Search Results (764)

Wild ales represent a diverse category of spontaneously fermented beers, influenced by complex microbial populations and variable ingredients. This study employed an integrated metabolomic profiling approach combining proton nuclear magnetic resonance (¹H NMR) spectroscopy, liquid chromatography–mass spectrometry (LC-MS), and spectrophotometric assays (DPPH and FRAP) to characterize the molecular composition and antioxidant potential of 22 wild ales from six countries. A total of 53 compounds were identified and quantified using NMR, while 62 compounds were identified by using LC-MS. The compounds in question included organic acids, amino acids, sugars, alcohols, bitter acids, phenolic compounds, and others. Ingredient-based clustering revealed that the addition of dark fruits resulted in a significant increase in the polyphenolic content and antioxidant activity. Concurrently, herb-infused and light-fruit beers exhibited divergent phytochemical profiles. Prolonged aging (>18 months) has been demonstrated to be associated with increased levels of certain amino acids, fermentation-derived aldehydes, and phenolic degradation products. However, the influence of maturation duration on the antioxidant capacity was found to be less significant than that of the type of fruit. Country-specific metabolite trends were revealed, indicating the influence of regional brewing practices on beer composition. Correlation analysis was employed to identify the major contributors to antioxidant activity, with salicylic, dihydroxybenzoic, and 4-hydroxybenzoic acids being identified as the most significant. These findings underscore the biochemical intricacy of wild ales and exemplify metabolomics’ capacity to correlate compositional variation with functionality and authenticity in spontaneously fermented beverages. Full article

Mining and analyzing information from multiple sources—such as geophysics and geochemistry—is a key aspect of big data-driven mineral prediction. Clustering, which groups large datasets based on distance metrics, is an essential method in multidimensional data analysis. The Two-Step Clustering (TSC) approach offers advantages by handling both categorical and continuous variables and automatically determining the optimal number of clusters. In this study, we applied the TSC method to mineral prediction in the northeastern margin of the Jiaolai Basin by: (i) converting residual gravity and magnetic anomalies into categorical variables using Ward clustering; and (ii) transforming 13 stream sediment elements into independent continuous variables through factor analysis. The results showed that clustering is sensitive to categorical variables and performs better with fewer categories. When variables share similar distribution characteristics, consistency between geophysical discretization and geochemical boundaries also influences clustering results. In this study, the (3 × 4) and (4 × 4) combinations yielded optimal clustering results. Cluster 3 was identified as a favorable zone for gold deposits due to its moderate gravity, low magnetism, and the enrichment in F1 (Ni–Cu–Zn), F2 (W–Mo–Bi), and F3 (As–Sb), indicating a multi-stage, shallow, hydrothermal mineralization process. This study demonstrates the effectiveness of combining Ward clustering for variable transformation with TSC for the integrated analysis of categorical and numerical data, confirming its value in multi-source data research and its potential for further application. Full article

Alkaline carbonatite complexes are formed by magmatic, hydrothermal, and weathering geological events, which modify the minerals present in the rocks, resulting in ores with varied metallurgical behavior. To better spatially distinguish ores with distinct plant responses, creating a 3D geometallurgical block model was necessary. To establish the clusters, four different algorithms were tested: K-Means, Hierarchical Agglomerative Clustering, dual-space clustering (DSC), and clustering by autocorrelation statistics. The chosen method was DSC, which can consider the multivariate and spatial aspects of data simultaneously. To better understand each cluster’s mineralogy, an XRD analysis was conducted, shedding light on why each cluster performs differently in the plant: cluster 0 contains high magnetite content, explaining its strong magnetic yield; cluster 3 has low pyrochlore, resulting in reduced flotation yield; cluster 2 shows high pyrochlore and low gangue minerals, leading to the best overall performance; cluster 1 contains significant quartz and monazite, indicating relevance for rare earth elements. A hierarchical indicator kriging workflow incorporating a stochastic partial differential equation (SPDE) trend model was applied to spatially map these domains. This improved the deposit’s circular geometry reproduction and better represented the lithological distribution. The elaborated model allowed the identification of four geometallurgical zones with distinct mineralogical profiles and processing behaviors, leading to a more robust model for operational decision-making. Full article

