Background Neuronal uptake and subsequent spread of proteopathic seeds, such as αS (alpha-synuclein), Tau, and TDP-43, contribute to neurodegeneration. The cellular machinery participating in this process is poorly understood. One proteinopathy called multisystem proteinopathy (MSP) is associated with dominant mutations in Valosin Containing Protein (VCP). MSP patients have muscle and neuronal degeneration characterized by aggregate pathology that can include αS, Tau and TDP-43. Methods We performed a fluorescent cell sorting based genome-wide CRISPR-Cas9 screen in αS biosensors. αS and TDP-43 seeding activity under varied conditions was assessed using FRET/Flow biosensor cells or immunofluorescence for phosphorylated αS or TDP-43 in primary cultured neurons. We analyzed in vivo seeding activity by immunostaining for phosphorylated αS following intrastriatal injection of αS seeds in control or VCP disease mutation carrying mice. Results One hundred fifty-four genes were identified as suppressors of αS seeding. One suppressor, VCP when chemically or genetically inhibited increased αS seeding in cells and neurons. This was not due to an increase in αS uptake or αS protein levels. MSP-VCP mutation expression increased αS seeding in cells and neurons. Intrastriatal injection of αS preformed fibrils (PFF) into VCP-MSP mutation carrying mice increased phospho αS expression as compared to control mice. Cells stably expressing fluorescently tagged TDP-43 C-terminal fragment FRET pairs (TDP-43 biosensors) generate FRET when seeded with TDP-43 PFF but not monomeric TDP-43. VCP inhibition or MSP-VCP mutant expression increases TDP-43 seeding in TDP-43 biosensors. Similarly, treatment of neurons with TDP-43 PFFs generates high molecular weight insoluble phosphorylated TDP-43 after 5 days. This TDP-43 seed dependent increase in phosphorlyated TDP-43 is further augmented in MSP-VCP mutant expressing neurons. Conclusion Using an unbiased screen, we identified the multifunctional AAA ATPase VCP as a suppressor of αS and TDP-43 aggregate seeding in cells and neurons. VCP facilitates the clearance of damaged lysosomes via lysophagy. We propose that VCP’s surveillance of permeabilized endosomes may protect against the proteopathic spread of pathogenic protein aggregates. The spread of distinct aggregate species may dictate the pleiotropic phenotypes and pathologies in VCP associated MSP.
Miscalculating the volumes of water withdrawn for irrigation, the largest consumer of freshwater in the world, jeopardizes sustainable water management. Hydrological models quantify water withdrawals, but their estimates are unduly precise. Model imperfections need to be appreciated to avoid policy misjudgements.
Introduction Shared on-demand mobility services emerge at a fast pace, changing the landscape of public transport. However, shared mobility services are largely designed without considering the access needs of people with disabilities, putting these passengers at risk of exclusion. Recognising that accessibility is best addressed at the design stage and through direct participation of persons with disabilities, the objective of this study was to explore disabled users’ views on the following emerging shared mobility services: (a) ride pooling, (b) microtransit, (c) motorbike taxis, (d) robotaxis, (f) e-scooter sharing, and (g) bike sharing. Methodolgy Using an online mobility survey, we sampled disabled users’ (1) views on accessibility, (2) use intention, and (3) suggestions for improving accessibility. The results reflect the responses of 553 individuals with different types of disabilities from 21 European countries. Results Projected accessibility and use intention were greatest for microtransit, robotaxis, and ride pooling across different disabilities. In contrast, motorbike taxis, e-scooter sharing, and bike sharing were viewed as least accessible and least attractive to use, especially by persons with physical, visual, and multiple disabilities. Despite differences in projected accessibility, none of the shared mobility services would fulfil the access needs of disabled persons in their current form. Suggestions for increasing the accessibility of these services included (a) an ondemand door-to-door service, (b) an accessible booking app, (c) real-time travel information, and (d) the necessity of accommodating wheelchairs. Conclusions Our findings highlight the need for improving both vehicles and service designs to cater for the access needs of persons with disabilities and provide policymakers with recommendations for the design of accessible mobility solutions.
