Showing papers by "University of Maribor published in 2020"

Measurement of R (D) and R (D?) with a Semileptonic Tagging Method

Abstract: The experimental results on the ratios of branching fractions $\mathcal{R}(D) = {\cal B}(\bar{B} \to D \tau^- \bar{ u}_{\tau})/{\cal B}(\bar{B} \to D \ell^- \bar{ u}_{\ell})$ and $\mathcal{R}(D^*) = {\cal B}(\bar{B} \to D^* \tau^- \bar{ u}_{\tau})/{\cal B}(\bar{B} \to D^* \ell^- \bar{ u}_{\ell})$, where $\ell$ denotes an electron or a muon, show a long-standing discrepancy with the Standard Model predictions, and might hint to a violation of lepton flavor universality. We report a new simultaneous measurement of $\mathcal{R}(D)$ and $\mathcal{R}(D^*)$, based on a data sample containing $772 \times 10^6$ $B\bar{B}$ events recorded at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB $e^+ e^-$ collider. In this analysis the tag-side $B$ meson is reconstructed in a semileptonic decay mode and the signal-side $\tau$ is reconstructed in a purely leptonic decay. The measured values are $\mathcal{R}(D)= 0.307 \pm 0.037 \pm 0.016$ and $\mathcal{R}(D^*) = 0.283 \pm 0.018 \pm 0.014$, where the first uncertainties are statistical and the second are systematic. These results are in agreement with the Standard Model predictions within $0.2$, $1.1$ and $0.8$ standard deviations for $\mathcal{R}(D)$, $\mathcal{R}(D^*)$ and their combination, respectively. This work constitutes the most precise measurements of $\mathcal{R}(D)$ and $\mathcal{R}(D^*)$ performed to date as well as the first result for $\mathcal{R}(D)$ based on a semileptonic tagging method.

Forecasting COVID-19

Abstract: The World Health Organization declared the coronavirus disease 2019 a pandemic on March 11th, pointing to the over 118,000 cases in over 110 countries and territories around the world at that time At the time of writing this manuscript, the number of confirmed cases has been surging rapidly past the half-million mark, emphasizing the sustained risk of further global spread Governments around the world are imposing various containment measures while the healthcare system is bracing itself for tsunamis of infected individuals that will seek treatment It is therefore important to know what to expect in terms of the growth of the number of cases, and to understand what is needed to arrest the very worrying trends To that effect, we here show forecasts obtained with a simple iteration method that needs only the daily values of confirmed cases as input The method takes into account expected recoveries and deaths, and it determines maximally allowed daily growth rates that lead away from exponential increase toward stable and declining numbers Forecasts show that daily growth rates should be kept at least below 5% if we wish to see plateaus any time soon—unfortunately far from reality in most countries to date We provide an executable as well as the source code for a straightforward application of the method on data from other countries © Copyright © 2020 Perc, Gorisek Miksic, Slavinec and Stožer

Interpretability of machine learning‐based prediction models in healthcare

Abstract: There is a need of ensuring machine learning models that are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable machine learning models allow healthcare experts to make reasonable and data-driven decisions to provide personalized decisions that can ultimately lead to higher quality of service in healthcare. Generally, we can classify interpretability approaches in two groups where the first focuses on personalized interpretation (local interpretability) while the second summarizes prediction models on a population level (global interpretability). Alternatively, we can group interpretability methods into model-specific techniques, which are designed to interpret predictions generated by a specific model, such as a neural network, and model-agnostic approaches, which provide easy-to-understand explanations of predictions made by any machine learning model. Here, we give an overview of interpretability approaches and provide examples of practical interpretability of machine learning in different areas of healthcare, including prediction of health-related outcomes, optimizing treatments or improving the efficiency of screening for specific conditions. Further, we outline future directions for interpretable machine learning and highlight the importance of developing algorithmic solutions that can enable machine-learning driven decision making in high-stakes healthcare problems.

Dynamics of oligomer populations formed during the aggregation of Alzheimer's Aβ42 peptide.

