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Assessing different temporal scales of calcium dynamics in networks of beta cell populations
Jan Zmazek, Maša Skelin, Rene Markovič, Jurij Dolenšek, Marko Marhl, Andraž Stožer, Marko Gosak, 2021, izvirni znanstveni članek

Opis: Beta cells within the pancreatic islets of Langerhans respond to stimulation with coherent oscillations of membrane potential and intracellular calcium concentration that presumably drive the pulsatile exocytosis of insulin. Their rhythmic activity is multimodal, resulting from networked feedback interactions of various oscillatory subsystems, such as the glycolytic, mitochondrial, and electrical/calcium components.How these oscillatory modules interact and affect the collective cellular activity, which is a prerequisite for proper hormone release, is incompletely understood. In the present work, we combined advanced confocal Ca2+ imaging in fresh mouse pancreas tissue slices with time series analysis and network science approaches to unveil the glucosedependent characteristics of different oscillatory components on both the intra- and inter-cellular level. Our results reveal an interrelationship between the metabolically driven low-frequency component and the electrically driven high-frequency component, with the latter exhibiting the highest bursting rates around the peaks of the slow component and the lowest around the nadirs. Moreover, the activity, as well as the average synchronicity of the fast component, considerably increased with increasing stimulatory glucose concentration, whereas the stimulation level did not affect any of these parameters in the slow component domain. Remarkably, in both dynamical components, the average correlation decreased similarly with intercellular distance, which implies that intercellular communication affects the synchronicity of both types of oscillations. To explore the intra-islet synchronization patterns in more detail, we constructed functional connectivity maps. The subsequent comparison of network characteristics of different oscillatory components showed more locally clustered and segregated networks of fast oscillatory activity, while the slow oscillations were more global, resulting in several long-range connections and a more cohesive structure. Besides the structural differences, we found a relatively weak relationship between the fast and slow network layer, which suggests that different synchronization mechanisms shape the collective cellular activity in islets, a finding which has to be kept in mind in future studies employing different oscillations for constructing networks.
Ključne besede: islets of Langerhans, beta cell network, calcium oscillations, multimodal activity analysis, confocal imaging, functional connectivity, multiplex network
Objavljeno v DKUM: 06.06.2024; Ogledov: 67; Prenosov: 4
.pdf Celotno besedilo (9,40 MB)
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Research trends in motivation and weight loss : a bibliometric-based review
Uroš Železnik, Peter Kokol, Jasmina Starc, Danica Železnik, Jernej Završnik, Helena Blažun Vošner, 2023, pregledni znanstveni članek

Opis: Obesity is a complex disease that, like COVID-19, has reached pandemic proportions. Consequently, it has become a rapidly growing scientific field, represented by an extensive body of research publications. Therefore, the aim of this study was to present the research trends in the scientific literature on motivation and weight loss. Because traditional knowledge synthesis approaches are not appropriate for analyzing large corpora of research evidence, we utilized a novel knowledge synthesis approach called synthetic knowledge synthesis (SKS) to generate new holistic insights into obesity research focusing on motivation. SKS is a triangulation of bibliometric analysis, bibliometric mapping, and content analysis. Using it, we analyzed the corpus of publications retrieved from the Scopus database, using the search string TITLE-ABS-KEY((obesity or overweight) and “weight loss” and motiv*) in titles, keywords, and abstracts, without any additional inclusion or exclusion criteria. The search resulted in a corpus of 2301 publications. The United States of America, the United Kingdom, and Australia were the most productive countries. Four themes emerged, namely, weight loss and weight-loss maintenance through motivational interventions, lifestyle changes supported by smart ICT, maintaining sustainable weight with a healthier lifestyle, and weight management on the level of primary healthcare and bariatric surgery. Further, we established that the volume of research literature is growing, as is the scope of the research. However, we observed a regional concentration of research and its funding in developed countries and almost nonexistent research cooperation between developed and less-developed countries.
Ključne besede: obesity, weight loss, motivation, synthetic knowledge synthesis, bibliometrics, content analysis
Objavljeno v DKUM: 05.06.2024; Ogledov: 57; Prenosov: 0
.pdf Celotno besedilo (2,16 MB)
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Materials for HybridNeuro webinar titled "Validation of results: statistical models and MU identification accuracy"
Aleš Holobar, Nina Murks, 2024, zaključena znanstvena zbirka raziskovalnih podatkov

