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Women in science are not a ‘problem to be fixed’
The 129S1/SvlmJ mouse strain recapitulates severe hypertensive target organ damage under moderate angiotensin II–induced hypertension
Inside Mexico’s stem-cell industry
Application of treatment response assessment maps (TRAMs), based on delayed-contrast MRI for radiomic characterization of breast lesions
Valorization of coal fly ash into a magnetic Fe₃O₄-decorated composite for Cu(II) removal from aqueous systems
Abstract Coal fly ash is generated in large quantities by coal-fired power plants and is commonly disposed of as waste despite containing reactive mineral phases. This study investigates the valorization of coal fly ash into a magnetic Fe₃O₄-decorated composite for Cu(II) removal from aqueous systems, linking waste reutilization with the development of low-cost and magnetically recoverable materials for water treatment. The composite was synthesized via a coprecipitation–thermal treatment route, promoting the deposition of iron oxide on chemically activated fly ash. The physicochemical properties of the resulting material were characterized using X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and N₂ adsorption–desorption (BET) analysis. Cu(II) concentrations before and after adsorption were quantified by atomic absorption spectroscopy (AAS). Batch adsorption experiments were performed under fixed experimental conditions to evaluate the influence of composite composition and contact time on Cu(II) removal. The Fe₃O₄-decorated fly ash composite exhibited enhanced magnetic recoverability and improved Cu(II) removal compared with non-magnetic fly ash, enabling efficient solid–liquid separation. Under the selected experimental conditions, approximately 80% Cu(II) removal was achieved, yielding an adsorption capacity of ~0.32 mg g⁻ 1 at an initial Cu(II) concentration of 4 mg L⁻ 1 and a contact time of 120 min. Kinetic analysis indicated that Cu(II) uptake followed pseudo-second-order behavior, suggesting chemisorption-controlled adsorption. The developed material is positioned as a low-cost, sustainable, and magnetically recoverable adsorbent for coal fly ash valorization rather than as a high-capacity adsorbent. The findings highlight the role of chemical activation and magnetic modification in tailoring fly ash-based composites and provide a basis for further performance optimization.
Long-term thrombus-free left atrial appendage occlusion via magnetofluids
Structure and mechanism of the human bile acid transporter OSTα–OSTβ
Feather aerodynamics suggest importance of lift and flow predictability over drag minimization
Abstract Partly overlapping feathers form a large part of birds’ wing surfaces, but in many species the outermost feathers split, making each feather function as an independent wing. These feathers are complex structures that evolved to fulfil both aerodynamic and structural functions. Yet relatively little is known about how the profile shape and microstructures of feathers impact aerodynamic performance. Here we determined, using fluid dynamic modelling, the aerodynamic capabilities of a section of the primary flight feather forming the leading edge of the split wing tip of a Jackdaw ( Corvus monedula ). Our findings demonstrate that the feather section exhibits a relatively high performance, with lift comparable to manmade aerofoils, however, there is a drag penalty associated with the feather shaft. The model’s vortex shedding behaviour shows low amplitude temporal fluctuations in lift, compared to manmade aerofoils. Notably, the aerodynamic pitch torque around the shaft varies with angle of attack. This, when combined with the built-in pitch-up twist of the feather implies a passive pitch control mechanism for the feather. Taken together, our findings suggest evolutionary adaptations of the flow around the feather, which could be of interest when designing micro-air vehicles and wind turbines.
Multi-omics analysis reveals sevoflurane exacerbates cognitive impairment in diabetic mice by disrupting lipid metabolism
Abstract Diabetes-associated cognitive dysfunction represents a global health challenge, yet the mechanisms by which anesthetics modulate cognitive function in diabetic states remain poorly understood. We systematically compared the effects of 2-hour brief exposure to sevoflurane (SEV) and propofol (PRO) on cognitive function and neuropathology in streptozotocin (STZ) -induced diabetic mice. Morris water maze and Y-maze tests revealed that SEV significantly exacerbated spatial memory and learning deficits in mice, while PRO showed no significant effects. Additionally, diabetic mice exhibited reduced NeuN + neurons, increased β-amyloid deposition, and decreased SYN expression in the hippocampal CA1 region as examined by Immuno-fluorescence staining. Neither short-term SEV nor PRO exposure aggravated neuronal structural damage. Further transcriptomics revealed both anesthetics affected hippocampal neuron differentiation, but SEV uniquely perturbed fatty acid metabolism pathways. Metabolomics identified SEV-induced disruptions in lipid metabolism, marked by elevated hippocampal free fatty acids, phospholipids, as well as reduced lysophospholipids and acylcarnitine. Integrated multi-omics analysis demonstrated that SEV impaired cognition by suppressing fatty acid oxidation and dysregulating glycerophospholipid metabolism. These findings highlight the critical impact of anesthetic selection in diabetic populations.
