Search Results (168,959)

Flower color, as an important trait of ornamental plants, has been a research hotspot in recent years. In this study, we selected Prunus campanulata (Maxim.) (ZH, red), P. dielsiana (Schneid.) (WH, white), and two cherry blossom varieties ‘Yanzhi Fei’ (FH, deep pink) and ‘Yanzhi Xue’ (XH, pinkish white) obtained by open-pollination hybridization as material. By means of bioinformatics methods such as metabolomics and transcriptomics, it is expected to deeply study the molecular mechanism of the gradient changes in flower color between the parents and offspring of cherry blossoms. Metabolomics analysis indicated that a total of 84 flavonoid related metabolites were identified, among which 31 were associated with the anthocyanin metabolic pathway, including three major types of anthocyanin substances: cyanidin, delphinidin, and malvidin. Transcriptome analysis showed that a total of 7712 differential genes were detected between P. campanulata and P. dielsiana; there were 3948 differential genes between P. campanulata and ‘Yanzhi Xue’, 2802 between P. campanulata and ‘Yanzhi Fei’, and 2511 between ‘Yanzhi Xue’ and ‘Yanzhi Fei’. After screening based on anthocyanin accumulation, nine key enzyme genes were obtained. Joint analysis showed that the relative expression trends of structural genes such as PAL, 4CL, CHI, DFR, and CYP75B in the samples were consistent with those of anthocyanins, and they had a high correlation with downstream metabolites. The results of this study lay a certain scientific foundation for the future directional improvement and breeding of cherry blossom colors. Full article

“Rotten” sea ice, ice in an advanced stage of melt, represents an important but understudied habitat in the rapidly changing Arctic. As Arctic warming accelerates, this late-season ice type will become more prevalent, yet little is known about its microbial inhabitants or their roles in Arctic marine biogeochemical cycles. We examined microbial communities (prokaryote and algal abundance, 16S and 18S rRNA gene and transcript sequencing) and biogeochemical properties of rotten sea ice and earlier-season ice near Utqiaġvik, Alaska, USA. Rotten ice was comparatively warm, isothermal, and largely drained of brine, with extensive, interconnected pore networks linked to melt ponds above and seawater below. Unlike earlier-season ice, fluids saturating rotten ice were vertically homogeneous in pH, dissolved inorganic carbon, prokaryote and phytoplankton abundance, and microbial community composition. However, particulate carbon and nitrogen exhibited strong vertical gradients, with the highest concentrations near the surface. Microbial communities in rotten ice were significantly different from those in earlier-season ice and varied between individual floes. These findings indicate that rotten ice constitutes a distinct microbial habitat and may serve as an important source of nutrient-rich particulate matter in the future Arctic Ocean during the summer melt season. Full article

The growing demand for marine resource development and in-depth exploration of the marine environment has positioned soft biomimetic underwater vehicles (SBUVs) as a research hotspot in the fields of underwater equipment and soft robotics. SBUVs are characterized by bodies made of flexible and extensible materials, integrating the dual advantages of softness and biomimetics. They can achieve muscle-like continuous deformation to efficiently absorb collision energy, while mimicking the propulsion mechanisms of marine organisms—such as fish and jellyfish—through undulating body movements or cavity contraction and relaxation. Such biomimetic propulsion is highly compatible with the flexible actuation of soft materials, enabling excellent environmental adaptability while maintaining favorable propulsion efficiency. Compared with traditional rigid underwater vehicles, SBUVs offer higher degrees of freedom, superior environmental adaptability, enhanced impact resistance and greater motion flexibility. This review systematically summarizes typical actuation methods for SBUVs—including fluid-powered actuation, shape memory alloy actuation, and electroactive polymer actuation—elaborating on their working principles, key technological advances, and representative application cases on SBUVs. These actuation mechanisms each offer distinct advantages. Fluid-powered systems are valued for high power density and precise motion control through direct fluidic force transmission. Shape memory alloys provide high force output and accurate positional recovery via controlled thermal phase changes. Meanwhile, electroactive polymers stand out for their rapid (often millisecond-scale) dynamic response, low hysteresis, and fine, muscle-like deformation under electrical stimuli. Current challenges are also analyzed, such as limited actuation efficiency, material durability issues, and system integration difficulties. Despite these constraints, SBUVs show broad application prospects in marine resource exploration, ecological monitoring, and underwater engineering operations. Future research should prioritize the development of novel materials, coordinated optimization of actuation and control systems, and breakthroughs in core technologies to accelerate the practical implementation and industrialization of SBUVs. Full article

