Search Results (1,019)

Background/Objectives: Human epidermal growth factor receptor 2 (HER2)-positive breast cancer is linked to poorer overall survival and a higher risk of brain metastases compared to HER2-negative breast cancer. Current preclinical studies lack robust HER2+ metastatic syngeneic mouse models for investigating targeted and immunomodulatory therapies. This study aims to develop effective HER2+ mouse models to investigate response dynamics to HER2-targeted therapy and immunotherapy. Methods: The human HER2 gene (WT or mutant p.A775_G776insYVMA, GFP-tagged at the C-terminus) was introduced into triple-negative breast cancer (TNBC) mouse mammary carcinoma cells with known metastatic potential (4T1 and EO771) via lentiviral transduction. HER2 expression and phosphorylation were analyzed using Western blotting and immunohistochemistry. Tumors were treated with HER2-targeted therapy (trastuzumab and tucatinib), immune checkpoint blockade (anti-PD-1 and anti-CTLA-4), and anti-HER2 antibody–drug conjugate (ADC) to evaluate treatment efficacy. Metastatic potential was assessed with brain fluorescence imaging. Statistical analysis included ANOVA and Kaplan–Meier tests. Results: Newly established lines demonstrated expression of HER2+, with HER2^YVMA lines showing higher phosphorylation than HER2^WT lines. Cells were tumorigenic, demonstrating in vivo tumor take rates at 100% for 4T1-HER2 and 15–30% for EO771-HER2. HER2 overexpression led to a 30% increase in spontaneous brain metastasis in the 4T1-HER2 models. Trastuzumab alone did not reduce primary tumor size but significantly reduced brain GFP signal by 17% ± 8% and 26% ± 7% in the 4T1-HER2^WT and 4T1-HER2^YVMA models, respectively. Combinational therapies with anti-HER2 therapy and immune checkpoint blockade effectively suppressed primary tumor growth and prolonged survival in EO771-HER2^YVMA model. T-Dxd, but not T-DM1, demonstrated partial treatment response in the EO771-HER2^WT model. Conclusions: HER2+ syngeneic tumor models were developed that spontaneously metastasize to the brain and demonstrate variable responses to immunotherapies and ADCs. These models are valuable for advancing molecular imaging modalities for HER2+ brain metastasis, studying blood–brain barrier penetration of HER2-targeted drugs, and exploring the combination of therapies, including immunotherapy. Full article

Arctic–boreal wetlands and lakes are among the most significant and most uncertain natural sources of atmospheric methane. Rapid Arctic amplification, permafrost thaw, hydrological change, and increasing ecosystem productivity are expected to intensify methane emissions from high-latitude landscapes. Yet, significant uncertainties persist in quantifying their magnitude, seasonality, and spatial distribution. This review synthesizes the current state of the art in monitoring methane emissions from Arctic–boreal wetlands and lakes through complementary bottom-up and top-down approaches. We examine Earth observation (EO) capabilities, including optical, thermal infrared (TIR), and synthetic aperture radar (SAR) missions, as well as new emerging satellite platforms. We also assess in situ measurement networks, wetland and lake inventories, empirical and process-based models, and atmospheric inversion frameworks. Key gaps remain in representing small waterbodies, shoreline heterogeneity, winter emissions, inventory harmonization, and integration between atmospheric retrievals and surface-based flux models. Moreover, advances in multi-sensor data fusion, explainable artificial intelligence (XAI), physics-informed inversion methods, and geospatial foundation models offer strong potential to reduce these uncertainties. A coordinated integration of satellite observations, field measurements, and transparent modeling frameworks is essential to improve Arctic–boreal methane budgets and strengthen projections of climate feedback in a rapidly warming region. Full article

