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Nearly half of the world’s Nature Index chemistry research is now done in China
BMA-YOLO: an object detection model for microscopic images of mouse embryos
Imaging hidden objects with consumer LiDAR via motion-induced sampling
Optimizing hepatitis C diagnosis through reinforcement learning feature selection and multi-model machine learning evaluation
Plant‑mediated silver quantum dots from Drimia maritima: antioxidant modulation and ROS‑induced apoptosis in breast cancer cells
Abstract Green nanotechnology offers a sustainable approach to improving the therapeutic efficacy of plant‑derived bioactive compounds while reducing environmental and biological toxicity. In this study, silver quantum dots (Ag QDs) were synthesized via an eco‑friendly green route using the aqueous bulb extract of Drimia maritima (L.) Stearn ( D. maritima) , which served as a natural reducing and stabilizing agent. Physicochemical characterization confirmed the successful formation of well‑defined nanoscale Ag QDs with appropriate structural features. The antioxidant potential of the biosynthesized Ag QDs was systematically evaluated using various in vitro assays, including DPPH, ABTS, nitric oxide scavenging, ferric reducing antioxidant power (FRAP), and metal‑chelating activity. The results revealed a dual antioxidant behavior: Ag QDs showed enhanced free‑radical scavenging capacity, whereas the crude extract exhibited stronger reducing and chelating activities. The anticancer efficacy of Ag QDs was assessed in MCF‑7 human breast cancer cells. MTT assay results revealed a significant cytotoxic effect, with an IC₅₀ value of approximately 14.7 µg/mL, approaching that of cisplatin and markedly lower than that of the crude extract. Mechanistic studies indicated that Ag QDs induced excessive intracellular ROS generation, leading to mitochondrial dysfunction and activation of the intrinsic apoptotic pathway, characterized by pronounced caspase‑9 and caspase‑3 activation. These effects were accompanied by modulation of apoptosis‑ and redox‑related gene expression, favoring pro‑apoptotic signaling. Confocal microscopy further confirmed apoptotic morphology, including chromatin condensation and cytoskeletal disruption. Overall, D. maritima ‑derived Ag QDs emerge as potent pro‑oxidant nanostructures capable of inducing ROS‑mediated mitochondrial apoptosis, highlighting their promise as a sustainable nanotherapeutic platform for breast cancer treatment.
Neodymium magnetic field meets nanocatalysis: a sustainable route to novel azines and condensed heterocycles
Abstract A sustainable and effective approach for manufacturing heterocyclic compounds was established utilizing Fe₃O₄ nanoparticles in the presence of a neodymium static magnetic field (about 9000 G). Pyrimidine, benzimidazole, quinoxaline, and benzodiazepine derivatives were swiftly synthesized at ambient temperature with excellent efficiency; no reaction transpired in the absence of a magnetic field. TEM and VSM investigations validated nanoscale dimensions (9–50 nm) and robust magnetic characteristics, facilitating efficient catalysis and straightforward recovery. The produced compounds were validated using FT-IR, 1 H NMR, 13 C NMR, and elemental analysis. The technology markedly decreased reaction time, reduced energy consumption, eliminated hazardous chemicals, and offers a sustainable pathway for synthesizing physiologically relevant heterocycles.
Hybrid deep learning-based multimodal framework for plant leaf disease classification using RGB, Excess Green (ExG), and pseudo-thermal representations with MobileNetV2
Abstract Plant diseases are a serious danger to the world’s food security, because they lower agricultural output and increase economic losses. Due to subjectivity, fluctuating lighting, and environmental unpredictability, traditional visual examination techniques are frequently incorrect. The Excess Green (ExG) vegetation index and pseudo-thermal representations produced from RGB pictures are two synthetically developed complementary representations that are integrated with RGB imagery in this study’s lightweight multimodal deep learning system to address these issues. Histogram shifting and pseudo-infrared color mapping are used in a reproducible picture alteration pipeline to create the pseudo-thermal modality, which allows for extra visual signals without the need for specific thermal sensors. In order to classify plant diseases while preserving computational efficiency, the suggested framework uses MobileNetV3-Small backbones to extract modality-specific characteristics. This is followed by feature-level fusion. The publicly accessible Ginger Leaf Dataset, which includes RGB pictures of ginger leaves in four different conditions—Damage-Pest, Dehydrated, Healthy, and Leaf-blight—was used for the experiments. For training, validation, and testing, the dataset was split using a stratified 70:15:15 split. Python-based preprocessing procedures were used to create the extra modalities (ExG and pseudo-thermal representations) from the original RGB images. The experimental results show that the combination of the representations with RGB images can enhance the classification performance compared with the unimodal RGB-based models. Ablation experiments are also conducted to examine the contributions of different modalities to the overall categorization accuracy. The experimental results show that plant disease recognition can be improved with the help of efficient computing by combining lightweight convolutional neural networks with computationally generated visual representations.
