Search Results (502)

For the validation of approaches like fracture mechanics used for the description of crack propagation in high-strength bolting assemblies, it is often necessary to determine the size of an existing crack in the tested assembly. However, since the fatigue crack typically initiates in the root of the first load bearing thread, it is not directly accessible during fatigue testing. The crack size determination method presented here achieves further propagation of the original fatigue crack after the fatigue test by hydrogen-induced cracking under constant load. The characteristic microstructure of the resulting fracture surface then allows for the determination of the original crack size. In the present study, the fatigue crack sizes in M12 bolting assemblies are determined and a corresponding linear elastic fracture mechanics model is validated. The presented method reliably leads to a failure in the plane of the original fatigue crack and allows for a precise measurement of the crack length. The validated fracture mechanics model describes the crack propagation reasonably well, taking into account the overall service life. Overall, the presented method is a valuable addition to existing crack size determination methods and a versatile tool in the further development of more advanced fracture mechanics models for high-strength bolting assemblies. Full article

Satellite-derived bathymetry (SDB) enhances monitoring capabilities in the context of global change and provides a cost-effective alternative to traditional in situ methods. However, a significant gap remains in the accuracy of SDB at shallow water depths (0–10 m), particularly in complex coastal settings. In this study, we developed a two-step methodology to improve shallow water depth estimates using empirical models and multi-temporal Sentinel-2 satellite imagery. Ten Sentinel-2 images from a one-year period were analysed using the Lyzenga and Stumpf empirical reference models, followed by the application of an empirical generalised linear model (GLM). Composite images were created by combining pixel values across the temporal dataset and compared with individual image results within the model. The validation results confirmed that the GLM outperformed the reference empirical models. The optimal selection of multi-temporal images demonstrated superior performance compared to single-image regression, achieving a 42% reduction in RMSE and a minimum MAE of 0.34 m. Furthermore, enhanced outlier identification within the multi-temporal analysis reduces local anomalies and enables further improvements in accuracy. These findings underscore the enhanced capability of GLM and multi-temporal images for improving the accuracy of SDB, with a relevant impact on many coastal monitoring applications and potential for scalable implementation in other regions. Full article

Accurately characterizing historical drought events is critical for understanding their spatial and temporal variability and for improving future drought projections. This study investigates extreme drought years across Europe using three complementary drought indicators: the Palmer drought severity index (PDSI, based on tree-ring width), the standardized precipitation evapotranspiration index (SPEI, based on stable oxygen isotopes in tree rings), and the soil moisture index (SMI, based on high-resolution climate modeling). We analyze the common period 1766–2018 simultaneously across all three reconstructions to enable direct cross-indicator comparisons, a scope not typical of prior single-indicator studies. When analyzing year-to-year variability, the driest European years differ by indicator (PDSI—1874, SPEI—2003, and SMI—1868). Quantitatively, the values exhibited are as follows: PDSI 1874 (M = −1.97; A = 64.4%), SPEI 2003 (M = −1.16; A = 90.1%), and SMI 1868 (M = 0.21; A = 83.4%). Multi-year extremes also diverge: while PDSI identifies 1941–1950 as the driest years (M = −0.82; A = 42.1%), SPEI highlights 2011–2018 (M = −0.36; A = 46.6%), and SMI points to 1781–1790 as the driest years, followed by 2011–2018. Trends in drought-covered areas show a significant European-scale increase for SMI (+0.52%/decade, p < 0.05) and regional increases for MED in SMI (~+1.1%/decade, p < 0.001) and for CEU in SPEI (+0.42%/decade, p < 0.05) and SMI (+0.6%/decade, p < 0.001). At the regional scale (Mediterranean—MED, central Europe—CEU, and northern Europe—NEU), the driest years/decades and spatial footprints vary by indicator, yet all the indicators consistently identify drought hotspots such as the MED. We also found that drought is significantly influenced by large-scale atmospheric drivers. A canonical correlation analysis (CCA) between summer geopotential height at 500 mb (Z500) and drought reconstructions indicates that drought-affected regions are, in general, associated with atmospheric blocking. The canonical series are significantly correlated at r = 0.82 (p < 0.001), with explained variances of 12.78% (PDSI), 8.41% (SPEI), and 14.58% (SMI). Overall, our study underscores the value of multi-indicator approaches: individual indicators provide distinct but complementary perspectives on European drought dynamics, improving the historical context for assessing future risk. Full article

