Search Results (2,097)

Morbid obesity is a complex, multifactorial disorder characterized by metabolic and inflammatory dysregulation. The aim of this study was to observe changes in obese patients adhering to a personalized nutrition plan based on multi-omic data. This study included 14 adult patients with a body mass index (BMI) > 40 kg/m² who were consecutively recruited from those presenting to our outpatient clinic and who met the inclusion criteria. Clinical, biochemical, hormonal, and glycomic parameters were assessed, along with whole-genome sequencing (WGS) that included a focused analysis of obesity-associated genes and an extended analysis encompassing genes related to cardiometabolic disorders, hereditary cancer risk, and nutrigenetic profiles. Patients were stratified into nutrigenetic clusters using a patented unsupervised machine learning platform (German Patent Office, No. DE 20 2025 101 197 U1), which was employed to generate personalized nutrigenetic dietary recommendations for patients with morbid obesity to follow over a six-month period. At baseline, participants exhibited elevated glucose, insulin, homeostatic model assessment for insulin resistance (HOMA-IR), triglycerides, and C-reactive protein (CRP) levels, consistent with insulin resistance and chronic low-grade inflammation. The majority of participants harbored risk alleles within the fat mass and obesity-associated gene (FTO) and the interleukin-6 gene (IL-6), together with multiple additional significant variants identified across more than 40 genes implicated in metabolic regulation and nutritional status. Using an AI-driven clustering model, these genetic polymorphisms delineated a uniform cluster of patients with morbid obesity. The mean GlycanAge index (56 ± 12.45 years) substantially exceeded chronological age (32 ± 9.62 years), indicating accelerated biological aging. Following a six-month personalized nutrigenetic dietary intervention, significant reductions were observed in both BMI (from 52.09 ± 7.41 to 34.6 ± 9.06 kg/m², p < 0.01) and GlycanAge index (from 56 ± 12.45 to 48 ± 14.83 years, p < 0.01). Morbid obesity is characterized by a pro-inflammatory and metabolically adverse molecular signature reflected in accelerated glycomic aging. Personalized nutrigenetic dietary interventions, derived from AI-driven analysis of whole-genome sequencing (WGS) data, effectively reduced both BMI and biological age markers, supporting integrative multi-omics and machine learning approaches as promising tools in precision-based obesity management. Full article

Illegal dumping poses serious risks to ecosystems and human health, requiring effective and timely monitoring strategies. Advances in uncrewed aerial vehicles (UAVs), photogrammetry, and deep learning (DL) have created new opportunities for detecting and characterizing waste objects over large areas. Within the framework of the EMERITUS Project, an EU Horizon Europe initiative supporting the fight against environmental crimes, this study evaluates the performance of pre-trained prompt-based multimodal (PBM) DL models integrated into ArcGIS Pro for object detection and segmentation. To test such models, UAV surveys were specially conducted at a semi-controlled test site in northern Italy, producing very high-resolution orthoimages and video frames populated with simulated waste objects such as tyres, barrels, and sand piles. Three PBM models (CLIPSeg, GroundingDINO, and TextSAM) were tested under varying hyperparameters and input conditions, including orthophotos at multiple resolutions and frames extracted from UAV-acquired videos. Results show that model performance is highly dependent on object type and imagery resolution. In contrast, within the limited ranges tested, hyperparameter tuning rarely produced significant improvements. The evaluation of the models was performed using low IoU to generalize across different types of detection models and to focus on the ability of detecting object. When evaluating the models with orthoimagery, CLIPSeg achieved the highest accuracy with F1 scores up to 0.88 for tyres, whereas barrels and ambiguous classes consistently underperformed. Video-derived (oblique) frames generally outperformed orthophotos, reflecting a closer match to model training perspectives. Despite the current limitations in performances highlighted by the tests, PBM models demonstrate strong potential for democratizing GeoAI (Geospatial Artificial Intelligence). These tools effectively enable non-expert users to employ zero-shot classification in UAV-based monitoring workflows targeting environmental crime. Full article

1,3-Dipolar cycloaddition reactions of nitrile imines are a powerful tool for the construction of spirocyclic frameworks, yet controlling chemoselectivity remains challenging when dipolarophiles contain multiple reactive sites. In this study, we investigated the cycloaddition of nitrile imines with 5-arylmethylene-2-methylthiohydantoins, which possess both exocyclic C=C and endocyclic C=N bonds. Nitrile imines were generated from hydrazonoyl chlorides under basic conditions and reacted with the thiohydantoin substrates under optimized reaction conditions. The cycloaddition proceeded smoothly, affording spiro-fused thiohydantoin–pyrazoline derivatives. In all cases, the reaction occurred selectively at the exocyclic C=C bond, while the C=N bond remained unreactive even in the presence of excess dipole. This chemoselectivity is attributed to the greater steric accessibility of the exocyclic double bond. These results clarify key factors governing nitrile imine chemoselectivity and provide a reliable approach to structurally complex spirocyclic thiohydantoin derivatives. Full article

