Search Results (2,419)

The Tuztaşı low-sulfidation epithermal Au–Ag deposit (Biga Peninsula,Türkiye) records a multi-stage hydrothermal history that can be interpreted through the trace and rare-earth-element (REE) chemistry of quartz. High-precision LA-ICP-MS analyses of five representative quartz samples (23 ablation spots; 10 analytically robust) reveal two fluid stages. Early fluids were cold, dilute meteoric waters (δ¹⁸O₍H₂O₎ ≈ –6.8 to +0.7‰), whereas later fluids circulated deeper, interacted with felsic basement rocks, and evolved in composition. Mineralized quartz displays marked enrichment in As (raw mean = 2 854 ± 6 821 ppm; filtered mean = 70 ± 93 ppm; one spot 16,775 ppm), K (498 ± 179 ppm), and Sb (57.8 ± 113 ppm), coupled with low Ti/Al (<0.005) and elevated Ge/Si (0.14–0.65 µmol mol⁻¹). Chondrite-normalized REE patterns show pronounced but variable LREE enrichment ((La/Yb)_n ≤ 45.3; ΣLREE/ΣHREE up to 10.8) and strongly positive Eu anomalies (δEu ≤ 9.3) with slightly negative Ce anomalies (δCe ≈ 0.29); negligible Ce–Eu covariance (r² ≈ 0.05) indicates discrete redox pulses. These signatures indicate chemically evolved, reducing fluids conducive to Au–Ag deposition. By contrast, barren quartz is characterized by lower pathfinder-element contents, less fractionated REE profiles, higher Ti/Al, and weaker Eu anomalies. A composite exploration toolkit emerges: As > 700 ppm, As/Sb > 25, Ti/Al < 0.005, Ge/Si > 0.15 µmol mol⁻¹, and δEu ≫ 1 reliably identify ore-bearing zones when integrated with δ¹⁸O data and fluid-inclusion microthermometry from earlier studies on the same vein system. This study provides one of the first systematic applications of integrated trace-element and REE analysis of quartz to a Turkish low-sulfidation epithermal system, offering an applicable model for vectoring mineralization in analogous settings worldwide Full article

Uveal melanoma (UM), the most common primary intraocular malignancy in adults, poses a unique clinical challenge due to its high propensity for liver metastasis and poor responsiveness to conventional therapies. Despite the expanding landscape of immunotherapy in oncology, progress in managing metastatic uveal melanoma (mUM) remains limited, and no universally accepted standard of care has been established. In this review, we examine the current state and evolving strategies in immunotherapy for mUM, focusing on immune checkpoint inhibitors (ICIs), T cell receptor (TCR)-engineered therapies, and tumor-targeted vaccines. We also present a meta-analytical comparison of clinical outcomes between ICI monotherapy and combination regimens, alongside the recently FDA-approved T cell engager tebentafusp. Our analysis indicates that the triple combination of Ipilimumab, anti-PD-1 agents, and tebentafusp significantly enhances objective response rates, disease control rates, 1-year overall survival rates, and median overall survival (mOS) compared to ICI monotherapy alone. However, this enhanced efficacy is accompanied by increased toxicity due to broader immune activation. In contrast, tebentafusp offers superior tumor specificity and a more favorable safety profile in HLA-A*02:01-positive patients, positioning it as a preferred therapeutic option for this genetically defined subset of UM. Additionally, early-phase studies involving dendritic cell-based immunotherapies and peptide vaccines has shown encouraging signs of tumor-specific immune activation, along with improved tolerability. Collectively, this review underscores the urgent need for more precise and effective immunotherapeutic approaches tailored to the unique biology of mUM. Full article

