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.

Development of an ultra-thin fibroin membrane for RPE cell transplantation

Purpose: One of the challenges associated with cell-based therapies for repairing the retina is the development of suitable materials on which to grow and transplant retinal cells. Using the ARPE-19 cell line, we have previously demonstrated the feasibility of growing RPE-derived cells on membranes prepared from the silk protein fibroin. The present study was aimed at developing a porous, ultra-thin fibroin membrane that might better support development of apical-basal polarity in culture, and to extend this work to primary cultures of human RPE cells. Methods: Ultra-thin fibroin membranes were prepared using a highly polished casting table coated with Topas® (a cyclic olefin copolymer) and a 1:0.03 aqueous solution of fibroin and PEO (Mv 900 000 g/mol). Following drying, the membranes were water annealed to make them water-stable, washed in water to remove PEO, sterilised by treatment with 95% ethanol, and washed extensively in saline. Primary cultures containing human RPE cells were established from donor posterior eye cups and maintained in DMEM/F12 medium supplemented with 10% fetal bovine serum and antibiotics. First passage cultures were seeded onto fibroin membranes pre-coated with vitronectin and grown for 6 weeks in medium supplemented with 1% serum. Comparative cultures were established on porous 1.0 µm pore PET membrane (Millipore) and using ARPE-19 cells. Results: The fibroin membranes displayed an average thickness of 3 µm and contained numerous dimples/pore-like structures of up to 3-5 µm in diameter. The primary cultures predominantly contained pigmented epithelial cells, but mesenchymal cells (presumed fibroblasts) were also often present. Passaged cultures appeared to attach equally well to either fibroin or PET membranes. Over time cells on either material adopted a more cobblestoned morphology. Conclusions: Progress has been made towards developing a porous ultra-thin fibroin membrane that supports cultivation of RPE cells. Further studies are required to determine the degree of membrane permeability and RPE polarity.

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.

Advantages of tandem LC-MS for the rapid assessment of tissue-specific metabolic complexity using a pentafluorophenylpropyl stationary phase

In this study, a tandem LC-MS (Waters Xevo TQ) MRM-based MS method was developed for rapid, broad profiling of hydrophilic metabolites from biological samples, in either positive or negative ion modes without the need for an ion pairing reagent, using a reversed-phase pentafluorophenylpropyl (PFPP) column. The developed method was successfully applied to analyze various biological samples from C57BL/6 mice, including urine, duodenum, liver, plasma, kidney, heart, and skeletal muscle. As result, a total 112 of hydrophilic metabolites were detected within 8 min of running time to obtain a metabolite profile of the biological samples. The analysis of this number of hydrophilic metabolites is significantly faster than previous studies. Classification separation for metabolites from different tissues was globally analyzed by PCA, PLS-DA and HCA biostatistical methods. Overall, most of the hydrophilic metabolites were found to have a "fingerprint" characteristic of tissue dependency. In general, a higher level of most metabolites was found in urine, duodenum, and kidney. Altogether, these results suggest that this method has potential application for targeted metabolomic analyzes of hydrophilic metabolites in a wide ranges of biological samples.

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.

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.

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.

Advances in the systemic therapy of malignant pleural mesothelioma

Malignant pleural mesothelioma is an aggressive thoracic malignancy associated with exposure to asbestos, and its incidence is anticipated to increase during the first half of this century. Chemotherapy is the mainstay of treatment, yet sufficiently robust evidence to substantiate the current standard of care has emerged only in the past 5 years. This Review summarizes the evidence supporting the clinical activity of chemotherapy, discusses the use of end points for its assessment and examines the influence of clinical and biochemical prognostic factors on the natural history of malignant pleural mesothelioma. Early-phase clinical trials of second-line and novel agents are emerging from an increased understanding of mesothelioma cell biology. Coupled with high-quality translational research, such developments have real potential to improve the outlook of patients at a time of increasing incidence.

In arrayed ranks array technology in the study of mesothelioma steven

Mesothelioma is a rare malignancy arising from mesothelial cells lining the pleura and peritoneum. Advances in modern technology have allowed the development of array based approaches to the study of disease allowing researchers the opportunity to study many genes or proteins in a high-throughput fashion. This review describes the current knowledge surrounding array based approaches with respect to mesothelioma research. © 2009 by the International Association for the Study of Lung Cancer.

Cyclooxygenase-2 protein levels are independent of epidermal growth factor receptor expression or activation in operable non-small cell lung cancer

Both cyclooxygenase (COX)-2 and epidermal growth factor receptor (EGFR) are thought to play important roles in the pathogenesis of non-small cell lung cancer (NSCLC). A number of in vitro studies have postulated a link between EGFR activation and subsequent COX-2 upregulation. The relationship between these factors has not been established in patients with NSCLC. COX-2 and EGFR expression were studied in 172 NSCLC specimens using standard immunohistochemical techniques. Western blotting was used to determine COX-2 and EGFR levels in five NSCLC cell lines. The effect of treatment with EGF on COX-2 expression in A549 cells was assessed. Results: Both EGFR and COX-2 are overexpressed in NSCLC. The predominant pattern of COX-2 and EGFR staining was cytoplasmic. Membranous EGFR staining was seen in 23.3% of cases. There was no relationship between COX-2 and EGFR expression and survival or any clinicopathological features. No correlation was seen between EGFR expression and COX-2 expression in the immunohistochemical series or in the cell lines. Treatment with EGF did not upregulate COX-2 levels in A549 cells, either in serum free or serum-supplemented conditions. Conclusions: Although COX-2 and EGFR are over-expressed in NSCLC neither was of prognostic significance in this series of cases. There is no correlation between these two factors in either tumour samples or cell lines. Although these factors show no correlation in NSCLC, they remain potential, though independent targets for treatment. © 2004 Elsevier Ireland Ltd. All rights reserved.

Quantitative proteomic approach to study subcellular localization of membrane proteins

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.