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Attention/Transformer-based artificial intelligence models for carotid segmentation and intima media Thickness/Plaque area measurements in Japanese ultrasound scans
No full textBackground and Motivation: In carotid ultrasound imaging, robust plaque segmentation and measurement are key requirements for dependable diagnosis and cardiovascular disease (CVD) risk evaluation. The latest techniques based on solo single stage deep learning UNet (M1:SS-UNet) leads to inconsistency in low contrast scans. We hypothesize that variants of UNet such as transformers can be more powerful paradigms. Method: Design and develop novel single-stage UNet-based transformer (M6:SSwAttSkip-XmerBot-UNet) for the far wall segmentation and intima-media thickness/plaque area measurements in Japanese diabetic cohort. We use augmented data and cross-validation protocol for performance evaluation and generalization. Performance evaluation includes 16 novel metrics, namely model GFLOPS, model size, jaccard index, performance to complexity, and training time. We benchmarked our novel transformer system against six models utilizing single and double stage attention-based configurations namely M1:SS-UNet, single-stage with attention-UNet (M2:SSwAttSkip-UNet), double-stage with attention-UNet (M3:DSwAttSkip-UNet), double-stage with attention in decoder UNet (M4:DSwAttDeco-UNet), single stage with transformer in the bottle neck layer (M5:SSTransBot-UNet) and single stage with swin-transformer in every layer(M7:SSSwin-UNet). Results: The results showed that M6:SSwAttSkip-XmerBot-UNet achieved a perfect normalization score of 100%, while M7:SSSwin-UNet and M5:SSTransBot-UNet followed closely by 86.07%. These models consistently outperformed M1:SS-UNet, M2:SSwAttSkip-UNet, M3:DSwAttSkip-UNet and M4:DSwAttDeco-UNet across all the 16 metrics. The final ranking of the UNet models were: SSwAttSkip-XmerBot-UNet > SSSwin-UNet > SSTransBot-UNet > DSwAttDeco-UNet > SS-UNet > SSwAttSkip-UNet > DSwAttSkip-UNet. M6:SSwAttSkip-XmerBot-UNet demonstrates comparable effectiveness with greater efficiency, making it a strong alternative. Conclusions: We conclude that transformer-based models like M6:SSwAttSkip-XmerBot-UNet provide highly accurate, reliable, and automated technique that segments and measures the risk of CVD
Probiotic Fermentation: A Strategy to Induce the Significant Amplification of Phenolics and Bioactivity in Milk Thistle Seeds
No full textThis study investigates how fermentation of milk thistle seeds (MTSs) by Saccharomyces cerevisiae var. boulardii, alone or with Lacticaseibacillus rhamnosus, affects phenolic compounds content and bioactivity of the resulting extracts. Microwave-assisted extraction parameters were optimized for maximal yield and validated for scale-up. The extracts were analyzed for total phenolic compounds (TPCs), total flavonoid compounds (TFCs), and bioactivities including antioxidant, antimicrobial, and prebiotic effects. Optimal extraction conditions were 70% ethanol, liquid to solid (L/S) ratio 30 mL/g, 180 W power, and 3 min duration, enabling energy-efficient recovery of antioxidants with higher yields than previously reported. Solid-state fermentation with S. boulardii significantly enhanced extraction efficiency, doubling TPC (647.6 ± 24.4 mg GAE/g dm) and TFC (87.04 ± 6.88 mg QE/g dm) contents, and antioxidant capacity (4.27 ± 0.19 mmol Fe2+/g dm) compared to non-fermented MTSs. Fermented extracts fully inhibited Staphylococcus aureus and partially Escherichia coli and Candida albicans. They also promoted the growth of probiotics such as S. boulardii and lactic acid bacteria strains, while non-fermented extracts showed opposite effects. These findings highlight the potential of MTS fermentation as a sustainable strategy to enhance bioactive compound yield and develop functional supplements that support human and animal health
Semi-Interpenetrated Poly(N,N-Diethylacrylamide)/Poly(Methyl Vinyl Ether)/Graphene-Oxide Composite Hydrogel: Synthesis, Characterization, and Swelling Behavior
No full textThe study introduces a novel class of thermo-responsive semi-interpenetrated hydrogel networks based on poly (N,N-diethylacrylamide) (PDEA) and poly (methyl vinyl ether) (PMVE), along with their graphene oxide (GO)-based composites. Poly (N,N-diethylacrylamide) was synthesized via free-radical polymerization using varying crosslinker concentrations. The swelling behavior of the hydrogels and composites was systematically investigated as a function of temperature and pH. Both pure and GO-incorporated hydrogels exhibited lower critical solution temperature (LCST)-type thermal responsiveness, with transition temperatures in the range of 40°C–45°C. Incorporation of GO enhanced swelling capacity, thermal