In a European context, facing pressure to digitalize public administration, the integration of artificial intelligence (AI) at the local level remains a deeply uneven and empirically poorly understood process. This study investigates the degree of adoption of artificial intelligence (AI) in local public administrations in Germany, exploring territorial disparities and institutional factors influencing this transition. Based on a national sample of 347 municipalities, this research proposes a composite AI adoption index, built by integrating six relevant indicators (including the use of conversational bots and the automation of internal and decision-making processes). In the simulations, local administration profiles were differentiated according to factors such as IT staff (with a weight of 30%), the degree of urbanization (25%), and participation in digital networks (20%), reflecting significant structural variations between regions. The analysis model used is a multilevel one, which highlights the combined influences of local and regional factors. The results indicate a clear stratification of digital innovation capacity, with significant differences between eastern and western Germany, as well as between urban and rural environments. The study contributes to the specialized literature by developing a replicable analytical tool and provides public policy recommendations for reducing interregional digital divides. Full article

Generative AI tools are becoming increasingly common in education. They make many tasks easier, but they also raise questions about how students interact with information and whether their ability to think critically might be affected. Although these tools are now part of many learning processes, we still do not fully understand how they influence cognitive behavior or digital maturity. This study proposes a model to help identify different user profiles based on how they engage with AI in educational contexts. The approach combines fuzzy clustering, the Analytic Hierarchy Process (AHP), and explainable AI techniques (SHAP and LIME). It focuses on five dimensions: how AI is used, how users verify information, the cognitive effort involved, decision-making strategies, and reflective behavior. The model was tested on data from 1273 users, revealing three main types of profiles, from users who are highly dependent on automation to more autonomous and critical users. The classification was validated with XGBoost, achieving over 99% accuracy. The explainability analysis helped us understand what factors most influenced each profile. Overall, this framework offers practical insight for educators and institutions looking to promote more responsible and thoughtful use of AI in learning. Full article

Fruits and vegetables are crucial components of a healthy diet, which are susceptible to pests. Therefore, the application of pesticides is a basic manner of crop chemical protection. The aim of this study was a comprehensive analysis of pesticide occurrence in 1114 samples of fruits and vegetables. A unified multi-analytical protocol was used composed of primary–secondary amine/graphitized carbon black/magnesium sulfate to purify samples with diversified profile of interfering substances. Moreover, the obtained analytical data were used to evaluate the critical acute health risk in subpopulations of children and adults within European limits criteria. Out of 550 pesticides analyzed, 38 and 69 compounds were noted in 58.6% of fruits and 44.2% of vegetables, respectively. Acetamiprid (14.1% of all detections) and captan (11.3%) occurred the most frequently in fruits, while pendimethalin (10.6%) and azoxystrobin (8.6%) occurred the most frequently in vegetables. A total of 28% of vegetable and 43% of fruit samples were multiresidues with up to 13 pesticides in dill, reaching a final concentration of 0.562 mg kg⁻¹. Maximum residue level (MRL) was exceeded in 7.9% of fruits and 7.3% of vegetables, up to 7900% MRL for chlorpyrifos in dill (0.79 mg kg⁻¹). Notably, 8 out of 38 pesticides found in fruits (21%; 1.2% for carbendazim) and 24 out of 69 compounds in vegetables (35%, 7.4% for chlorpyrifos) were not approved in the EU. Concentrations of pesticides exceeding MRL were used to assess acute health risk for children and adults. Moreover, the incidence of acute health risk was proved for children consuming parsnip with linuron (156%). In other cases, it was below 100%, indicating that Polish food is safe. The work provides reliable and representative scientific data on the contamination of fruits and vegetables with pesticides. It highlights the importance of legislative changes to avoid the occurrence of not approved pesticides in the EU, increasing food and health safety. Full article

