Cytokine synthesis by intestinal intraepithelial lymphocytes. Both gamma/delta T cell receptor-positive and alpha/beta T cell receptor-positive T cells in the G1 phase of cell cycle produce IFN-gamma and IL-5

Murine intestinal intraepithelial lymphocytes (IEL) have been shown to contain subsets of alpha/beta TCR+ and gamma/delta TCR+ T cells that spontaneously produce cytokines such as IFN-gamma and IL-5. We have now determined the nature and cell cycle stage of these cytokine-producing T lymphocytes in EIL by using IFN-gamma- and IL-5-specific ELISPOT assay, cytokine-specific mRNA-cDNA dot-blot hybridization and polymerase chain reaction, and flow cytometry (FACS) for DNA analysis. When CD3+ T cells from IEL of normal C3H/HeN mice were separated into low and high density fractions by discontinuous Percoll gradients, IFN-gamma and IL-5 spot-forming cells were only found in the former population. Analysis of mRNA for these cytokines by both IFN-gamma- and IL-5-specific dot-blot hybridization and polymerase chain reaction revealed that higher levels of message for IFN-gamma and IL-5 were also seen in the low density fraction. However, cell cycle analysis of these two fractions by FACS using propidium iodide showed a similar pattern of cell cycle stages in both low and high density populations (G0 + G1 approximately 96 to 98% and S/G2 + M approximately 2 to 4%). Finally, mRNA from gamma/delta TCR+ and alpha/beta TCR+ T cells in both low and high density fractions of IEL were analyzed for IFN-gamma and IL-5 message by polymerase chain reaction. After 35 cycles of amplification, both gamma/delta TCR+ and alpha/beta TCR+ T cells in the low density fraction expressed higher levels of message for these two cytokines when compared with the high density population. These results have now shown that both gamma/delta and alpha/beta TCR+ IEL can be separated into low and high density subsets and both fractions possess a similar stage of cell cycle. However, only the low density cells (in G1 phase) of both gamma/delta and alpha/beta TCR types possess increased cytokine-specific mRNA and produce the cytokines IFN-gamma and IL-5. Our results suggest that alpha/beta TCR+ and gamma/delta TCR+ IEL can produce cytokines without cell proliferation.

CD4+ tumor infiltrating lymphocytes are prognostic and independent of R-IPI in patients with DLBCL receiving R-CHOP chemo-immunotherapy

Despite the Revised International Prognostic Index's (R-IPI) undoubted utility in diffuse large B-cell lymphoma (DLBCL), significant clinical heterogeneity within R-IPI categories persists. Emerging evidence indicates that circulating host immunity is a robust and R-IPI independent prognosticator, most likely reflecting the immune status of the intratumoral microenvironment. We hypothesized that direct quantification of immunity within lymphomatous tissue would better permit stratification within R-IPI categories. We analyzed 122 newly diagnosed consecutive DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemo-immunotherapy. Median follow-up was 4 years. As expected, the R-IPI was a significant predictor of outcome with 5-year overall survival (OS) 87% for very good, 87% for good, and 51% for poor-risk R-IPI scores (P < 0.001). Consistent with previous reports, systemic immunity also predicted outcome (86% OS for high lymphocyte to monocyte ratio [LMR], versus 63% with low LMR, P = 0.01). Multivariate analysis confirmed LMR as independently prognostic. Flow cytometry on fresh diagnostic lymphoma tissue, identified CD4+ T-cell infiltration as the most significant predictor of outcome with ≥23% infiltration dividing the cohort into high and low risk groups with regard to event-free survival (EFS, P = 0.007) and OS (P = 0.003). EFS and OS were independent of the R-IPI and LMR. Importantly, within very good/good R-IPI patients, CD4+ T-cells still distinguished patients with different 5 year OS (high 96% versus low 63%, P = 0.02). These results illustrate the importance of circulating and local intratumoral immunity in DLBCL treated with R-CHOP.

