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Global demand for fossil fuels is projected to decline by up to 75 percent by 2050, requiring new economic strategies for fossil exporters to promote diversification. This study employs an inter-country input-output model to assess sector interdependence in Saudi Arabia and explore the macroeconomic impacts of investing in Power-to-X (PtX) plants. We analyze four configurations: pure solar and hybrid solar-wind systems, producing either green hydrogen or ammonia. Our comparative analysis shows that hybrid plants appear to have stronger domestic effects than pure solar configurations, while producing green ammonia instead of hydrogen generates stronger relative employment. These trends remain consistent across different sensitivities and domestic supply shares. However, the differences between configurations and sensitivities decrease with increasing domestic supply. Currently Saudi Arabia has limited developed manufacturing sectors and the economy is heavily dependent on exports of crude oil and related products-a situation often referred to as the “resource curse”. For all PtX configurations, the construction sector therefore emerges as the largest contributor to domestic employment and value added. At current import shares, foreign effects, particularly in China and the United States, account for about half of the total impact, especially in sectors such as electronics and machinery. Without embedding the hydrogen supply chain into their industrial landscape by starting to produce parts of clean technologies locally, exporters risk substantial value creation occurring predominantly abroad. Overall, our research highlights the need for targeted policy measures to capitalize on the „renewables pull effect“ when installing PtX plants for green energy exports.

Epilepsy is characterized by recurrent, unprovoked seizures. Accurate prediction of seizure occurrence has long been a clinical goal since this would allow to optimize patient treatment, prevent injuries due to seizures, and alleviate the patient burden of unpredictability. Advances in implantable electroencephalographic (EEG) devices, allowing for long-term interictal EEG recordings, have facilitated major progress in this field. Recently, it has been discovered that interictal brain activity demonstrates circadian and multi-dien cycles that are strongly aligned, or phase locked, with seizure risk. Thus, cyclical brain activity patterns have been used to forecast seizures. However, in the effort to develop a clinically useful EEG based seizure forecasting system, challenges remain. Firstly, multiple EEG features demonstrate cyclical patterns, but it remains unclear which feature is best suited for predicting seizures. Secondly, the technology for long-term EEG recording is currently limited in both spatial and temporal sampling resolution. In this study, we compare five established EEG metrics:synchrony, spatial correlation, temporal correlation, signal variance which have been motivated from critical dynamics theory, and interictal epileptiform discharge (IED) which are a traditional marker of seizure propensity. We assess their effectiveness in detecting 24-h and seizure cycles as well as their robustness under spatial and temporal subsampling. Analyzing intracranial EEG data from 23 patients, we report that all examined features exhibit 24-h cycles. Spatial correlation, signal variance, and synchrony showed the highest phase locking with seizures, while IED rates were the lowest. Notably, spatial and temporal correlation were also found to be highly correlated to each other, as were signal variance and IED—suggesting some features may reflect similar aspects of cortical dynamics, whereas others provide complementary information. All features proved robust under subsampling, indicating that the dynamic properties of interictal activity evolve slowly and are not confined to specific brain regions. Our results may aid future translational research by assisting in design and testing of EEG based seizure forecasting systems.

In addition to static loads, structural glazing joints in glass and facade construction in many regions are subject to extraordinary effects such as earthquakes. Seismic actions are characterized by a randomly recurring and dynamic load that affects the structural behavior of the viscoelastic material. Publications on the load-bearing behavior and design of structural glazing joints against seismic loading have not systematically considered these effects. In this paper, relevant parameters influencing the seismic loading of structural glazing joints are determined, evaluated, and narrowed down to areas of practical relevance as part of a theoretical stress analysis. On this basis, extensive experimental investigations of the low-cycle fatigue behavior of structural glazing joints are presented. Cyclic stress-strain curves are determined and compared with quasi-static reference tests to describe the basic low-cycle fatigue behavior. The influence of the previously determined parameters on the cyclic load-bearing behavior can thus be determined and presented. These investigations provide an important basis for describing the behavior under typical seismic random loading. In particular, the type of load application caused by the different construction types of glass and facade buildings can be mentioned as a decisive influencing factor in addition to the frequency of building construction. The results provide a first important contribution to the modeling and design of structural glazing joints against earthquake effects. Within the framework of the IGF project "RISIKO", further extensive scientific investigations will be carried out on this basis in order to develop a design model for structural glazing joints in disaster scenarios.

