Solution self-assembly of hybrid block copolymers containing poly(ethylene glycol) and amphiphilic beta-strand peptide sequences

The self-assembly in aqueous solution of hybrid block copolymers consisting of amphiphilic β-strand peptide sequences flanked by one or two PEG chains was investigated by means of circular dichroism spectroscopy, small-angle X-ray scattering, and transmission electron microscopy. In comparison with the native peptide sequence, it was found that the peptide secondary structure was stabilized against pH variation in the di-and tri-block copolymers with PEG. Small-angle X-ray scattering indicated the presence of fibrillar structures, the dimensions of which are comparable to the estimated width of a β-strand (with terminal PEG chains in the case of the copolymers). Transmission electron microscopy on selectively stained and dried specimens shows directly the presence of fibrils. It is proposed that these fibrils result from the hierarchical self-assembly of peptide β-strands into helical tapes, which then stack into fibrils.

The production, purification and characterisation of two novel alpha-D-mannosidases from Aspergillus phoenicis

1,6-alpha-D-Mannosidase from Aspergillits phoenicis was purified by anion-exchange chromatography, chromatofocussing and size-exclusion chromatography. The apparent molecular weight was 74 kDa by SDS-PAGE and 81 kDa by native-PAGE. The isoelectric point was 4.6. 1,6-alpha-D-Mannosidase had a temperature optimum of 60 degrees C, a pH optimum of 4.0-4.5. a K-m of 14 mM with alpha-D-Manp-(1 -> 6)-D-Manp as substrate. It was strongly inhibited by Mn2+ and did not need Ca2+ or any other metal cofactor of those tested. The enzyme cleaves specifically (1 -> 6)-linked mannobiose and has no activity towards any other linkages, p-nitrophenyl-alpha-D-mannopyranoside or baker's yeast mannan. 1,3(1,6)-alpha-D-Mannosidase from A. phoenicis was purified by anion-exchange chromatography, chromatofocus sing and size-exclusion chromatography. The apparent molecular weight was 97 kDa by SDS-PAGE and 110 kDa by native-PAGE. The 1,3(1,6)-alpha-D-mannosidase enzyme existed as two charge isomers or isoforms. The isoelectric points of these were 4.3 and 4.8 by isoelectric focussing. It cleaves alpha-D-Manp-(1 -> 3)-D-Manp 10 times faster than alpha-D-Manp-(1 -> 6)-D-Manp, has very low activity towards p-nitrophenyl-alpha-D-mannopyranoside and baker's yeast mannan, and no activity towards alpha-D-Manp-(1 -> 2)-D-Manp. The activity towards (1 -> 3)-linked mannobiose is strongly activated by 1 mM Ca2+ and inhibited by 10 mM EDTA, while (1 -> 6)-activity is unaffected, indicating that the two activities may be associated with different polypeptides. It is also possible that one polypeptide may have two active sites catalysing distinct activities. (c) 2005 Elsevier Ltd. All rights reserved.

A sparse parallel hybrid Monte Carlo algorithm for matrix computations

In this paper we introduce a new algorithm, based on the successful work of Fathi and Alexandrov, on hybrid Monte Carlo algorithms for matrix inversion and solving systems of linear algebraic equations. This algorithm consists of two parts, approximate inversion by Monte Carlo and iterative refinement using a deterministic method. Here we present a parallel hybrid Monte Carlo algorithm, which uses Monte Carlo to generate an approximate inverse and that improves the accuracy of the inverse with an iterative refinement. The new algorithm is applied efficiently to sparse non-singular matrices. When we are solving a system of linear algebraic equations, Bx = b, the inverse matrix is used to compute the solution vector x = B(-1)b. We present results that show the efficiency of the parallel hybrid Monte Carlo algorithm in the case of sparse matrices.

Synthesis, x-ray structure and anion binding properties of a cryptand-like hybrid calixpyrrole

The novel cryptand in/out-3, containing two tripyrrolemethane units briged by three 1,3- diisopropylidenbenzene arms was readily synthesized by a convergent three-step synthesis. It binds fluoride by inclusion with excellent selectivity with respect to a number of other tested anions. The structure of the free receptor and that of its fluoride complex were investigated in solution by NMR spectroscopy. The solid state X-ray structure of the free cryptand 3 was also determined.

A hybrid data assimilation scheme for model parameter estimation: application to morphodynamic modelling

We present a novel algorithm for joint state-parameter estimation using sequential three dimensional variational data assimilation (3D Var) and demonstrate its application in the context of morphodynamic modelling using an idealised two parameter 1D sediment transport model. The new scheme combines a static representation of the state background error covariances with a flow dependent approximation of the state-parameter cross-covariances. For the case presented here, this involves calculating a local finite difference approximation of the gradient of the model with respect to the parameters. The new method is easy to implement and computationally inexpensive to run. Experimental results are positive with the scheme able to recover the model parameters to a high level of accuracy. We expect that there is potential for successful application of this new methodology to larger, more realistic models with more complex parameterisations.

