Search Results (213)

Background/Objectives: Humulus lupulus (hops) are a perennial, dioecious plant widely cultivated for beer production, used for their distinguishing aroma and bitterness—traits that confer high added value status. Various hop-derived compounds have been reported to exhibit antioxidant, antimicrobial, antiproliferative and other bioactive effects. This systematic review and meta-analysis assesses the impact of hop compounds on the viability of diverse cancer cell lines. Methods: A comprehensive literature search was performed following PRISMA guidelines. Data were synthesized via multivariate meta-analysis and meta-regression, using IC₅₀ values as the effect size. Key variables included assay type (SRB, tetrazolium salt-based, crystal violet), exposure duration (24, 48, 72 h), specific hop compound and cancer cell line. Results: Of 622 articles identified, 61 met eligibility criteria, yielding 354 individual experiments. Meta-regression of xanthohumol (XN) IC₅₀ values across SRB, tetrazolium and crystal violet assays revealed no statistically significant differences at 24 h (p = 0.77), 48 h (p = 0.35) and 72 h (p = 0.70), supporting the interchangeability of the methods. Meta-analysis confirmed that hop constituents inhibit cancer cell proliferation; XN emerged as the most potent flavonoid (IC₅₀ = 16.89 μM at 72 h), while lupulone was the strongest compound overall (IC₅₀ = 5.00 μM at 72 h). Crude hop extracts demonstrated greater antiproliferative selectivity for cancer versus non-cancer cells (IC₅₀ = 35.23 vs. 43.80 μg/mL at 72 h). Conclusions: Hop compounds, and particularly bitter acids, demonstrate promising antiproliferative activity against cancer cells with comparatively low toxicity to healthy cells. Furthermore, our analysis confirms the comparability of SRB, tetrazolium-based and crystal violet assays, supporting the robust integration of antiproliferative data. Full article

Heterogeneous information network (HIN) embedding transforms multi-type nodes into low-dimensional vectors to preserve structural and semantic information for downstream tasks. However, it struggles with multiplex networks where nodes connect via diverse semantic paths (metapaths). Information fusion mainly improves the quality of node embedding by fully exploiting the structure and hidden information within the network. Current metapath-based methods ignore information from intermediate nodes along paths, depend on manually defined metapaths, and overlook implicit relationships between nodes sharing similar attributes. Our objective is to develop an adaptive framework that overcomes limitations in existing metapath-based embedding (incomplete information aggregation, manual path dependency, and ignorance of latent semantics) to learn more discriminative embeddings. We propose an adaptive multi-hop neighbor information fusion model for heterogeneous network embedding (ANHNE), which: (1) autonomously extracts composite metapaths (weighted combinations of relations) via a multipath aggregation matrix to mine hierarchical semantics of varying lengths for task-specific scenarios; (2) projects heterogeneous nodes into a unified space and employs hierarchical attention to selectively fuse neighborhood features across metapath hierarchies; and (3) enhances semantics by identifying potential node correlations via cosine similarity to construct implicit connections, enriching network structure with latent information. Extensive experimental results on multiple datasets show that ANHNE achieves more precise embeddings than comparable baseline models. Full article

Hyperspectral sparse unmixing (SU) networks have recently received considerable attention due to their model hyperspectral images (HSIs) with a priori spectral libraries and to capture nonlinear features through deep networks. This method effectively avoids errors associated with endmember extraction, and enhances the unmixing performance via nonlinear modeling. However, two major challenges remain: the use of large spectral libraries with high coherence leads to computational redundancy and performance degradation; moreover, certain feature extraction models, such as Transformer, while exhibiting strong representational capabilities, suffer from high computational complexity. To address these limitations, this paper proposes a hyperspectral unmixing dual-branch network integrating an adaptive hop-aware GCN and neighborhood offset Mamba that is termed DGMNet. Specifically, DGMNet consists of two parallel branches. The first branch employs the adaptive hop-neighborhood-aware GCN (AHNAGC) module to model global spatial features. The second branch utilizes the neighborhood spatial offset Mamba (NSOM) module to capture fine-grained local spatial structures. Subsequently, the designed Mamba-enhanced dual-stream feature fusion (MEDFF) module fuses the global and local spatial features extracted from the two branches and performs spectral feature learning through a spectral attention mechanism. Moreover, DGMNet innovatively incorporates a spectral-library-pruning mechanism into the SU network and designs a new pruning strategy that accounts for the contribution of small-target endmembers, thereby enabling the dynamic selection of valid endmembers and reducing the computational redundancy. Finally, an improved ESS-Loss is proposed, which combines an enhanced total variation (ETV) with an $l_{1/2}$ sparsity constraint to effectively refine the model performance. The experimental results on two synthetic and five real datasets demonstrate the effectiveness and superiority of the proposed method compared with the state-of-the-art methods. Notably, experiments on the Shahu dataset from the Gaofen-5 satellite further demonstrated DGMNet’s robustness and generalization. Full article