Abstract: Oligomeric species populated during the aggregation of the Aβ42 peptide have been identified as potent cytotoxins linked to Alzheimer's disease, but the fundamental molecular pathways that control their dynamics have yet to be elucidated. By developing a general approach that combines theory, experiment and simulation, we reveal, in molecular detail, the mechanisms of Aβ42 oligomer dynamics during amyloid fibril formation. Even though all mature amyloid fibrils must originate as oligomers, we found that most Aβ42 oligomers dissociate into their monomeric precursors without forming new fibrils. Only a minority of oligomers converts into fibrillar structures. Moreover, the heterogeneous ensemble of oligomeric species interconverts on timescales comparable to those of aggregation. Our results identify fundamentally new steps that could be targeted by therapeutic interventions designed to combat protein misfolding diseases.

Erratum: The Belle II Physics Book (Progress of Theoretical and Experimental Physics (2019) 2019 (123C01) DOI: 10.1093/ptep/ptz106)

Abstract: In the original version of this manuscript, an error was introduced on pp352. '2.7nb:1.6nb' has been corrected to '2.4nb:1.3nb' in the current online and printed version. doi:10.1093/ptep/ptz106.

Sustainable technologies for liposome preparation

Abstract: Liposome possesses a great number of advantages and therefore they can be used for a variety of applications. Among other things, they can serve as useful drug carriers in preclinical and clinical trials. Supercritical fluids assisted technology is an appropriate method for liposome preparation because of its nontoxicity to the environment enables particle size manipulation and solvent-free production. Thus, the use of supercritical fluids (SCFs) provides advanced technology for liposome preparation and may become the dominant technology for their preparation in the future. This review discusses the classification of liposome and gives a short overview of their applications and conventional methods of preparation. Emphasis is placed on the use of various supercritical fluids assisted methods for liposome preparation and their advantages and disadvantages. The reader is also updated about recent developments in supercritical fluids assisted liposome production technology.

Early detection of type 2 diabetes mellitus using machine learning-based prediction models.

Abstract: Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformation into a scoring formula. The increasing volume of electronically collected data opened the opportunity to develop more complex, accurate prediction models that can be continuously updated using machine learning approaches. This study compares machine learning-based prediction models (i.e. Glmnet, RF, XGBoost, LightGBM) to commonly used regression models for prediction of undiagnosed T2DM. The performance in prediction of fasting plasma glucose level was measured using 100 bootstrap iterations in different subsets of data simulating new incoming data in 6-month batches. With 6 months of data available, simple regression model performed with the lowest average RMSE of 0.838, followed by RF (0.842), LightGBM (0.846), Glmnet (0.859) and XGBoost (0.881). When more data were added, Glmnet improved with the highest rate (+ 3.4%). The highest level of variable selection stability over time was observed with LightGBM models. Our results show no clinically relevant improvement when more sophisticated prediction models were used. Since higher stability of selected variables over time contributes to simpler interpretation of the models, interpretability and model calibration should also be considered in development of clinical prediction models.

Potential effects of "social" distancing measures and school lockdown on child and adolescent mental health.

