Search Results (38,892)

Background: Hand, foot, and mouth disease (HFMD) remains a major public-health concern in China. While the monovalent EV-A71 vaccine has effectively reduced EV-A71–associated cases, it offers no protection against CV-A16. The introduction of a bivalent EV-A71/CV-A16 vaccine may offer broader protection, but its economic viability under different immunization strategies remains uncertain. Methods: We developed a dynamic transmission model integrated with cost-effectiveness analysis to assess the epidemiological and economic impact of a hypothetical bivalent EV-A71/CV-A16 vaccine in China. Based on the immunization program policy, seven vaccination strategies, vaccine effectiveness (VE) levels ranging from 50–95% against EV-A71/CV-A16, and coverage levels from 0–95% were evaluated. The threshold vaccine price (TVP) was derived based on incremental cost-effectiveness ratio (ICER) calculations. Cost-effectiveness was assessed using willingness-to-pay (WTP) thresholds defined as 1–3 times the gross domestic product (GDP) per capita. Results: The mean cost of two doses of the monovalent EV-A71 vaccine was USD133.0 (95% CI: 126.9–139.1). Strategy 2, which targeted individuals unvaccinated with the monovalent EV-A71 vaccine, demonstrated the most favorable cost-effectiveness. At 45% coverage and 85% vaccine effectiveness, the estimated threshold price per dose was USD 107.7 (95% CI: 103.4–112.0), with threshold vaccine prices increasing as coverage declined. When vaccination coverage exceeded 80%, the threshold vaccine price decreased substantially, falling below USD 45.9 (95% CI: 43.5–48.3) per dose. Conclusions: Large-scale inclusion in the national immunization program may not be economically justified at current cost levels. Targeted voluntary vaccination of unvaccinated, susceptible populations represents a more cost-effective and practical strategy during the early stage of vaccine introduction. Full article

As a potential strategic resource of critical metals, deep-sea cobalt-rich crusts represent one of the most promising metal reservoirs within oceanic seamount systems, and their metallogenic mechanism constitutes a frontier topic in deep-sea geoscience research. This review focuses on the cobalt-rich crusts from the Magellan Seamount region in the northwestern Pacific and synthesizes existing geological, mineralogical, and geochemical studies to systematically elucidate their mineralization processes and metal enrichment mechanisms from a microstructural perspective, with particular emphasis on cobalt enrichment and its controlling factors. Based on published observations and experimental evidence, the formation of cobalt-rich crusts is divided into three stages: (1) Mn/Fe colloid formation—At the chemical interface between oxygen-rich bottom water and the oxygen minimum zone (OMZ), Mn²⁺ and Fe²⁺ are oxidized to form hydrated oxide colloids such as δ-MnO₂ and Fe(OH)₃. (2) Key metal adsorption—Colloidal particles adsorb metal ions such as Co²⁺, Ni²⁺, and Cu²⁺ through surface complexation and oxidation–substitution reactions, among which Co²⁺ is further oxidized to Co³⁺ and stably incorporated into MnO₆ octahedral vacancies. (3) Colloid deposition and mineralization—Mn–Fe colloids aggregate, dehydrate, and cement on the exposed seamount bedrock surface to form layered cobalt-rich crusts. This process is dominated by the Fe/Mn redox cycle, representing a continuous evolution from colloidal reactions to solid-phase mineral formation. Biological processes play a crucial catalytic role in the microstructural evolution of the crusts. Mn-oxidizing bacteria and extracellular polymeric substances (EPS) accelerate Mn oxidation, regulate mineral-oriented growth, and enhance particle cementation, thereby significantly improving the oxidation and adsorption efficiency of metal ions. Tectonic and paleoceanographic evolution, seamount topography, and the circulation of Antarctic Bottom Water jointly control the metallogenic environment and metal sources, while crystal defects, redox gradients, and biological activity collectively drive metal enrichment. This review establishes a conceptual framework of a multi-level metallogenic model linking macroscopic oceanic circulation and geological evolution with microscopic chemical and biological processes, providing a theoretical basis for the exploration, prediction, and sustainable development of potential cobalt-rich crust deposits. Full article

