Search Results (5,148)

Sugar amino acids (SAAs) represent a privileged class of molecular chimeras that uniquely merge the structural rigidity of carbohydrates with the functional display of amino acids. These hybrid molecules have garnered significant attention as programmable conformational constraints, offering a powerful strategy to overcome the inherent limitations of peptide-based therapeutics, such as proteolytic instability and conformational ambiguity. The strategic incorporation of SAAs into peptide backbones, particularly within cyclic frameworks, allows for the rational design of peptidomimetics with pre-organized secondary structures, enhanced metabolic stability, and improved physicochemical properties. This review provides a comprehensive analysis of the synthetic methodologies developed to access the diverse structural landscape of SAAs, with a focus on modern, stereoselective strategies that yield versatile building blocks for peptide chemistry. A critical examination of the structural impact of SAA incorporation reveals their profound ability to induce and stabilize specific secondary structures, such as β- and γ-turns. Furthermore, a comparative analysis positions SAAs in the context of other widely used peptidomimetic scaffolds, highlighting their unique advantages in combining conformational control with tunable hydrophilicity. We surveyed the application of SAA-containing cyclic peptides as therapeutic agents, with a detailed case study on gramicidin S analogs that underscores the power of SAAs in elucidating complex structure–activity relationships. Finally, this review presents a forward-looking perspective on the challenges and future directions of the field, emphasizing the transformative potential of computational design, artificial intelligence, and advanced bioconjugation techniques to accelerate the development of next-generation SAA-based therapeutics. Full article

Prescribed burning significantly influences the microbial communities and physicochemical characteristics of forest soils. However, studies on the impacts of prescribed burning on the stability of soil microbial co-occurrence networks, as well as on the combined effects of post-fire soil depth gradients and their interactions on soil physicochemical properties and microbial communities, remain poorly understood. This study was conducted in a subtropical Pinus yunnanensis plantation that has undergone annual prescribed burns since 2007. Using 16S and ITS rRNA gene sequencing techniques alongside analyses of soil physicochemical properties, we collected and examined soil samples from different depths (0–5 cm, 5–10 cm, and 10–20 cm) in June 2024. The study found that prescribed burning enhanced the complexity and stability of bacterial co-occurrence networks, boosting both the diversity (prescribed burning/unburned control: 3/1) and the abundance (prescribed burning/unburned control: 8/2) of key taxa, which were essential for maintaining bacterial community network stability. However, it also intensified competitive interactions (prescribed burning/unburned control: 0.3162/0.0262) within the community. Moreover, prescribed burning had a significant effect on the diversity, structure, and composition of microbial communities and the physicochemical properties in the 0–5 cm soil layer, while also showing notable effects in the 5–20 cm layer. Prescribed burning also enhanced the coupling between the soil environment and bacterial community composition. The bacterial community showed negative correlations with most physicochemical properties. Soil organic matter (SOM) (p = 0.002) and available potassium (AK) (p = 0.042) were identified as key determinants shaping the post-fire bacterial community structure. The relationship between physicochemical parameters and fungal community composition was weaker. Urease (UE) (p = 0.036) and total potassium (TK) (p = 0.001) emerged as two key factors influencing the composition of post-fire fungal communities. These results elucidate the distinct functional roles of bacteria and fungi in post-fire ecosystem recovery, emphasizing their contributions to maintaining the stability and functionality of microbial communities. The study provides valuable insights for refining prescribed burning management strategies to promote sustainable forest ecosystem recovery. Full article

