The Mediterranean diet (MD), rich in minimally processed plant foods and in monounsaturated fats but low in saturated fats, meat, and dairy products, represents one of the most studied diets for cardiovascular health. It has been shown, from both observational and randomized controlled trials, that MD reduces body weight, improves cardiovascular disease surrogates such as waist-to-hip ratios, lipids, and inflammation markers, and even prevents the development of fatal and nonfatal cardiovascular disease, diabetes, obesity, and other diseases. However, it is unclear whether it offers cardiovascular benefits from its individual components or as a whole. Furthermore, limitations in the methodology of studies and meta-analyses have raised some concerns over its potential cardiovascular benefits. MD is also associated with characteristic changes in the intestinal microbiota, mediated through its constituents. These include increased growth of species producing short-chain fatty acids, such as Clostridium leptum and Eubacterium rectale, increased growth of Bifidobacteria, Bacteroides, and Faecalibacterium prausnitzii species, and reduced growth of Firmicutes and Blautia species. Such changes are known to be favorably associated with inflammation, oxidative status, and overall metabolic health. This review will focus on the effects of MD on cardiovascular health through its action on gut microbiota. Full article

Yaks are the main pillar of plateau animal husbandry and the material basis of local herdsmen’s survival. The level of mineral elements in the body is closely related to the production performance of yaks. In this study, we performed a comprehensive analysis of rumen epithelial morphology, transcriptomics and metagenomics to explore the dynamics of rumen functions, microbial colonization and functional interactions in yaks from birth to adulthood. Bacteria, eukaryotes, archaea and viruses colonized the rumen of yaks from birth to adulthood, with bacteria being the majority. Bacteroidetes and Firmicutes were the dominant phyla in five developmental stages, and the abundance of genus Lactobacillus and Fusobacterium significantly decreased with age. Glycoside hydrolase (GH) genes were the most highly represented in five different developmental stages, followed by glycosyltransferases (GTs) and carbohydrate-binding modules (CBMs), where the proportion of genes coding for CBMs increased with age. Integrating host transcriptome and microbial metagenome revealed 30 gene modules related to age, muscle layer thickness, nipple length and width of yaks. Among these, the MEmagenta and MEturquoise were positively correlated with these phenotypic traits. Twenty-two host genes involved in transcriptional regulation related to metal ion binding (including potassium, sodium, calcium, zinc, iron) were positively correlated with a rumen bacterial cluster 1 composed of Alloprevotella, Paludibacter, Arcobacter, Lactobacillus, Bilophila, etc. Therefore, these studies help us to understand the interaction between rumen host and microorganisms in yaks at different ages, and further provide a reliable theoretical basis for the development of feed and mineral element supplementation for yaks at different ages. Full article

The receptor for advanced glycation end-products (RAGE) has a central function in orchestrating inflammatory responses in multiple disease states including chronic obstructive pulmonary disease (COPD). RAGE is a transmembrane pattern recognition receptor with particular interest in lung disease due to its naturally abundant pulmonary expression. Our previous research demonstrated an inflammatory role for RAGE following acute exposure to secondhand smoke (SHS). However, chronic inflammatory mechanisms associated with RAGE remain ambiguous. In this study, we assessed transcriptional outcomes in mice exposed to chronic SHS in the context of RAGE expression. RAGE knockout (RKO) and wild-type (WT) mice were delivered nose-only SHS via an exposure system for six months and compared to control mice exposed to room air (RA). We specifically compared WT + RA, WT + SHS, RKO + RA, and RKO + SHS. Analysis of gene expression data from WT + RA vs. WT + SHS showed FEZ1, Slpi, and Msln as significant at the three-month time point; while RKO + SHS vs. WT + SHS identified cytochrome p450 1a1 and Slc26a4 as significant at multiple time points; and the RKO + SHS vs. WT + RA revealed Tmem151A as significant at the three-month time point as well as Gprc5a and Dynlt1b as significant at the three- and six-month time points. Notable gene clusters were functionally analyzed and discovered to be specific to cytoskeletal elements, inflammatory signaling, lipogenesis, and ciliogenesis. We found gene ontologies (GO) demonstrated significant biological pathways differentially impacted by the presence of RAGE. We also observed evidence that the PI3K-Akt and NF-κB signaling pathways were significantly enriched in DEGs across multiple comparisons. These data collectively identify several opportunities to further dissect RAGE signaling in the context of SHS exposure and foreshadow possible therapeutic modalities. Full article