To improve the oral bioavailability of oleanolic acid (OA), this study developed a menthol–fatty acid-based hydrophobic deep eutectic solvent (HDES) system. Through a comprehensive evaluation using in vitro simulated digestion and Caco-2 cell transport models, the short-chain HDES was found to increase the apparent in vitro bioavailability index of OA by 9.3-fold compared to conventional ethanol systems, with efficacy showing clear fatty acid chain-length dependence. The mechanism was systematically investigated through spectral characterization and cellular studies, revealing a two-stage enhancement process: during the digestion phase, HDES significantly improved OA bioaccessibility to 14.30% compared to 4.90% with ethanol; during the absorption phase, it markedly increased cellular uptake to 25.79% versus 4.71% with ethanol. Molecular analysis indicated that the optimal hydrophobicity and diffusion properties of HDES contributed to this enhancement. This study reveals a fatty acid chain-length-dependent mechanism in HDES-facilitated OA delivery, providing a tunable strategy for enhancing the absorption of hydrophobic bioactive compounds. Full article

With the increasing penetration of renewable energy and electric vehicles (EVs), virtual power plants (VPPs) have become a key mechanism for coordinating distributed energy resources and flexible loads to participate in electricity markets. However, the uncertainties of renewable generation and EV user behavior pose significant challenges to bidding strategies and real-time execution. This study proposes a two-stage optimal bidding strategy for VPPs by integrating vehicle-to-grid (V2G) technology. An aggregated EV schedulable-capacity model is established to characterize the time-varying charging and discharging capability boundaries of the EV fleet. A unified day-ahead and real-time optimization framework is further developed to ensure coordinated bidding and scheduling. Case studies on a modified IEEE-33 bus system demonstrate that the proposed strategy significantly enhances renewable energy utilization and market revenues, validating the effectiveness of coordinated V2G operation and multi-type flexible load control. Full article

Background/Objectives: Malaria remains a major global health burden, increasingly complicated by resistance to artemisinin-based therapies. Because artemisinin activation depends on heme and porphyrin chemistry, we sought to exploit host red blood cell (RBC) heme metabolism as a therapeutic vulnerability. This study aims to develop and evaluate a host-directed “bait-and-kill” strategy that selectively sensitizes malaria-infected RBCs to artemisinin. Methods: We integrated quantitative proteomics, erythropoiesis transcriptomic analyses, flow cytometry, and in vitro malaria culture assays to characterize heme metabolism in mature RBCs and Plasmodium falciparum-infected RBCs (iRBCs). The heme precursor 5-aminolevulinic acid (ALA) was used to induce porphyrin accumulation, and dihydroartemisinin (DHA) was applied as the killing agent. Drug synergy, porphyrin accumulation, reactive oxygen species (ROS) induction, and parasite survival were assessed, including ring-stage survival assays using artemisinin-resistant clinical isolates. Results: Mature RBCs retain a truncated heme biosynthesis pathway capable of accumulating porphyrin intermediates, while uninfected RBCs are impermeable to ALA. In contrast, iRBCs exhibit increased membrane permeability, allowing selective ALA uptake and porphyrin accumulation. ALA alone did not induce cytotoxicity or ROS, whereas DHA induced ROS and parasite killing. The ALA + DHA combination resulted in synergistic parasite elimination, including complete clearance of artemisinin-resistant P. falciparum isolates from the Greater Mekong Subregion, with no recrudescence observed over three weeks of culture. Evidence supports a predominant role for host-derived heme metabolites in mediating this synergy. Conclusions: The bait-and-kill strategy selectively exploits host RBC heme metabolism to restore and enhance artemisinin efficacy while sparing uninfected cells. Using clinically safe compounds, this host-directed approach provides a promising, resistance-bypassing framework for malaria treatment and combination drug development. Full article

