Valeur pronostique de CD73 et des lymphocytes T CD8 et optimisation de la vaccination de type GVAX dans le cancer de la prostate

CD73 est un ecto-enzyme qui a été associé à la suppression de l'immunité anti-tumorale. Ses valeurs pronostiques et thérapeutiques ont été mises de l'avant dans plusieurs types de cancer. La première hypothèse du projet est que l'expression de CD73 dans la tumeur prédit le pronostic des patients atteints du cancer de la prostate. L'expression de CD73 a été étudiée par immunofluorescence dans des échantillons de tumeur. Puis, des analyses univariées et multivariées ont été conduites pour déterminer si l'expression de CD73 permet de prédire la récidive biochimique des patients. Nous avons déterminé que CD73 prédit indépendamment le pronostic des patients atteints du cancer de la prostate. De plus, nous avons déterminé que son expression dans le tissu normal adjacent ou dans la tumeur prédit différemment la survenue de la récidive biochimique. La deuxième hypothèse est que l'inhibition de CD73 permet d'améliorer l'efficacité d'un vaccin thérapeutique contre le cancer de la prostate. L'effet d'un vaccin de type GVAX a été étudié dans des souris CD73KO ou en combinaison avec un anticorps ciblant CD73. Nous avons observé que l'efficacité du vaccin était augmentée dans les souris où CD73 était absent. Cependant, la combinaison avec l'anti-CD73 n'a pas permis d'améliorer l'efficacité.

Regulatory T and B cells in asthmatic women: variations from pregnancy to postpartum Treg and Breg: pregnancy to postpartum

BACKGROUND: Allergic asthma and rhinitis are common in pregnancy. The immune mechanisms underlying the effects of pregnancy in asthma and vice-versa are not completely understood. OBJECTIVES: This work aimed to study the evolution of regulatory T and B cells in asthmatic pregnant women, from late pregnancy till postpartum. METHODS: Four groups of women were enrolled for this study: third trimester pregnant women, asthmatic (n=24) and healthy (n=43), and non-pregnant women, asthmatic (n=33) and healthy (n=35). Pregnant women were also evaluated postpartum (>6 weeks after delivery). Blood samples were taken from each woman and flow cytometry was used to characterize circulating regulatory T and B cells. Foxp3 expression was assessed within CD4DimCD25Hi regulatory T cells. RESULTS: In asthmatic and healthy pregnant women, regulatory T cells did not oscillate significantly from pregnancy to postpartum, but CD24HiCD38Hi regulatory B cells, decreased in pregnancy, rose significantly postpartum. Foxp3 expression in regulatory T cells was also impaired during pregnancy in asthmatic and healthy pregnant women, recovering postpartum. Nevertheless, asthmatic pregnant women presented higher Foxp3 expression than healthy pregnant women (p=0.007), probably due to the use of control medication. CONCLUSIONS: Women with controlled asthma present variations in regulatory cell subsets during pregnancy and postpartum. The similar pattern observed for Foxp3 expression and CD24HiCD38Hi regulatory B cells during this period corroborates the interaction established between regulatory T and B cells in immune responses. Considering the immunomodulatory potential of these immune mediators, more studies are needed to evaluate their relation with asthma and rhinitis complications in pregnancy.

Étude du niveau de B Lymphocyte Stimulator (BLyS) et de son impact sur les lymphocytes B en relation avec l’infection et la résistance au virus d’immunodéficience humaine (VIH) chez des travailleuses du sexe au Bénin

