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.

Optimising fed-batch production of recombinant proteins using the baculovirus expression vector system

Fed-batch culture can offer significant improvement in recombinant protein production compared to batch culture in the baculovirus expression vector system (BEVS), as shown by Nguyen et al. (1993) and Bedard et al. (1994) among others. However, a thorough analysis of fed-batch culture to determine its limits in improving recombinant protein production over batch culture has yet to be performed. In this work, this issue is addressed by the optimisation of single-addition fed-batch culture. This type of fed-batch culture involves the manual addition of a multi-component nutrient feed to batch culture before infection with the baculovirus. The nutrient feed consists of yeastolate ultrafiltrate, lipids, amino acids, vitamins, trace elements, and glucose, which were added to batch cultures of Spodoptera frugiperda (Sf9) cells before infection with a recombinant Autographa californica nuclear polyhedrosis virus (Ac-NPV) expressing beta-galactosidase (beta-Gal). The fed-batch production of beta-Gal was optimised using response surface methods (RSM). The optimisation was performed in two stages, starting with a screening procedure to determine the most important variables and ending with a central-composite experiment to obtain a response surface model of volumetric beta-Gal production. The predicted optimum volumetric yield of beta-Gal in fed-batch culture was 2.4-fold that of the best yields in batch culture. This result was confirmed by a statistical analysis of the best fed-batch and batch data (with average beta-Gal yields of 1.2 and 0.5 g/L, respectively) obtained from this laboratory. The response surface model generated can be used to design a more economical fed-batch operation, in which nutrient feed volumes are minimised while maintaining acceptable improvements in beta-Gal yield. (C) 1998 John Wiley & Sons, Inc.

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.

Gene transfer and expression of a non-viral polycation-based vector in CD4(+) cells

CD4-selective targeting of an antibody-polycation-DNA complex was investigated The complex was synthesized with the anti-CD4 monoclonal antibody B-F5, polylysine(268) (pLL) and either the pGL3 control vector containing the luciferase reporter gene or the pGeneGrip vector containing the green fluorescent protein (GFP) gene. B-F5-pLL-DNA complexes inhibited the binding of I-125-B-F5 to CD4(+) Jurkat cells, while complexes synthesised either without B-F5 or using a non-specific mouse IgG1 antibody had little or no effect Expression of the luciferase reporter gene was achieved in Jurkat cells using the B-F5-pLL-pGL3 complex and was enhanced in the presence of PMA. Negligible luciferase activity was defected with the non-specific antibody complex in Jurkat cells or with the B-F5-pLL-pGL3 complex in the CD4(-) K-562 cells. Using complexes synthesised with the pGeneGrip vector, the transfection efficiency in Jurkat and K-562 cells was examined using confocal microscopy. More than 95% of Jurkat cells expressed GFP and the level of this expression was markedly enhanced by PMA. Negligible GFP expression was seen in K-562 cells or when B-F5 was replaced by a nonspecific antibody. Using flow cytometry, fluorescein-labelled complex showed specific targeting to CD4(+) cells in a mixed cell population from human peripheral blood. These studies demonstrate the selective transfection of CD4(+) T-lymphoid cells using a polycation-based gene delivery system. The complex may provide a means of delivering anti-HIV gene therapies to CD4(+) cells in vivo.

Computational chemistry on Fujitsu vector-parallel processors: Development and performance of applications software

In this and a preceding paper, we provide an introduction to the Fujitsu VPP range of vector-parallel supercomputers and to some of the computational chemistry software available for the VPP. Here, we consider the implementation and performance of seven popular chemistry application packages. The codes discussed range from classical molecular dynamics to semiempirical and ab initio quantum chemistry. All have evolved from sequential codes, and have typically been parallelised using a replicated data approach. As such they are well suited to the large-memory/fast-processor architecture of the VPP. For one code, CASTEP, a distributed-memory data-driven parallelisation scheme is presented. (C) 2000 Published by Elsevier Science B.V. All rights reserved.

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.

Lentiviral vector-mediated tyro sinase-related protein 2 gene transfer to dendritic cells for the therapy of melanoma

Dendritic cells (DCs) are the most potent professional antigen-presenting cells (APCs), which play a vital role in primary immune responses. Introducing genes into DCs will allow constitutive expression of the encoded proteins and thus prolong the presentation of the antigens derived therefrom. In addition, multiple and unidentified epitopes encoded by the entire tumor-associated antigen (TAA) gene may enhance T cell activation. This study demonstrated that an HIV-1-based lentiviral vector conferred efficient gene transfer to DCs. The transgene, murine tyrosinase-related protein 2 (mTRP-2), encodes a clinically relevant melanoma-associated antigen (MAA), which has been found to be a tumor rejection antigen for B16 melanoma. The transfer and proper processing of mTRP-2 in DCs, in terms of RNA transcription activity and protein expression, were verified by RT-PCR and specific antibody, respectively. Administration of mTRP-2 gene-modified DCs (DC-HR'CmT2) to C57BL/6 mice evoked strong protection against tumor challenge, for which the presence of CD4(+) and CD8(+) cells during both the priming and challenge phase was essential. In a therapy model, our results showed that four of seven mice with preestablished tumor remained tumor free for 80 days after therapeutic vaccination. Given the results shown in this study, mTRP-2 gene transfer to DCs provides a potential therapeutic strategy for the management of melanoma, especially in the early stage of the disease.

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.