Iskanje po katalogu digitalne knjižnice

Opis: Aims: Understanding protein–ligand binding site behavior is central to structure-based drug design. Weanalyzed amino acid composition and interactions in protein–ligand small-molecule binding sites anddeveloped a novel method for binding site prediction.Materials and methods: We analyzed the PDBBind+ database, which contains the largest protein–ligand binding site dataset known to us, using existing cheminformatics packages and in-house code.We used the resulting data to train a binding site prediction model.Results: Within solvent-accessible binding regions, tryptophan, phenylalanine, tyrosine, methionine,and glycine, were enriched. Interaction analysis revealed hydrophobic contacts as the most frequent,followed by hydrogen bonds, water-bridged hydrogen bonds, salt bridges, π–π, π–cation, and occa-sional halogen interactions. We introduced the amino acid binding site enrichment index (ABSE), tosupport small-molecule binding site detection, and developed a model that discriminates binding sitesequences from protein surface patches with 0.91 accuracy.Conclusions: This work offers interpretable composition–interaction relationships and practical tool forbinding site characterization. To facilitate application, we provide a free, open-source, fast, bindingsiteidentification tool (AABS), available at https://gitlab.com/Jukic/aabs. We anticipate that these findingsand tool will advance binding site prediction and accelerate computationally intensive drug discoverywithin medicinal chemistry.
Ključne besede: protein surface analysis, small-molecule binding site detection, machine learning, cheminformatics, amino acidindex, binding site, mall-molecule–protein interactions, in-silico drug design
Objavljeno v DKUM: 08.12.2025; Ogledov: 0; Prenosov: 0
Celotno besedilo (7,50 MB)

Opis: This study focuses on the surface and subsurface characterization of a pharmaceutical tablet containing sodium metamizolate (NaMET), with an emphasis on mass spectrometry using time-of-flight secondary ion mass spectrometry (ToF-SIMS). ToF-SIMS enabled the identification of NaMET-specific fragment ions, which served as signals for the determination of the spatial distribution of this active pharmaceutical ingredient (API) within the tablet matrix. A ToF-SIMS fragmentation mechanism for NaMET was proposed based on the ToF-SIMS spectra analysis measured on a NaMET reference standard. 3D ToF-SIMS imaging showed heterogeneous localization of the API across a 5 μm depth. Complementary techniques, including 3D profilometry and atomic force microscopy (AFM), provided surface roughness and morphological data, while X-ray photoelectron spectroscopy (XPS) confirmed the elemental composition and chemical states. Depth profiling by XPS further supported the non-uniform distribution of NaMET.
Ključne besede: matamizole, sodium metamizolate, ToF-SIMS, XPS, surface analysis, API
Objavljeno v DKUM: 10.11.2025; Ogledov: 0; Prenosov: 6
Celotno besedilo (9,03 MB)

Opis: Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning to predict outlet moisture content, leveraging a dataset of 3826 observations from an operational dryer. The effects of inlet moisture, target air temperature, and material discharge interval on thermal behavior of the system were evaluated through linear regression and t-test, which provided interpretable insights into process dependencies. Three neural network architectures (LSTM, GRU, and TCN) were benchmarked for multivariate time-series forecasting of outlet corn moisture, with hyperparameters optimized using grid search to ensure fair performance comparison. Results demonstrated GRU’s superior performance in the context of absolute deviations, achieving the lowest mean absolute error (MAE = 0.304%) and competitive mean squared error (MSE = 0.304%), compared to LSTM (MAE = 0.368%, MSE = 0.291%) and TCN (MAE = 0.397%, MSE = 0.315%). While GRU excelled in average prediction accuracy, LSTM’s lower MSE highlighted its robustness against extreme deviations. The hybrid methodology bridges statistical insights for interpretability with deep learning’s dynamic predictive capabilities, offering a scalable framework for real-time process optimization. By combining traditional analytical methods (e.g., regression and t-test) with deep learning-driven forecasting, this work advances intelligent monitoring and control of industrial drying systems, enhancing process stability, ensuring compliance with moisture standards, and indirectly supporting energy efficiency by reducing over drying and enabling more consistent operation.
Ključne besede: advanced drying technologies, continuous flow drying, time-series forecasting, LSTM, GRU, TCN, deep learning, statistical analysis, optimization of the drying process
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (3,02 MB)
Gradivo ima več datotek! Več...

Opis: Background: Evolutionary game theory (EGT), originating from Darwinian competition studies, offers a powerful framework for understanding complex healthcare interactions where multiple stakeholders with conflicting interests evolve strategies over time. Unlike traditional game theory, EGT accounts for bounded rationality and strategic evolution through imitation and selection. Aims and objectives: In our study, we use Synthetic Knowledge Synthesis (SKS) that integrates descriptive bibliometrics and bibliometric mapping to systematically analyze the application of EGT in healthcare. The SKS aimed to identify prolific research topics, suitable publishing venues, and productive institutions/countries for collaboration and funding. Data was harvested from the Scopus bibliographic database, encompassing 539 publications from 2000 to June 2025, Results: Production dynamics is revealing an exponential growth in scholarly output since 2019, with peak productivity in 2024. Descriptive bibliometrics showed China as the most prolific country (376 publications), followed by the United States and the United Kingdom. Key institutions are predominantly Chinese, and top journals include PLoS One and Frontiers in Public Health. Funding is primarily from Chinese entities like the National Natural Science Foundation of China. Bibliometric mapping identified five key research themes: game theory in cancer research, evolution game-based simulation of supply management, evolutionary game theory in epidemics, evolutionary games in trustworthy connected public health, and evolutionary games in collaborative governance. Conclusions: Despite EGT’s utility, significant research gaps exist in methodological robustness, data availability, contextual modelling, and interdisciplinary translation. Future research should focus on integrating machine learning, longitudinal data, and explicit ethical frameworks to enhance EGT’s practical application in adaptive, patient-centred healthcare systems
Ključne besede: evolutionary games theory, healthcare, complex healthcare systems, synthetic knowledge synthesis, thematic analysis
Objavljeno v DKUM: 29.10.2025; Ogledov: 0; Prenosov: 4
Celotno besedilo (587,94 KB)

Opis: This paper aims to assess the impact of certain economic factors on pollution for selected 112 countries worldwide. Due to data availability, carbon dioxide emissions in tonnes per capita were chosen as the dependent variable measuring pollution. Based on panel data and generalized moments method, the relationship between economic activity and environmental pollution was estimated. The results show that in the whole sample, as well as for two subsamples of developed and undeveloped countries, carbon dioxide emissions are statistically significantly affected by gross domestic product per capita, energy intensity and renewable energy consumption. The linear effect of economic activity positively effects the pollution, while the quadratic relationship is negative. Thus, the validity of inverted-U curve of the environmental Kuznets curve has been demonstrated. The estimated effect of economic activity measured by gross domestic product per capita on pollution does not differ in developed and undeveloped countries.
Ključne besede: environmental Kuznets curve, carbon dioxide emissions, renewable energy, industrialisation, urbanisation, economic development, cross-section analysis
Objavljeno v DKUM: 21.10.2025; Ogledov: 0; Prenosov: 4
Celotno besedilo (704,44 KB)
Gradivo ima več datotek! Več...