The treatment of inflammatory bowel diseases (IBDs), particularly ulcerative colitis and Crohn’s disease, remains a challenge. As the available therapeutic options have limited efficacy and various side effect, there is a need to identify new inflammatory modulators that can influence IBD. Natural polyphenols and polyphenol-rich extracts have been found to have preventive and therapeutic potential, including various anti-inflammatory effects. In this study, the inhibition of the formation of mediators associated with intestinal inflammation, remodelling, and angiogenesis by the spent hop extract (SHE), a polyphenol-rich extract from Humulus lupulus L., and its flaxseed polysaccharide-based encapsulates was examined using tumour necrosis factor alpha (TNF-α)-stimulated human small intestinal epithelial (HIEC-6) and large intestinal epithelial (CCD841CoN) cells. Also, we assessed the activity of the tested agents after in the vitro-simulated gastrointestinal digestion process. SHE strongly inhibited the expression of pro-inflammatory cytokines, mainly IL-1β and TNF-α, as well as the expression and activity of type IV collagenases (MMP-2 and MMP-9); these effects resulted from the suppression of NF-κB, ERK and Akt signalling pathways. We also proved the protective effect of encapsulation process against the reduction in the bioaccessibility of SHE, observed under the influence of digestion process. Our results provide initial evidence on the potential utility of SHE and its encapsulates in IBD. Full article

Humulus lupulus, commonly known as hop, is a climbing plant whose female cones impart beer’s characteristic bitterness and aroma and also serve as a preservative. In this study, we conducted a meta-analysis to investigate the antimicrobial activity of hop compounds and extracts against various microorganisms by statistically synthesizing minimum inhibitory concentration (MIC) values. From the 2553 articles retrieved from the comprehensive literature search, 18 provided data on MIC values for six hop compounds, and three extract types tested against 55 microbial strains’ MIC values corresponded to 24 and 48 h incubation periods with compounds or extracts. The results indicate that xanthohumol (a flavonoid) and lupulone (a bitter acid) exhibit potent antimicrobial activity against most tested microorganisms, particularly food spoilage bacteria [21.92 (95%CI 9.02–34.83), and 12.40 (95%CI 2.66–22.14) μg/mL, respectively, for 24 h of treatment]. Furthermore, hydroalcoholic extracts demonstrated greater efficacy compared to supercritical CO₂ (SFE) extracts, which showed limited antimicrobial effects against both probiotic and non-probiotic strains. These findings underscore the need for standardized, evidence-based protocols—including uniform microbial panels and consistent experimental procedures—to reliably evaluate the antimicrobial properties of hop-derived compounds and extracts. Taken together, our findings ultimately chart a path toward evidence based antimicrobial tests that could inform food-preservation strategies and inspire the development of plant-based antimicrobials. Full article

To quantitatively describe the frequency domain spectroscopy (FDS) characteristics of transformer oil-paper insulation under varying temperature, moisture, and aging conditions, a modified Cole–Cole model is introduced. This model decomposes the dielectric spectrum into polarization, DC conduction, and hopping conduction components, with parameters reflecting insulation characteristics. Methods for determining initial parameter values and optimizing the objective function are proposed. Using a three-electrode setup, FDS measurements were conducted on oil-paper insulation samples at different temperatures, and extracted parameters were analyzed for their variation patterns. Within the frequency range of 1.98 × 10⁻⁴ Hz to 1 × 10³ Hz, the model achieves a goodness-of-fit (R²) exceeding 0.97 for both real and imaginary permittivity components, with the sum of squared errors reduced from 259 to 57.35 at 70 °C, outperforming the fundamental Cole–Cole and Ekanayake’s models. Temperature significantly affects the relaxation and DC conductivity components; both adhere to the Arrhenius equation, enabling precise condition assessment of transformer insulation. Full article