Abstract: Age-related metabolic and renal changes predispose older people to an increased risk of diabetes mellitus and diabetic kidney disease, respectively. As the prevalence of the ageing population is increasing, because of increased life expectancy, the prevalence of older people with diabetic kidney disease is likely to increase. Diabetic kidney disease is associated with an increased risk of adverse outcomes and increased costs to healthcare systems. The management includes promotion of a healthy lifestyle and control of cardiovascular risk factors such as hyperglycaemia, hypertension and dyslipidaemia. Older people are a heterogeneous group of people from a community-living fit and independent person to a fully dependent individual residing in a care home. Therefore, management in this age group should be based on a patient’s functional level adopting tight metabolic control in the fit individual and relaxed targets in the frail person. However, despite the maximum available therapy, a significant number of patients with diabetic kidney disease still progress to renal failure and experience adverse cardiac outcomes. Therefore, future research is required to explore methods of early detection of diabetic kidney disease and to investigate novel therapeutic interventions to further improve the outcomes.Age-related metabolic and renal changes predispose older people to an increased risk of diabetes mellitus and diabetic kidney disease, respectively. As the prevalence of the ageing population is increasing, because of increased life expectancy, the prevalence of older people with diabetic kidney disease is likely to increase. Diabetic kidney disease is associated with an increased risk of adverse outcomes and increased costs to healthcare systems. The management includes promotion of a healthy lifestyle and control of cardiovascular risk factors such as hyperglycaemia, hypertension and dyslipidaemia. Older people are a heterogeneous group of people from a community-living fit and independent person to a fully dependent individual residing in a care home. Therefore, management in this age group should be based on a patient’s functional level adopting tight metabolic control in the fit individual and relaxed targets in the frail person. However, despite the maximum available therapy, a significant number of patients with diabetic kidney disease still progress to renal failure and experience adverse cardiac outcomes. Therefore, future research is required to explore methods of early detection of diabetic kidney disease and to investigate novel therapeutic interventions to further improve the outcomes.Age-related metabolic and renal changes predispose older people to an increased risk of diabetes mellitus and diabetic kidney disease, respectively. As the prevalence of the ageing population is increasing, because of increased life expectancy, the prevalence of older people with diabetic kidney disease is likely to increase. Diabetic kidney disease is associated with an increased risk of adverse outcomes and increased costs to healthcare systems. The management includes promotion of a healthy lifestyle and control of cardiovascular risk factors such as hyperglycaemia, hypertension and dyslipidaemia. Older people are a heterogeneous group of people from a community-living fit and independent person to a fully dependent individual residing in a care home. Therefore, management in this age group should be based on a patient’s functional level adopting tight metabolic control in the fit individual and relaxed targets in the frail person. However, despite the maximum available therapy, a significant number of patients with diabetic kidney disease still progress to renal failure and experience adverse cardiac outcomes. Therefore, future research is required to explore methods of early detection of diabetic kidney disease and to investigate novel therapeutic interventions to further improve the outcomes.

Sex Differences in Mate Preferences Across 45 Countries: A Large-Scale Replication

Abstract: Considerable research has examined human mate preferences across cultures, finding universal sex differences in preferences for attractiveness and resources as well as sources of systematic cultural variation. Two competing perspectives-an evolutionary psychological perspective and a biosocial role perspective-offer alternative explanations for these findings. However, the original data on which each perspective relies are decades old, and the literature is fraught with conflicting methods, analyses, results, and conclusions. Using a new 45-country sample (N = 14,399), we attempted to replicate classic studies and test both the evolutionary and biosocial role perspectives. Support for universal sex differences in preferences remains robust: Men, more than women, prefer attractive, young mates, and women, more than men, prefer older mates with financial prospects. Cross-culturally, both sexes have mates closer to their own ages as gender equality increases. Beyond age of partner, neither pathogen prevalence nor gender equality robustly predicted sex differences or preferences across countries.

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 groups of more than two players. 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. Secondly, 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.

Application of bibliometrics in medicine: a historical bibliometrics analysis.

Abstract: Background The application of bibliometrics in medicine enables one to analyse vast amounts of publications and their production patterns on macroscopic and microscopic levels. Objectives The aim of the study was to analyse the historical perspective of research literature production regarding application of bibliometrics in medicine. Methods Publications related to application of bibliometrics in medicine from 1970 to 2018 were harvested from the Scopus bibliographic database. Reference Publication Year Spectroscopy was triangulated with the VOSViewer to identify historical roots and evolution of topics and clinical areas. Results The search resulted in 6557 publications. The literature production trend was positive. Historical roots analysis identified 33 historical roots and 16 clinical areas where bibliometrics was applied. Discussion The increase in productivity in application of bibliometrics in medicine might be attributed to increased use of quantitative metrics in research evaluation, publish or perish phenomenon and the increased use of evidence-based medicine. Conclusion The trend of the literature production was positive. Medicine was in the forefront of knowledge development in bibliometrics. reference publication year spectroscopy proved to be an accurate method which was able to identify most of the historical roots.

Optimization of identifiability for efficient community detection

Abstract: Many physical and social systems are best described by networks. And the structural properties of these networks often critically determine the properties and function of the resulting mathematical models. An important method to infer the correlations between topology and function is the detection of community structure, which plays a key role in the analysis, design, and optimization of many complex systems. The nonnegative matrix factorization has been used prolifically to that effect in recent years, although it cannot guarantee balanced partitions, and it also does not allow a proactive computation of the number of communities in a network. This indicates that the nonnegative matrix factorization does not satisfy all the nonnegative low-rank approximation conditions. Here we show how to resolve this important open problem by optimizing the identifiability of community structure. We propose a new form of nonnegative matrix decomposition and a probabilistic surrogate learning function that can be solved according to the majorization–minimization principle. Extensive in silico tests on artificial and real-world data demonstrate the efficient performance in community detection, regardless of the size and complexity of the network.

