Rheological Properties of Organoclay Suspensions in Epoxy Network Precursors

This paper deals with the evolution of the state of dispersion of organically modified montmorillonites in epoxy or amine precursors. The epoxy prepolymer is a diglycidyl ether of bisphenol A (DGEBA) and the curing agent is an aliphatic diamine with a polyoxypropylene backbone (Jeffamine D2000). The clay dispersion is evaluated at the platelet scale (nanoscopic scale) from X-ray spectrometry [wide-angle X-ray diffraction (WAXD) and small-angle X-ray scattering (SAXS)] and at the aggregates scale (microscopic scale) from rheological analysis. The organoclays used form gels in the monomers above the percolation threshold if no shear is applied and present a mechanical gel/sol transition when shear stress increases. Gel strength and viscosity at high shear rates are linked to the nanometric state of dispersion and reveal the existence of two different organizations depending on organoclay/monomer interactions: (i) When the clay shows good interactions with the monomer, a significant swelling of the clay galleries by the monomer is obtained. These swollen particles lead to formation of weak gels which after shearing give high relative viscosity fluids. (ii) When the clay develops poor interactions with the monomer, the clay tends to reduce its exchange surface with the monomer and leads to a strongly connected gel. Shear breaks down this physical network leading to a very low relative viscosity fluid composed of nonswollen particles keeping a high aspect ratio. (C) 2003 Elsevier B.V All rights reserved.

Structure-function studies of the plant cyclotides: The role of a circular protein backbone

The traditional idea of proteins as linear chains of amino acids is being challenged with the discovery of miniproteins that contain a circular backbone. The cyclotide family is the largest group of circular proteins and is characterized by an amide-circularized protein backbone and six conserved cysteine residues. These conserved cysteines are paired to form a knotted network of disulfide bonds. The combination of the circular backbone and a cystine knot, known as the cyclic cystine knot (CCK) motif, confers exceptional stability upon the cyclotides. This review discusses the role of the circular backbone based on studies of both the oxidative folding of kalata B1, the prototypical cyclotide, and a comparison of the structure and activity of kalata B1 and its acyclic permutants.

Factors influencing the stability of cyclotides: Proteins with a circular backbone and cystine knot motif

Cyclotides are a large family of mini-proteins that have the distinguishing features of a head-to-tail cyclised backbone and a cystine knot formed by six conserved cysteine residues. They are present in plants from the Rubiaceae, Violaceae and Cucurbitaceae families. The unique structural features of the cyclotides make them extremely resistant to chemical, thermal and proteolytic degradation. In this article we review recent Studies from our laboratory that dissect the role of the individual structural elements in defining the stability of cyclotides. The resistance of cyclotides to chemical and proteolytic degradation is in large part due to the cystine knot, whereas the thermal stability is I composite of several features including the cystine knot, the cyclic backbone and the hydrogen bonding network. A range of biological activities of cyclotides is critically dependent oil the presence of the cyclic backbone.

Spectral properties of the tandem Jackson network, seen as a quasi-birth-and-death process

Quasi-birth-and-death (QBD) processes with infinite “phase spaces” can exhibit unusual and interesting behavior. One of the simplest examples of such a process is the two-node tandem Jackson network, with the “phase” giving the state of the first queue and the “level” giving the state of the second queue. In this paper, we undertake an extensive analysis of the properties of this QBD. In particular, we investigate the spectral properties of Neuts’s R-matrix and show that the decay rate of the stationary distribution of the “level” process is not always equal to the convergence norm of R. In fact, we show that we can obtain any decay rate from a certain range by controlling only the transition structure at level zero, which is independent of R. We also consider the sequence of tandem queues that is constructed by restricting the waiting room of the first queue to some finite capacity, and then allowing this capacity to increase to infinity. We show that the decay rates for the finite truncations converge to a value, which is not necessarily the decay rate in the infinite waiting room case. Finally, we show that the probability that the process hits level n before level 0 given that it starts in level 1 decays at a rate which is not necessarily the same as the decay rate for the stationary distribution.

Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Capillary network models for transport in packed beds: Considerations of pore aspect ratio

The conventional analysis for the estimation of the tortuosity factor for transport in porous media is modified here to account for the effect of pore aspect ratio. Structural models of the porous medium are also constructed for calculating the aspect ratio as a function of porosity. Comparison of the model predictions with the extensive data of Currie (1960) for the effective diffusivity of hydrogen in packed beds shows good agreement with a network model of randomly oriented intersecting pores for porosities upto about 50 percent, which is the region of practical interest. The predictions based on this network model are also found to be in better agreement with the data of Currie than earlier expressions developed for unconsolidated and grainy media.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Modeling the fluid-flow-induced stress and collapse in a dendritic network

An analytical approach to the stress development in the coherent dendritic network during solidification is proposed. Under the assumption that stresses are developed in the network as a result of the friction resisting shrinkage-induced interdendritic fluid flow, the model predicts the stresses in the solid. The calculations reflect the expected effects of postponed dendrite coherency, slower solidification conditions, and variations of eutectic volume fraction and shrinkage. Comparing the calculated stresses to the measured shear strength of equiaxed mushy zones shows that it is possible for the stresses to exceed the strength, thereby resulting in reorientation or collapse of the dendritic network.

An Activated O N Acyl Transfer Auxiliary: Efficient Amide-Backbone Substitution of Hindered "Difficult" Peptides

Overcoming the phenomenon known as difficult synthetic sequences has been a major goal in solid-phase peptide synthesis for over 30 years. In this work the advantages of amide backbone-substitution in the solid-phase synthesis of difficult peptides are augmented by developing an activated N-alpha-acyl transfer auxiliary. Apart from disrupting troublesome intermolecular hydrogen-bonding networks, the primary function of the activated N-alpha-auxiliary was to facilitate clean and efficient acyl capture of large or beta-branched amino acids and improve acyl transfer yields to the secondary N-alpha-amine. We found o-hydroxyl-substituted nitrobenzyl (Hnb) groups were suitable N-alpha-auxiliaries for this purpose. The relative acyl transfer efficiency of the Hnb auxiliary was superior to the 2-hydroxy-4-methoxybenzyl (Hmb) auxiliary with protected amino acids of varying size. Significantly, this difference in efficiency was more pronounced between more sterically demanding amino acids. The Hnb auxiliary is readily incorporated at the N-alpha-amine during SPPS by reductive alkylation of its corresponding benzaldehyde derivative and conveniently removed by mild photolysis at 366 nm. The usefulness of the Hnb auxiliary for the improvement of coupling efficiencies in the chain-assembly of difficult peptides was demonstrated by the efficient Hnb-assisted Fmoc solid-phase synthesis of a known hindered difficult peptide sequence, STAT-91. This work suggests the Hnb auxiliary will significantly enhance our ability to synthesize difficult polypeptides and increases the applicability of amide-backbone substitution.

An object-oriented neural network approach to short-term traffic forecasting

This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90-94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93-95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction. (C) 2001 Elsevier Science B.V. All rights reserved.

Inter-subband redundancy prediction using neural network for video coding

High performance video codec is mandatory for multimedia applications such as video-on-demand and video conferencing. Recent research has proposed numerous video coding techniques to meet the requirement in bandwidth, delay, loss and Quality-of-Service (QoS). In this paper, we present our investigations on inter-subband self-similarity within the wavelet-decomposed video frames using neural networks, and study the performance of applying the spatial network model to all video frames over time. The goal of our proposed method is to restore the highest perceptual quality for video transmitted over a highly congested network. Our contributions in this paper are: (1) A new coding model with neural network based, inter-subband redundancy (ISR) prediction for video coding using wavelet (2) The performance of 1D and 2D ISR prediction, including multiple levels of wavelet decompositions. Our result shows a short-term quality enhancement may be obtained using both 1D and 2D ISR prediction.