Infection with a Brazilian isolate of Zika virus generates RIG‐I stimulatory RNA and the viral NS5 protein blocks type I IFN induction and signalling

Zika virus (ZIKV) is a major public health concern in the Americas. We report that ZIKV infection and RNA extracted from ZIKV infected cells potently activated the induction of type I interferons (IFNs). This effect was fully dependent on the mitochondrial antiviral signalling protein (MAVS), implicating RIG‐I‐like receptors (RLRs) as upstream sensors of viral RNA. Indeed, RIG‐I and the related RNA sensor MDA5 contributed to type I IFN induction in response to RNA from infected cells. We found that ZIKV NS5 from a recent Brazilian isolate blocked type I IFN induction downstream of RLRs and also inhibited type I IFN receptor (IFNAR) signalling. We defined the ZIKV NS5 nuclear localization signal and report that NS5 nuclear localization was not required for inhibition of signalling downstream of IFNAR. Mechanistically, NS5 blocked IFNAR signalling by both leading to reduced levels of STAT2 and by blocking phosphorylation of STAT1, two transcription factors activated by type I IFNs. Taken together, our observations suggest that ZIKV infection induces a type I IFN response via RLRs and that ZIKV interferes with this response by blocking signalling downstream of RLRs and IFNAR

Semicausal operations are semilocalizable

We prove a conjecture by DiVincenzo, which in the terminology of Preskill et al. [quant-ph/0102043] states that ``semicausal operations are semilocalizable''. That is, we show that any operation on the combined system of Alice and Bob, which does not allow Bob to send messages to Alice, can be represented as an operation by Alice, transmitting a quantum particle to Bob, and a local operation by Bob. The proof is based on the uniqueness of the Stinespring representation for a completely positive map. We sketch some of the problems in transferring these concepts to the context of relativistic quantum field theory.Comment: 4 pages, 1 figure, revte

Imitation modeling of the static process loading milling cutters

Myelin is a multilayered membrane that ensheathes axonal fibers in the vertebrate nervous system, allowing fast propagation of nerve action potentials. It contains densely packed lipids, lacks an actin-based cytocortex, and requires myelin basic protein (MBP) as its major structural component. This protein is the basic constituent of the proteinaceous meshwork that is localized between adjacent cytoplasmic membranes of the myelin sheath. Yet, it is not clear how MBP influences the organization and dynamics of the lipid constituents of myelin. Here, we used optical stimulated emission depletion super-resolution microscopy in combination with fluorescence correlation spectroscopy to assess the characteristics of diffusion of different fluorescent lipid analogs in myelin membrane sheets of cultured oligodendrocytes and in micrometer-sized domains that were induced by MBP in live epithelial PtK2 cells. Lipid diffusion was significantly faster and less anomalous both in oligodendrocytes and inside the MBP-rich domains of PtK2 cells compared with undisturbed live PtK2 cells. Our data show that MBP reorganizes lipid diffusion, possibly by preventing the buildup of an actin-based cytocortex and by preventing most membrane proteins from entering the myelin sheath region. Yet, in contrast to myelin sheets in oligodendrocytes, the MBP-induced domains in epithelial PtK2 cells demonstrate no change in lipid order, indicating that segregation of long-chain lipids into myelin sheets is a process specific to oligodendrocytes

Computing the shortest elementary flux modes in genome-scale metabolic networks

This article is available open access through the publisher’s website through the link below. Copyright @ The Author 2009.Motivation: Elementary flux modes (EFMs) represent a key concept to analyze metabolic networks from a pathway-oriented perspective. In spite of considerable work in this field, the computation of the full set of elementary flux modes in large-scale metabolic networks still constitutes a challenging issue due to its underlying combinatorial complexity. Results: In this article, we illustrate that the full set of EFMs can be enumerated in increasing order of number of reactions via integer linear programming. In this light, we present a novel procedure to efficiently determine the K-shortest EFMs in large-scale metabolic networks. Our method was applied to find the K-shortest EFMs that produce lysine in the genome-scale metabolic networks of Escherichia coli and Corynebacterium glutamicum. A detailed analysis of the biological significance of the K-shortest EFMs was conducted, finding that glucose catabolism, ammonium assimilation, lysine anabolism and cofactor balancing were correctly predicted. The work presented here represents an important step forward in the analysis and computation of EFMs for large-scale metabolic networks, where traditional methods fail for networks of even moderate size. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online (http://bioinformatics.oxfordjournals.org/cgi/content/full/btp564/DC1).Fundação Calouste Gulbenkian, Fundação para a Ciência e a Tecnologia (FCT) and Siemens SA Portugal

