Search Results (10,645)

We present a comprehensive mathematical analysis of a within-host dual-target HIV dynamics model, which explicitly incorporates the virus’s interactions with its two primary cellular targets: CD4${}^{+}$ T cells and macrophages. The model is formulated as a system of five nonlinear delay differential equations, integrating three distinct discrete time delays to account for critical intracellular processes such as the development of productively infected cells and the maturation of new virions. We first establish the model’s biological well-posedness by proving the non-negativity and boundedness of solutions, ensuring all trajectories remain within a feasible region. The basic reproduction number, ${\mathcal{R}}_0^d$, is derived using the next-generation matrix method and serves as a sharp threshold for disease dynamics. Analytical results demonstrate that the infection-free equilibrium is globally asymptotically stable (GAS) when ${\mathcal{R}}_0^d\le 1$, guaranteeing viral eradication from any initial state. Conversely, when ${\mathcal{R}}_0^d>1$, a unique endemic equilibrium emerges and is proven to be GAS, representing a state of chronic infection. These global stability properties are rigorously established for both the non-delayed and delayed systems using carefully constructed Lyapunov functions and functionals, coupled with LaSalle’s invariance principle. A sensitivity analysis identifies viral production rates ($p_1,p_2$) and infection rates (${\beta }_1,{\beta }_2$) as the most influential parameters on ${\mathcal{R}}_0^d$, while the viral clearance rate (m) and maturation delay (${\tau }_3$) have a suppressive effect. The model is extended to evaluate antiretroviral therapy (ART), revealing a critical treatment efficacy threshold ${ϵ}^{cr}$ required to suppress the virus. Numerical simulations validate all theoretical findings and further investigate the dynamics under varying treatment efficacies and maturation delays, highlighting how these factors can shift the system from persistence to clearance. This study provides a rigorous mathematical framework for understanding HIV dynamics, with actionable insights for designing targeted treatment protocols aimed at achieving viral suppression. Full article

Bamboo rhizomes, the belowground stems of bamboo, play a crucial role in ecosystem functioning and material cycling; however, they have long been regarded as forest residues, receiving limited research attention. This review systematically summarizes current knowledge on the anatomical structure, chemical composition, physical and mechanical properties, and applications of bamboo rhizomes, thereby highlighting their potential for high–value utilization. Based on existing studies, a three-tier framework of rhizome characteristics is proposed: (1) age–driven changes, including lignin deposition, cellulose distribution, and cell wall development; (2) interspecific differences in chemical and anatomical traits, which modulate mechanical performance and durability; and (3) functional differentiation between rhizomes and culms, reflecting adaptation to belowground environments. Within this framework, the structural, chemical, and physicomechanical properties of bamboo rhizomes exhibit tight coupling, thus providing theoretical guidance for species selection, harvesting strategies, and processing. Moreover, bamboo rhizomes have been applied in handicrafts, agricultural organic fertilizers, and composite materials, and they show emerging potential in high-friction functional materials and bio–based composites. Nevertheless, systematic investigations remain limited, particularly regarding structure–property relationships, interspecific performance variability, and optimized processing technologies. Therefore, future research should focus on multidimensional characterization, elucidation of structure–property coupling mechanisms, and development of high–value processing techniques, in order to promote the transformation of bamboo rhizomes into value–added products, thereby supporting green bamboo industry development and the “Bamboo Instead of Plastic” initiative. Full article

Glioblastoma multiforme (GBM) remains a devastating brain tumor with poor prognosis, traditionally viewed as non-metastatic. The recent detection of circulating tumor cells (CTCs) in glioblastoma challenges this long-held view and opens new opportunities for liquid biopsy in neuro-oncology. This review summarizes current understanding of glioblastoma CTCs, emphasizing their unique properties, detection technologies, and differences compared to CTCs in extracranial cancers. Key challenges include their rarity, the absence of epithelial markers, and the presence of the blood–brain barrier. Despite the need for specialized enrichment approaches, CTC analysis in glioblastoma can offer helpful information regarding tumor heterogeneity, treatment response, and minimal residual disease. We discuss emerging clinical studies leveraging CTCs for early relapse detection and therapy monitoring. Integrating CTC phenotyping with molecular and functional characterization may enhance future personalized treatment strategies in glioblastoma. Refined CTC methodologies combined with other liquid biopsy modalities may transform glioblastoma management, improving patient outcomes through less invasive, dynamic tumor surveillance. Full article

