Profiling event logs to configure risk indicators for process delays

Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators(PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company.

A data mining driven crash risk profiling method for road asset management

This thesis takes a new data mining approach for analyzing road/crash data by developing models for the whole road network and generating a crash risk profile. Roads with an elevated crash risk due to road surface friction deficit are identified. The regression tree model, predicting road segment crash rate, is applied in a novel deployment coined regression tree extrapolation that produces a skid resistance/crash rate curve. Using extrapolation allows the method to be applied across the network and cope with the high proportion of missing road surface friction values. This risk profiling method can be applied in other domains.

Controlling buoyancy-driven profiling floats for applications in ocean observation

Establishing a persistent presence in the ocean with an autonomous underwater vehicle (AUV) capable of observing temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we examine the utility of vehicles that can only control their depth in the water column for such extended deployments. We present a strategy that utilizes ocean model predictions to facilitate a basic level of autonomy and enables general control for these profiling floats. The proposed method is based on experimentally validated techniques for utilizing ocean current models to control autonomous gliders. With the appropriate vertical actuation, and utilizing spatio–temporal variations in water speed and direction, we show that general controllability results can be met. First, we apply an A* planner to a local controllability map generated from predictions of ocean currents. This computes a path between start and goal waypoints that has the highest likelihood of successful execution. A computed depth plan is generated with a model-predictive controller (MPC), and selects the depths for the vehicle so that ambient currents guide it toward the goal. Mission constraints are included to simulate and motivate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA, USA, that show encouraging results in the ability of a drifting vehicle to reach a desired location.

A data mining driven risk profiling method for road asset management

Road surface skid resistance has been shown to have a strong relationship to road crash risk, however, applying the current method of using investigatory levels to identify crash prone roads is problematic as they may fail in identifying risky roads outside of the norm. The proposed method analyses a complex and formerly impenetrable volume of data from roads and crashes using data mining. This method rapidly identifies roads with elevated crash-rate, potentially due to skid resistance deficit, for investigation. A hypothetical skid resistance/crash risk curve is developed for each road segment, driven by the model deployed in a novel regression tree extrapolation method. The method potentially solves the problem of missing skid resistance values which occurs during network-wide crash analysis, and allows risk assessment of the major proportion of roads without skid resistance values.

Development of an integrated metabolomic profiling approach for infectious diseases research

Metabolomic profiling offers direct insights into the chemical environment and metabolic pathway activities at sites of human disease. During infection, this environment may receive important contributions from both host and pathogen. Here we apply an untargeted metabolomics approach to identify compounds associated with an E. coli urinary tract infection population. Correlative and structural data from minimally processed samples were obtained using an optimized LC-MS platform capable of resolving ~2300 molecular features. Principal component analysis readily distinguished patient groups and multiple supervised chemometric analyses resolved robust metabolomic shifts between groups. These analyses revealed nine compounds whose provisional structures suggest candidate infection-associated endocrine, catabolic, and lipid pathways. Several of these metabolite signatures may derive from microbial processing of host metabolites. Overall, this study highlights the ability of metabolomic approaches to directly identify compounds encountered by, and produced from, bacterial pathogens within human hosts.

Metabolic urinary profiling of alcohol hepatotoxicity and intervention effects of Yin Chen Hao Tang in rats using ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry

This paper was designed to study metabonomic characters of the hepatotoxicity induced by alcohol and the intervention effects of Yin Chen Hao Tang (YCHT), a classic traditional Chinese medicine formula for treatment of jaundice and liver disorders in China. Urinary samples from control, alcohol- and YCHT-treated rats were analyzed by ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) in positive ionization mode. The total ion chromatograms obtained from the control, alcohol- and YCHT-treated rats were easily distinguishable using a multivariate statistical analysis method such as the principal components analysis (PCA). The greatest difference in metabolic profiling was observed from alcohol-treated rats compared with the control and YCHT-treated rats. The positive ions m/z 664.3126 (9.00 min) was elevated in urine of alcohol-treated rats, whereas, ions m/z 155.3547 (10.96 min) and 708.2932 (9.01 min) were at a lower concentration compared with that in urine of control rats, however, these ions did not indicate a statistical difference between control rats and YCHT-treated rats. The ion m/z 664.3126 was found to correspond to ceramide (d18:1/25:0), providing further support for an involvement of the sphingomyelin signaling pathway in alcohol hepatotoxicity and the intervention effects of YCHT.