The combination of ionic liquids (ILs) and hydrogen peroxide is one of the most promising approaches for the substitution of conventionally used hypergolic bipropellants consisting of nitrogen tetroxide and hydrazines. In this work, protic ionic liquids with imidazolium-based cationic structures and thiocyanate anions are presented as new fuels for green hypergolic bipropellants for the first time. All seven presented substances are hypergolic with hydrogen peroxide and surpass the density specific impulse of the previously mentioned conventional bipropellant. One of them stands out in particular, as no other transition metal-, hydride- and boron-free ionic liquid with an ignition delay time as short as 7.3 ms was found with hydrogen peroxide so far. This extremely good ignition behavior of the, at room temperature solid, IL offers great potential not only for hybrid propellants, but also for liquid hypergolic bipropellant combinations: out of a mixture of 35 wt% [Him][SCN] and 65 wt% [Emim][SCN], a remarkable new liquid bipropellant combination (named HIM_35) is formed with a very low ignition delay time of 16.7 ms and a particularly high maximum density specific impulse ρ Isp of 429 s⋅g⋅cm- 3. https://authors.elsevier.com/a/1fUbQ3iH4IHs3
Based on the work by Boukamp (Boukamp, 2017), the method of Fuoss and Kirkwood (Fuoss and Kirkwood, 1941) is applied to derive an analytical distribution function of relaxation times for physics based porous electrode impedance cases. These impedance models are typically described by transcendental transfer functions. The porous electrode impedance treated here reflects a balance of the effective ionic and electronic impedances inside a porous electrode consisting of particles. Therefore, first the DFRT of the single particle interface impedance is derived. This includes treatment of charge transfer, double layer charging, solid state diffusion inside the particles, open-circuit voltage variations due to solid-state concentration, and insulating layers surrounding the particles. The resulting single particle DFRT relations are then incorporated into a mathematical description of the porous electrode DFRT. The results show that the DFRT of the porous electrode can be clearly separated into distributions of time constants corresponding to charge transfer, solid state diffusion and in case of intercalating particles, like in lithium-ion batteries, a third distribution of time constants is identified. A novelty of this work is the explicit treatment of the low-frequency capacitance and the resulting distribution of time constants in porous electrode systems. Analytical relations for the individual time constants are derived and reported. Since the ideal distribution of time constants can be represented by a series of R||C circuit elements, validation is performed by reconstruction of the impedance spectra, based on the analytical results.
In a context of accelerated human-induced biodiversity loss, remote sensing (RS) is emerging as a promising tool to map plant biodiversity from space. Proposed approaches often rely on the Spectral Variation Hypothesis (SVH), linking the heterogeneity of terrestrial vegetation to the variability of the spectroradiometric signals. Yet, due to observational limitations, the SVH has been insufficiently tested, remaining unclear which metrics, methods, and sensors could provide the most reliable estimates of plant biodiversity. Here we assessed the potential of RS to infer plant biodiversity using radiative transfer simulations and inversion. We focused specifically on “functional diversity,” which represents the spatial variability in plant functional traits. First, we simulated vegetation communities and evaluated the information content of different functional diversity metrics (FDMs) derived from their optical reflectance factors (R) or the corresponding vegetation “optical traits,” estimated via radiative transfer model inversion. Second, we assessed the effect of the spatial resolution, the spectral characteristics of the sensor, and signal noise on the relationships between FDMs derived from field and remote sensing datasets. Finally, we evaluated the plausibility of the simulations using Sentinel-2 (multispectral, 10 m pixel) and DESIS (hyperspectral, 30 m pixel) imagery acquired over sites of the Functional Significance of Forest Biodiversity in Europe (FunDivEUROPE) network. We demonstrate that functional diversity can be inferred both by reflectance and optical traits. However, not all the FDMs tested were suited for assessing plant functional diversity from RS. Rao's Q index, functional dispersion, and functional richness were the best-performing metrics. Furthermore, we demonstrated that spatial resolution is the most limiting RS feature. In agreement with simulations, Sentinel-2 imagery provided better estimates of plant diversity than DESIS, despite the coarser spectral resolution. However, Sentinel-2 offered inaccurate results at DESIS spatial resolution. Overall, our results identify the strengths and weaknesses of optical RS to monitor plant functional diversity. Future missions and biodiversity products should consider and benefit from the identified potentials and limitations of the SVH.
