Search Results (3,157)

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy, characterized by low tumor cellularity, a dense stromal response, and intricate cellular and molecular interactions within the tumor microenvironment (TME). Although bulk omics technologies have enhanced our understanding of the molecular landscape of PDAC, the specific contributions of non-malignant immune and stromal components to tumor progression and therapeutic response remain poorly understood. Methods: We explored genome-wide DNA methylation and transcriptomic data from the Cancer Genome Atlas Pancreatic Adenocarcinoma cohort (TCGA-PAAD) to profile the immune composition of the TME and uncover gene co-expression networks. Bioinformatic analyses included DNA methylation profiling followed by hierarchical deconvolution, epigenetic age estimation, and a weighted gene co-expression network analysis (WGCNA). Results: The unsupervised clustering of methylation profiles identified two major tumor groups, with Group 2 (n = 98) exhibiting higher tumor purity and a greater frequency of KRAS mutations compared to Group 1 (n = 87) (p < 0.0001). The hierarchical deconvolution of DNA methylation data revealed three distinct TME subtypes, termed hypo-inflamed (immune-deserted), myeloid-enriched, and lymphoid-enriched (notably T-cell predominant). These immune clusters were further supported by co-expression modules identified via WGCNA, which were enriched in immune regulatory and signaling pathways. Conclusions: This integrative epigenomic–transcriptomic analysis offers a robust framework for stratifying PDAC patients based on the tumor immune microenvironment (TIME), providing valuable insights for biomarker discovery and the development of precision immunotherapies. Full article

Grape pomace (i.e., the residual skins, seeds, and pulp left after vinification) retains up to 70% of the fruit’s original phenolic compounds and is also rich in dietary fiber. As such, because this by-product is generated in large quantities worldwide and its disposal is both technologically problematic and costly, reusing it as a food ingredient could simultaneously mitigate environmental burdens, lower winery waste-management expenses, and enhance the nutritional profile of fortified foods. In this context, this study investigated the nutritional enrichment of dark chocolate by incorporating flour produced from red (cv. Syrah) and white (cv. Arinto) grape pomace at three levels (5, 10, and 15% w/w). Formulated chocolates and controls were manufactured under industrial tempering conditions and subsequently analyzed for protein, lipids, sugars, dietary fiber, total phenolic content, antioxidant capacity (DPPH and ORAC), color, texture, and consumer perception (hedonic test). All fortified samples showed higher fiber and antioxidant activity than the control, with “White_15” showing higher fiber content (43.1%) and “Red_5” for ORAC (69,483 µmol TE/100 g) and DPPH (6587 µmol TE/100 g). Dietary fiber showed an increase in content with the increase in grape pomace incorporation, regardless of the type (red or white). Texture softening was observed in all fortified chocolates independently of the incorporation level or type (red or white). Principal Component Analysis (PCA) and hierarchical clustering confirmed clear separation between control and fortified chocolates based on the parameters analyzed. Sensory evaluation with untrained panelists revealed good overall acceptability across all formulations. These findings demonstrate that grape pomace flour can be effectively valorized as a functional ingredient in dark chocolates, supporting circular economy practices in the wine and confectionery sectors while delivering products with enhanced health-promoting attributes (nutritional and antioxidant). Full article

Multidrug-resistant (MDR) Shiga toxin-producing Escherichia coli O157 (STEC O157) is a public health threat. This study analyzed publicly available surveillance data collected by the National Antimicrobial Resistance Monitoring System (NARMS) to assess temporal and regional differences and co-resistance patterns in MDR STEC O157 human clinical isolates across the United States. Co-resistance patterns were assessed by hierarchical clustering and Phi coefficient network analyses. A negative binomial regression model estimated the incidence rate ratios (IRRs) for the number of antimicrobial classes to which an isolate was resistant, across years and geographic regions. Out of 1955 isolates, 151 (7.57%) were MDR. The most important clusters were Cluster 1 (n = 1632), which included susceptible isolates, and Cluster 3 (n = 255), comprising the majority of the MDR isolates, having a high resistance prevalence to tetracyclines (TET) (0.97), folate pathway inhibitors (FPI) (0.77), and phenicols (PHN) (0.49). In the co-resistance network, TET, FPI, and PHN served as central hubs, with large nodes and thick edges, suggesting that they are frequently co-selected. The highest IRRs were observed in Regions 6 (IRR = 2.72) and 9 (IRR = 2.00), compared to Region 4. Compared to 2010, a significant increase in the IRR was observed in each year from 2015 to 2021 (IRRs 2.5–4.38). Antimicrobial stewardship programs and public health interventions targeting MDR E. coli O157 are needed to mitigate the emergence of antimicrobial resistance. Full article