Sustainability research has seen tremendous growth as a field of study in recent years, evolving and changing in scope along the way. In this review, we track the growth and development of sustainability from 1980 to 2024 within the academic literature, utilize a comparison of works conducted with U.S.- and Chinese-based samples to demonstrate how different countries may influence sustainability practices, and outline possible areas for future research on this topic. As the largest economies in the world and the largest emitters of greenhouse gases, the U.S. and China can have a substantial impact on reducing climate change if they commit to sustainability. Articles for this review were acquired using a Scopus search for titles containing “sustainability AND China” and also titles containing “sustainability AND United States”, with years set to 1980–2024. It was also supplemented by a Google Scholar search for studies based in the U.S. and China. This review provides an overall comparison of the two literatures on sustainability from the U.S. and China. Ethical implications of sustainability in the U.S. and China are discussed. It appears that China is clearly positioned to lead the world in clean energy production, both by installing sustainable energy domestically and by selling such technology (e.g., solar) to other countries, because the Chinese government has prioritized this effort. The U.S. has made progress on the clean energy front, but that progress varies depending on the level of commitment from the federal government and may need to be driven by market and consumer demand. We hope that this review will aid in stimulating further investigation to advance the underlying research streams that we identify in this review, along with broadening the focus of sustainability to the global scale. Full article

Climate-smart forestry (CSF) is a comprehensive approach that aims to sustainably enhance wood productivity (production), improve forest resilience and adaptation, sequester carbon (mitigation), and support broader development goals. This strategy is profoundly linked with Forest Genetic Resources (FGR), which are crucial for the adaptive capacity and long-term sustainability of forest ecosystems in the face of the escalating climatic changes. Climate change presents significant risks, including increased air temperatures, altered precipitation regimes, and a rise in extreme weather events, leading to tree mortality, shifts in vegetation distribution, and a potential loss of critical forest functions and services, such as carbon sequestration capacity. While forests have inherent resilience, the rapidity and magnitude of projected changes may exceed their natural adaptive capacity, potentially resulting in local extinction and degradation of ecosystems. This review explores various facets of the interplay between CSF and FGR, emphasizing their role in sustainable forest management. Key areas of focus include: (1) Genetic Diversity, (2) Genotype Selection and Breeding, (3) Modern Breeding Techniques, (4) Molecular Breeding, (5) Genomic Prediction (GP), (6) Breeding Programs, (7) Silvicultural Practices, (8) Adaptation Mechanisms, (9) Phenotypic Plasticity, (10) Migration, particularly Assisted Gene Flow (AGF) and (11) Reproductive Material Management. Ultimately, the study highlights the crucial role of FGR in the resilience of forest ecosystems and proposes future actions for their integration into CSF strategies, including in situ and ex situ conservation, assisted migration, advanced research and development, community involvement, and supportive policy frameworks, all vital for the long-term sustainability and vitality of forest ecosystems in a changing climate. Full article

