Exploration of alpine orographic precipitation patterns with radar image processing and clustering techniques

A methodology of exploratory data analysis investigating the phenomenon of orographic precipitation enhancement is proposed. The precipitation observations obtained from three Swiss Doppler weather radars are analysed for the major precipitation event of August 2005 in the Alps. Image processing techniques are used to detect significant precipitation cells/pixels from radar images while filtering out spurious effects due to ground clutter. The contribution of topography to precipitation patterns is described by an extensive set of topographical descriptors computed from the digital elevation model at multiple spatial scales. Additionally, the motion vector field is derived from subsequent radar images and integrated into a set of topographic features to highlight the slopes exposed to main flows. Following the exploratory data analysis with a recent algorithm of spectral clustering, it is shown that orographic precipitation cells are generated under specific flow and topographic conditions. Repeatability of precipitation patterns in particular spatial locations is found to be linked to specific local terrain shapes, e.g. at the top of hills and on the upwind side of the mountains. This methodology and our empirical findings for the Alpine region provide a basis for building computational data-driven models of orographic enhancement and triggering of precipitation. Copyright (C) 2011 Royal Meteorological Society .

Using free data mining software and clustering algorithms to find predictors from student qualifications

In this project a research both in finding predictors via clustering techniques and in reviewing the Data Mining free software is achieved. The research is based in a case of study, from where additionally to the KDD free software used by the scientific community; a new free tool for pre-processing the data is presented. The predictors are intended for the e-learning domain as the data from where these predictors have to be inferred are student qualifications from different e-learning environments. Through our case of study not only clustering algorithms are tested but also additional goals are proposed.

Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.

Clustering of risk behaviors with cigarette consumption: a population-based survey.

OBJECTIVE: This study assessed clustering of multiple risk behaviors (i.e., low leisure-time physical activity, low fruits/vegetables intake, and high alcohol consumption) with level of cigarette consumption. METHODS: Data from the 2002 Swiss Health Survey, a population-based cross-sectional telephone survey assessing health and self-reported risk behaviors, were used. 18,005 subjects (8052 men and 9953 women) aged 25 years old or more participated. RESULTS: Smokers more frequently had low leisure time physical activity, low fruits/vegetables intake, and high alcohol consumption than non- and ex-smokers. Frequency of each risk behavior increased steadily with cigarette consumption. Clustering of risk behaviors increased with cigarette consumption in both men and women. For men, the odds ratios of multiple (> or =2) risk behaviors other than smoking, adjusted for age, nationality, and educational level, were 1.14 (95% confidence interval: 0.97, 1.33) for ex-smokers, 1.24 (0.93, 1.64) for light smokers (1-9 cigarettes/day), 1.72 (1.36, 2.17) for moderate smokers (10-19 cigarettes/day), and 3.07 (2.59, 3.64) for heavy smokers (> or =20 cigarettes/day) versus non-smokers. Similar odds ratios were found for women for corresponding groups, i.e., 1.01 (0.86, 1.19), 1.26 (1.00, 1.58), 1.62 (1.33, 1.98), and 2.75 (2.30, 3.29). CONCLUSIONS: Counseling and intervention with smokers should take into account the strong clustering of risk behaviors with level of cigarette consumption.

Clustering compositional data trajectories

Our essay aims at studying suitable statistical methods for the clustering ofcompositional data in situations where observations are constituted by trajectories ofcompositional data, that is, by sequences of composition measurements along a domain.Observed trajectories are known as “functional data” and several methods have beenproposed for their analysis.In particular, methods for clustering functional data, known as Functional ClusterAnalysis (FCA), have been applied by practitioners and scientists in many fields. To ourknowledge, FCA techniques have not been extended to cope with the problem ofclustering compositional data trajectories. In order to extend FCA techniques to theanalysis of compositional data, FCA clustering techniques have to be adapted by using asuitable compositional algebra.The present work centres on the following question: given a sample of compositionaldata trajectories, how can we formulate a segmentation procedure giving homogeneousclasses? To address this problem we follow the steps described below.First of all we adapt the well-known spline smoothing techniques in order to cope withthe smoothing of compositional data trajectories. In fact, an observed curve can bethought of as the sum of a smooth part plus some noise due to measurement errors.Spline smoothing techniques are used to isolate the smooth part of the trajectory:clustering algorithms are then applied to these smooth curves.The second step consists in building suitable metrics for measuring the dissimilaritybetween trajectories: we propose a metric that accounts for difference in both shape andlevel, and a metric accounting for differences in shape only.A simulation study is performed in order to evaluate the proposed methodologies, usingboth hierarchical and partitional clustering algorithm. The quality of the obtained resultsis assessed by means of several indices

Solving Large Location-Allocation problems by Clustering and Simulated Annealing

Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable

Interaction of cationic ultrasmall superparamagnetic iron oxide nanoparticles with human melanoma cells.

