Influenza A virus-specific CD8.sup.+ T-cell responses : from induction to function

Seasonal influenza virus infection is a leading cause of illness and mortality in young children and the elderly each year. Current influenza vaccines generate protective antibody responses; however, these must be given annually to provide protection against serologically distinct viruses. By contrast, CD8.sup.+ T cells are capable of recognizing conserved antigenic determinants within the influenza virion and, as such, may provide protection against a number of variant strains of the virus. CD8.sup.+ T cells play a critical key role in controlling and resolving influenza virus infections via the production of cytokines and cytolytic mediators. This article focuses on the induction of the influenza-specific CD8.sup.+ T-cell response and how these cells acquire and maintain effector function after induction. Moreover, we discuss how cytotoxic T-lymphocyte function correlates with protection following vaccination.

Generation of recombinant influenza A viruses lacking immunodominant CD8+ T cell epitopes

The induction of a cytotoxic T lymphocyte (CTL) response following influenza infection can lead to the formation of immunity capable of recognizing viruses of a different antigenicity. Our ability to exploit such broadly reactive responses in vaccination strategies is hampered by a lack of understanding on the regulation of CTL responses. In this report, we describe the utilization of reverse genetics to produce a range of recombinant viruses lacking immunodominant murine CTL epitopes. Recombinant viruses lacking the epitopes had indistinguishable growth properties in vitro and in vivo compared with the wild-type virus. Analysis of a primary immune response to these viruses showed that mutation of the anchor-binding residue leads to a loss of a response to that epitope, but no compensating increase in responses to other immunodominant epitopes. The utilization of reverse genetics and the murine model of influenza infection hold great promise for elucidating the factors regulating the CTL response.

Beyond e-commerce: an entrepreneurial business modelling method for profitable e-venturing

E- business is used as a term that embraces e-commerce, its commercial exchange or transaction component. Both are subsets of a larger concept, e-venturing. E-business is a major disruptive innovation that is rapidly changing many of the accepted norms of effective management. So the paper revisits and reassesses several established principles of economics, strategy and entrepreneurship to place them in the context of the forces driving the emerging e-business economy. Entrepreneurship is applied as a 'framework enrichener', model-building tool and critical organisational behaviour to guide integration of e-business strategy into the total organisational strategy of a profit-seeking firm This permits development of a new business modelling process, labelled 'map and locate', that adapts a combination of entrepreneurial and strategic imperatives to the internet environment. The process assumes that value-provision, competitive distinction and profitability are the three essentials of any successful e-business strategy design and execution. Apart from its general conceptual role of linking strategic and entrepreneurial thinking, the 'map and locate' modelling process can be used as a practical tool for specific performance in a variety of circumstances. It focuses on learning and the development of metrics useful for measuring progress towards achievement of target outcomes.

A multi-vector, multi-envelope HIV-1 vaccine

The St. Jude Children's Research Hospital (St. Jude) HIV-1 vaccine program is based on the observation that multiple, antigenically distinct HIV-1 envelope protein structures are capable of mediating HIV-1 infection. A cocktail vaccine comprising representatives of these diverse structures (immunotypes) is therefore considered necessary to elicit lymphocyte populations that prevent HIV-1 infection. This strategy is reminiscent of that used to design a currently licensed and successful 23-valent pneumococcus vaccine. Three recombinant vector systems are used for the delivery of envelope cocktails (DNA, vaccinia virus, and purified protein) and each of these has been tested individually in phase I safety trials. A fourth clinical trial, in which diverse envelopes and vectors are combined in a prime-boost vaccination regimen, has been FDA-approved and is expected to commence in 2007. This trial will continue to test the hypothesis that a multivector, multi-envelope vaccine can elicit diverse 8- and T-cell populations that can prevent HIV-1 infections in humans.