Background: Alginate is a naturally occurring anionic polysaccharide extracted from brown marine algae and widely explored for biomedical applications due to its biocompatibility and functional versatility. This study aims to extract and compare alginates from two Red Sea brown algae, Turbinaria ornata (TA) and Hormophysa cuneiformis (HA), and to evaluate how structural differences influence their therapeutic properties. Methods: Alginate was isolated by sequential acid–alkaline extraction and characterized using FTIR, XRD, TGA, elemental analysis, and HPLC. Biological activities were assessed through antioxidant, anti-inflammatory, antidiabetic, neuroprotective, and hepatoprotective assays, supported by molecular docking and gene ontology interaction analysis. Results: Distinct physicochemical variations were observed between HA and TA. TA exhibited stronger antioxidant (IC₅₀ = 25.89 µg/mL), anti-inflammatory (COX-1 IC₅₀ = 69.61 µg/mL), antidiabetic (α-amylase IC₅₀ = 45.14 µg/mL), and hepatoprotective activities (IC₅₀ = 118.21 µg/mL), whereas HA displayed superior neuroprotective potential through butyrylcholinesterase inhibition (IC₅₀ = 39.01 µg/mL). Molecular docking supported the in vitro findings by confirming interactions with key protein targets associated with oxidative stress and metabolic pathways. Conclusions: Structural variation between species-derived alginates directly impacts their biological activities. TA represents a promising candidate for metabolic and anti-inflammatory therapies, while HA may be more suitable for neuroprotective interventions. These results emphasize the importance of source-specific alginate selection for developing targeted pharmaceutical applications. Full article

Background/Objectives: The classification of patients with diabetes into phenotypes with distinct risks and therapeutic needs is crucial for individualized care. We recently introduced a clustering model for gestational diabetes mellitus (GDM). This study aims to further characterize the proposed clusters and to identify cluster-specific differences in glucometabolic parameters during early pregnancy in an independent cohort. The metabolic profiles and dietary habits of GDM clusters will be compared with those of a normal glucose-tolerant (NGT) control group. Methods: 1088 women (195 who developed GDM and 893 who remained NGT) underwent a broad risk evaluation at early pregnancy. GDM patients were further categorized into the three proposed GDM subtypes (CL1 to CL3). Results: Among GDM patients, 7.7% were classified as CL1, 35.9% as CL2, and 56.4% as CL3. CL1 showed higher age, pregestational BMI, and increased glucose concentrations both at fasting and during the diagnostic oral glucose tolerance test. CL2 was characterized by elevated BMI and fasting glucose, while CL3 showed higher glucose concentrations after the oral glucose load, with BMI levels comparable to NGT mothers. Women in the CL1 group exhibited impaired insulin sensitivity and β-cell function at early pregnancy and showed elevated lipid levels. Compared to NGT women, a positive family history of diabetes was more prevalent in CL1 and CL3, but not in CL2. Dietary patterns were similar across all groups. Conclusions: Our study showed distinct alterations in glucometabolic parameters already at early pregnancy among GDM subtypes. Patients in CL1 exhibited the most unfavorable risk constellation and could benefit from lifestyle changes and nutrition therapy in early pregnancy, despite showing similar dietary patterns as the NGT group. Full article

This study uses industrial wastewater from an aluminum factory to evaluate the phycoremediation efficiency of two green microalgal strains, Dictyosphaerium sp. and Tetradesmus obliquus. The industrial wastewater contained high levels of pollutants, including COD, ammonium, nitrate, phosphate, and heavy metal ions (Al³⁺, Cu²⁺, Cr³⁺, Zn²⁺, Mn²⁺, Cd²⁺). Four biomass conditions were tested: free-living cells (active living cells), immobilized cells (entrapped within alginate), dried biomass (non-living dried cells), and acid-treated dried biomass (chemically modified for enhanced adsorption). Both strains demonstrated significant pollutant removal, with living biomass (free and immobilized) achieving the highest nutrient and organic pollutant removal, and non-living biomass (dried and acid-treated) being more efficient for rapid heavy metal removal. Tetradesmus obliquus showed superior performance across most parameters, while Dictyosphaerium sp. exhibited the highest aluminum removal (99.4%, reducing Al from 481.2 mg/L to 10.2 mg/L). These findings highlight the potential of microalgae-based approaches and support species-specific strategies for cost-effective and sustainable phycoremediation of industrial wastewater. Full article