Hydraulic thrust cylinders in hard-rock tunnel boring machines (TBMs) often exhibit slow response and sluggish acceleration during start-up, which degrades early-stage tracking performance and limits overall operational accuracy. Most existing studies primarily enhance start-up behavior through advanced control algorithms, yet the achievable improvement is ultimately constrained by the system’s flow–pressure capacity. Meanwhile, reported system-level optimization approaches are either difficult to implement under practical TBM operating conditions or fail to consistently deliver high-accuracy tracking. To address these limitations, this paper proposes a “dual-pump–single-cylinder” control framework for the TBM thrust system, where a large-displacement pump serves as the main supply and a parallel small-displacement pump provides auxiliary flow compensation to mitigate the start-up flow deficit. Building on this architecture, an adaptive compensation algorithm is developed for the auxiliary pump, with its output updated online according to the system’s dynamic states, including displacement error and velocity-related error components. Comparative simulations and test-bench experiments show that, compared with a single-pump scheme, the proposed method notably accelerates cylinder start-up while effectively suppressing overshoot and oscillations, thereby improving both transient smoothness and tracking accuracy. This study provides a feasible and engineering-oriented solution for achieving “rapid and smooth start-up” of TBM hydraulic cylinders. Full article

Chloroplasts are key organelles in plants that carry out photosynthesis, convert light energy into chemical energy, and synthesize organic compounds. In this study, a stably heritable chlorophyll-deficient mutant was screened from the ethyl methanesulfonate-induced mutation library of Wuyunjing 21 (WYJ21). This mutant was designated as chlorophyll deficient 6 (cde6). The cde6 mutant exhibits a low chlorophyll content, photosynthetic defects, an impaired chloroplast structure, a significant reduction in the number of stacked thylakoid layers, and a yellow-green leaf phenotype in the early tillering stage. Through MutMap analysis, it was found that the cde6 mutant harbors a single-base mutation (T→A) in the LOC_Os07g38300 gene. This mutation results in an amino acid substitution from valine (Val) to aspartic acid (Asp) in the encoded protein, thereby affecting the protein’s structure and function. The mutation of CDE6 leads to decreased expression of genes related to chloroplast development and chlorophyll biosynthesis. Further studies revealed that the CDE6, a potential chloroplast ribosome recycle factor, leads to high temperature sensitivity in rice when mutated. As high-temperature stress is a primary constraint to global rice productivity, the identification of CDE6 provides a genetic target for improving thermotolerance. In conclusion, these findings demonstrate that CDE6 plays a crucial role in chloroplast biogenesis and provide new insights into its regulatory function in high-temperature tolerance. Full article

Some Sacoglossa sea slugs are capable of stealing and maintaining functional intracellular chloroplasts—kleptoplasts—from their macroalgal prey for periods of up to several months, a process known as kleptoplasty. Although the cultivation of these marine invertebrates under laboratory conditions is crucial for research in various fields (e.g., endosymbiosis, animal physiology, discovery of new marine natural products), rearing protocols are scarce. This study presents a standardized protocol for the laboratory rearing of large numbers of the sacoglossan tropical sea slug Elysia crispata. The detailed protocol successfully facilitated embryonic development, larval metamorphosis, and juvenile-to-adult transition, allowing the rearing of multiple generations. Two groups, characterized by acquiring different kleptoplasts, were obtained by feeding the sea slugs with two different prey macroalgae: Bryopsis sp. and Acetabularia acetabulum. Usually referred to as lettuce sea slug among marine aquarium hobbyists, E. crispata is a highly valued organism for its striking appearance and ability to control nuisance algal growth in reef aquariums. This protocol allows experimental reproducibility and access to specimens under different development stages, potentially boosting research on kleptoplasty while also contributing to reducing the impact of the marine aquarium trade on natural populations. Full article

Mass production in product design typically relies on standardized geometries and dimensions to accommodate a broad user population. However, when products are required to interface directly with the human body, such generalized design approaches often result in inadequate fit and reduced user comfort. This limitation highlights the necessity of fully personalized design methodologies based on individual anthropometric characteristics. This paper presents a novel application that automates the design of custom-fit sunglasses through the integration of Artificial Intelligence (AI) and Computational Design. The system is implemented using both textual (Python™ version 3.10.11) and visual (Grasshopper 3D™ version 1.0.0007) programming environments. The proposed workflow consists of the following four main stages: (a) acquisition of user facial images, (b) AI-based detection of facial landmarks, (c) three-dimensional reconstruction of facial features via an optimization process, and (d) generation of a personalized sunglass frame, exported as a three-dimensional model. The application demonstrates a robust performance across a diverse set of test images, consistently generating geometries that conformed closely to each user’s facial morphology. The accurate recognition of facial features enables the successful generation of customized sunglass frame designs. The system is further validated through the fabrication of a physical prototype using additive manufacturing, which confirms both the manufacturability and the fit of the final design. Overall, the results indicate that the combined use of AI-driven feature extraction and parametric Computational Design constitutes a powerful framework for the automated development of personalized wearable products. Full article