With the continuing architectural trend toward transparent structures, the use of glass in the construction industry has increased significantly in recent years. The characteristic brittle failure behavior of glass is counteracted by the use of Laminated Safety Glass (LSG) in order to meet the safety-related requirements for breakage, post-breakage, and residual load-bearing capacity. For LSG, at least two panes of glass are laminated to form a monolithic composite with a polymer interlayer. During the manufacturing process, geometric imperfections ("planarity deviations" or "roller waves") in the thermally toughened glass can lead to permanent tensile stress in the thickness direction of the laminate. This stress, especially under environmental conditions (humidity and temperature), has not been considered in current quality assurance measures or scientific studies on the structural performance of laminated safety glass. This paper analyzes the performance of PVB composite samples under tensile load in the thickness direction of the laminate. Two PVB products are considered under two environmental conditions. Based on quasi-static tensile tests to determine a primary strength, the influence of load duration is analyzed in creep tests at different load levels. On the one hand, a relevant correlation between load level and failure time could be determined. Firstly, a relevant correlation between load level and failure time was established. Secondly, a relationship between quasi-static strength and creep performance is observed and a model for predicting the failure time of such specimens is proposed.

Abstract Extracellular matrix (ECM) deposition after central nervous system (CNS) injury leads to inhibitory scarring in humans and other mammals, whereas it facilitates axon regeneration in the zebrafish. However, the molecular basis of these different fates is not understood. Here, we identify small leucine-rich proteoglycans (SLRPs) as a contributing factor to regeneration failure in mammals. We demonstrate that the SLRPs chondroadherin, fibromodulin, lumican, and prolargin are enriched in rodent and human but not zebrafish CNS lesions. Targeting SLRPs to the zebrafish injury ECM inhibits axon regeneration and functional recovery. Mechanistically, we find that SLRPs confer mechano-structural properties to the lesion environment that are adverse to axon growth. Our study reveals SLRPs as inhibitory ECM factors that impair axon regeneration by modifying tissue mechanics and structure, and identifies their enrichment as a feature of human brain and spinal cord lesions. These findings imply that SLRPs may be targets for therapeutic strategies to promote CNS regeneration.

Cystic fibrosis (CF) is a monogenetic disease caused by an impairment of the cystic fibrosis transmembrane conductance regulator (CFTR). CF affects multiple organs and is associated with acute and chronic inflammation. In 2020, Elexacaftor–Tezacaftor–Ivacaftor (ETI) was approved to enhance and restore the remaining CFTR functionality. This study investigates cellular innate immunity, with a focus on neutrophil activation and phenotype, comparing healthy volunteers with patients with CF before (T1, n = 13) and after six months (T2, n = 11) of ETI treatment. ETI treatment reduced sweat chloride (T1: 95 mmol/l (83|108) vs. T2: 32 mmol/l (25|62), p < 0.01, median, first|third quartile) and significantly improved pulmonal function (FEV1 T1: 2.66 l (1.92|3.04) vs. T2: 3.69 l (3.00|4.03), p < 0.01). Moreover, there was a significant decrease in the biomarker human epididymis protein 4 (T1: 6.2 ng/ml (4.6|6.3) vs. T2: 3.0 ng/ml (2.2|3.7), p < 0.01) and a small but significant decrease in matrix metallopeptidase 9 (T1: 45.5 ng/ml (32.5|140.1) vs. T2: 28.2 ng/ml (18.2|33.6), p < 0.05). Neutrophil phenotype (CD10, CD11b, CD62L, and CD66b) and function (radical oxygen species generation, chemotactic and phagocytic activity) remained largely unaffected by ETI treatment. Likewise, monocyte phenotype and markers of platelet activation were similar at T1 and T2. In summary, the present study confirmed a positive impact on patients with CF after ETI treatment. However, neither beneficial nor harmful effects of ETI treatment on cellular innate immunity could be detected, possibly due to the study population consisting of patients with well-controlled CF.