Part segmentation can be used to overcome limitations of additive manufacturing (AM) processes such as Direct Energy Deposition of Metals (DED). In this case subparts of soft martensitic steel 1.4313 produced by conventional manufacturing (CM) and AM are joined by laser welding. This paper reports the difference in thermal conductivity of conventional and additive manufactured parts. The thermal conductivity was calculated from the thermal diffusivity, the specific heat, and the bulk density. Furthermore, the temperature was measured during welding and the microstructure analyzed. The far field temperature was measured using eight K-type thermocouples and the microstructure was analyzed by metallography and light microscopy. The results showed that the thermal conductivity of AM material is 8% lower and therefore the heating rate 5% lower compared to CM material. The lower thermal conductivity is explained in the literature by its higher dislocation density, unfavorable alloying element distribution and a lower rest austenite content. AM introduces structural complexity that hampers electron and phonon transport, thereby reducing the thermal conductivity despite similar base chemical compositions. The heat-affected zone is only clearly visible on the CM side due to carbide formation. In DED parts, it comes to different phases in non-equilibrium states, which complicates the identification of carbides and the HAZ. The findings are important for the design of hybrid components to improve the the joint integrity and functionality of hybrid parts. Full article

Amphidinols (AM) are a diverse group of bioactive polyketides produced by dinoflagellates of the genus Amphidinium, known for their hemolytic, antifungal, and cytotoxic activities. This work presents the assessment of AM profiles in a comprehensive number of strains, whose species boundaries were previously established through detailed taxonomic analysis. Using UHPLC-MS/MS, we characterized the spectrum of AM analogs in 54 Amphidinium strains isolated from diverse geographical locations. In addition, toxicity was assessed using brine shrimp assays, which revealed significant inter- and intraspecific variability. Despite the broad diversity in AM content, no clear correlation was observed between total AM levels and toxicity across all strains. Multivariate analysis grouped the strains into clusters distinguished by distinct AM profiles and toxicity levels, suggesting that AM production alone does not predict toxicity. Our findings highlight the complexity of Amphidinium bioactivity, emphasizing the influence of strain-specific factors and other bioactive compounds. This work highlights the importance of integrating chemical, genetic, and biological assessments to understand better the factors that govern toxicity in this genus, with implications for ecological studies and the monitoring of harmful dinoflagellates. Full article

Fibrocapsa japonica (Raphidophyceae) is a cosmopolitan species frequently associated with harmful algal blooms (HABs) and fish mortality events, representing a potential threat to aquaculture and coastal ecosystems. This study provides the first comprehensive morphological, phylogenetic, pigmentary, and toxicological characterization of F. japonica strains isolated from Argentina. Light and transmission electron microscopy confirmed key diagnostic features of the species, including anterior flagella and the conspicuous group of mucocyst in the posterior region. Phylogenetic analysis based on the LSU rDNA D1–D2 region revealed monophyletic relationships with strains from geographically distant regions. Pigment analysis by HPLC identified chlorophyll-a (62.3 pg cell⁻¹) and fucoxanthin (38.4 pg cell⁻¹) as the main dominant pigments. Cytotoxicity assays using RTgill-W1 cells exposed for 2 h to culture supernatants and intracellular extracts showed strain-specific effects. The most toxic strain (LPCc049) reduced gill cell viability down to 53% in the supernatant exposure, while LC₅₀ values ranged from 1.6 × 10⁴ to 4.7 × 10⁵ cells mL⁻¹, depending directly on the strain and treatment type. No brevetoxins (PbTx-1, -2, -3, -6, -7, -8, -9, -10, BTX-B1 and BTX-B2) were detected by LC–MS/MS, suggesting that the cytotoxicity may be linked to the production of reactive oxygen species (ROS), polyunsaturated fatty acids (PUFAs), or hemolytic compounds, as previously hypothesized in the literature. These findings offer novel insights into the toxic potential of F. japonica in South America and underscore the need for further research to elucidate the mechanisms underlying its ichthyotoxic effect. Full article

