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Interpretive and predictive tools are needed to assist in the understanding of cell invasion processes. Cell invasion involves cell motility and proliferation, and is central to many biological processes including developmental morphogenesis and tumor invasion. Experimental data can be collected across a wide range of scales, from the population scale to the individual cell scale. Standard continuum or discrete models used in isolation are insufficient to capture this wide range of data. We develop a discrete cellular automata model of invasion with experimentally motivated rules. The cellular automata algorithm is applied to a narrow two-dimensional lattice and simulations reveal the formation of invasion waves moving with constant speed. The simulation results are averaged in one dimension-these data are used to identify the time history of the leading edge to characterize the population-scale wave speed. This allows the relationship between the population-scale wave speed and the cell-scale parameters to be determined. This relationship is analogous to well-known continuum results for Fisher's equation. The cellular automata algorithm also produces individual cell trajectories within the invasion wave that are analogous to cell trajectories obtained with new experimental techniques. Our approach allows both the cell-scale and population-scale properties of invasion to be predicted in a way that is consistent with multiscale experimental data. Furthermore we suggest that the cellular automata algorithm can be used in conjunction with individual data to overcome limitations associated with identifying cell motility mechanisms using continuum models alone.

BackgroundDuring September 2009, a large dust storm was experienced in Sydney, New South Wales, Australia. Extremely high levels of particulate matter were recorded, with daily average levels of coarse matter (<10 μm) peaking over 11,000 μg/m3 and fine (<2.5 μm) over 1,600 μg/m3. We conducted an analysis to determine whether the dust storm was associated with increases in all-cause, cardiovascular, respiratory and asthma-related emergency department presentations and hospital admissions.MethodsWe used distributed-lag Poisson generalized models to analyse the emergency department presentations and hospital admissions adjusted for pollutants, humidity, temperature and day of week and seasonal effects to obtain estimates of relative risks associated with the dust storm.ResultsThe dust storm period was associated with large increases in asthma emergency department visits (relative risk 1.23, 95% confidence interval 1.10-1.38, p < 0.01), and to a lesser extent, all emergency department visits (relative risk 1.04, 95% confidence interval 1.03-1.06, p < 0.01) and respiratory emergency department visits (relative risk 1.20, 95% confidence interval 1.15-1.26, p < 0.01). There was no significant increase in cardiovascular emergency department visits (p = 0.09) or hospital admissions for any reason. Age-specific analyses showed the dust storm was associated with increases in all-cause and respiratory emergency department visits in the ≥65 year age group; the ≤5 year group had higher risks of all-cause, respiratory and asthma-related emergency department presentations.ConclusionsWe recommend public health measures, especially targeting asthmatics, should be implemented during future dust storm events.

Inconsistent and incomplete outcome reporting may make estimates of treatment effects from published randomized trials unreliable. We aimed to determine outcome reporting practices and source of differences in reporting quality among randomized trials of primary immunosuppression in kidney transplantation. We searched the Cochrane Renal Group's Specialized Register, 2000–2012, specified four core outcomes we expected trials to report, and recorded if and how completely each was reported. We identified 179 trials. One hundred sixty‐eight (94%) reported death, 145 (81%) as number dead and 119 (66%) as time to death. One hundred sixty‐five (92%) reported graft loss, 158 (88%) as number with graft loss and 127 (71%) as time to graft loss. One hundred twenty‐one (68%) reported creatinine and 114 (64%) estimated GFR (eGFR). One hundred forty‐one (79%) provided complete reports of number dead, 95 (53%) censored and 99 (55%) uncensored number with graft loss. Seventy‐three (41%) provided complete reports of time to death, 67 (37%) censored and 31 (17%) uncensored time to graft loss. Complete reporting of graft function was infrequent: 62 (35%) eGFR and 50 (28%) creatinine. All four outcomes were reported in any form in 61 (34%) and completely in 28 (16%) trials. No single trial or journal characteristic was consistently associated with complete outcome reporting. Outcome reporting in kidney transplant trials is inconsistent and frequently incomplete, and published estimates of treatment effects may be unreliable.

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BackgroundLittle is known about the potential of syndromic surveillance to provide early warning of pertussis outbreaks. We conducted a time series analysis to assess whether an emergency department (ED) cough syndrome would respond to changes in the incidence of pertussis in children aged under 10 years in New South Wales (NSW), Australia, and to evaluate the timing of any association. A further aim was to assess the lag between the onset of pertussis symptoms and case notification in the infectious diseases surveillance system in NSW.MethodsUsing routinely collected data, we prepared a daily count time series of visits to NSW EDs assigned a provisional diagnosis of cough. Separate daily series were prepared for three independent variables: notifications of cases of pertussis and influenza and ED visits with bronchiolitis (a proxy measure of respiratory syncytial virus (RSV) infection). The study period was 1/1/2007-31/12/2010. A negative binomial multivariate model was used to assess associations between the outcome and independent variables. We also evaluated the median delay in days between the estimated onset of a case of pertussis and the date the local public health authority was notified of that case.ResultsWhen notified pertussis increased by 10 cases in one day, ED visits with cough increased by 5.2% (95% confidence interval (CI): 0.5%-10.0%) seven days later. Daily increases in the other independent variables had a smaller impact on cough visits. When notified influenza increased by 10 cases in one day, ED visits with cough increased by 0.8% (95% CI: 0%-1.7%) seven days later. When ED visits with bronchiolitis increased by 10 visits in one day, ED visits with cough increased by 4.8% (95% CI: 1.2%-8.6%) one day earlier. The median interval between estimated onset of pertussis and case notification was seven days.ConclusionsPertussis appears to be an important driver of ED visits with cough in children aged under 10 years. However, the median delay in notification of cases of pertussis was similar to the lag in the pertussis-associated short-term increases in ED visits with cough. Elevations in RSV and influenza activity may also explain increases in the ED cough syndrome. Real time monitoring of ED visits with cough in children is therefore unlikely to consistently detect a potential outbreak of pertussis before passive surveillance.

Sample size calculations before conducting a health study or clinical trial are important to provide evidence that the proposed study is capable of detecting real associations between study factors. This review aims to clarify statistical issues related to the calculation of sample sizes and is illustrated with an example of a recent study design to improve health outcomes related to water and sewage in NSW Aboriginal communities. The effect of power, significance level and effect size on sample size are discussed. Calculations of sample sizes for individual-based studies are modified for more complex trial designs by multiplying individual-based estimates by an inflationary factor.