Showing papers by "Jožef Stefan Institute published in 2020"

The joint evaluated fission and fusion nuclear data library, JEFF-3.3

Abstract: The joint evaluated fission and fusion nuclear data library 3.3 is described. New evaluations for neutron-induced interactions with the major actinides $^{235}\hbox {U}$, $^{238}\hbox {U}$ and $^{239}\hbox {Pu}$, on $^{241}\hbox {Am}$ and $^{23}\hbox {Na}$, $^{59}\hbox {Ni}$, Cr, Cu, Zr, Cd, Hf, W, Au, Pb and Bi are presented. It includes new fission yields, prompt fission neutron spectra and average number of neutrons per fission. In addition, new data for radioactive decay, thermal neutron scattering, gamma-ray emission, neutron activation, delayed neutrons and displacement damage are presented. JEFF-3.3 was complemented by files from the TENDL project. The libraries for photon, proton, deuteron, triton, helion and alpha-particle induced reactions are from TENDL-2017. The demands for uncertainty quantification in modeling led to many new covariance data for the evaluations. A comparison between results from model calculations using the JEFF-3.3 library and those from benchmark experiments for criticality, delayed neutron yields, shielding and decay heat, reveals that JEFF-3.3 performes very well for a wide range of nuclear technology applications, in particular nuclear energy.

Quantum chaos challenges many-body localization.

Abstract: Characterizing states of matter through the lens of their ergodic properties is a fascinating new direction of research. In the quantum realm, the many-body localization (MBL) was proposed to be the paradigmatic ergodicity breaking phenomenon, which extends the concept of Anderson localization to interacting systems. At the same time, random matrix theory has established a powerful framework for characterizing the onset of quantum chaos and ergodicity (or the absence thereof) in quantum many-body systems. Here we numerically study the spectral statistics of disordered interacting spin chains, which represent prototype models expected to exhibit MBL. We study the ergodicity indicator $g={log}_{10}({t}_{\mathrm{H}}/{t}_{\mathrm{Th}})$, which is defined through the ratio of two characteristic many-body time scales, the Thouless time ${t}_{\mathrm{Th}}$ and the Heisenberg time ${t}_{\mathrm{H}}$, and hence resembles the logarithm of the dimensionless conductance introduced in the context of Anderson localization. We argue that the ergodicity breaking transition in interacting spin chains occurs when both time scales are of the same order, ${t}_{\mathrm{Th}}\ensuremath{\approx}{t}_{\mathrm{H}}$, and $g$ becomes a system-size independent constant. Hence, the ergodicity breaking transition in many-body systems carries certain analogies with the Anderson localization transition. Intriguingly, using a Berezinskii-Kosterlitz-Thouless correlation length we observe a scaling solution of $g$ across the transition, which allows for detection of the crossing point in finite systems. We discuss the observation that scaled results in finite systems by increasing the system size exhibit a flow towards the quantum chaotic regime.

Interplay Between NLRP3 Inflammasome and Autophagy.

Abstract: The NLRP3 inflammasome is cytosolic multi-protein complex that induces inflammation and pyroptotic cell death in response to both pathogen (PAMPs) and endogenous activators (DAMPs). Recognition of PAMPs or DAMPs leads to formation of the inflammasome complex, which results in activation of caspase-1, followed by cleavage and release of pro-inflammatory cytokines. Excessive activation of NLRP3 inflammasome can contribute to development of inflammatory diseases and cancer. Autophagy is vital intracellular process for recycling and removal of damaged proteins and organelles, as well as destruction of intracellular pathogens. Cytosolic components are sequestered in a double-membrane vesicle-autophagosome, which then fuses with lysosome resulting in degradation of the cargo. The autophagy dysfunction can lead to diseases with hyperinflammation and excessive activation of NLRP3 inflammasome and thus acts as a major regulator of inflammasomes. Autophagic removal of NLRP3 inflammasome activators, such as intracellular DAMPs, NLRP3 inflammasome components, and cytokines can reduce inflammasome activation and inflammatory response. Likewise, inflammasome signaling pathways can regulate autophagic process necessary for balance between required host defense inflammatory response and prevention of excessive and detrimental inflammation. Autophagy has a protective role in some inflammatory diseases associated with NLRP3 inflammasome, including gouty arthritis, familial Mediterranean fever (FMF), and sepsis. Understanding the interregulation between these two essential biological processes is necessary to comprehend the biological mechanisms and designing possible treatments for multiple inflammatory diseases.

Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data

Abstract: Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community. Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.

Functional diversity of small nucleolar RNAs.