The forest–atmosphere exchange of carbon and water is regulated by meteorological conditions as well as canopy properties such as leaf area index (LAI, m² m⁻²), photosynthetic capacity (PC μmol m⁻² s⁻¹), or surface conductance in optimal conditions (G_{s, opt}, mmol m⁻² s⁻¹), which can vary seasonally and inter-annually. This variability is well understood for deciduous species but is poorly characterized in evergreen forests. Here, we quantify the seasonal dynamics of a temperate evergreen eucalypt forest with estimates of LAI, litterfall, carbon and water fluxes, and meteorological conditions from measurements and model simulations. We merged MODIS Enhanced Vegetation Index (EVI) values with site-based LAI measurements to establish a 17-year sequence of monthly LAI. We ran the Community Atmosphere Biosphere Land Exchange model (CABLE-POP (version r5046)) with constant and varying LAI for our site to quantify the influence of seasonal canopy dynamics on carbon and water fluxes. We observed that the peak of LAI occurred in late summer–early autumn, with a higher and earlier peak occurring in years when summer rainfall was greater. Seasonality in litterfall and allocation of net primary productivity (F_NPP) to leaf growth (a_f, 0–1) drove this pattern, suggesting a complete renewal of the canopy before the timing of peak LAI. Litterfall peaked in spring, followed by a high a_f in summer, at the end of which LAI peaked, and PC and G_s,opt reached their maximum values in autumn, resulting from a combination of high LAI and efficient mature leaves. These canopy dynamics helped explain observations of maximum gross ecosystem production (F_GEP) in spring and autumn and net ecosystem carbon loss in summer at our site. Inter-annual variability in LAI was positively correlated with Net Ecosystem Production (F_NEP). It would be valuable to apply a similar approach to other temperate evergreen forests to identify broad patterns of seasonality in leaf growth and turnover. Because incorporating dynamic LAI was insufficient to fully capture the dynamics of F_GEP, observations of seasonal variation in photosynthetic capacity, such as from solar-induced fluorescence, should be incorporated in land surface models to improve ecosystem flux estimates in evergreen forests. Full article

X-ray nanotomography is a powerful tool for the characterization of nanoscale materials and structures, but it is difficult to implement due to the competing requirements of X-ray flux and spot size. Due to this constraint, state-of-the-art nanotomography is predominantly performed at large synchrotron facilities. We present a laboratory-scale nanotomography instrument that achieves nanoscale spatial resolution while addressing the limitations of conventional tomography tools. The instrument combines the electron beam of a scanning electron microscope (SEM) with the precise, broadband X-ray detection of a superconducting transition-edge sensor (TES) microcalorimeter. The electron beam generates a highly focused X-ray spot on a metal target held micrometers away from the sample of interest, while the TES spectrometer isolates target photons with a high signal-to-noise ratio. This combination of a focused X-ray spot, energy-resolved X-ray detection, and unique system geometry enables nanoscale, element-specific X-ray imaging in a compact footprint. The proof of concept for this approach to X-ray nanotomography is demonstrated by imaging 160 nm features in three dimensions in six layers of a Cu-SiO${}_2$ integrated circuit, and a path toward finer resolution and enhanced imaging capabilities is discussed. Full article