Osteoarthritis (OA) is a multifactorial chronic musculoskeletal disorder characterised by cartilage degradation, synovial inflammation, and subchondral bone remodelling. Conventional diagnostic modalities, including radiographic imaging and symptom-based assessments, primarily detect disease in its later stages, limiting the potential for timely intervention. Inflammatory biomarkers, particularly Interleukin-6 (IL-6), Tumour Necrosis Factor-alpha (TNF-α), and Myeloperoxidase (MPO), have emerged as biologically relevant indicators of disease activity, with potential applications as companion diagnostics in precision medicine. This review examines the diagnostic and prognostic relevance of IL-6, TNF-α, and MPO in OA, focusing on their mechanistic roles in inflammation and joint degeneration, particularly through the activity of fibroblast-like synoviocytes (FLSs). The influence of sample type (serum, plasma, synovial fluid) and analytical performance, including enzyme-linked immunosorbent assay (ELISA), is discussed in the context of biomarker detectability. Advanced statistical and computational methodologies, including rank-based analysis of covariance (ANCOVA), discriminant function analysis (DFA), and Cox proportional hazards modelling, are explored for their capacity to validate biomarker associations, adjust for demographic variability, and stratify patient risk. Further, the utility of synthetic data generation, hierarchical clustering, and dimensionality reduction techniques (e.g., t-distributed stochastic neighbour embedding) in addressing inter-individual variability and enhancing model generalisability is also examined. Collectively, this synthesis supports the integration of biomarker profiling with advanced analytical modelling to improve early OA detection, enable patient-specific classification, and inform the development of targeted therapeutic strategies. Full article

In this paper, a class of fully probabilistic time series models based on Gaussian Vector Mixtures (VMs), i.e., on linear combinations of multivariate Gaussian distributions, is proposed to model electricity Day Ahead Market (DAM) hourly prices and to generate consistent related DAM prices dynamic scenarios. These models, based on latent variables, intrinsically allow for organizing DAM data in hierarchically organized clusters, and for recreating the delicate balance of price spikes and baseline price dynamics present in the DAM data. The latent variables and the parameters of these models have a simple and clear interpretation in terms of market phenomenology, like market conditions, spikes and night/day seasonality. In the machine learning community, different to current deep learning models, VMs and the other members of the class discussed in the paper could be seen as just ‘oldish’ probabilistic models. In this paper it is shown, on the contrary, that they are still worthy models, excellent at extracting relevant features from data, and directly interpretable as a subset of the regime switching autoregressions still currently largely used in the econometric community. In addition, it is shown how they can include mixtures of mixtures, thus allowing for the unsupervised detection of hierarchical structures in the data. It is also pointed out that, as such, VMs cannot fully accommodate the autocorrelation information intrinsic to DAM data time series, hence extensions of VMs are needed. The paper is thus divided into two parts. In the first part, VMs are estimated and used to model daily vector sequences of 24 prices, thus assessing their scenario generation capability. In this part, it is shown that VMs can very well preserve and encode infra-day dynamic structure like autocorrelation up to 24 lags, but also that they cannot handle inter-day structure. In the second part, these mixtures are dynamically extended to incorporate dynamic features typical of hidden Markov models, thus becoming Vector Hidden Markov Mixtures (VHMMs) of Gaussian distributions, endowed with daily latent dynamics. VHMMs are thus shown to be very much able to model both infra-day and inter-day phenomenology, hence able to include autocorrelation beyond 24 lags. Building on the VM discussion on latent variables and mixtures of mixtures, these models are also shown to possess enough internal structure to exploit and carry forward hierarchical clustering also in their dynamics, their small number of parameters still preserving a simple and clear interpretation in terms of market phenomenology and in terms of standard econometrics. All these properties are thus also available to their regime switching counterparts from econometrics. In practice, these very simple models, bridging machine learning and econometrics, are able to learn latent price regimes from historical data in an unsupervised fashion, enabling the generation of realistic market scenarios while maintaining straightforward econometrics-like explainability. Full article