Cnidium monnieri (L.) Cusson is a species of Umbelliferae plants, and it is one of China’s traditional medicinal herbs, widely distributed in China owing to its strong adaptability in fields. In this article, the research progress on the taxonomy, distribution, cultivation techniques, active components, analysis methods, antibacterial and insecticidal properties, and ecological applications of C. monnieri was reviewed. The main active components in C. monnieri are coumarins (mainly osthole) and volatile compounds, exhibiting multiple pharmacological effects, e.g., anti-inflammatory, antibacterial, antioxidant, anti-tumor, and immune-regulating effects. Some modern analytical techniques (e.g., HPLC, GC-MS, and UPLC-QTOF-MS) have enabled more precise detection and quality control of these chemical components in C. monnieri. The specific active constituents in C. monnieri (e.g., coumarins and volatile components) exhibit significant inhibitory effects against various pathogenic fungi and insect pests. Simultaneously, the resources provided during its flowering stage (e.g., pollen and nectar) and the specific volatiles released can repel herbivorous insect pests while attracting natural enemies, such as ladybugs, lacewings, and hoverflies, thereby enhancing ecological control of insect pests in farmland through a “push–pull” strategy. Additionally, C. monnieri has the ability to accumulate heavy metals, e.g., Zn and Cu, indicating its potential value for ecological restoration in agroecosystems. Overall, C. monnieri has medicinal, ecological, and economic value. Future research should focus on regulating active-component synthesis, improving our understanding of ecological mechanisms, and developing standardized cultivation systems to enhance the applications of C. monnieri in modernized traditional Chinese medicine and green agriculture production. Full article

The article focuses on the design and construction of a 3D printer capable of printing both traditional cement and alkali-activated cement (AAC). Research into alkali-activated cements, commonly known as geopolymers, has progressed beyond the basic research stage, with the current challenge being the implementation of practical applications. These include solving the shaping issues of AAC paste and forming the final shape of a given product. One of the most advanced methods for achieving this is through 3D printing. The printer was created by modifying the open-source RatRig V-Core 3D printer ecosystem design to fit this purpose. Based on these modifications, an appropriate material composition was determined, and printing tests were conducted, allowing development conclusions to be drawn. A three-dimensional model of the structure was first created using Autodesk Inventor 2024 CAD software, and critical load-bearing components were validated through simulation. Special attention was given to cost-effective manufacturability, with custom parts produced using 3D printing, while additional components (e.g., bearings, fasteners) were selected from commercial catalogs. Finally, test prints using the specified material composition were performed to examine potential construction improvements for the 3D printer and assess material properties. The core concept of the cement printer lies in the material deposition method, specifically, in achieving effective extrusion of the paste. Five different versions of this were tested, which will be discussed in detail. Full article

Liver cancer, also known as hepatocellular carcinoma (HCC), remains a major global health concern, with high mortality driven by late-stage diagnosis, limited treatment efficacy, and frequent therapeutic resistance. Matrix metalloproteinases (MMPs), a large family of zinc-dependent endopeptidases, are central to the biological processes that drive liver tumor initiation and progression. By degrading and reorganizing extracellular matrix components, MMPs facilitate tumor expansion, tissue invasion, and metastatic dissemination. In addition, these enzymes regulate the availability of growth factors, cytokines, and chemokines, thereby influencing angiogenesis, inflammation, immune cell recruitment, and the development of an immunosuppressive tumor microenvironment. Aberrant expression or activity of multiple MMP family members is consistently associated with aggressive clinicopathologic features, including vascular invasion, increased metastatic potential, and reduced patient survival, highlighting their promise as prognostic markers and actionable therapeutic targets. Past attempts to modulate MMP activity were hindered by broad inhibition profiles and dose-limiting toxicities, underscoring the need for improved specificity and delivery strategies. Recent advances in molecular design, biologics engineering, and nanotechnology have revitalized interest in MMP targeting by enabling more selective, context-dependent modulation of proteolytic activity. Preclinical studies demonstrate that carefully tuned MMP inhibition can limit tumor invasion, enhance anti-angiogenic responses, and potentially improve the efficacy of existing systemic therapies, including immuno-oncology agents. This review synthesizes current knowledge on the multifaceted roles of MMPs in HCC pathobiology and evaluates emerging therapeutic strategies that may finally unlock the clinical potential of targeting these proteases. Full article

