/GAF), and infrared (IR) imaging. LQAF values, the Pentacam Nucleus Staging score and the relative lens reflectivity were extracted to estimate lens opacification. Mean opinion scores of FAF and IR image quality were compiled by medical readers. A regression model for predicting image quality was developed using a convolutional neural network (CNN). Correlation analysis was conducted to assess the association of lens scores, with retinal image quality derived from human or CNN annotations.Results Retinal image quality was generally high across all imaging modalities (IR (8.25±1.99) >GAF >BAF (6.6±3.13)). CNN image quality prediction was excellent (average mean absolute error (MAE) 0.9). Predictions were comparable to human grading. Overall, LQAF showed the highest correlation with image quality grading criteria for all imaging modalities (eg, Pearson correlation±CI −0.35 (−0.50 to 0.18) for BAF/LQAF). BAF image quality was most vulnerable to an increase in lenticular metrics, while IR (−0.19 (−0.38 to 0.01)) demonstrated the highest resilience.Conclusion The use of CNN-based retinal image quality assessment achieved excellent results. The study highlights the vulnerability of BAF to lenticular remodelling. These results can aid in the development of cut-off values for clinical studies, ensuring reliable data collection for the monitoring of retinal diseases.

Abstract Foam formation is a massive challenge in submerged aerated bioprocesses, e.g., in beer fermentation. While the use of antifoam may easily overcome foaming at laboratory scale, it is often an unattractive solution since the challenge remains in future upscaling, as reduced mass transfer and extra steps in product purification and analytics result in increased costs. Interestingly, the number of studies tackling this challenge is relatively low, although literature suggests a range of alternatives, from avoiding foaming to means of controlling or even using foaming as an in situ product removal. Here we give an overview of the topic in five subsections. (1) We argue that a sound understanding of the molecular origin of foaming can facilitate solutions for overcoming the challenge while introducing some long-known challenges (i.e., in beer fermentation). We then review in (2) the apparent avoidance of foam formation before we in (3) summarize possibilities to reduce and control foam after its formation. Subsequently, in (4), we discuss possible solutions that take advantage of foam formation, for example, via foam fractionation for in situ product removal. Finally, in (5), we provide an overview of microbial strain engineering approaches to cope with some aspects of foaming in fermentations. With this review, we would like to sensitize and inform the interested reader while offering an overview of the current literature for the expert, particularly with regard to the foam special issue in Discover Chemical Engineering.

This paper deals with double-pulse laser-induced breakdown spectroscopy (LIBS) underwater, which is a promising analytical method for elemental analysis in the deep sea up to a water depth of 6000 m. A double-pulse laser with a wavelength of 1064 nm is used, which provides a pulse energy of up to 266 mJ for each laser pulse (in single pulse mode), a pulse width of 5–7 ns and a pulse delay in the range of 0.5 to 20 µs. In the double-pulse LIBS method, the first laser pulse creates a cavity on the material surface, and then the second laser pulse forms the plasma in this cavity. It is expected that the plasma is affected by the cavity’s size and lifetime. For this reason, the influence of focus position, pulse energy and pulse delay on the cavity and plasma formation at shallow water depth has been investigated.