Mechanical tissue properties increasingly serve as pivotal phenotypic characteristics that are subject to change during development or pathological progression. The quantification of such material properties often relies on physical contact between a load-applying probe and an exposed sample surface. For most tissues, meeting these requirements entails an invasive preparation, which poses the risk of yielding mechanical properties that do not portray the physiological state of a tissue within a functioning organism. Brillouin microscopy has emerged as a non-invasive, optical technique that enables the assessment of mechanical cell and tissue properties with high spatio-temporal resolution. In optically transparent specimens, it does not require animal sacrifice, tissue dissection or sectioning. However, the extent to which results obtained from Brillouin microscopy allow to infer conclusions about potential results obtained with a contact-based technique, and vice versa , is unclear. Sources for discrepancies include the varying characteristic temporal and spatial scales, the directionality of measurement, environmental factors, and mechanical moduli probed. In this work, we addressed those aspects by quantifying the mechanical properties of acutely dissected murine retinae using Brillouin microscopy and atomic force microscopy (AFM)-based indentation measurements. Our results show a distinct mechanical profile of the retinal layers with respect to the Brillouin frequency shift, the Brillouin linewidth and the apparent Young’s modulus. Contrary to previous reports, our findings do not support a simple correlative relationship between Brillouin frequency shift and apparent Young’s modulus. Additionally, the divergent sensitivities of Brillouin microscopy and AFM-indentation measurements to structural features, as visualized by transmission electron microscopy, to cross-linking or changes post mortem underscore the dangers of assuming interchangeability between the two methods. In conclusion, our study advocates for viewing Brillouin microscopy and AFM-based indentation measurements as complementary tools, discouraging direct comparisons a priori and suggesting their combined use for a more comprehensive understanding of tissue mechanical properties.

Abstract Graphene‐based materials are among the most promising candidates for studying superconductivity arising from reduced dimensionality. Apart from doping by twisted stacking, superconductivity can also be achieved by metal‐intercalation of stacked graphene sheets, where the properties depend on the choice of the metal atoms and the number of graphene layers. Many different and even unconventional pairing mechanisms and symmetries are predicted in the literature for graphene monolayers and few‐layers. However, those theoretical predictions have yet to be verified experimentally. Here, it is shown that potassium‐intercalated epitaxial bilayer graphene is a superconductor with a critical temperature of Tc = 3.6 ± 0.1 K. By scanning‐tunneling microscopy and angle‐resolved photoelectron spectroscopy, the physical mechanisms are analyzed in great detail, using laboratory equipment. The data demonstrate that electron–phonon coupling is the driving force enabling superconductivity. Although the consideration of an s‐wave pairing symmetry is sufficient to explain the experimental data, evidence is found for the existence of multiple energy gaps. Furthermore, it is shown that low‐dimensional effects are most likely the cause of a gap ratio of 6.1 ± 0.2 that strongly exceeds the Bardeen‐Cooper‐Schrieffer (BCS) value of 3.52 for conventional superconductors. These results highlight the importance of reduced dimensionality yielding unusual superconducting properties of K‐intercalated epitaxial bilayer graphene.

The mechanical response of materials to dynamic loading is often quantified by the frequency-dependent complex modulus. Probing materials directly in the frequency domain faces technical challenges such as a limited range of frequencies, long measurement times, or small sample sizes. Furthermore, many biological samples, such as cells or tissues, can change their properties upon repetitive probing at different frequencies. Therefore, it is common practice to extract the material properties by fitting predefined mechanical models to measurements performed in the time domain. This practice, however, precludes the probing of unique and yet unexplored material properties. In this report, we demonstrate that the frequency-dependent complex modulus can be robustly retrieved in a model-independent manner directly from time-dependent stress-strain measurements. While applying a rolling average eliminates random noise and leads to a reliable complex modulus in the lower frequency range, a Fourier transform with a complex frequency helps to recover the material properties at high frequencies. Finally, by properly designing the probing procedure, the recovery of reliable mechanical properties can be extended to an even wider frequency range. Our approach can be used with many state-of-the-art experimental methods to interrogate the mechanical properties of biological and other complex materials.

In processes such as development and regeneration, where large cellular and tissue rearrangements occur, cell fate and behaviour are strongly influenced by tissue mechanics. While most well-established tools probing mechanical properties require an invasive sample preparation, confocal Brillouin microscopy captures mechanical parameters optically with high resolution in a contact-free and label-free fashion. In this work, we took advantage of this tool and the transparency of the highly regenerative axolotl to probe its mechanical properties in vivo for the first time. We mapped the Brillouin frequency shift with high resolution in developing limbs and regenerating digits, the most studied structures in the axolotl. We detected a gradual increase in the cartilage Brillouin frequency shift, suggesting decreasing tissue compressibility during both development and regeneration. Moreover, we were able to correlate such an increase with the regeneration stage, which was undetected with fluorescence microscopy imaging. The present work evidences the potential of Brillouin microscopy to unravel the mechanical changes occurring in vivo in axolotls, setting the basis to apply this technique in the growing field of epimorphic regeneration.