L’infection au VIH s’accompagne souvent de dérégulations du compartiment des lymphocytes B qui nuisent à la génération de réponses efficaces. En effet, détectées tôt après l’infection, ces dérégulations perdurent, ne sont pas totalement restaurées par la thérapie, et mènent souvent à des manifestations auto-immunes et lymphomes. Une étude longitudinale de notre groupe, effectuée avec des cellules mononucléées du sang circulant provenant de patients VIH+ avec différents types de progression clinique, a démontré qu’un niveau élevé de BLyS chez des individus VIH+ progresseurs était associé à une dérégulation des fréquences de populations de cellules B avec augmentation de cellules innées de la zone marginale (MZ) présentant des caractéristiques d’immaturité et d’activation. Au contraire, chez des individus VIH+ non-progresseurs avirémiques ou contrôleurs d’élite, les niveaux de BLyS étaient dans la normale et ce sont les fréquences de cellules B MZ plus matures qui étaient diminuées. La résistance au VIH pourrait aussi impliquer le contrôle de BLyS et son impact sur les cellules B. De ce fait, nous avons préalablement recruté une cohorte de travailleuses du sexe (TS) à Cotonou (Bénin) dans laquelle nous avons identifié des femmes qui demeurent séronégatives malgré une exposition soutenue au virus. Nous avons mesuré les niveaux de BLyS dans le sang et dans les lavages cervico-vaginaux (CVL) de TS VIH- et les avons comparés à ceux mesurés chez des TS VIH+ et un groupe contrôle de non-TS VIH- . Nous avons trouvé que les niveaux de BLyS dans le sang et le CVL des TS VIH- étaient inférieurs à ceux des TS VIH+ et des non-TS VIH-. Le niveau d’expression de BLyS à la surface des lymphocytes T, monocytes et cellules dendritiques de TS VIH- était augmenté, mais à un niveau moindre que les TS VIH+. Chez les TS VIH+, les hauts niveaux de BLyS étaient concomitants avec une dérégulation du compartiment B caractérisée par une hyperglobulinémie, une augmentation de la fréquence de populations avec un profil immature/inné et une plus grande proportion de plasmablastes IgG vs IgA. Au contraire, les niveaux inférieurs de BLyS dans le sang des TS VIH- coïncident avec un compartiment B préservé, révélant que les lymphocytes B MZ peuvent être impliqués dans l’immunité naturelle au VIH. Ces résultats démontrent l’importance du contrôle des niveaux de BLyS et du maintien de l’intégrité du compartiment B dans la résistance au VIH.

Étude du niveau de B Lymphocyte Stimulator (BLyS) et de son impact sur les lymphocytes B en relation avec l’infection et la résistance au virus d’immunodéficience humaine (VIH) chez des travailleuses du sexe au Bénin

L’infection au VIH s’accompagne souvent de dérégulations du compartiment des lymphocytes B qui nuisent à la génération de réponses efficaces. En effet, détectées tôt après l’infection, ces dérégulations perdurent, ne sont pas totalement restaurées par la thérapie, et mènent souvent à des manifestations auto-immunes et lymphomes. Une étude longitudinale de notre groupe, effectuée avec des cellules mononucléées du sang circulant provenant de patients VIH+ avec différents types de progression clinique, a démontré qu’un niveau élevé de BLyS chez des individus VIH+ progresseurs était associé à une dérégulation des fréquences de populations de cellules B avec augmentation de cellules innées de la zone marginale (MZ) présentant des caractéristiques d’immaturité et d’activation. Au contraire, chez des individus VIH+ non-progresseurs avirémiques ou contrôleurs d’élite, les niveaux de BLyS étaient dans la normale et ce sont les fréquences de cellules B MZ plus matures qui étaient diminuées. La résistance au VIH pourrait aussi impliquer le contrôle de BLyS et son impact sur les cellules B. De ce fait, nous avons préalablement recruté une cohorte de travailleuses du sexe (TS) à Cotonou (Bénin) dans laquelle nous avons identifié des femmes qui demeurent séronégatives malgré une exposition soutenue au virus. Nous avons mesuré les niveaux de BLyS dans le sang et dans les lavages cervico-vaginaux (CVL) de TS VIH- et les avons comparés à ceux mesurés chez des TS VIH+ et un groupe contrôle de non-TS VIH- . Nous avons trouvé que les niveaux de BLyS dans le sang et le CVL des TS VIH- étaient inférieurs à ceux des TS VIH+ et des non-TS VIH-. Le niveau d’expression de BLyS à la surface des lymphocytes T, monocytes et cellules dendritiques de TS VIH- était augmenté, mais à un niveau moindre que les TS VIH+. Chez les TS VIH+, les hauts niveaux de BLyS étaient concomitants avec une dérégulation du compartiment B caractérisée par une hyperglobulinémie, une augmentation de la fréquence de populations avec un profil immature/inné et une plus grande proportion de plasmablastes IgG vs IgA. Au contraire, les niveaux inférieurs de BLyS dans le sang des TS VIH- coïncident avec un compartiment B préservé, révélant que les lymphocytes B MZ peuvent être impliqués dans l’immunité naturelle au VIH. Ces résultats démontrent l’importance du contrôle des niveaux de BLyS et du maintien de l’intégrité du compartiment B dans la résistance au VIH.

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