Search Results (20,066)

Diols represent a structurally diverse class of compounds with considerable biological and functional significance. Herein, we describe the synthesis of 1,1-bis(4-ethylphenyl)propan-1,2-diol (BEPP) via a Grignard reaction. The structure of BEPP was unambiguously elucidated by ¹H and ¹³C nuclear magnetic resonance (NMR), heteronuclear multiple-bond correlation (HMBC), high-resolution mass spectrometry (HRMS), and infrared (IR) spectroscopy. Full article

Dietary polyphenols are recognized as crucial modulators of gastrointestinal motility, holding therapeutic promise for conditions like irritable bowel syndrome, postoperative ileus, and functional dyspepsia. However, their reported effects are heterogeneous, ranging from spasmolytic to prokinetic. This review aims to clarify these inconsistencies by synthesizing experimental evidence on structure–activity relationships and underlying mechanisms. Relevant publications were identified in PubMed and Google Scholar using terms related to polyphenols and gastrointestinal motility. References were selected for relevance, and the narrative review integrates findings from in vitro, ex vivo, in vivo, and clinical studies. Across various experimental models, polyphenols function as multi-target modulators of gastrointestinal smooth muscle. The primary mechanisms identified involve the blockade of voltage-dependent L-type Ca²⁺ channels, activation of K⁺ channels (BK, K_ATP), and modulation of the NO/cGMP and cAMP/PKA pathways. Flavones and multiple flavonols consistently demonstrate spasmolytic activity via Ca²⁺ channel antagonism. In contrast, flavanones engage BK and K_ATP channels to induce membrane hyperpolarization. Complex extracts from plants like ginger and turmeric exhibit mixed pro- or antimotility effects, reflecting the diverse profiles of their constituent compounds. While robust ex vivo pharmacology and some in vivo and human data exist, a high degree of dataset heterogeneity and inconsistent reporting impedes direct translational efforts. Polyphenols are promising multi-mechanistic modulators of gastrointestinal motility with clear structure–activity patterns. To advance their clinical application, future research must focus on establishing standardized in vivo pharmacokinetics, conducting targeted structure–activity studies, employing bioassay-guided fractionation, and designing rigorous clinical trials. Full article

Phosphorus (P) is essential to life yet constrained by finite reserves, heterogeneous distribution, and strong chemical binding to soil minerals. Pedogenesis progressively alters the availability of P: in ‘young’ soils, P associated with Ca and Mg is relatively labile, while in ‘old’ soils, acidification and leaching deplete base cations, shifting P into organic matter and recalcitrant Al- and Fe-bound pools. Podzolized soils (Spodosols) provide a unique lens for studying this transition because podzolization vertically segregates these dynamics into distinct horizons. Organic cycling dominates the surface horizon, while downward translocation of Al, Fe, and humus creates a spodic horizon that immobilizes P through sorption and co-precipitation in amorphous organometal complexes. This spatial separation establishes two contrasting P pools—biologically dynamic surface P and mineral-stabilized deep P—that may be variably accessible to plants and microbes depending on depth, chemistry, and hydrology. We synthesize mechanisms of spodic P retention and liberation, including redox oscillations, ligand exchange, root exudation, and physical disturbance, and contrast these with strictly mineral-driven or biologically dominated systems. We further propose that podzols serve as natural experimental models for ecosystem aging, allowing researchers to explore how P cycling reorganizes as soils develop, how vertical stratification structures biotic strategies for nutrient acquisition, and how deep legacy P pools may be remobilized under environmental change. By framing podzols as a spatial analogue of long-term weathering, this paper identifies them as critical systems for advancing our understanding of nutrient limitation, biogeochemical cycling, and sustainable management of P in diverse ecosystems. Full article