With the development of various new types of instrumental aroma sensing technologies, there is a need for methodologies that help developers and users evaluate the performance of the different devices. This study introduces a simple method that uses standard coffee beverages, reproducible worldwide, thus allowing users to compare aroma sensing devices and technologies globally. Eight different variations of commercial coffee capsules were used to brew espresso coffees (40 mL), consisting of either Arabica coffee or a blend of Robusta and Arabica coffee, covering a wide range of sensory attributes. The AlphaMOS Astree electronic tongue (equipped with sensors based on chemically modified field-effect transistor technology) and the AlphaMOS Heracles NEO and the Volatile Scout3 electronic noses (both using separation technology based on gas chromatography) were used to describe the taste and odor profiles of the freshly brewed coffee samples and also to compare them to the various sensory characteristics declared on the original packaging, such as intensity, roasting, acidity, bitterness, and body. Linear discriminant analysis (LDA) results showed that these technologies were able to classify the samples similarly to the pattern of the coffees based on the human sensory characteristics. In general, the arrangement of the different coffee types in the LDA results—i.e., the similarities and dissimilarities in the types based on their taste or smell—was the same in the case of the Astree electronic tongue and the Heracles electronic nose, while slightly different arrangements were found for the Scout3 electronic nose. The results of the Astree electronic tongue and those of the Heracles electronic nose showed the taste and smell profiles of the decaffeinated coffees to be different from their caffeinated counterparts. The Heracles and Scout3 electronic noses provided high accuracies in classifying the samples based on their odor into the sensory classes presented on the coffee capsules’ packaging. Despite the technological differences in the investigated devices, the introduced coffee test could assess the similarities in the taste and odor profiling capacities of the aroma fingerprinting technologies. Since the coffee capsules used for the test can be purchased all over the world in the same quality, these coffees can be used as global standard samples during the comparison of different devices applying different measurement technologies. The test can be used to evaluate instrumentational and data analytical developments worldwide and to assess the potential of novel, cost-effective, accurate, and rapid solutions for quality assessments in the food and beverage industry. Full article

Background/Objectives: The Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway is a critical mediator of immune regulation, inflammation, and cancer progression. Although implicated in colorectal cancer (CRC) pathogenesis, its molecular heterogeneity and clinical significance remain insufficiently characterized—particularly within early-onset CRC (EOCRC) and across diverse treatment and demographic contexts. We present AI-HOPE-JAK-STAT, a novel conversational artificial intelligence platform built to enable the real-time, natural language-driven exploration of JAK/STAT pathway alterations in CRC. The platform integrates clinical, genomic, and treatment data to support dynamic, hypothesis-generating analyses for precision oncology. Methods: AI-HOPE-JAK-STAT combines large language models (LLMs), a natural language-to-code engine, and harmonized public CRC datasets from cBioPortal. Users define analytical queries in plain English, which are translated into executable code for cohort selection, survival analysis, odds ratio testing, and mutation profiling. To validate the platform, we replicated known associations involving JAK1, JAK3, and STAT3 mutations. Additional exploratory analyses examined age, treatment exposure, tumor stage, and anatomical site. Results: The platform recapitulated established trends, including improved survival among EOCRC patients with JAK/STAT pathway alterations. In FOLFOX-treated CRC cohorts, JAK/STAT-altered tumors were associated with significantly enhanced overall survival (p < 0.0001). Stratification by age revealed survival advantages in younger (age < 50) patients with JAK/STAT mutations (p = 0.0379). STAT5B mutations were enriched in colon adenocarcinoma and correlated with significantly more favorable trends (p = 0.0000). Conversely, JAK1 mutations in microsatellite-stable tumors did not affect survival, emphasizing the value of molecular context. Finally, JAK3-mutated tumors diagnosed at Stage I–III showed superior survival compared to Stage IV cases (p = 0.00001), reinforcing stage as a dominant clinical determinant. Conclusions: AI-HOPE-JAK-STAT establishes a new standard for pathway-level interrogation in CRC by empowering users to generate and test clinically meaningful hypotheses without coding expertise. This system enhances access to precision oncology analyses and supports the scalable, real-time discovery of survival trends, mutational associations, and treatment-response patterns across stratified patient cohorts. Full article

Brachial plexus root avulsion [BPRA] and concomitant spinal cord injury [SCI] represent devastating injuries that come with limited hope for recovery owing to the adult spinal cord’s loss of intrinsic ability to spontaneously regenerate. BPRA/SCI is an enormous public health issue the world over, and its catastrophic impact goes beyond the patient, the family, businesses, and national health budgets, draining billions of dollars annually. The rising population and economic growth have seen the incidence of SCI surging. Genomic, transcriptomic, and proteomic studies have yielded loads of information on the various molecular events that precede, regulate, and support both regenerative and degenerative pathways post-SCI. Metabolomics, on the other hand, comes in as the search for a cure and the objective monitoring of SCI severity and prognosis remains on the horizon. Despite the large number of review articles on metabolomics and its application fields such as in cancer and diabetes research, there is no comprehensive review on metabolite profiling to study disease mechanisms, biomarkers, or neuroprotection in SCI. First, we present a short review on BPRA/SCI. Second, we discuss potential benefits of metabolomics as applied in BPRA/SCI cases. Next, a look at the analytical techniques that are used in metabolomics. Next, we present an overview of the studies that have used metabolomics to reveal SCI metabolic fingerprints and point out areas of further investigation. Finally, we discuss future research directions. Full article