The Arctic Ocean is turning blue. Abrupt Arctic warming and amplification is driving rapid sea ice decline and irreversible deglaciation of Greenland. The already emerging, substantial consequences for the planet and society are intensifying and yet, model-based projections lack validatory consensus. To date, we cannot anticipate how a blue Arctic will respond to and amplify an increasingly warmer future climate, nor how it will impact the wider planet and society. Climate projections are inconclusive as we critically lack key Arctic geological archives that preserved the answers. This “Arctic Challenge” of global significance can only be addressed by investigating the processes, consequences, and impacts of past “greenhouse” (warmer-than-present) climate states. To address this challenge, the ERC Synergy Grant project Into the Blue (i2B) is undertaking a program of research focused on retrieving new Arctic geological archives of past warmth and key breakthroughs in climate model performance to deliver a ground-breaking, synergistic framework to answer the central question: “Why and what were the global ramifications of a “blue” (ice-free) Arctic during past warmer-than-present climates?” Here, we present the proposed research plan that will be conducted as part of this program. Into the Blue will quantify cryosphere (sea ice and land ice) change in a warmer world that will form the scientific basis for understanding the dynamics of Arctic cryosphere and ocean changes to enable the quantitative assessment of the impact of Arctic change on ocean biosphere, climate extremes, and society that will underpin future cryosphere-inclusive IPCC assessments. Full article

The study of neuronal electrical activity and spatial organization is essential for uncovering the mechanisms that regulate neuronal electrophysiology and function. Mathematical models have been utilized to analyze the structural properties of neuronal networks, predict connectivity patterns, and examine how morphological changes impact neural network function. In this study, we aimed to explore the role of the actin cytoskeleton in neuronal signaling via primary cilia and to elucidate the role of the actin network in conjunction with neuronal electrical activity in shaping spatial neuronal formation and organization, as demonstrated by relevant mathematical models. Our proposed model is based on the polygamma function, a mathematical application of ramification, and a geometrical definition of the actin cytoskeleton via complex numbers, ring polynomials, homogeneous polynomials, characteristic polynomials, gradients, the Dirac delta function, the vector Laplacian, the Goldman equation, and the Lie bracket of vector fields. We were able to reflect the effects of neuronal electrical activity, as modeled by the Van der Pol equation in combination with the actin cytoskeleton, on neuronal morphology in a 2D model. In the next step, we converted the 2D model into a 3D model of neuronal electrical activity, known as a core-shell model, in which our generated membrane potential is compatible with the neuronal membrane potential (in millivolts, mV). The generated neurons can grow and develop like an organoid brain based on the developed mathematical equations. Furthermore, we mathematically introduced the signal transduction of primary cilia in neurons. Additionally, we proposed a geometrical model of the neuronal branching pattern, which we described as ramification, that could serve as an alternative mathematical explanation for the branching pattern emanating from the neuronal soma. In conclusion, we highlighted the relationship between the actin cytoskeleton and the signaling processes of primary cilia. We also developed a 3D model that integrates the geometric organization unique to neurons, which contains soma and branches, such that the mathematical model represents the interaction between the actin cytoskeleton and neuronal electrical activity in generating action potentials. Next, we could generalize the model into a cluster of neurons, similar to an organoid brain model. This mathematical framework offers promising applications in artificial intelligence and advancements in neural networks. Full article

Background/Objectives: Cancer remains one of the leading causes of mortality worldwide, while natural antioxidants have emerged as promising therapeutic agents in cancer treatment. Although fucoidan from brown algae shows anticancer potential, its efficacy is limited by bioavailability challenges, and the synergistic effects of combining it with gold nanoparticles remain unexplored. Methods: Fucoidan was extracted from Sargassum cinereum and Turbinaria decurrens. F-AuNPs were produced utilizing fucoidan as both a reducing and stabilizing agent. The nanoparticles were analyzed by UV-Vis spectroscopy, FTIR, TEM, XRD, DLS, TAG, and zeta potential evaluation. The antioxidant activity was evaluated by DPPH and FRAP tests. Cytotoxicity was determined against HepG2, THP-1, and BNL cells, utilizing MTT and SRB tests. Flow cytometry was utilized to assess the cell cycle, while molecular docking was carried out to examine binding to oncogenic proteins. Results: T. decurrens produced higher polysaccharides rich in fucoidan content (235.9 mg/g dry weight) and stated higher antioxidant activity (FRAP: 9.21 μg TE mg⁻¹; DPPH: 4.48 μg TE mg⁻¹) in comparison to S. cinereum. F-AuNPs showed potent cytotoxicity toward HepG2 cells, with IC₅₀ values and cytotoxicity toward HepG2 cells, with IC₅₀ values of 377.6 μg/mL for S. cinereum and 449.5 μg mL⁻¹ for T. decurrens. Molecular docking revealed robust binding of fucoidan to COX-2 (−7.1 kcal mol⁻¹) and TERT (−5.4 kcal mol⁻¹). Conclusions: Fucoidan and F-AuNPs reveal remarkable antioxidant and anticancer properties. Nanoparticle formulation greatly improves bioactivity, underscoring its promise as a synergistic approach for cancer treatment by influencing oxidative stress and cancer-associated pathways. Full article

