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Biosynthesis of glycosylphosphatidylinositol (GPI) is required for anchoring proteins to the plasma membrane, and is essential for the integrity of the fungal cell wall. Here, we use a reporter gene-based screen in Saccharomyces cerevisiae for the discovery of antifungal inhibitors of GPI-anchoring of proteins, and identify the oligocyclopropyl-containing natural product jawsamycin (FR-900848) as a potent hit. The compound targets the catalytic subunit Spt14 (also referred to as Gpi3) of the fungal UDP-glycosyltransferase, the first step in GPI biosynthesis, with good selectivity over the human functional homolog PIG-A. Jawsamycin displays antifungal activity in vitro against several pathogenic fungi including Mucorales, and in vivo in a mouse model of invasive pulmonary mucormycosis due to Rhyzopus delemar infection. Our results provide a starting point for the development of Spt14 inhibitors for treatment of invasive fungal infections. Biosynthesis of glycosylphosphatidylinositol (GPI) is essential for the integrity of the fungal cell wall. Here, the authors show that the natural product jawsamycin inhibits GPI biosynthesis by targeting a subunit of the fungal UDP-glycosyltransferase, and displays pronounced activity against pathogenic fungi of the order Mucorales.

Relating the attenuation and velocity dispersion of seismic waves to fluid pressure diffusion (FDP) by means of numerical simulations is essential for constraining the mechanical and hydraulic properties of heterogeneous porous rocks. This, in turn, is of significant importance for a wide range of prominent applications throughout the Earth, environmental, and engineering sciences, such as, for example, geothermal energy production, hydrocarbon exploration, nuclear waste disposal, and CO 2 storage. In order to assess the effects of wave-induced FDP in heterogeneous porous rocks, we simulate time-harmonic oscillatory tests based on a finite element (FE) discretization of Biot’s equations in the time-frequency domain for representative elementary volumes (REVs) of the considered rock masses. The major challenge for these types of simulations is the creation of adequate computational meshes, which resolve the numerous and complex interfaces between the heterogeneities and the embedding background. To this end, we have developed a novel method based on adaptive mesh refinement (AMR), which allows for the fully automatic creation of meshes for strongly heterogenous media. The key concept of the proposed method is to start from an initially uniform coarse mesh and then to gradually refine elements which have non-empty overlaps with the embedded heterogeneities. This results in a hierarchy of non-uniform meshes with a large number of elements close to the interfaces, which do, however, not need to be explicitly resolved. This dramatically simplifies and accelerates the laborious and time-consuming process of meshing strongly heterogeneous poroelastic media, thus enabling the efficient simulation of REVs containing heterogeneities of quasi-arbitrary complexity. After a detailed description of the methodological foundations, we proceed to demonstrate that the FE discretization with low-order FE has a unique solution and hence does not present spurious modes. We assess the practical effectiveness and accuracy of the proposed method by means of four case studies of increasing complexity.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The hydraulic and mechanical characterization of fractures is crucial for a wide range of pertinent applications, such as geothermal energy production, hydrocarbon exploration, CO2 sequestration, and nuclear waste disposal. Direct hydraulic and mechanical testing of individual fractures along boreholes does, however, tend to be slow and cumbersome. To alleviate this problem, we propose to estimate the effective hydraulic aperture and the mechanical compliance of isolated fractures intersecting a borehole through a Bayesian Markov chain Monte Carlo (MCMC) inversion of full-waveform tube-wave data recorded in a vertical seismic profiling (VSP) setting. The solution of the corresponding forward problem is based on a recently developed semi-analytical solution. This inversion approach has been tested for and verified on a wide range of synthetic scenarios. Here, we present the results of its application to observed hydrophone VSP data acquired along a borehole in the underground Grimsel Test Site in the central Swiss Alps. While the results are consistent with the corresponding evidence from televiewer data and exemplarily illustrate the advantages of using a computationally expensive stochastic, instead of a deterministic inversion approach, they also reveal the inherent limitation of the underlying semi-analytical forward solver.

Artworks are an inseparable part of the cultural heritage of societies and provide us with a unique look at cultural developments through time and space. For the best possible conservation, it is paramount to know the constituent materials, condition, and construction techniques of the objects (e.g. painting on wood, fresco, sculpture). Such information is required not only for the surfaces of the objects, but also for the interiors; in the imaging discipline, this is known as depth imaging. Here, we introduce a new method for noninvasive depth imaging as an alternative to traditional non-invasive methods when the latter cannot be used to obtain the required information. We use ultrasonic transverse-wave transmission measurements and turn them into virtual reflection measurements. We achieve this by applying seismic interferometry with active sources. Obtaining reflection measurements by seismic interferometry allows us to apply an advanced imaging technique – prestack depth migration, as used in seismic exploration – to produce a high-resolution depth image of an object. We apply our method to ultrasonic data recorded on a mockup of a painting on a wooden support. We validate our method by comparing our results with an image from X-ray computed tomography.