Lithium-ion batteries are widely used across diverse applications due to their high energy density, long cycle life, and fast charging capabilities. As battery-powered systems become increasingly critical, accurate estimation of the Remaining Useful Life (RUL) is essential for ensuring reliability, safety, and effective maintenance planning. This work investigates Evidential Deep Learning (EDL) for data-driven RUL estimation and introduces a novel risk-aware loss function designed to enhance both predictive accuracy and uncertainty quantification in the End-of-Life (EoL) region, where precise and trustworthy predictions are most needed. Using a publicly available dataset of lithium iron phosphate (LFP) cells, we benchmark the proposed approach against a baseline Conv–LSTM model, Monte Carlo (MC) Dropout, and Deep Ensembles. The results show that integrating the risk-aware loss into the EDL framework substantially improves the calibration of predictive uncertainty while achieving state-of-the-art accuracy near EoL. Unlike MC Dropout and Deep Ensembles, which exhibit increasing or unstable uncertainty as degradation accelerates, the proposed EDL model demonstrates a consistent reduction in uncertainty and significantly higher reliability in late-stage predictions. The findings indicate that the risk-aware evidential framework offers a reliable and computationally efficient solution for battery RUL estimation, enabling more informed decision-making in both safety-critical and consumer-oriented applications. Full article

Rare Earth Elements (REEs) represent strategic and critical raw materials for the energy transition and must therefore be integrated into efficient and functional recycling processes. Their adoption in electric motors is rapidly expanding, raising significant challenges for end-of-life (EoL) management, starting from the collection phase. In this context, this work proposes the integration of an image-based classification framework within the Waste Electrical and Electronic Equipment (WEEE) recycling pipeline to selectively identify electric motors containing permanent magnets (PMs) and direct them toward dedicated recycling processes for rare earth recovery. The proposed methodology relies on a Discriminative Transfer Learning (DTL) approach based on a ResNeXt convolutional neural network (CNN), adapted to a proprietary and heterogeneous dataset of electric motors acquired in an industrial recycling facility. The objective is twofold: first, to identify motors containing PMs; second, to classify motors into construction categories according to their likelihood of incorporating PMs. Experimental results show promising performance in terms of PM-containing motor detection capability, establishing a robust foundation for the automated recovery of REEs at an industrial scale. Furthermore, the model’s generalization capabilities can be further enhanced through the expansion of collaborative datasets and the integration of advanced scanning technologies. Full article

Spring phytoplankton bloom mechanisms in the Bohai Sea show clear spatial differences, but the physical–biological coupling in the ice-covered Liaodong Bay (LDB) remains poorly understood. Utilizing satellite observations and high-resolution reanalysis data from 2009 to 2023, this study explores the drivers of spring blooms through generalized additive models (GAMs) and the Equation of State of Seawater (EOS). The results reveal pronounced regional heterogeneity. In the southern Bohai Sea, bloom dynamics are co-regulated by a complex combination of nutrient availability and localized physical mixing. In contrast, blooms in LDB are predominantly driven by the shoaling of the mixed layer depth (MLD), a physical state intrinsically linked to winter sea-ice melt. Linear decomposition of water density via EOS quantitatively demonstrates that spring stratification in LDB is salinity-dominated (contributing ~60.7%), rather than thermally driven. The rapid influx of low-salinity meltwater forms a strong halocline that suppresses vertical mixing and physically compresses the MLD into the euphotic zone. Consistent with Sverdrup’s Critical Depth Theory, this inferred physical pathway effectively alleviates light limitation and acts as the primary trigger for the early bloom peak timing. This complete melting–freshening–stratification–light coupling chain provides a novel physical perspective on how mid-latitude marginal sea ecosystems respond to climate change, distinct from canonical polar light-limitation models. Full article

The growing interest in bio-based and biodegradable polymer composites reinforced with agricultural waste reflects global efforts to reduce dependence on fossil resources and improve the sustainability of materials. However, biocomposites are not necessarily more sustainable, and their environmental performance requires careful life cycle assessment (LCA). This review critically analyses recent LCA studies of biodegradable biocomposites reinforced with agricultural waste, focusing on methodological choices, data quality, results and limitations. A systematic literature review was conducted using the Scopus database, focusing on studies from the last five years. Selected studies were examined using a structure consistent with ISO 14040, with defined data extraction categories and key questions. The analysis shows that although biocomposites often demonstrate advantages in terms of climate change and fossil resource depletion compared to traditional materials, the results vary significantly depending on the definition of the functional unit, geographical context, processing pathways, and data assumptions. Limitations include reliance on laboratory data, uncertainties, incomplete system boundaries, inconsistent allocation methods, and limited end-of-life (EoL) modelling. Overall, the review highlights the need for improved data quality, performance-based functional units, geographically representative inventories, and more standardised LCA practices to ensure meaningful comparisons and support the sustainable development of biocomposites. Full article