The global sleep aids market has grown significantly due to rising stress and increased awareness of sleep’s importance. Melatonin, available in the EU as a prescription or over-the-counter medicine, depending on the country, is also permitted in dietary supplements with country-specific limits. Recent reports indicate concerning levels of excessive melatonin in EU dietary supplements, necessitating accurate quantification methods. We developed and validated, by applying accuracy profiles, ISO17025-compliant, rapid ultra-high performance liquid chromatography (UHPLC) methodologies coupled with either diode array detection (DAD) or high-resolution accurate mass spectrometry (HRAM MS). The cost-effective UHPLC-DAD method is suitable for medicines and most dietary supplements, except those more complex herbal matrices containing passionflower, hop, hemp, lime tree or lavender or their extracts, where UHPLC-HRAM MS is recommended due to selectivity issues of the DAD methodology. To demonstrate the applicability, we analyzed 50 dietary supplements claiming to contain melatonin—25 from legal supply chains and 25 from suspicious sources claiming therapeutic melatonin content. Our findings confirmed previous reports of high melatonin content in online products, especially when purchased through rogue internet pharmacies. Moreover, 12% of legal supply chain samples violated current legislation through unauthorized health claims or contained at least triple the melatonin amount permitted in Belgium. This research provides reliable analytical methods for regulatory bodies and confirms the circulation of non-compliant melatonin-containing dietary supplements in the EU market, even in the legal supply chain. Full article

Four different yeast strains were isolated from industrial gluten-free bread (GFB) purchased from a local supermarket. These strains, including Hyphopichia burtonii, Wickerhamomyces anomalus, Saccharomycopsis fibuligera, and Cyberlindnera fabianii, are responsible for spoilage, which consists of white powdery and filamentous colonies due to the fragmentation of hyphae into short-length fragments (dust-type spots) that is typical of the spoilage produced by chalk yeasts. The isolated strains were identified using genomic analysis. Among them, C. fabianii was also isolated, which is a rare ascomycetous opportunistic yeast species with low virulence attributes, uncommonly implicated in bread spoilage. The yeast growth was studied in vitro on Malt Extract Agar (MEA) at two temperatures (20 and 25 °C) and at different Aws (from 0.99 to 0.90). It was inferred that the temperature did not influence the growth. On the contrary, different Aws reduced the growth, but all the yeast strains could grow until a minimum Aw of about 0.90. Different preservatives (ethanol, hop extract, and sorbic and propionic acids) were used to prevent the growth. In MEA, the growth was reduced but not inhibited. In addition, the vapor-phase antimicrobial activity of different preservatives such as ethanol and hop extract was studied in MEA. Both preservatives completely inhibited the yeast growth either at 20 or at 25 °C. Both preservatives were found in GFB slices. Contrary to hop extract, 2% (v/w) ethanol completely inhibited all the strains. The spoilage was also confirmed by the presence of various compounds typically present in yeasts, derived from sugar fermentation and amino acid degradation. These compounds included alcohols, ketones, organic acids, and esters, and they were identified at higher concentrations in the spoiled samples than in the unspoiled samples. The concentration of acetic acid was low only in the spoiled samples, as this compound was consumed by yeasts, which are predominately present in the spoiled samples, to produce acetate esters. Full article

Osteoporosis is characterized by an imbalance between bone formation and resorption, leading to decreased bone mass and an increased fracture risk, mainly associated with aging. Current treatments include anti-resorptive and anabolic drugs. However, these often have side effects, leading many patients to seek natural biological alternatives. We have demonstrated previously that hops extract, rich in compounds with estrogenic activity classified as phytoestrogens, exerts osteogenic effects by promoting the osteoblastic differentiation of bone marrow stem cells (BMSCs) while inhibiting osteoclast activity in vitro. In our study, young male Sprague Dawley rats were randomized into two groups: one received hops extract (LPL, 1% w/v in drinking water) for two months, and the other drank water alone (C). The rats were euthanized, and their femurs were dissected and processed for static histomorphometry and bone biomechanics. Additionally, BMSCs were isolated from the humeri to evaluate their osteogenic potential. Our result demonstrated that LPL treatment enhanced the osteogenic potential of humeral BMSCs in ex vivo assays, upregulating osteogenic genes and downregulating pro-resorptive markers. These findings correlated with improved femoral bone microarchitecture and biomechanical parameters. In conclusion, a two-month treatment with LPL enhanced the osteogenic capacity of BMSCs, improving bone microarchitecture and biomechanical properties. These results suggest its potential as a natural alternative for promoting bone health. Full article

