Estimating actual SARS-CoV-2 infections from secondary data

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation-rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

Lagrangian solver for coupling hydrodynamics with biokinetic conversion modelling in anaerobic digesters

Conventional anaerobic digestion models used in wastewater treatment plants suffer from inaccuracies due to the limited consideration given to hydrodynamics within the digester tank. A solution to this is to combine computational fluid dynamics simulations with anaerobic models. This paper introduces a novel methodology in the form of a software toolbox that implements the standard anaerobic digestion model no.1 in C++ and can interface with particle-based Lagrangian simulations. This method provides significantly more insights into the biochemical conversion process by accounting for the impact of the hydrodynamics on the biochemical reactions. The paper presents the background of the method along with a conceptual and numerical verification. It also presents a case study of a 3D lab scale digester comparing the results from the solver with the standard anaerobic digestion model. This integrated approach can be used by operators and designers for optimisations and also for predictive modelling.

Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations

Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images.

Automatic modelling of human musculoskeletal ligaments - Framework overview and model quality evaluation

BACKGROUND

Accurate segmentation of connective soft tissues in medical images is very challenging, hampering the generation of geometric models for bio-mechanical computations. Alternatively, one could predict ligament insertion sites and then approximate the shapes, based on anatomical knowledge and morphological studies.

OBJECTIVE

In this work, we describe an integrated framework for automatic modelling of human musculoskeletal ligaments.

METHOD

We combine statistical shape modelling with geometric algorithms to automatically identify insertion sites, based on which geometric surface/volume meshes are created. As clinical use case, the framework has been applied to generate models of the forearm interosseous membrane. Ligament insertion sites in the statistical model were defined according to anatomical predictions following a published approach.

RESULTS

For evaluation we compared the generated sites, as well as the ligament shapes, to data obtained from a cadaveric study, involving five forearms with 15 ligaments. Our framework permitted the creation of models approximating ligaments' shapes with good fidelity. However, we found that the statistical model trained with the state-of-the-art prediction of the insertion sites was not always reliable. Average mean square errors as well as Hausdorff distances of the meshes could increase by an order of magnitude, as compared to employing known insertion locations of the cadaveric study. Using those, an average mean square error of 0.59 mm and an average Hausdorff distance of less than 7 mm resulted, for all ligaments.

CONCLUSIONS

The presented approach for automatic generation of ligament shapes from insertion points appears to be feasible but the detection of the insertion sites with a SSM is too inaccurate, thus making a patient-specific approach necessary.

Nonreactive spreading at high temperature: molten metals and oxides on molybdenum

The spontaneous spreading of small liquid metal (Cu, Ag, Au) and oxide drops on Mo substrates has been studied using a drop transfer setup combined with high-speed video. Under the experimental conditions used in this work, spreading occurs in the absence of interfacial reactions or ridging. The analysis of the spreading data indicates that dissipation at the triple junction (that can be described in terms of a triple-line friction) is playing a dominant role in the movement of the liquid front. This is due, in part, to the much stronger atomic interactions in high-temperature systems when compared to organic liquids. As a result of this analysis, a comprehensive view of spreading emerges in which the strength of the atomic interactions (solid-liquid, liquid-liquid) determines the relative roles of viscous impedance and dissipation at the triple junction in spreading kinetics.

Characterization of cyclic AMP-dependent phosphorylation of neuronal membrane proteins

We examined the patterns of cyclic AMP-dependent protein phosphorylation in membranes prepared from rat cortical synaptosomes following gel electrophoresis and autoradiography. We determined the optimum pH (6.2), time (20 s), Mg2+ concentration (10 mM) and cyclic AMP concentration (5 microM) for the reaction. We also found that the detergents Triton X-100 and gramicidin S enhanced cyclic AMP-dependent protein phosphorylation. Inhibitors of the Na+, K+ ATPase (ouabain, NaF, vanadate) enhanced protein phosphorylation. This effect occurred in the presence but not in the absence of detergent. The addition of purified bovine brain cyclic AMP-dependent protein kinase catalytic subunit enhanced membrane protein phosphorylation. The addition of homogeneous neural (bovine brain) and non-neural (bovine skeletal muscle) cyclic AMP-dependent protein kinase type II regulatory subunit partially inhibited protein phosphorylation. Both neural and non-neural regulatory subunits behaved similarly. In addition to cyclic AMP-dependent phosphorylation, the alpha-subunit of pyruvate dehydrogenase (Mr = 41,000) is phosphorylated in a cyclic AMP-independent fashion. We also examined the phosphorylation pattern of membranes prepared from rat heart and found that the number of acceptor substrates was much less than that from the nervous system.

The influence of portosystemic shunting on zinc and vitamin A metabolism in liver cirrhosis

To elucidate the possible role of portosystemic shunting on zinc and vitamin A deficiency which has been described in patients with cirrhosis of the liver, a study on 37 hospitalized patients with liver cirrhosis was performed. Patients with surgical portosystemic shunt were found to have a significantly lower levels of zinc, vitamin A and retinol-binding protein (RBP) than controls and patients with cirrhosis without shunt. Patients with portal hypertension--considered to have spontaneous shunting--also has lower levels than those without this symptom. A significant correlation between zinc and vitamin A and RBP levels, respectively, was found. Also an increased renal zinc output was demonstrated. An influence of portosystemic shunting on zinc deficiency and subsequent vitamin A deficiency by decreased RBP release is concluded. The importance of these metabolic disorders for clinical symptoms is discussed.