Under global climate change, the response of mountain forest soil carbon pools to elevation is central to carbon cycle research, and Pinus yunnanensis stands, which span a wide elevation range, serve as a typical subject for studying how soil properties in mountain ecosystems respond to elevation gradients. To reveal the variation patterns and underlying regulatory mechanisms of soil nutrients and organic carbon components in Pinus yunnanensis stands across different altitudinal gradients, this study took Pinus yunnanensis stands at three altitude gradients (1604 m, 2377 m, 3206 m) within Yunnan Province as research objects, collected stratified soil samples, and determined soil chemical properties, organic carbon components, enzyme activity, and microbial biomass. The results showed that changes in elevation significantly influence soil nutrient content: soil pH gradually decreases with increasing elevation; soil organic carbon, total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus, and readily available potassium first increase then decrease with elevation, reaching their highest levels at Jin’an Town (JA); total phosphorus and total potassium gradually increase with elevation, peaking at Xiaozhongdian Town (XZD); particulate organic carbon, mineral-bound organic carbon, and microbial biomass carbon follow similar patterns to organic carbon, all showing enrichment in the surface layer; JA exhibits the highest carbon cycle enzyme activity and bacterial biomass, while XZD shows dominant fungal biomass. Partial Least Squares Path Modeling (PLS-PM) analysis indicates that elevation strongly positively drives microbial biomass, indirectly regulating enzyme activity and chemical properties, ultimately jointly influencing organic carbon components. In conclusion, soil properties varied markedly, and under stable precipitation, the thermal gradient emerged as the primary driver; the mid-elevation site (2377 m) showed optimal soil functioning, with peak nutrient and carbon stocks linked to heightened microbial and enzymatic activity, and path modeling confirmed that temperature, via microbial mediation, is the key regulator of soil organic carbon dynamics in these pine forests. Full article

Collective intelligence within a quantum-informed cybernetic paradigm presents a transformative perspective to examine adaptability and resilience in Internet of Things (IoT) systems. This paper introduces Cogitor5, a fifth-order cybernetic system that builds upon the foundational principles of the fourth-order COgITOR framework, a liquid computational system designed for complex adaptive processes. The term COgITOR is etymologically linked to the Latin passive verb cogĭtur, translating to “He is gathered,” in contrast to the more commonly recognized active form cogito, meaning “I gather” or “I think,” as famously articulated by Descartes. In contrast to conventional binary systems, Cogitor5 functions as a simulation-based complex adaptive system, inspired by a population of nano agents represented by nanoparticles suspended in a colloidal medium. These agents exhibit autonomous interactions within the solvent, featuring quantum-enabled properties that facilitate advanced self-organization and coevolutionary dynamics. This intricate model captures the complexities of agent interaction, offering a refined representation of their evolving collective intelligence. The study redefines collective intelligence as emergent process intelligence, relevant to the adaptive capacities of both biological and cybernetic systems. By utilizing metacybernetic principles in conjunction with theories of complex adaptive systems, this paper investigates how IoT networks can evolve to enhance agency trajectory formation and increase adaptability. Cogitor5 serves as an innovative computational framework for addressing the inherent complexities of IoT, providing clarity in examining self-organization, self-regulation, self-maintenance, and sustainability, thus elevating system viability. The methodology encompasses the modeling of collective and process intelligence within the scope of Mindset Agency Theory (MAT), an advanced metacybernetic model that allows for evaluable characteristics. Furthermore, this approach integrates theoretical modelling and a practical case study implemented in Matlab^® to illustrate agency functionality within a dynamic system simulating failures in the nodes of an electric grid. Full article