Grapefruit (Citrus × paradisi Macfad.) is susceptible to Huanglongbing (HLB) disease, which prominently affects tree health and leads to a substantial loss of productivity. HLB-affected trees exhibit a nutritional imbalance expressed in either deficiencies or toxicities of the essential minerals required for plant growth, as well as changes in the production of plant metabolites. Hence, understanding foliar nutritional and metabolite fluctuations as HLB-elicited symptoms progress can assist growers in improving tree health management strategies. This study evaluated changes in foliar nutrient and phloem sap amino acid concentrations of HLB-affected grapefruit trees showing a mixed canopy of HLB-induced blotchy mottle and asymptomatic mature leaves. The trees used in our experiment were fruit-bearing seven-year-old grapefruit trees (cv ‘Rio Red’ on sour orange rootstock) grown in South Texas. Two types of foliage from HLB-affected trees were studied, (a) HLB-symptomatic and confirmed Candidatus Liberibacter asiaticus (CLas)-positive (IS) and (b) CLas-negative and HLB-asymptomatic (IA) mature leaves, which were compared to asymptomatic and CLas-free mature foliage from healthy trees (HY) in terms of their leaf nutrient and phloem sap amino acid contents. Hierarchical clustering based on leaf nutrient contents showed that 70% of IA samples clustered with HY samples, thus indicating that the levels of some nutrients were statistically similar in these two types of samples. The concentrations of the macronutrients N, Ca, Mg, and S and the micronutrients Mn and B were significantly reduced in HLB-symptomatic (IS) leaves, as compared to their IA and HY counterparts, which did not show statistically significant differences. Conversely, leaf Na concentration was approximately two-fold higher in leaves from HLB-affected trees (IA and IS) independent of symptom expression as compared to leaves from healthy trees. Significantly higher concentrations of glutamine and the S-containing amino acids taurine and cystathionine were observed in the IS leaves relative to the phloem sap of IA leaves from HLB-affected trees. In contrast, the phloem sap of IA (14%) and IS (41%) leaves from HLB-affected trees exhibited lower levels of γ-amino butyric acid (GABA) as compared to HY leaves. The results of this study highlight the changes in leaf nutrient and phloem sap amino acid profiles following CLas infection and HLB symptom development in grapefruit, and we discuss these results considering the strategies that growers can implement to correct the nutritional deficiencies and/or toxicities induced by this disease. Full article

In the face of climate challenges and growing social inequalities, ESG (Environmental, Social, Governance) has become a key framework for sustainable development. Within the EU, forestry—covering about one third of Europe—is increasingly addressed through ESG principles in the 2030 New EU Forest Strategy (NSF 2030). This study aims to systematize the diversity and similarities of EU Member States’ forest policies using ESG indicators aligned with NFS 2030 objectives. We do not assess policy outcomes but rather identify clusters of countries with similar forest-economy profiles to fill a research gap and support more coherent strategies. Using hierarchical clustering on selected ESG indicators, we find very high variability in EU forest policies. The results confirm that NFS 2030 can serve as an analytical tool to identify clusters of countries with similar ESG profiles and tailor policies to their contexts. The identification of eight clusters per ESG segment underscores the need for a differentiated, flexible approach to achieving common EU forest objectives. Despite similarities within clusters, diverse economic, environmental, and social conditions often require differentiated policies tailored to each country’s unique context. Full article