Application of a hybrid tracking algorithm to motion analysis

An algorithm for tracking multiple feature positions in a dynamic image sequence is presented. This is achieved using a combination of two trajectory-based methods, with the resulting hybrid algorithm exhibiting the advantages of both. An optimizing exchange algorithm is described which enables short feature paths to be tracked without prior knowledge of the motion being studied. The resulting partial trajectories are then used to initialize a fast predictor algorithm which is capable of rapidly tracking multiple feature paths. As this predictor algorithm becomes tuned to the feature positions being tracked, it is shown how the location of occluded or poorly detected features can be predicted. The results of applying this tracking algorithm to data obtained from real-world scenes are then presented.

Modeling the tetraphenylalanine-PEG hybrid amphiphile: from DFT calculations on the peptide to molecular dynamics simulations on the conjugate

The conformational properties of the hybrid amphiphile formed by the conjugation of a hydrophobic peptide with four phenylalanine (Phe) residues and hydrophilic poly(ethylene glycol), have been investigated using quantum mechanical calculations and atomistic molecular dynamics simulations. The intrinsic conformational preferences of the peptide were examined using the building-up search procedure combined with B3LYP/ 6-31G(d) geometry optimizations, which led to the identification of 78, 78, and 92 minimum energy structures for the peptides containing one, two, and four Phe residues. These peptides tend to adopt regular organizations involving turn-like motifs that define ribbon or helicallike arrangements. Furthermore, calculations indicate that backbone ... side chain interactions involving the N-H of the amide groups and the pi clouds of the aromatic rings play a crucial role in Phe-containing peptides. On the other hand,MD simulations on the complete amphiphile in aqueous solution showed that the polymer fragment rapidly unfolds maximizing the contacts with the polar solvent, even though the hydrophobic peptide reduce the number of waters of hydration with respect to an individual polymer chain of equivalent molecular weight. In spite of the small effect of the peptide in the hydrodynamic properties of the polymer, we conclude that the two counterparts of the amphiphile tend to organize as independent modules.

Modular non-computational-connectionist-hybrid neural network approach to robotic systems

Spiking neural networks are usually limited in their applications due to their complex mathematical models and the lack of intuitive learning algorithms. In this paper, a simpler, novel neural network derived from a leaky integrate and fire neuron model, the ‘cavalcade’ neuron, is presented. A simulation for the neural network has been developed and two basic learning algorithms implemented within the environment. These algorithms successfully learn some basic temporal and instantaneous problems. Inspiration for neural network structures from these experiments are then taken and applied to process sensor information so as to successfully control a mobile robot.

Exploiting a hybrid environmental design strategy in the continental climate of Beijing

The built environment in China is required to achieve a 50% reduction in carbon emissions by 2020 against the 1980 design standard. A particular challenge is how to maintain acceptable comfort conditions through the hot humid summers and cold desiccating winters of its continental climate regions. Fully air-conditioned sealed envelopes, often fully glazed, are becoming increasingly common in these regions. Remedial strategies involve technical refinements to the air-handling equipment and a contribution from renewable energy sources in an attempt to achieve the prescribed net reduction in energy use. However an alternative hybrid environmental design strategy is developed in this research project. It exploits observed temperate periods of weeks, days, even hours in duration to free-run an office and exhibition building configured to promote natural stack ventilation when ambient conditions permit and mechanical ventilation when conditions require it, the two modes delivered through the same physical infrastructure. The proposal is modelled in proprietary software and the methodology adopted is described. The challenge is compounded by its first practical application to an existing reinforced concrete frame originally designed to receive a highly glazed envelope. This original scheme is reviewed in comparison. Furthermore the practical delivery of the proposal value engineered out a proportion of the ventilation stacks. The likely consequence of this for the environmental performance of the building is investigated through a sensitivity study.

J-measure based hybrid pruning for complexity reduction in classification rules

Prism is a modular classification rule generation method based on the ‘separate and conquer’ approach that is alternative to the rule induction approach using decision trees also known as ‘divide and conquer’. Prism often achieves a similar level of classification accuracy compared with decision trees, but tends to produce a more compact noise tolerant set of classification rules. As with other classification rule generation methods, a principle problem arising with Prism is that of overfitting due to over-specialised rules. In addition, over-specialised rules increase the associated computational complexity. These problems can be solved by pruning methods. For the Prism method, two pruning algorithms have been introduced recently for reducing overfitting of classification rules - J-pruning and Jmax-pruning. Both algorithms are based on the J-measure, an information theoretic means for quantifying the theoretical information content of a rule. Jmax-pruning attempts to exploit the J-measure to its full potential because J-pruning does not actually achieve this and may even lead to underfitting. A series of experiments have proved that Jmax-pruning may outperform J-pruning in reducing overfitting. However, Jmax-pruning is computationally relatively expensive and may also lead to underfitting. This paper reviews the Prism method and the two existing pruning algorithms above. It also proposes a novel pruning algorithm called Jmid-pruning. The latter is based on the J-measure and it reduces overfitting to a similar level as the other two algorithms but is better in avoiding underfitting and unnecessary computational effort. The authors conduct an experimental study on the performance of the Jmid-pruning algorithm in terms of classification accuracy and computational efficiency. The algorithm is also evaluated comparatively with the J-pruning and Jmax-pruning algorithms.

