Mapping the landscape of the lymphocytic choriomeningitis virus stable signal peptide reveals novel functional domains

The stable signal peptide (SSP) of the lymphocytic choriomeningitis virus surface glycoprotein precursor has several unique characteristics. The SSP is unusually long, at 58 amino acids, and contains two hydrophobic domains, and its sequence is highly conserved among both Old and New World arenaviruses. To better understand the functions of the SSP, a panel of point and deletion mutants was created by in vitro mutagenesis to target the highly conserved elements within the SSP. We were also able to confirm critical residues required for separate SSP functions by trans-complementation. Using these approaches, it was possible to resolve functional domains of the SSP. In characterizing our SSP mutants, we discovered that the SSP is involved in several distinct functions within the viral life cycle, beyond translocation of the viral surface glycoprotein precursor into the endoplasmic reticulum lumen. The SSP is required for efficient glycoprotein expression, posttranslational maturation cleavage of GP1 and GP2 by SKI-1/S1P protease, glycoprotein transport to the cell surface plasma membrane, formation of infectious virus particles, and acid pH-dependent glycoprotein-mediated cell fusion.

Specificity grafting of human antibody frameworks selected from a phage display library: generation of a highly stable humanized anti-CD22 single-chain Fv fragment

A prerequisite for the enrichment of antibodies screened from phage display libraries is their stable expression on a phage during multiple selection rounds. Thus, if stringent panning procedures are employed, selection is simultaneously driven by antigen affinity, stability and solubility. To take advantage of robust pre-selected scaffolds of such molecules, we grafted single-chain Fv (scFv) antibodies, previously isolated from a human phage display library after multiple rounds of in vitro panning on tumor cells, with the specificity of the clinically established murine monoclonal anti-CD22 antibody RFB4. We show that a panel of grafted scFvs retained the specificity of the murine monoclonal antibody, bound to the target antigen with high affinity (6.4-9.6 nM), and exhibited exceptional biophysical stability with retention of 89-93% of the initial binding activity after 6 days of incubation in human serum at 37degreesC. Selection of stable human scaffolds with high sequence identity to both the human germline and the rodent frameworks required only a small number of murine residues to be retained within the human frameworks in order to maintain the structural integrity of the antigen binding site. We expect this approach may be applicable for the rapid generation of highly stable humanized antibodies with low immunogenic potential.

Colloidal stable silica encapsulated nano-magnetic composite as a novel bio-catalyst carrier

A colloidal stable silica-encapsulated magnetic nano-composite of a controlled dimension is, for the first time, employed to carry beta-lactamase via chemical linkage on the silica overlayer: activity study reflects that this new type of immobilisation allows site (enzyme) isolation, accessibility as good as free enzyme and recovery & reusability upon application of magnetic separation.

Dipeptide nanotubes, with n-terminally located omega-amino acid residues, that are stable proteolytically, thermally, and over a wide range of pH

Two dipeptides containing an N-terminally positioned omega-amino acid residue (beta-alanine/delta-amino valeric acid) self-assembles to form nanotubes in the solid state as well as in aqueous solution. In spite of having hollow nanotubular structures in the solid state and in solution, their self-assembling nature in these two states are different and this leads to the formation of different internal diameters of these nanotubes in solution and in solid state structure. These nanotubes are stable proteolytically, thermally, and over a wide range of pH values (1-13). The role of water molecules in nanotube formation has been investigated in the solid state. These nanotubes can be considered as a new class of dipeptide nanotubes as they are consisting of N-terminally located protease resistant omega-amino acid residues and C-terminally positioned alpha-amino acid residues. These dipeptides can form an interesting class of short peptidic structure that can give rise to stable nanotubular structure upon self-assembly and these nanotubes can be explored in future for potential nanotechnological applications.

A stable pure hydroxyl layer on Pt{110}-(1x2)

The adsorption of water and coadsorption with oxygen on the missing-row reconstructed Pt{110}-(1x2) surface was studied by using temperature-programmed desorption (TPD) and X-ray photoelectron spectroscopy. Coadsorbed oxygen below saturation (<0.65 +/- 0.05 ML) leads to the formation of 014, which occupies sites near the ridge Pt atoms, In contrast to the more closely packed Pt{111} Surface, OH appears not to form hydrogen bonds with coadsorbed water molecules and is stable after the desorption of water tip to about 205 K (as determined by TPD). Because OH and atomic oxygen compete for adsorption sites, water dissociation is only observed for oxygen coverages below saturation. In the absence of coadsorbed oxygen, water stays intact at all temperatures and forms a strongly bound layer of 2 ML coverage oil the clean Pt{110}-(1x2) surface at temperatures between 140 and 175 K.