Increased tubulointerstitial recruitment of human CD141(hi) CLEC9A(+) and CD1c(+) myeloid dendritic cell subsets in renal fibrosis and chronic kidney disease

Dendritic cells (DCs) play critical roles in immune-mediated kidney diseases. Little is known, however, about DC subsets in human chronic kidney disease, with previous studies restricted to a limited set of pathologies and to using immunohistochemical methods. In this study, we developed novel protocols for extracting renal DC subsets from diseased human kidneys and identified, enumerated, and phenotyped them by multicolor flow cytometry. We detected significantly greater numbers of total DCs as well as CD141(hi) and CD1c(+) myeloid DC (mDCs) subsets in diseased biopsies with interstitial fibrosis than diseased biopsies without fibrosis or healthy kidney tissue. In contrast, plasmacytoid DC numbers were significantly higher in the fibrotic group compared with healthy tissue only. Numbers of all DC subsets correlated with loss of kidney function, recorded as estimated glomerular filtration rate. CD141(hi) DCs expressed C-type lectin domain family 9 member A (CLEC9A), whereas the majority of CD1c(+) DCs lacked the expression of CD1a and DC-specific ICAM-3-grabbing nonintegrin (DC-SIGN), suggesting these mDC subsets may be circulating CD141(hi) and CD1c(+) blood DCs infiltrating kidney tissue. Our analysis revealed CLEC9A(+) and CD1c(+) cells were restricted to the tubulointerstitium. Notably, DC expression of the costimulatory and maturation molecule CD86 was significantly increased in both diseased cohorts compared with healthy tissue. Transforming growth factor-β levels in dissociated tissue supernatants were significantly elevated in diseased biopsies with fibrosis compared with nonfibrotic biopsies, with mDCs identified as a major source of this profibrotic cytokine. Collectively, our data indicate that activated mDC subsets, likely recruited into the tubulointerstitium, are positioned to play a role in the development of fibrosis and, thus, progression to chronic kidney disease.

Terrestrial invertebrates of dry river beds are not simply subsets of riparian assemblages

Dry river beds are common worldwide and are rapidly increasing in extent due to the effects of water management and prolonged drought periods due to climate change. While attention has been given to the responses of aquatic invertebrates to drying rivers, few studies exist on the terrestrial invertebrates colonizing dry river beds. Dry river beds are physically harsh and they often differ substantially in substrate, topography, microclimate and inundation frequency from adjacent riparian zones. Given these differences, we predicted that dry river beds provide a unique habitat for terrestrial invertebrates, and that their assemblage composition differs from that in adjacent riparian zones. Dry river beds and riparian zones in Australia and Italy were sampled for terrestrial invertebrates with pitfall traps. Sites differed in substrate type, climate and flow regime. Dry river beds contained diverse invertebrate assemblages and their composition was consistently different from adjacent riparian zones, irrespective of substrate, climate or hydrology. Although some taxa were shared between dry river beds and riparian zones, 66 of 320 taxa occurred only in dry river beds. Differences were due to species turnover, rather than shifts in abundance, indicating that dry river bed assemblages are not simply subsets of riparian assemblages. Some spatial patterns in invertebrate assemblages were associated with environmental variables (irrespective of habitat type), but these associations were statistically weak. We suggest that dry river beds are unique habitats in their own right. We discuss potential human stressors and management issues regarding dry river beds and provide recommendations for future research.

Human CD141(+) (BDCA-3)(+) dendritic cells (DCs) represent a unique myeloid DC subset that cross-presents necrotic cell antigens

The characterization of human dendritic cell (DC) subsets is essential for the design of new vaccines. We report the first detailed functional analysis of the human CD141(+) DC subset. CD141(+) DCs are found in human lymph nodes, bone marrow, tonsil, and blood, and the latter proved to be the best source of highly purified cells for functional analysis. They are characterized by high expression of toll-like receptor 3, production of IL-12p70 and IFN-beta, and superior capacity to induce T helper 1 cell responses, when compared with the more commonly studied CD1c(+) DC subset. Polyinosine-polycytidylic acid (poly I:C)-activated CD141(+) DCs have a superior capacity to cross-present soluble protein antigen (Ag) to CD8(+) cytotoxic T lymphocytes than poly I:C-activated CD1c(+) DCs. Importantly, CD141(+) DCs, but not CD1c(+) DCs, were endowed with the capacity to cross-present viral Ag after their uptake of necrotic virus-infected cells. These findings establish the CD141(+) DC subset as an important functionally distinct human DC subtype with characteristics similar to those of the mouse CD8 alpha(+) DC subset. The data demonstrate a role for CD141(+) DCs in the induction of cytotoxic T lymphocyte responses and suggest that they may be the most relevant targets for vaccination against cancers, viruses, and other pathogens.