When rockfalls hit and damage linear stakes such as roads or railways, the access to critical infrastructures (hospitals, schools, factories …) might be disturbed or stopped. Rockfall risk management often involves building protective structures that are traditionally based on the intensive use of resources such as steel or concrete. However, these solutions are expensive, considering their construction and maintenance, and it is very difficult to protect long linear stakes. A more sustainable and effective risk management strategy could be to account for changes on rockfall activity related to weather conditions. By integrating sustainability principles, we can implement mitigation measures that are less resource-intensive and more adaptable to environmental changes. For instance, instead of solely relying on physical barriers, solutions could include measures such as restriction of access, monitoring and mobilization of emergency kits containing eco-friendly materials. A critical step in developing such a strategy is accurately predicting periods of increased rockfall activity according to meteorological triggers. In this paper, we test four machine learning models to predict rockfalls on the National Road 1 at La Réunion, a key road for the socio-economic life of the island. Rainfall and rockfall data are used as inputs of the predictive models. We show that a set of features derived from the rainfall and rockfall data can predict rockfall with performances very close and almost slightly better than the standard expert model used for operational management. Metrics describing the performance of these models are translated in operational terms, such as road safety or the duration of road closings and openings, providing actionable insights for sustainable risk management practices. Full article

Background: The hunt for naturally occurring antiviral compounds to combat viral infection was expedited when COVID-19 and Ebola spread rapidly. Phytochemicals from Nyctanthes arbor-tristis Linn were evaluated as significant inhibitors of these viruses. Methods: Computational tools and techniques were used to assess the binding pattern of phytochemicals from Nyctanthes arbor-tristis Linn to Ebola virus VP35, SARS-CoV-2 protease, Nipah virus glycoprotein, and chikungunya virus. Results: Virtual screening and AutoDock analysis revealed that arborside-C, beta amyrin, and beta-sitosterol exhibited a substantial binding affinity for specific viral targets. The arborside-C and beta-sitosterol molecules were shown to have binding energies of −8.65 and −9.11 kcal/mol, respectively, when interacting with the major protease. Simultaneously, the medication remdesivir exhibited a control value of −6.18 kcal/mol. The measured affinity of phytochemicals for the other investigated targets was −7.52 for beta-amyrin against Ebola and −6.33 kcal/mol for nicotiflorin against Nipah virus targets. Additional molecular dynamics simulation (MDS) conducted on the molecules with significant antiviral potential, specifically the beta-amyrin-VP35 complex showing a stable RMSD pattern, yielded encouraging outcomes. Conclusions: Arborside-C, beta-sitosterol, beta-amyrin, and nicotiflorin could be established as excellent natural antiviral compounds derived from Nyctanthes arbor-tristis Linn. The virus-suppressing phytochemicals in this plant make it a compelling target for both in vitro and in vivo research in the future. Full article

The wood-based panel industry is experiencing an excessive accumulation of solid residues from the production of medium-density fiberboard (MDF) panels and moldings. It is possible to create new MDF products with acceptable physical and mechanical properties by revaluing MDF residues. Additionally, those products’ thermal properties can be improved by incorporating phase change materials (PCMs). This study aims to develop a wood-based fiberboard made of MDF residues, capable of storing thermal energy. Two types of PCMs (liquid and microencapsulated), two PCM ratios (2% and 6%), and two types of adhesives (urea-formaldehyde and phenol-formaldehyde) were used to produce eight different types of panels. The vertical density profile, thickness swelling, water absorption, internal bond (IB), and static bending properties—modulus of elasticity (MOE) and modulus of rupture (MOR)—were determined for each panel type. The specific heat of the panels was also determined. The results show the panels’ densities were greater than 700 kg/m³. Thickness swelling in water improved by 23% compared to the reference value of the control panel PCMs after PCM incorporation. The highest IB value was 1.30 MPa, which is almost three times the minimum required by regulation standards. The incorporation of PCMs reduced the panels’ bending properties compared to the properties of the control panels. Even though the values obtained are sufficient to comply with the minimum values set out in ANSI standard A208.2 with an MOE value of 2072.4 MPa and the values obtained are sufficient to comply with the minimum standards with an MOE value of 2072.4 MPa and an MOR value of 16.4 MPa, when microencapsulated PCM is used, the specific heat of the panels is increased by more than 100% over that of the control panels. This study developed fiberboards with adequate physical and mechanical properties and capable of storing thermal energy. Full article

