Slovak Academy of Sciences
  • Bratislava, Slovakia
Recent publications
The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.
The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.
Background Physical exercise has favorable effects on the structure of gut microbiota and metabolite production in sedentary subjects. However, little is known whether adjustments in an athletic program impact overall changes of gut microbiome in high-level athletes. We therefore characterized fecal microbiota and serum metabolites in response to a 7-week, high-intensity training program and consumption of probiotic Bryndza cheese. Methods Fecal and blood samples and training logs were collected from young competitive male ( n = 17) and female ( n = 7) swimmers. Fecal microbiota were categorized using specific primers targeting the V1–V3 region of 16S rDNA, and serum metabolites were characterized by NMR-spectroscopic analysis and by multivariate statistical analysis, Spearman rank correlations, and Random Forest models. Results We found higher α-diversity, represented by the Shannon index value (HITB-pre 5.9 [± 0.4]; HITB-post 6.4 [± 0.4], p = 0.007), (HIT-pre 5.5 [± 0.6]; HIT-post 5.9 [± 0.6], p = 0.015), after the end of the training program in both groups independently of Bryndza cheese consumption. However, Lactococcus spp . increased in both groups, with a higher effect in the Bryndza cheese consumers (HITB-pre 0.0021 [± 0.0055]; HITB-post 0.0268 [± 0.0542], p = 0.008), (HIT-pre 0.0014 [± 0.0036]; HIT-post 0.0068 [± 0.0095], p = 0.046). Concomitant with the increase of high-intensity exercise and the resulting increase of anaerobic metabolism proportion, pyruvate ( p [HITB] = 0.003; p [HIT] = 0.000) and lactate ( p [HITB] = 0.000; p [HIT] = 0.030) increased, whereas acetate ( p [HITB] = 0.000; p [HIT] = 0.002) and butyrate ( p [HITB] = 0.091; p [HIT] = 0.019) significantly decreased. Conclusions Together, these data demonstrate a significant effect of high-intensity training (HIT) on both gut microbiota composition and serum energy metabolites. Thus, the combination of intensive athletic training with the use of natural probiotics is beneficial because of the increase in the relative abundance of lactic acid bacteria.
Crystallization kinetics of rapidly quenched Fe-Sn-B alloys under non-isothermal conditions were studied using differential scanning calorimetry. Formation of crystalline phases was analyzed by X-ray diffraction. Nominal chemical compositions were Fe81Sn7B12, (Fe3Co1)81Sn7B12 and (Fe81Sn7B12)99Cu1. Alloys were prepared by planar flow casting in the form of ribbons approximately 20 µm thick and 6 mm wide. Mechanism of crystallization was studied under framework of the Johnson-Mehl-Avrami-Kolmogorov model. Alloys exhibit two stages of crystallization. Results show decrease in activation energy of the first stage of crystallization with addition of Cu and increase with addition of Co. Crystallization mechanism of the first stage of crystallization for Fe81Sn7B12 and (Fe81Sn7B12)99Cu1 alloy starts as growth with increasing nucleation rate and continues as growth with decreasing nucleation rate. Addition of Co changes mechanism of crystallization. Which in case of (Fe3Co1)81Sn7B12 alloy starts as a growth with increasing nucleation rate. Then changes to growth with decreasing nucleation rate. After which nucleation rate decreases to zero. Rest of crystallization stage is governed by growth of pre-existing nuclei. In the first stage of crystallization α-Fe phase with bcc structure crystallizes from amorphous matrix. In the second stage of crystallization the remaining amorphous matrix crystalizes into tetragonal Fe2B phase and hexagonal FeSn phase. After the first stage of crystallization, 50 % to 55 % volume of studied alloys were crystalized. Addition of Cu decreases crystalline size of α-Fe crystallites by 60 % and decreases concentration of Sn in α-Fe phase by 0.8 at. %. Addition of Co doesn't affect the size of α-Fe crystallites and decreases the concentration of Sn in α-Fe phase by 1.7 at. %.