Excessive nitrogen export from lowland polders is a key contributor to cultural eutrophication in downstream aquatic ecosystems. This study investigated the spatiotemporal characteristics, migration pathways, and sources of nitrogen pollution in a typical polder system. Eight surface water sampling campaigns were conducted at 13 sites in Quyuan Polder, Dongting Lake, from 2022 to 2023, combining ArcGIS spatial analysis, multivariate statistics, and dual-isotope (δ¹⁵N-NO⁻), δ¹⁸O-NO₃⁻) techniques. Nitrate and ammonium nitrogen dominated the nitrogen pool, accounting for ~76% of total nitrogen. Concentrations were higher in the dry season (2.48 mg/L) than in the wet season (1.89 mg/L) and differed significantly among hydrological periods (p < 0.05). Within the polder, total nitrogen and ammonium nitrogen were elevated, whereas nitrate nitrogen was higher at the outlet, reflecting distinct nitrogen profiles along the hydrological gradient. Nitrogen transport patterns were largely consistent with flow direction, driven by both upstream inputs and in situ generation. Isotopic signatures indicated that nitrate originated mainly from ammonium fertilizer and soil nitrogen, with contributions from manure and sewage. These findings enhance understanding of nitrogen dynamics in lowland catchments and provide a scientific basis for targeted pollution control in polder waters. Full article

Distributed Acoustic Sensing (DAS) is an emerging technology in the maritime domain, enabling the use of existing fiber optic cables to detect acoustic signals in the marine environment. In this study, we present an automated signal detection and classification framework for DAS data that supports near-real-time processing. Using data from the SHEFA-2 cable between the Faroe and Shetland Islands, we develop a method to identify acoustic signals and generate both labeled and unlabeled datasets based on their spectral characteristics. Principal component analysis (PCA) is used to explore separability in the labeled data, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is applied to classify unlabeled data. Experimental validation using clustering metrics shows that with the full dataset, we can achieve a Davies–Bouldin Index of 0.828, a Silhouette Score of 0.124, and a Calinski–Harabasz Index of 189.8. The clustering quality degrades significantly when more than 20% of the labeled data is excluded, highlighting the importance of maintaining sufficient labeled samples for robust classification. Our results demonstrate the potential to distinguish between signal sources such as ships, vehicles, earthquakes, and possible cable damage, offering valuable insights for maritime monitoring and security. Full article

Marine shelled organisms exhibit diverse growth strategies shaped by species-specific traits and environmental conditions that critically influence their ecological roles, particularly within Marine Animal Forests (MAF), which are structurally complex habitats and biodiversity-rich habitats. This review compiles and compares empirical growth data for 16 bivalve and gastropod species across seven families, classified as full MAF contributors (Pinna nobilis, Flexopecten glaber, Pecten maximus, and Placopecten magellanicus), partial MAF contributors (Cerastoderma edule, C. glaucum, Chamelea gallina, Ruditapes philippinarum, Mercenaria mercenaria, Panopea generosa, Anadara kagoshimensis, A. inaequivalvis, and Tegillarca granosa), and ecologically relevant non-MAF species (Buccinum undatum, Hexaplex trunculus, and Rapana venosa). Age estimation methods included direct techniques, such as shell growth ring and opercular annulus analysis, alongside indirect approaches, such as length-frequency analysis, stable isotope profiling, and mark–recapture studies. Growth trajectories were modelled using von Bertalanffy growth function (VBGF) parameters to estimate the shell size from ages 1 to 4. Based on these estimates, species were categorised into slow, moderate, fast, and exceptional growth groups. These classifications were further explored through hierarchical clustering that grouped species according to their VBGF-derived growth values, revealing consistent and contrasting life history strategies. This comparative analysis should enhance the understanding of molluscan growth dynamics and support the conservation and management of MAF-associated ecosystems by informing restoration planning, guiding species selection, and contributing to evidence-based policy development. Full article

