Disorder-Promoted Splitting in Quasiparticle Interference at Nesting Vectors.

Inelastic interactions of quantum systems with the environment usually wash coherent effects out. In the case of Friedel oscillations, the presence of disorder leads to a fast decay of the oscillation amplitude. Here we show both experimentally and theoretically that in three-dimensional topological insulator Bi2Te3 there is a nesting-induced splitting of coherent scattering vectors which follows a peculiar evolution in energy. The effect becomes experimentally observable when the lifetime of quasiparticles shortens due to disorder. The amplitude of the splitting allows an evaluation of the lifetime of the electrons. A similar phenomenon should be observed in any system with a well-defined scattering vector regardless of its topological properties.

Module-detection approaches for the integration of multilevel omics data highlight the comprehensive response of Aspergillus fumigatus to caspofungin.

BACKGROUND: Omics data provide deep insights into overall biological processes of organisms. However, integration of data from different molecular levels such as transcriptomics and proteomics, still remains challenging. Analyzing lists of differentially abundant molecules from diverse molecular levels often results in a small overlap mainly due to different regulatory mechanisms, temporal scales, and/or inherent properties of measurement methods. Module-detecting algorithms identifying sets of closely related proteins from protein-protein interaction networks (PPINs) are promising approaches for a better data integration. RESULTS: Here, we made use of transcriptome, proteome and secretome data from the human pathogenic fungus Aspergillus fumigatus challenged with the antifungal drug caspofungin. Caspofungin targets the fungal cell wall which leads to a compensatory stress response. We analyzed the omics data using two different approaches: First, we applied a simple, classical approach by comparing lists of differentially expressed genes (DEGs), differentially synthesized proteins (DSyPs) and differentially secreted proteins (DSePs); second, we used a recently published module-detecting approach, ModuleDiscoverer, to identify regulatory modules from PPINs in conjunction with the experimental data. Our results demonstrate that regulatory modules show a notably higher overlap between the different molecular levels and time points than the classical approach. The additional structural information provided by regulatory modules allows for topological analyses. As a result, we detected a significant association of omics data with distinct biological processes such as regulation of kinase activity, transport mechanisms or amino acid metabolism. We also found a previously unreported increased production of the secondary metabolite fumagillin by A. fumigatus upon exposure to caspofungin. Furthermore, a topology-based analysis of potential key factors contributing to drug-caused side effects identified the highly conserved protein polyubiquitin as a central regulator. Interestingly, polyubiquitin UbiD neither belonged to the groups of DEGs, DSyPs nor DSePs but most likely strongly influenced their levels. CONCLUSION: Module-detecting approaches support the effective integration of multilevel omics data and provide a deep insight into complex biological relationships connecting these levels. They facilitate the identification of potential key players in the organism's stress response which cannot be detected by commonly used approaches comparing lists of differentially abundant molecules.

Pathophysiological changes of inflammatory syndrome in multiple sclerosis after instituting therapeutic plasmapheresis.

In autoimmune conditions affecting the central and peripheral CNS as well as in multiple sclerosis (MS), the inflammatory syndrome is present with the onset of this disease. The present paper aimed to highlight the inflammatory syndrome based on the leukergia test, the total blood viscosity test, blood filterability test as well as on other tests. The early instituting of the therapeutic plasmapheresis beneficially modified the clinical status, the biological and pathophysiological behavior of the patient's illness. Objective of the paper: The aim was to highlight the importance, advantages, and pathophysiological changes after therapeutic plasmapheresis in five cases, in patients hospitalized with the diagnosis of multiple sclerosis. Material and method: In order to emphasize the inflammatory syndrome, the determination of leukergia assay, the total blood viscosity test and the blood filterability test were added to regular examinations, conducted on the batch of patients included in the study. Results and discussions: As a result of using therapeutic plasmapheresis, the inflammatory parameters in patients with multiple sclerosis improved beneficially as it was proven by the values of inflammatory tests before and after plasmapheresis. Conclusions: In the treatment of multiple sclerosis, plasmapheresis proved to be a medical method that significantly reduced autoimmune inflammatory "installed" syndrome.

Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers.

In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models.

Low temperature ferromagnetism in chemically ordered FeRh nanocrystals.

In sharp contrast to previous studies on FeRh bulk, thin films, and nanoparticles, we report the persistence of ferromagnetic order down to 3 K for size-selected 3.3 nm diameter nanocrystals embedded into an amorphous carbon matrix. The annealed nanoparticles have a B2 structure with alternating atomic Fe and Rh layers. X-ray magnetic dichroism and superconducting quantum interference device measurements demonstrate ferromagnetic alignment of the Fe and Rh magnetic moments of 3 and 1µ(B), respectively. The ferromagnetic order is ascribed to the finite-size induced structural relaxation observed in extended x-ray absorption spectroscopy.

Atomic-scale engineering of magnetic anisotropy of nanostructures through interfaces and interlines.

The central goals of nanoscale magnetic materials science are the self-assembly of the smallest structure exhibiting ferromagnetic hysteresis at room temperature, and the assembly of these structures into the highest density patterns. The focus has been on chemically ordered alloys combining magnetic 3d elements with polarizable 5d elements having high spin-orbit coupling and thus yielding the desired large magneto-crystalline anisotropy. The chemical synthesis of nanoparticles of these alloys yields disordered phases requiring annealing to transform them to the high-anisotropy L1(0) structure. Despite considerable efforts, so far only part of the nanoparticles can be transformed without coalescence. Here we present an alternative approach to homogeneous alloys, namely the creation of nanostructures with atomically sharp bimetallic interfaces and interlines. They exhibit unexpectedly high magnetization reversal energy with values and directions of the easy magnetization axes strongly depending on chemistry and texture. We find significant deviations from the expected behaviour for commonly used element combinations. Ab-initio calculations reproduce these results and unravel their origin.

Model of the synthesis of trisporic acid in Mucorales showing bistability.

An important substance in the signalling between individuals of Mucor-like fungi is trisporic acid (TA). This compound, together with some of its precursors, serves as a pheromone in mating between (+)- and (-)-mating types. Moreover, intermediates of the TA pathway are exchanged between the two mating partners. Based on differential equations, mathematical models of the synthesis pathways of TA in the two mating types of an idealised Mucor-fungus are here presented. These models include the positive feedback of TA on its own synthesis. The authors compare three sub-models in view of bistability, robustness and the reversibility of transitions. The proposed modelling study showed that, in a system where intermediates are exchanged, a reversible transition between the two stable steady states occurs, whereas an exchange of the end product leads to an irreversible transition. The reversible transition is physiologically favoured, because the high-production state of TA must come to an end eventually. Moreover, the exchange of intermediates and TA is compared with the 3-way handshake widely used by computers linked in a network.

Highly anisotropic Dirac cones in epitaxial graphene modulated by an island superlattice.

We present a new method to engineer the charge carrier mobility and its directional asymmetry in epitaxial graphene by using metal cluster superlattices self-assembled onto the moiré pattern formed by graphene on Ir(111). Angle-resolved photoemission spectroscopy reveals threefold symmetry in the band structure associated with strong renormalization of the electron group velocity close to the Dirac point giving rise to highly anisotropic Dirac cones. We further find that the cluster superlattice also affects the spectral-weight distribution of the carbon bands as well as the electronic gaps between graphene states.