Metabolite profiling and pathway analysis of acute hepatitis rats by UPLC-ESI MS combined with pattern recognition methods

BACKGROUND & AIMS Metabolomics is comprehensive analysis of low-molecular-weight endogenous metabolites in a biological sample. It could enable mapping of perturbations of early biochemical changes in diseases and hence provide an opportunity to develop predictive biomarkers that could provide valuable insights into the mechanisms of diseases. The aim of this study was to elucidate the changes in endogenous metabolites and to phenotype the metabolic profiling of d-galactosamine (GalN)-inducing acute hepatitis in rats by UPLC-ESI MS. METHODS The systemic biochemical actions of GalN administration (ip, 400 mg/kg) have been investigated in male wistar rats using conventional clinical chemistry, liver histopathology and metabolomic analysis of UPLC- ESI MS of urine. The urine was collected predose (-24 to 0 h) and 0-24, 24-48, 48-72, 72-96 h post-dose. Mass spectrometry of the urine was analysed visually and via conjunction with multivariate data analysis. RESULTS Results demonstrated that there was a time-dependent biochemical effect of GalN dosed on the levels of a range of low-molecular-weight metabolites in urine, which was correlated with developing phase of the GalN-inducing acute hepatitis. Urinary excretion of beta-hydroxybutanoic acid and citric acid was decreased following GalN dosing, whereas that of glycocholic acid, indole-3-acetic acid, sphinganine, n-acetyl-l-phenylalanine, cholic acid and creatinine excretion was increased, which suggests that several key metabolic pathways such as energy metabolism, lipid metabolism and amino acid metabolism were perturbed by GalN. CONCLUSION This metabolomic investigation demonstrates that this robust non-invasive tool offers insight into the metabolic states of diseases.

What are the offence and offender risk factors for Indigenous repeat drink drivers in Queensland?

In Australia and internationally, there is scant information about Indigenous repeat drink drivers. The aim was to identify the risk factors associated with repeat offending. De-identified data on drink driving convictions by offenders identifying as Indigenous in Queensland between 2006 and 2010 were examined. A range of univariate analyses were used to compare first time and repeat offenders on gender, age, court location and region (based on the accessibility/remoteness index of Australia), blood alcohol concentration and sentencing severity. Multivariate logistic regression adjusted for confounding variables. Convictions for repeat offenders were more likely from locations other than ‘major cities’ with the association strongest for courts in the ‘very remote’ region (OR=2.75, 2.06-3.76, p<.001). Indigenous offenders 40 years or older were found to be at reduced risk in comparison to offenders aged 15-24 years (OR=0.68, 0.54-0.86, p=0.01). After controlling for confounding factors, gender, sentencing severity and blood alcohol concentration levels were not significantly associated with recidivism. The association of recidivism and remoteness is consistent with higher rates of alcohol-related transport accidents involving Indigenous Australians in isolated areas. This study provides a platform for future research and allows for early attempts to address the need for intervention to reduce Indigenous drink driving recidivism.

Integrative genomic profiling reveals conserved genetic mechanisms for tumorigenesis in common entities of non-Hodgkin's lymphoma

Recent developments in genomic technologies have resulted in increased understanding of pathogenic mechanisms and emphasized the importance of central survival pathways. Here, we use a novel bioinformatic based integrative genomic profiling approach to elucidate conserved mechanisms of lymphomagenesis in the three commonest non-Hodgkin's lymphoma (NHL) entities: diffuse large B-cell lymphoma, follicular lymphoma, and B-cell chronic lymphocytic leukemia. By integrating genome-wide DNA copy number analysis and transcriptome profiling of tumor cohorts, we identified genetic lesions present in each entity and highlighted their likely target genes. This revealed a significant enrichment of components of both the apoptosis pathway and the mitogen activated protein kinase pathway, including amplification of the MAP3K12 locus in all three entities, within the set of genes targeted by genetic alterations in these diseases. Furthermore, amplification of 12p13.33 was identified in all three entities and found to target the FOXM1 oncogene. Amplification of FOXM1 was subsequently found to be associated with an increased MYC oncogenic signaling signature, and siRNA-mediated knock-down of FOXM1 resulted in decreased MYC expression and induced G2 arrest. Together, these findings underscore genetic alteration of the MAPK and apoptosis pathways, and genetic amplification of FOXM1 as conserved mechanisms of lymphomagenesis in common NHL entities. Integrative genomic profiling identifies common central survival mechanisms and highlights them as attractive targets for directed therapy.