Mentha essential oils (EOs), renowned for their distinctive aromas and diverse biological activities, represent a key focus in phytochemical and pharmacological research. While numerous reviews have documented the general properties of mint EOs, a systematic and critical synthesis of recent advances linking chemotypic diversity to biosynthetic mechanisms and evidence-based health benefits remains lacking. This review aims to address this gap by comprehensively analyzing the structural variability of EOs across major Mentha species, elucidating the chemotype-dependent enzymatic and genetic regulation within the plastidial methylerythritol phosphate (MEP) pathway, and evaluating preclinical and clinical evidence supporting their health-promoting activities, including antimicrobial, antioxidant, anti-inflammatory, digestive, respiratory, cognitive-enhancing, and anticancer effects. By integrating findings from cutting-edge transcriptomic and genomic studies, we highlight how genetic variations and epigenetic factors influence monoterpene biosynthesis and ultimately shape bioactivity profiles. Furthermore, we critically assess challenges related to EO standardization, bioavailability, and clinical translation, and propose interdisciplinary strategies, such as metabolic engineering, nano-delivery systems, and structured clinical trial designs to overcome these barriers. This review not only consolidates the current understanding of Mentha Eos, but also provides a forward-looking perspective on their potential applications in functional foods, pharmaceuticals, and personalized health products. Full article

Background/Objectives: Sleep disturbances are recognized as a common feature of Long COVID but detailed investigation into the specific nature of these sleep symptoms remain limited. This study analyzes comprehensive sleep questionnaire data from a Long COVID clinic to better characterize the nature and prevalence of sleep complaints in this population. Methods: We conducted a cross-sectional analysis of 200 adults referred to the Stanford Long COVID Clinic. Patients completed an intake questionnaire including three sleep-related items (unrefreshing sleep, insomnia, daytime sleepiness) rated on a 0–5 Likert scale. Additionally, patients completed the Alliance Sleep Questionnaire (ASQ), incorporating the Insomnia Severity Index, Epworth Sleepiness Scale, reduced Morningness–Eveningness Questionnaire, and modules for parasomnia, restless legs, and breathing symptoms. We calculated the prevalence of six sleep symptom domains. Standardized symptom data were analyzed using principal component analysis (PCA) and K-means clustering (k = 2) to explore latent phenotypes and used logistic regression to assess associations between demographic and clinical variables and each sleep complaint. Results: Sleep-related breathing complaints affected 57.5% of participants, insomnia 42.5%, and excessive daytime sleepiness 28.5%. Parallel analysis supported a nine-factor structure explaining ~90% of variance, with varimax rotation yielding interpretable domains such as insomnia/unrefreshing sleep, fatigue/post-exertional malaise, parasomnias, and respiratory symptoms. Gaussian mixture modeling favored a two-cluster solution (n = 94 and n = 106); one cluster represented a higher-burden phenotype characterized by greater BMI, insomnia, daytime sleepiness, gastrointestinal symptoms, and parasomnias. Logistic models using factor scores predicted insomnia with high accuracy (AUC = 0.90), EDS moderately well (AUC = 0.81), but extreme chronotype poorly (AUC = 0.39). In adjusted models, hospitalization during acute COVID-19 was significantly associated with insomnia (OR 4.41; 95% CI 1.27–15.36). Participants identifying as multiracial had higher odds of insomnia (OR 3.22; 95% CI 1.00–10.34), though this narrowly missed statistical significance. No other predictors were significant. Conclusions: Sleep disturbances are frequent and diverse in Long COVID. Factor analysis showed overlapping domains, while clustering identified a higher-burden phenotype marked by more severe sleep and systemic complaints. Symptom-based screening may help target those at greatest risk. Full article

Portfolio optimization is a cornerstone of modern financial decision-making, traditionally based on the mean–variance model introduced by Markowitz. However, this framework relies on restrictive assumptions—such as normally distributed returns and symmetric risk preferences—that often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return maximization, mean absolute deviation (MAD) minimization, and entropy-based diversification into a unified optimization structure: the Mean–Deviation–Entropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average magnitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are considered—Shannon entropy, Tsallis entropy, and cumulative residual Sharma–Taneja–Mittal entropy (CR-STME)—to explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and entropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical mean–variance and mean–MAD models indicate that the MDE model achieves improved diversification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allocation, and decentralized finance, especially under high-uncertainty market conditions. Full article