Many ancient manuscripts are littered with wax drippings from the candles used while writing or reading them. Bacterial and fungal species associated with the wax in a manuscript containing an unusually large number of wax drops were analysed by metabarcoding with Oxford Nanopore Sequencing (MinION). In addition, culturable fungi and bacteria were isolated from the wax and tested for enzyme activities. The mechanism of colonisation of wax drops by airborne microorganisms was also reproduced. Imaging by electron microscopy showed the presence of mycelium and fungal fruiting structures on the wax particles. Wax is not a substance whose addition makes the paper more biodegradable, as the colonisation experiment conducted in this study has shown. However, the microanalysis highlighted that the drops represented points of accumulation of dust and material eroded from the pages that acted as nutritional hotspots for the development of a particular assemblage of microbial species.
In this paper, we adopt a new approach to study the controllability and observability of linear quaternion-valued systems (QVS) from the point of complex-valued systems, which is much different from the method used in the previous paper. We show the equivalence relation of complete controllability for linear QVS and its complex-valued system. Then we establish two effective criteria for controllability and observability of the linear QVS in the sense of complex representation. In addition, we give a direct method to solve the control function. Finally, we use numerical examples to illustrate our theoretical results.
Hypothesis One of the highlighted properties of Ti3C2Tx MXene compared to other 2D nanomaterials is its hydrophilicity. However, the broad range of static contact angles of Ti3C2Tx reported in the literature is misleading. To elucidate the experimental values of the static contact angles and get reproducible contact angle data, it is wiser to perform the advancing and receding contact angle measurements on smooth and compact Ti3C2Tx layers and focus on deep understanding of the physical basis behind the wettability, which is provided by contact angle hysteresis. Experiments Measurements of the advancing and receding contact angle on mono-, bi, and trilayer Ti3C2Tx on two different substrates were performed. As substrates, UV-ozone treated silicon wafer and silicon wafer functionalized by (3-aminopropyl)triethoxysilane, were used. Findings The values of the advancing contact angle on Ti3C2Tx on both substrates were proved to be independent of the number of Ti3C2Tx layers, demonstrating a negligible effect of the background substrate wettability. In addition, a giant contact angle hysteresis (44–52 °) was observed on very smooth surface, most likely as a result of chemical heterogeneity arising from the diversity of surface terminal groups (F, O, and OH). The findings reported in this study provide a comprehensive understanding of the wettability of MXene.
Parallel factor analysis (PARAFAC) is a powerful tool for detecting latent components in human electroencephalogram (EEG) in the time-space-frequency domain. As an essential parameter, the number of latent components should be set in advance. However, any component number selection method already proposed in the literature became a rule of thumb. Existing studies have demonstrated the methods’ performance on artificial data with a simplified structure, often not mimicking a real data character. On the other hand, the ground-truth latent structure is not always known for real-world data. With the objective to provide a comprehensive overview of component number selection methods and discuss their applicability to EEG, our study focuses on nontrivial and nonnegative simulated data structures resembling real EEG properties as closely as possible. This is achieved through an accurate head model and well-controlled cortical activation sources. By considering different noise levels and disruptions from the optimal structure, the performance of the twelve component number selection methods is closely inspected. Moreover, we validate a new approach for component number selection, which we recently proposed and applied to EEG tasks. We found that methods based on the eigenvalue analysis, variance explained, or presence of redundant components are inappropriate for component number selection in EEG tensor decomposition. On the other hand, three existing methods and the newly proposed approach produced promising results on nontrivial simulated EEG data. Nevertheless, component number selection for PARAFAC analysis of EEG is a complex yet unresolved problem, and new approaches are needed.
