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Institution

University of Maribor

EducationMaribor, Slovenia
About: University of Maribor is a education organization based out in Maribor, Slovenia. It is known for research contribution in the topics: Population & KEKB. The organization has 3987 authors who have published 13077 publications receiving 258339 citations. The organization is also known as: Univerza v Mariboru.


Papers
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Journal ArticleDOI
TL;DR: It is shown that sharing information about strategy choice between players residing on two different networks reinforces the evolution of cooperation.
Abstract: More often than not, bad decisions are bad regardless of where and when they are made. Information sharing might thus be utilized to mitigate them. Here we show that sharing the information about strategy choice between players residing on two different networks reinforces the evolution of cooperation. In evolutionary games the strategy reflects the action of each individual that warrants the highest utility in a competitive setting. We therefore assume that identical strategies on the two networks reinforce themselves by lessening their propensity to change. Besides network reciprocity working in favour of cooperation on each individual network, we observe the spontaneous emerge of correlated behaviour between the two networks, which further deters defection. If information is shared not just between individuals but also between groups, the positive effect is even stronger, and this despite the fact that information sharing is implemented without any assumptions with regards to content.

104 citations

Journal ArticleDOI
TL;DR: This work proposes a new form of nonnegative matrix decomposition and a probabilistic surrogate learning function that can be solved according to the majorization–minimization principle, and shows how to resolve this important open problem by optimizing the identifiability of community structure.
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.

103 citations

Journal ArticleDOI
TL;DR: Understanding how cannabinoids are able to regulate essential cellular processes involved in tumorigenesis, such as progression through the cell cycle, cell proliferation and cell death, as well as the interactions between cannabinoids and the immune system, are crucial for improving existing and developing new therapeutic approaches for cancer patients.
Abstract: The plant Cannabis sativa L. has been used as an herbal remedy for centuries and is the most important source of phytocannabinoids. The endocannabinoid system (ECS) consists of receptors, endogenous ligands (endocannabinoids) and metabolizing enzymes, and plays an important role in different physiological and pathological processes. Phytocannabinoids and synthetic cannabinoids can interact with the components of ECS or other cellular pathways and thus affect the development/progression of diseases, including cancer. In cancer patients, cannabinoids have primarily been used as a part of palliative care to alleviate pain, relieve nausea and stimulate appetite. In addition, numerous cell culture and animal studies showed antitumor effects of cannabinoids in various cancer types. Here we reviewed the literature on anticancer effects of plant-derived and synthetic cannabinoids, to better understand their mechanisms of action and role in cancer treatment. We also reviewed the current legislative updates on the use of cannabinoids for medical and therapeutic purposes, primarily in the EU countries. In vitro and in vivo cancer models show that cannabinoids can effectively modulate tumor growth, however, the antitumor effects appear to be largely dependent on cancer type and drug dose/concentration. Understanding how cannabinoids are able to regulate essential cellular processes involved in tumorigenesis, such as progression through the cell cycle, cell proliferation and cell death, as well as the interactions between cannabinoids and the immune system, are crucial for improving existing and developing new therapeutic approaches for cancer patients. The national legislation of the EU Member States defines the legal boundaries of permissible use of cannabinoids for medical and therapeutic purposes, however, these legislative guidelines may not be aligned with the current scientific knowledge.

103 citations

Journal ArticleDOI
TL;DR: The purpose of this work is to provide a balanced view of further uses of NAC as a dietary supplement, and to improve the understanding of Nac applicability.
Abstract: N-acetylcysteine (NAC), a plant antioxidant naturally found in onion, is a precursor to glutathione. It has been used as a drug since the 1960s and is listed on the World Health Organization (WHO) Model List of Essential Medicines as an antidote in poisonings. There are numerous other uses or proposed uses in medicine that are still in preclinical and clinical investigations. NAC is also used in food supplements and cosmetics. Despite its abundant use, there are projections that the NAC global market will grow in the next five years; therefore, the purpose of this work is to provide a balanced view of further uses of NAC as a dietary supplement. Although NAC is considered a safe substance, the results among clinical trials are sometimes controversial or incomplete, like for many other antioxidants. More clinical trials are underway that will improve our understanding of NAC applicability.

103 citations

Journal ArticleDOI
TL;DR: An extensive overview of the chemical composition, nutritional properties, and antioxidant and antimicrobial activities of chia, along with extraction methods used to produce chia oil, will be discussed.
Abstract: Chia (Salvia hispanica L.) is a small seed that comes from an annual herbaceous plant, Salvia hispanica L. In recent years, usage of Chia seeds has tremendously grown due to their high nutritional and medicinal values. Chia was cultivated by Mesopotamian cultures, but then disappeared for centuries until the middle of the 20th century, when it was rediscovered. Chia seeds contain healthy ω-3 fatty acids, polyunsaturated fatty acids, dietary fiber, proteins, vitamins, and some minerals. Besides this, the seeds are an excellent source of polyphenols and antioxidants, such as caffeic acid, rosmarinic acid, myricetin, quercetin, and others. Today, chia has been analyzed in different areas of research. Researches around the world have been investigating the benefits of chia seeds in the medicinal, pharmaceutical, and food industry. Chia oil is today one of the most valuable oils on the market. Different extraction methods have been used to produce the oil. In the present study, an extensive overview of the chemical composition, nutritional properties, and antioxidant and antimicrobial activities, along with extraction methods used to produce chia oil, will be discussed.

103 citations


Authors

Showing all 4077 results

NameH-indexPapersCitations
Ignacio E. Grossmann11277646185
Mirjam Cvetič8945627867
T. Sumiyoshi8885562277
M. Bračko8773830195
Xin-She Yang8544461136
Matjaž Perc8440022115
Baowen Li8347723080
S. Nishida8267827709
P. Križan7874926408
S. Korpar7861523802
Attila Szolnoki7623120423
H. Kawai7647722713
John Shawe-Taylor7250352369
Matjaz Perc5714812886
Mitja Lainscak5528722004
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202352
2022135
2021809
2020870
2019832
2018756