Understanding the light adaptation of plants is critical for conservation. Platycrater arguta, an endangered deciduous shrub endemic to East Asia, possesses high ornamental and phylogeographic value. However, the weak environmental adaptability of P. arguta species has limited its general growth and conservation. To obtain a deeper understanding of the P. arguta growth conditions, we examined the leaf morphology and physiology via anatomical and chloroplast ultrastructural analyses following exposure to different natural light intensities (full light, 40%, and 10%). The findings indicated that P. arguta seedings in the 10% light intensity had significantly improved leaf morphological characteristics and specific leaf area compared to those exposed to other intensities. The net photosynthetic rate, chlorophyll (Chl) content, photosynthetic nitrogen use efficiency (PNUE), and photosynthetic phosphorus use efficiency (PPUE) exhibited marked increases at a 10% light intensity compared to both 40% light and full light intensities, whereas the light compensation point and dark respiration levels reached their lowest values under the 10% light condition. With reduced light, leaf thickness, palisade tissue, spongy tissue, and stomatal density significantly decreased, whereas the stomatal length, stomatal width, and stomatal aperture were significantly elevated. When exposed to 10% light intensity, the ultrastructure of chloroplasts was well developed, chloroplasts and starch grain size, the number of grana, and thylakoids all increased significantly, while the number of plastoglobules was significantly reduced. Relative distance phenotypic plasticity index analysis exhibited that P. arguta adapts to varying light environments predominantly by adjusting PPUE, Chl b, PNUE, chloroplast area, and the activity of PSII reaction centers. We proposed that P. arguta efficiently utilizes low light to reconfigure its energy metabolism by regulating its leaf structure, photosynthetic capacity, nutrient use efficiency, and chloroplast development. Full article

Facing the significant challenge of 3D object detection in complex weather conditions and road environments, existing algorithms based on single-frame point cloud data struggle to achieve desirable results. These methods typically focus on spatial relationships within a single frame, overlooking the semantic correlations and spatiotemporal continuity between consecutive frames. This leads to discontinuities and abrupt changes in the detection outcomes. To address this issue, this paper proposes a multi-frame 3D object detection algorithm based on a deformable spatiotemporal Transformer. Specifically, a deformable cross-scale Transformer module is devised, incorporating a multi-scale offset mechanism that non-uniformly samples features at different scales, enhancing the spatial information aggregation capability of the output features. Simultaneously, to address the issue of feature misalignment during multi-frame feature fusion, a deformable cross-frame Transformer module is proposed. This module incorporates independently learnable offset parameters for different frame features, enabling the model to adaptively correlate dynamic features across multiple frames and improve the temporal information utilization of the model. A proposal-aware sampling algorithm is introduced to significantly increase the foreground point recall, further optimizing the efficiency of feature extraction. The obtained multi-scale and multi-frame voxel features are subjected to an adaptive fusion weight extraction module, referred to as the proposed mixed voxel set extraction module. This module allows the model to adaptively obtain mixed features containing both spatial and temporal information. The effectiveness of the proposed algorithm is validated on the KITTI, nuScenes, and self-collected urban datasets. The proposed algorithm achieves an average precision improvement of 2.1% over the latest multi-frame-based algorithms. Full article

Quaking aspen is an important deciduous tree species across interior western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (<0.09 ha or 30 m²), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. Using observed annual phenology of aspen across the Southern Rockies and leveraging the frequent temporal resolution of S1 and S2, ecologically relevant seasonal imagery composites were developed. We derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best-performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing products in both accuracy (0.93 average F1-score) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America. Full article