Assessing the influence of building data choice on wildland–urban interface delineation in mainland Portugal
Abstract The delineation of the Wildland-Urban Interface (WUI) is fundamental for wildfire risk management, yet it is highly sensitive to the underlying building data used. Global building datasets offer unprecedented coverage but may introduce biases by including different types of built-up structures, potentially leading to an overestimation of exposure and a misallocation of critical resources. This study aims to map the WUI and assess wildfire exposure in mainland Portugal by comparing the efficacy of different building datasets: the official residential database (BGE21), Microsoft Building Footprints (MSB24), and the World Settlement Footprint (WSF19). We employed a point-based mapping methodology (100-m radius, > 6.17 buildings/km 2 ) to classify WUI into Intermix and Interface zones. Our analysis revealed that the choice of dataset drastically alters WUI estimates. MSB24, which includes all structure types, identified 67% more buildings than the residential-focused BGE21, resulting in a 73% larger WUI area. Spatial agreement was low, with only 46% of the total WUI area being identified by all three datasets, falling to just 29% for the more vulnerable Intermix zones. While MSB24 showed high recall (0.97 Intermix , 0.99 Interface ), its precision was low (0.43 Intermix , 0.67 Interface ), confirming a significant overestimation of critical zones. Analysis of wildfire perimeters (2000–2023) showed that burned area within the WUI was disproportionately higher in years with smaller total fire extent (e.g., 2006, 2018) rather than in megafire years (e.g., 2003, 2017). We conclude that while global datasets like MSB24 are valuable for emergency response due to their high coverage, their use for preventive planning and resource allocation may cause inefficiencies. We recommend that local authorities prioritize validated residential data, like BGE21, for strategic wildfire prevention and mitigation planning in Portugal to ensure resources are targeted efficiently.
Comparative in vitro and in vivo assessment of three experimental extracellular matrix meshes for soft tissue remodeling
Development and validation of single-item experience sampling measures of wellbeing in teens
Abstract What are you feeling right now? Were you aware of where your mind was a moment ago ? Questions like these provide simple, face valid measures of momentary experience. Ultra-brief measures using the experience sampling method (ESM), consisting of a few items that can be completed in about a minute, may also be highly scalable and increase engagement in applied contexts. We developed single-item measures of teen wellbeing using ESM in four domains: awareness, connection, insight, and purpose. Then, we evaluated item relevance and clarity through user testing with 12 teens aged 14–18 years old and made revisions based on their feedback. Finally, we tested the new items in the context of ESM with 156 teens aged 13–18 years old, over a period of 8 days with ESM questions sent via text message three times each day outside school hours. We found one or more items for each domain with acceptable validity, response variability, and a normal response distribution. We assessed convergent, divergent, and predictive validity, and found significant relationships for comparison measures of each type. Future research should further investigate using these measures in the context of interventions that include training in these skills-based domains.
The brain’s code seems to be in constant flux. Neuroscientists are baffled
A properties prediction strategy of aluminum anode foils through machine learning based on feature selection and stacking learning models
BCDX2–CX3 and DX2–CX3 complexes assemble and stabilize RAD51 filaments
Increasing the ecological footprint significantly reduces life expectancy and increases infant mortality rates in Ethiopia
Experimental modeling and multi-objective optimization of Micro Arc Oxidation process parameters of aluminium alloy joints
Abstract Micro Arc Oxidation (MAO) is an advanced electrochemical surface-processing technique capable of producing hard, dense and adherent ceramic coatings on aluminium alloys, exhibiting superior wear and corrosion properties than traditional anodizing. This research project produced MAO coating on CMT-welded AA6082-T6 aluminium alloy joints and the effect of current density, the oxidation time and the distance in between the electrodes on the coating characteristics were systematically studied using Response Surface Methodology (RSM) based parametric mathematical modelling (PMM). The PMMs generated very accurate predictions of the porosity and hardness of the aluminium alloy joints with an error margin less than 2% and 99% confidence level. Detailed characterization was done to test model predictions. The analysis of the SEM showed how the crater-type discharge channels, micropores, and the patterns of molten-oxide resolidification changed, and optimal parameters resulted in homogeneous and compact structures. The thickness variation between about 58 μm to 110 μm with current density was established by cross-sectional SEM. The XRD patterns showed that, γ-Al 2 O 3 , θ-Al 2 O 3 and α- Al 2 O 3 phases existed and were transformed with α- Al 2 O 3 enrichment at moderate current density (0.19 A/cm 2 ) that increased peak hardness. High density of current (0.25 A/cm 2 ) inhibited the formation of α- Al 2 O 3 and high porosity. Optimized MAO conditions of 0.19 A/cm 2 current density, 20 min oxidation time, and 6-cm spacing between electrodes gave minimum porosity (2.07 vol) and maximum hardness (1459.36 HV). The coatings also exhibited positive wear behaviour, and decreased friction coefficient was observed due to high density of ceramic phase. The overall results of modelling and characterization offer a solid system of application of MAO coating on welded aluminium structures in the high-performance engineering applications.
Longitudinal characterization of COVID-19 across a surveillance transition in Japan: thirteen epidemic waves, 2020–2025
Abstract Long-term comparison of COVID-19 epidemic waves is challenging because surveillance systems and reporting practices change over time. In Japan, official reporting shifted repeatedly between January 2020 and December 2025, from comprehensive nationwide notification to restricted reporting and sentinel-based surveillance. Using publicly available national data on reported cases and deaths, we characterized 13 epidemic waves while explicitly accounting for reporting transitions. We described weekly incidence and mortality rates per 100,000 population using population-based indicators to enable comparison across heterogeneous surveillance frameworks. Early waves showed very high observed case fatality ratios. Observed case fatality ratios declined markedly in subsequent waves, although estimates varied with reporting criteria and adjustments for reporting limitations, including periods when mild cases were excluded. Death counts were available only through week 18 of 2023, precluding evaluation of population-based mortality and observed case fatality ratios after the transition to sentinel-based surveillance. This transparent bridging framework enables longitudinal, comparability-oriented description of 13 epidemic waves in Japan across the 2023 surveillance transition. These findings should be interpreted as descriptive patterns observed under evolving surveillance contexts, and causal attribution is beyond the scope of this study.