Showing papers by "Jožef Stefan Institute published in 2019"

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Abstract: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Cysteine Cathepsins and Their Extracellular Roles: Shaping the Microenvironment

Abstract: For a long time, cysteine cathepsins were considered primarily as proteases crucial for nonspecific bulk proteolysis in the endolysosomal system. However, this view has dramatically changed, and cathepsins are now considered key players in many important physiological processes, including in diseases like cancer, rheumatoid arthritis, and various inflammatory diseases. Cathepsins are emerging as important players in the extracellular space, and the paradigm is shifting from the degrading enzymes to the enzymes that can also specifically modify extracellular proteins. In pathological conditions, the activity of cathepsins is often dysregulated, resulting in their overexpression and secretion into the extracellular space. This is typically observed in cancer and inflammation, and cathepsins are therefore considered valuable diagnostic and therapeutic targets. In particular, the investigation of limited proteolysis by cathepsins in the extracellular space is opening numerous possibilities for future break-through discoveries. In this review, we highlight the most important findings that establish cysteine cathepsins as important players in the extracellular space and discuss their roles that reach beyond processing and degradation of extracellular matrix (ECM) components. In addition, we discuss the recent developments in cathepsin research and the new possibilities that are opening in translational medicine.

Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network.

Abstract: Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP is a potential solution investigated in our study. We analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms, resulting in over 700 h of signals after preprocessing, belonging to 510 subjects. We then used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections. We have shown in a leave-one-subject-out experiment that the network is able to model the dependency between PPG and BP, achieving mean absolute errors of 9.43 for systolic and 6.88 for diastolic BP. Additionally we have shown that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult. We have made crucial parts of our study, especially the list of used subjects and our neural network code, publicly available, in an effort to provide a solid baseline and simplify potential comparison between future studies on an explicit MIMIC III subset.

Bad metallic transport in a cold atom Fermi-Hubbard system

Abstract: Strong interactions in many-body quantum systems complicate the interpretation of charge transport in such materials. To shed light on this problem, we study transport in a clean quantum system: ultracold lithium-6 in a two-dimensional optical lattice, a testing ground for strong interaction physics in the Fermi-Hubbard model. We determine the diffusion constant by measuring the relaxation of an imposed density modulation and modeling its decay hydrodynamically. The diffusion constant is converted to a resistivity by using the Nernst-Einstein relation. That resistivity exhibits a linear temperature dependence and shows no evidence of saturation, two characteristic signatures of a bad metal. The techniques we developed in this study may be applied to measurements of other transport quantities, including the optical conductivity and thermopower.

The future for plasma science and technology

Abstract: The application of gas discharge plasmas has assumed an important place in many manufacturing processes. Plasma methods contribute significantly to the economic prosperity of industrialized societies. However, plasma is mainly an enabling method and therefore its role remains often hidden. Hence the success of plasma technologies is described for different examples and commercial areas. From these examples and emerging applications, the potential of plasma technologies is discussed. Economic trends are anticipated together with research needs. The community of plasma scientists strongly believes that more exciting advances will continue to foster innovations and discoveries in the first decades of the 21st century, if research and education will be properly funded and sustained by public bodies and industrial investors.

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Abstract: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only). We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

An ultra-stable gold-coordinated protein cage displaying reversible assembly.

Abstract: Symmetrical protein cages have evolved to fulfil diverse roles in nature, including compartmentalization and cargo delivery1, and have inspired synthetic biologists to create novel protein assemblies via the precise manipulation of protein-protein interfaces. Despite the impressive array of protein cages produced in the laboratory, the design of inducible assemblies remains challenging2,3. Here we demonstrate an ultra-stable artificial protein cage, the assembly and disassembly of which can be controlled by metal coordination at the protein-protein interfaces. The addition of a gold (I)-triphenylphosphine compound to a cysteine-substituted, 11-mer protein ring triggers supramolecular self-assembly, which generates monodisperse cage structures with masses greater than 2 MDa. The geometry of these structures is based on the Archimedean snub cube and is, to our knowledge, unprecedented. Cryo-electron microscopy confirms that the assemblies are held together by 120 S-Aui-S staples between the protein oligomers, and exist in two chiral forms. The cage shows extreme chemical and thermal stability, yet it readily disassembles upon exposure to reducing agents. As well as gold, mercury(II) is also found to enable formation of the protein cage. This work establishes an approach for linking protein components into robust, higher-order structures, and expands the design space available for supramolecular assemblies to include previously unexplored geometries.