In recent years, cycling mobility in urban environments across Spain has grown significantly, driven by sustainability policies and behavioral shifts following the COVID-19 pandemic. However, this growth has been accompanied by an increase in accidents in urban areas, where more than 72.6% of cyclist accidents are concentrated, with large cities being the most affected. This study aims to explore and analyze the factors influencing cycling accidents in Spanish municipalities with populations exceeding 50,000, during the period of 2020–2023. A total of 24 variables were analyzed, encompassing not only innovative cyclist infrastructure network features (line connectivity), but also urban morphology and street infrastructure, weather conditions and mobility (all transportation modes). The methodological approach combines Principal Component Analysis (PCA) with two negative binomial regression models: one addressing all cycling accidents, and another focusing specifically on collisions between cyclists and motor vehicles. PCA shows the complex relations between urban features when comparing cyclist accidents among cities. The main results from the Negative Binomial analysis show that increased bicycle lane length significantly reduces cycling accident risk, while higher intersections with traffic signal density are associated with a greater likelihood of car–bicycle crashes. These findings emphasize the importance of cycling infrastructure provision and intersection design and regulation as key policy levers for improving urban cyclist safety. Future research should seek to corroborate these results through micro-spatial analyses and accident geolocation, assessing their severity and accounting for more detailed data on cycling infrastructure. Finally, the results’ discussion underscores the importance of implementing holistic urban mobility strategies that prioritize cyclist safety. Full article

Postprandial plasma amino acid (AA) kinetics serve as essential indicators of digestive efficiency and systemic metabolic status in pigs. Traditional kinetic analysis relies on Non-Linear Least Squares (NLS) regression using compartmental models, yet these methods typically demand repeated blood sampling and precise initialization to ensure convergence. In this study, we developed a Physics-Informed Neural Network (PINN) framework by integrating mechanistic Ordinary Differential Equations (ODEs) directly into the deep learning loss function. The framework was evaluated using a benchmark dataset. Specifically, we performed a retrospective analysis by downsampling the original high-frequency data to simulate dense and sparse sampling strategies. The results demonstrate that while both models exhibit high fidelity under dense sampling, PINN maintains superior robustness and predictive accuracy under data-constrained conditions. Under the sparse sampling scenario, PINN reduced the Root Mean Square Error (RMSE) compared to NLS in key metabolic profiles, such as Methionine in the FAA group (p < 0.01) and Lysine in the HYD group (p < 0.05). Unlike NLS, which is sensitive to initial guesses, PINN successfully utilized physical laws as a regularization term to robustly solve the inverse problem, demonstrating superior parameter identification stability and predictive consistency under data-constrained conditions compared to NLS. We concluded that the PINN framework provides a reliable and consistent alternative for modeling the AA dynamics. In the future, it may be possible to reconstruct highly accurate physiological trajectories under optimized sparse sampling conditions. Full article

Quantum Key Distribution (QKD) networks guarantee information-theoretical security of exchanged keys, but key rates are still limited. This makes efficient and adaptive routing a critical challenge, especially in meshed topologies without quantum repeaters. Conventional shortest path routing approaches struggle to cope with dynamic key store filling levels and changes in network topologies, which leads to load imbalance and blocked connections. In this work, we propose an adaptive routing framework based on Deep Reinforcement Learning (DRL) for hop-wise end-to-end routing in unknown meshed QKD networks. The agent leverages Graph Attention Networks (GATs) to process the network states of varying topologies, enabling generalization across previously unseen meshed networks without topology-specific retraining. The agent is trained on random graphs with 10 to 20 nodes and learns a routing policy that explicitly balances key consumption across the network by utilizing a reward function that is based on the entropy of key store filling levels. We evaluate the proposed approach on the 14-node NSFNET topology under time-varying traffic demands. Simulation results demonstrate that the DRL-based routing significantly outperforms hop-based and weighted shortest path benchmarks, achieving up to a 18.7% increase in mean key store filling levels while completely avoiding key store depletion. These results highlight the potential of graph-based DRL methods for scalable, adaptive, and resource-efficient routing in future QKD networks. Full article