The rising incidence of cancer has demanded the development of new anti-cancer chemical sources. The presence of phenolics in hazelnut cell cultures has led to the development of new and potential pharmacotherapeutic uses. Hazelnut extract has emerged as a promising candidate due to its high phytochemical content. HCT-116 colorectal cancer IC₅₀ cell viability of Palaz and Tombul hazelnut extracts was determined as 400 μg/mL and 200 μg/mL, respectively. Flow cytometry annexin V-fluorescein isothiocyante (FITC) apoptosis detection indicated apoptosis of Tombul hazelnut extract and Palaz hazelnut extract as 23.53% and 17.47%, respectively. The apoptosis result of flow cytometry was also supported at the protein level. Hazelnut extracts resulted in an increased loss of mitochondrial membrane potential as well. The loss of mitochondrial membrane potential has significantly increased from an average of 0.61% to 16.17% in Tombul hazelnut extract and to 20.38% in Palaz hazelnut extract. This is further supported by screening MICU1, MICU2, PPAR-γ, PPARGC1A, UCP1, UCP2, and UCP3 gene expressions. Targeting apoptosis pathways, particularly mitochondrial membrane potential, is an effective strategy for cancer prevention and treatment. Hazelnut extract contains phenolic compounds, which activate these pathways, resulting in enhanced apoptosis in colorectal cancer cells. The phenolic contents of Palaz and Tombul hazelnut extracts were determined as 271.72 ± 5.3 mg gallic acid equivalent (GAE)/100 g sample dry weight (DW) and 85.23 ± 2.2 mg GAE/100 g sample DW, respectively. Further, hazelnut extract may reduce oxidative stress, contributing to its anti-cancer properties. The extracts could be utilized as functional ingredients in foods and nutraceuticals to assist with cancer prevention and treatment. Full article

Moringa oleifera has been recognized for its adaptability, nutritional richness, and multipurpose potential, particularly in resource-limited regions. While most research has focused on its leaves, moringa seeds remain underutilized despite their broad applicability in the environmental, agricultural, and food sectors. This review systematically and critically examines recent scientific literature on the use of M. oleifera seeds across these fields, emphasizing their functional value, applications, and challenges for sustainable use. The review follows the SALSA methodology (Search, Appraisal, Synthesis, and Analysis), a structured and iterative framework designed to identify, evaluate, and integrate scientific evidence from diverse sources. The analysis encompasses three main areas: (i) water treatment, where moringa seed extracts have achieved turbidity removal efficiencies above 90% and effective adsorption of dyes and potentially toxic elements; (ii) agriculture, where seed-derived fertilizers improve soil fertility, nutrient availability, and crop yield compared to conventional inputs; and (iii) the food industry, where moringa seed derivatives enhance the nutritional, functional, and antioxidant properties of bakery, beverage, and oil-based products. Overall, M. oleifera seeds emerge as a versatile and sustainable resource with proven potential as a natural coagulant, biofertilizer, and nutraceutical ingredient. By integrating findings from both English and Spanish language studies, this work highlights their contribution to sustainable water management, agricultural productivity, and food innovation, while emphasizing the need for further safety evaluation and process optimization to support large-scale application. Full article

Some cast metallic alloys for ultra-high-temperature structural applications can have better mechanical properties compared with Ni-based superalloys. Research on the directional solidification (DS) of such alloys is limited. The production of DS components of these alloys with “tailor-made” microstructures in different parts of the component has not been considered. This paper attempts to address these issues. A bar of the RCCA/RM(Nb)IC with nominal composition 3.5Al–4Crc6Ge–1Hf–5Mo–36Nb–22Si–1.5Sn–20Ti–1W (at.%) was directionally grown using OFZ processing, where the growth rate R increased from 1.2 to 6 and then to 15 cm/h. The paper studies how the macrosegregation of the elements affected the microstructure in different parts of the bar. It was shown that the synergy of macrosegregation and growth rate produced microstructures from the edge to the centre of the OFZ bar and along the length of the OFZ bar that differed in type and chemical composition as R increased. Contamination with oxygen was confined to the “root” of the part of the bar that was grown with R = 1.2 cm/h. The concentrations of elements in the bar were related (a) to each of the parameters VEC, Δχ, and δ for different sections, (i) across the thickness and (ii) along the length of the bar, or to each other for different sections of the bar, and demonstrated the synergy and entanglement of processing, parameters, and elements. In the centre of the bar, the phases were the Nb_ss and Nb₅Si₃ for all R values. In the bar, the silicide formed with Nb/(Ti + Hf) less or greater than one. There was synergy of solutes in the solid solution and the silicide for all R values, and synergy and entanglement of the two phases. Owing to the synergy and entanglement of processing, parameters, elements, and phases, properties would “emerge” in each part of the bar. The creep and oxidation properties of the bar were calculated as guided by the alloy design methodology NICE. It was suggested that, in principle, a component based on a metallic UHTM with “functionally graded” composition, microstructure and properties could be directionally grown. Full article