Uncovering latent jet substructure

Abstract: We apply techniques from Bayesian generative statistical modeling to uncover hidden features in jet substructure observables that discriminate between different a priori unknown underlying short distance physical processes in multijet events. In particular, we use a mixed membership model known as latent Dirichlet allocation to build a data-driven unsupervised top-quark tagger and $t\overline{t}$ event classifier. We compare our proposal to existing traditional and machine learning approaches to top-jet tagging. Finally, employing a toy vector-scalar boson model as a benchmark, we demonstrate the potential for discovering new physics signatures in multijet events in a model independent and unsupervised way.

Surface Modification to Improve Properties of Materials

Abstract: Surface properties of modern materials are usually inadequate in terms of wettability, adhesion properties, biocompatibility etc., so they should be modified prior to application or any further processing such as coating with functional materials. Both the morphological properties and chemical structure/composition should be modified in order to obtain a desired surface finish. Various treatment procedures have been employed, and many are based on the application of non-equilibrium gaseous media, especially gaseous plasma. Although such treatments have been studied extensively in past decades and actually commercialized, the exact mechanisms of interaction between reactive gaseous species and solid materials is still inadequately understood. This special issue provides recent trends in nanostructuring and functionalization of solid materials with the goal of improving their functional properties.

Multi-level chirality in liquid crystals formed by achiral molecules.

Abstract: Complex materials often exhibit a hierarchical structure with an intriguing mechanism responsible for the 'propagation' of order from the molecular to the nano- or micro-scale level. In particular, the chirality of biological molecules such as nucleic acids and amino acids is responsible for the helical structure of DNA and proteins, which in turn leads to the lack of mirror symmetry of macro-bio-objects. To fully understand mechanisms of cross-level order transfer there is an intensive search for simpler artificial structures exhibiting hierarchical arrangement. Here we present complex systems built of achiral molecules that show four levels of structural chirality: layer chirality, helicity of a basic repeating unit, mesoscopic helix and helical filaments. The structures are identified by a combination of hard and soft x-ray diffraction measurements, optical studies and theoretical modelling. Similarly to many biological systems, the studied materials exhibit a coupling between chirality at different levels.

Evaluating time series forecasting models: An empirical study on performance estimation methods

Abstract: Performance estimation aims at estimating the loss that a predictive model will incur on unseen data. These procedures are part of the pipeline in every machine learning project and are used for assessing the overall generalisation ability of predictive models. In this paper we address the application of these methods to time series forecasting tasks. For independent and identically distributed data the most common approach is cross-validation. However, the dependency among observations in time series raises some caveats about the most appropriate way to estimate performance in this type of data and currently there is no settled way to do so. We compare different variants of cross-validation and of out-of-sample approaches using two case studies: One with 62 real-world time series and another with three synthetic time series. Results show noticeable differences in the performance estimation methods in the two scenarios. In particular, empirical experiments suggest that cross-validation approaches can be applied to stationary time series. However, in real-world scenarios, when different sources of non-stationary variation are at play, the most accurate estimates are produced by out-of-sample methods that preserve the temporal order of observations.

A Roadmap for HEP Software and Computing R&D for the 2020s

Abstract: Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.