A triple-random ensemble classification method for mining multi-label data

This paper presents a triple-random ensemble learning method for handling multi-label classification problems. The proposed method integrates and develops the concepts of random subspace, bagging and random k-label sets ensemble learning methods to form an approach to classify multi-label data. It applies the random subspace method to feature space, label space as well as instance space. The devised subsets selection procedure is executed iteratively. Each multi-label classifier is trained using the randomly selected subsets. At the end of the iteration, optimal parameters are selected and the ensemble MLC classifiers are constructed. The proposed method is implemented and its performance compared against that of popular multi-label classification methods. The experimental results reveal that the proposed method outperforms the examined counterparts in most occasions when tested on six small to larger multi-label datasets from different domains. This demonstrates that the developed method possesses general applicability for various multi-label classification problems.

A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data

Background: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are commonly with extremely high feature-to-sample ratio. In addition to the essential objectives such as to reduce data noise, to reduce data redundancy, to improve sample classification accuracy, and to improve model generalization property, feature selection also helps biologists to focus on the selected genes to further validate their biological hypotheses.
Results: In this paper we describe an improved hybrid system for gene selection. It is based on a recently proposed genetic ensemble (GE) system. To enhance the generalization property of the selected genes or gene subsets and to overcome the overfitting problem of the GE system, we devised a mapping strategy to fuse the goodness information of each gene provided by multiple filtering algorithms. This information is then used for initialization and mutation operation of the genetic ensemble system.
Conclusion: We used four benchmark microarray datasets (including both binary-class and multi-class classification problems) for concept proving and model evaluation. The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly. The MF-GE system is very flexible as various combinations of multiple filters and classifiers can be incorporated based on the data characteristics and the user preferences.

Differential expression of ATP7A, ATP7B and CTR1 in adult rat dorsal root ganglion tissue

Background: ATP7A, ATP7B and CTR1 are metal transporting proteins that control the cellular disposition of copper and platinum drugs, but their expression in dorsal root ganglion (DRG) tissue and their role in platinum-induced neurotoxicity are unknown. To investigate the DRG expression of ATP7A, ATP7B and CTR1, lumbar DRG and reference tissues were collected for real time quantitative PCR, RT-PCR, immunohistochemistry and Western blot analysis from healthy control adult rats or from animals treated with intraperitoneal oxaliplatin (1.85 mg/kg) or drug vehicle twice weekly for 8 weeks.
Results: In DRG tissue from healthy control animals, ATP7A mRNA was clearly detectable at levels similar to those found in the brain and spinal cord, and intense ATP7A immunoreactivity was localised to the cytoplasm of cell bodies of smaller DRG neurons without staining of satellite cells, nerve fibres or co-localisation with phosphorylated heavy neurofilament subunit (pNF-H). High levels of CTR1 mRNA were detected in all tissues from healthy control animals, and strong CTR1 immunoreactivity was associated with plasma membranes and vesicular cytoplasmic structures of the cell bodies of larger-sized DRG neurons without co-localization with ATP7A. DRG neurons with strong expression of ATP7A or CTR1 had distinct cell body size profiles with minimal overlap between them. Oxaliplatin treatment did not alter the size profile of strongly ATP7A-immunoreactive neurons but significantly reduced the size profile of strongly CTR1-immunoreactive neurons. ATP7B mRNA was barely detectable, and no specific immunoreactivity for ATP7B was found, in DRG tissue from healthy control animals.
Conclusions: In conclusion, adult rat DRG tissue exhibits a specific pattern of expression of copper transporters with distinct subsets of peripheral sensory neurons intensely expressing either ATP7A or CTR1, but not both or ATP7B. The neuron subtype-specific and largely non-overlapping distribution of ATP7A and CTR1 within rat DRG tissue may be required to support the potentially differing cuproenzyme requirements of distinct subsets of sensory neurons, and could influence the transport and neurotoxicity of oxaliplatin.

Joint learning and dictionary construction for pattern recognition

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.