Background: Platelet hyperreactivity contributes to occlusive thrombus formation in vessels, precipitating acute cardiovascular events such as myocardial infarction and stroke. Traditional Chinese Medicine (TCM) has been used for centuries, and numerous TCM herbs have been reported to exert anti-inflammatory and anticoagulant effects. Objectives: We sought to identify key compounds within the TCM-derived herbal extracts that regulate platelet activity. Methods: Crude and fractioned herbal extracts were screened for their ability to inhibit platelet activation in response to multiple agonists. Platelet aggregation and flow cytometry were used to assess the potency and selectivity of the compounds within the extracts. Results: Three extracts, di gu pi (DGP), san qi (SQ), and zi cao (ZC), demonstrated inhibitory activity and were subsequently fractionated. Fractions derived from DGP, the root bark of Lycium chinense, inhibited platelet aggregation and suppressed integrin activation and granule secretion downstream of collagen receptor signaling. Further analysis identified the oxidized lipids 9(S)-hydroxy-9Z,11E-octadecadienoic acid (9-HODE), 13(S)-HODE, and 13(S)-hydroxy-9Z,11E,15Z-octadecatrienoic acid (13-HOTrE) as constituents of the bioactive fractions. Both 13-HODE and 13-HOTrE selectively inhibited collagen-mediated platelet aggregation without affecting thrombin-induced activation. Conclusions: Collectively, these findings identify oxylipins in TCM as promising candidates for the development of antiplatelet therapies targeting platelet activity and thrombosis. These oxylipins may represent novel approaches for thrombosis and have high therapeutic potential for development as next-generation antiplatelet drugs. Full article

The widespread use of petroleum derivatives in industrial settings poses a challenge due to their toxicity and the difficulty of removing them from tanks, pipes, and equipment. Conventional degreasers are generally expensive, toxic, and harmful to workers’ health and the environment. In this study, an environmentally friendly biodetergent formulated from natural ingredients was produced in a pilot plant with 480 L h⁻¹ capacity, in 250 L homogenizers, at 3500 rpm and 80 °C, and its performance evaluated under different operating conditions. Furthermore, the biodetergent efficiency was compared with that of commercial degreasers commonly used in industrial settings. Stability tests indicated 100% stable emulsion with 2.0% fatty alcohol and 1.0% stabilizing gum after one week of storage. In application tests, the biodetergent promoted up to 100% removal of heavy fuel oil (OCB1) and diesel from metal surfaces, both in concentrated and (1:1 v/v) diluted forms. In direct comparisons, the product performed equally or better than commercial degreasers, notably removing >95% of OCB1 in 10 min and maintaining efficiency after multiple reuse cycles. Unlike acidic or solvent-based formulations, the biodetergent did not induce corrosion on pieces or release toxic vapors when applied to heated surfaces. In summary, the developed bioproduct demonstrated industrial scalability and high efficiency, constituting a sustainable alternative for petrochemical cleaning operations in onshore and offshore environments. Full article

This study offers a comprehensive generalization of the Gould–Hopper polynomials and their Appell-type analogs. Employing the quasi-monomiality approach, we delineate fundamental analytical characteristics, including recurrence relations, associated multiplicative and differential operators, and governing differential equations. Additionally, we derive series representations and determinantal expressions for this newly defined polynomial family. Within this framework, several significant subclasses are introduced and examined, such as the generalized Gould–Hopper-based Appell polynomials. The formulation is further extended using fractional operator techniques to explore their intrinsic structural attributes. Moreover, we construct and investigate new families, namely, the generalized Gould–Hopper-based Bernoulli, Gould–Hopper-based Euler, and Gould–Hopper-based Genocchi polynomials, emphasizing their operational and algebraic properties. Collectively, these findings advance the theory of special functions and provide a foundation for potential applications in mathematical physics and the study of differential equations. Full article