In the background of sustainable development in the construction industry, recycled cement mortar (RCM) has emerged as a research hotspot due to its eco-friendly features, where mechanical properties serve as critical indicators for evaluating its engineering applicability. This study proposes an artificial neural network (ANN) model optimized by intelligent algorithms, including the GWO (grey wolf optimizer), PSO (particle swarm optimization), and a GA (genetic algorithm), to predict the compressive strength of recycled mortar. By integrating experimental and prediction data, we establish a comprehensive database with eight input variables, including the water–cement ratio (W/C), cement–sand ratio (C/S), fly ash content (FA), aggregate replacement rate (ARR), and curing age. The predictive performance of neural network models with different database sizes (database 1: experimental data of RCM; database 2: experimental data of RCM and ordinary mortar; database 3: model prediction data of RCM, experimental data of RCM, and ordinary mortar) is analyzed. The results show that the intelligent optimization algorithms significantly enhance the predictive performance of the ANN model. Among them, the PSO-ANN model demonstrates optimal performance, with R² = 0.92, MSE = 0.007, and MAE = 0.0632, followed by the GA-ANN model and the GWO-ANN model. SHAP analysis reveals that the W/C, C/S, and curing age are the key variables influencing the compression strength. Furthermore, the size of the dataset does not significantly influence the computation time for the above models but is primarily governed by the complexity of the optimization algorithms. This study provides an efficient data-driven method for the mix design of RCM and a theoretical support for its engineering applications. Full article

Active restoration structures such as microtopographic water-harvesting designs are widely implemented in dryland ecosystems to improve soil moisture, reduce erosion, and promote vegetation recovery. We assessed the combined effects of planted species identity, planting diversity (mono-, bi- and multi-species mixtures), and micro-catchment (half-moon) structures on seedling performance and spontaneous natural regeneration in a hyper-arid restoration pilot site in Sharaan National Park, northwest Saudi Arabia. Thirteen native plant species, of which four—Ochradenus baccatus, Haloxylon persicum, Haloxylon salicornicum, and Acacia gerrardii—formed the dominant planted treatments, were established in 18 half-moons and monitored for survival, growth, and natural recruitment. Seedling survival after 20 months differed significantly among planting treatments, increasing from 58% in mono-plantings to 69% in bi-plantings and 82% in multi-plantings (binomial GLMM, p < 0.001), indicating a positive effect of planting diversity on establishment. Growth traits (height, collar diameter, and crown dimensions) were synthesized into an Overall Growth Index (OGI) and an entropy-weighted OGI (EW-OGI). Mixed-effects models revealed strong species effects on both indices (F_12,369 ≈ 7.2, p < 0.001), with O. baccatus and H. persicum outperforming other taxa and cluster analysis separating “fast expanders”, “moderate growers”, and “decliners”. Trait-based modeling showed that lateral crown expansion was the main driver of overall performance, whereas stem thickening and fruit production contributed little. Between 2022 and 2024, half-moon soils exhibited reduced electrical conductivity and exchangeable Na, higher organic carbon, and doubled available P, consistent with emerging positive soil–plant feedbacks. Spontaneous recruits were dominated by perennials (≈67% of richness), with perennial dominance increasing from mono- to multi-plantings, although Shannon diversity differences among treatments were small and non-significant. The correlation between OGI and spontaneous richness was positive but weak (r = 0.29, p = 0.25), yet plots dominated by O. baccatus hosted nearly two additional spontaneous species relative to other plantings, highlighting its strong facilitative role. Overall, our results show that half-moon micro-catchments, especially when combined with functionally diverse native plantings, can simultaneously improve soil properties and promote biotic facilitation, fostering a transition from active intervention to passive, self-sustaining restoration in hyper-arid environments. Full article