Stable approach based diagonal recurrent quantum neural networks for identification of nonlinear systems
Abstract Identification of nonlinear dynamics from input-output data is crucial in many fields where conventional linear models fail to capture nonlinear dynamics of complex systems. Although recurrent neural network architectures have the potential to deal with these problems, they often face limitations in stability, memory capacity, and convergence efficiency. Recent developments in quantum neural networks (QNNs) offer a promising alternative due to their inherent parallelism and high-dimensional processing power. However, the application of QNNs in dynamic nonlinear modeling is still underexplored, especially with regard to stability-guaranteed learning strategies. To address this gap, a novel Diagonal Recurrent Quantum Neural architecture with Lyapunov Stability (DRQNN-LS) has been developed, which combines the structural simplicity of diagonal recurrent networks harnessing the capabilities of quantum learning algorithms and the mathematical rigor of Lyapunov stability theory. Stable convergence and efficient parameter tuning are ensured by deriving adaptive learning rates through Lyapunov analysis. The proposed model is evaluated through three scenarios: a mathematical nonlinear system, a chaotic Henon map, and a practical DC motor system. Comparative analysis with other models demonstrates the exceptional capabilities of DRQNN-LS in terms of the RMSE, MSE, and FIT metrics. The obtained responses validate the effectiveness and robustness of DRQNN-LS for modeling highly nonlinear and real-world systems.
Do wet or dry soils trigger thunderstorms? It depends on how the wind blows
Industrial internet data management framework with blockchain integration for data integrity assurance and access control resolution
Respiratory sound analysis in a rabbit tracheomalacia model
Wind shear enhances soil moisture influence on rapid thunderstorm growth
Abstract Convective storms can develop rapidly, creating hazards to local populations through intense precipitation, strong winds and lightning 1 . The large-scale environment in which thunderstorms develop is often well captured in forecast systems, yet predicting where individual storms will initiate remains a fundamental challenge. It is known that differential heating driven by soil moisture (SM) patterns creates atmospheric circulations that favour convective initiation over drier soils 2,3 , whereas wind shear between low and mid levels can enhance storm growth 4,5 . Here we show that the most extreme initiations are especially favoured over SM contrasts by means of an interaction with wind shear. Analysing 2.2 million afternoon events across sub-Saharan Africa, we find 68% more initiations classed as extreme given favourable (versus unfavourable) soil conditions, with greatest vertical storm growth occurring where SM-driven circulations oppose the direction of shear-induced cloud displacement. Developing clouds follow the mid-level wind direction and, where this opposes the low-level flow, rainfall is strongly correlated with locally drier soils. Although such shear conditions are particularly common over tropical north Africa, the effect favours negative SM–precipitation feedbacks globally. The combination of SM heterogeneity and wind shear provides a potentially important source of predictability for where deep convection develops, particularly for the most rapidly developing thunderstorms.
An MIEN1-based hexamer peptide (LA3IK) inhibits EGF-driven oncogenic signaling in prostate cancer by disrupting EGFR–ERBB2 heterodimerization
Spatiotemporal dynamics of heat stress and cold stress on UK rapeseed cropping over 1961–2020
Abstract Most temperature stress research in oil crops has focused on either heat or cold stress with analyses of the effects of both in combination rare. For the UK, neither the spatiotemporal hot spots of temperature stressed arable areas nor the comparative trends of heat and cold stresses for rapeseed cropping under climate change are understood. This study investigated the spatiotemporal heat and cold stresses for UK rapeseed over 1961–2020, and quantified the normalized rapeseed production loss index ( f RPL ) induced by heat stress during flowering. Stress indices including a literature derived heat stress index ( f HS ), cold degree days ( CDD ), with historical land cover and crop productivity data were used to comparatively study both stresses and to estimate f RPL . Results showed increasing f HS , particularly during flowering (April to May) and main yield-forming reproductive stages (spanning flowering through pod and seed development from April to July) over the study period, with f HS being negatively correlated with latitude. The decreasing values of CDD and frequency of cold stress suggest cold stress decreased during the flowering, vegetative (September to November) and reproductive stages. Notably, this study observed that annually at the UK level heat stress was increasing at a faster rate than cold stress was decreasing during flowering. The increasing values of f RPL , with significant differences between decades and regions, suggested an increasing production loss. These results presented a potentially trend of increasing heat stress impacts on future rapeseed production and further work is required to understand the specific impacts and mitigation strategies for addressing UK food security.