Despite significant medical advancements, cancer remains the second leading cause of death in the US, causing over 600,000 deaths per year. One emerging field, pathway analysis, is promising but still relies on manually derived wet lab data, which is time-consuming to acquire. This work proposes an efficient, effective, end-to-end framework for Artificial Intelligence (AI)-based pathway analysis that predicts both cancer severity and mutation progression in order to recommend possible treatments. The proposed technique involves a novel combination of time-series machine learning models and pathway analysis. First, mutation sequences were isolated from The Cancer Genome Atlas (TCGA) Database. Then, a novel preprocessing algorithm was used to filter key mutations by mutation frequency. This data was fed into a Recurrent Neural Network (RNN) that predicted cancer severity. The model probabilistically used the RNN predictions, information from the preprocessing algorithm, and multiple drug-target databases to predict future mutations and recommend possible treatments. This framework achieved robust results and Receiver Operating Characteristic (ROC) curves (a key statistical metric) with accuracies greater than 60%, similar to existing cancer diagnostics. In addition, preprocessing played a key role in isolating a few hundred key driver mutations per cancer stage, consistent with current research. Heatmaps based on predicted gene frequency were also generated, highlighting key mutations in each cancer. Overall, this work is the first to propose an efficient, cost-effective end-to-end framework for projecting cancer prognosis and providing possible treatments without relying on expensive, time-consuming wet lab work. Full article

Table question answering has been studied using datasets drawn from a variety of tabular sources and task formats. However, most publicly available resources have been created in high-resource languages such as English. For low-resource languages, researchers are often required to construct new datasets or translate existing ones, which incurs substantial time, effort, and financial cost. In contrast to natural language text, table data consists of structured entries whose interpretation is less affected by language-specific syntax or word order. In this work, we present a cost-effective strategy for multilingual table QA that relies on selectively translating only the questions of existing datasets. Leveraging the language-agnostic structure of tables, our approach maintains the original table content while translating queries into multiple target languages. To address possible performance drops caused by using table data in the source language rather than the target language, we apply cross-lingual adaptation techniques using contrastive learning and adversarial training. In addition, to strengthen reasoning ability while avoiding degradation in languages not seen during pre-training, we perform supplementary pre-training of a RoBERTa-based multilingual encoder with SQL-derived table data. Finally, we extend our investigation beyond encoder-based architectures and evaluate decoder-only large language models under the same multilingual table QA setting. The experiments show that LLaMA-3 models exhibit strong cross-lingual generalization even without using translated table context and often achieve competitive performance using only Korean table data. Moreover, the performance gap among training configurations such as translated queries or translated datasets is notably smaller compared to encoder-based models, highlighting the inherent multilingual robustness of modern LLMs. We further evaluate LLaMA-3 models on domain-specific table datasets and observe that domain knowledge acquired from Korean tables transfers effectively across languages even without multilingual supervision, underscoring the potential of LLMs for specialized multilingual table reasoning. These findings demonstrate that LLMs can serve as an effective alternative for multilingual table QA, particularly in low-resource or partially translated environments. Full article

Detecting trace metals in soil across geologically diverse terrains remains challenging due to complex mineral–metal interactions and the limited spatial coverage of traditional geochemical tests. This study develops a scalable VIS–NIR–SWIR spectroscopy and machine learning (ML) framework to predict and map soil concentrations of Cr, As, Cu, and Cd in the Aligudarz District, located within the geotectonically complex Sanandaj–Sirjan Zone of western Iran. Laboratory reflectance spectra (~350–2500 nm) obtained from 110 soil samples were pre-processed using derivative filtering, scatter-correction techniques, and genetic algorithm (GA)-based wavelength optimisation to enhance diagnostic absorption features linked to Fe-oxides, clay minerals, and carbonates. Multiple ML-based approaches, including artificial neural networks (ANNs), support vector regression (SVR), and partial least squares regression (PLSR), as well as stepwise multiple linear regression (SMLR), were compared using nested, spatial, and external validation. Nonlinear models, particularly ANNs, exhibited the highest predictive accuracy, with strong generalisation confirmed via an independent test set. GA-selected wavelengths and derivative-enhanced spectra revealed mineralogical controls on metal retention, confirming that spectral predictions reflect underlying geological processes. Ordinary kriging of spectral-ML residuals generated spatially consistent metal-distribution maps that aligned well with local and regional geological features. The integrated framework demonstrates high predictive accuracy and operational scalability, providing a reproducible, field-ready method for rapid geochemical assessment. The findings highlight the potential of VIS–NIR–SWIR spectroscopy, combined with advanced modelling and geostatistics, to support environmental monitoring, mineral exploration, and risk assessment in geologically complex terrains. Full article