Astaxanthin, a high-value keto-carotenoid with potent antioxidant and health-promoting properties, has gained global attention as a sustainable nutraceutical and biotechnological product. The green microalgae Haematococcus pluvialis and Chromochloris zofingiensis represent two promising natural producers, yet they differ markedly in physiology, productivity, and industrial scalability. This review provides a focused comparative analysis of these two species, emphasizing their quantitative performance differences. H. pluvialis can accumulate astaxanthin up to ~3–5% of dry biomass but typically reaches biomass densities of only 5–10 g L⁻¹, whereas C. zofingiensis achieves ultrahigh biomass concentrations of 100–220 g L⁻¹ under heterotrophic fed-batch fermentation, although its astaxanthin content is much lower (~0.1–0.5% DW). While H. pluvialis remains the benchmark for natural astaxanthin due to its exceptionally high cellular content, its thick cell wall, slow growth, and strict phototrophic requirements impose major cost and operational barriers. In contrast, C. zofingiensis exhibits rapid and flexible growth under heterotrophic, mixotrophic, or phototrophic conditions and can achieve ultrahigh biomass in fermentation, though its ketocarotenoid flux and astaxanthin accumulation remain comparatively limited. Meanwhile, a rapidly growing patent landscape demonstrates global technological competition, with major portfolios emerging in China, the United States, and Europe, spanning chemical synthesis, microbial fermentation, algal metabolic engineering, and high-density cultivation methods. These patents reveal clear innovation trends—ranging from solvent-free green synthesis routes to engineered microalgae and yeast chassis for enhanced astaxanthin production—which increasingly shape industrial development strategies. By synthesizing recent advances in metabolic engineering, two-stage cultivation, and green extraction technologies, this review identifies key knowledge gaps and outlines a practical roadmap for developing next-generation astaxanthin biorefineries, with an emphasis on scalable production and future integration into broader biorefinery frameworks. The findings aim to guide future research and provide actionable insights for scaling sustainable, cost-effective production of natural astaxanthin. Full article

Altitude-driven environmental changes influence the persistence of soil organic carbon (SOC) via microbial metabolic pathways. However, the degree to which the network robustness of microbial communities directly predicts the persistence of organic carbon in alpine mountain forests remains unclear. This study focused on the Qilian Sabina przewalskii forest, situated along an altitude gradient of 2900–3400 m in the Qilian Mountains, systematically exploring the organization of soil microbial communities, the co-occurrence networks’ robustness, and their predictive capacity for organic carbon storage. The results indicate that altitude, as a critical driving factor, not only alters the physicochemical properties, microbial composition, and diversity of the soil but also significantly impacts its complexity and network robustness. The complexity and robustness of the microbial network are highest in the mid-altitude region (3100–3200 m), which is conducive to the development of robust microbial networks. Both bacterial α diversity and network robustness exhibit positive correlations with SOC, whereas fungal diversity shows a negative correlation with SOC. Furthermore, statistical modeling revealed that indices of microbial co-occurrence network robustness were stronger predictors of SOC storage than classical alpha-diversity indices. The structural equation model reveals that microbial biomass nitrogen (MBN) serves as a key mediating factor linking microbial diversity and SOC. Soil characteristics emerge as the primary direct driving factor, whereas the robustness of microbial networks exerts a significant yet minor direct and mediating influence. This study underscores that the robustness of microbial networks, rather than their diversity, is a critical predictor of soil organic carbon in high-altitude mountain forests. It offers a novel theoretical framework for understanding the mechanisms of the carbon cycle in mountain forest ecosystems in the context of climate warming. Full article