Phytoplankton are single-celled microorganisms with short generation times that may comprise high diversity in genetic and phenotypic traits, allowing them to acclimate to changes rapidly. High intraspecific genetic variation is well known in phytoplankton, but less is known about variation in physiological traits. To investigate variability and plasticity in genetic, morphological, and physiological traits of the toxigenic diatom genus Pseudo-nitzschia in a global warming scenario, we exposed 40 strains of the cold-water P. seriata to different temperatures (2 °C, 6 °C and 10 °C). The maximum growth rate and cellular toxin content showed extensive intraspecific variation, whereas morphological and genetic variation was minor. Thermal reaction norms showed a general increase in growth rate with increasing temperature; however, three distinct types of thermal responses were found among the 40 strains. All 40 strains contained toxins (domoic acid) in both exponential and stationary growth phase, and toxin content increased significantly with temperature. Most strains (>87%) contained measurable levels of domoic acid at all three temperatures. In conclusion, P. seriata shows extensive intraspecific variation in measured physiological traits like growth and toxin content, a variation exceeding the response of each strain to increases in temperature. Intraspecific variation in harmful species thus needs attention for the future understanding of food web dynamics, as well as the management and forecasting of harmful blooms. Full article

This study aimed to evaluate the effect of the continuous use of the settling chamber for solids removal in the cultivation of the marine shrimp Penaeus vannamei and the halophyte Salicornia neei in an aquaponic system with bioflocs. Two treatments were tested: with settling and without settling. Each experimental unit consisted of an 800 L tank for shrimp rearing (stocking density of 375 shrimp m⁻³) and a hydroponic bench of 0.33 m² for 28 seedlings (84 plants m⁻²). In the treatment without settling, water was continuously pumped to the hydroponic bench. In the treatment with settling, the water was first pumped to the chamber, and the overflow was then distributed across each irrigation channel, returning to the tank by gravity. To maintain the concentration of suspended solids in the shrimp culture, solids that accumulated in the settling chamber were pumped back into the tank every 30 min. During the 54-day trial, the reduction in suspended solids in the treatment with settling led to an increase in TAN and NO₂ levels, while the concentration of NO₃ remained stable. Although water quality parameters were more stable in the treatment without settling, no significant differences were observed between the treatments regarding plant and shrimp production indices. These results demonstrate the feasibility of cultivating P. vannamei and S. neei in a biofloc-based aquaponic system without the continuous use of a settling chamber during the pre-grow phase (until 10 g), offering a potential method for simplifying aquaponic system design. Full article

We present a two-dimensional reconstruction of blocking frequency indices in the Atlantic-European region spanning the last 400 years. Our approach is based on a simple field reconstruction scheme similar to the principal component regression method. The particularity of our reconstruction scheme is that we select the blocking predictors using observed and reconstructed surface temperature and precipitation gridded data based on the correlation stability criteria. This approach avoids the problem of non-stationarity between predictand and predictors that commonly affects the quality of climate field reconstructions. First, we reconstruct the blocking field back to 1891 using observed gridded surface temperature and precipitation data. Then, the reconstruction is extended back in time to 1602 using seasonal-resolution paleo-reanalysis temperature and precipitation fields. The reconstruction is validated against various observed blocking frequency fields and climate reconstruction indices. The methodology presented in this study offers an opportunity for extracting paleo-weather signals from seasonal-resolution gridded datasets, which enables an improved understanding of the forcing of low-frequency variability for atmospheric blockings and related extremes. Full article