Adolescents living with HIV face unique challenges when transitioning from pediatric to adult care—including stigma, disclosure concerns, and loss of support—that undermine continuity. We systematically searched PubMed, Embase, and Web of Science through August 2025 in accordance with PRISMA 2020. Two reviewers independently screened records and appraised risk of bias using Joanna Briggs Institute (JBI) tools by study design; appraisals informed interpretation but did not determine inclusion. Thirty-two studies met criteria; given heterogeneity, findings were narratively synthesized. Post-transition outcomes were suboptimal: retention often declined to ~70% by two years and viral suppression to roughly two-thirds, below global targets. Older transfer age and minimal gaps between pediatric discharge and adult enrollment were associated with better retention. Structured programs combining early preparation, coordinated handoffs, psychosocial/peer support, and youth-friendly adult services consistently improved engagement (often >90% 12-month retention), though improvements in viral suppression were less uniform. JBI appraisal indicated mostly moderate methodological quality with common risks from confounding and incomplete follow-up, tempering certainty. Purposeful, well-supported transition models are essential to sustain treatment success into adulthood; future work should evaluate scalable delivery and long-term outcomes across diverse settings. Full article

RNA viruses remain a significant public health concern due to their rapid evolution, genetic variability, and capacity to trigger recurrent epidemics and pandemics. Over the last decade, natural products have gained attention as a valuable source of antiviral candidates, offering structural diversity, accessibility, and favorable safety profiles. This review highlights key replication mechanisms of RNA viruses and their associated therapeutic targets, including RNA-dependent RNA polymerase, viral proteases, and structural proteins mediating entry and maturation. We summarize recent advances in the identification of bioactive compounds such as flavonoids, alkaloids, terpenes, lectins, and polysaccharides that exhibit inhibitory activity against clinically relevant pathogens, including the Influenza A virus (IAV), human immunodeficiency viruses (HIV), dengue virus (DENV), Zika virus (ZIKV), and Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Special emphasis is placed on the integration of in silico screening, in vitro validation, and nanotechnology-based delivery systems that address challenges of stability, bioavailability, and specificity. Furthermore, the growing role of artificial intelligence, drug repurposing strategies, and curated antiviral databases is discussed as a means to accelerate therapeutic discovery. Despite persistent limitations in clinical translation and standardization, natural products represent a promising and sustainable platform for the development of next-generation antivirals against RNA viruses. Full article

To mitigate the threats posed by habitat fragmentation due to rapid urbanization on bird diversity, this study introduces an innovative framework for analyzing the synergistic effects of habitat quality (HQ), ecological network connectivity (ENC), and bird richness (BR) in the Pearl River Delta National Forest Urban Agglomeration (PRDNFUA). The framework, based on a stratified ecological network perspective that distinguishes between urban agglomeration and urban core areas, incorporates different types of ecological corridors (interactive corridors and self-corridors), providing a novel approach for effectively quantifying and spatially visualizing the temporal and spatial evolution of the “HQ–ENC–BR” synergy. By integrating geographic detectors through ternary plot analysis combined with a zonation model, this study identified the synergetic effects of HQ and ENC on BR observed during 2015–2020 and proposed strategies for optimizing “HQ–ENC–BR” synergy. The results indicate that between 2015 and 2020, (1) the Pearl River Estuary and coastal areas are hotspots for bird distribution and also represent gaps in ecological network protection. (2) The positive synergistic effects between ecological network structure (HQ, ENC) and function (BR) have gradually strengthened and are stronger than the effects of individual factors; this synergy is especially significant in urban agglomerations and interactive corridors and is particularly pronounced in the northern cities. (3) The area overlap between the optimized ecological network and bird richness hotspots will increase by approximately 78.2%. The proposed ecological network optimization strategies are scientifically sound and offer valuable suggestions for improving bird diversity patterns in the PRDNFUA. These findings also provide empirical support for the United Nations Sustainable Development Goals (SDG 11: Sustainable Cities and Communities; SDG 15: Life on Land). Full article