Humans ignore motion and stereo cues in favor of a fictional stable world

As we move through the world, our eyes acquire a sequence of images. The information from this sequence is sufficient to determine the structure of a three-dimensional scene, up to a scale factor determined by the distance that the eyes have moved [1, 2]. Previous evidence shows that the human visual system accounts for the distance the observer has walked [3,4] and the separation of the eyes [5-8] when judging the scale, shape, and distance of objects. However, in an immersive virtual-reality environment, observers failed to notice when a scene expanded or contracted, despite having consistent information about scale from both distance walked and binocular vision. This failure led to large errors in judging the size of objects. The pattern of errors cannot be explained by assuming a visual reconstruction of the scene with an incorrect estimate of interocular separation or distance walked. Instead, it is consistent with a Bayesian model of cue integration in which the efficacy of motion and disparity cues is greater at near viewing distances. Our results imply that observers are more willing to adjust their estimate of interocular separation or distance walked than to accept that the scene has changed in size.

Efficient FPGA-based regular expression pattern matching

An approach to the automatic generation of efficient Field Programmable Gate Arrays (FPGAs) circuits for the Regular Expression-based (RegEx) Pattern Matching problems is presented. Using a novel design strategy, as proposed, circuits that are highly area-and-time-efficient can be automatically generated for arbitrary sets of regular expressions. This makes the technique suitable for applications that must handle very large sets of patterns at high speed, such as in the network security and intrusion detection application domains. We have combined several existing techniques to optimise our solution for such domains and proposed the way the whole process of dynamic generation of FPGAs for RegEX pattern matching could be automated efficiently.

Minimum stable convergence criteria for stochastic diffusion search

An analysis of Stochastic Diffusion Search (SDS), a novel and efficient optimisation and search algorithm, is presented, resulting in a derivation of the minimum acceptable match resulting in a stable convergence within a noisy search space. The applicability of SDS can therefore be assessed for a given problem.

Generalized matching networks and their properties

In this paper, we introduce two kinds of graphs: the generalized matching networks (GMNs) and the recursive generalized matching networks (RGMNs). The former generalize the hypercube-like networks (HLNs), while the latter include the generalized cubes and the star graphs. We prove that a GMN on a family of k-connected building graphs is -connected. We then prove that a GMN on a family of Hamiltonian-connected building graphs having at least three vertices each is Hamiltonian-connected. Our conclusions generalize some previously known results.

Ferric and ferrous iron in nitroso-myoglobin: computer simulations of stable and metastable states and their infrared spectra

The binding of NO to iron is involved in the biological function of many heme proteins. Contrary to ligands like CO and O-2, which only bind to ferrous (Fe-II) iron, NO binds to both ferrous and ferric (Fe-II) iron. In a particular protein, the natural oxidation state can therefore be expected to be tailored to the required function. Herein, we present an ob initio potential-energy surface for ferric iron interacting with NO. This potential-energy surface exhibits three minima corresponding to eta'-NO coordination (the global minimum), eta(1)-ON coordination and eta(2) coordination. This contrasts with the potential-energy surface for Fe-II-NO, which ex- hibits only two minima (the eta(2) coordination mode for Fe-II is a transition state, not a minimum). In addition, the binding energies of NO are substantially larger for Fe-III than for Fe-II. We have performed molecular dynamics simulations for NO bound to ferric myoglobin (Mb(III)) and compare these with results obtained for Mb(II). Over the duration of our simulations (1.5 ns), all three binding modes are found to be stable at 200 K and transiently stable at 300 K, with eventual transformation to the eta(1)-NO global-minimum conformation. We discuss the implication of these results related to studies of rebinding processes in myoglobin.

A stable one-step-ahead predictive control of non-linear systems

In this paper stability of one-step ahead predictive controllers based on non-linear models is established. It is shown that, under conditions which can be fulfilled by most industrial plants, the closed-loop system is robustly stable in the presence of plant uncertainties and input–output constraints. There is no requirement that the plant should be open-loop stable and the analysis is valid for general forms of non-linear system representation including the case out when the problem is constraint-free. The effectiveness of controllers designed according to the algorithm analyzed in this paper is demonstrated on a recognized benchmark problem and on a simulation of a continuous-stirred tank reactor (CSTR). In both examples a radial basis function neural network is employed as the non-linear system model.

Stable receding horizon control based on recurrent networks

The last decade has seen the re-emergence of artificial neural networks as an alternative to traditional modelling techniques for the control of nonlinear systems. Numerous control schemes have been proposed and have been shown to work in simulations. However, very few analyses have been made of the working of these networks. The authors show that a receding horizon control strategy based on a class of recurrent networks can stabilise nonlinear systems.

Stable linearization using multilayer neural networks

The main limitation of linearization theory that prevents its application in practical problems is the need for an exact knowledge of the plant. This requirement is eliminated and it is shown that a multilayer network can synthesise the state feedback coefficients that linearize a nonlinear control affine plant. The stability of the linearizing closed loop can be guaranteed if the autonomous plant is asymptotically stable and the state feedback is bounded.

Stable reduced-order models for discrete-time systems

The Routh-stability method is employed to reduce the order of discrete-time system transfer functions. It is shown that the Routh approximant is well suited to reduce both the denominator and the numerator polynomials, although alternative methods, such as PadÃ�Â(c)-Markov approximation, are also used to fit the model numerator coefficients.