The large-scale deployment of electric power wireless private networks (EPWPNs) has significantly increased the number of base stations in substations, transmission corridors, and distribution terminals, leading to rapidly rising electricity expenditure for continuous wireless coverage and power-grid monitoring services. However, the increasing number of base stations deployed across substations and distribution networks has led to rising electricity expenditure, making cost-effective energy supply a critical challenge. To reduce the operating costs of base station clusters and enhance the economic efficiency of power supply, this paper proposes a multimodal power consumption optimization method that coordinates wind energy, solar energy, and energy storage based on user interaction behavior. First, considering user interaction characteristics and the complementarity of multiple energy sources, a dual-layer cellular network architecture consisting of macro- and micro-base stations is constructed. This architecture incorporates grid power purchases, wind power generation, and photovoltaic energy. An optimization model is then developed, which includes both equipment operation constraints and energy interaction constraints. Second, the key factors influencing energy consumption are analyzed using operational research methods. The existence of an optimal solution for the energy consumption function is demonstrated based on the Weierstrass optimization theorem. An energy-saving strategy for base stations under user group access is then derived using Karush–Kuhn–Tucker (KKT) conditions. Through spatio-temporal (ST) dynamic analysis, the coupling relationships among wind power, solar energy, energy storage, and grid electricity purchases are quantified. Based on this analysis, a multimodal cost optimization scheme utilizing dynamic bandwidth allocation is proposed. Simulation results demonstrate that, compared with traditional single-source power supply models and representative existing optimization schemes, the proposed multimodal energy scheduling framework can significantly reduce the operating cost of base station clusters while maintaining communication performance. Full article

The Atlantic City–South Pass (ACSP) orogenic gold district, Wind River Mountains, Wyoming, occurs in the Archean South Pass Greenstone Belt primarily within greywackes and igneous rocks metamorphosed to the upper greenschist–lower amphibolite facies. Approximately 10 Mt of gold has been produced from pyrite and arsenopyrite-bearing quartz veins in deformation zones at the brittle–ductile transition. Multiple generations of primary and/or pseudosecondary fluid inclusions in gold-bearing quartz veins include one- and two-phase gaseous CO₂-CH₄ ± N₂ inclusions and two- and three-phase gaseous CO₂-CH₄-H₂O inclusions with rare NaCl daughter minerals. These primary/pseudosecondary inclusions show a broad range of homogenization temperatures (T_h) of 177.2 to 420.0 °C, with salinities of halite-bearing inclusions of >26 wt. % NaCl, with a high concentration of CaCl₂. Secondary aqueous inclusions formed at lower values of T_h (80.9 to 243.4 °C, with one outlier of 301.1 °C). Carbon from graphitic schists associated with gold-quartz veins yields values of δ¹³C = −28.5 to −19.1 per mil, suggesting that the light C isotope compositions of some carbonates (δ¹³C = −11.0 to −1.5 per mil) involved exchange reactions with graphite in the schists. Isotopic compositions of sulfur in sulfides (δ³⁴S = −1.0 to 3.6 per mil), oxygen in vein quartz (δ¹⁸O = 7.36 to 10.38 per mil), and hydrogen in fluid inclusions in vein quartz (δD = −125 to −55 per mil) are permissive of both magmatic-hydrothermal and metamorphic dehydration models for the origin of gold mineralization. However, a potential source of magmatic–hydrothermal fluids, the post-metamorphic Louis Lake granodiorite was unlikely to transport gold in a vapor state to become focused into shear zones as previously proposed. We favor a metamorphic dehydration model in which gold was derived from the South Pass supracrustal sequence and deposited in second-order shear zones that are spatially related to the first-order Roundtop Mountain Deformation Zone. Full article

Accurate channel estimation is critical for enabling effective directional beamforming and spectrally efficient transmission in beamspace massive multiple-input multiple-output (MIMO) systems. However, conventional model-driven algorithms are derived from idealized mathematical models and typically suffer severe performance degradation under model mismatches caused by complex and nonideal propagation environments. Although data-driven deep learning (DL) approaches can learn channel characteristics from data, they typically require large-scale training datasets and demonstrate limited generalization capability. To overcome these limitations, we propose a model-data hybrid-driven network (MD-HDN) scheme to address the wideband beamspace channel estimation problem. In the MD-HDN scheme, we unfold the vector approximate message passing (VAMP) algorithm into a trainable network, where a novel shrinkage function is introduced to enhance the estimation accuracy. Extensive numerical results confirm that the proposed MD-HDN scheme can significantly outperform existing schemes under various signal-to-noise ratio (SNR), and achieve substantial improvements in both estimation accuracy and robustness. Full article