What are the offence and offender risk factors for Indigenous repeat drink drivers in Queensland Australia?

Background Road crashes contribute to high injury rates in Indigenous population in Aust, NZ, Canada and USA Alcohol one of the leading risk factors for road crashes: 31.5% Indigenous drivers in remote areas over 0.05mg/100ml, compared to 7.4 percent of non-Indigenous counterparts 17.5% Indigenous drivers in regional areas over 0.05mg/100ml compared to 3.8 percent of non-Indigenous counterparts Indigenous peoples are overrepresented in alcohol-related fatality rates, with this group 40% more likely to be killed

Gene expression profiling in human breast cancer – toward personalised therapeutics?

The most integrated approach toward understanding the multiple molecular events and mechanisms by which cancer may develop is the application of gene expression profiling using microarray technologies. As molecular alterations in breast cancer are complex and involve cross-talk between multiple cellular signalling pathways, microarray technology provides a means of capturing and comparing the expression patterns of the entire genome across multiple samples in a high throughput manner. Since the development of microarray technologies, together with the advances in RNA extraction methodologies, gene expression studies have revolutionised the means by which genes suitable as targets for drug development and individualised cancer treatment can be identified. As of the mid-1990s, expression microarrays have been extensively applied to the study of cancer and no cancer type has seen as much genomic attention as breast cancer. The most abundant area of breast cancer genomics has been the clarification and interpretation of gene expression patterns that unite both biological and clinical aspects of tumours. It is hoped that one day molecular profiling will transform diagnosis and therapeutic selection in human breast cancer toward more individualised regimes. Here, we review a number of prominent microarray profiling studies focussed on human breast cancer and examine their strengths, their limitations, clinical implications including prognostic relevance and gene signature significance along with potential improvements for the next generation of microarray studies.

Secretome and degradome profiling shows that Kallikrein-related peptidases 4, 5, 6, and 7 induce TGFβ-1 signaling in ovarian cancer cells

Kallikrein-related peptidases, in particular KLK4, 5, 6 and 7 (4-7), often have elevated expression levels in ovarian cancer. In OV-MZ-6 ovarian cancer cells, combined expression of KLK4-7 reduces cell adhesion and increases cell invasion and resistance to paclitaxel. The present work investigates how KLK4-7 shape the secreted proteome ("secretome") and proteolytic profile ("degradome") of ovarian cancer cells. The secretome comparison consistently identified >900 proteins in three replicate analyses. Expression of KLK4-7 predominantly affected the abundance of proteins involved in cell-cell communication. Among others, this includes increased levels of transforming growth factor β-1 (TGFβ-1). KLK4-7 co-transfected OV-MZ-6 cells share prominent features of elevated TGFβ-1 signaling, including increased abundance of neural cell adhesion molecule L1 (L1CAM). Augmented levels of TGFβ-1 and L1CAM upon expression of KLK4-7 were corroborated in vivo by an ovarian cancer xenograft model. The degradomic analysis showed that KLK4-7 expression mostly affected cleavage sites C-terminal to arginine, corresponding to the preference of kallikreins 4, 5 and 6. Putative kallikrein substrates include chemokines, such as growth differentiation factor 15 (GDF 15) and macrophage migration inhibitory factor (MIF). Proteolytic maturation of TGFβ-1 was also elevated. KLK4-7 have a pronounced, yet non-degrading impact on the secreted proteome, with a strong association between these proteases and TGFβ-1 signaling in tumor biology. © 2013 Federation of European Biochemical Societies.

Integrating time forgetting mechanisms into topic-based user interest profiling

The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

Elucidating the host–pathogen interaction between human colorectal cells and invading Enterovirus 71 using transcriptomics profiling

Enterovirus 71 (EV71) is one of the main etiological agents for Hand, Foot and Mouth Disease (HFMD) and has been shown to be associated with severe clinical manifestation. Currently, there is no antiviral therapeutic for the treatment of HFMD patients owing to a lack of understanding of EV71 pathogenesis. This study seeks to elucidate the transcriptomic changes that result from EV71 infection. Human whole genome microarray was employed to monitor changes in genomic profiles between infected and uninfected cells. The results reveal altered expression of human genes involved in critical pathways including the immune response and the stress response. Together, data from this study provide valuable insights into the host–pathogen interaction between human colorectal cells and EV71.