Fast cross-validation of kernel fisher discriminant classifiers

Given n training examples, the training of a Kernel Fisher Discriminant (KFD) classifier corresponds to solving a linear system of dimension n. In cross-validating KFD, the training examples are split into 2 distinct subsets for a number of times (L) wherein a subset of m examples is used for validation and the other subset of(n - m) examples is used for training the classifier. In this case L linear systems of dimension (n - m) need to be solved. We propose a novel method for cross-validation of KFD in which instead of solving L linear systems of dimension (n - m), we compute the inverse of an n × n matrix and solve L linear systems of dimension 2m, thereby reducing the complexity when L is large and/or m is small. For typical 10-fold and leave-one-out cross-validations, the proposed algorithm is approximately 4 and (^4/9n) times respectively as efficient as the naive implementations. Simulations are provided to demonstrate the efficiency of the proposed algorithms.

Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering

Ranking is an important task for handling a large amount of content. Ideally, training data for supervised ranking would include a complete rank of documents (or other objects such as images or videos) for a particular query. However, this is only possible for small sets of documents. In practice, one often resorts to document rating, in that a subset of documents is assigned with a small number indicating the degree of relevance. This poses a general problem of modelling and learning rank data with ties. In this paper, we propose a probabilistic generative model, that models the process as permutations over partitions. This results in super-exponential combinatorial state space with unknown numbers of partitions and unknown ordering among them. We approach the problem from the discrete choice theory, where subsets are chosen in a stagewise manner, reducing the state space per each stage significantly. Further, we show that with suitable parameterisation, we can still learn the models in linear time. We evaluate the proposed models on two application areas: (i) document ranking with the data from the recently held Yahoo! challenge, and (ii) collaborative filtering with movie data. The results demonstrate that the models are competitive against well-known rivals.

X4 and R5 HIV-1 have distinct post-entry requirements for uracil DNA glycosylase during infection of primary cells

It has been assumed that R5 and X4 HIV utilize similar strategies to support viral cDNA synthesis post viral entry. In this study, we provide evidence to show that R5 and X4 HIV have distinct requirements for host cell uracil DNA glycosylase (UNG2) during the early stage of infection. UNG2 has been previously implicated in HIV infection, but its precise role remains controversial. In this study we show that, although UNG2 is highly expressed in different cell lines, UNG2 levels are low in the natural host cells of HIV. Short interfering RNA knockdown of endogenous UNG2 in primary cells showed that UNG2 is required for R5 but not X4 HIV infection and that this requirement is bypassed when HIV enters the target cell via vesicular stomatitis virus envelope-glycoprotein-mediated endocytosis. We also show that short interfering RNA knockdown of UNG2 in virus-producing primary cells leads to defective R5 HIV virions that are unable to complete viral cDNA synthesis. Quantitative PCR analysis revealed that endogenous UNG2 levels are transiently up-regulated post HIV infection, and this increase in UNG2 mRNA is ∼10–20 times higher in R5 versus X4 HIV-infected cells. Our data show that both virion-associated UNG2 and HIV infection-induced UNG2 expression are critical for reverse transcription during R5 but not X4 HIV infection. More importantly, we have made the novel observation that R5 and X4 HIV have distinct host cell factor requirements and differential capacities to induce gene expression during the early stages of infection. These differences may result from activation of distinct signaling cascades and/or infection of divergent T-lymphocyte subpopulations.

Labeling of multiple HIV-1 proteins with the biarsenical-tetracysteine system

Due to its small size and versatility, the biarsenical-tetracysteine system is an attractive way to label viral proteins for live cell imaging. This study describes the genetic labeling of the human immunodeficiency virus type 1 (HIV-1) structural proteins (matrix, capsid and nucleocapsid), enzymes (protease, reverse transcriptase, RNAse H and integrase) and envelope glycoprotein 120 with a tetracysteine tag in the context of a full-length virus. We measure the impact of these modifications on the natural virus infection and, most importantly, present the first infectious HIV-1 construct containing a fluorescently-labeled nucleocapsid protein. Furthermore, due to the high background levels normally associated with the labeling of tetracysteine-tagged proteins we have also optimized a metabolic labeling system that produces infectious virus containing the natural envelope glycoproteins and specifically labeled tetracysteine-tagged proteins that can easily be detected after virus infection of T-lymphocytes. This approach can be adapted to other viral systems for the visualization of the interplay between virus and host cell during infection.