Abstract: Small nucleolar RNAs (snoRNAs) are short non-protein-coding RNAs with a long-recognized role in tuning ribosomal and spliceosomal function by guiding ribose methylation and pseudouridylation at targeted nucleotide residues of ribosomal and small nuclear RNAs, respectively. SnoRNAs are increasingly being implicated in regulation of new types of post-transcriptional processes, for example rRNA acetylation, modulation of splicing patterns, control of mRNA abundance and translational efficiency, or they themselves are processed to shorter stable RNA species that seem to be the principal or alternative bioactive isoform. Intriguingly, some display unusual cellular localization under exogenous stimuli, or tissue-specific distribution. Here, we discuss the new and unforeseen roles attributed to snoRNAs, focusing on the presumed mechanisms of action. Furthermore, we review the experimental approaches to study snoRNA function, including high resolution RNA:protein and RNA:RNA interaction mapping, techniques for analyzing modifications on targeted RNAs, and cellular and animal models used in snoRNA biology research.

Recent Progress in Lithium Niobate: Optical Damage, Defect Simulation, and On‐Chip Devices

Abstract: Lithium niobate (LN) is one of the most important synthetic crystals. In the past two decades, many breakthroughs have been made in material technology, theoretical understanding, and application of LN crystals. Recent progress in optical damage, defect simulation, and on-chip devices of LN are explored. Optical damage is one of the main obstacles for the practical usage of LN crystals. Recent results reveal that doping with ZrO2 not only leads to better optical damage resistance in the visible but also improves resistance in the ultraviolet region. It is still awkward to extract defect characteristics and their relationship with the physical properties of LN crystals directly from experimental investigations. Recent simulations provide detailed descriptions of intrinsic defect models, the site occupation of dopants and the variation of energy levels due to extrinsic defects. LN is considered to be one of the most promising platforms for integrated photonics. Benefiting from advances in smart-cut, direct wafer bonding and layer transfer techniques, great progress has been made in the past decade for LNs on insulators. Recent progress on on-chip LN micro-photonic devices and nonlinear optical effects, in particular photorefractive effects, are briefly reviewed.

Ergodicity breaking transition in finite disordered spin chains

Abstract: We study disorder-induced ergodicity breaking transition in high-energy eigenstates of interacting spin-1/2 chains. Using exact diagonalization, we introduce a cost function approach to quantitatively compare different scenarios for the eigenstate transition. We study ergodicity indicators such as the eigenstate entanglement entropy and the spectral level spacing ratio, and we consistently find that an (infinite-order) Berezinskii-Kosterlitz-Thouless transition yields a lower cost function when compared to a finite-order transition. Interestingly, we observe that the ergodicity breaking transition in systems studied by exact diagonalization (with around 20 lattice sites) takes place at disorder values lower than those reported in previous works. As a consequence, the crossing point in finite systems exhibits nearly thermal properties, i.e., ergodicity indicators at the transition are close to the random matrix theory predictions.

Gapless ground state in the archetypal quantum kagome antiferromagnet ZnCu3(OH)6Cl2

Abstract: Spin liquids are exotic phases of quantum matter that challenge Landau’s paradigm of symmetry-breaking phase transitions. Despite strong exchange interactions, spins do not order or freeze down to zero temperature. Although well established for one-dimensional quantum antiferromagnets, in higher dimensions where quantum fluctuations are less acute, realizing and understanding such states is a major issue, both theoretically and experimentally. In this regard, the simplest nearest-neighbour Heisenberg antiferromagnet Hamiltonian on the highly frustrated kagome lattice has proven to be a fascinating and inspiring model. The exact nature of its ground state remains elusive and the existence of a spin-gap is the first key issue to be addressed to discriminate between the various classes of proposed spin liquids. Here, through low-temperature NMR contrast experiments on high-quality single crystals, we single out the kagome susceptibility and the corresponding dynamics in the kagome archetype, the mineral herbertsmithite, ZnCu3(OH)6Cl2. We firmly conclude that this material does not harbour any spin-gap, which restores a convergence with recent numerical results promoting a gapless Dirac spin liquid as the ground state of the Heisenberg kagome antiferromagnet. Herbertsmithite is an experimental realization of the so-called quantum kagome antiferromagnet, a system that is predicted to host a spin liquid state down to zero temperature. Detailed NMR measurements now confirm that this is the case, and that its ground state is indeed gapless.

Ferroelectric-Ferroelastic Phase Transition in a Nematic Liquid Crystal.