The plants from the Ocimum genus, belonging to the Labiatae family, serve as important bioresources of essential oils (EOs) rich in biologically active secondary metabolites, widely used in medicine, food, and cosmetics. This study explored the volatile composition, enantiomeric distribution, and in vitro biological activities of EOs from three Ocimum species native to Nepal: O. tenuiflorum L., O. basilicum L., and O. americanum L. EOs were extracted via hydro-distillation and analyzed using gas chromatography–mass spectrometry (GC-MS) for chemical profiling and chiral GC-MS for enantiomeric composition. Hierarchical cluster analysis was performed for major chemotypes. Antioxidant activity was assessed using DPPH and ABTS assays. Antimicrobial efficacy was evaluated using the microbroth dilution method, and cytotoxicity was tested on NIH-3T3 (normal) and MCF-7 (breast cancer) cell lines via the Cell Counting Kit-8 assay. EO yield was highest in O. tenuiflorum (1.67 ± 0.13%) during autumn and lowest in O. americanum (0.35 ± 0.02%) during winter among all Ocimum spp. The major compounds identified in O. tenuiflorum were eugenol (32.15–34.95%), trans-β-elemene (29.08–32.85%), and β–caryophyllene (19.85–21.64%). In O. americanum, the major constituents included camphor (51.33–65.88%), linalool (9.72–9.91%), germacrene D (7.75–1.83%), and β–caryophyllene (6.35–3.97%). For O. basicilum, EO was mainly composed of methyl chavicol (62.16–64.42%) and linalool (26.92–27.05%). The oxygenated monoterpenes were a dominant class of terpenes in the EOs except for O. tenuiflorum (sesquiterpene hydrocarbon). A hierarchical cluster analysis based on the compositions of EOs revealed at least three different chemotypes in Ocimum species. Chiral GC-MS analysis revealed β-caryophyllene and germacrene D as enantiomerically pure, with linalool consistently dominant in its levorotatory form. O. tenuiflorum exhibited the strongest antimicrobial activity, particularly against Candida albicans, and showed notable anticancer activity against MCF-7 cells (IC₅₀ = 23.43 µg/mL), with lower toxicity to normal cells. It also demonstrated the highest antioxidant activity (DPPH IC₅₀ = 69.23 ± 0.10 µg/mL; ABTS IC₅₀ = 9.05 ± 0.24 µg/mL). The EOs from Ocimum species possess significant antioxidant, antimicrobial, and cytotoxic properties, especially O. tenuiflorum. These findings support their potential application as natural agents in medicine, food, and cosmetics, warranting further validation. Full article

With the significant increase in solar power generation’s proportion in power systems, the uncertainty of its power output poses increasingly severe challenges to grid operation. In recent years, solar forecasting models have achieved remarkable progress, with various developed models each exhibiting distinct advantages and characteristics. To address complex and variable geographical and meteorological conditions, it is necessary to adopt a multi-model fusion approach to leverage the strengths and adaptability of individual models. This paper proposes a photovoltaic power prediction framework based on multi-stage ensemble learning, which enhances prediction robustness by integrating the complementary advantages of heterogeneous models. The framework employs a three-level optimization architecture: first, a recursive feature elimination (RFE) algorithm based on LightGBM–XGBoost–MLP weighted scoring is used to screen high-discriminative features; second, mutual information and hierarchical clustering are utilized to construct a heterogeneous model pool, enabling competitive intra-group and complementary inter-group model selection; finally, the traditional static weighting strategy is improved by concatenating multi-model prediction results with real-time meteorological data to establish a time-period-based dynamic weight optimization module. The performance of the proposed framework was validated across multiple dimensions—including feature selection, model screening, dynamic integration, and comprehensive performance—using measured data from a 75 MW photovoltaic power plant in Inner Mongolia and the open-source dataset PVOD. Full article