Background: Embryoid bodies (EBs) play a central role in organoid engineering, where their formation fidelity and size critically influence downstream differentiation outcomes. Current EB production workflows primarily rely on retrospective quality assessment, which limits reproducibility in high-throughput culture systems. Objective: This study aimed to develop a prospective, non-invasive framework that integrates early-phase bright-field time-lapse imaging with a three-dimensional convolutional neural network to predict EB formation outcomes and final EB diameter within the microwell platform. Methods: Time-lapse image sequences collected during the first hours after cell seeding on the microwell array were used to train 3D-CNN models for classification (formation vs. non-formation) and regression (final diameter). A balanced dataset was constructed through under-sampling, and five-fold cross-validation with data augmentation was applied to evaluate model performance. Results: The classification model achieved an accuracy of 96.5%, reliably distinguishing between successful and failed EB formation using short-duration image sequences. The regression model predicted the final EB diameter with a mean absolute error of ±7.1 µm, reflecting strong agreement with measured values and capturing seeding-density-dependent size variations. Conclusions: Early aggregation dynamics captured by bright-field time-lapse imaging contain sufficient spatiotemporal information to enable accurate, prospective EB quality prediction. The proposed framework provides a label-free and automation-compatible strategy for improving reproducibility in large-scale EB manufacturing and supports the future development of adaptive and closed-loop organoid culture systems for clinical applications. Full article

Ochratoxin A (OTA) is a globally distributed mycotoxin that poses serious threats to food safety and human health due to its nephrotoxic, hepatotoxic, and carcinogenic properties. Previous enzymatic detoxification strategies for OTA have been constrained by low degradation efficiency or poor soluble expression of highly active enzymes. In this study, a bacterial strain with strong OTA-degrading activity was isolated and identified as Acinetobacter kookii AK4, which degraded 95.44% of 1 μg/mL OTA within 6 h. The predominant OTA-degrading activity was derived from intracellular enzymes. Through genome mining and experimental validation, gene2102 was identified as encoding an amidohydrolase. The enzyme was designated AMH2102 and was heterologously expressed in Escherichia coli. Codon optimization combined with fusion of an N-terminal SUMO tag increased the soluble expression of AMH2102 by 14.81-fold, enabling complete (100%) OTA degradation within 3 min. Overall, this study achieved the identification of an efficient OTA-degrading strain and enzyme and explored strategies for improving enzyme expression, yielding effective outcomes that provide useful references for future studies on strain mining and enzyme engineering. Full article

Increasing electric vehicle (EV) adoption raises concerns about EV waste management and the impact on the environment. To improve energy efficiency and exploit their remaining usable capacity, the retired batteries may be repurposed in second-life applications. This paper predicts the usable second-life capacity of retired EV batteries, considering the European Union (EU) regulation regarding the mandatory recycled critical material quotas in newly manufactured batteries from 2031 onwards. Based on political influences and the market’s capacity to return to pre-pandemic values, four scenarios are proposed regarding future EV sales in the EU market. The algorithm implemented in Matlab R2025a indicates the batteries that must be recycled to meet the mandatory targets and the ones that can be repurposed as battery energy storage systems. Historical data and future predictions are used to determine the number of EV batteries sold, lifetime, the market’s chemistry share and the usable capacity for second life. The annual mandatory recycled critical material content is compared to the available recyclable mass from both retired batteries in the current year and those that are already active in their second life. The economic analysis reveals the scenario with the highest total revenue, including the cascade benefits and recycling value. Full article

Global agri-food systems face a critical conflict between the need to feed a growing population and the imperative to mitigate its substantial environmental impact, including 23% of global greenhouse gas emissions and 70% of freshwater withdrawals. This bibliometric review maps the scientific landscape of Machine Learning (ML) research applied to sustainable agri-food systems. Using a structured bibliometric protocol, we analyzed 648 scientific documents from Scopus (2010–2025) to map the evolution, collaborative networks, and thematic trends in this domain. Results reveal a field that has grown exponentially until 2021, primarily driven by contributions from Computer Science (26%) and Engineering (21%), with key publications in journals such as Computers and Electronics in Agriculture (22 papers, 2631 citations). While China and India lead in productivity (80% of top authors), high-impact research remains strongly linked to international collaborations with institutions in the U.S. and EU. Current ML efforts focus on technical optimization—such as precision irrigation, pest detection, and yield prediction—but fall short in addressing social equity and climate resilience. The study concludes that while ML holds significant promise for sustainable agri-food processing and system optimization, future progress depends on overcoming fragmented regional collaborations and integrating holistic frameworks, such as life-cycle assessment, to ensure resilient and equitable food systems. Full article

