Identification, characterisation and expression analysis of natural killer receptor genes in chlamydia pecorum infected koalas (phascolarctos cinereus)

BACKGROUND: Koalas (Phascolarctos cinereus), an iconic Australian marsupial, are being heavily impacted by the spread of Chlamydia pecorum, an obligate intracellular bacterial pathogen. Koalas vary in their response to this pathogen, with some showing no symptoms, while others suffer severe symptoms leading to infertility, blindness or death. Little is known about the pathology of this disease and the immune response against it in this host. Studies have demonstrated that natural killer (NK) cells, key components of the innate immune system, are involved in the immune response to chlamydial infections in humans. These cells can directly lyse cells infected by intracellular pathogens and their ability to recognise these infected cells is mediated through NK receptors on their surface. These are encoded in two regions of the genome, the leukocyte receptor complex (LRC) and the natural killer complex (NKC). These two families evolve rapidly and different repertoires of genes, which have evolved by gene duplication, are seen in different species. METHODS: In this study we aimed to characterise genes belonging to the NK receptor clusters in the koala by searching available koala transcriptomes using a combination of search methods. We developed a qPCR assay to quantify relative expression of four genes, two encoded within the NK receptor cluster (CLEC1B, CLEC4E) and two known to play a role in NK response to Chalmydia in humans (NCR3, PRF1). RESULTS: We found that the NK receptor repertoire of the koala closely resembles that of the Tasmanian devil, with minimal genes in the NKC, but with lineage specific expansions in the LRC. Additional genes important for NK cell activity, NCR3 and PRF1, were also identified and characterised. In a preliminary study to investigate whether these genes are involved in the koala immune response to infection by its chlamydial pathogen, C. pecorum, we investigated the expression of four genes in koalas with active chlamydia infection, those with past infection and those without infection using qPCR. This analysis revealed that one of these four, CLEC4E, may be upregulated in response to chlamydia infection. CONCLUSION: We have characterised genes of the NKC and LRC in koalas and have discovered evidence that one of these genes may be upregulated in koalas with chlamydia, suggesting that these receptors may play a role in the immune response of koalas to chlamydia infection.

Construction of wavelets and multiwavelets basis : a generalized method

Multiwavelets are wavelets with multiplicity r, that is r scaling functions and r wavelets, which define multiresolution analysis similar to scalar wavelets. They are advantageous over scalar wavelets since they simultaneously posse symmetry and orthogonality. In this work, a new method for constructing multiwavelets with any approximation order is presented. The method involves the derivation of a matrix equation for the desired approximation order. The condition for approximation order is similar to the conditions in the scalar case. Generalized left eigenvectors give the combinations of scaling functions required to reconstruct the desired spline or super function. The method is demonstrated by constructing a specific class of symmetric and non-symmetric multiwavelets with different approximation orders, which include Geranimo-Hardin-Massopust (GHM), Daubechies and Alperts like multi-wavelets, as parameterized solutions. All multi-wavelets constructed in this work, posses the good properties of orthogonality, approximation order and short support.

EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.

Identification, distribution and expression of natriuretic peptide receptors in trout

This study identified cell receptors for trout natriuretic peptide hormone. These hormones protect the heart and maintain fluid balance. Receptor populations on cells depend on whether the animal lives in freshwater or saltwater. Receptors are widely distributed and different receptor types perform different tasks. Their original evolutionary role is suggested.

Incorporating both positive and negative association rules into the analysis of outbound tourism in Hong Kong

This article presents a novel approach to data mining that incorporates both positive and negative association rules into the analysis of outbound travelers. Using datasets collected from three large-scale domestic tourism surveys on Hong Kong residents' outbound pleasure travel, different sets of targeted rules were generated to provide promising information that will allow practitioners and policy makers to better understand the important relationship between condition attributes and target attributes. This article will be of interest to readers who want to understand methods for integrating the latest data mining techniques into tourism research. It will also be of use to marketing managers in destinations to better formulate strategies for receiving outbound travelers from Hong Kong, and possibly elsewhere.

Mycoplasmas in Australian fur seals (Arctocephalus pusillus doriferus) : identification and association with abortion

Bacteria from the genus Mycoplasma are common inhabitants of the respiratory, gastrointestinal, and genital tracts of mammals. The understanding of the pathological significance of mycoplasmas in seals is poor, as few studies have utilized the specific culture techniques required to isolate these bacteria. The current study surveyed for the Mycoplasma species present in Australian fur seals (Arctocephalus pusillus doriferus) and investigated the association between infection and pathology. Mycoplasmas were found in the nasal cavities of 55/80 (69%) of apparently healthy individuals. Isolates from 18 individuals were investigated through 16S ribosomal RNA sequencing, and 3 species were identified: M. zalophi, M. phocae, and Mycoplasma sp. (GenBank no. EU714238.1), all of which had previously been isolated from Northern Hemisphere pinnipeds. In addition, mycoplasmas were isolated from the lungs of 4 out of 16 juveniles and 1 out of 5 adults sampled at necropsy. Isolates obtained were M. zalophi, Mycoplasma sp. EU714238.1, and M. phocicerebrale, but infection was not associated with lung pathology in these age classes. Inflammatory disease processes of the heart and/or lungs were present in 12 out of 32 (38%) aborted fetuses on microscopic examination. Predominant findings were interstitial pneumonia, pericarditis, and myocarditis. Mycoplasma phocicerebrale was isolated from the thymus of an aborted fetus, and 3 out of 11 (27%) fetuses with inflammatory heart or lung lesions were PCR-positive for Mycoplasma. In conclusion, several species of Mycoplasma are part of the normal flora of the nasal cavity of Australian fur seals, and some mycoplasmas may be associated with abortion in this species of seal.

Text-dependent speaker recognition using wavelets and neural networks

An intelligent system for text-dependent speaker recognition is proposed in this paper. The system consists of a wavelet-based module as the feature extractor of speech signals and a neural-network-based module as the signal classifier. The Daubechies wavelet is employed to filter and compress the speech signals. The fuzzy ARTMAP (FAM) neural network is used to classify the processed signals. A series of experiments on text-dependent gender and speaker recognition are conducted to assess the effectiveness of the proposed system using a collection of vowel signals from 100 speakers. A variety of operating strategies for improving the FAM performance are examined and compared. The experimental results are analyzed and discussed.

Development of a speaker recognition system using wavelets and artificial neural networks

This paper addresses the problem of speaker recognition from speech signals. The study focuses on the development of a speaker recognition system comprising two modules: a wavelet-based feature extractor, and a neural-network-based classifier. We have conducted a number of experiments to investigate the applicability of Discrete Wavelet Transform (D WT) in extracting discriminative features from the speech signals, and have examined various models from the Adaptive Resonance Theory (ART) family of neural networks in classijjing the extracted features. The results indicate that DWT could be a potential feature extraction tool for speaker recognition. In addition, the ART-based classijiers have yielded very promising recognition accuracy at more than 81%.

Mass spectrometry cancer data classification using wavelets and genetic algorithm

This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.

Carrier's liability under the Hague, Hague-Visby and Hamburg Rules

Provides a critical analysis of the carrier's liability under the Hague, Hague-Visby and Hamburg Rules. Focusing on Australian and English jurisprudence, the work also demonstrates that, quite contrary to prevailing opinions, the Hamburg Rules do not materially change (still less increase) the carrier's existing liability.

Identification of novel therapeutics for complex diseases from genome-wide association data

Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.