The analytical solutions for groundwater inflow into tunnels are usually developed under the condition of circular tunnels. However, real-world tunnels often have non-circular cross-sections, such as arched, lens-shaped, or egg-shaped profiles. Accurately assessing water inflow for these diverse tunnel shapes remains challenging. To address this gap, this study developed a closed-form analytical solution for water inflow into a deeply buried arched tunnel using the conformal mapping method. When the tunnel circumference degenerates to a circle, the analytical solution degenerates to the widely used Goodman’s equation. The solution also showed excellent agreement with numerical simulations carried out using COMSOL. Based on the analytical solution, the impact of various factors on water inflow Q was further discussed: (1) Q decreases as the boundary distance $D^{\ast }$ increases. And the boundary inclination angle ($\alpha -\pi /2$) significantly affects Q only when the boundary is close to the tunnel ($D^{\ast }<20$); (2) Q increases quickly with the upper arc radius $r_1^{\ast }$, while it shows minimal variation with the change in the lower arc radius $r_2^{\ast }$. The findings provide a theoretical foundation for characterizing water inflow into arched tunnels, thereby supporting improved tunnel planning and grouting system design. Full article

The increasing popularity of “superseeds” such as flax, sesame, amaranth and quinoa as functional foods raises the need for robust analytical methods for authentication purposes. In this work, a standardized workflow for the extraction, characterization and identification of unique peptides that may be used as markers to distinguish superseed species was investigated. Ammonium bicarbonate/urea (Ambi/urea) extraction, sodium dodecyl sulfate (SDS) buffer and trichloroacetic acid (TCA) precipitation were initially implemented and, based on the level and composition of the extracted proteins, the SDS buffer protocol was selected. Electrophoresis analysis revealed consistent protein profiles between biological replicates from each of the eleven seed species, confirming the reproducibility of the SDS buffer protocol. Targeted mass spectrometry successfully identified species-specific peptide markers for six of eleven superseeds investigated, including peptides from conlinins in flaxseed (WVQQAK), 11S globulins in sesame (LVYIER), oleosin in quinoa (DVGQTIESK), agglutin-like lectins in amaranth (CAGVSVIR), as well as cupin-like proteins in poppy seeds (INIVNSQK) and edestins in hemp seeds (FLQLSAER). Moreover, proteome cross-analysis allowed us to disqualify the isomeric peptide LTALEPTNR from 11S globulins present in amaranth and quinoa. However, no reliable markers were identified for chia, canihua, basil, black cumin, and psyllium seeds under current conditions. While this targeted proteomics approach shows promise for superseed authentication, comprehensive method validation and alternative strategies for marker-deficient species are required before routine implementation. Full article

This article presents an empirical evaluation of the technical and economic performance of a building-integrated photovoltaic (PV) system implemented at the Faculty of Architecture and Urbanism of the University of Cuenca, Ecuador. This study explores both stages of deployment, beginning with a 7.7 kWp pilot system and later scaling to a full 75.6 kWp configuration. This hourly monitoring of power exchanges with utility was conducted over several months using high-resolution instrumentation and cloud-based analytics platforms. A detailed comparison between projected energy output, recorded production, and real energy consumption was carried out, revealing how seasonal variability, cloud cover, and academic schedules influence system behavior. The findings also include a comparison between billed and actual electricity prices, as well as an analysis of the system’s payback period under different cost scenarios, including state-subsidized and real-cost frameworks. The results confirm that energy exports are frequent during weekends and that daily generation often exceeds on-site demand on non-working days. Although the university benefits from low electricity tariffs, the system demonstrates financial feasibility when broader public cost structures are considered. This study highlights operational outcomes under real-use conditions and provides insights for scaling distributed generation in institutional settings, with particular relevance for Andean urban contexts with similar solar profiles and tariff structures. Full article