Carburizing process optimization requires accurate prediction of multiple interrelated outcomes, yet existing models either oversimplify the physics or require prohibitively large datasets. Here, we present a physics-informed machine learning (PIML) cascade model for vacuum carburizing of 20Cr2Ni4 gear steel that predicts surface carbon content, maximum hardness, and effective case depth through a three-stage sequential architecture. The model integrates Fick’s diffusion law and empirical carbon–hardness relationships with ensemble learning using physics-derived features to reduce data requirements while maintaining interpretability. Validation against experimental data yields coefficient of determination values of 0.968 (surface carbon, RMSE = 0.0023 wt%), 0.963 (maximum hardness, RMSE = 1.27 HV), and 0.999 (case depth, RMSE = 0.0053 mm) on physics-augmented test data; leave-one-out cross-validation (LOOCV) on original experimental data yields $R^2$ = 0.87–0.95, representing true generalization capability. Feature importance analysis reveals that physics-derived features collectively account for over 70% of the prediction power, with the characteristic diffusion length ($\sqrt{Dt}$) contributing 42.2%, followed by temperature-related features (22.4%) and time-related features (14.8%). Compared to pure physics-based and data-driven approaches, the proposed framework achieves superior accuracy for case depth prediction while preserving physical consistency. The methodology demonstrates potential for adaptation to other vacuum-carburizing applications with similar Cr-Ni steel compositions, although extension to fundamentally different processes (e.g., gas carburizing and nitriding) would require process-specific recalibration. Full article

Hemostasis depends on the coordinated interaction between platelets, coagulation factors, endothelium, and shear forces. Current point-of-care (POC) assays evaluate isolated components of haemostasis or operate under nearly static conditions, limiting their ability to reproduce physiological thrombus formation. In this study, we performed the technical validation of Smart Clot, a fully automated, microfluidic POC platform that quantifies platelet aggregation, fibrin formation, and total thrombus growth under controlled arterial shear using unmodified whole blood. Recalcified citrated blood was perfused over collagen at $\dot{\gamma }$_w = 300 s⁻¹. Dual-channel epifluorescence microscopy acquired platelet and fibrin(ogen) signals at 1 frame per second. Integrated Density time-series were fitted with a five-parameter logistic model; first derivatives and their integrals yielded standardized pseudo-volumes for platelets, fibrin(ogen), and total thrombus. Sixty-two healthy donors established reference distributions; one-hundred-thirteen patients on antithrombotic therapy assessed pharmacodynamic sensitivity. Platelet-derived parameters were approximately normally distributed, whereas fibrin(ogen) and total thrombus values followed log-normal patterns. Anticoagulants and antiplatelet agents produced graded, mechanism-consistent inhibition of all thrombus components. Cardiopulmonary bypass samples showed profound but transient suppression of platelet and fibrin activity. Across activity ranges, multiple statistical assessments supported high analytical repeatability. Smart Clot provides rapid, reproducible, flow-aware quantification of platelet–fibrin dynamics, capturing pharmacological modulation and peri-operative impairment with high sensitivity. These results support its potential as a next-generation POC assay for physiological hemostasis assessment. Full article

Mushrooms are valued for their nutritional qualities and have been used in traditional medicine since the Neolithic era. They exhibit various bioactivities, including antioxidant, hypocholesterolemic, immunomodulatory, and anticancer effects. The anticancer effects arise via direct action on tumor cells and indirect modulation of the immune system; the latter is the predominant mechanism. Numerous studies indicate that various mushroom species are potent immunostimulants because their cell wall polysaccharides and proteoglycans are recognized by intestinal immune cells. This enhances antitumor immunity through multiple molecular pathways. However, their direct effects on cancer cells are of questionable physiological relevance due to bioavailability constraints. Nevertheless, we hypothesize that the accumulation of non-absorbed polysaccharides in the gastrointestinal tract positions mushrooms as dual-action agents with the potential to treat colorectal cancer by providing indirect immunomodulation and direct local tumor suppression. Conversely, the direct anticancer effects of mushrooms are generally attributed to bioactive secondary metabolites that influence essential cellular processes, including signaling pathways, cell cycle regulation, apoptosis, autophagy, cellular migration, invasion, and cancer stem cell characteristics. Beyond these anticancer effects, clinical evidence suggests that certain mushroom-derived substances can improve survival outcomes for cancer patients and provide supportive care benefits in oncology, thereby improving quality of life. Specifically, mushrooms may mitigate the side effects of chemotherapy and radiotherapy, bolster immune function often suppressed by cancer treatments, and enhance overall well-being. In this review, we discuss the therapeutic benefits of dietary and medicinal mushrooms in cancer care, as well as unresolved challenges and future research directions. Full article