stability, and responsiveness due to additional hydrophilic sites and physical crosslinking effects. Swelling studies under acidic (pH 2), neutral (pH 7), and mildly alkaline (pH 10) conditions demonstrated that GO-based composites consistently outperformed pure hydrogels, reaching maximum swelling at alkaline pH due to deprotonation-induced electrostatic repulsion. The point of zero charge (pHpzc) for both systems was determined to be approximately 7.9, indicating predominantly basic surface characteristics. Structural analyses using FTIR confirmed successful polymerization and GO incorporation, while SEM imaging revealed uniform, crosslinked morphologies. The results highlight that GO incorporation significantly improves hydrogel performance, making these composites promising candidates for smart adsorption, controlled release, and efficient treatment of dye-contaminated wastewater
New fixed point theorems by applying Singh’s framework to iterated ρ-Ćirić contractions
No full textThis paper develops new unified fixed point theorems by extending the classical Ćirić contractions through the iterative framework introduced by Singh in his generalization of Kannan-type mappings. We first revisit the two well-known definitions of Ćirić contractions in their standard forms and then introduce the concepts of ρ-Ćirić contraction and generalized ρ-Ćirić contraction, in which the contractive condition is imposed on higher iterates of the mapping. These extended classes retain the intrinsic structure of Ćirić-type mappings while significantly enlarging the family of operators that admit a unique fixed points. For each class, we establish comprehensive fixed point theorems ensuring existence, uniqueness, and global convergence of the associated Picard iteration. Our results unify and generalize several classical fixed-point theorems, including those of Banach, Kannan, Chatterjea, and the original Ćirić contractions, which arise as special cases within this broader framework. Illustrative examples are provided to demonstrate the applicability and sharpness of the proposed generalizations
Gentiana lutea Root Extract Attenuates Radiation-Induced Damage in Human PBMCs in vitro
No full textBackground: Ionizing radiation (IR) has the potential to harm cellular macromolecules, raising concerns across a range ofexposure contexts. This study investigates the radioprotective potential of Gentiana lutea root extract (GRE), a naturalproduct known for its antioxidant properties. Research Design: The GRE composition was analysed using ultra-performanceliquid chromatography (UPLC). Peripheral blood mononuclear cells were treated with 0.25-2 mg/mL GRE 24 hours prior to0.5 and 2 Gy gamma radiation exposure (Co-60 source). Oxidative stress and antioxidant responses were evaluated via lipidperoxidation products, reduced glutathione, and pro-oxidant/antioxidant balance assays. Genomic damage was assessedusing the micronuclei test, comet and colorimetric DNA fragmentation assay, and DNA repair was assessed throughevaluation of gene expression levels that are involved in base excision repair. Results: The presence of gentiopicroside,loganic acid, sweroside, swertiamarin, and rosmarinic acid was confirmed by UPLC analysis. GRE pretreatment significantlyimproved cell viability, reduced oxidative and genomic damage, and enhanced antioxidative responses in irradiated cells.Conclusions: Natural compounds in GRE induce cellular responses that attenuate sublethal radiation damage in humanPBMCs in vitro, supporting its further evaluation as a potential radioprotective strategy
Exploring the interplay between formation mechanisms and luminescence of lignin carbon quantum dots from spruce biomass
No full textThis study investigates the intricate relationship between the formation mechanisms and luminescent properties of lignin-derived carbon quantum dots (LG CQDs) synthesized from spruce biomass by hydrothermal treatment. A comprehensive understanding of LG CQDs structure and its photoluminescence requires insights into the native architecture of lignin and the distribution of its acidolysis-derived fragments. Research showed how these lignin-derived units interact with dopant molecules in three different approaches during synthesis, contributing to core and surface structures that govern optical behavior. In our experiments, m-aminophenylboronic acid and varying amounts of HCl were employed as dopants to further tune the structural and luminescent features of the CQDs. The presence of functional groups such as amino, hydroxyl, and boronic acid groups was found to be crucial for enhancing the luminescent properties of the CQDs. Our findings reveal a clear correlation between structural features and luminescent properties, emphasizing the role of surface chemistry in tuning emission characteristics. These insights provide a foundation for the rational design of LG CQDs with tailored luminescent properties, advancing their potential applications in sustainable optoelectronics, sensing, and bioimaging