Previous studies have described vehicle acceleration profiles in non-car-following scenarios; however, the underlying mechanisms governing these profiles remain incompletely understood. This study aims to enhance the understanding of these mechanisms by proposing an improved model based on an optimal control problem with two bounded conditions (OCP2B), segmenting vehicle acceleration curves into three distinct phases. Specifically, the proposed model imposes constraints on acceleration through maximum jerk and maximum acceleration functions, thereby capturing essential dynamics previously unexplained by conventional models. Our key contributions include establishing a comprehensive analytical framework for accurately describing vehicle acceleration profiles and elucidating critical characteristics overlooked in the prior literature. Our findings demonstrate that incorporating human-centric considerations, such as driving comfort, significantly enhances the model’s practical applicability. Moreover, the proposed approach provides crucial insights for designing autonomous vehicle (CAV) trajectories consistent with human driving behaviors and effectively predicts the movements of human-driven vehicles (HVs), thus facilitating smoother interactions and potentially reducing conflicts between CAVs and HVs. Full article

This study investigates the fabrication, nanomechanical behavior, and tribological performance of nanostructured superlattice coatings (NSCs) composed of alternating TiAlSiNb-N/TiCr-CN bilayers. Deposited via High-Power Ion-Plasma Magnetron Sputtering (HiPIPMS) onto 100Cr6 steel substrates, the coatings achieved nanohardness values of ~25 GPa and elastic moduli up to ~415 GPa. A novel empirical method was applied to extract stress–strain field (SSF) gradient and divergence profiles from nanoindentation load–displacement data. These profiles revealed complex, depth-dependent oscillations attributed to alternating strain-hardening and strain-softening mechanisms. Fourier analysis identified dominant spatial wavelengths, DWL, ranging from 4.3 to 42.7 nm. Characteristic wavelengths WL1 and WL2, representing fine and coarse oscillatory modes, were 8.2–9.2 nm and 16.8–22.1 nm, respectively, aligning with the superlattice period and grain-scale features. The hyperfine structure exhibited non-stationary behavior, with dominant wavelengths decreasing from ~5 nm to ~1.5 nm as the indentation depth increased. We attribute the SSF gradient and divergence spatial oscillations to alternating strain-hardening and strain-softening deformation mechanisms within the near-surface layer during progressive loading. This cyclic hardening–softening behavior was consistently observed across all NSC samples, suggesting it represents a general phenomenon in thin film/substrate systems under incremental nanoindentation loading. The proposed SSF gradient–divergence framework enhances nanoindentation analytical capabilities, offering a tool for characterizing thin-film coatings and guiding advanced tribological material design. Full article

The generation of realistic electrical load profiles is essential for advancing smart grid analytics, demand forecasting, and privacy-preserving data sharing. Traditional approaches often rely on large, high-resolution datasets and complex recurrent neural architectures, which can be unstable or ineffective when training data are limited. This paper proposes a data-driven framework based on a lightweight 1D Convolutional Wasserstein GAN with Gradient Penalty (Conv1D-WGAN-GP) for generating high-fidelity synthetic 24 h load profiles. The model is specifically designed to operate on small- to medium-sized datasets, where recurrent models often fail due to overfitting or training instability. The approach leverages the ability of Conv1D layers to capture localized temporal patterns while remaining compact and stable during training. We benchmark the proposed model against vanilla GAN, WGAN-GP, and Conv1D-GAN across four datasets with varying consumption patterns and sizes, including industrial, agricultural, and residential domains. Quantitative evaluations using statistical divergence measures, Real-vs-Synthetic Distinguishability Score, and visual similarity confirm that Conv1D-WGAN-GP consistently outperforms baselines, particularly in low-data scenarios. This demonstrates its robustness, generalization capability, and suitability for privacy-sensitive energy modeling applications where access to large datasets is constrained. Full article

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors. Full article