The MS risk allele of CD40 is associated with reduced cell-membrane bound expression in antigen presenting cells: Implications for gene function

Human genetic and animal studies have implicated the costimulatory molecule CD40 in the development of multiple sclerosis (MS). We investigated the cell specific gene and protein expression variation controlled by the CD40 genetic variant(s) associated with MS, i.e. the T-allele at rs1883832. Previously we had shown that the risk allele is expressed at a lower level in whole blood, especially in people with MS. Here, we have defined the immune cell subsets responsible for genotype and disease effects on CD40 expression at the mRNA and protein level. In cell subsets in which CD40 is most highly expressed, B lymphocytes and dendritic cells, the MS-associated risk variant is associated with reduced CD40 cell-surface protein expression. In monocytes and dendritic cells, the risk allele additionally reduces the ratio of expression of full-length versus truncated CD40 mRNA, the latter encoding secreted CD40. We additionally show that MS patients, regardless of genotype, express significantly lower levels of CD40 cell-surface protein compared to unaffected controls in B lymphocytes. Thus, both genotype-dependent and independent down-regulation of cell-surface CD40 is a feature of MS. Lower expression of a co-stimulator of T cell activation, CD40, is therefore associated with increased MS risk despite the same CD40 variant being associated with reduced risk of other inflammatory autoimmune diseases. Our results highlight the complexity and likely individuality of autoimmune pathogenesis, and could be consistent with antiviral and/or immunoregulatory functions of CD40 playing an important role in protection from MS. © 2015 Field et al.

Development of a particle number and particle mass vehicle emissions inventory for an urban fleet

Motor vehicles are major emitters of gaseous and particulate pollution in urban areas, and exposure to particulate pollution can have serious health effects, ranging from respiratory and cardiovascular disease to mortality. Motor vehicle tailpipe particle emissions span a broad size range from 0.003-10µm, and are measured as different subsets of particle mass concentrations or particle number count. However, no comprehensive inventories currently exist in the international published literature covering this wide size range. This paper presents the first published comprehensive inventory of motor vehicle tailpipe particle emissions covering the full size range of particles emitted. The inventory was developed for urban South-East Queensland by combining two techniques from distinctly different disciplines, from aerosol science and transport modelling. A comprehensive set of particle emission factors were combined with traffic modelling, and tailpipe particle emissions were quantified for particle number (ultrafine particles), PM1, PM2.5 and PM10 for light and heavy duty vehicles and buses. A second aim of the paper involved using the data derived in this inventory for scenario analyses, to model the particle emission implications of different proportions of passengers travelling in light duty vehicles and buses in the study region, and to derive an estimate of fleet particle emissions in 2026. It was found that heavy duty vehicles (HDVs) in the study region were major emitters of particulate matter pollution, and although they contributed only around 6% of total regional vehicle kilometres travelled, they contributed more than 50% of the region’s particle number (ultrafine particles) and PM1 emissions. With the freight task in the region predicted to double over the next 20 years, this suggests that HDVs need to be a major focus of mitigation efforts. HDVs dominated particle number (ultrafine particles) and PM1 emissions; and LDV PM2.5 and PM10 emissions. Buses contributed approximately 1-2% of regional particle emissions.

Development of a particle number and particle mass emissions inventory for an urban fleet : a study in South-East Queensland