To address the matching challenge between the High Resolution Imaging Science Experiment (HiRISE) Digital Elevation Model (DEM) and the Mars Orbiter Laser Altimeter (MOLA) DEM, we propose a terrain matching framework based on the combination of point cloud coarse alignment and fine alignment methods. Firstly, we achieved global coarse localization of the HiRISE DEM through nearest neighbor matching of key Intrinsic Shape Signatures (ISS) points in the Fast Point Feature Histograms (FPFH) feature space. We introduced a graph matching strategy to mitigate gross errors in feature matching, employing a numerical method of non-cooperative game theory to solve the extremal optimization problem under Karush–Kuhn–Tucker (KKT) conditions. Secondly, to handle the substantial resolution disparities between the MOLA DEM and HiRISE DEM, we devised a smoothing weighting method tailored to enhance the Voxelized Generalized Iterative Closest Point (VGICP) approach for fine terrain registration. This involves leveraging the Euclidean distance between distributions to effectively weight loss and covariance, thereby reducing the results’ sensitivity to voxel radius selection. Our experiments show that the proposed algorithm improves the accuracy of terrain registration on the proposed Curiosity landing area’s, Mawrth Vallis, data by nearly 20%, with faster convergence and better algorithm robustness. Full article

This study presents an approach to forecast outbreaks of Dendrolimus sibiricus, a significant pest affecting taiga ecosystems. Leveraging comprehensive datasets encompassing climatic variables and forest attributes from 15,000 taiga parcels in the Krasnoyarsk Krai region, we employ genetic programming-based predictive modeling. Our methodology utilizes Random Forest algorithm to develop robust forecasting model through integrated data analysis techniques. By optimizing hyperparameters within the predictive model, we achieved heightened accuracy, reaching a maximum precision of 0.9941 in forecasting pest outbreaks up to one year in advance. Full article

In the realm of computer vision, the integration of advanced techniques into the pre-processing of RGB-D camera inputs poses a significant challenge, given the inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for the robust 3D segmentation of objects in RGB-D imagery. Traditional computer vision systems face limitations in simultaneously capturing precise object boundaries and achieving high-precision object detection on depth maps, as they are mainly proposed for RGB cameras. To address this challenge, FusionVision adopts an integrated approach by merging state-of-the-art object detection techniques, with advanced instance segmentation methods. The integration of these components enables a holistic (unified analysis of information obtained from both color RGB and depth D channels) interpretation of RGB-D data, facilitating the extraction of comprehensive and accurate object information in order to improve post-processes such as object 6D pose estimation, Simultanious Localization and Mapping (SLAM) operations, accurate 3D dataset extraction, etc. The proposed FusionVision pipeline employs YOLO for identifying objects within the RGB image domain. Subsequently, FastSAM, an innovative semantic segmentation model, is applied to delineate object boundaries, yielding refined segmentation masks. The synergy between these components and their integration into 3D scene understanding ensures a cohesive fusion of object detection and segmentation, enhancing overall precision in 3D object segmentation. Full article

Postural adaptation is a prominent feature in horses affected by laminitis. Laminitis induces intense pain, especially in the forelimbs, prompting affected horses to assume a caudally displaced trunk posture, resulting in the hyperflexion of the thoracolumbar spine. This study assessed the nature and prevalence of thoracolumbar injuries in horses with chronic laminitis compared to horses without it. Sixty horses were used (thirty laminitic and thirty non-laminitic) of different athletic purposes and ages (2–20 years). The experimental protocol entailed a single assessment of horses’ thoracolumbar spines, utilizing physical examination by MACCTORE, a scoring system developed specifically for this study. Additional evaluations included the Grimace Equine Pain Scale (HGS) and ultrasound exams. Statistical tests were used to compare values (Mann–Whitney or t-test) and lesions prevalences (Fisher) between groups (p < 0.05). The results showed a higher pain manifestation (HGS and heart rate, p < 0.0001) and thoracolumbar-spine-injury levels in chronic laminitis horses, both in MACCTORE clinical examinations (11.7 ± 4.8 vs. 4.2 ± 3.3, p < 0.0001) and general ultrasonographic indices (39.6 ± 12.0 vs. 20.7 ± 7.1, p < 0.0001), including specific examination approaches for various spinal elements. Horses with laminitis presented with a 14-fold higher prevalence of ultrasound-relevant lesions in the thoracolumbar spine (CI: 4.4 to 50.6, p < 0.0001) compared to controls. These findings constitute new evidence of an association between chronic laminitis and the presence of thoracolumbar spine injuries in horses, which may be confirmed by more sophisticated study designs. Full article