We study a Pierce sheaf representation of pseudo EMV-algebras which are a non-commutative generalization of MV-algebras, pseudo MV-algebras and of generalized Boolean algebras, so that the top element is not assumed a priori. We present one sheaf using a Boolean type of representation and the main results are concerning the Hausdorff sheaf representation of representable pseudo EMV-algebras. For this aim, we study also the space of maximal ideals not necessarily normal and the space of minimal prime ideals to give conditions when these spaces are compact/locally compact in the hull-kernel topology and when they imply existence of a top element.
Antimicrobial hydrogels have enticed a major concern for repairing soft tissues, particularly prohibiting bacterial infections that are frequently accompanied by impaired wound healing. Nevertheless, the development of new antibacterial hydrogel ingrained with excellent cell affinity is one of the robust challenges. This study aims for the first time to design a new class of antibacterial hydrogels with high biocompatibility through the formation of a water-soluble polyelectrolyte complex by performing a physical crosslinking reaction between the cationic trimethyl chitosan chloride (TMC) and anionic carboxymethyl starch (CMS) polymers. The structure of as-prepared hydrogels was characterized using different spectral and surface techniques including FTIR, ¹H-NMR, SEM, and XRD. The data stated that the hydrogels prepared with a high TMC content possess a high surface area and small pore size compared to other samples, suggesting more occurred interactions with CMS chains. Then, the antibacterial activity was investigated against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) as two pathogenic bacteria. The as-developed hydrogel with a high TMC mass achieved superior inhibition zones with diameters of 26 and 24 mm for E. coli and S. aureus, respectively, compared to that of pure TMC (20, and 18 mm). Moreover, the cytotoxicity of the hydrogels was examined against two normal cell lines such as VERO and lung (Wi38) cells lines. The cell viability of hydrogel was recorded 100% up to concentrations lower than 62.5, and 125 μg/mL for normal lung and VERO cell lines, respectively. Graphical abstract
Who is responsible when an AI machine causes something to go wrong? Or is there a gap in the ascription of responsibility? Answers range from claiming there is a unique responsibility gap, several different responsibility gaps, or no gap at all. In a nutshell, the problem is as follows: on the one hand, it seems fitting to hold someone responsible for a wrong caused by an AI machine; on the other hand, there seems to be no fitting bearer of responsibility for this wrong. In this article , we focus on a particular (aspect of the) AI responsibility gap: it seems fitting that someone should bear the legal consequences in scenarios involving AI machines with design defects; however, there seems to be no such fitting bearer. We approach this problem from the legal perspective, and suggest vicarious liability of AI manufacturers as a solution to this problem. Our proposal comes in two variants: the first one has a narrower range of application, but can be easily integrated in current legal frameworks; the second one requires a revision of current legal frameworks, but has a wider range of application. The latter variant employs a broadened account of vicarious liability. We emphasise strengths of the two variants and finally highlight how vicarious liability offers important insights for addressing a moral AI responsibility gap.
Emulsions are traditionally used as galenic forms for the local or systemic delivery of drugs and nutritional supplements. They are also used in cosmetics. In the 21st century, there is no area where nanotechnology does not intervene, so nanoemulsions have been prepared and characterized. This new generation of delivery forms can better protect active encapsulated ingredients from degradation. At the same time, they can also enhance their solubility in water, adjust their bioavailability, and allow the specific/modified release of the active ingredients from these lipid nanocarriers. Nanoemulsions in the food industry are of great importance for food protection and biological enrichment of foods with valuable ingredients. In nanoemulsions, natural vegetable oils are used for the oil phase, emulsifiers, biosurfactants, cosurfactants, and encapsulated active ingredients. This contribution is focused on the current findings related to the application of food-grade nanoemulsions—a useful class of nanocarriers composed of biocompatible and biodegradable vehicles that protect bioactive components, modify bioavailability, and allow the advanced delivery of bioactive ingredients. Attention is paid to encapsulated vitamins, antioxidants, omega-3 fatty acids, phytochemicals, and other nutraceuticals/dietary supplements or ingredients used for food fortification to meet the status of foods for special medical purposes and, of course, edible coatings and smart packaging materials, which are used to extend the shelf life and improve the quality of food.
Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely‐used, state‐of‐the‐art, stand‐scale forest models against field measurements of forest structure and eddy‐covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models’ performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapor pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi‐model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe’s common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.
Lecanosticta acicola is a pine needle pathogen causing brown spot needle blight that results in premature needle shedding with considerable damage described in North America, Europe, and Asia. Microsatellite and mating type markers were used to study the population genetics, migration history, and reproduction mode of the pathogen, based on a collection of 650 isolates from 27 countries and 26 hosts across the range of L. acicola. The presence of L. acicola in Georgia was confirmed in this study. Migration analyses indicate there have been several introduction events from North America into Europe. However, some of the source populations still appear to remain unknown. The populations in Croatia and western Asia appear to originate from genetically similar populations in North America. Intercontinental movement of the pathogen was reflected in an identical haplotype occurring on two continents, in North America (Canada) and Europe (Germany). Several shared haplotypes between European populations further suggests more local pathogen movement between countries. Moreover, migration analyses indicate that the populations in northern Europe originate from more established populations in central Europe. Overall, the highest genetic diversity was observed in south‐eastern USA. In Europe, the highest diversity was observed in France, where the presence of both known pathogen lineages was recorded. Less than half of the observed populations contained mating types in equal proportions. Although there is evidence of some sexual reproduction taking place, the pathogen spreads predominantly asexually and through anthropogenic activity. The pine needle pathogen Lecanosticta acicola has been introduced into Europe on several separate occasions with human activity supporting the pathogen's onwards spread from already established European populations into new areas.
A new class of implicit Hadamard fractional differential equations with Riemann-Stieltjes integral boundary conditions is studied in this research paper. The existence and uniqueness results of the aforesaid problem are investigated using Schauder’s fixed point theorem and Banach’s contraction mapping principle. A simulative example is given to highlight the acquired outcomes.
Bee pollen (BP) and bee bread (BB) have attracted great attention due to their biological activities including antibacterial activity. However, the mechanism of antibacterial activity is largely unknown. Therefore, we aimed to characterise the antibacterial effect of BP and BB aqueous extracts against bacterial pathogens (Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Proteus mirabilis and Enterococcus faecalis) and identify the key compound(s) responsible for this effect. Here, we demonstrate that BP and particularly BB extracts display antibacterial activity which is significantly increased in the presence of glucose. Immunoblot analysis of extracts revealed the presence of MRJP1 in all analysed BP and BB samples and the enzyme glucose oxidase (GOX) in the majority of BB samples. Treatment of extracts with catalase resulted in the restoration of bacterial growth but only in those samples where glucose supplementation caused the enhancement of antibacterial activity. Our findings provide a deeper understanding of antibacterial activity of BP/BB which is mediated by the enzymatic activity of bee-derived GOX.
The presented overview deals with the study of the luminescence properties of lanthanide ions incorporated into different dielectric crystalline materials for use in photonics and optoelectronics. From the crystalline materials, non-centrosymmetric hexagonal crystals of LiNbO3, Al2O3 and ZnO, together with the centrosymmetric cubic crystal of diamond, were chosen. The above-mentioned materials represent a certain cross-section through various crystal structure geometries with different internal bonding of atoms which represent different crystal vicinity for the incorporated Er ions. During more than ten years of our research, each of the crystals was doped with erbium ions and the resulting structural and luminescence properties were studied in detail and compared between the mentioned crystalline materials to find similar behaviour for erbium ions in the different crystalline materials. To better understand the incorporation of erbium in the studied crystalline materials, theoretical simulations of different erbium-doped crystal models were carried out. In the calculations, cohesive energies of the structures and erbium defect-formation energies were compared in order to find the most favourable erbium positions in the crystals. Also, from the geometry optimization calculations, the optimal geometry arrangements in the vicinity of erbium ions in different crystals were studied and visualized. The results of the theoretical simulations confirmed the experimental results - i.e., from all the theoretical erbium-doped crystal models, the most stable structures contained erbium in the substitutional positions with octahedral oxygen coordination.