Opis: This dataset contains a collection of teaching materials that were used in the HybridNeuro project webinar titled "Validation of results: statistical models and MU identification accuracy". The webinar was presented by Aleš Holobar and covered the complexities of motor unit (MU) identification accuracy, regression analysis and Bayesian models. The primary aim of the webinar was to spark a robust discussion within the scientific community, particularly focusing on the application and implications of linear mixed models and Bayesian regression in the realm of MU identification. The teaching materials include Matlab and R source code for statistical analysis of the included data, as well as three examples of MU identification results in CSV format (from both synthetic and experimental HDEMG signals). The presentation slides in PDF format are also included. The dataset is approximately 9 MB in size.
Ključne besede: HybridNeuro, webinar, teaching materials, statistical models, regression analysis, motor unit identification, matlab, rstudio, statistics, surface high density electromyogram (HDEMG), tibialis anterior, dataset
Objavljeno v DKUM: 30.05.2024; Ogledov: 126; Prenosov: 12
.pdf Celotno besedilo (108,35 KB)
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Principal component analysis and manifold learning techniques for the design of brain-computer interfaces based on steady-state visually evoked potentials
Bartu Yesilkaya, Ebru Sayilgan, Yilmaz Kemal Yuce, Matjaž Perc, Yalcin Isler, 2023, izvirni znanstveni članek

Opis: Steady-state visually evoked potentials (SSVEP) are stochastic and nonstationary bioelectric signals. Because of these properties, it is difficult to achieve high classification accuracy, especially when many considered features lead to a complex structure. We therefore propose a manifold learning framework to decrease the number of features and to classify SSVEP data by comparing lower dimensional matrices with well-known machine learning algorithms. We use the AVI-SSVEP Dataset, which includes stimuli at seven different frequencies and 15360 samples per person. The SSVEP features are extracted from relevant and distinctive frequency-domain, time-domain, and time–frequency domain properties, creating a total of 55 feature vectors. We then analyze and compare five divergent manifold learning methods with respect to their performance on nine different machine-learning algorithms. Among all considered manifold learning methods, we show that the Principal Component Analysis has the best classifier performance with an average of 22 components. Moreover, the Naive Bayes classifier with the Principal Component Analysis achieves the maximum accuracy of 50.0%–80.95% for a 7-class classification problem. Our research thus shows that the proposed analytical framework can significantly improve the decoding accuracy of 7-class SSVEP problems, and that it exhibits notable robustness and efficiency for small group datasets.
Ključne besede: manifold learning, brain-computer interface, steady-state visual evoked potential, principal component analysis, feature reduction
Objavljeno v DKUM: 29.05.2024; Ogledov: 83; Prenosov: 0
.pdf Celotno besedilo (816,82 KB)
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Evaluating seagrass meadow dynamics by integrating field-based and remote sensing techniques
Danijel Ivajnšič, Martina Orlando-Bonaca, Daša Donša, Jaša Veno Grujić, Domen Trkov, Borut Mavrič, Lovrenc Lipej, 2022, izvirni znanstveni članek