Molten chlorides, such as MgCl2-KCl-NaCl, are promising advanced high-temperature (up to 800 °C) thermal energy storage (TES) materials in next-generation concentrating solar power (CSP) plants. However, their high corrosivity to commercial Fe-Cr-Ni alloys impedes the commercial applications of chloride-TES. In this work, we investigated the corrosion of two selected commercial Fe-based alloys (SS 310 and In 800H) in molten MgCl2-KCl-NaCl salt, aiming to study the feasibility of affordable Fe-based alloys instead of expensive Ni-based alloys in the chloride-TES system. The alloy samples were immersed in the liquid-Mg-purified molten salt at 700 °C for 2000 h under a protective inert gas atmosphere. After the corrosion test, SEM-EDX microstructural analysis and mass loss analysis showed that corrosion rates of the immersed alloy samples were lower than 15 µm/year, and the corrosion rates had a decreasing tendency with increasing immersion time during the 2000-hour test. To our best knowledge, this is the first experimental demonstration that corrosion rates of the Fe-based alloys in molten MgCl2-KCl-NaCl salt at 700 °C can be controlled below the target (15 µm/year) proposed by the US Department of Energy (DOE). Using affordable Fe-based alloys as main structural materials, the cost of chloride-TES (27 USD/kWh) could be comparable to that of commercial nitrate-TES (20–33 USD/kWh). Taking advantage of chloride-TES with higher operating temperature, the next-generation CSP plant could use an advanced power cycle (e.g., sCO2 Brayton) to have a much higher energy conversion efficiency, leading to a significantly lower Levelized Cost of Electricity (LCOE) than the current commercial CSP plant.
Monitoring and understanding urban development requires up-to-date information on multiple urban land-use classes. Manual classification and deep learning approaches based on very-high resolution imagery have been applied successfully, but the required resources limits their capacity to map urban land use at larger scales. Here, we use a combination of open-source satellite imagery, constituting of data from Sentinel-1 and Sentinel-2, and socioeconomic data, constituting of points-of-interest and spatial metrics from road networks to classify urban land-use at a national scale, using a deep learning approach. A related challenge for large-scale mapping is the availability of ground truth data. Therefore, we focus our analysis on the transferability of our classification approach, using ground truth labels from a nationwide land-use dataset for the Netherlands. By dividing the country into four regions, we tested whether a combination of satellite data and socioeconomic data increases the transferability of the classification approach, compared to using satellite data only. The results indicate that socioeconomic data increases the overall accuracy of the classification for the Netherlands by 3 percentage points. In a transfer learning approach we find that adding socioeconomic data increases the accuracy between 3 and 5 percentage points when trained on three regions and tested on the independent fourth one. In the case of training and testing on one region and testing on another, the increase in overall accuracy increased up to 9 percentage points. In addition, we find that our deep learning approach consistently outperforms a random forest model, used here as benchmark, in all of the abovementioned experiments. Overall, we find that socioeconomic data increases the accuracy of urban land use classification, but variations between experiments are large.