In this article, we report a comprehensive analysis of the chemical composition and biological activity of Dysphania ambrosioides essential oil (DA-EO) originating from Bulgaria. Gas chromatography–mass spectrometry (GC–MS) analysis led to the identification of 53 constituents, revealing a complex phytochemical profile. The results classify the investigated oil as a thymol–carvacrol chemotype, dominated by oxygenated monoterpenes (56.79%), with thymol (19.45%) and carvacrol (14.30%) as the major components. This compositional profile differs markedly from the ascaridole-rich chemotypes commonly reported in the literature. The biological activity of DA-EO was evaluated through its antimicrobial, antioxidant, and anti-inflammatory properties. The oil exhibited broad-spectrum antimicrobial activity against pathogenic microorganisms such as S. aureus, E. coli, and L. monocytogenes. Antioxidant assays (HPSA, HRSA) indicated moderate activity, closely associated with the terpenoid composition of the oil. The anti-inflammatory potential, assessed via inhibition of albumin denaturation (IAD), was analyzed using nonlinear four-parameter (4PL) and five-parameter (5PL) logistic models. The obtained IC₅₀ values (67.0–77.0 µg/mL) were comparable to those of the reference drug ibuprofen, highlighting the significant potential of DA-EO as a natural therapeutic agent. Full article

Essential oils (EOs) from combined plant materials offer a promising alternative to conventional extraction by enhancing chemical diversity and bioactivity. This study evaluated the chemical composition and insecticidal properties of individual and combined plant EOs from Cymbopogon citratus, Eucalyptus camaldulensis, Eucalyptus lehmannii, Salvia rosmarinus and Thymus vulgaris were evaluated against aphids. Binary and ternary combinations were prepared in equal proportions prior to hydrodistillation. GC-MS analysis revealed significant compositional shifts in EOs from combined plant materials. Major compounds in individual oils included citral (53.11%) and neral (29.14%) in C. citratus, thymol (70.84%) in T. vulgaris, and eucalyptol as the predominant compound in E. camaldulensis (66.51%), E. lehmannii (56.99%) and S. rosmarinus (46.56%), respectively. In the combined oils, the relative abundance of these constituents was altered, and in some cases new constituents were introduced. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) revealed that combined plant EOs clustered near their parental oils, indicating compositional inheritance. Contact toxicity assay against Aphis fabae demonstrated enhanced efficacy of the combined oils, with reduced LC₅₀ values (1.39 µL mL⁻¹ for E. camaldulensis + T. vulgaris) and synergistic interactions, indicated by a co-toxicity coefficient (CTC) of 221.58 and elevated synergistic factors. Pearson correlation analysis and Partial Least Squares (PLS) regression jointly identified Acorenone B and thymol as negatively, and caryophyllene as positively correlated compounds, all with relatively high contribution to insecticidal activity, ranking highest with a Variable Importance in Projection (VIP) scores > 1.0. While PLS model had modest predictive power, the integration of these statistical approaches supports the insecticidal potential of combined plant-derived EOS in laboratory bioassays and indicates their relevance to sustainable crop protection. Full article