Most current graph neural network (GNN)-based social recommendation systems mainly extract negative samples from explicit feedback, and are unable to accurately learn the boundaries of similar positive and negative samples, which leads to misjudgment of user preferences. For this reason, we propose to introduce the hop-mixing technique to synthesize hard negative samples for users to fully explore their preferences. Firstly, positive sample information is injected into the original negative samples in each layer to generate augmented negative samples that are very similar to the positive samples. Then the super-enhanced negative samples with the highest inner product score with the positive samples are identified from each layer, and finally, the super-enhanced negative samples from each layer are aggregated and pooled to obtain the final hard negative samples. Subsequently, a graph fusion mechanism is used to aggregate user representations from the social graph and the user–item bipartite graph. Comparative experiments on two real datasets and ten baseline models are conducted, and the results show that the proposed method has certain performance advantages over other state-of-the-art recommendation models. Full article

Spent hops from the supercritical extraction process represent a valuable source of xanthohumol (XN), a prenylated flavonoid with demonstrated anticancer, antidiabetic, antibacterial, and anti-inflammatory properties. However, XN is thermally sensitive and readily isomerizes into the less bioactive iso-XN at elevated temperatures, necessitating mild extraction conditions. Previous studies have shown that the pretreatment of plant biomass can enhance the extraction efficiency of bioactive compounds. In this study, various pretreatment methods—including physical (freeze–thaw, ultrasound, and microwave), chemical (acid and base hydrolysis), and enzymatic approaches—were applied to spent hops prior to extraction, and XN yields were compared to those obtained from untreated samples. The experiments, performed in triplicate, yielded meaningful results which helped understand the raw material’s behavior in applied conditions. Due to the compound’s high thermal sensitivity, ultrasound and microwave pretreatments require precise control to prevent excessive temperature increases, making low-temperature methods more suitable. Additionally, exposure to elevated pH adversely affected XN extraction efficiency, limiting the applicability of strong alkaline pretreatments. Among the evaluated methods, freeze–thaw pretreatment proved to be a simple and effective strategy, enhancing XN extraction yields by up to 10.7 ± 0.7% through the optimization of soaking time, the solid-to-liquid ratio, and the thawing temperature. Identifying an inexpensive and efficient pretreatment method could reduce extraction time while improving yield, contributing to the sustainable utilization of spent hops as an XN source. Full article

Objective: This study aimed to investigate the effects of neuromuscular electrical stimulation (NMES) on lower limb muscle synergies during the single-leg hop test in martial artists after anterior cruciate ligament (ACL) reconstruction. Methods: Twenty-four martial artists who underwent ACL reconstruction were recruited and performed a single-leg hop test under two conditions: with NMES (ES) and without NMES (CON). The ES condition involved using Compex SP 8.0 to deliver biphasic symmetrical wave stimulation. Jump performance metrics and electromyographic (EMG) signals were recorded. Muscle synergies of the lower limbs were extracted using non-negative matrix factorization (NMF) to analyze patterns of muscle coordination. Results: Compared with the CON condition, the ES condition significantly reduced the jump time (0.13 ± 0.05 vs. 0.18 ± 0.09; F = 5.660; p = 0.022) and significantly increased the contact time (0.53 ± 0.12 vs. 0.43 ± 0.21; F = 4.013; p = 0.049). Muscle synergy analysis revealed three distinct synergy patterns under both conditions. For synergy pattern 1, compared with the CON condition, the muscle weightings of the rectus femoris and tibialis anterior muscles were significantly increased under the ES condition (p < 0.001). For synergy pattern 2, compared with the CON condition, the muscle weighting of the lateral gastrocnemius muscle was significantly increased under the ES condition (p < 0.001). Additionally, the activation timing of synergy pattern 2 was significantly reduced under the ES condition (p = 0.001). Conclusion: Neuromuscular electrical stimulation enhances jump performance and alters muscle synergy patterns in martial artists after ACL reconstruction. The findings suggest that NMES can promote better lower limb muscle coordination during jumping tasks, potentially aiding in postoperative rehabilitation and performance optimization. Full article