Identifying sea ice types in the early stages of development from L-band SAR imagery remains an active research area during the Arctic freeze-up period. We used ScanSAR C- and L-band imagery from RADARSAT-2, ALOS PALSAR and ALOS-2 PALSAR-2, to identify ice types in the North Water Polynya (NOW) and Victoria Strait (VS) region of the Canadian Arctic. We investigated the HH-polarized microwave backscatter coefficient (σHH⁰) and its GLCM texture parameters for six ice classes and open water. We found very low σHH⁰ for nilas at both C- and L-band. Although similar σHH⁰ found for grey ice at both frequencies, σHH⁰ decrease with increasing ice thickness at L-band from grey ice, whereas, at C-band, σHH⁰ increases from grey to grey-white ice and then decreases as the ice grows. GLCM texture parameters show lower values for L-band than C-band; however, separability among classes was found only for a few selected parameters. We used the support vector machine (SVM) algorithm for ice type classification from SAR scenes using σHH⁰ and GLCM texture statistics. Due to overlapping σHH⁰ signatures at C-band, early-stage ice classes were substantially misclassified. L-band identified early-stage ice classes with higher accuracy compared to C-band but misclassified thicker ice types and open water. L-band alone provided very good classification results (~80% accuracy) and combining L- and C-band (i.e., dual-frequency approach) further increased accuracy to >90%. C-band alone resulted in the lowest accuracy of <60%. We acknowledge that developing a universal ice classification is still a challenge and requires some manual supervision to adopt variable ice conditions into the classification method. However, a dual-frequency approach can achieve higher classification accuracy than conventionally used single-frequency approaches. This research highlights the value of upcoming L-Band SAR missions to improve sea ice classification in regions where a variety of ice types exist, including many thinner types, which are now dominating an increasingly warming Arctic.
Research on the automatic analysis of sonar images has focused on classical, i.e. non deep learning based, approaches for a long time. Over the past 15 years, however, the application of deep learning in this research field has constantly grown. This paper gives a broad overview of past and current research involving deep learning for feature extraction, classification, detection and segmentation of sidescan and synthetic aperture sonar imagery. Most research in this field has been directed towards the investigation of convolutional neural networks (CNN) for feature extraction and classification tasks, with the result that even small CNNs with up to four layers outperform conventional methods. The purpose of this work is twofold. On one hand, due to the quick development of deep learning it serves as an introduction for researchers, either just starting their work in this specific field or working on classical methods for the past years, and helps them to learn about the recent achievements. On the other hand, our main goal is to guide further research in this field by identifying main research gaps to bridge. We propose to leverage the research in this field by combining available data into an open source dataset as well as carrying out comparative studies on developed deep learning methods.
Super-resolution is an essential task in remote sensing. It can enhance low-resolution remote sensing images and benefit downstream tasks such as building extraction and small object detection. However, existing remote sensing image super-resolution methods may fail in many real-world scenarios because they are trained on synthetic data generated by a single degradation model or on a limited amount of real data collected from specific satellites. To achieve super-resolution of real-world remote sensing images with different qualities in a unified framework, we propose a practical degradation model and a kernel-aware network (KANet). The proposed degradation model includes blur kernels estimated from real images and blur kernels generated from pre-defined distributions, which improves the diversity of training data and covers more real-world scenarios. The proposed KANet consists of a kernel prediction subnetwork and a kernel-aware super-resolution subnetwork. The former estimates the blur kernel of each image, making it possible to cope with real images of different qualities in an adaptive way. The latter iteratively solves two subproblems, degradation and high-frequency recovery, based on unfolding optimization. Furthermore, we propose a kernel-aware layer to adaptively integrate the predicted blur kernel into super-resolution process. The proposed KANet achieves state-of-the-art performance for real-world image super-resolution and outperforms the competing methods by 0.2–0.8 dB in the peak signal-to-noise ratio (PSNR). Extensive experiments on both synthetic and real-world images demonstrate that our approach is of high practicability and can be readily applied to high-resolution remote sensing applications.
