Comparing methods for modelling spreading cell fronts

Spreading cell fronts play an essential role in many physiological processes. Classically, models of this process are based on the Fisher-Kolmogorov equation; however, such continuum representations are not always suitable as they do not explicitly represent behaviour at the level of individual cells. Additionally, many models examine only the large time asymptotic behaviour, where a travelling wave front with a constant speed has been established. Many experiments, such as a scratch assay, never display this asymptotic behaviour, and in these cases the transient behaviour must be taken into account. We examine the transient and asymptotic behaviour of moving cell fronts using techniques that go beyond the continuum approximation via a volume-excluding birth-migration process on a regular one-dimensional lattice. We approximate the averaged discrete results using three methods: (i) mean-field, (ii) pair-wise, and (iii) one-hole approximations. We discuss the performace of these methods, in comparison to the averaged discrete results, for a range of parameter space, examining both the transient and asymptotic behaviours. The one-hole approximation, based on techniques from statistical physics, is not capable of predicting transient behaviour but provides excellent agreement with the asymptotic behaviour of the averaged discrete results, provided that cells are proliferating fast enough relative to their rate of migration. The mean-field and pair-wise approximations give indistinguishable asymptotic results, which agree with the averaged discrete results when cells are migrating much more rapidly than they are proliferating. The pair-wise approximation performs better in the transient region than does the mean-field, despite having the same asymptotic behaviour. Our results show that each approximation only works in specific situations, thus we must be careful to use a suitable approximation for a given system, otherwise inaccurate predictions could be made.

Fast and accurate business process drift detection

Business processes are prone to continuous and unexpected changes. Process workers may start executing a process differently in order to adjust to changes in workload, season, guidelines or regulations for example. Early detection of business process changes based on their event logs – also known as business process drift detection – enables analysts to identify and act upon changes that may otherwise affect process performance. Previous methods for business process drift detection are based on an exploration of a potentially large feature space and in some cases they require users to manually identify the specific features that characterize the drift. Depending on the explored feature set, these methods may miss certain types of changes. This paper proposes a fully automated and statistically grounded method for detecting process drift. The core idea is to perform statistical tests over the distributions of runs observed in two consecutive time windows. By adaptively sizing the window, the method strikes a trade-off between classification accuracy and drift detection delay. A validation on synthetic and real-life logs shows that the method accurately detects typical change patterns and scales up to the extent it is applicable for online drift detection.

Bitboard methods for games

Bitboards allow the efficient encoding of games for computer play and the application of fast bitwiseparallel algorithms for common game-related operations. This article describes: (1) a selection of bitboard techniques including an introduction to bitboards and bitwise operations; (2) a classification scheme that distinguishes filter, query and update methods, and; (3) a sampling of bitboard algorithms for a range of games other than chess, with notes on their performance and practical application.

Colonoscopy preparation in children: Safety, efficacy, and tolerance of high- versus low-volume cleansing methods

Background: The use of large-volume electrolyte balanced solutions as preparation for colonoscopy often results in poor patient compliance and acceptance. The tolerance, safety, and efficacy of high-versus low volume colon-cleansing methods as preparation for colonoscopy in children were compared by randomized operator-blinded trial. Methods: Twenty-nine children ages 3.6-14.6 years had either high-volume nasogastric balanced polyethylene glycol electrolyte lavage (20 ml/kg/h) until the effluent was clear (n = 15), or two oral doses of sodium phosphate solution (22.5-45 ml) separated by oral fluid intake (n = 14). Results: Both preparations were equally effective. The low-volume preparation was better tolerated and caused less discomfort that the high-volume preparation, judging by serial nurse observations. The incidence of abdominal symptoms, diarrhea, sleep disturbance, and vomiting was not significantly different between the two groups. Both groups had a small reduction in mean hematocrit and serum calcium levels. The sodium phosphate preparation caused increases in mean serum sodium concentrations from 140 to 145 mmol/L and serum phosphate concentrations from 1.41 to 2.53 mmol/L. Ten hours after the commencement of the preanesthetic fast, these concentrations had returned to normal. Conclusions: There are advantages in terms of tolerance, discomfort, and case of administration with acceptable colonic cleansing with the use of the less-invasive oral sodium phosphate low-volume colon-cleansing preparation in children. Safe use requires ensuring an adequate oral fluid intake during the preparation time and avoidance of use in patients with renal insufficiency.