Opis: Marine phanerogams are considered biological sentinels or indicators since any modification in seagrass meadow distribution and coverage signals negative changes in the marine environment. In recent decades, seagrass meadows have undergone global losses at accelerating rates, and almost one-third of their coverage has disappeared globally. This study focused on the dynamics of seagrass meadows in the northern Adriatic Sea, which is one of the most anthropogenically affected areas in the Mediterranean Sea. Seagrass distribution data and remote sensing products were utilized to identify the stable and dynamic parts of the seagrass ecosystem. Different seagrass species could not be distinguished with the Sentinel-2 (BOA) satellite image. However, results revealed a generally stable seagrass meadow (283.5 Ha) but, on the other hand, a stochastic behavior in seagrass meadow retraction (90.8 Ha) linked to local environmental processes associated with anthropogenic activities or climate change. If systemized, this proposed approach to monitoring seagrass meadow dynamics could be developed as a spatial decision support system for the entire Mediterranean basin. Such a tool could serve as a key element for decision makers in marine protected areas and would potentially support more effective conservation and management actions in these highly productive and important environments.
Ključne besede: Adriatic Sea, seagrass meadow, change analysis, Cimodocea nodosa, image classifiers, Sentinel-2, marine biology, hydrobiology
Objavljeno v DKUM: 17.05.2024; Ogledov: 102; Prenosov: 4
.pdf Celotno besedilo (3,45 MB)
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The Thermochemical Conversion of Municipal Solid Waste by Torrefaction Process
Maja Ivanovski, Darko Goričanec, Danijela Urbancl, 2023, izvirni znanstveni članek

Opis: In this work, the thermochemical properties of municipal solid waste (MSW) are studied using the torrefaction process as the main method for investigation. Torrefaction experiments were carried out using an electric laboratory furnace, at temperatures of 200, 250, and 300 °C. The residence time was set to 90 min. Proximate and ultimate analysis were performed on the torrefied MSW samples and compared with the properties of the raw MSW samples. In addition, the thermal properties of the obtained torrefied MSW samples were evaluated by thermogravimetric analysis (TGA) and derivative thermogravimetric analysis (DTG). The following could be stated: the obtained results showed that mass and energy yields (MY and EY, respectively) decrease with increasing when torrefaction temperature, while the heating values (HHV) increased under the same conditions (from 24.3 to 25.1 MJ/kg). Elemental analysis showed an increase in carbon content (C), from 45.7 ± 0.9 to 52.8 ± 1.05 wt.%, and decrease in oxygen content (O), from 45.6 ± 0.9 to 39.5 ± 0.8 wt.%, when torrefaction temperature is increased, which is consistent with the general definition of the torrefaction process. In addition, enhancement factors (EFs) and fuel ratios (FRs) were calculated, which ranged from 1.00 to 1.02 and 0.16 to 0.23, respectively. Some anomalies were observed during the thermal analysis, which are assumed to be related to the composition of the selected MSW. This study therefore shows that torrefaction pretreatment can improve the physicochemical properties of raw MSW to a level comparable to coal, and could contribute to a better understanding of the conversion of MSW into a valuable, solid biofuel.
Ključne besede: biomass, municipal solid waste, torrefication, energy yield, thermogravimetric analysis
Objavljeno v DKUM: 10.05.2024; Ogledov: 148; Prenosov: 11
.pdf Celotno besedilo (1,63 MB)
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Advancements in data analysis for the work-sampling method
Borut Buchmeister, Nataša Vujica-Herzog, 2024, izvirni znanstveni članek

Opis: The work-sampling method makes it possible to gain valuable insights into what is happening in production systems. Work sampling is a process used to estimate the proportion of shift time that workers (or machines) spend on different activities (within productive work or losses). It is estimated based on enough random observations of activities over a selected period. When workplace operations do not have short cycle times or high repetition rates, the use of such a statistical technique is necessary because the labor sampling data can provide information that can be used to set standards. The work-sampling procedure is well standardized, but additional contributions are possible when evaluating the observations. In this paper, we present our contribution to improving the decision-making process based on work-sampling data. We introduce a correlation comparison of the measured hourly shares of all activities in pairs to check whether there are mutual connections or to uncover hidden connections between activities. The results allow for easier decision-making (conclusions) regarding the influence of the selected activities on the triggering of the others. With the additional calculation method, we can uncover behavioral patterns that would have been overlooked with the basic method. This leads to improved efficiency and productivity of the production system.
Ključne besede: work sampling, observations, analysis, proportions, correlations, interdependence between activities
Objavljeno v DKUM: 09.05.2024; Ogledov: 128; Prenosov: 7
.pdf Celotno besedilo (2,19 MB)
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