Motor vehicles are a major source of gaseous and particulate matter pollution in urban areas, particularly of ultrafine sized particles (diameters < 0.1 µm). Exposure to particulate matter has been found to be associated with serious health effects, including respiratory and cardiovascular disease, and mortality. Particle emissions generated by motor vehicles span a very broad size range (from around 0.003-10 µm) and are measured as different subsets of particle mass concentrations or particle number count. However, there exist scientific challenges in analysing and interpreting the large data sets on motor vehicle emission factors, and no understanding is available of the application of different particle metrics as a basis for air quality regulation. To date a comprehensive inventory covering the broad size range of particles emitted by motor vehicles, and which includes particle number, does not exist anywhere in the world. This thesis covers research related to four important and interrelated aspects pertaining to particulate matter generated by motor vehicle fleets. These include the derivation of suitable particle emission factors for use in transport modelling and health impact assessments; quantification of motor vehicle particle emission inventories; investigation of the particle characteristic modality within particle size distributions as a potential for developing air quality regulation; and review and synthesis of current knowledge on ultrafine particles as it relates to motor vehicles; and the application of these aspects to the quantification, control and management of motor vehicle particle emissions. In order to quantify emissions in terms of a comprehensive inventory, which covers the full size range of particles emitted by motor vehicle fleets, it was necessary to derive a suitable set of particle emission factors for different vehicle and road type combinations for particle number, particle volume, PM1, PM2.5 and PM1 (mass concentration of particles with aerodynamic diameters < 1 µm, < 2.5 µm and < 10 µm respectively). The very large data set of emission factors analysed in this study were sourced from measurement studies conducted in developed countries, and hence the derived set of emission factors are suitable for preparing inventories in other urban regions of the developed world. These emission factors are particularly useful for regions with a lack of measurement data to derive emission factors, or where experimental data are available but are of insufficient scope. The comprehensive particle emissions inventory presented in this thesis is the first published inventory of tailpipe particle emissions prepared for a motor vehicle fleet, and included the quantification of particle emissions covering the full size range of particles emitted by vehicles, based on measurement data. The inventory quantified particle emissions measured in terms of particle number and different particle mass size fractions. It was developed for the urban South-East Queensland fleet in Australia, and included testing the particle emission implications of future scenarios for different passenger and freight travel demand. The thesis also presents evidence of the usefulness of examining modality within particle size distributions as a basis for developing air quality regulations; and finds evidence to support the relevance of introducing a new PM1 mass ambient air quality standard for the majority of environments worldwide. The study found that a combination of PM1 and PM10 standards are likely to be a more discerning and suitable set of ambient air quality standards for controlling particles emitted from combustion and mechanically-generated sources, such as motor vehicles, than the current mass standards of PM2.5 and PM10. The study also reviewed and synthesized existing knowledge on ultrafine particles, with a specific focus on those originating from motor vehicles. It found that motor vehicles are significant contributors to both air pollution and ultrafine particles in urban areas, and that a standardized measurement procedure is not currently available for ultrafine particles. The review found discrepancies exist between outcomes of instrumentation used to measure ultrafine particles; that few data is available on ultrafine particle chemistry and composition, long term monitoring; characterization of their spatial and temporal distribution in urban areas; and that no inventories for particle number are available for motor vehicle fleets. This knowledge is critical for epidemiological studies and exposure-response assessment. Conclusions from this review included the recommendation that ultrafine particles in populated urban areas be considered a likely target for future air quality regulation based on particle number, due to their potential impacts on the environment. The research in this PhD thesis successfully integrated the elements needed to quantify and manage motor vehicle fleet emissions, and its novelty relates to the combining of expertise from two distinctly separate disciplines - from aerosol science and transport modelling. The new knowledge and concepts developed in this PhD research provide never before available data and methods which can be used to develop comprehensive, size-resolved inventories of motor vehicle particle emissions, and air quality regulations to control particle emissions to protect the health and well-being of current and future generations.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Agglutinins from marine macroalgae of the southeastern United States

Protein extracts from 22 species of marine macroalgae from Florida and North Carolina were compared for their abilities to agglutinate sheep and rabbit erythrocytes. Protein extracts from 21 algal species agglutinated rabbit erythrocytes compared to 19 for sheep erythrocytes. However, agglutination by brown algal extracts was variable. The agglutination produced by protein extracts from Dictyota dichotoma could be blocked by addition of polyvinylpyrrolidone. Protein extracts from North Carolina macroalgae were also tested against five bacterial species. Three of these agglutinated bacterial cells. Ulva curvata and Bryopsis plumosa agglutinated all five species. Protein extracts from five species of Florida algae were tested for their effects on mitogenesis in mouse splenocytes and human lymphocytes. Gracilaria tikvahiae HBOI Strain G-5, Ulva rigida and Gracilaria verrucosa HBOI Strain G-16S stimulated mitogenesis in mouse splenocytes, while Gracilaria tikvahiae HBOI Strain G-16stimulated mitogenesis in human lymphocytes.

Multicast traffic filtering for sampled value process bus networks

Ethernet is a key component of the standards used for digital process buses in transmission substations, namely IEC 61850 and IEEE Std 1588-2008 (PTPv2). These standards use multicast Ethernet frames that can be processed by more than one device. This presents some significant engineering challenges when implementing a sampled value process bus due to the large amount of network traffic. A system of network traffic segregation using a combination of Virtual LAN (VLAN) and multicast address filtering using managed Ethernet switches is presented. This includes VLAN prioritisation of traffic classes such as the IEC 61850 protocols GOOSE, MMS and sampled values (SV), and other protocols like PTPv2. Multicast address filtering is used to limit SV/GOOSE traffic to defined subsets of subscribers. A method to map substation plant reference designations to multicast address ranges is proposed that enables engineers to determine the type of traffic and location of the source by inspecting the destination address. This method and the proposed filtering strategy simplifies future changes to the prioritisation of network traffic, and is applicable to both process bus and station bus applications.

Molecular classification of cutaneous malignant melanoma by gene expression profiling

The most common human cancers are malignant neoplasms of the skin. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm. Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

Multifractal characterisation and analysis of complex networks

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model