A simple and fast two-locus quality control test to detect false positives due to batch effects in genome-wide association studies

The impact of erroneous genotypes having passed standard quality control (QC) can be severe in genome-wide association studies, genotype imputation, and estimation of heritability and prediction of genetic risk based on single nucleotide polymorphisms (SNP). To detect such genotyping errors, a simple two-locus QC method, based on the difference in test statistic of association between single SNPs and pairs of SNPs, was developed and applied. The proposed approach could detect many problematic SNPs with statistical significance even when standard single SNP QC analyses fail to detect them in real data. Depending on the data set used, the number of erroneous SNPs that were not filtered out by standard single SNP QC but detected by the proposed approach varied from a few hundred to thousands. Using simulated data, it was shown that the proposed method was powerful and performed better than other tested existing methods. The power of the proposed approach to detect erroneous genotypes was approximately 80% for a 3% error rate per SNP. This novel QC approach is easy to implement and computationally efficient, and can lead to a better quality of genotypes for subsequent genotype-phenotype investigations.

FreeS: A fast algorithm to discover frequent free subtrees using a novel canonical form

Web data can often be represented in free tree form; however, free tree mining methods seldom exist. In this paper, a computationally fast algorithm FreeS is presented to discover all frequently occurring free subtrees in a database of labelled free trees. FreeS is designed using an optimal canonical form, BOCF that can uniquely represent free trees even during the presence of isomorphism. To avoid enumeration of false positive candidates, it utilises the enumeration approach based on a tree-structure guided scheme. This paper presents lemmas that introduce conditions to conform the generation of free tree candidates during enumeration. Empirical study using both real and synthetic datasets shows that FreeS is scalable and significantly outperforms (i.e. few orders of magnitude faster than) the state-of-the-art frequent free tree mining algorithms, HybridTreeMiner and FreeTreeMiner.

Inspiration sources for Australian fast fashion design: Tapping into consumer desire

Purpose The research purpose was to identify both the inspiration sources used by fast fashion designers and ways the designers sort information from the sources during the product development process. Design/methodology/approach This is a qualitative study, drawing on semi-structured interviews conducted with the members of the in-house design teams of three Australian fast fashion companies. Findings Australian fast fashion designers rely on a combination of trend data, sales data, product analysis and travel for design development ideas. The designers then use the consensus and embodiment methods to interpret and synthesise information from those inspiration sources. Research limitations/implications The empirical data used in the analysis were limited by interviewing fashion designers within only three Australian companies. Originality/value This research augments knowledge of fast fashion product development, in particular designers’ methods and approaches to product design within a volatile and competitive market.

Clinical Accuracy of the MedGem Indirect Calorimeter for Measuring Resting Energy Expenditure in Cancer Patients

OBJECTIVE: To compare, in patients with cancer and in healthy subjects, measured resting energy expenditure (REE) from traditional indirect calorimetry to a new portable device (MedGem) and predicted REE. DESIGN: Cross-sectional clinical validation study. SETTING: Private radiation oncology centre, Brisbane, Australia. SUBJECTS: Cancer patients (n = 18) and healthy subjects (n = 17) aged 37-86 y, with body mass indices ranging from 18 to 42 kg/m(2). INTERVENTIONS: Oxygen consumption (VO(2)) and REE were measured by VMax229 (VM) and MedGem (MG) indirect calorimeters in random order after a 12-h fast and 30-min rest. REE was also calculated from the MG without adjustment for nitrogen excretion (MGN) and estimated from Harris-Benedict prediction equations. Data were analysed using the Bland and Altman approach, based on a clinically acceptable difference between methods of 5%. RESULTS: The mean bias (MGN-VM) was 10% and limits of agreement were -42 to 21% for cancer patients; mean bias -5% with limits of -45 to 35% for healthy subjects. Less than half of the cancer patients (n = 7, 46.7%) and only a third (n = 5, 33.3%) of healthy subjects had measured REE by MGN within clinically acceptable limits of VM. Predicted REE showed a mean bias (HB-VM) of -5% for cancer patients and 4% for healthy subjects, with limits of agreement of -30 to 20% and -27 to 34%, respectively. CONCLUSIONS: Limits of agreement for the MG and Harris Benedict equations compared to traditional indirect calorimetry were similar but wide, indicating poor clinical accuracy for determining the REE of individual cancer patients and healthy subjects.