Ultrasmall superparamagnetic iron oxide nanoparticles (USPIONs) are currently under development for the intracellular delivery of therapeutics. However, the mechanisms of cellular uptake and the cellular reaction to this uptake, independent of therapeutics, are not well defined. The interactions of biocompatible cationic aminoUSPIONs with human cells was studied in 2D and 3D cultures using biochemical and electron microscopy techniques. AminoUSPIONs were internalized by human melanoma cells in 2D and 3D cultures. Uptake was clathrin mediated and the particles localized in lysosomes, inducing activation of the lysosomal cathepsin D and decreasing the expression of the transferrin receptor in human melanoma cells and/or skin fibroblasts. AminoUSPIONs deeply invaded 3D spheroids of human melanoma cells. Thus, aminoUSPIONs can invade tumors and their uptake by human cells induces cell reaction.

Magnetic and in vitro heating properties of implants formed in situ from injectable formulations and containing superparamagnetic iron oxide nanoparticles (SPIONs) embedded in silica microparticles for magnetically induced local hyperthermia

The biological and therapeutic responses to hyperthermia, when it is envisaged as an anti-tumor treatment modality, are complex and variable. Heat delivery plays a critical role and is counteracted by more or less efficient body cooling, which is largely mediated by blood flow. In the case of magnetically mediated modality, the delivery of the magnetic particles, most often superparamagnetic iron oxide nanoparticles (SPIONs), is also critically involved. We focus here on the magnetic characterization of two injectable formulations able to gel in situ and entrap silica microparticles embedding SPIONs. These formulations have previously shown suitable syringeability and intratumoral distribution in vivo. The first formulation is based on alginate, and the second on a poly(ethylene-co-vinyl alcohol) (EVAL). Here we investigated the magnetic properties and heating capacities in an alternating magnetic field (141 kHz, 12 mT) for implants with increasing concentrations of magnetic microparticles. We found that the magnetic properties of the magnetic microparticles were preserved using the formulation and in the wet implant at 37 degrees C, as in vivo. Using two orthogonal methods, a common SLP (20 Wg(-1)) was found after weighting by magnetic microparticle fraction, suggesting that both formulations are able to properly carry the magnetic microparticles in situ while preserving their magnetic properties and heating capacities. (C) 2010 Elsevier B.V. All rights reserved.

Discourse Type Clustering using POS n-gram Profiles and High-Dimensional Embeddings

Abstract: To cluster textual sequence types (discourse types/modes) in French texts, K-means algorithm with high-dimensional embeddings and fuzzy clustering algorithm were applied on clauses whose POS (part-ofspeech) n-gram profiles were previously extracted. Uni-, bi- and trigrams were used on four 19th century French short stories by Maupassant. For high-dimensional embeddings, power transformations on the chi-squared distances between clauses were explored. Preliminary results show that highdimensional embeddings improve the quality of clustering, contrasting the use of bi and trigrams whose performance is disappointing, possibly because of feature space sparsity.

Conserved chromosomal clustering of genes governed by chromatin regulators in Drosophila

BACKGROUND: The trithorax group (trxG) and Polycomb group (PcG) proteins are responsible for the maintenance of stable transcriptional patterns of many developmental regulators. They bind to specific regions of DNA and direct the post-translational modifications of histones, playing a role in the dynamics of chromatin structure. RESULTS: We have performed genome-wide expression studies of trx and ash2 mutants in Drosophila melanogaster. Using computational analysis of our microarray data, we have identified 25 clusters of genes potentially regulated by TRX. Most of these clusters consist of genes that encode structural proteins involved in cuticle formation. This organization appears to be a distinctive feature of the regulatory networks of TRX and other chromatin regulators, since we have observed the same arrangement in clusters after experiments performed with ASH2, as well as in experiments performed by others with NURF, dMyc, and ASH1. We have also found many of these clusters to be significantly conserved in D. simulans, D. yakuba, D. pseudoobscura and partially in Anopheles gambiae. CONCLUSION: The analysis of genes governed by chromatin regulators has led to the identification of clusters of functionally related genes conserved in other insect species, suggesting this chromosomal organization is biologically important. Moreover, our results indicate that TRX and other chromatin regulators may act globally on chromatin domains that contain transcriptionally co-regulated genes.