Background/Objectives: Citrus limon leaf essential oil (EO) is traditionally used for its calming and cognitive-enhancing properties. Although the chemical composition of C. limon leaf essential oils (EOs) obtained by means of hydrodistillation (HD) and solvent-free microwave extraction (SFME) has been previously characterized, the influence of the extraction method on their neuroprotective efficacy and dose–response effects remains insufficiently explored. In the present study, EOs obtained by means of HD (CEH) and SFME (CEM) were compared for their behavioral, biochemical, and in silico neuroprotective effects against scopolamine (SCOP)-induced cognitive and anxiety-like impairments in adult zebrafish. Methods: Adult Tübingen zebrafish were exposed to CEH or CEM via immersion at 10, 100, and 150 µL/L for 19 days prior to SCOP challenge (100 µM). Cognitive performance was evaluated using the Y-maze and novel object recognition (NOR) tests, while anxiety-like behavior was assessed using the novel tank test (NTT) and novel approach test (NAT). Brain acetylcholinesterase (AChE) activity and oxidative stress markers were quantified. Molecular docking analyses were conducted to investigate interactions between major EO constituents and AChE and monoamine oxidase A (MAO A). Results: Both CEH and CEM significantly attenuated SCOP-induced memory deficits, improved spontaneous alternation and NOR discrimination, and reduced anxiety-like behaviors. These effects were associated with AChE inhibition and restoration of redox balance. Notably, CEM generally exhibited stronger neurobehavioral and biochemical effects at comparable doses. In silico analyses supported these findings, revealing favorable binding affinities of key EO constituents toward cholinergic and monoaminergic targets. Conclusions: This study demonstrates that the extraction method influences the neuroprotective efficacy of C. limon leaf EOs. While both CEH and CEM exert antioxidant and cholinergic modulatory effects, CEM shows enhanced neuroprotective potential in a zebrafish model of SCOP-induced cognitive impairment, supporting the relevance of extraction-dependent biological profiling in EO-based neurotherapeutic research. Full article

Lantana camara L. (Verbenaceae) is a medicinal plant widely used in traditional medicine in Angola, especially for its anti-inflammatory effects. This study evaluated the chemical composition of L. camara essential oil from leaves (Lc-EO) collected in Uíge Province, Angola. GC–MS analysis enabled the identification of 96 volatile compounds, with sesquiterpenes and monoterpenes as the predominant constituents. Among them, β-caryophyllene (14.49%), sabinene (9.13%), bicyclogermacrene (8.18%), α-humulene (5.66%), nerolidol (5.29%), and 1,8-cineole (5.14%) were identified as major components. The antioxidant activity of Lc-EO was assessed using DPPH, ABTS, and superoxide anion (O₂^•−) assays. Lc-EO showed strong activity in the DPPH assay (IC₅₀ = 0.72 µg/mL), moderate activity in the ABTS assay (IC₅₀ = 87.5 µg/mL), but minimal effect on O₂^•− radicals (IC₅₀ = 1491 µg/mL). It also significantly inhibited lipid peroxidation (IC₅₀ = 236.2 µg/mL). The anti-inflammatory activity of Lc-EO was assessed through its ability to inhibit protein denaturation, exhibiting a moderate effect with 28% inhibition. In silico ADMET predictions suggested drug-like properties and low predicted systemic toxicity for major compounds. The Artemia salina lethality assay indicated moderate general toxicity (IC₅₀ = 154.1 µg/mL), whereas the MTT viability assay revealed higher cytotoxic potency of Lc-EO (IC₅₀ = 31.58 µg/mL), highlighting model-dependent differences in sensitivity. Overall, L. camara essential oil shows relevant bioactivity consistent with its traditional use, particularly antioxidant and anti-inflammatory effects, while its cytotoxicity highlights the need for safety evaluation. These findings indicate that the assayed oil is a promising source of bioactive compounds, but further studies are required to support its development as a safe pharmaceutical raw material. Full article

Accurate acquisition of low-observable targets with a minimal radar cross-section (RCS) poses a significant challenge for multi-source remote sensing systems, such as integrated radar–electro-optical (REO) platforms, particularly in complex electromagnetic environments characterized by strong noise interference and a high false-alarm rate. Conventional methods, which often treat data association and fusion from heterogeneous sensors as separate, offline processes, struggle with the dynamic uncertainties and real-time decision requirements of such scenarios. To address these limitations, this paper proposes a novel Evidence–Reinforcement Learning-based Decision and Control (ERL-DC) framework. It operates through a closed-loop architecture consisting of three core modules: A static assessment model for initial target prioritization, a Dempster–Shafer (D–S) evidence-based multi-source data decision generator for dynamic information fusion and uncertainty-aware target selection, and a Deep Reinforcement Learning (DRL) controller for noise-robust sensor steering. A high-fidelity simulation environment was developed to model the multi-source data stream, encompassing radar detection with clutter and false targets, as well as the physical constraints of the electro-optical (EO) servo system. Based on the averaged results from multiple Monte Carlo simulations, the proposed ERL-DC framework reduced the Average Decision Time (ADT) from 7.51 s to 4.53 s, corresponding to an absolute reduction of 2.98 s when compared to the conventional method integrating threshold logic with Model Predictive Control (MPC). Furthermore, the Net Discrimination Accuracy (NDA), derived from the statistical outcomes across all the simulation runs, exhibited an absolute increase of 37.8 percentage points, rising from 57.8% to 95.6%. These results indicate that ERL-DC achieves a more favorable trade-off in terms of scheduling efficiency, decision robustness, and resource utilization. The primary contribution is an intelligent, closed-loop architecture that tightly couples high-level evidential reasoning for multi-source data fusion with low-level adaptive control. Within the simulated environment characterized by clutter, false targets, and angular measurement noise, ERL-DC demonstrates improved target discrimination accuracy and decision efficiency compared to conventional methods. Future work will focus on online parameter adaptation and validation on physical platforms. Full article