Aminoglycoside antibiotics (AGs) are widely used in medicine and animal husbandry, but they pose significant risks due to residual toxicity and antibiotic resistance. In this study, a novel chemical sensor based on the laccase-like activity of CH₃COOAg was developed for the selective detection of AGs. CH₃COOAg exhibited varying degrees of laccase-like activity in different buffers (MES, HEPES, and NaAc) and H₂O, and five AGs showed distinct intensities of the inhibitory effect on the laccase-like activity of CH₃COOA in different buffers and H₂O. Therefore, a four-channel colorimetric sensor array was constructed in combination with the use of principal component analysis (PCA) and Hierarchical Cluster Analysis (HCA) for the efficient identification of five AGs (0.02–0.3 μM) in environment samples like tap and lake water. In addition, a colorimetric method was developed for kanamycin (KAN) detection in a honey sample with a linear range of 10–100 nM (R² = 0.9977). The method has excellent sensitivity with a limit of detection of 3.99 nM for KAN. This work not only provides a rapid and low-cost detection method for AG monitoring but also provides a reference for the design of non-copper laccase mimics. Full article

Background: Bronchiectasis is a chronic respiratory condition characterized by permanent bronchial dilation, recurrent infections, and progressive lung damage. A subset of patients, known as frequent exacerbators, experience multiple exacerbations annually, leading to accelerated lung function decline, hospitalizations, and reduced quality of life. The aim of this study is to identify distinct phenotypes and treatable traits in bronchiectasis frequent exacerbators, since it could be crucial for optimizing patient management. Research question: Could clinically distinct phenotypes and treatable traits be identified among frequent exacerbators with bronchiectasis to guide personalized management strategies? Methods: We analysed a cohort of 56 bronchiectasis frequent exacerbator patients using 21 clinically relevant variables, including pulmonary function tests, radiological patterns, and microbiological data. Hierarchical clustering and k-means algorithms were applied to identify subgroups. Key outcomes included cluster-specific characteristics, treatable traits, and their implications for management. Results: Four distinct clusters were identified: 1. Mild, idiopathic bronchiectasis (Cluster 1): Predominantly mild disease (FACED), idiopathic etiology (93.3%), and cylindrical bronchiectasis with moderate obstruction (60%). 2. Rheumatological and NTM-associated bronchiectasis (Cluster 2): Patients with systemic inflammatory diseases (50%) and NTMever (50%) but minimal infections by Pseudomonas aeruginosa. 3. Mild, post-infective bronchiectasis (Cluster 3): Exclusively mild disease, mixed idiopathic and post-infective etiologies, and preserved lung function. 4. Severe, chronic infection phenotype (Cluster 4): Severe disease with high colonization rates of Pseudomonas aeruginosa (71.4%), advanced structural damage (57.1% varicose, 50% cystic bronchiectasis), and frequent exacerbations. Interpretation: This analysis highlights the heterogeneity of bronchiectasis and its frequent exacerbator phenotype. The treatable traits framework underscores the importance of aggressive infection control and management of airway inflammation in severe cases, while milder clusters may benefit from preventive strategies. These findings support the integration of precision medicine in bronchiectasis care, focusing on phenotype-specific interventions to improve outcomes. Full article

Racetrack NbTi superconducting coil is a key component in Maglev train systems due to its excellent mechanical processing performance and lower construction cost. However, dynamic pressures during high-speed operations can influence contact pressures and cause internal filament damage, leading to critical current degradation and quench, which threaten the stable operation of the superconducting magnet. Considering that the NbTi coil has a typical hierarchical structure and comprises thousands of filaments, this study constructs an efficient multiscale framework combining the finite element method (FEM) and self-consistent clustering analysis (SCA) to study the multiscale responses of the NbTi coil. The mechanical responses of the two-scale racetrack coil under monotonic and periodic pressures are investigated, and the effects of the friction contacts between strands are also discussed. The study reveals that internal contacts significantly influence local contact pressures and microscopic stresses, and periodic loading leads to stress accumulation with cycle times. The proposed framework efficiently captures critical microscale responses and can be applied to other multiscale materials and structures. Full article