Metal halide perovskite (MHP)-based heterojunctions have become a forefront area in the research of optoelectronic functional materials due to their unique layered crystal structure, tunable band gaps, and exceptional optoelectronic properties. Recent studies have demonstrated that interface charge transfer is a crucial factor in determining the optoelectronic performance of the heterojunction devices. By constructing heterojunctions between MHPs and two-dimensional (2D) materials such as graphene, MoS₂, and WS₂, efficient electron–hole separation and transport can be achieved, significantly extending carrier lifetimes and suppressing non-radiative recombination. This results in enhanced response speed and energy conversion efficiency in photodetectors, photovoltaic devices, and light-emitting devices (LEDs). In these heterojunctions, the thickness of the MHP layer, interface defect density, and band alignment significantly influence carrier dynamics. Furthermore, techniques such as interface engineering, molecular passivation, and band engineering can effectively optimize charge separation efficiency and improve device stability. The integration of multilayer heterojunctions and flexible designs also presents new opportunities for expanding the functionality of high-performance optoelectronic devices. In this review, we systematically summarize the charge transfer mechanisms in MHP-based heterojunctions and highlight recent advances in their optoelectronic applications. Particular emphasis is placed on the influence of interfacial coupling on carrier generation, transport, and recombination dynamics. Furthermore, the ultrafast dynamic behaviors and band-engineering strategies in representative heterojunctions are elaborated, together with key factors and approaches for enhancing charge transfer efficiency. Finally, the potential of MHP heterojunctions for high-performance optoelectronic devices and emerging photonic systems is discussed. This review aims to provide a comprehensive theoretical and experimental reference for future research and to offer new insights into the rational design and application of flexible optoelectronics, photovoltaics, light-emitting devices, and quantum photonic technologies. Full article

Most polymeric plastics used as food packaging are obtained from petroleum or made with non-biodegradable synthetic molecules, which slowly degrade and leach into the environment, resulting in the accumulation of microplastics along the trophic chains. To mitigate these impacts, biodegradable packaging derived from agro-industrial biomass residues has emerged as a promising alternative. In this study, bio-based methylcellulose films reinforced with cellulose nanocrystals (CNCs) extracted from low-quality coffee beans were developed and fully characterized. The extracted CNCs presented a needle-like morphology, with an average height of 7.27 nm and a length of 221.34 nm, with 65.75% crystallinity, were stable at pH 7–8, and presented thermogravimetric mass loss of 8.0%. Methylcellulose films containing 0.6% w/w of CNC were produced by casting and characterized in terms of thermal, mechanical, and optical properties. Notably, the incorporation of CNCs resulted in significantly more flexible and less rigid films, as evidenced by the higher elongation at break (57.90%) and lower Young’s modulus (0.0015 GPa) compared to neat methylcellulose film. The tensile strength was not affected (p > 0.05). Additionally, the MCNC 0.6% films effectively blocked UV light in the 200–300 nm range without compromising transparency. Altogether, these findings underscore the MCNC 0.6% film as a flexible, biodegradable packaging material suitable for food industry application. Full article

Neuromorphic computing, an emerging computational paradigm, aims to overcome the bottlenecks of the traditional von Neumann architecture. Two-dimensional materials serve as ideal platforms for constructing artificial synaptic devices, yet existing devices based on these materials face challenges such as insufficient stability. Indium selenide (InSe), a two-dimensional semiconductor with unique properties, demonstrates significant potential in the field of neuromorphic devices, though its application research remains in the initial stage. This study presents an artificial synaptic device based on the InSe/Charge Trapping Layer (CTL)/h-BN heterojunction. By applying oxygen plasma treatment to h-BN to form a controllable charge-trapping layer, efficient regulation of carriers in the InSe channel is achieved. The device successfully emulates fundamental synaptic behaviors including paired-pulse facilitation and long-term potentiation/inhibition, exhibiting excellent reproducibility and stability. Through investigating the influence of electrical pulse parameters on synaptic weights, a structure–activity relationship between device performance and structural parameters is established. Experimental results show that the device features outstanding linearity and symmetry, realizing the simulation of key synaptic behaviors such as dynamic conversion between short-term and long-term plasticity. It possesses a high dynamic range ratio of 7.12 and robust multi-level conductance tuning capability, with stability verified through 64 pulse cycle tests. This research provides experimental evidence for understanding interfacial charge storage mechanisms, paves the way for developing high-performance neuromorphic computing devices, and holds broad application prospects in brain-inspired computing and artificial intelligence hardware. Full article