Showing papers by "University of Maribor published in 2021"

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Modeling compliance with COVID-19 prevention guidelines: the critical role of trust in science.

Abstract: The coronavirus pandemic is one of the biggest health crises of our time. In response to this global problem, various institutions around the world had soon issued evidence-based prevention guidelines. However, these guidelines, which were designed to slow the spread of COVID-19 and contribute to public well-being, are (deliberately) disregarded by some individuals. In the present study, we aimed to develop and test a multivariate model that could help us identify individual characteristics that make a person more/less likely to comply with COVID-19 prevention guidelines. A total of 525 attentive participants completed the online survey. The results of structural equation modeling (SEM) show that COVID-19 risk perception and trust in science both independently predict compliance with COVID-19 prevention guidelines, while the remaining variables in the model (political conservatism, religious orthodoxy, conspiracy ideation and intellectual curiosity) do so via the mediating role of trust in science. The described model exhibited an acceptable fit (χ2(1611) = 2485.84, p < .001, CFI = .91, RMSEA = .032, SRMR = .055). These findings thus provide empirical support for the proposed multivariate model and underline the importance of trust in science in explaining the different levels of compliance with COVID-19 prevention guidelines.

Evolutionary dynamics of higher-order interactions in social networks.

Abstract: We live and cooperate in networks. However, links in networks only allow for pairwise interactions, thus making the framework suitable for dyadic games, but not for games that are played in larger groups. Here, we study the evolutionary dynamics of a public goods game in social systems with higher-order interactions. First, we show that the game on uniform hypergraphs corresponds to the replicator dynamics in the well-mixed limit, providing a formal theoretical foundation to study cooperation in networked groups. Second, we unveil how the presence of hubs and the coexistence of interactions in groups of different sizes affects the evolution of cooperation. Finally, we apply the proposed framework to extract the actual dependence of the synergy factor on the size of a group from real-world collaboration data in science and technology. Our work provides a way to implement informed actions to boost cooperation in social groups. Alvarez-Rodriguez et al. examine group interactions by means of higher-order social networks. They propose a theoretical framework for studying real-world interactions and provide a case study of collaboration in science and technology.

Test of Lepton-Flavor Universality in B →k∗ℓ+ℓ- Decays at Belle

Abstract: We acknowledge support from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan, the Japan Society for the Promotion of Science (JSPS), and the Tau-Lepton Physics Research Center of Nagoya University; the Australian Research Council including Grants No. DP180102629, No. DP170102389, No. DP170102204, No. DP150103061, No. FT130100303; Austrian Science Fund (FWF); the National Natural Science Foundation of China under Contracts No. 11435013, No. 11475187, No. 11521505, No. 11575017, No. 11675166, No. 11705209; Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS), Grant No. QYZDJ-SSWSLH011; the CAS Center for Excellence in Particle Physics (CCEPP); the Shanghai Pujiang Program under Grant No. 18PJ1401000; the Ministry of Education, Youth and Sports of the Czech Republic under Contract No. LTT17020; the Carl Zeiss Foundation, the Deutsche Forschungsgemeinschaft, the Excellence Cluster Universe, and the VolkswagenStiftung; the Department of Science and Technology of India; the Istituto Nazionale di Fisica Nucleare of Italy; National Research Foundation (NRF) of Korea Grants No. 2016R1D1A1B01010135, No. 2016R1D1A1B02012900, No. 2018R1A2B3003643, No. 2018R1A6A1A06024970, No. 2018R1D1A1B07047294, No. 2019K1A3A7A09033840, No. 2019R1I1A3A01058933; Radiation Science Research Institute, Foreign Large-size Research Facility Application Supporting project, the Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information, and KREONET/GLORIAD the Polish Ministry of Science and Higher Education and the National Science Center; the Ministry of Science and Higher Education of the Russian Federation, Agreement No. 14.W03.31.0026; University of Tabuk research Grants No. S-1440-0321, No. S-0256-1438, and No. S-0280-1439 (Saudi Arabia); the Slovenian Research Agency; Ikerbasque, Basque Foundation for Science, Spain; the Swiss National Science Foundation; the Ministry of Education and the Ministry of Science and Technology of Taiwan; and the U.S. Department of Energy and the National Science Foundation.

Alkaline membrane fuel cells: anion exchange membranes and fuels

Abstract: Alkaline anion exchange membrane fuel cells (AAEMFC) are attracting ever-increasing attention, as they are promising electrochemical devices for energy production, presenting a viable opponent to the more researched proton exchange membrane fuel cells (PEMFCs). Consequently, great progress has been made in the area of designing and developing synthetic or naturally-derived anion exchange membrane (AEM), the properties of which have been discussed in this review, i.e. ionic conductivity, ion exchange capacity, fuel crossover, durability, stability and cell performance. Major groups of natural polymers (e.g. chitosan (CS)) and nanocellulose, together with modification/crosslinking routes, have been mentioned as more ecologically and economically viable raw materials for AEM processing compared to synthetic ones. Performances of fuel cells are also discussed, with different fuels used as anode feeds. Although the AEMFC technology is promising, the longevity challenges remain, originating from the still-limited long-term stability of hydroxide-conducting ionomers, particularly when operating at higher cell temperatures.