City size and the spreading of COVID-19 in Brazil.

Abstract: The current outbreak of the coronavirus disease 2019 (COVID-19) is an unprecedented example of how fast an infectious disease can spread around the globe (especially in urban areas) and the enormous impact it causes on public health and socio-economic activities. Despite the recent surge of investigations about different aspects of the COVID-19 pandemic, we still know little about the effects of city size on the propagation of this disease in urban areas. Here we investigate how the number of cases and deaths by COVID-19 scale with the population of Brazilian cities. Our results indicate small towns are proportionally more affected by COVID-19 during the initial spread of the disease, such that the cumulative numbers of cases and deaths per capita initially decrease with population size. However, during the long-term course of the pandemic, this urban advantage vanishes and large cities start to exhibit higher incidence of cases and deaths, such that every 1% rise in population is associated with a 0.14% increase in the number of fatalities per capita after about four months since the first two daily deaths. We argue that these patterns may be related to the existence of proportionally more health infrastructure in the largest cities and a lower proportion of older adults in large urban areas. We also find the initial growth rate of cases and deaths to be higher in large cities; however, these growth rates tend to decrease in large cities and to increase in small ones over time.

Methods and Models for Electric Load Forecasting: A Comprehensive Review

Abstract: Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.

All together to Fight COVID-19.

Abstract: Novel coronavirus disease (COVID-19), named a pandemic by the WHO, is the current global health crisis. National and international collaboration are indispensable for combating COVID-19 and other similar potential outbreaks. International efforts to tackle this complex problem have led to remarkable scientific advances. Yet, as a global society, we can and must take additional measures to fight this pandemic. Undoubtedly, our approach toward COVID-19 was not perfect, and testing has not been deployed fast enough to arrest the epidemic early on. It is critical that we revise our approaches to be more prepared for pandemics as a united body by promoting global cooperation and commitment.

Honey bee colony winter loss rates for 35 countries participating in the COLOSS survey for winter 2018–2019, and the effects of a new queen on the risk of colony winter loss

Abstract: This article presents managed honey bee colony loss rates over winter 2018/19 resulting from using the standardised COLOSS questionnaire in 35 countries (31 in Europe). In total, 28,629 beekeepers ...

Chaos and complexity in a fractional-order financial system with time delays

Abstract: Finance and economics are complex nonlinear systems that are affected by various external factors, including of course human action, bilateral relations, conflicts, and policy. Time delays in a financial system take into account the amount of time that passes from a particular policy or decision being made to it actually taking effect. It is thus important to consider time delays as an integral part of modeling in this field. Moreover, many features of financial systems cannot be expressed sufficiently precisely by means of integer-order calculus. Fractional-order calculus alleviates these shortcomings. The aim of this paper is therefore to study the dynamics and complexity in a fractional-order financial system with time delays. We observe fascinating transitions to deterministic chaos, including cascading period doubling, as well as high levels of complexity. This is particularly true in response to variations of derivative orders, which are thus identified as key system parameters.

Early spread of COVID-19 in Romania: imported cases from Italy and human-to-human transmission networks

Abstract: We describe the early spread of the novel coronavirus (COVID-19) and the first human-to-human transmission networks, in Romania. We profiled the first 147 cases referring to sex, age, place of residence, probable country of infection, return day to Romania, COVID-19 confirmation date and the probable modes of COVID-19 transmissions. Also, we analysed human-to-human transmission networks and explored their structural features and time dynamics. In Romania, local cycles of transmission were preceded by imported cases, predominantly from Italy. We observed an average of 4.8 days (s.d. = 4.0) between the arrival to a Romanian county and COVID-19 confirmation. Furthermore, among the first 147 COVID-19 patients, 88 were imported cases (64 carriers from Italy), 54 were domestic cases, while for five cases the source of infection was unknown. The early human-to-human transmission networks illustrated a limited geographical dispersion, the presence of super-spreaders and the risk of COVID-19 nosocomial infections. COVID-19 occurred in Romania through case importation from Italy. The largest share of the Romanian diaspora is concentrated especially in the northern parts of Italy, heavily affected by COVID-19. Human mobility (including migration) accounts for the COVID-19 transmission and it should be given consideration while tailoring prevention measures.