Accurate segmentation of soil pore structure is crucial for studying soil water migration, nutrient cycling, and gas exchange. However, the low-contrast and high-noise CT images in complex soil environments cause the traditional segmentation methods to have obvious deficiencies in accuracy and robustness. This paper proposes a hybrid model combining a Multi-Modal Low-Frequency Reconstruction algorithm (MMLFR) and UNet (MMLFR-UNet). MMLFR enhances the key feature expression by extracting the image low-frequency signals and suppressing the noise interference through the multi-scale spectral decomposition, whereas UNet excels in the segmentation detail restoration and complexity boundary processing by virtue of its coding-decoding structure and the hopping connection mechanism. In this paper, an undisturbed soil column was collected in Hainan Province, China, which was classified as Ferralsols (FAO/UNESCO), and CT scans were utilized to acquire high-resolution images and generate high-quality datasets suitable for deep learning through preprocessing operations such as fixed-layer sampling, cropping, and enhancement. The results show that MMLFR-UNet outperforms UNet and traditional methods (e.g., Otsu and Fuzzy C-Means (FCM)) in terms of Intersection over Union (IoU), Dice Similarity Coefficients (DSC), Pixel Accuracy (PA), and boundary similarity. Notably, this model exhibits exceptional robustness and precision in segmentation tasks involving complex pore structures and low-contrast images. Full article

Despite the growing adoption of Property Graph Databases, like Neo4j, interacting with them remains difficult for non-technical users due to the reliance on formal query languages. Natural Language Interfaces (NLIs) address this by translating natural language (NL) into Cypher. However, existing solutions are typically limited to high-resource languages; are difficult to adapt to evolving domains with limited annotated data; and often depend on Machine Learning (ML) approaches, including Large Language Models (LLMs), that demand substantial computational resources and advanced expertise for training and maintenance. We address these limitations by introducing a novel dependency-based, training-free, schema-agnostic Natural Language Interface (NLI) that converts NL queries into Cypher for querying Property Graphs. Our system employs a modular pipeline-integrating entity and relationship extraction, Named Entity Recognition (NER), semantic mapping, triple creation via syntactic dependencies, and validation against an automatically extracted Schema Graph. The distinctive feature of this approach is the reduction in candidate entity pairs using syntactic analysis and schema validation, eliminating the need for candidate query generation and ranking. The schema-agnostic design enables adaptation across domains and languages. Our system supports single- and multi-hop queries, conjunctions, comparisons, aggregations, and complex questions through an explainable process. Evaluations on real-world queries demonstrate reliable translation results. Full article

Bioactive phytochemicals, such as polyphenols, play vital roles in human health, but conventional extraction methods rely on hazardous solvents. This study establishes natural deep eutectic solvents (NADESs) as versatile and environmentally friendly alternatives for recovering a variety of bioactive compounds from plant materials. Five choline chloride-based NADESs were evaluated for their effectiveness in extracting betalains (from beetroot), carotenoids (from carrot and sweet potato), anthocyanins (from chokeberry pomace and red onion), and polyphenols (from Lonicera japonica flowers, hop cones, rowan berries, and spent coffee grounds). Notably, NADES2 outperformed water in betalain recovery (179.86 mg of betanin/100 g of beetroot), while NADES4 (choline chloride-urea, 1:2 molar ratio) matched the polyphenol extraction efficiency of ethanol. Using L. japonica flowers as a model for optimization, Response Surface Methodology (RSM) identified the solvent ratio and temperature as critical extraction parameters, using high ratios (12:1–15:1 v/w) and moderate heat (55–75 °C) to maximize recovery. NADES4 emerged as a high-performing solvent, achieving a total phenolic content (TPC) of 75.94 mg chlorogenic acid/g and antioxidant activity of 451.00 µmol Trolox/g under the following conditions: 60% aqueous dilution, 15:1 solvent ratio, and 80 °C, 30 min. These findings highlight NADESs as a green, tunable solvent system for phytochemical extraction across plant species, offering enhanced efficiency, reduced environmental impact, and alignment with sustainable practices. Full article