Floods pose significant risks to human lives, infrastructure, and the environment. Timely and accurate flood forecasting plays a pivotal role in mitigating these risks. This study proposes a Digital Twin proof-of-concept framework aimed at improving flood forecasting and validated its effectiveness through a pilot study of the 2021 flood event in Luxembourg. The baseline forecasting method combines GloFAS ensemble streamflow forecasts with a high-resolution flood hazard datacube generated using a LISFLOOD-FP hydrodynamic model and then averaging among the member forecasts. To dynamically update the flood forecasts and improve their accuracy, the framework integrates satellite-based Earth observations (EOs)—specifically Sentinel-1-derived flood probability maps from the Global Flood Monitoring service—via a particle filter-based data assimilation (DA) process. As such, the simulations with more coherence with the observed Sentinel-1-derived flood probability maps are prioritized. This results in a Digital Twin capable of delivering daily flood depth forecasts, at detailed spatial resolution, up to 30 days ahead, with reduced prediction uncertainty. Using the 2021 flood event, we evaluate the performance of the Digital Twin in assimilating EO data to refine hydraulic model simulations and issue accurate flood forecasts. Although certain challenges persist—particularly the difficulty in quantifying the error structure of GloFAS discharge forecasts—the proposed approach demonstrates clear improvements in forecast accuracy compared to open-loop simulations. As a result, the approach reduces water level prediction errors by an average of 15–33% and increases the Nash–Sutcliffe Efficiency of discharge predictions by approximately 15–36%. Future work will aim to refine the flood hazard datacube and advance the characterization and modeling of uncertainties associated with both GloFAS streamflow forecasts and Sentinel-1-derived flood maps, thereby further enhancing the system’s predictive capability. Full article

As an important ecological obstacle prone to climatic changes, the Tibetan Plateau has been transformed by retreating glaciers, degrading permafrost, and deteriorating grasslands. Recent ecological remote sensing evaluations typically use medium-resolution and single-source optical imagery, highlight natural factors while ignoring human impacts, and encounter difficulties with time-focused interpretability and continuity within complex terrains. This research proposes a theory combining large language models with satellite embedding to holistically examine the ecology of the Tibetan Plateau between 2000 and 2024. We created an ecological satellite embedding (ESE) model applying self-supervised learning to integrate 12 ecological variables into combined space and time representations as of 2024, according to the Prithvi-Earth Observation (Prithvi-EO) foundational model involving low-rank adaptation (LoRA). GeoChat reasoning was applied to turn the embedded variables into a comprehensive representation feature (CRF). Field research demonstrated strong accuracy for the fraction of absorbed photosynthetically active radiation (FAPAR, R² = 0.9923) and aboveground biomass (AGB, R² = 0.8690). Space and temporal analyses demonstrated a general ecology-dependent enhancement accompanied by significant space-based clustering (Moran’s I = 0.50–0.80), hotspots in humid southeastern areas, major upward trends in vegetation indices and productivity metrics (p < 0.05), and higher shifts in transition regions. Despite the marginal degradation risk, the grassland carrying capacity has expanded extensively in the main farming regions. The comprehensible CRF schema identified three management areas: potential risk, enhancement potential, and stable conservation management. This transferable modular approach connects expert reasoning with data-driven modeling, presenting adaptable methods for assessing ecosystems in high-altitude, data-sparse environments, and practical ways to promote ecological management. Full article