Algorithms for learning parsimonious context trees

Parsimonious context trees, PCTs, provide a sparse parameterization of conditional probability distributions. They are particularly powerful for modeling context-specific independencies in sequential discrete data. Learning PCTs from data is computationally hard due to the combinatorial explosion of the space of model structures as the number of predictor variables grows. Under the score-and-search paradigm, the fastest algorithm for finding an optimal PCT, prior to the present work, is based on dynamic programming. While the algorithm can handle small instances fast, it becomes infeasible already when there are half a dozen four-state predictor variables. Here, we show that common scoring functions enable the use of new algorithmic ideas, which can significantly expedite the dynamic programming algorithm on typical data. Specifically, we introduce a memoization technique, which exploits regularities within the predictor variables by equating different contexts associated with the same data subset, and a bound-and-prune technique, which exploits regularities within the response variable by pruning parts of the search space based on score upper bounds. On real-world data from recent applications of PCTs within computational biology the ideas are shown to reduce the traversed search space and the computation time by several orders of magnitude in typical cases.Peer reviewe

On the relationships between kinetic schemes and two-state single molecule trajectories

Trajectories of a signal that fluctuates between two states which originate from single molecule activities have become ubiquitous. Common examples are trajectories of ionic flux through individual membrane-channels, and of photon counts collected from diffusion, activity, and conformational changes of biopolymers. By analyzing the trajectory, one wishes to deduce the underlying mechanism, which is usually described by a multi-substate kinetic scheme. In previous works, we divided kinetic schemes that generate two-state trajectories into two types: reducible schemes and irreducible schemes. We showed that all the information in trajectories generated from reducible schemes is contained in the waiting time probability density functions (PDFs) of the two states. It follows that reducible schemes with the same waiting time PDFs are not distinguishable. In this work, we further characterize the topologies of kinetic schemes, now of irreducible schemes, and further study two-state trajectories from the two types of scheme. We suggest various methods for extracting information about the underlying kinetic scheme from the trajectory (e. g., calculate the binned successive waiting times PDF and analyze the ordered waiting times trajectory), and point out the advantages and disadvantages of each. We show that the binned successive waiting times PDF is not only more robust than other functions when analyzing finite trajectories, but contains, in most cases, more information about the underlying kinetic scheme than other functions in the limit of infinitely long trajectories. For some cases however, analyzing the ordered waiting times trajectory may supply unique information about the underlying kinetic scheme

Identification of an α(1→6) mannopyranosyltransferase (MptA), involved in Corynebacterium glutamicum lipomanann biosynthesis, and identification of its orthologue in Mycobacterium tuberculosis

Corynebacterium glutamicum and Mycobacterium tuberculosis share a similar cell wall architecture, and the availability of their genome sequences has enabled the utilization of C. glutamicum as a model for the identification and study of, otherwise essential, mycobacterial genes involved in lipomannan (LM) and lipoarabinomannan (LAM) biosynthesis. We selected the putative glycosyltransferase-Rv2174 from M. tuberculosis and deleted its orthologue NCgl2093 from C. glutamicum. This resulted in the formation of a novel truncated lipomannan (Cg-t-LM) and a complete ablation of LM/LAM biosynthesis. Purification and characterization of Cg-t-LM revealed an overall decrease in molecular mass, a reduction of α(1→6) and α(1→2) glycosidic linkages illustrating a reduced degree of branching compared with wild-type LM. The deletion mutant's biochemical phenotype was fully complemented by either NCgl2093 or Rv2174. Furthermore, the use of a synthetic neoglycolipid acceptor in an in vitro cell-free assay utilizing the sugar donor β-d-mannopyranosyl-1-monophosphoryl-decaprenol together with the neoglycolipid acceptor α-d-Manp-(1→6)-α-d-Manp-O-C8 as a substrate, confirmed NCgl2093 and Rv2174 as an α(1→6) mannopyranosyltransferase (MptA), involved in the latter stages of the biosynthesis of the α(1→6) mannan core of LM. Altogether, these studies have identified a new mannosyltransferase, MptA, and they shed further light on the biosynthesis of LM/LAM in Corynebacterianeae