The SARS-CoV-2 outbreak has already affected more than 555 million people, and 6.3 million people have died. Due to its high infectivity, it is crucial to track SARS-CoV-2 outbreaks early to prevent the spread of infection. Wastewater monitoring appears to be a powerful and effective tool for managing epidemiological situations. Due to emerging mutations of SARS-CoV-2, there is a need to monitor mutations in order to control the pandemic. Since the sequencing of randomly chosen individuals is time-consuming and expensive, sequencing of wastewater plays an important role in revealing the dynamics of infection in a population. The sampling method used is a crucial factor and significantly impacts the results. Wastewater can be collected as a grab sample or as a 24 h composite sample. Another essential factor is the sample volume, as is the method of transport used. This review discusses different pretreatment procedures and RNA extraction, which may be performed using various methods, such as column-based extraction, TRIzol, or magnetic extraction. Each of the methods has its advantages and disadvantages, which are described accordingly. RT-qPCR is a procedure that confirms the presence of SARS-CoV-2 genes before sequencing. This review provides an overview of currently used methods for preparing wastewater samples, from sampling to sequencing.
Silicon is absorbed as uncharged mono-silicic acid by plant roots through passive absorption of Lsi1, and influx transporter belonging to the aquaporin protein family. Lsi2 then actively effluxes silicon from root cells towards the xylem from where it is exported by Lsi6 for silicon distribution and accumulation to other parts. Recently, it was proposed that silicon nanoparticles (SiNPs) might share a similar route for their uptake and transport. SiNPs then initiate a cascade of morphophysiological adjustments that improve the plant physiology through regulating the expression of many photosynthetic genes and proteins along with photosystem I (PSI) and PSII assemblies. Subsequent improvement in photosynthetic performance and stomatal behaviour correspond to higher growth, development, and productivity. On many occasions, SiNPs have demonstrated a protective role during stressful environments by improving plant-water status, source-sink potential, reactive oxygen species (ROS) metabolism, and enzymatic profile. The present review comprehensively discusses the crop improvement potential of SiNPs stretching their role during optimal and abiotic stress conditions including salinity, drought, temperature, heavy metals, and ultraviolet (UV) radiation. Moreover, in the later section of this review, we offered the understanding that most of these upgrades can be explained by SiNPs intricate correspondence with phytohormones, antioxidants, and signalling molecules. SiNPs can modulate the endogenous phytohormones level such as abscisic acid (ABA), auxins (IAAs), cytokinins (CKs), ethylene (ET), gibberellins (GAs), and jasmonic acid (JA). Altered phytohormones level affects plant growth, development, and productivity at various organ and tissue levels. Similarly, SiNPs regulate the activities of catalase (CAT), ascorbate peroxidase (APX), superoxide dismutase (SOD), and ascorbate-glutathione (AsA-GSH) cycle leading to an upgraded defence system. At the cellular and subcellular levels, SiNPs crosstalk with various signalling molecules such as Ca2+, K+, Na+, nitric oxide (NO), ROS, soluble sugars, and transcription factors (TFs) was also explained.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
1,782 members
Enric Pardo
  • Institute of Electrical Engineering
Tomáš Čejka
  • Institute of Botany, Plant Science and Biodiversity Center
Zuzana Čiamporová-Zaťovičová
  • Plant Science and Biodiversity Centre
Stefan Janecek
  • Laboratory of Protein Evolution
Slavomir Adamcik
  • Department of Non-vascular Plants
Information
Address
Štefánikova 49, 81438, Bratislava, Slovakia
Head of institution
prof. RNDr. Pavol Šajgalík, DrSc.
Website
http://www.sav.sk