This work proposes a systematic study of different computational schemes for fluorine Nuclear Magnetic Resonance (¹⁹F NMR) chemical shifts, with special emphasis placed on the basis set issue. This study encompasses two stages of calculation, namely, the development of the computational schemes for the geometry optimization of fluorine compounds and the NMR chemical shift calculations. In both stages, the performance of different density functional theory functionals is considered against the method of coupled-cluster singles and doubles (CCSD), with the latter representing a theoretical reference in this work. This exchange-correlation functional study is accompanied with a basis set study in both stages of calculation. Basis sets of different families, sizes, and valence-splitting levels are considered. Various locally dense basis sets (LDBSs) are proposed for the calculation of ¹⁹F NMR chemical shifts, and their performance is assessed by comparison of the calculated chemical shifts with both theoretical and experimental reference data. Overall, the pcS-3/pcS-2 LDBS scheme is recommended as the most balanced locally dense basis set scheme for fluorine chemical shift calculations. Full article

In recent years, the exploration and utilization of geothermal energy have received growing attention as a sustainable alternative to conventional energy sources. Reliable, data-driven identification of geothermal reservoirs, particularly in crystalline basement terrains, is crucial for reducing exploration uncertainties and costs. In such geological settings, magnetic susceptibility, radioactive heat production, and seismic wave characteristics play a vital role in evaluating geothermal energy potential. Building on this foundation, our study integrates in situ and laboratory measurements, collected using advanced sensors from spatially diverse locations, with statistical and unsupervised artificial intelligence (AI) clustering models. This integrated framework improves the effectiveness and reliability of identifying clusters of potential geothermal sites. We applied this methodology to the migmatitic gneisses within the Simav Basin in western Türkiye. Among the statistical and AI-based models evaluated, Density-Based Spatial Clustering of Applications with Noise and Autoencoder-Based Deep Clustering identified the most promising and spatially confined subregions with high geothermal production potential. The potential geothermal sites identified by the AI models align closely with those identified by statistical models and show strong agreement with independent datasets, including existing drilling locations, thermal springs, and the distribution of major earthquake epicenters in the region. Full article

This study demonstrates that the ripeness of avocado fruits can be analyzed using frequency-dependent electrical conductivity and permittivity through a non-invasive Magnetic Induction Spectroscopy (MIS) method. Utilizing an MIS system for conductivity and permittivity measurements of a large sample set ($N=60$) of avocado fruits across multiple frequencies from 100 kHz to 3 MHz enables clear observation of their dispersion behavior and the evolution of their spectra over ripening time in a completely non-contact manner. For the entire sample batch, the conductivity spectrum exhibits a general upward shift and spectral flattening over ripening time. To further quantify these features, normalized gradient analysis and equivalent circuit modeling were employed, and statistical analysis confirmed the correlations between electrical parameters and ripening stages. The trend characteristics of the normalized gradient parameter $P_y$ provide a basis for defining the three ripening stages within the 22-day period: early pre-ripe stage (0–5 days), ripe stage (5–15 days), and overripe stage (after 15 days). The equivalent circuit model, which is both physically interpretable and fitted to experimental data, revealed that the ripening process of avocado fruits is characterized by a weakening of capacitive structures and an increase in extracellular solution conductivity, suggesting changes in cellular integrity and extracellular composition, respectively. The results also highlight significant inter-sample variability, which is inherent to biological samples. To further investigate individual conductivity variation trends, Gaussian Mixture Model (GMM) clustering and Principal Component Analysis (PCA) was conducted for exploratory sample classification and visualization. Through this approach, the sample set was classified into three categories, each corresponding to distinct conductivity variation patterns. Full article