Background: Second primary tumors (SPTs) are a major survivorship challenge in oral squamous cell carcinoma (OSCC), yet their biological phenotypes may differ according to exposure status and immune background. Methods: In this retrospective cohort (2011–2020), 242 surgically treated primary OSCC patients were classified as non-smoking, non-drinking (NSND; never smokers/never drinkers) or smoking and/or drinking (SD; any history of smoking and/or alcohol consumption). SPTs were categorized as extra-oral SPTs (eoSPTs) or multifocal oral SCC (mOSCC), with mOSCC (≥3) denoting ≥3 oral primaries. Immune background was assessed by documenting immune-modulating conditions (including oral lichen planus as an immune-mediated mucosal disorder). Multivariable logistic regression was used to evaluate predictors of eoSPTs and mOSCC. Results: SPT occurred in 82/242 (33.9%), comprising 54 eoSPT (22.3%) and 28 mOSCC (11.6%). Overall SPT prevalence was similar in NSND and SD patients (29.8% vs. 36.1%), but phenotype composition differed significantly (chi-square p = 0.004): eoSPTs were more common in SD (27.8% vs. 11.9%), whereas mOSCC was more common in NSND (17.9% vs. 8.2%); mOSCC (≥3) occurred in 10.7% of NSND versus 1.3% of SD patients. Immune-modulating conditions were associated with mOSCC but not eoSPTs. Within the immune-modulating spectrum, OLP showed strong phenotype specificity (0/20 eoSPTs; mOSCC in 7/20 [35.0%), particularly among NSND patients (38.9% with OLP vs. 12.1% without). In adjusted models, NSND status was associated with lower odds of eoSPT (OR 0.37, 95% CI 0.15–0.96), while OLP independently predicted mOSCC (OR 3.47, 95% CI 1.04–11.52). Conclusions: SPTs in OSCC comprise distinct phenotypes: SD patients predominantly develop eoSPTs consistent with carcinogen-associated aerodigestive field effects, whereas NSND patients exhibit an immune-associated, oral-restricted pattern with frequent mOSCC, supporting phenotype-tailored surveillance. Full article

Essential oils (EOs) have gained attention as biodegradable biopesticides for sustainable crop protection. This study investigated the chemical composition, antifungal activity and antioxidant potential of EOs from Mentha pulegium (EO-MP) and Cymbopogon citratus (EO-CC) against Bipolaris oryzae, the causal agent of rice brown spot, including the first quantitative determination of IC₅₀ values through standardized dose–response modeling and temporal evaluation of antifungal efficacy. Volatile profiles of both EOs were characterized by gas chromatography coupled with flame ionization detection (GC-FID) and gas chromatography–mass spectrometry (GC-MS). Antifungal activity was evaluated in vitro by a poisoned food assay at six concentrations ranging from 9.375 to 300 µL per plate (0.469–15.000 µL/mL PDA medium). Mycelial growth inhibition was assessed after 7 and 14 days of incubation. Antioxidant potential was determined by ferric reducing antioxidant power (FRAP) assay while protection against lipid oxidative damage was evaluated through inhibition of lipid peroxidation by the thiobarbituric acid reactive substances (TBARS) method. Both EO-MP and EO-CC exhibited strong, dose-dependent antifungal effects and achieved complete inhibition of mycelial growth at ≥37.50 µL per plate (1.875 µL/mL PDA) and ≥18.75 µL per plate (0.938 µL/mL PDA), respectively. EO-MP showed high reducing capacity (its FRAP value was 1.45 Trolox equivalent antioxidant capacity—TEAC) and high inhibition of lipid peroxidation (89.09%). Similarly, EO-CC exhibited a FRAP value of 1.55 TEAC and lipid peroxidation inhibition of 87.66%. These findings highlight the biofungicidal activity and multifunctional antioxidant-related properties of EOs from M. pulegium and C. citratus, supporting their potential application as eco-friendly tools for sustainable rice brown spot management. Full article