Local variations in mechanical properties are commonly observed in engineering structures, driven by complex manufacturing histories and harsh service environments. The evaluation of mechanical properties accurately constitutes a fundamental requirement for structural integrity assessment. The Instrumented Indentation Technique (IIT) can play a critical role in the in-site testing of local properties. However, it could be often a challenge to correlate indentation characteristics with uniaxial stress–strain relationships. In this study, we investigated quantitatively the connection between the indentation responses of commonly used metals and their plastic properties using the finite element inversion method. Materials typically exhibit plastic deformation mechanisms characterized by either linear or power-law hardening behaviors. Consequently, conventional prediction methods based on a single constitutive model may no longer be universally applicable. Hence, this study developed methods for acquiring mechanical properties suitable for either the power-law and linear hardening model, or combined, respectively, based on analyses of microstructures of materials exhibiting different hardening behaviors. We proposed a novel integrated IIT incorporating microstructures and material-specific constitutive models. Moreover, the inter-dependency between microstructural evolution and hardening behaviors was systematically investigated. The proposed method was validated on representative engineering steels, including austenitic stainless steel, structural steel, and low-alloy steel. The predicted deviations in yield strength and strain hardening exponent are broadly within 10%, with the maximum error at 12%. This study is expected to provide a fundamental framework for the advancement of IIT and structural integrity assessment. Full article

The search for more sustainable alternatives to traditional organic solvents, in the frame of the green chemistry approach, is leading to an increasing interest toward the exploration of deep eutectic solvents (DESs), especially natural-based ones (NADESs). The great ferment in the use of DESs as innovative media for many applications and in the research of novel types of DESs is not matched by an equal rigor in their characterization and in the study of their physico-chemical characteristics. Nevertheless, it is evident how comparative studies encompassing the investigation of a wide range of properties in relationship with the DESs structures would be beneficial for a rational development of the field. In this work a panel of DESs featuring choline acetate, L-carnitine and L-proline as hydrogen bond acceptor constituents (HBAs) and ethylene glycol, glycerol and levulinic acid as hydrogen bond donor constituents (HBDs) in 1:2 and 1:3 molar ratios have been prepared and characterized. Their density, viscosity and optical properties have been thoroughly investigated at various temperatures, analyzing the influence of their composition in terms of type of HBA, type of HBD and molar ratio on their properties. All the proposed DESs have also been thermally characterized by TGA and DSC, providing a description of their thermal behavior in a wide range of temperature and determining their thermal stability and thermal degradation profile. Full article

The global imperative for sustainable energy has catalyzed the pursuit of next-generation energy storage technologies that are intrinsically safe, economically viable, and scalable. Aqueous zinc-ion batteries (AZIBs) present a promising solution to meet these demands. However, the metallic Zn anode, the heart of this technology, suffers from fundamental electrochemical instabilities—manifesting as dendrite growth and rampant parasitic reactions (e.g., corrosion and passivation)—that critically curtail battery lifespan and impede practical application. This review offers a comprehensive overview of the latest strategies designed to achieve a highly reversible and stable Zn anode. We meticulously categorize and analyze these innovations through the three integral components of the AZIBs: (i) intrinsic anode engineering, (ii) interfacial electrolyte chemistry regulation, and (iii) separator-induced transport modulation. By delving into the core scientific mechanisms and critically evaluating each approach, this work synthesizes a holistic understanding of the structure-property-performance relationships. We conclude by identifying the persistent challenges and, more importantly, proposing visionary perspectives on future research directions. This review aims to serve as a scientific guide for the rational design of highly reversible Zn anodes, paving the way for the next generation of high-performance, commercially viable aqueous batteries. Full article

This study presents the development of a novel alumina-based ceramic composite designed for refractory applications in auxiliary components of sintering furnaces. The innovative concept relies on a three-phase microstructural architecture: a fine-grained alumina matrix improves cohesion, coarse particles act as crack propagation barriers, and spherical granules are intentionally introduced to increase porosity while preserving mechanical strength. This design reduces thermal capacity, enhancing the material’s energy efficiency under high-frequency thermal cycling and offering potential for operating cost reduction. A further novelty is the water-free forming process, which eliminates issues related to drying and deformation. The material was characterized using scanning electron microscopy (SEM), mechanical strength testing, and refractoriness under load (RUL) analysis to establish the structure–property relationships of the developed composite. The results demonstrate that the developed spherical alumina-based composite possesses excellent thermal and mechanical properties, making it a promising candidate for high-temperature industrial applications, particularly as auxiliary refractory plates. Full article