Most geophysical inverse problems are nonlinear and rely upon numerical forward solvers involving discretization and simplified representations of the underlying physics. As a result, forward modeling errors are inevitable. In practice, such model errors tend to be either completely ignored, which leads to biased and over-confident inversion results, or only partly taken into account using restrictive Gaussian assumptions. Here, we rely on deep generative neural networks to learn problem-specific low-dimensional probabilistic representations of the discrepancy between high-fidelity and low-fidelity forward solvers. These representations are then used to probabilistically invert for the model error jointly with the target geophysical property field, using the computationally-cheap, low-fidelity forward solver. To this end, we combine a Markov-chain-Monte-Carlo (MCMC) inversion algorithm with a trained convolutional neural network of the spatial generative adversarial network (SGAN) type, whereby at each MCMC step, the simulated low-fidelity forward response is corrected using a proposed model-error realization. Considering the crosshole ground-penetrating radar traveltime tomography inverse problem, we train SGAN networks on traveltime discrepancy images between: (1) curved-ray (high fidelity) and straight-ray (low fidelity) forward solvers; and (2) finite-difference-time-domain (high fidelity) and straight-ray (low fidelity) forward solvers. We demonstrate that the SGAN is able to learn the spatial statistics of the model error and that suitable representations of both the subsurface model and model error can be recovered by MCMC. In comparison with inversion results obtained when model errors are either ignored or approximated by a Gaussian distribution, we find that our method has lower posterior parameter bias and better explains the observed traveltime data.[...]

The chemical modification 2′‐O‐methyl of nucleosides is often used to increase siRNA stability towards nuclease activities. However, the metabolic fate of modified nucleosides remains unclear. Therefore, the aim of this study was to determine the mass balance, pharmacokinetic, and absorption, distribution, metabolism, and excretion (ADME)‐properties of tritium‐labeled 2′‐O‐methyluridine, following a single intravenous dose to male CD‐1 mice. The single intravenous administration of [5‐3H]‐2′‐O‐methyluridine was well tolerated in mice. Radioactivity was rapidly and widely distributed throughout the body and remained detectable in all tissues investigated throughout the observation period of 48 h. After an initial rapid decline, blood concentrations of total radiolabeled components declined at a much slower rate. [3H]‐2′‐O‐Methyluridine represented a minor component of the radioactivity in plasma (5.89% of [3H]‐AUC0‐48 h). Three [3H]‐2′‐O‐methyluridine metabolites namely uridine (M1), cytidine (M2), and uracil (M3) were the major circulating components representing 32.8%, 8.11%, and 23.6% of radioactivity area under the curve, respectively. The highest concentrations of total radiolabeled components and exposures were observed in kidney, spleen, pineal body, and lymph nodes. The mass balance, which is the sum of external recovery of radioactivity in excreta and remaining radioactivity in carcass and cage wash, was complete. Renal excretion accounted for about 52.7% of the dose with direct renal excretion of the parent in combination with metabolism to the endogenous compounds cytidine, uracil, cytosine, and cytidine.

The chemical modification 2′‐ O ‐methyl of nucleosides is often used to increase si RNA stability towards nuclease activities. However, the metabolic fate of modified nucleosides remains unclear. Therefore, the aim of this study was to determine the mass balance, pharmacokinetic, and absorption, distribution, metabolism, and excretion ( ADME )‐properties of tritium‐labeled 2′‐ O ‐methyluridine, following a single intravenous dose to male CD ‐1 mice. The single intravenous administration of [5‐ 3 H]‐2′‐ O ‐methyluridine was well tolerated in mice. Radioactivity was rapidly and widely distributed throughout the body and remained detectable in all tissues investigated throughout the observation period of 48 h. After an initial rapid decline, blood concentrations of total radiolabeled components declined at a much slower rate. [ 3 H]‐2′‐ O ‐Methyluridine represented a minor component of the radioactivity in plasma (5.89% of [ 3 H]‐ AUC 0‐48 h ). Three [ 3 H]‐2′‐ O ‐methyluridine metabolites namely uridine (M1), cytidine (M2), and uracil (M3) were the major circulating components representing 32.8%, 8.11%, and 23.6% of radioactivity area under the curve, respectively. The highest concentrations of total radiolabeled components and exposures were observed in kidney, spleen, pineal body, and lymph nodes. The mass balance, which is the sum of external recovery of radioactivity in excreta and remaining radioactivity in carcass and cage wash, was complete. Renal excretion accounted for about 52.7% of the dose with direct renal excretion of the parent in combination with metabolism to the endogenous compounds cytidine, uracil, cytosine, and cytidine.

Determination of SARS-CoV-2 antibody responses in the context of pre-existing immunity to circulating human coronavirus (HCoV) is critical for understanding protective immunity. Here we perform a multifactorial analysis of SARS-CoV-2 and HCoV antibody responses in pre-pandemic ( N  = 825) and SARS-CoV-2-infected donors ( N  = 389) using a custom-designed multiplex ABCORA assay. ABCORA seroprofiling, when combined with computational modeling, enables accurate definition of SARS-CoV-2 seroconversion and prediction of neutralization activity, and reveals intriguing interrelations with HCoV immunity. Specifically, higher HCoV antibody levels in SARS-CoV-2-negative donors suggest that pre-existing HCoV immunity may provide protection against SARS-CoV-2 acquisition. In those infected, higher HCoV activity is associated with elevated SARS-CoV-2 responses, indicating cross-stimulation. Most importantly, HCoV immunity may impact disease severity, as patients with high HCoV reactivity are less likely to require hospitalization. Collectively, our results suggest that HCoV immunity may promote rapid development of SARS-CoV-2-specific immunity, thereby underscoring the importance of exploring cross-protective responses for comprehensive coronavirus prevention. How the immune responses induced by SARS-CoV-2 and human coronavirus (hCoV) crosstalk is still unclear. Here the authors profile the humoral responses of prepandemic and SARS-CoV-2-infected donors to find that higher hCoV antibody titers are associated with SARS-CoV-2 negativity, and with reduced hospitalization in SARS-CoV-2 positive patients.