Litchi (Litchi chinensis Sonn.) is a pillar of the tropical agricultural economy in southern China, yet its production faces increasing instability due to climate change. Traditional agronomic models often fail to capture the complex, non-linear interactions between meteorological drivers and yield formation in perennial fruit trees. To address this challenge, the study constructed a yield prediction framework using an optimized Random Forest (RF) model integrated with interpretable machine learning (SHAP), based on a comprehensive dataset from 17 major production regions in Hainan Province (2000–2022). The model demonstrated robust predictive capability at the provincial scale (R² = 0.564, RMSE = 2.1 t/ha) and high consistency across regions (R² ranging from 0.51 to 0.94). Feature importance analysis revealed that heat accumulation (specifically growing degree days above 20 °C) is the dominant driver, explaining over 85% of yield variability. Crucially, scenario simulations uncovered asymmetric climate risks across phenological stages: while moderate warming generally enhances yield by promoting vegetative growth and ripening, it acts as a stressor during the Fruit Development stage, where temperatures exceeding 26 °C trigger yield decline. Furthermore, the yield penalty for drought during Flowering (−8.09%) far outweighed the marginal benefits of surplus rainfall, identifying this window as critically sensitive to water deficits. These findings underscore the necessity of phenology-aligned adaptation strategies—specifically, securing irrigation during flowering and deploying cooling interventions during fruit development—providing a data-driven basis for climate-smart management in tropical agriculture. Full article

Understanding the complex interplay between environmental factors and fruit quality development requires sophisticated analytical approaches linking cellular architecture to environmental conditions. This study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth, thereby advancing beyond traditional methods that are primarily focused on postharvest analysis. By extracting detailed three-dimensional structural parameters, we reveal tissue porosity and heterogeneity influenced by crop load, maturity timing and canopy position, offering insights into internal quality attributes. Employing correlation analysis, Principal Component Analysis, Canonical Correlation Analysis, and Structural Equation Modeling, we identify temperature as the primary environmental driver, particularly during early developmental stages (45 Days After Full Bloom, DAFB), and uncover nonlinear, hierarchical effects of preharvest environmental factors such as vapor pressure deficit, relative humidity, and light on quality traits. Machine learning models (Multiple Linear Regression, Random Forest, XGBoost) achieve high predictive accuracy (R² > 0.99 for Multiple Linear Regression), with temperature as the key predictor. These baseline results represent findings from a single growing season and require validation across multiple seasons and cultivars before operational application. Temporal analysis highlights the importance of early-stage environmental conditions. Integrating structural and environmental data through innovative visualization tools, such as anatomy-based radar charts, facilitates comprehensive interpretation of complex interactions. This multidisciplinary framework enhances predictive precision and provides a baseline methodology to support precision orchard management under typical agricultural variability. Full article

Addressing the difficulty of intuitively identifying and effectively correcting induced heave error in multibeam measurements, this paper proposes a two-stage methodology comprising error identification and correction. This scheme includes an error discrimination method based on regression diagnostics and an error correction method based on Partial Least Squares Regression (PLSR). By establishing a mathematical model between bathymetric discrepancies and attitude parameters, statistical diagnosis and effective identification of the error are achieved. To further mitigate the impact of induced heave error on bathymetric data, an elimination model based on PLSR is developed, enabling high-precision prediction and compensation of the induced heave error. Validation using field survey data demonstrates that this method can effectively estimate the installation offset parameters of the attitude sensor. After correction, the root mean square of bathymetric discrepancies between adjacent survey lines is reduced by approximately 78.8%, periodic stripe-shaped distortions along the track direction are essentially eliminated, and the quality of terrain mosaicking is significantly improved. This provides an effective solution for controlling induced heave error under complex topographic conditions. Full article