Cartilage tissue engineering aims to restore damaged cartilage using biomaterials, cells, and/or biological cues to support cell growth and tissue repair. Although in the past decades scientific advances have moved the field forward, their translation to a clinical setting is still hampered. One major hurdle to take is to reduce process variability to ensure a predictable biological outcome. Using enabling technologies such as bioprinting has shown the potential to improve process robustness. However, developing bioinks that balance printability with biological functionality remains a major challenge. This study presents the development and structure–property relationships of a novel gelatin-based hydrogel blend, GelMA/GelMA-AEMA, optimized for extrusion-based bioprinting (EBB) while maintaining the crucial biological properties of GelMA for tissue engineering applications. The novel GelMA/GelMA-AEMA blend demonstrated superior flowability and printability compared to GelMA, effectively addressing common 3D-printing defects such as filament shape inhomogeneity. A systematic rheological characterization revealed that the blend exhibits a softer, elastically dominated structure with improved compliance. The blend behaves as a yield-stress fluid with a strong shear-thinning degree, making it highly suitable for EBB. The superior flow properties of the blend are deemed to enhance bond slippage and stress-induced orientation of its more imperfect gel structure, resulting in greater macroscopic deformation and enhanced print fidelity. In addition, histological assessment of a 21-day in vitro study with iPSC-derived chondrocytes suggested that the blend is at least equally performant as GelMA in supporting matrix formation. Histological analysis shows similar matrix deposition profiles, whereas gene expression analysis and compression tests even have suggested superior characteristics for cartilage TE. This study emphasizes the central role of rheology in bioink development and provides foundations for future material development for EBB, with potential implications for cartilage tissue engineering. Full article

Ultra-high-performance concrete (UHPC) is increasingly employed in long-span and heavily loaded structural applications; however, the accurate prediction of its flexural capacity remains a significant challenge because of the complex interactions among geometric parameters, reinforcement details, and advanced material properties. Existing design codes and single-architecture machine learning models often struggle to capture these nonlinear relationships, particularly when experimental datasets are limited in size and diversity. This study proposes a compact hybrid CNN–Transformer model that combines convolutional layers for local feature extraction with self-attention mechanisms for modeling long-range dependencies, enabling robust learning from a database of 120 UHPC beam tests drawn from 13 laboratories worldwide. The model’s predictive performance is benchmarked against conventional design codes, analytical and semi-empirical formulations, and alternative machine learning approaches including Convolutional Neural Networks (CNN), eXtreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN). Results show that the proposed architecture achieves the highest accuracy with an R² of 0.943, an RMSE of 41.310, and a 25% reduction in RMSE compared with the best-performing baseline, while maintaining strong generalization across varying fiber dosages, reinforcement ratios, and shear-span ratios. Model interpretation via SHapley Additive exPlanations (SHAP) analysis identifies key parameters influencing capacity, providing actionable design insights. The findings demonstrate the potential of hybrid deep-learning frameworks to improve structural performance prediction for UHPC beams and lay the groundwork for future integration into reliability-based design codes. Full article

Healthcare is a complex sociotechnical system consisting of several groups of people interacting with each other to provide patient care. Employee commitment, empowerment, and continuous learning are crucial factors in this system. This study aims to investigate the relationship between dialogue and inquiry, a significant component of individual learning, and employee commitment in the healthcare industry. Based on organizational learning theory (OLT) and organizational commitment theory (OCT), a conceptual model was developed, and hypotheses were tested by collecting data from 346 employees working in a multi-specialty hospital in southern India. After checking the psychometric properties of the survey instrument, structural equation modeling was used to analyze data. The results indicate that (i) dialogue and inquiry positively predicts empowerment and employee commitment, (ii) empowerment is a precursor to employee commitment, and (iii) empowerment mediates the relationship between dialogue and inquiry and employee commitment. The results also support the moderating effect of system connection in the relationship between dialogue inquiry and empowerment. Further, strategic leadership interacts with empowerment to positively influence employee commitment. The findings provide valuable insights to the administrators and decision-makers in the healthcare industry for enhancing employee commitment necessary to provide low-cost and high-quality patient care. The conceptual model is first of its kind with regard to healthcare industry in India and hence makes a pivotal contribution to the advancement of literature on healthcare. Full article

