Workflow resource pattern modelling and visualization

Workflow patterns have been recognized as the theoretical basis to modeling recurring problems in workflow systems. A form of workflow patterns, known as the resource patterns, characterise the behaviour of resources in workflow systems. Despite the fact that many resource patterns have been discovered, people still preclude them from many workflow system implementations. One of reasons could be obscurityin the behaviour of and interaction between resources and a workflow management system. Thus, we provide a modelling and visualization approach for the resource patterns, enabling a resource behaviour modeller to intuitively see the specific resource patterns involved in the lifecycle of a workitem. We believe this research can be extended to benefit not only workflow modelling, but also other applications, such as model validation, human resource behaviour modelling, and workflow model visualization.

Retrieving information from microblog using pattern mining and relevance feedback

Retrieving information from Twitter is always challenging due to its large volume, inconsistent writing and noise. Most existing information retrieval (IR) and text mining methods focus on term-based approach, but suffers from the problems of terms variation such as polysemy and synonymy. This problem deteriorates when such methods are applied on Twitter due to the length limit. Over the years, people have held the hypothesis that pattern-based methods should perform better than term-based methods as it provides more context, but limited studies have been conducted to support such hypothesis especially in Twitter. This paper presents an innovative framework to address the issue of performing IR in microblog. The proposed framework discover patterns in tweets as higher level feature to assign weight for low-level features (i.e. terms) based on their distributions in higher level features. We present the experiment results based on TREC11 microblog dataset and shows that our proposed approach significantly outperforms term-based methods Okapi BM25, TF-IDF and pattern based methods, using precision, recall and F measures.

Scoring-thresholding pattern based text classifier

A big challenge for classification on text is the noisy of text data. It makes classification quality low. Many classification process can be divided into two sequential steps scoring and threshold setting (thresholding). Therefore to deal with noisy data problem, it is important to describe positive feature effectively scoring and to set a suitable threshold. Most existing text classifiers do not concentrate on these two jobs. In this paper, we propose a novel text classifier with pattern-based scoring that describe positive feature effectively, followed by threshold setting. The thresholding is based on score of training set, make it is simple to implement in other scoring methods. Experiment shows that our pattern-based classifier is promising.

Interobserver variability of radiation therapists aligning to fiducial markers for prostate radiation therapy

As the use of fiducial markers (FMs) for the localisation of the prostate during external beam radiation therapy (EBRT) has become part of routine practice, radiation therapists (RTs) have become increasingly responsible for online image interpretation. The aim of this investigation was to quantify the limits of agreement (LoA) between RTs when localising to FMs with orthogonal kilovoltage (kV) imaging. Methods Six patients receiving prostate EBRT utilising FMs were included in this study. Treatment localisation was performed using kV imaging prior to each fraction. Online stereoscopic assessment of FMs, performed by the treating RTs, was compared with the offline assessment by three RTs. Observer agreement was determined by pairwise Bland-Altman analysis. Results Stereoscopic analysis of 225 image pairs was performed online at the time of treatment, and offline by three RT observers. Eighteen pairwise Bland-Altman analyses were completed to assess the level of agreement between observers. Localisation by RTs was found to be within clinically acceptable 95% LoAs. Conclusions Small differences between RTs, in both the online and offline setting, were found to be within clinically acceptable limits. RTs were able to make consistent and reliable judgements when matching FMs on planar kV imaging.

Fabrication and characterisation of graphene oxide-epoxy nanocomposite

Adequate amount of graphene oxide (GO) was firstly prepared by oxidation of graphite and GO/epoxy nanocomposites were subsequently prepared by typical solution mixing technique. X-ray diffraction (XRD) pattern, X-ray photoelectron (XPS), Raman and Fourier transform infrared (FTIR) spectroscopy indicated the successful preparation of GO. Scanning electron microscopy (SEM) and Transmission electron microscopy (TEM) images of the graphite oxide showed that they consist of a large amount of graphene oxide platelets with a curled morphology containing of a thin wrinkled sheet like structure. AFM image of the exfoliated GO signified that the average thickness of GO sheets is ~1.0 nm which is very similar to GO monolayer. Mechanical properties of as prepared GO/epoxy nanocomposites were investigated. Significant improvements in both Young’s modulus and tensile strength were observed for the nanocomposites at very low level of GO loading. The Young’s modulus of the nanocomposites containing 0.5 wt% GO was 1.72 GPa, which was 35 % higher than that of the pure epoxy resin (1.28 GPa). The effective reinforcement of the GO based epoxy nanocomposites can be attributed to the good dispersion and the strong interfacial interactions between the GO sheets and the epoxy resin matrices.

A pattern based two-stage text classifier

In a classification problem typically we face two challenging issues, the diverse characteristic of negative documents and sometimes a lot of negative documents that are closed to positive documents. Therefore, it is hard for a single classifier to clearly classify incoming documents into classes. This paper proposes a novel gradual problem solving to create a two-stage classifier. The first stage identifies reliable negatives (negative documents with weak positive characteristics). It concentrates on minimizing the number of false negative documents (recall-oriented). We use Rocchio, an existing recall based classifier, for this stage. The second stage is a precision-oriented “fine tuning”, concentrates on minimizing the number of false positive documents by applying pattern (a statistical phrase) mining techniques. In this stage a pattern-based scoring is followed by threshold setting (thresholding). Experiment shows that our statistical phrase based two-stage classifier is promising.