Abstract: Ferroelectric ordering in liquids is a fundamental question of physics. Here, we show that ferroelectric ordering of the molecules causes the formation of recently reported splay nematic liquid-crystalline phase. As shown by dielectric spectroscopy, the transition between the uniaxial and the splay nematic phase has the characteristics of a ferroelectric phase transition, which drives an orientational ferroelastic transition via flexoelectric coupling. The polarity of the splay phase was proven by second harmonic generation imaging, which additionally allowed for determination of the splay modulation period to be of the order of 5-10 microns, also confirmed by polarized optical microscopy. The observations can be quantitatively described by a Landau-de Gennes type of macroscopic theory.

COVID-19 pandemic changes the food consumption patterns.

Abstract: Background: The COVID-19 pandemic affects all aspects of human life including their food consumption. The changes in the food production and supply processes introduce changes to the global dietary patterns. Scope and Approach: To study the COVID-19 impact on food consumption process, we have analyzed two data sets that consist of food preparation recipes published before (69,444) and during the quarantine (10,009) period. Since working with large data sets is a time-consuming task, we have applied a recently proposed artificial intelligence approach called DietHub. The approach uses the recipe preparation description (i.e. text) and automatically provides a list of main ingredients annotated using the Hansard semantic tags. After extracting the semantic tags of the ingredients for every recipe, we have compared the food consumption patterns between the two data sets by comparing the relative frequency of the ingredients that compose the recipes. Key Findings and Conclusions: Using the AI methodology, the changes in the food consumption patterns before and during the COVID-19 pandemic are obvious. The highest positive difference in the food consumption can be found in foods such as “Pulses/ plants producing pulses”, “Pancake/Tortilla/Outcake”, and “Soup/pottage”, which increase by 300%, 280%, and 100%, respectively. Conversely, the largest decrease in consumption can be food for food such as “Order Perciformes (type of fish)”, “Corn/cereals/grain”, and “Wine-making”, with a reduction of 50%, 40%, and 30%, respectively. This kind of analysis is valuable in times of crisis and emergencies, which is a very good example of the scientific support that regulators require in order to take quick and appropriate response.

Findings of the 2020 Conference on Machine Translation (WMT20)

Abstract: This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.

Machine Learning and End-to-End Deep Learning for the Detection of Chronic Heart Failure From Heart Sounds

Abstract: Chronic heart failure (CHF) affects over 26 million of people worldwide, and its incidence is increasing by 2% annually. Despite the significant burden that CHF poses and despite the ubiquity of sensors in our lives, methods for automatically detecting CHF are surprisingly scarce, even in the research community. We present a method for CHF detection based on heart sounds. The method combines classic Machine-Learning (ML) and end-to-end Deep Learning (DL). The classic ML learns from expert features, and the DL learns from a spectro-temporal representation of the signal. The method was evaluated on recordings from 947 subjects from six publicly available datasets and one CHF dataset that was collected for this study. Using the same evaluation method as a recent PhysoNet challenge, the proposed method achieved a score of 89.3, which is 9.1 higher than the challenge’s baseline method. The method’s aggregated accuracy is 92.9% (error of 7.1%); while the experimental results are not directly comparable, this error rate is relatively close to the percentage of recordings labeled as “unknown” by experts (9.7%). Finally, we identified 15 expert features that are useful for building ML models to differentiate between CHF phases (i.e., in the decompensated phase during hospitalization and in the recompensated phase) with an accuracy of 93.2%. The proposed method shows promising results both for the distinction of recordings between healthy subjects and patients and for the detection of different CHF phases. This may lead to the easier identification of new CHF patients and the development of home-based CHF monitors for avoiding hospitalizations.

The structure of human thyroglobulin.

Abstract: Thyroglobulin (TG) is the protein precursor of thyroid hormones, which are essential for growth, development and the control of metabolism in vertebrates1,2. Hormone synthesis from TG occurs in the thyroid gland via the iodination and coupling of pairs of tyrosines, and is completed by TG proteolysis3. Tyrosine proximity within TG is thought to enable the coupling reaction but hormonogenic tyrosines have not been clearly identified, and the lack of a three-dimensional structure of TG has prevented mechanistic understanding4. Here we present the structure of full-length human thyroglobulin at a resolution of approximately 3.5 A, determined by cryo-electron microscopy. We identified all of the hormonogenic tyrosine pairs in the structure, and verified them using site-directed mutagenesis and in vitro hormone-production assays using human TG expressed in HEK293T cells. Our analysis revealed that the proximity, flexibility and solvent exposure of the tyrosines are the key characteristics of hormonogenic sites. We transferred the reaction sites from TG to an engineered tyrosine donor–acceptor pair in the unrelated bacterial maltose-binding protein (MBP), which yielded hormone production with an efficiency comparable to that of TG. Our study provides a framework to further understand the production and regulation of thyroid hormones. The cryo-electron microscopy structure of human thyroglobulin reveals that proximity, flexibility and solvent exposure are key characteristics of its hormonogenic tyrosine pairs, and provides a framework for understanding the formation of thyroid hormones.