In mammals, the suprachiasmatic nucleus (SCN), located in the hypothalamus serves as the master biological clock and precisely regulates circadian rhythms through a complex network structure. As a central pacemaker, the SCN has two primary functions: one is to synchronize the daily rhythms in physiological and behavioral activities; the other is to entrain the endogenous rhythms to the external light–dark cycle. A deep understanding of the SCN network structure is crucial for elucidating the functional mechanisms of the biological clock system. In this review, we systematically summarized the impact of the SCN network structure on functional regulation under light stimulation based on mathematical models. Studies have shown that the coupling between the light-sensitive subgroups in the left and right nuclei of the SCN can enhance the entrainment ability. As an integrated network structure, the SCN may have the characteristics of the small-world network or the scale-free network, as these properties are more conducive to the realization of functions. Additionally, the higher-order coupling mechanism within the SCN can effectively expand the entrainment range. These theoretical research results offer new insights into the relationship between the SCN network and functions and provide crucial theoretical guidance and validation directions for subsequent experimental research. Full article

Ultrasonic wave attenuation in biological tissues arises from complex interactions between mechanical, structural, and fluidic properties, making it essential to identify dominant mechanisms for accurate simulation and device design. This work introduces a novel integration of experimentally measured tissue parameters into time-explicit nonlinear acoustic wave simulations, in which the equations are directly solved in the time domain using an explicit solver. This approach captures the full transient waveform without relying on frequency-domain simplifications, offering a more realistic representation of ultrasound propagation in heterogeneous media. The study estimates both sound diffusivity and viscous damping parameters (dynamic and bulk viscosity) for a broad range of ex vivo tissues (skin, adipose tissue, skeletal muscle, trabecular/cortical bone, liver, myocardium, kidney, tendon, ligament, cartilage, and gray/white brain matter). Four regression models (power law, linear, exponential, logarithmic) were applied to characterize their frequency dependence between 0.5 and 5 MHz. Results show that attenuation is more strongly driven by bulk viscosity than dynamic viscosity, particularly in fluid-rich tissues such as liver and myocardium, where compressional damping dominates. The power-law model consistently provided the best fit for all attenuation metrics, revealing a scale-invariant frequency relationship. Tissues such as cartilage and brain showed weaker viscous responses, suggesting the need for alternative modeling approaches. These findings not only advance fundamental understanding of attenuation mechanisms but also provide validated parameters and modeling strategies to improve predictive accuracy in therapeutic ultrasound planning and the design of non-invasive, tissue-specific acoustic devices. Full article

Static chambers combined with isotopic (δ¹³C and δ¹⁸O) and flux (CO₂ and CH₄) measurements were applied, to explore the effects of mosses and long-term nitrogen (N) addition at two levels (22.5 and 45 kg N ha⁻¹ yr⁻¹) on δ¹³C and δ¹⁸O values of respired CO₂ across three autumn seasons under a temperate forest (northeastern China) and their relationships with CO₂ and CH₄ fluxes and with soil properties. Mosses generally depleted δ¹³C and enriched δ¹⁸O in respired CO₂, likely by altering soil microenvironments or/and substrate use. The effect of N addition on the δ¹³C and δ¹⁸O values of respired CO₂ varied with years, and its interaction with mosses had no effects on the isotopic values. The removal of mosses decreased CO₂ fluxes and the addition of N at a high dose increased CH₄ fluxes. The δ¹³C and δ¹⁸O values of respired CO₂ decreased at soil moisture levels below and above an optimum, and the moisture-dependent effect became more pronounced for the δ¹⁸O than for the δ¹³C. The results of structural equation modeling showed that 70% of the variability of δ¹³C values of respired CO_2. was accounted for by the N addition, mosses, soil moisture, and CH₄ and CO₂ fluxes, while only 22% of the variability of δ¹⁸O values of respired CO₂ was explained by these factors. The results highlight that moss–soil interaction drives the isotopic shifts, which is modulated by N availability. Soil moisture regulates the δ¹⁸O values of respired CO₂, but its drivers remain poorly understood. Future work should target processes influencing the δ¹⁸O shifts of respired CO₂ and deep soil property interactions. Full article