Water’s unique physicochemical properties arise from its dynamic hydrogen-bonding network, yet the precise molecular threshold at which these cooperative behaviors emerge remains a key question. This study employed nuclear magnetic resonance (NMR) spectroscopy and density functional theory (DFT) calculations to investigate the evolution of hydrogen bonding strength in small water clusters, ranging from dimers to pentamers. The observed exponential increase in NMR chemical shift up to the pentamer reflects growing hydrogen bond cooperativity, identifying the (H₂O)₅ cluster as a critical structural and energetic threshold. At this size, the network achieves sufficient connectivity to support key bulk-like phenomena such as proton transfer and dielectric relaxation. These conclusions were corroborated by complementary FT-IR and electrochemical impedance spectroscopy (EIS) measurements of bulk water. Our results position the water pentamer as the molecular onset of emergent solvent behavior, effectively bridging the divide between discrete clusters and the macroscopic properties of liquid water. Full article

Functional magnetic resonance imaging (fMRI) has become instrumental in researching the functioning of the brain. One application of fMRI is investigating the brains of people with autism spectrum disorder (ASD). The Autism Brain Imaging Data Exchange (ABIDE) facilitates this research through its extensive data-sharing initiative. While ABIDE offers raw data and data preprocessed with various atlases, independent component analysis (ICA) for dimensionality reduction remains underutilized. ICA is a data-driven way to reduce dimensionality without prior assumptions on delineations. Additionally, ICA separates the noise from the signal, and the signal components correspond well to functional brain networks called resting-state networks (RSNs). Currently, no large, readily available dataset preprocessed with ICA exists. Here, we address this gap by presenting ABIDE’s data preprocessed to extract ICA-based resting-state networks, which are publicly available. These RSNs unveil neural activation clusters without atlas constraints, offering a perspective on ASD analyses that complements the predominantly atlas-based literature. This contribution provides a resource for further research into ASD, benchmarking between methodologies, and the development of new analytical approaches. Full article

A comprehensive characterization of two pressed-curd cheeses produced from ewe’s milk using enzymatic coagulation—Manchego cheese (with Protected Designation of Origin, PDO) and Castellano cheese (with Protected Geographical Indication, PGI)—was performed throughout the manufacturing process (industrial or traditional) and ripening stages (2, 9, 30, 90, and 180 days). Proton high-resolution magic angle spinning nuclear magnetic resonance (¹H HRMAS NMR) spectroscopy, combined with Principal Component Analysis (PCA) and cluster analysis, was applied to intact cheese samples. The combination of this spectroscopic technique with chemometric methods allows for the characterization of each type of sheep milk cheese according to its geographical origin and production method (artisanal or industrial), as well as the estimation of ripening time. The results demonstrate that HRMAS NMR spectroscopy enables the rapid and direct analysis of cheese samples, providing a comprehensive profile of their metabolites—a metabolic ‘fingerprint’. Full article

In this study, an innovative dental functionalized magnetic nanomaterial was developed by incorporating hydrophilic magnetic clusters as an alternative to conventional isolated magnetic nanoparticles, introducing a novel structural and functional concept in dental applications. The ~100 nm magnetic clusters—composed of densely packed 7 nm Fe₃O₄ nanoparticles—were sequentially coated with a silica (SiO₂) layer (3–5 nm) to improve chemical and mechanical stability, followed by an outer calcium hydroxide [Ca(OH)₂] layer to enhance bioactivity and optical integration. This bilayer architecture enables magnetic field-assisted positioning and improved dispersion within dental resin matrices. Silver nanoparticles were incorporated to enhance antimicrobial activity and reduce biofilm formation. The synthesis process was environmentally friendly and scalable. Comprehensive physicochemical characterization confirmed the material’s functional performance. Saturation magnetization decreased progressively with surface functionalization, from 62 to 14 emu/g, while the zeta potential became increasingly negative (from −2.42 to −22.5 mV), supporting its ability to promote apatite nucleation. The thermal conductivity (0.527 W/m·K) closely matched that of human dentin (0.44 W/m·K), and the colorimetric analysis showed improved brightness (ΔL = 5.3) and good color compatibility (ΔE = 11.76). These results indicate that the functionalized magnetic nanomaterial meets essential criteria for restorative use and holds strong potential for future clinical applications. Full article

