Detecting and sorting targeting peptides wtih neural networks and support vector machines

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

Modifying insect population age structure to control vector-borne disease

Age is a critical determinant of the ability of most arthropod vectors to transmit a range of human pathogens. This is due to the fact that most pathogens require a period of extrinsic incubation in the arthropod host before pathogen transmission can occur. This developmental period for the pathogen often comprises a significant proportion of the expected lifespan of the vector. As such, only a small proportion of the population that is oldest contributes to pathogen transmission. Given this, strategies that target vector age would be expected to obtain the most significant reductions in the capacity of a vector population to transmit disease. The recent identification of biological agents that shorten vector lifespan, such as Wolbachia, entomopathogenic fungi and densoviruses, offer new tools for the control of vector-borne diseases. Evaluation of the efficacy of these strategies under field conditions will be possible due to recent advances in insect age-grading techniques. Implementation of all of these strategies will require extensive field evaluation and consideration of the selective pressures that reductions in vector longevity may induce on both vector and pathogen.

Evidence of a spotted fever-like rickettsia and a potential new vector from northeastern Australia

A spotted fever-like rickettsia was identified in a Hemaphysalis tick by polymerase chain reaction (PCR) amplification and sequencing of the 16S rDNA, ompA, and ompB genes. A comparison of these nucleotide sequences with those of other spotted fever group (SFG) rickettsiae revealed that the Hemaphysalis tick rickettsia was distinct from other previously reported strains. Phylogenetic analysis based on both ompA and ompB also indicates that the strain’s closest relatives are the agents of Thai tick typhus (Rickettsia honei strain TT-118) and Flinders Island spotted fever (R. honei). This study represents the first report of an R. honei-like agent from a Hemaphysalis tick in Australia and of a spotted fever group rickettsia from Cape York Peninsula, Queensland.

Field prevalence of Wolbachia in the mosquito vector Aedes albopictus

The endosymbiotic bacteria in the genus Wolbachia have been proposed as a potential candidate to deliver pathogen-blocking genes into natural populations of medically important insects. The successful application of Wolbachia in insect vector control depends on the ability of the agent to successfully invade and maintain itself at high frequency under field conditions. Here, we evaluated the prevalence of Wolbachia infections in a field population of the Wolbachia-superinfected mosquito Aedes albopictus. A field prevalence of 100% (n = 1,016) was found in a single population in eastern Thailand via polymerase chain reaction (PCR) testing of Wolbachia both from individual parent females and their corresponding F1 offspring. This is the first report of accurate Wolbachia prevalence in a field population of an insect disease vector. The prevalence of superinfection was estimated to be 99.41%. All single-infected individual mosquitoes (n = 6) were found to harbor group A Wolbachia. For this particular population, none was found to be single-infected with group B Wolbachia. Our results also show that PCR testing of field materials alone without checking F1 offspring overestimated the natural prevalence of single infection. Thus, the confirmation of infection status by means of F1 offspring was critical to the accurate estimates of Wolbachia prevalence under field conditions.

Prospects for control of African trypanosomiasis by tsetse vector manipulation

The extensive antigenic variation phenomena African trypanosomes display in their mammalian host have hampered efforts to develop effective vaccines against trypanosomiasis. Human disease management aims largely to treat infected hosts by chemotherapy, whereas control of animal diseases relies on reducing tsetse populations as well as on drug therapy. The control strategies for animal diseases are carried out and financed by livestock owners, who have an obvious economic incentive. Sustaining largely insecticide-based control at a local level and relying on drugs for treatment of infected hosts for a disease for which there is no evidence of acquired immunity could prove extremely costly in the long run. It is more likely that a combination of several methods in an integrated, phased and area-wide approach would be more effective in controlling these diseases and subsequently improving agricultural output. New approaches that are environmentally acceptable, efficacious and affordable are clearly desirable for control of various medically and agriculturally important insects including tsetse. Here, Serap Aksoy and colleagues discuss molecular genetic approaches to modulate tsetse vector competence.

Semliki Forest virus as an expression vector in insect cell lines

Studies were undertaken to determine if replication-deficient Semliki Forest virus expression vectors could be successfully used to express foreign gene constructs in insect cell lines. Using green fluorescent protein (GFP) as a marker we recorded infection levels of nearly 100% in the Aedes albopictus cell lines C6/36 and Aa23T, as well as in the Ae. aegypti cell line MOS20. The virus was capable of infecting an Anopheles gambiae cell line MOS55. The amount of GFP protein produced in each cell line was quantified. Northern analysis of viral transcription revealed the presence of novel transcripts in Aa23T, C6/36, and MOS55 cell lines, but not in the BHK or MOS20. The initial characterization of these transcripts is described.

Control of vector-borne disease by genetic manipulation of insect populations: technological requirements and research priorities

The possibility of controlling vector-borne disease through the development and release of transgenic insect vectors has recently gained popular support and is being actively pursued by a number of research laboratories around the world. Several technical problems must be solved before such a strategy could be implemented: genes encoding refractory traits (traits that render the insect unable to transmit the pathogen) must be identified, a transformation system for important vector species has to be developed, and a strategy to spread the refractory trait into natural vector populations must be designed. Recent advances in this field of research make it seem likely that this technology will be available in the near future. In this paper we review recent progress in this area as well as argue that care should be taken in selecting the most appropriate disease system with which to first attempt this form of intervention. Much attention is currently being given to the application of this technology to the control of malaria, transmitted by Anopheles gambiae in Africa. While malaria is undoubtedly the most important vector-borne disease in the world and its control should remain an important goal, we maintain that the complex epidemiology of malaria together with the intense transmission rates in Africa may make it unsuitable for the first application of this technology. Diseases such as African trypanosomiasis, transmitted by the tsetse fly, or unstable malaria in India may provide more appropriate initial targets to evaluate the potential of this form of intervention.

Modification of arthropod vector competence via symbiotic bacteria

Some of the world's most devastating diseases are transmitted by arthropod vectors. Attempts to control these arthropods are currently being challenged by the widespread appearance of insecticide resistance. It is therefore desirable to develop alternative strategies to complement existing methods of vector control. In this review, Charles Beard, Scott O'Neill, Robert Tesh, Frank Richards and Serap Aksoy present an approach for introducing foreign genes into insects in order to confer refractoriness to vector populations, ie. the inability to transmit disease-causing agents. This approach aims to express foreign anti-parasitic or anti-viral gene products in symbiotic bacteria harbored by insects. The potential use of naturally occurring symbiont-based mechanisms in the spread of such refractory phenotypes is also discussed.

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.

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.