3D Cultures of Parkinson's Disease-Specific Dopaminergic Neurons for High Content Phenotyping and Drug Testing

Abstract: Parkinson's disease (PD)-specific neurons, grown in standard 2D cultures, typically only display weak endophenotypes. The cultivation of PD patient-specific neurons, derived from induced pluripotent stem cells carrying the LRRK2-G2019S mutation, is optimized in 3D microfluidics. The automated image analysis algorithms are implemented to enable pharmacophenomics in disease-relevant conditions. In contrast to 2D cultures, this 3D approach reveals robust endophenotypes. High-content imaging data show decreased dopaminergic differentiation and branching complexity, altered mitochondrial morphology, and increased cell death in LRRK2-G2019S neurons compared to isogenic lines without using stressor agents. Treatment with the LRRK2 inhibitor 2 (Inh2) rescues LRRK2-G2019S-dependent dopaminergic phenotypes. Strikingly, a holistic analysis of all studied features shows that the genetic background of the PD patients, and not the LRRK2-G2019S mutation, constitutes the strongest contribution to the phenotypes. These data support the use of advanced in vitro models for future patient stratification and personalized drug development.

Overview of the JET preparation for deuterium–tritium operation with the ITER like-wall

Abstract: For the past several years, the JET scientific programme (Pamela et al 2007 Fusion Eng. Des. 82 590) has been engaged in a multi-campaign effort, including experiments in D, H and T, leading up to 2020 and the first experiments with 50%/50% D–T mixtures since 1997 and the first ever D–T plasmas with the ITER mix of plasma-facing component materials. For this purpose, a concerted physics and technology programme was launched with a view to prepare the D–T campaign (DTE2). This paper addresses the key elements developed by the JET programme directly contributing to the D–T preparation. This intense preparation includes the review of the physics basis for the D–T operational scenarios, including the fusion power predictions through first principle and integrated modelling, and the impact of isotopes in the operation and physics of D–T plasmas (thermal and particle transport, high confinement mode (H-mode) access, Be and W erosion, fuel recovery, etc). This effort also requires improving several aspects of plasma operation for DTE2, such as real time control schemes, heat load control, disruption avoidance and a mitigation system (including the installation of a new shattered pellet injector), novel ion cyclotron resonance heating schemes (such as the threeions scheme), new diagnostics (neutron camera and spectrometer, active Alfven eigenmode antennas, neutral gauges, radiation hard imaging systems…) and the calibration of the JET neutron diagnostics at 14 MeV for accurate fusion power measurement. The active preparation of JET for the 2020 D–T campaign provides an incomparable source of information and a basis for the future D–T operation of ITER, and it is also foreseen that a large number of key physics issues will be addressed in support of burning plasmas.

Rare-earth (Gd 3+ ,Yb 3+ /Tm 3+ , Eu 3+ ) co-doped hydroxyapatite as magnetic, up-conversion and down-conversion materials for multimodal imaging

Abstract: Taking advantage of the flexibility of the apatite structure, nano- and micro-particles of hydroxyapatite (HAp) were doped with different combinations of rare earth ions (RE3+ = Gd, Eu, Yb, Tm) to achieve a synergy among their magnetic and optical properties and to enable their application in preventive medicine, particularly diagnostics based on multimodal imaging. All powders were synthesized through hydrothermal processing at T ≤ 200 °C. An X-ray powder diffraction analysis showed that all powders crystallized in P63/m space group of the hexagonal crystal structure. The refined unit-cell parameters reflected a decrease in the unit cell volume as a result of the partial substitution of Ca2+ with smaller RE3+ ions at both cation positions. The FTIR analysis additionally suggested that a synergy may exist solely in the triply doped system, where the lattice symmetry and vibration modes become more coherent than in the singly or doubly doped systems. HAp:RE3+ optical characterization revealed a change in the energy band gap and the appearance of a weak blue luminescence (λex = 370 nm) due to an increased concentration of defects. The “up”- and the “down”-conversion spectra of HAp:Gd/Yb/Tm and HAp:Gd/Eu powders showed characteristic transitions of Tm3+ and Eu3+, respectively. Furthermore, in contrast to diamagnetic HAp, all HAp:RE3+ powders exhibited paramagnetic behavior. Cell viability tests of HAp:Gd/Yb/Tm and HAp:Gd/Eu powders in human dental pulp stem cell cultures indicated their good biocompatibility.