A generic classifier-ensemble approach for biomedical named entity recognition

In named entity recognition (NER) for biomedical literature, approaches based on combined classifiers have demonstrated great performance improvement compared to a single (best) classifier. This is mainly owed to sufficient level of diversity exhibited among classifiers, which is a selective property of classifier set. Given a large number of classifiers, how to select different classifiers to put into a classifier-ensemble is a crucial issue of multiple classifier-ensemble design. With this observation in mind, we proposed a generic genetic classifier-ensemble method for the classifier selection in biomedical NER. Various diversity measures and majority voting are considered, and disjoint feature subsets are selected to construct individual classifiers. A basic type of individual classifier – Support Vector Machine (SVM) classifier is adopted as SVM-classifier committee. A multi-objective Genetic algorithm (GA) is employed as the classifier selector to facilitate the ensemble classifier to improve the overall sample classification accuracy. The proposed approach is tested on the benchmark dataset – GENIA version 3.02 corpus, and compared with both individual best SVM classifier and SVM-classifier ensemble algorithm as well as other machine learning methods such as CRF, HMM and MEMM. The results show that the proposed approach outperforms other classification algorithms and can be a useful method for the biomedical NER problem.

Modern enhancements in teaching design theories to mechanical engineering students

This paper describes the application of computer aided design (CAD) in teaching advanced design methodologies to fourth-year undergraduate students majoring in mechanical engineering. This involves modern enhancements in teaching strategies for subjects such as design-for-X (DFx) and failure mode effect analysis (FMEA) concepts, which are traditionally categorised as advanced design methodologies. The main subsets of DFx including design-for-assembly (DFA), design-for-disassembly (DFD), design-for-manufacturing (DFM), design-for-environment (DFE) and design-for-recyclability (DFR) were covered by studying various engineering and consumer products. The unit was designed as a combination of practical hands-on workshop-based classes along with a software-based evaluation of different products. In addition to CAD, finite element modelling techniques were utilised to enhance the students’ understanding of design faults and failures. The inquiry into teaching practice and design of this fourth-year unit was carried out during past two years and it revealed some interesting outcomes from our teaching practice in terms of students’ learning experiences. Finally, the paper discusses some critical factors in the context of teaching advanced design methodologies to the undergraduates in mechanical engineering and even manufacturing engineering.

Bone marrow chimeric mice reveal a role for CX3CR1 in maintenance of the monocyte-derived cell population in the olfactory neuroepithelium

Macrophages in the olfactory neuroepithelium are thought to play major roles in tissue homeostasis and repair. However, little information is available at present about possible heterogeneity of these monocyte-derived cells, their turnover rates, and the role of chemokine receptors in this process. To start addressing these issues, this study used Cx3cr1gfp mice, in which the gene sequence for eGFP was knocked into the CX3CR1 gene locus in the mutant allele. Using neuroepithelial whole-mounts from Cx3cr1gfp/+ mice, we show that eGFP+ cells of monocytic origin are distributed in a loose network throughout this tissue and can be subdivided further into two immunophenotypically distinct subsets based on MHC-II glycoprotein expression. BM chimeric mice were created using Cx3cr1gfp/+ donors to investigate turnover of macrophages (and other monocyte-derived cells) in the olfactory neuroepithelium. Our data indicate that the monocyte-derived cell population in the olfactory neuroepithelium is actively replenished by circulating monocytes and under the experimental conditions, completely turned over within 6 months. Transplantation of Cx3cr1gfp/gfp (i.e., CX3CR1-deficient) BM partially impaired the replenishment process and resulted in an overall decline of the total monocyte-derived cell number in the olfactory epithelium. Interestingly, replenishment of the CD68lowMHC-II+ subset appeared minimally affected by CX3CR1 deficiency. Taken together, the established baseline data about heterogeneity of monocyte-derived cells, their replenishment rates, and the role of CX3CR1 provide a solid basis to further examine the importance of different monocyte subsets for neuroregeneration at this unique frontier with the external environment.