(1) Background: The complexity of today’s healthcare system requires the formation of highly cohesive work teams that guarantee safe and high-quality care. Clinical simulation has become established as a pedagogical strategy capable of promoting the collaborative skills of teams of students and healthcare professionals. The objective of this study was to analyze the influence of learning through clinical simulation on group cohesion in nursing student teams. (2) Methods: A pre–post quasi-experimental study without a control group was conducted with final-year nursing students using the short Spanish version of the Group Environment Questionnaire, validated for nursing students. This questionnaire was administered twice, before and after participation in clinical simulation sessions. (3) Results: Clinical simulation significantly increased group cohesion in most items and in all dimensions with moderate to large effect sizes (r > 0.5). The Group Integration-Task (GI-T) dimension showed the greatest improvement after clinical simulation. Although causal relationships cannot be established, the results suggest an association between exposure to clinical simulation and increased group cohesion. (4) Conclusions: Clinical simulation was associated with significant improvements in both task-oriented and social dimensions of group cohesion among nursing students. These findings suggest that clinical simulation may enhance collaboration, communication, and commitment to shared goals within student teams. Future studies including control groups are needed to confirm these associations and further explore the impact of clinical simulation on team performance in both student and healthcare professional contexts. Full article

Betaine, a simple natural zwitterion, is currently attracting widespread attention. Although historically labeled as an osmoregulator in agriculture and a methyl donor in animal nutrition, the molecule is now being repositioned at the forefront of green chemistry and materials science due to its unique physicochemical structure. This review critically explores the expanding horizon of betaine applications, bridging the gap between its established biological functions and its emerging roles in recently reported technologies, such as deep eutectic solvents (DESs), cocrystal engineering, and sustainable polymer synthesis. Beyond summarizing its versatile functionality across biomedicine, food science, and industrial formulations, we provide a comprehensive bibliometric analysis to map the evolution of research trends, identifying a clear focus toward industrial ecology and advanced materials. By synthesizing current advancements and discussing potential future directions, this work highlights betaine not merely as a supplement, but as a versatile molecular component with potential applications in sustainable materials and chemical engineering processes. Full article

Background/Objectives: Franz Tappeiner (1816–1902) is often celebrated as a pioneer of alpine medicine and the founder of Tappeiner Promenade in Meran (South Tyrol, Italy). However, his legacy extends far beyond the scenic infrastructure, encompassing a comprehensive vision of physical activity as a public health intervention. His multidisciplinary practice anticipated the principles of contemporary rehabilitation, preventive medicine, and climate-sensitive public health. Methods: This historical public health analysis, combining biographical, contextual, and material–spatial approaches, reinterprets Tappeiner’s writings, institutional engagements, and civic projects through the lens of modern public health frameworks. Drawing on primary materials (e.g., published articles, autobiographical fragments, and commemorative texts) and recent evidence from rehabilitation and environmental health research, these contributions were contextualized. Results: Tappeiner’s early focus on infectious disease prevention (e.g., cholera and tuberculosis) transitioned into a strategic emphasis on recovery and behavioral therapy through environmental design. The walking therapy model of Max Joseph Oertel, locally realized in the Tappeiner Promenade, prefigured modern concepts such as structured green rehabilitation, walkability, and urban-health citizenship. His systematic integration of graded walking into civic infrastructure represents one of the earliest documented examples of embedding physical activity promotion at the population level. He contributed substantial personal funds to the path’s construction, embedding therapeutic gradients, curating vegetation, and promoting inclusive design to support convalescence. Contemporary research supports the intuition that green, low- to moderate-intensity walking improves cardiometabolic health, psychological well-being, and functional capacity. Moreover, his integrative ethos, merging clinical medicine, civic ethics, and spatial intervention, parallels contemporary eco-social models of public health. Conclusions: Franz Tappeiner’s career exemplifies a still-relevant model of physician leadership that is empirically grounded, socially accountable, and ecologically attuned, with physical activity promotion embedded as a central element of his public health vision. His work invites reflection on how medical professionals can shape not only individual care but also urban environments and collective health futures. Full article