Entanglement and matrix elements of observables in interacting integrable systems

Abstract: We study the bipartite von Neumann entanglement entropy and matrix elements of local operators in the eigenstates of an interacting integrable Hamiltonian (the paradigmatic spin-1/2 XXZ chain), and we contrast their behavior with that of quantum chaotic systems. We find that the leading term of the average (over all eigenstates in the zero magnetization sector) eigenstate entanglement entropy has a volume-law coefficient that is smaller than the universal (maximal entanglement) one in quantum chaotic systems. This establishes the entanglement entropy as a powerful measure to distinguish integrable models from generic ones. Remarkably, our numerical results suggest that the volume-law coefficient of the average entanglement entropy of eigenstates of the spin-1/2 XXZ Hamiltonian is very close to, or the same as, the one for translationally invariant quadratic fermionic models. We also study matrix elements of local operators in the eigenstates of the spin-1/2 XXZ Hamiltonian at the center of the spectrum. For the diagonal matrix elements, we show evidence that the support does not vanish with increasing system size, while the average eigenstate-to-eigenstate fluctuations vanish in a power-law fashion. For the off-diagonal matrix elements, we show that they follow a distribution that is close to (but not quite) log-normal, and that their variance is a well-defined function of $\ensuremath{\omega}={E}_{\ensuremath{\alpha}}\ensuremath{-}{E}_{\ensuremath{\beta}}$ (${{E}_{\ensuremath{\alpha}}}$ are the eigenenergies) proportional to $1/D$, where $D$ is the Hilbert space dimension.

Diagnostics for plasma control - : From ITER to DEMO

Abstract: The plasma diagnostic and control (D&C) system for a future tokamak demonstration fusion reactor (DEMO) will have to provide reliable operation near technical and physics limits, while its fron ...

Mechanisms of Copper-Based Catalyst Deactivation during CO2 Reduction to Methanol

Abstract: Despite the fact that the methanol synthesis process includes industrially some of the most important catalytic chemical reactions, it is still not clear how different gaseous species impact cataly...

The Alkyne Moiety as a Latent Electrophile in Irreversible Covalent Small Molecule Inhibitors of Cathepsin K

Abstract: Irreversible covalent inhibitors can have a beneficial pharmacokinetic/pharmacodynamics profile but are still often avoided due to the risk of indiscriminate covalent reactivity and the resulting adverse effects. To overcome this potential liability, we introduced an alkyne moiety as a latent electrophile into small molecule inhibitors of cathepsin K (CatK). Alkyne-based inhibitors do not show indiscriminate thiol reactivity but potently inhibit CatK protease activity by formation of an irreversible covalent bond with the catalytic cysteine residue, confirmed by crystal structure analysis. The rate of covalent bond formation ( kinact) does not correlate with electrophilicity of the alkyne moiety, indicative of a proximity-driven reactivity. Inhibition of CatK-mediated bone resorption is validated in human osteoclasts. Together, this work illustrates the potential of alkynes as latent electrophiles in small molecule inhibitors, enabling the development of irreversible covalent inhibitors with an improved safety profile.

Observation of Coexistence of Yu-Shiba-Rusinov States and Spin-Flip Excitations.

Abstract: We investigate the spectral evolution in different metal phthalocyanine molecules on NbSe2 surface using scanning tunnelling microscopy (STM) as a function of the coupling with the substrate. For manganese phthalocyanine (MnPc), we demonstrate a smooth spectral crossover from Yu-Shiba-Rusinov (YSR) bound states to spin-flip excitations. This has not been observed previously and it is in contrast to simple theoretical expectations. We corroborate the experimental findings using numerical renormalization group calculations. Our results provide fundamental new insight on the behavior of atomic scale magnetic/SC hybrid systems, which is important, for example, for engineered topological superconductors and spin logic devices.

Supervised and unsupervised neural approaches to text readability

Abstract: We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable across languages and allows adaptation to a specific readability task and data set. By systematic comparison of several neural architectures on a number of benchmark and new labelled readability datasets in two languages, this study also offers a comprehensive analysis of different neural approaches to readability classification. We expose their strengths and weaknesses, compare their performance to current state-of-the-art classification approaches to readability, which in most cases still rely on extensive feature engineering, and propose possibilities for improvements.