Background: Nanomedicine approaches for cancer therapy face significant challenges, including a poor tumor accumulation, limited therapeutic efficacy, and systemic toxicity. We hypothesized that controlling the clustering of poly(acrylic acid-co-maleic acid) (PAM)-coated superparamagnetic iron oxide nanoparticles (SPIONs) would enhance their magnetic properties for improved targeting, while enabling a pH-responsive drug release in tumor microenvironments. Methods: PAM-stabilized SPION clusters were synthesized via arrested precipitation, characterized for physicochemical and magnetic properties, and evaluated for doxorubicin loading and pH-dependent release. A dual targeting approach combining antibody conjugation with magnetic guidance was assessed in cellular models, including a novel alternating magnetic field (AMF) pre-treatment protocol. Results: PAM-SPION clusters demonstrated controlled size distributions (60–100 nm), excellent colloidal stability, and enhanced magnetic properties, particularly for larger crystallites (13 nm). The formulations exhibited a pH-responsive drug release (8.5% at pH 7.4 vs. 14.3% at pH 6.5) and a significant enhancement of AMF-triggered release (17.5%). The dual targeting approach achieved an 8-fold increased cellular uptake compared to non-targeted formulations. Most notably, the novel AMF pre-treatment protocol demonstrated an 87% improved therapeutic efficacy compared to conventional post-treatment applications. Conclusions: The integration of targeting antibodies, magnetic guidance, and a pH-responsive PAM coating creates a versatile theranostic platform with significantly enhanced drug delivery capabilities. The unexpected synergistic effect of the AMF pre-treatment represents a promising new approach for improving the therapeutic efficacy of nanoparticle-based cancer treatments. Full article

Obesity is a complex, diverse, and multifactorial disease that has become a significant public health concern. It is a modifiable risk factor for developing type 2 diabetes (T2D). The current classification systems rely on anthropometric measurements, such as body mass index (BMI), which cannot capture the physiopathological diversity of this disease. This study aimed to analyze the metabolic signatures of obesity and diabetes using ¹H-nuclear magnetic resonance (NMR). Obese patients with BMI ≥ 25 kg/m² (according to the Asian cut-off value) with different diabetes status scheduled to undergo metabolic-bariatric surgery at three hospitals were prospectively recruited for this study. Plasma samples of 111 obese patients and 26 healthy controls were analyzed by ¹H-NMR. When compared among groups with different diabetes statuses, four clusters with no differences in BMI but different metabolomics profiles were obtained. These clusters highlight intricate metabolic relationships associated with obesity and diabetes. This study demonstrated the benefits of using precision techniques like ¹H-NMR to better early detection, substantially decreasing the risk of developing T2D and its related complications. This study is the first to report on metabolic markers and altered metabolic profiles of T2D and prediabetes among obese Malaysians with a BMI cut-off value for the Asian population. Full article