We provide detailed background, theoretical and practical, on the specific heat of minerals and mixtures thereof, 'astro-materials,' as well as background information on common minerals and other relevant solid substances found on the surfaces of solar system bodies. Furthermore, we demonstrate how to use specific heat and composition data for lunar samples and meteorites as well as a new database of endmember mineral heat capacities (the result of an extensive literature review) to construct reference models for the isobaric specific heat c P as a function of temperature for common solar system materials. Using a (generally linear) mixing model for the specific heat of minerals allows extrapolation of the available data to very low and very high temperatures, such that models cover the temperature range between 10 K and 1000 K at least (and pressures from zero up to several kbars). We describe a procedure to estimate c P (T) for virtually any solid solar system material with a known mineral composition, e.g., model specific heat as a function of temperature for a number of typical meteorite classes with known mineralogical compositions. We present, as examples, the c P (T) curves of a number of well-described laboratory regolith analogs, as well as for planetary ices and 'tholins' in the outer solar system. Part II will review and present the heat capacity database for minerals and compounds and part III is going to cover applications, standard reference compositions, c P (T) curves, and a comparison with new and literature experimental data. Supplementary information: The online version contains supplementary material available at 10.1007/s10765-022-03046-5.
Dynamical changes in the ionosphere and thermosphere during geomagnetic storm times can have a significant impact on our communication and navigation applications, as well as satellite orbit determination and prediction activities. Because of the complex electrodynamics coupling processes during storms, which cannot be fully described with the sparse set of thermosphere–ionosphere (TI) observations, it is crucial to accurately model the state of the TI system. The approximation closest to the true state can be obtained by assimilating relevant measurements into physics-based models. Thermospheric mass density (TMD) derived from satellite measurements is ideal to improve the thermosphere through data assimilation. Given the coupled nature of the TI system, the changes in the thermosphere will also influence the ionosphere state. This study presents a quantification of the changes and improvement of the model state produced by assimilating TMD not only for the thermosphere density but also for the ionosphere electron density under storm conditions. TMD estimates derived from a single Swarm satellite and the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) physics-based model are used for the data assimilation. The results are presented for a case study during the St. Patricks Day storm 2015. It is shown that the TMD data assimilation generates an improvement of the model’s thermosphere density of up to 40% (measured along the orbit of the non-assimilated Swarm satellites). The model’s electron density during the course of the storm has been improved by approximately 8 and 22% relative to Swarm-A and GRACE, respectively. The comparison of the model’s global electron density against a high-quality 3D electron density model, generated through assimilation of total electron content, shows that TMD assimilation modifies the model’s ionosphere state positively and negatively during storm time. The major improvement areas are the mid-low latitudes during the storm’s recovery phase. Graphical Abstract
Steady plane turbulent free-surface flow over a slightly wavy bottom is considered for very large Reynolds numbers, very small bottom slopes, and Froude numbers close to the critical value 1. As in previous works, the slope and the deviation from the critical Froude number are assumed to be coupled such that turbulence modeling is not required. The amplitudes of the periodic bottom elevations, however, are assumed to be half an order of magnitude larger than in the previous case of bumps or ramps of finite length. Asymptotic expansions give a steady-state version of an extended Korteweg–deVries (KdV) equation for the surface elevation. The extension consists of a forcing term due to the unevenness of the bottom and a damping term due to friction at the bottom. Other flow quantities, such as pressure, flow velocity components, local Froude number and bottom friction force, can be expressed in terms of the surface elevation. Exact solutions of the extended KdV equation, describing stationary cnoidal waves, are obtained for bottoms of particular periodic shapes. As a limiting case, the solitary waves over a bottom ramp are re-obtained in accord with previous results.
In this study, we apply particle image velocimetry (PIV), hot-wire anemometry (HWA), and large-eddy simulation (LES) to identify and characterize a key mechanism by which high-intensity turbulence measured in the “Hi-Pilot” burner is generated. Large-scale oscillation of the high-velocity jet core about its own mean axial centerline is identified as a dominant feature of the turbulent flow field produced by this piloted Bunsen burner. This oscillation is linked to unsteady flow separation along the expanding section of the reactant nozzle and appears stochastic in nature. It occurs over a range of frequencies (100–300 Hz) well below where the turbulent kinetic energy (TKE) spectrum begins to follow a – 5/3 power law and results in a flow with significant scale separation in the TKE spectrum. Although scale separation and intermittency are not unusual in turbulent flows, this insight should inform analysis and interpretation of previous, and future studies of this unique test case.