This study investigates the effect of tensile strain during high-temperature carbonization on the microstructural development and mechanical properties of polyacrylonitrile (PAN)-based carbon fibers. The wet-spun stabilized PAN precursor fibers were carbonized at 1400 °C under various tensile draw ratios (0%, 5%, 10%, and 15%), followed by stress-free graphitization at 2400 °C in an argon atmosphere for 1 h to isolate the effects of the carbonization-stage tension. Structural characterization using XRD, 2D-XRD, Raman spectroscopy, and HR-TEM revealed that moderate tensile strain (5–10%) promoted significant improvements in crystallinity, orientation, and graphene layer alignment. Notably, the fiber drawn at 10% performed the best, with a reduced interlayer spacing (d₀₀₂), increased lateral crystallite size (L_a), high orientation factor, and minimal turbostratic disorder. These structural developments translated into the best mechanical properties, including a tensile strength of ~2.44 GPa, a Young’s modulus of ~408.6 GPa, and the highest measured density (1.831 g/cm³). In contrast, excessive strain (15%) induced microstructural defects and reduced performance, underscoring the detrimental effects of overstretching. The findings highlight the critical role of draw control during carbonization in optimizing the structure–property relationships of carbon fibers, offering valuable insight for the design of high-performance fiber processing strategies. Full article

Geopolymers represent an innovative and environmentally sustainable alternative to traditional construction materials, offering significant potential for reducing anthropogenic CO₂ emissions. Among these, phosphoric acid-activated metakaolin-based systems have attracted increasing attention for their chemical and thermal resilience. In this study, we present a comprehensive structural and mechanical evaluation of metakaolin-based geopolymers synthesized across a wide range of Al/P molar ratios (0.8–4.0). Six formulations were systematically prepared and analyzed using X-ray powder diffraction (XRPD), small-angle X-ray scattering (SAXS), Fourier-transform infrared spectroscopy (FTIR), solid-state nuclear magnetic resonance (ssNMR), and complementary mechanical testing. The novelty of this work lies in the integrated mapping of composition–structure–property relationships across the broad Al/P spectrum under controlled synthesis, combined with the rare application of SAXS to reveal composition-dependent nanoscale domains (~18–50 nm). We identify a stoichiometric window at Al/P ≈ 1.5, where complete acid consumption leads to a structurally homogeneous AlVI–O–P network, yielding the highest compressive strength. In contrast, acid-rich systems exhibit divergent flexural and compressive behaviors, with enhanced flexural strength linked to hydrated silica domains arising from metakaolin dealumination, quantitatively tracked by ²⁹Si MAS NMR. XRPD further reveals the formation of uncommon Si–P crystalline phases (SiP₂O₇, Si₅P₆O₂₅) under low-temperature curing in acid-rich compositions. Together, these findings provide new insights into the nanoscale structuring, phase evolution, and stoichiometric control of silica–alumino–phosphate geopolymers, highlighting strategies for optimizing their performance in demanding thermal and chemical environments. Full article

NIN-like proteins (NLPs) are conserved, plant-specific transcription factors that play crucial roles in the nitrate signaling response, plant growth and development, and abiotic stress responses. However, their functions have not been explored in sweet potato. In this study, we identified 7 NLPs in cultivated hexaploid sweet potato (Ipomoea batatas, 2n = 6x = 90), 9 NLPs in the diploid relative Ipomoea trifida (2n = 2x = 30), and 12 NLPs in Ipomoea triloba (2n = 2x = 30) via genome structure analysis and phylogenetic characterization, respectively. The protein physiological properties, chromosome localization, phylogenetic relationships, syntenic analysis maps, gene structure, promoter cis-acting regulatory elements, and protein interaction networks were systematically investigated to explore the possible roles of homologous NLPs in the nitrate signaling response, growth and development, and abiotic stress responses in sweet potato. The expression profiles of the identified NLPs in different tissues and treatments revealed tissue specificity and various expression patterns in sweet potato and its two diploid relatives, supporting differences in the evolutionary trajectories of the hexaploid sweet potato. These results are a critical first step in understanding the functions of sweet potato NLPs and offer more candidate genes for improving nitrogen use efficiency and increasing yield in cultivated sweet potato. Full article