Water conveyance channels, as critical components of water diversion projects, feature numerous structures, complex configurations, and intensive operational management requirements, making them vulnerable to multiple risks, such as extreme flooding, channel blockage, structural failures, and management deficiencies. To ensure an accurate assessment of the operational safety risk, this study proposes a comprehensive risk assessment framework that integrates risk probability and risk loss. The former is quantified using the Consequence Reverse Diffusion Method (CRDM), which systematically identifies and categorizes key factors of primary dike failure modes into four domains: hydrological characteristics, channel morphology, engineering structures, and operational management. The latter is assessed by integrating socioeconomic impacts, including population exposure, infrastructure investment, and industrial and agricultural production. A structured assessment framework is established through systematic indicator selection, justified weight assignment, and standardized scoring criteria. Application of the framework to Yangtze-to-Huaihe Water Diversion Project (Henan Reach) reveals that the risk probability across four segments falls within the (1, 3) range, indicating a generally low to moderate risk profile, while channel morphology shows greater spatial variability than hydrological, structural, and management indicators, driven by local differences in crossing structure density, sinuosity, and regime coefficients. Meanwhile, the segments along the Qingshui River face higher risk losses owing to their upstream location and large-scale water supply capacity, resulting in a relatively higher comprehensive risk level. Full article

In industrial settings, defect detection using deep learning typically requires large numbers of defective samples. However, defective products are rare on production lines, creating a scarcity of defect samples and an overabundance of samples that contain only background. We introduce ImbDef-GAN, a sample imbalance generative framework, to address three persistent limitations in defect image generation: unnatural transitions at defect background boundaries, misalignment between defects and their masks, and out-of-bounds defect placement. The framework operates in two stages: (i) background image generation and (ii) defect image generation conditioned on the generated background. In the background image-generation stage, a lightweight StyleGAN3 variant jointly generates the background image and its segmentation mask. A Progress-coupled Gated Detail Injection module uses global scheduling driven by training progress and per-pixel gating to inject high-frequency information in a controlled manner, thereby enhancing background detail while preserving training stability. In the defect image-generation stage, the design augments the background generator with a residual branch that extracts defect features. By blending defect features with a smoothing coefficient, the resulting defect boundaries transition more naturally and gradually. A mask-aware matching discriminator enforces consistency between each defect image and its mask. In addition, an Edge Structure Loss and a Region Consistency Loss strengthen morphological fidelity and spatial constraints within the valid mask region. Extensive experiments on the MVTec AD dataset demonstrate that ImbDef-GAN surpasses existing methods in both the realism and diversity of generated defects. When the generated data are used to train a downstream detector, YOLOv11 achieves a 5.4% improvement in mAP@0.5, indicating that the proposed approach effectively improves detection accuracy under sample imbalance. Full article

With the increased demand for wearable sensors, image representations—such as persistence images and Gramian angular fields—transformed from time-series data have been investigated to address challenges in wearables arising from physiological variations, sensor noise, and limitations in capturing contextual information. To preserve the lightweight structural design of models, knowledge distillation (KD) has also been employed alongside image representations during training to distill smaller and more efficient models. Although image representations play a key role in providing richer and more informative features in training a model, their effectiveness within the KD framework has not been thoroughly explored. In this paper, we focus on image representation-driven KD to investigate whether these representations can provide useful knowledge leading to improved time-series interpretation in activity classification tasks. We explore the benefits of integrating image representations into KD, and we analyze the interplay between representation richness and model compactness with different combinations of teacher and student networks. We also introduce diverse KD strategies to utilize image representations, and we demonstrate the strategies with various perspectives, such as analysis of noises, generalizability, and compatibility, across datasets of varying scales to obtain comprehensive and insightful observations. These offer valuable insights for designing efficient and high-performance wearable sensor-based systems. Full article