Background/Objectives: The insula is a deep, functionally heterogeneous region involved in various pathological conditions. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising therapeutic avenue for neuromodulation, yet very few studies have directly investigated its effects on insular activity. Moreover, empirical evidence of target engagement of this region remains scarce. This study aimed to stimulate the insula with rTMS and assess blood oxygen level-dependent (BOLD) signal modulation using concurrent functional magnetic resonance imaging (fMRI). Methods: Ten participants were recruited, six of whom underwent a single session of 5 Hz high-frequency rTMS over the right insular cortex inside the MRI scanner. Stimulation was delivered using a compatible MRI-B91 TMS coil. Stimulation consisted of 10 trains of 10 s each, with a 50 s interval between trains. Frameless stereotactic neuronavigation ensured precise targeting. Paired t-tests were used to compare the mean BOLD signal obtained between stimulation trains with resting-state fMRI acquired before the rTMS stimulation session. A significant cluster threshold of q < 0.01 (False Discovery Rate; FDR) with a minimum cluster size of 10 voxels was applied. Results: Concurrent rTMS-fMRI revealed the significant modulation of BOLD activity within insular subregions. Increased activity was observed in the anterior, middle, and middle-inferior insula, while decreased activity was identified in the ventral anterior and posterior insula. Additionally, two participants reported transient dysgeusia following stimulation, which provides further evidence of insular modulation. Conclusions: These findings provide preliminary evidence that rTMS can modulate distinct subregions of the insular cortex. The combination of region-specific BOLD responses and stimulation-induced dysgeusia supports the feasibility of using rTMS to modulate insular activity. Full article

Phenylene-based bis-oxamate polydentate ligands offer a unique opportunity for creating a large variety of coordination compounds, in which paramagnetic metal ions are strongly magnetically coupled. The employment of imino nitroxyl (IN) radicals as supplementary ligands confers numerous benefits, including the strong ferromagnetic interaction between Ni and IN. Furthermore, the chelating IN can act as a capping ligand, thereby impeding the formation of coordination polymers. In this study, we present the molecular and crystal structure and experimental and theoretical magnetic behavior of an exceptional neutral trinuclear complex [Ni(L³⁻)₂(IN)₃]∙5CH₃OH (1) (L is N,N′-1,3-phenylenebis-oxamic acid; IN is [4,4,5,5-tetramethyl-2-(6-methylpyridin-2-yl)-4,5-dihydro-1H-imidazol-1-yl]oxidanyl radical) with a cyclic triangular arrangement. Moreover, in this compound three Ni²⁺ ions are linked by the two bis-oxamate ligands playing a rare tritopic function due to an unprecedented triple deprotonation of the related meta-phenylene-bis(oxamic acid). The main evidence of such a deprotonation of the ligand is the neutrality of the cluster, since there are no anions or cations compensating for its charge in the crystals of the compound. Despite the presence of six possible magnetic couplings in the trinuclear cluster 1, its behavior was reproduced with a high degree of accuracy using a three-J model and ZFS, under the assumption that the three different Ni-IN interactions are equal to each other, whereas only two equivalent-in-value Ni-Ni interactions were taken into account, with the third one being equated to zero. Our study indicates the presence of two opposite-in-nature types of magnetic interactions within the triangular core. DFT and CASSCF/NEVPT2 calculations were completed to support the experimental magnetic data simulation. Full article

Anopheles darlingi is a major malaria vector in South America. Understanding its population dynamics is critical for designing effective vector control strategies. While various Anopheles collection methods exist, they may sample distinct populations. Microsatellite genotyping across nine loci was performed to characterize An. darlingi populations, which were collected in French Guiana between 6:30 p.m. and 7:00 a.m. using human landing catch (HLC) or Mosquito Magnet^® (MM) traps. Traps were arranged in a 3 × 3 Latin square design to minimize possible effects of geographical position. Pairwise F_ST index and discriminant analyses of principal components (DAPC) were used to make comparisons. A total of 431 An. darlingi were analyzed. No significant genetic differentiation was observed between collection methods or time slots (F_ST values non-significant, p > 0.25), with DAPC revealing a single genetic cluster. Despite documented phenotypic variations, no significant population structure was detected among An. darlingi sampled in a rural village in French Guiana via collection methods or time slots. These findings confirm that mosquitoes collected with these various methods or time slots are suitable for the molecular studies of An. darlingi in French Guiana. In this context, Mosquito Magnet^® traps could also represent an alternative to the now controversial human landing catch. Full article