Nanoscale spatiotemporal diffusion modes measured by simultaneous confocal and stimulated emission depletion nanoscopy imaging

The diffusion dynamics in the cellular plasma membrane provide crucial insights into molecular interactions, organization, and bioactivity. Beam-scanning fluorescence correlation spectroscopy combined with super-resolution stimulated emission depletion nanoscopy (scanning STED–FCS) measures such dynamics with high spatial and temporal resolution. It reveals nanoscale diffusion characteristics by measuring the molecular diffusion in conventional confocal mode and super-resolved STED mode sequentially for each pixel along the scanned line. However, to directly link the spatial and the temporal information, a method that simultaneously measures the diffusion in confocal and STED modes is needed. Here, to overcome this problem, we establish an advanced STED–FCS measurement method, line interleaved excitation scanning STED–FCS (LIESS–FCS), that discloses the molecular diffusion modes at different spatial positions with a single measurement. It relies on fast beam-scanning along a line with alternating laser illumination that yields, for each pixel, the apparent diffusion coefficients for two different observation spot sizes (conventional confocal and super-resolved STED). We demonstrate the potential of the LIESS–FCS approach with simulations and experiments on lipid diffusion in model and live cell plasma membranes. We also apply LIESS–FCS to investigate the spatiotemporal organization of glycosylphosphatidylinositol-anchored proteins in the plasma membrane of live cells, which, interestingly, show multiple diffusion modes at different spatial positions

A priori probability that a qubit-qutrit pair is separable

We extend to arbitrarily coupled pairs of qubits (two-state quantum systems) and qutrits (three-state quantum systems) our earlier study (quant-ph/0207181), which was concerned with the simplest instance of entangled quantum systems, pairs of qubits. As in that analysis -- again on the basis of numerical (quasi-Monte Carlo) integration results, but now in a still higher-dimensional space (35-d vs. 15-d) -- we examine a conjecture that the Bures/SD (statistical distinguishability) probability that arbitrarily paired qubits and qutrits are separable (unentangled) has a simple exact value, u/(v Pi^3)= >.00124706, where u = 2^20 3^3 5 7 and v = 19 23 29 31 37 41 43 (the product of consecutive primes). This is considerably less than the conjectured value of the Bures/SD probability, 8/(11 Pi^2) = 0736881, in the qubit-qubit case. Both of these conjectures, in turn, rely upon ones to the effect that the SD volumes of separable states assume certain remarkable forms, involving "primorial" numbers. We also estimate the SD area of the boundary of separable qubit-qutrit states, and provide preliminary calculations of the Bures/SD probability of separability in the general qubit-qubit-qubit and qutrit-qutrit cases.Comment: 9 pages, 3 figures, 2 tables, LaTeX, we utilize recent exact computations of Sommers and Zyczkowski (quant-ph/0304041) of "the Bures volume of mixed quantum states" to refine our conjecture

Neural networks in pulsed dipolar spectroscopy : a practical guide

This work was funded by a grant from Leverhulme Trust (RPG-2019-048). Studentship funding and technical support from MathWorks are gratefully acknowledged. This research was supported by grants from NVIDIA and utilised NVIDIA Tesla A100 GPUs through the Academic Grants Programme. We also acknowledge funding from the Royal Society (University Research Fellowship for JEL) and EPSRC (EP/R513337/1 studentship for HR and EP/L015110/1 studentship for MJT).This is a methodological guide to the use of deep neural networks in the processing of pulsed dipolar spectroscopy (PDS) data encountered in structural biology, organic photovoltaics, photosynthesis research, and other domains featuring long-lived radical pairs and paramagnetic metal ions. PDS uses distance dependence of magnetic dipolar interactions; measuring a single well-defined distance is straightforward, but extracting distance distributions is a hard and mathematically ill-posed problem requiring careful regularisation and background fitting. Neural networks do this exceptionally well, but their “robust black box” reputation hides the complexity of their design and training – particularly when the training dataset is effectively infinite. The objective of this paper is to give insight into training against simulated databases, to discuss network architecture choices, to describe options for handling DEER (double electron-electron resonance) and RIDME (relaxation-induced dipolar modulation enhancement) experiments, and to provide a practical data processing flowchart.Publisher PDFPeer reviewe