Using qualia information to identify lexical semantic classes in an unsupervised clustering task

Acquiring lexical information is a complex problem, typically approached by relying on a number of contexts to contribute information for classification. One of the first issues to address in this domain is the determination of such contexts. The work presented here proposes the use of automatically obtained FORMAL role descriptors as features used to draw nouns from the same lexical semantic class together in an unsupervised clustering task. We have dealt with three lexical semantic classes (HUMAN, LOCATION and EVENT) in English. The results obtained show that it is possible to discriminate between elements from different lexical semantic classes using only FORMAL role information, hence validating our initial hypothesis. Also, iterating our method accurately accounts for fine-grained distinctions within lexical classes, namely distinctions involving ambiguous expressions. Moreover, a filtering and bootstrapping strategy employed in extracting FORMAL role descriptors proved to minimize effects of sparse data and noise in our task.

Clustering of cardiovascular risk factors mimicking the human metabolic syndrome X in eNOS null mice.

AIMS/HYPOTHESIS: The metabolic syndrome comprises a clustering of cardiovascular risk factors but the underlying mechanism is not known. Mice with targeted disruption of endothelial nitric oxide synthase (eNOS) are hypertensive and insulin resistant. We wondered, whether eNOS deficiency in mice is associated with a phenotype mimicking the human metabolic syndrome. METHODS AND RESULTS: In addition to arterial pressure and insulin sensitivity (euglycaemic hyperinsulinaemic clamp), we measured the plasma concentration of leptin, insulin, cholesterol, triglycerides, free fatty acids, fibrinogen and uric acid in 10 to 12 week old eNOS-/- and wild type mice. We also assessed glucose tolerance under basal conditions and following a metabolic stress with a high fat diet. As expected eNOS-/- mice were hypertensive and insulin resistant, as evidenced by fasting hyperinsulinaemia and a roughly 30 percent lower steady state glucose infusion rate during the clamp. eNOS-/- mice had a 1.5 to 2-fold elevation of the cholesterol, triglyceride and free fatty acid plasma concentration. Even though body weight was comparable, the leptin plasma level was 30% higher in eNOS-/- than in wild type mice. Finally, uric acid and fibrinogen were elevated in the eNOS-/- mice. Whereas under basal conditions, glucose tolerance was comparable in knock out and control mice, on a high fat diet, knock out mice became significantly more glucose intolerant than control mice. CONCLUSIONS: A single gene defect, eNOS deficiency, causes a clustering of cardiovascular risk factors in young mice. We speculate that defective nitric oxide synthesis could trigger many of the abnormalities making up the metabolic syndrome in humans.

PhD Students’ Research Group Social Capital in Two Countries: A Clustering Approach with Duocentred Network Measures

The article examines the structure of the collaboration networks of research groups where Slovenian and Spanish PhD students are pursuing their doctorate. The units of analysis are student-supervisor dyads. We use duocentred networks, a novel network structure appropriate for networks which are centred around a dyad. A cluster analysis reveals three typical clusters of research groups. Those which are large and belong to several institutions are labelled under a bridging social capital label. Those which are small, centred in a single institution but have high cohesion are labelled as bonding social capital. Those which are small and with low cohesion are called weak social capital groups. Academic performance of both PhD students and supervisors are highest in bridging groups and lowest in weak groups. Other variables are also found to differ according to the type of research group. At the end, some recommendations regarding academic and research policy are drawn

Clustering of health behaviours in adult survivors of childhood cancer and the general population.

BACKGROUND: Little is known about engagement in multiple health behaviours in childhood cancer survivors. METHODS: Using latent class analysis, we identified health behaviour patterns in 835 adult survivors of childhood cancer (age 20-35 years) and 1670 age- and sex-matched controls from the general population. Behaviour groups were determined from replies to questions on smoking, drinking, cannabis use, sporting activities, diet, sun protection and skin examination. RESULTS: The model identified four health behaviour patterns: 'risk-avoidance', with a generally healthy behaviour; 'moderate drinking', with higher levels of sporting activities, but moderate alcohol-consumption; 'risk-taking', engaging in several risk behaviours; and 'smoking', smoking but not drinking. Similar proportions of survivors and controls fell into the 'risk-avoiding' (42% vs 44%) and the 'risk-taking' cluster (14% vs 12%), but more survivors were in the 'moderate drinking' (39% vs 28%) and fewer in the 'smoking' cluster (5% vs 16%). Determinants of health behaviour clusters were gender, migration background, income and therapy. CONCLUSION: A comparable proportion of childhood cancer survivors as in the general population engage in multiple health-compromising behaviours. Because of increased vulnerability of survivors, multiple risk behaviours should be addressed in targeted health interventions.