Sustainable renewable energy supply networks optimization – The gradual transition to a renewable energy system within the European Union by 2050

Abstract: In order to achieve the goal of a carbon neutral EU by 2050 and meet the climate targets of the Paris Agreement, a sustainable, efficient, competitive and secure energy system needs to be developed. This paper presents the synthesis of sustainable renewable energy supply networks within the EU-27, proposing a stepwise energy transition in the transport and power sectors, achieving a carbon net neutral target by 2050. A multi-period mixed-integer programming model is developed, with the objective of maximizing sustainability net present value, considering different biomass and waste resources for the production of biofuels, renewable electricity, hydrogen, food and bioproducts, employing different types of technologies. The results show that, with further development of existing technologies, the goal of a carbon-neutral EU can be achieved without compromising food production. Wind farms have proven to be the most promising solution at present for the rapid expansion of electricity generation from renewable energy sources, while the importance of solar photovoltaics is increasing over the years, reaching the 43% share of electricity generation from RES in 2050. Moreover, the energy transition within the EU could have a significant positive impact on the economic, environmental and also social aspects of sustainability, with more than 1.5 million new job opportunities created across the EU over the next 30 years.

A global analysis of the impact of COVID-19 stay-at-home restrictions on crime.

Abstract: The stay-at-home restrictions to control the spread of COVID-19 led to unparalleled sudden change in daily life, but it is unclear how they affected urban crime globally. We collected data on daily counts of crime in 27 cities across 23 countries in the Americas, Europe, the Middle East and Asia. We conducted interrupted time series analyses to assess the impact of stay-at-home restrictions on different types of crime in each city. Our findings show that the stay-at-home policies were associated with a considerable drop in urban crime, but with substantial variation across cities and types of crime. Meta-regression results showed that more stringent restrictions over movement in public space were predictive of larger declines in crime.

Bioethanol Production by Enzymatic Hydrolysis from Different Lignocellulosic Sources

Abstract: As the need for non-renewable sources such as fossil fuels has increased during the last few decades, the search for sustainable and renewable alternative sources has gained growing interest. Enzymatic hydrolysis in bioethanol production presents an important step, where sugars that are fermented are obtained in the final fermentation process. In the process of enzymatic hydrolysis, more and more new effective enzymes are being researched to ensure a more cost-effective process. There are many different enzyme strategies implemented in hydrolysis protocols, where different lignocellulosic biomass, such as wood feedstocks, different agricultural wastes, and marine algae are being used as substrates for an efficient bioethanol production. This review investigates the very recent enzymatic hydrolysis pathways in bioethanol production from lignocellulosic biomass.

(Bio)nanotechnology in Food Science-Food Packaging.

Abstract: Background: Bionanotechnology, as a tool for incorporation of biological molecules into nanoartifacts, is gaining more and more importance in the field of food packaging. It offers an advanced expectation of food packaging that can ensure longer shelf life of products and safer packaging with improved food quality and traceability. Scope and approach: This review recent focuses on advances in food nanopackaging, including bio-based, improved, active, and smart packaging. Special emphasis is placed on bio-based packaging, including biodegradable packaging and biocompatible packaging, which presents an alternative to most commonly used non-degradable polymer materials. Safety and environmental concerns of (bio)nanotechnology implementation in food packaging were also discussed including new EU directives. Conclusions: The use of nanoparticles and nanocomposites in food packaging increases the mechanical strength and properties of the water and oxygen barrier of packaging and may provide other benefits such as antimicrobial activity and light-blocking properties. Concerns about the migration of nanoparticles from packaging to food have been expressed, but migration tests and risk assessment are unclear. Presumed toxicity, lack of additional data from clinical trials and risk assessment studies limit the use of nanomaterials in the food packaging sector. Therefore, an assessment of benefits and risks must be defined.