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.

Fabrication and mechanical and thermal behaviour of graphene oxide/epoxy nanocomposites

Bulk amount of graphite oxide was prepared by oxidation of graphite using the modified Hummers method and its ultrasonication in organic solvents yielded graphene oxide (GO). X-ray diffraction (XRD) pattern, X-ray photoelectron (XPS), Raman and Fourier transform infrared (FTIR) spectroscopy indicated the successful preparation of GO. XPS survey spectrum of GO revealed the presence of 66.6 at% C and 30.4 at% O. Scanning electron microscopy (SEM) and Transmission electron microscopy (TEM) images of the graphene oxide showed that they consist of a large amount of graphene oxide platelets with a curled morphology containing of a thin wrinkled sheet like structure. AFM image of the exfoliated GO signified that the average thickness of GO sheets is ~1.0 nm which is very similar to GO monolayer. GO/epoxy nanocomposites were prepared by typical solution mixing technique and influence of GO on mechanical and thermal properties of nanocomposites were investigated. As for the mechanical behaviour of GO/epoxy nanocomposites, 0.5 wt% GO in the nanocomposite achieved the maximum increase in the elastic modulus (~35%) and tensile strength (~7%). The TEM analysis provided clear image of microstructure with homogeneous dispersion of GO in the polymer matrix. The improved strength properties of GO/epoxy nanocomposites can be attributed to inherent strength of GO, the good dispersion and the strong interfacial interactions between the GO sheets and the polymer matrix. However, incorporation of GO showed significant negative effect on composite glass transition temperature (Tg). This may arise due to the interference of GO on curing reaction of epoxy.

Accurate pattern prediction of very closely spaced mismatched antennas using measured active element patterns

A method for prediction of the radiation pattern of N strongly coupled antennas with mismatched sources is presented. The method facilitates fast and accurate design of compact arrays. The prediction is based on the measured N-port S parameters of the coupled antennas and the N active element patterns measured in a 50 ω environment. By introducing equivalent power sources, the radiation pattern with excitation by sources with arbitrary impedances and various decoupling and matching networks (DMN) can be accurately predicted without the need for additional measurements. Two experiments were carried out for verification: pattern prediction for parasitic antennas with different loads and for antennas with DMN. The difference between measured and predicted patterns was within 1 to 2 dB.

Daytime pattern of post-exercise protein intake affects whole-body protein turnover in resistance-trained males

Background The pattern of protein intake following exercise may impact whole-body protein turnover and net protein retention. We determined the effects of different protein feeding strategies on protein metabolism in resistance-trained young men. Methods: Participants were randomly assigned to ingest either 80g of whey protein as 8x10g every 1.5h (PULSE; n=8), 4x20g every 3h (intermediate, INT; n=7), or 2x40g every 6h (BOLUS; n=8) after an acute bout of bilateral knee extension exercise (4x10 repetitions at 80% maximal strength). Whole-body protein turnover (Q), synthesis (S), breakdown (B), and net balance (NB) were measured throughout 12h of recovery by a bolus ingestion of [ 15N]glycine with urinary [15N]ammonia enrichment as the collected end-product. Results PULSE Q rates were greater than BOLUS (?19%, P<0.05) with a trend towards being greater than INT (?9%, P=0.08). Rates of S were 32% and 19% greater and rates of B were 51% and 57% greater for PULSE as compared to INT and BOLUS, respectively (P<0.05), with no difference between INT and BOLUS. There were no statistical differences in NB between groups (P=0.23); however, magnitude-based inferential statistics revealed likely small (mean effect90%CI; 0.590.87) and moderate (0.800.91) increases in NB for PULSE and INT compared to BOLUS and possible small increase (0.421.00) for INT vs. PULSE. Conclusion We conclude that the pattern of ingested protein, and not only the total daily amount, can impact whole-body protein metabolism. Individuals aiming to maximize NB would likely benefit from repeated ingestion of moderate amounts of protein (?20g) at regular intervals (?3h) throughout the day.

Expression pattern of the cannabinoid receptor genes in the frontal cortex of mood disorder patients and mice selectively bred for high and low fear

Although the endocannabinoid system (ECS) has been implicated in brain development and various psychiatric disorders, precise mechanisms of the ECS on mood and anxiety disorders remain unclear. Here, we have investigated developmental and disease-related expression pattern of the cannabinoid receptor 1 (CB1) and the cannabinoid receptor 2 (CB2) genes in the dorsolateral prefrontal cortex (PFC) of humans. Using mice selectively bred for high and low fear, we further investigated potential association between fear memory and the cannabinoid receptor expression in the brain. The CB1, not the CB2, mRNA levels in the PFC gradually decrease during postnatal development ranging in age from birth to 50 years (r 2 > 0.6 & adj. p < 0.05). The CB1 levels in the PFC of major depression patients were higher when compared to the age-matched controls (adj. p < 0.05). In mice, the CB1, not the CB2, levels in the PFC were positively correlated with freezing behavior in classical fear conditioning (p < 0.05). These results suggest that the CB1 in the PFC may play a significant role in regulating mood and anxiety symptoms. Our study demonstrates the advantage of utilizing data from postmortem brain tissue and a mouse model of fear to enhance our understanding of the role of the cannabinoid receptors in mood and anxiety disorders