Innovative Extraction Techniques Using Deep Eutectic Solvents and Analytical Methods for the Isolation and Characterization of Natural Bioactive Compounds from Plant Material

Abstract: The growing interest of the food, pharmaceutical and cosmetics industries in naturally occurring bioactive compounds or secondary plant metabolites also leads to a growing demand for the development of new and more effective analysis and isolation techniques. The extraction of bioactive compounds from plant material has always been a challenge, accompanied by increasingly strict control requirements for the final products and a growing interest in environmental protection. However, great efforts have been made in this direction and today a considerable number of innovative extraction techniques have been developed using green, environmentally friendly solvents. These solvents include the deep eutectic solvents (DES) and their natural equivalents, the natural deep eutectic solvents (NADES). Due to their adjustable physical-chemical properties and their green character, it is expected that DES/NADES could be the most widely used solvents in the future, not only in extraction processes but also in other research areas such as catalysis, electrochemistry or organic synthesis. Consequently, this review provided an up-to-date systematic overview of the use of DES/NADES in combination with innovative extraction techniques for the isolation of bioactive compounds from various plant materials. The topicality of the field was confirmed by a detailed search on the platform WoS (Web of Science), which resulted in more than 100 original research papers on DES/NADES for bioactive compounds in the last three years. Besides the isolation of bioactive compounds from plants, different analytical methods are presented and discussed.

Diabetes and metabolic syndrome as risk factors for COVID-19.

Abstract: Background and aims Clinical evidence exists that patients with diabetes are at higher risk for Coronavirus disease 2019 (COVID-19). We investigated the physiological origins of this clinical observation linking diabetes with severity and adverse outcome of COVID-19. Methods Publication mining was applied to reveal common physiological contexts in which diabetes and COVID-19 have been investigated simultaneously. Overall, we have acquired 1,121,078 publications from PubMed in the time span between 01-01-2000 and 17-04-2020, and extracted knowledge graphs interconnecting the topics related to diabetes and COVID-19. Results The Data Mining revealed three pathophysiological pathways linking diabetes and COVID-19. The first pathway indicates a higher risk for COVID-19 because of a dysregulation of Angiotensin-converting enzyme 2. The other two important physiological links between diabetes and COVID-19 are liver dysfunction and chronic systemic inflammation. A deep network analysis has suggested clinical biomarkers predicting the higher risk: Hypertension, elevated serum Alanine aminotransferase, high Interleukin-6, and low Lymphocytes count. Conclusions The revealed biomarkers can be applied directly in clinical practice. For newly infected patients, the medical history needs to be checked for evidence of a long-term, chronic dysregulation of these biomarkers. In particular, patients with diabetes, but also those with prediabetic state, deserve special attention.

Discontinuous Transitions and Rhythmic States in the D-Dimensional Kuramoto Model Induced by a Positive Feedback with the Global Order Parameter.

Abstract: From fireflies to cardiac cells, synchronization governs important aspects of nature, and the Kuramoto model is the staple for research in this area. We show that generalizing the model to oscillators of dimensions higher than 2 and introducing a positive feedback mechanism between the coupling and the global order parameter leads to a rich and novel scenario: the synchronization transition is explosive at all even dimensions, whilst it is mediated by a time-dependent, rhythmic, state at all odd dimensions. Such a latter circumstance, in particular, differs from all other time-dependent states observed so far in the model. We provide the analytic description of this novel state, which is fully corroborated by numerical calculations. Our results can, therefore, help untangle secrets of observed time-dependent swarming and flocking dynamics that unfold in three dimensions, and where this novel state could thus provide a fresh perspective for as yet not understood formations.

String Universality and Non-Simply-Connected Gauge Groups in 8D

Abstract: We present a consistency condition for 8D N=1 supergravity theories with nontrivial global structure G/Z for the non-Abelian gauge group, based on an anomaly involving the Z 1-form center symmetry. The interplay with other swampland criteria identifies the majority of 8D theories with gauge group G/Z, which have no string theory realization, as inconsistent quantum theories when coupled to gravity. While this condition is equivalent to geometric properties of elliptic K3 surfaces in F-theory compactifications, it constrains the unexplored landscape of gauge groups in other 8D string models.