Hybrid proton and electron transport in peptide fibrils

Protons and electrons are being exploited in different natural charge transfer processes. Both types of charge carriers could be, therefore, responsible for charge transport in biomimetic self-assembled peptide nanostructures. The relative contribution of each type of charge carrier is studied in the present work for fi brils self-assembled from amyloid- β derived peptide molecules, in which two non-natural thiophene-based amino acids are included. It is shown that under low humidity conditions both electrons and protons contribute to the conduction, with current ratio of 1:2 respectively, while at higher relative humidity proton transport dominates the conductance. This hybrid conduction behavior leads to a bimodal exponential dependence of the conductance on the relative humidity. Furthermore, in both cases the conductance is shown to be affected by the peptide folding state under the entire relative humidity range. This unique hybrid conductivity behavior makes self-assembled peptide nanostructures powerful building blocks for the construction of electric devices that could use either or both types of charge carriers for their function.

Synthesis and characterization of inorganic-organic hybrid gallium selenides

Two semiconducting hybrid gallium selenides, [Ga6Se9(C6H14N2)4][H2O] (1) and [C6H14N2][Ga4Se6(C6H14N2)2] (2), were prepared using a solvothermal method in the pres-ence of 1,2-diaminocyclohexane (1,2-DACH). Both materials consist of neutral inorganic layers, in which 1,2-DACH is co-valently bonded to gallium. In (1), the organic amine acts as a monodentate and a bidentate ligand, while in (2) bidentate and uncoordinated 1,2-DACH molecules coexist.

A frequency-independent boundary element method for scattering by two-dimensional screens and apertures

We propose and analyse a hybrid numerical–asymptotic hp boundary element method (BEM) for time-harmonic scattering of an incident plane wave by an arbitrary collinear array of sound-soft two-dimensional screens. Our method uses an approximation space enriched with oscillatory basis functions, chosen to capture the high-frequency asymptotics of the solution. We provide a rigorous frequency-explicit error analysis which proves that the method converges exponentially as the number of degrees of freedom N increases, and that to achieve any desired accuracy it is sufficient to increase N in proportion to the square of the logarithm of the frequency as the frequency increases (standard BEMs require N to increase at least linearly with frequency to retain accuracy). Our numerical results suggest that fixed accuracy can in fact be achieved at arbitrarily high frequencies with a frequency-independent computational cost, when the oscillatory integrals required for implementation are computed using Filon quadrature. We also show how our method can be applied to the complementary ‘breakwater’ problem of propagation through an aperture in an infinite sound-hard screen.

Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing nonlinearity

We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing nonlinearity and using a quasi-static variational assimilation algorithm as a comparison. Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering (1) assimilation window length and observation interval size and (2) ensemble size to investigate the influence of hybrid background error covariance matrices and nonlinearity on the performance of the methods. For short assimilation windows with close to linear dynamics, it has been shown that all hybrid methods show an improvement in RMSE compared to the traditional methods. For long assimilation window lengths in which nonlinear dynamics are substantial, the variational framework can have diffculties fnding the global minimum of the cost function, so we explore a quasi-static variational assimilation (QSVA) framework. Of the hybrid methods, it is seen that under certain parameters, hybrid methods which do not use a climatological background error covariance do not need QSVA to perform accurately. Generally, results show that the ETKS and hybrid methods that do not use a climatological background error covariance matrix with QSVA outperform all other methods due to the full flow dependency of the background error covariance matrix which also allows for the most nonlinearity.

A new hybrid BCI paradigm based on P300 and SSVEP

Background: P300 and steady-state visual evoked potential(SSVEP) approaches have been widely used for brain–computer interface (BCI) systems. However, neither of these approaches can work for all subjects. Some groups have reported that a hybrid BCI that combines two or more approaches might provide BCI functionality to more users. Hybrid P300/SSVEP BCIs have only recently been developed and validated, and very few avenues to improve performance have been explored. New method: The present study compares an established hybrid P300/SSVEP BCIs paradigm to a new paradigm in which shape changing, instead of color changing, is adopted for P300 evocation to decrease the degradation on SSVEP strength. Result: The result shows that the new hybrid paradigm presented in this paper yields much better performance than the normal hybrid paradigm. Comparison with existing method: A performance increase of nearly 20% in SSVEP classification is achieved using the new hybrid paradigm in comparison with the normal hybrid paradigm.Allthe paradigms except the normal hybrid paradigm used in this paper obtain 100% accuracy in P300 classification. Conclusions: The new hybrid P300/SSVEP BCIs paradigm in which shape changing, instead of color changing, could obtain as high classification accuracy of SSVEP as the traditional SSVEP paradigm and could obtain as high classification accuracy of P300 as the traditional P300 paradigm. P300 did not interfere with the SSVEP response using the new hybrid paradigm presented in this paper, which was superior to the normal hybrid P300/SSVEP paradigm.