Significant price spikes occurred as early as 2021, initially driven by low gas storage levels, a post-pandemic economic rebound and then exacerbated by the Russian invasion of Ukraine. The situation had a range of wide-ranging consequences, from rising inflation, increasing energy poverty, food insecurity, business bankruptcies and recession. A well-known tool to protect energy consumers from energy price spikes, while at the same time contributing to the development of sustainable technologies, is Power Purchase Agreements. PPAs are long-term bilateral contracts for the purchase and sale of a certain amount of electricity, usually generated from renewable sources. The primary goal of this paper is to assess how the risk associated with PPAs has evolved between 2020 and 2023. It aims to examine whether, after the events in 2022, PPAs remain a robust solution that protects the off-taker from energy price spikes, ensures greater energy budget stability and enables savings. To achieve this, the probability of PPA prices being higher than market prices is evaluated, considering the changing market landscape. Furthermore, this paper intends to gain a thorough understanding of each risk related to PPAs and the best strategies for mitigating it, to maximize the protection of the off-taker. Full article

As some of the most threatened ecosystems in the world, the declining condition and coverage of coastal habitats results in the loss of the myriad ecosystem services they provide. Due to the variability in physical and biological characteristics across sites, it is imperative to increase location-based information to inform local management projects, which will potentially help to reestablish functions of coastal habitats. Since oysters are often used in restoration projects, this study quantified spatial and temporal patterns in eastern oyster spat settlement in a bar-built estuary in northeast Florida, USA that is host to a robust population of intertidal oyster reefs. Spat settlement was found to occur from April to October with small peaks in the spring and large ones around September. Inter-annual differences in spat settlement were likely influenced by existing environmental conditions and heavily affected by large-scale events such as tropical cyclones. Variations in regional spat settlements are possibly driven by the residence times of the watersheds, the density of adult populations, and the location of the spat collectors. The results of this study illustrate place-based variability in oyster settlement patterns and underscore the importance of local monitoring for oyster resource management, restoration, and research. Full article

Due to the increasing populations of anthelmintic-resistant gastrointestinal nematodes and as a consequence of the adverse effects of synthetic drugs, this study focuses on the search for secondary metabolites with nematocidal activity from the edible mushroom Pleurotus djamor using The proton nuclear magnetic resonance (¹H-NMR) metabolomics. The highest activity was shown by the ethyl acetate fractions of mycelium (EC₅₀ 290.8 µg/mL) and basidiomes (EC₅₀ 282.7 µg/mL). Principal component analysis (PCA) and hierarchical data analysis (HCA) of the ¹H-NMR metabolic profiles data showed that the ethanolic extracts, the ethyl acetate, butanol, and water fractions from mycelium have different metabolic profiles than those from basidiomes, while low polarity (hexane) fractions from both stages of fungal development show similar profiles. Orthogonal partial least squares discriminant analysis (OPLS-DA) allowed the identification of signals in the ¹H-NMR metabolic profile associated with nematocidal activity. The signals yielded via OPLS-DA and bidimensional NMR analysis allowed the identification of uracil as a component in the ethyl acetate fraction from basidiomes, with an EC₅₀ of 237.7 µg/mL. The results obtained showed that chemometric analyses of the ¹H-NMR metabolic profiles represent a viable strategy for the identification of bioactive compounds from samples with complex chemical profiles. Full article