Dispersion-managed electromagnetic pulse transparency in arrays of coupled microcavities
No full textWe examine theoretically the transparency of electromagnetic pulses through an infinite one-dimensional array of coupled optical microcavities uniformly filled with superconducting qubits-one per cavity. Two types of hybrid matter-light waves, i.e., polaritons and self-induced transparency solitons, govern the transparency of electromagnetic radiation in these media. The spectrum of linear excitations, i.e., polaritons, consists of two branches separated by a relatively wide forbidden band. In the nonlinear regime, the dispersion relation of the carrier wave is determined by soliton width that is controlled by the reciprocal qubit frequency. The separate dispersion curves lie within the polariton forbidden band. Soliton transparency requires that the carrier wave frequency exceeds a threshold value; the latter depends strongly on the pulse width. We find that for pulses with widths ranging from ultrashort to an intermediate limit, the threshold is of the order of the gap frequency value in the photon spectrum. For wider pulses, the threshold frequency gradually decreases to values that are toward the edge of the polariton lower band, provided the soliton width is larger than a critical value. In the overcritical regime, the bandgap appears in the spectrum of the soliton carrier wave, while a twin transparency window appears in the soliton pulse dispersion law. A possible experimental observation of the predicted effects within the proposed setup would be of interest in understanding the properties of self-induced transparency in general and applications in the design of quantum technological devices.Peer-reviewed version of the article available at: [https://vinar.vin.bg.ac.rs/handle/123456789/16116
Synthesized lignin oligomers, method for obtaining and use thereof
No full textPredmetni pronalazak se odnosi na sintetisane oligomere lignina, način dobijanja i njihovu upotrebu.The present invention relates to synthesized lignin oligomers, method for obtaining and use thereof.Broj prijave: P-2024/1027Podaci o nosiocu prava: UNIVERZITET U BEOGRADU, Studentski trg 1, 11000 Beograd, RSPriznati datum podnošenja prijave: 30.12.2021
S1PR1 genetic variants may modulate disability outcomes and therapy response in relapsing-remitting multiple sclerosis
No full textRaw Dataset file for genotyping by sequencing of Sphingosine-1-phosphate receptor 1 in a group of relapsing-remitting multiple sclerosis (RRMS) patients subjected for immunomodulatory therapy, from the population of Serbia
Numerical simulation-driven machine learning and particle swarm optimization of burner fuel distribution for cleaner combustion in a thermal power plant
No full textCoal-fired power plants remain important energy source in many countries, but releasing significant nitrogen oxides (NOx) emissions with serious environmental and health impacts. This study focuses on optimizing lignite combustion in a large utility boiler to minimize NOx emission by adjusting fuel distribution over the burner tiers. The proposed methodology integrates an in-house developed numerical code, correlation analysis, Extreme Gradient Boosting (XGBoost) model and Particle Swarm Optimization (PSO) algorithm. A database of computational fluid dynamics (CFD) simulations was generated to develop machine learning (ML) models for predicting NOx emission and furnace exit gas temperature (FEGT). Based on the ML models, PSO was applied to minimize NOx emissions while maintaining FEGT close to reference case. The framework is applied to a real scale utility boiler, demonstrating practical applicability and measurable environmental impact. Its multidisciplinary significance lies in the synergistic combination of CFD, artificial intelligence, and PSO for combustion control in large-scale energy system. Numerical simulation confirmed the accuracy of the PSO-based optimization, showing excellent agreement between predicted and simulated NOx emission and FEGT. The optimized case corresponds to a 30% reduction in NOx emission compared to the reference scenario. The results also indicated risk of localized overheating and slagging at the furnace walls and possible problems with ascending flame, highlighting the importance of adjusting the operating conditions and flame control to ensure stable boiler performance. The presented comprehensive method provides a novel and effective solution to improve the environmental performance of thermal power plants through intelligent optimization of combustion parameters