Test of lepton flavor universality and search for lepton flavor violation in B → Kℓℓ decays

Abstract: We present measurements of the branching fractions for the decays B → Kμ+μ− and B → Ke+e−, and their ratio (RK), using a data sample of 711 fb−1 that contains 772 × 106 $$ B\overline{B} $$ events. The data were collected at the ϒ(4S) resonance with the Belle detector at the KEKB asymmetric-energy e+e− collider. The ratio RK is measured in five bins of dilepton invariant-mass-squared (q2): q2 ∈ (0.1, 4.0), (4.00, 8.12), (1.0, 6.0), (10.2, 12.8) and (> 14.18) GeV2/c4, along with the whole q2 region. The RK value for q2 ∈ (1.0, 6.0) GeV2/c4 is $$ {1.03}_{-0.24}^{+0.28} $$ ± 0.01. The first and second uncertainties listed are statistical and systematic, respectively. All results for RK are consistent with Standard Model predictions. We also measure CP-averaged isospin asymmetries in the same q2 bins. The results are consistent with a null asymmetry, with the largest difference of 2.6 standard deviations occurring for the q2 ∈ (1.0, 6.0) GeV2/c4 bin in the mode with muon final states. The measured differential branching fractions, $$ d\mathrm{\mathcal{B}} $$ /dq2, are consistent with theoretical predictions for charged B decays, while the corresponding values are below the expectations for neutral B decays. We have also searched for lepton-flavor-violating B → Kμ±e∓ decays and set 90% confidence-level upper limits on the branching fraction in the range of 10−8 for B+ → K+μ±e∓, and B0 → K0μ±e∓ modes.

Ionic Dendrimer Based Polyamide Membranes for Ion Separation

Abstract: Separating low/high-valent ions with sub-nanometer sizes is a crucial yet challenging task in various areas (e.g., within environmental, healthcare, chemical, and energy engineering). Satisfying high separation precision requires membranes with exceptionally high selectivity. One way to realize this is constructing well-designed ion-selective nanochannels in pressure-driven membranes where the separation mechanism relies on combined steric, dielectric exclusion, and Donnan effects. To this aim, charged nanochannels in polyamide (PA) membranes are created by incorporating ionic polyamidoamine (PAMAM) dendrimers via interfacial polymerization. Both sub-10 nm sizes of the ionic PAMAM dendrimer molecules and their gradient distributions in the PA nanofilms contribute to the successful formation of defect-free PA nanofilms, containing both internal (intramolecular voids) and external (interfacial voids between the ionic PAMAM dendrimers and the PA matrix) nanochannels for fast transport of water molecules. The external nanochannels with tunable ionizable groups endow the PA membranes with both high low/high-valent co-ion selectivity and chemical cleaning tolerance, while the ion sieving/transport mechanism was analyzed by employing the Donnan steric pore model with dielectric exclusion.

Nicotinamide for the treatment of heart failure with preserved ejection fraction

Abstract: Heart failure with preserved ejection fraction (HFpEF) is a highly prevalent and intractable form of cardiac decompensation commonly associated with diastolic dysfunction. Here, we show that diastolic dysfunction in patients with HFpEF is associated with a cardiac deficit in nicotinamide adenine dinucleotide (NAD+). Elevating NAD+ by oral supplementation of its precursor, nicotinamide, improved diastolic dysfunction induced by aging (in 2-year-old C57BL/6J mice), hypertension (in Dahl salt-sensitive rats), or cardiometabolic syndrome (in ZSF1 obese rats). This effect was mediated partly through alleviated systemic comorbidities and enhanced myocardial bioenergetics. Simultaneously, nicotinamide directly improved cardiomyocyte passive stiffness and calcium-dependent active relaxation through increased deacetylation of titin and the sarcoplasmic reticulum calcium adenosine triphosphatase 2a, respectively. In a long-term human cohort study, high dietary intake of naturally occurring NAD+ precursors was associated with lower blood pressure and reduced risk of cardiac mortality. Collectively, these results suggest NAD+ precursors, and especially nicotinamide, as potential therapeutic agents to treat diastolic dysfunction and HFpEF in humans.

Stability and Stabilization in Probability of Probabilistic Boolean Networks

Abstract: This article studies the stability in probability of probabilistic Boolean networks and stabilization in the probability of probabilistic Boolean control networks. To simulate more realistic cellular systems, the probability of stability/stabilization is not required to be a strict one. In this situation, the target state is indefinite to have a probability of transferring to itself. Thus, it is a challenging extension of the traditional probability-one problem, in which the self-transfer probability of the target state must be one. Some necessary and sufficient conditions are proposed via the semitensor product of matrices. Illustrative examples are also given to show the effectiveness of the derived results.

Complex networks and deep learning for EEG signal analysis

Abstract: Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human's physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain-computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.

International experiences with co-production and people centredness offer lessons for covid-19 responses.

Abstract: Eva Turk and colleagues believe that there is much to learn from the experiences of low and middle income countries in co-producing knowledge and working with communities to find feasible and acceptable solutions to healthcare concerns

A look into the future of the COVID-19 pandemic in Europe: an expert consultation.