The increased decentralization of renewable energy has increased curtailment rates in stagnating demand zones, increasing the levelized cost of energy (LCOE). The geographically dynamic nature of gravity energy storage (GES) is emerging in the field of mechanical energy storage, over pumped hydro. However, GES costs vary geospatially, specifically in decentralized suburban areas, due to the impact of urban socioeconomics. This study aims to find a mathematical approximation of a cost-optimized location for suburban Solar–GES hybrid systems in curtailment-prone areas. A multi-parameterization model mathematically programmed land, transmission, supply chain and excavation costs into geospatial matrix approximations for suburban areas of 2500 km² in Fukuoka and Ibaraki in Japan. It was found that SPV-GES location-dependent costs were mainly affected by distance from the city’s economic center and flat plains in suburbs, and supply chain and transmission costs optimized the location-dependent cost for GES at a specific point. It was also found that flat terrains were more economical than mountainous terrains due to high GES supply chain costs. With GES found to be cost-competitive compared to other storage technologies in Japan, this study reveals that GES introduction benefits the LCOE of suburban, decentralized SPV when curtailment is >50% irrespective of terrain. Full article

This article presents a methodology for evaluating the susceptibility of landfill areas to develop landslides by analyzing Synthetic Aperture Radar (SAR) satellite products. The deformation velocity of the landfills is computed through the Persistent Scatterer Method on SAR imagery. These data, combined with a deformation model based on the shallow water equations (SWE), form the foundation for a Monte Carlo experiment that extrapolates the current state of the landfill into the future. The results of this simulation are then employed to determine the probability of a landslide occurrence. In order to validate the methodology effectiveness, a case study is conducted on a landfill in Zaldibar, Spain, revealing its effectiveness in estimating the probability of landfill landslides. This innovative approach emerges as an asset in large landfill management, acting as a proactive tool for identifying high-risk sites and preventing potential landslides, ultimately safeguarding human life and the environment. By providing insights into landslide probabilities, this study enhances decision-making processes and facilitates the development of intervention strategies in the domain of landfill risk assessment and management. Full article

Biological membranes are composed of a lipid bilayer with embedded proteins, including ion channels like the epithelial sodium channel (ENaC), which are critical for sodium homeostasis and implicated in arterial hypertension (HTN). Changes in the lipid composition of the plasma membrane can significantly impact cellular processes related to physiological functions. We hypothesized that the observed overexpression of ENaC in neutrophils from HTN patients might result from alterations in the structuring domains within the plasma membrane, disrupting the endocytic processes responsible for ENaC retrieval. This study assessed the structural lipid composition of neutrophil plasma membranes from HTN patients along with the expression patterns of key elements regulating ENaC at the plasma membrane. Our findings suggest alterations in microdomain structure and SGK1 kinase activity, which could prolong ENaC presence on the plasma membrane. Additionally, we propose that the proteasomal and lysosomal degradation pathways are insufficient to diminish ENaC presence at the plasma membrane in HTN. These results highlight the importance of understanding ENaC retrieval mechanisms and suggest that targeting these mechanisms could provide insights for developing drugs to prevent and treat HTN. Full article

The present study investigated microstructural evolution and changes in tensile properties of an Al-Si piston alloy subjected to thermal exposures at 250 and 350 °C for 150, 300, and 500 h. Microstructural and nanoscale precipitates were characterized using a combination of high-angle annular dark field-scanning transmission electron microscopy (HAADF-STEM) images and scanning electron microscopy (SEM). The tensile testing was performed. The results demonstrated that the thermal exposure induced granulation of the δ-Al₃CuNi particles, alongside precipitation of the θ-Al₂Cu phase particles and AlCu clusters within the matrix. Specifically, an increase in the size and number density of the θ-Al₂Cu phase particles was observed with exposure time at 250 °C. Conversely, at 350 °C, the θ-Al₂Cu particles exhibited a gradual increase in size with prolonged thermal exposure, coupled with a decrease in their number density. AlCu clusters precipitated solely at a thermal exposure temperature of 350 °C, with precipitation intensifying over time. Moreover, a decrease in the alloy’s tensile strength and an increase in elongation were noted after thermal exposure. Finally, the present study discussed the precipitation mechanisms of θ-Al₂Cu particles and AlCu clusters within the grains, suggesting that the AlCu clusters exerted a more effective strengthening effect compared to the θ-Al₂Cu particles. Full article