Propylene Epoxidation using Molecular Oxygen over Copper- and Silver-Based Catalysts: A Review

Abstract: Propylene oxide (PO) is a versatile chemical, mainly used in the synthesis of polyurethane plastics. Propylene epoxidation using molecular oxygen could replace the tedious current synthesis protoco...

N-Graphene Nanowalls via Plasma Nitrogen Incorporation and Substitution: The Experimental Evidence

Abstract: Incorporating nitrogen (N) atom in graphene is considered a key technique for tuning its electrical properties. However, this is still a great challenge, and it is unclear how to build N-graphene with desired nitrogen configurations. There is a lack of experimental evidence to explain the influence and mechanism of structural defects for nitrogen incorporation into graphene compared to the derived DFT theories. Herein, this gap is bridged through a systematic study of different nitrogen-containing gaseous plasma post-treatments on graphene nanowalls (CNWs) to produce N-CNWs with incorporated and substituted nitrogen. The structural and morphological analyses describe a remarkable difference in the plasma–surface interaction, nitrogen concentration and nitrogen incorporation mechanism in CNWs by using different nitrogen-containing plasma. Electrical conductivity measurements revealed that the conductivity of the N-graphene is strongly influenced by the position and concentration of C–N bonding configurations. These findings open up a new pathway for the synthesis of N-graphene using plasma post-treatment to control the concentration and configuration of incorporated nitrogen for application-specific properties.

From mobility patterns to behavioural change: leveraging travel behaviour and personality profiles to nudge for sustainable transportation

Abstract: Rendering transport behaviours more sustainable is a pressing issue of our times. In this paper, we rely on the deep penetration of mobile phones in order to influence citizens’ behavior through data-driven mobility and persuasive profiles. Our proposed approach aims to nudge users on a personalized level in order to change their mobility behavior and make more sustainable choices. To achieve our goal, first we leverage pervasive mobile sensing to uncover users’ mobility patterns and use of transportation modes. Second, we construct users’ persuadability profiles by considering their personality and mobility behavior. With the use of the aforementioned information we generate personalized interventions that nudge users to adopt sustainable transportation habits. These interventions rely on persuasive technologies and are embedded in a route planning application for smartphones. A pilot study with 30 participants using the system for 6 weeks provided fairly positive evaluation results in terms of the acceptance of our approach and revealed instances of behavioural change.

μ → e γ selecting scalar leptoquark solutions for the ( g − 2 ) e , μ puzzles

Abstract: We investigate all potentially viable scenarios that can produce the chiral enhancement required to simultaneously explain the $(g\ensuremath{-}2{)}_{e}$ and $(g\ensuremath{-}2{)}_{\ensuremath{\mu}}$ data with either a single scalar leptoquark or a pair of scalar leptoquarks. We provide a classification of these scenarios in terms of their ability to satisfy the existing limits on the branching ratio for the $\ensuremath{\mu}\ensuremath{\rightarrow}e\ensuremath{\gamma}$ process. The simultaneous explanation of the $(g\ensuremath{-}2{)}_{e,\ensuremath{\mu}}$ discrepancies, coupled with the current experimental data, implies that the $(g\ensuremath{-}2{)}_{e}$ loops are exclusively due to the charm-quark propagation, whereas the $(g\ensuremath{-}2{)}_{\ensuremath{\mu}}$ loops are due to the top-quark propagation. The scenarios where the $(g\ensuremath{-}2{)}_{e}$ loops are due to the top (bottom) quark propagation are, at best, approximately 9 (3) orders of magnitude away from the experimental limit on the $\ensuremath{\mu}\ensuremath{\rightarrow}e\ensuremath{\gamma}$ branching ratio. All in all, there are only three particular scenarios that can pass the $\ensuremath{\mu}\ensuremath{\rightarrow}e\ensuremath{\gamma}$ test and simultaneously create a large enough impact on the $(g\ensuremath{-}2{)}_{e,\ensuremath{\mu}}$ discrepancies when the new physics is based on the Standard Model fermion content. These are the ${S}_{1}$, ${R}_{2}$, and ${S}_{1}a{S}_{3}$ scenarios, where the first two are already known to be phenomenologically viable candidates with respect to all other flavor and collider data constraints. We show that the third scenario---where the right-chiral couplings to charged leptons are due to ${S}_{1}$, the left-chiral couplings to charged leptons are due to ${S}_{3}$, and the two leptoquarks mix through the Standard Model Higgs field---cannot address the $(g\ensuremath{-}2{)}_{e}$ and $(g\ensuremath{-}2{)}_{\ensuremath{\mu}}$ discrepancies at the $1\ensuremath{\sigma}$ level due to an interplay between ${K}_{L}^{0}\ensuremath{\rightarrow}{e}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\mu}}^{\ensuremath{\mp}}$, $Z\ensuremath{\rightarrow}{e}^{+}{e}^{\ensuremath{-}}$, and $Z\ensuremath{\rightarrow}{\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}}$ data despite the ability of that scenario to avoid the $\ensuremath{\mu}\ensuremath{\rightarrow}e\ensuremath{\gamma}$ limit.

