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.

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.

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.

Fully automated software solution for protein quantitation by global metabolic labeling with stable isotopes

Metabolic stable isotope labeling is increasingly employed for accurate protein (and metabolite) quantitation using mass spectrometry (MS). It provides sample-specific isotopologues that can be used to facilitate comparative analysis of two or more samples. Stable Isotope Labeling by Amino acids in Cell culture (SILAC) has been used for almost a decade in proteomic research and analytical software solutions have been established that provide an easy and integrated workflow for elucidating sample abundance ratios for most MS data formats. While SILAC is a discrete labeling method using specific amino acids, global metabolic stable isotope labeling using isotopes such as (15)N labels the entire element content of the sample, i.e. for (15)N the entire peptide backbone in addition to all nitrogen-containing side chains. Although global metabolic labeling can deliver advantages with regard to isotope incorporation and costs, the requirements for data analysis are more demanding because, for instance for polypeptides, the mass difference introduced by the label depends on the amino acid composition. Consequently, there has been less progress on the automation of the data processing and mining steps for this type of protein quantitation. Here, we present a new integrated software solution for the quantitative analysis of protein expression in differential samples and show the benefits of high-resolution MS data in quantitative proteomic analyses.

Amplifying vibrational circular dichroism by manipulation of the electronic manifold

Vibrational circular dichroism is a powerful technique to study the stereochemistry of chiral molecules, but often suffers from small signal intensities. Electrochemical modulation of the energies of the electronically excited state manifold is now demonstrated to lead to an order of magnitude enhancement of the differential absorption. Quantum-chemical calculations show that increased mixing between ground and excited states is at the origin of this amplification.

Stable Nonlinear Receding Horizon Regulator Using RBF Neural Network Models

The general stability theory of nonlinear receding horizon controllers has attracted much attention over the last fifteen years, and many algorithms have been proposed to ensure closed-loop stability. On the other hand many reports exist regarding the use of artificial neural network models in nonlinear receding horizon control. However, little attention has been given to the stability issue of these specific controllers. This paper addresses this problem and proposes to cast the nonlinear receding horizon control based on neural network models within the framework of an existing stabilising algorithm.

Exact design manifold control of a class of nonlinear singularly perturbed systems

The integral manifold approach captures from a geometric point of view the intrinsic two-time-scale behavior of singularly perturbed systems. An important class of nonlinear singularly perturbed systems considered in this note are fast actuator-type systems. For a class of fast actuator-type systems, which includes many physical systems, an explicit corrected composite control, the sum of a slow control and a fast control, is derived. This corrected control will steer the system exactly to a required design manifold.