Accurately predicting aging of lithium-ion batteries would help to prolong their lifespan, but remains a challenge owing to the complexity and interrelation of different aging mechanisms. As a result, aging prediction often relies on empirical or data-driven approaches, which obtain their performance from analyzing large datasets. However, these datasets are expensive to generate and the models are agnostic of the underlying physics and thus difficult to extrapolate to new conditions. In this article, a physical model is used to predict capacity fade caused by solid-electrolyte interphase (SEI) growth in 62 automotive cells, aged with 28 different protocols. Three protocols parametrize the time, current and temperature dependence of the model, the state of charge dependence results from the anode’s open circuit voltage curve. The model validation with the remaining 25 protocols shows a high predictivity with a root-mean squared error of 1.28%. A case study with the so-validated model shows that the operating window, i.e. maximum and minimum state of charge, has the largest impact on SEI growth, while the influence of the applied current is almost negligible. Thereby the presented model is a promising approach to better understand, quantify and predict aging of lithium-ion batteries.
Microstructure and electrochemical properties of the cathode catalyst layers (CCL) of a polymer electrolyte membrane fuel cells (PEMFC) have great impact on the performance and durability of the cell. The aim of this work is to establish a link between CCL structure and fuel cell performance. To obtain CCLs with unique structures six types of electrodes were prepared, each with a different coating technique but with the same Pt loading. The coating techniques are airbrush, screen printing, inkjet printing, dry spraying, doctor blade and drop casting. Moreover, intrinsic properties of PEMFC electrodes such as porosity, permeability, diffusivity as well as ionomer distribution are determined by Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and Atomic Force Microscopy (AFM). Overall, 12 parameters have been evaluated. Generally, CCLs with low fractions of uncovered Pt/C show higher performance at low current densities. In this case the more homogeneous ionomer distribution leads to a higher catalyst utilization. At high current densities transport properties of the CCL have to be considered in addition to the catalyst utilization to explain their performance. The CCL prepared by screen printing shows a low fraction of uncovered Pt/C in combination with good transport properties, leading to the best performance at high currents.
The dynamic behaviour of crystals in convecting fluids determines how magma bodies solidify. In particular, it is often important to estimate how long crystals stay in suspension in the host liquid before being deposited at its bottom (or top, for light crystals and bubbles of volatiles). We perform a systematic 3D numerical study of particle-laden Rayleigh-Bénard convection, and derive a robust model for the particle residence time. For Rayleigh numbers higher than 10⁷, inertial particles' trajectories exhibit a monotonic transition from fluid tracer-like to free-fall dynamics, the control parameter being the ratio between particle Stokes velocity and the mean amplitude of the fluid velocity. The average settling rate is proportional to the particle Stokes velocity in both the end-member regimes, but the distribution of residence times differs markedly from one to the other. For lower Rayleigh numbers (<107), an interaction between large-scale circulation and particle motion emerges, increasing the settling rates on average. Nevertheless, the mean residence time does not exceed the terminal time, i.e. the settling time from a quiescent fluid, by a factor larger than four. An exception are simulations with only a slightly super-critical Rayleigh number (∼104), for which stationary convection develops and some particles become trapped indefinitely. 2D simulations of the same problem overestimate the flow-particle interaction – and hence the residence time – for both high and low Rayleigh numbers, which stresses the importance of using 3D geometries for simulating particle-laden flows. We outline how our model can be used to explain the depth changes of crystal size distribution in sedimentary layers of magmatic intrusions that are thought to have formed via settling of a crystal cargo, and discuss how the micro-structural observations of solidified intrusions can be used to infer the past convective velocity of magma.
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