The application of dolomitic limestone is a recommended practice for improving pastures established on acidic soils. On the other hand, pasture availability should determine the adjustment of the biotic load. The aim of this study is to evaluate the potential of pasture plant community composition as an indicator to assess the effects of intensification strategies in the Montado ecosystem, specifically soil pH correction and/or increasing animal stocking rate. Forty-eight sampling areas of a biodiverse pasture were monitored on a 4-ha plot located at the Mitra farm (Évora district; southern Portugal). The experimental design included four treatments: with and without limestone application (respectively, DL and WDL) × traditional low stocking rate (LSR, 7 sheep ha⁻¹) and high stocking rate (HSR, 18 sheep ha⁻¹). Floristic composition, structural parameters, and diversity metrics were recorded and analyzed using multivariate statistical tools. Pasture diversity was assessed through the computation of richness indices, with plant species identified as ecological indicators representative of each study area. The results showed Rumex pulcher, Trifolium subterraneum, Plantago lanceolata, and Lolium rigidum as botanical indicators of the four treatments of this study, respectively, LSR in untreated soil, HSR in untreated soil, HSR in treated soil, and LSR in treated soil. The results also show that soil amendment led to a more distinct and stable pasture floristic composition (PFC) compared to untreated areas. Conversely, the stocking rate (SR) played a secondary but still ecologically relevant role. Notably, HSR appears to reduce the need for lime application to achieve a balanced floristic composition and desirable plant community structure, potentially lowering soil amendment costs without compromising pasture quality. In LSR areas, the application of lime was essential to significantly improve the floristic richness, the vegetation cover, and the presence of legumes. Full article

Deoxynivalenol (DON) is a trichothecene mycotoxin produced by Fusarium species that frequently contaminates cereal crops, representing a major threat to food safety, public health, and agricultural productivity. Its remarkable chemical stability during food processing presents significant challenges for effective detoxification. Among the available mitigation strategies, biological approaches have emerged as particularly promising, as they exploit enzymatic systems capable of converting DON into metabolites with substantially reduced toxicity. In this study, we provide a comprehensive analysis of the structural and evolutionary mechanisms underlying DON detoxification across three kingdoms of life. We investigated the fungal glutathione S-transferase Fhb7, the bacterial DepA/DepB epimerization pathway, and the plant SPG glyoxalase using integrative bioinformatics, phylogenetics, molecular modeling, and docking simulations. The selected enzymatic systems employ distinct yet complementary strategies: Fhb7 conjugates DON with glutathione and disrupts its epoxide ring, DepA/DepB converts it into the less toxic 3-epi-DON through stereospecific epimerization, and SPG glyoxalase mediates DON isomerization. Despite their mechanistic differences, these enzymes share key adaptive features that enable efficient DON recognition and detoxification. This work provides an integrative view of cross-kingdom enzymatic strategies for DON degradation, offering insights into their evolution and functional diversity. These findings open avenues for biotechnological applications, including the development of DON-resistant crops and innovative solutions to reduce mycotoxin contamination in the food chain. Full article

This study presents an analysis of regional sea level variability on various seafloor structures. The main aim of this paper was to determine the regional trends of sea level changes (time span of 29 years) in the area of the ocean trench and submarine canyon, in the area of seamounts and corrugations, and then to compare the models of the seafloor with the models of the sea surface. We used hybrid datasets, including satellite altimetric time series, multibeam bathymetric soundings, GEBCO products, free-air gravity anomaly models, and mean dynamic ocean topography models. Radar remote sensing and spaceborne radar technologies are essential in capturing the long-term dynamics of sea surface variability regarding seafloor topography. The values of regional sea level change trends in the seamounts and corrugation region are two times higher (from 2.56 ± 0.10 mm/yr to 7.66 ± 0.18 mm/yr) than in the trench and canyon region (1.75 ± 0.01 mm/yr to 3.65 ± 0.07 mm/yr). In the region of trench and canyon, i.e., on narrow and deep forms of the seafloor, the values of regional trends are stable and regular. In the region of seamounts and corrugations, where the depth is more diverse, regional trend values are higher and irregular. Study results show that regional sea level fluctuations can be the consequence of the diversified structure of the seafloor. The region of the trench and canyon, although characterized by high susceptibility to climatic phenomena, presents lower amplitudes of sea level changes than the subregion of seamounts and corrugations, where the bathymetry of the seafloor is more complex. Full article