Abstract: How will the coronavirus disease 2019 (COVID-19) pandemic develop in the coming months and years? Based on an expert survey, we examine key aspects that are likely to influence the COVID-19 pandemic in Europe. The challenges and developments will strongly depend on the progress of national and global vaccination programs, the emergence and spread of variants of concern (VOCs), and public responses to non-pharmaceutical interventions (NPIs). In the short term, many people remain unvaccinated, VOCs continue to emerge and spread, and mobility and population mixing are expected to increase. Therefore, lifting restrictions too much and too early risk another damaging wave. This challenge remains despite the reduced opportunities for transmission given vaccination progress and reduced indoor mixing in summer 2021. In autumn 2021, increased indoor activity might accelerate the spread again, whilst a necessary reintroduction of NPIs might be too slow. The incidence may strongly rise again, possibly filling intensive care units, if vaccination levels are not high enough. A moderate, adaptive level of NPIs will thus remain necessary. These epidemiological aspects combined with economic, social, and health-related consequences provide a more holistic perspective on the future of the COVID-19 pandemic.

Optimizing nonlinear charging times of electric vehicle routing with genetic algorithm

Abstract: With the rising share of electric vehicles used in the service industry, the optimization of their specific constraints is gaining importance. Lowering energy consumption, time of charging and the strain on the electric grid are just some of the issues that must be tackled, to ensure a cleaner and more efficient industry. This paper presents a Two-Layer Genetic Algorithm (TLGA) for solving the capacitated Multi-Depot Vehicle Routing Problem with Time Windows (MDVRPTW) and Electric Vehicles (EV) with partial nonlinear recharging times (NL) – E-MDVRPTW-NL. Here, the optimization goal is to minimize driving times, number of stops at electric charging stations and time of recharging while taking the nonlinear recharging times into account. This routing problem closes the gap between electric vehicle routing problem research on the one hand and its applications to several problems with numerous real-world constraints of electric vehicles on the other. Next to the definition and the formulation of the E-MDVRPTW-NL, this paper presents the evolutionary method for solving this problem using the Genetic Algorithm (GA), where a novel two-layer genotype with multiple crossover operators is considered. This allows the GA to not only solve the order of the routes but also the visits to electric charging stations and the electric battery recharging times. Various settings of the proposed method are presented, tested and compared to competing meta-heuristics using well-known benchmarks with the addition of charging stations.

A critical review on multifunctional smart materials ‘nanographene’ emerging avenue: nano-imaging and biosensor applications

Abstract: Smart electronic materials ‘nanographene’ stated, its significant authentication has undergone massive improvements and has emerged as a ‘material of the century’ in materialize fields of ‘Chemical...

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

Abstract: The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world.

Socio-demographic and health factors drive the epidemic progression and should guide vaccination strategies for best COVID-19 containment.

Abstract: We propose and study an epidemiological model on a social network that takes into account heterogeneity of the population and different vaccination strategies. In particular, we study how the COVID-19 epidemics evolves and how it is contained by different vaccination scenarios by taking into account data showing that older people, as well as individuals with comorbidities and poor metabolic health, and people coming from economically depressed areas with lower quality of life in general, are more likely to develop severe COVID-19 symptoms, and quicker loss of immunity and are therefore more prone to reinfection. Our results reveal that the structure and the spatial arrangement of subpopulations are important epidemiological determinants. In a healthier society the disease spreads more rapidly but the consequences are less disastrous as in a society with more prevalent chronic comorbidities. If individuals with poor health are segregated within one community, the epidemic outcome is less favorable. Moreover, we show that, contrary to currently widely adopted vaccination policies, prioritizing elderly and other higher-risk groups is beneficial only if the supply of vaccine is high. If, however, the vaccination availability is limited, and if the demographic distribution across the social network is homogeneous, better epidemic outcomes are achieved if healthy people are vaccinated first. Only when higher-risk groups are segregated, like in elderly homes, their prioritization will lead to lower COVID-19 related deaths. Accordingly, young and healthy individuals should view vaccine uptake as not only protecting them, but perhaps even more so protecting the more vulnerable socio-demographic groups.

Affective Interpersonal Touch in Close Relationships: A Cross-Cultural Perspective.

Abstract: Interpersonal touch behavior differs across cultures, yet no study to date has systematically tested for cultural variation in affective touch, nor examined the factors that might account for this variability. Here, over 14,000 individuals from 45 countries were asked whether they embraced, stroked, kissed, or hugged their partner, friends, and youngest child during the week preceding the study. We then examined a range of hypothesized individual-level factors (sex, age, parasitic history, conservatism, religiosity, and preferred interpersonal distance) and cultural-level factors (regional temperature, parasite stress, regional conservatism, collectivism, and religiosity) in predicting these affective-touching behaviors. Our results indicate that affective touch was most prevalent in relationships with partners and children, and its diversity was relatively higher in warmer, less conservative, and religious countries, and among younger, female, and liberal people. This research allows for a broad and integrated view of the bases of cross-cultural variability in affective touch.