Electrolyte Reactivity in the Double Layer in Mg Batteries: An Interface Potential-Dependent DFT Study

Abstract: The electrochemical degradation of two solvent-based electrolytes for Mg-metal batteries is investigated through a Grand canonical DFT approach. Both electrolytes are highly reactive in the double layer region where the solvated species have no direct contact with the Mg-surface, hence emphasising that surface reactions are not the only phenomena responsible for electrolyte degradation. Applied to dimethoxyethane (DME) and ethylene carbonate (EC), the present methodology shows that both solvents should thermodynamically decompose in the double layer prior to the Mg 2+ /Mg 0 reduction, leading to electrochemically inactive reaction products. Based on thermodynamic considerations, Mg 0 deposition should not be possible, which is not in agreement with experiments, at least for DME-based electrolytes. This apparent contradiction is here addressed through the rationalization of the electrochemical mechanism underlying solvent electro-activation. An extended operation potential window (OPW) is defined, in which the Mg 2+ /Mg 0 reduction can compete with electrolyte decomposition, thus enabling battery operation beyond the solvated species thermodynamic stability. The chemical study of the degradation products is in excellent agreement with experiments and it offer rationale for the Mg-battery failure in EC electrolyte and 2 capacity fade in DME electrolyte. Potential-dependent approach proposed herein is thus able to successfully tackle the challenging problem of interface electrochemistry. Being fully transferable to any other electrochemical systems, this methodology should provide rational guidelines for the development of viable electrolytes for multivalent batteries, and more generally, energy conversion and storage devices.

Effects of Nonthermal Plasma on Morphology, Genetics and Physiology of Seeds: A Review.

Abstract: Nonthermal plasma (NTP), or cold plasma, has shown many advantages in the agriculture sector as it enables removal of pesticides and contaminants from the seed surface, increases shelf life of crops, improves germination and resistance to abiotic stress. Recent studies show that plasma treatment indeed offers unique and environmentally friendly processing of different seeds, such as wheat, beans, corn, soybeans, barley, peanuts, rice and Arabidopsis thaliana, which could reduce the use of agricultural chemicals and has a high potential in ecological farming. This review covers the main concepts and underlying principles of plasma treatment techniques and their interaction with seeds. Different plasma generation methods and setups are presented and the influence of plasma treatment on DNA damage, gene expression, enzymatic activity, morphological and chemical changes, germination and resistance to stress, is explained. Important plasma treatment parameters and interactions of plasma species with the seed surface are presented and critically discussed in correlation with recent advances in this field. Although plasma agriculture is a relatively new field of research, and the complex mechanisms of interactions are not fully understood, it holds great promise for the future. This overview aims to present the advantages and limitations of different nonthermal plasma setups and discuss their possible future applications.

Eigenstate Entanglement Entropy in Random Quadratic Hamiltonians

Abstract: The eigenstate entanglement entropy is a powerful tool to distinguish integrable from generic quantum-chaotic models In integrable models, the average eigenstate entanglement entropy (over all Hamiltonian eigenstates) has a volume-law coefficient that generally depends on the subsystem fraction In contrast, it is maximal (subsystem fraction independent) in quantum-chaotic models Using random matrix theory for quadratic Hamiltonians, we obtain a closed-form expression for the average eigenstate entanglement entropy as a function of the subsystem fraction We test it against numerical results for the quadratic Sachdev-Ye-Kitaev model and show that it describes the results for the power-law random banded matrix model (in the delocalized regime) We show that localization in quasimomentum space produces (small) deviations from our analytic predictions

Increasing the oxygen-evolution reaction performance of nanotubular titanium oxynitride-supported Ir nanoparticles by a strong metal - support Interaction

Abstract: This study targets one of the grand challenges of electrochemical hydrogen production: a durable and cost-effective oxygen-evolution catalyst. We present a thin-film composite electrode with a uniq...