Abstract Background Quantitative phase imaging (QPI) is an established tool for the marker-free classification and quantitative characterization of biological samples. For spherical objects, such as cells in suspension, microgel beads, or liquid droplets, a single QPI image is sufficient to extract the radius and the average refractive index. This technique is invaluable, as it allows the characterization of large sample populations at high measurement rates. However, until now, no universal software existed that could perform this type of analysis. Besides the choice of imaging modality and the variety in imaging software, the main difficulty has been to automate the entire analysis pipeline from raw data to ensemble statistics. Results We present DryMass, a powerful tool for QPI that covers all relevant steps from loading experimental data (multiple file formats supported), computing the phase data (built-in, automated hologram analysis), performing phase background corrections (offset, tilt, second order polynomial) to fitting scattering models (light projection, Rytov approximation, Mie simulations) to spherical phase objects for the extraction of dry mass, radius, and average refractive index. The major contribution of DryMass is a user-convenient, reliable, reproducible, and automated analysis pipeline for an arbitrary number of QPI datasets of arbitrary sizes. Conclusion DryMass is a leap forward for data analysis in QPI, as it not only makes it easier to visualize raw QPI data and reproduce previous results in the field, but it also opens up QPI analysis to users without a background in programming or phase imaging.

Summary: Atomic force microscopy (AFM) is widely used for quantifying the mechanical properties of soft materials such as cells. AFM force-indentation curves are conventionally fitted with a Hertzian model to extract elastic properties. These properties solely are, however, insufficient to describe the mechanical properties of cells. Here, we expand the analysis capabilities to describe the viscoelastic behavior while using the same force-indentation curves. Our model gives an explicit relation of force and indentation and extracts physically meaningful mechanical parameters. We first validated the model on simulated force-indentation curves. Then, we applied the fitting model to the force-indentation curves of two hydrogels with different crosslinking mechanisms. Finally, we characterized HeLa cells in two cell cycle phases, interphase and mitosis, and showed that mitotic cells have a higher apparent elasticity and a lower apparent viscosity. Our study provides a simple method, which can be directly integrated into the standard AFM framework for extracting the viscoelastic properties of materials.

Quantitative measurements of physical parameters become increasingly important for understanding biological processes. Brillouin microscopy (BM) has recently emerged as one technique providing the 3D distribution of viscoelastic properties inside biological samples − so far relying on the implicit assumption that refractive index (RI) and density can be neglected. Here, we present a novel method (FOB microscopy) combining BM with optical diffraction tomography and epifluorescence imaging for explicitly measuring the Brillouin shift, RI, and absolute density with specificity to fluorescently labeled structures. We show that neglecting the RI and density might lead to erroneous conclusions. Investigating the nucleoplasm of wild-type HeLa cells, we find that it has lower density but higher longitudinal modulus than the cytoplasm. Thus, the longitudinal modulus is not merely sensitive to the water content of the sample − a postulate vividly discussed in the field. We demonstrate the further utility of FOB on various biological systems including adipocytes and intracellular membraneless compartments. FOB microscopy can provide unexpected scientific discoveries and shed quantitative light on processes such as phase separation and transition inside living cells.

Abstract Background Atomic force microscopy (AFM) allows the mechanical characterization of single cells and live tissue by quantifying force-distance (FD) data in nano-indentation experiments. One of the main problems when dealing with biological tissue is the fact that the measured FD curves can be disturbed. These disturbances are caused, for instance, by passive cell movement, adhesive forces between the AFM probe and the cell, or insufficient attachment of the tissue to the supporting cover slide. In practice, the resulting artifacts are easily spotted by an experimenter who then manually sorts out curves before proceeding with data evaluation. However, this manual sorting step becomes increasingly cumbersome for studies that involve numerous measurements or for quantitative imaging based on FD maps. Results We introduce the Python package nanite, which automates all basic aspects of FD data analysis, including data import, tip-sample separation, base line correction, contact point retrieval, and model fitting. In addition, nanite enables the automation of the sorting step using supervised learning. This learning approach relates subjective ratings to predefined features extracted from FD curves. For ratings ranging from 0 to 10, our approach achieves a mean squared error below 1.0 rating points and a classification accuracy between good and poor curves that is above 87%. We showcase our approach by quantifying Young’s moduli of the zebrafish spinal cord at different classification thresholds and by introducing data quality as a new dimension for quantitative AFM image analysis. Conclusion The addition of quality-based sorting using supervised learning enables a fully automated and reproducible FD data analysis pipeline for biological samples in AFM.

Personalised medicine requires cell cultures from defined genetic backgrounds, but providing sufficient numbers of cells is a challenge. Here the authors develop gene cocktails to expand primary cells from a variety of different tissues and species, and show that expanded endothelial and hepatic cells retain properties of the differentiated phenotype.

The massively parallel nature of biological information processing plays an important role due to its superiority in comparison to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.