Computer simulations of seasonal outbreak and diurnal vertical migration of cyanobacteria

Algal blooms caused by cyanobacteria are characterized by two features with different time scales: one is seasonal outbreak and collapse of a bloom and the other is diurnal vertical migration. Our two-component mathematical model can simulate both phenomena, in which the state variables are nutrients and cyanobacteria. The model is a set of one-dimensional reaction-advection-diffusion equations, and temporal changes of these two variables are regulated by the following five factors: (1) annual variation of light intensity, (2) diurnal variation of light intensity, (3) annual variation of water temperature, (4) thermal stratification within a water column and (5) the buoyancy regulation mechanism. The seasonal change of cyanobacteria biomass is mainly controlled by factors, (1), (3) and (4), among which annual variations of light intensity and water temperature directly affect the maximum growth rate of cyanobacteria. The latter also contributes to formation of the thermocline during the summer season. Thermal stratification causes a reduction in vertical diffusion and largely prevents mixing of both nutrients and cyanobacteria between the epilimnion and the hypolimnion. Meanwhile, the other two factors, (2) and (5), play a significant role in diurnal vertical migration of cyanobacteria. A key mechanism of vertical migration is buoyancy regulation due to gas-vesicle synthesis and ballast formation, by which a quick reversal between floating and sinking becomes possible within a water column. The mechanism of bloom formation controlled by these five factors is integrated into the one-dimensional model consisting of two reaction-advection-diffusion equations.

Computer-assisted pathological immunohistochemistry scoring is more time-effective than conventional scoring, but provides no analytical advantage

Computer-assisted pathological immunohistochemistry scoring is more time-effective than conventional scoring, but provides no analytical advantage

Do general practitioners change how they use the computer during consultations with a significant psychological component?

objectives: To describe the patterns of computer use during patient visits to family doctors and to determine whether doctors alter their pattern of computer use in consultations which have significant psychological content.

Computer simulating a trial of a load-bearing implant: example of an intramedullary prosthesis

Computational modelling is becoming ever more important for obtaining regulatory approval for new medical devices. An accepted approach is to infer performance in a population from an analysis conducted for an idealised or ‘average’ patient; we present here a method for predicting the performance of an orthopaedic implant when released into a population—effectively simulating a clinical trial. Specifically we hypothesise that an analysis based on a method for predicting the performance in a population will lead to different conclusions than an analysis based on an idealised or ‘average’ patient. To test this hypothesis we use a finite element model of an intramedullary implant in a bone whose size and remodelling activity is different for each individual in the population. We compare the performance of a low Young’s modulus implant (View the MathML source) to one with a higher Young’s modulus (200 GPa). Cyclic loading is applied and failure is assumed when the migration of the implant relative to the bone exceeds a threshold magnitude. The analysis for an idealised of ‘average’ patient predicts that the lower modulus device survives longer whereas the analysis simulating a clinical trial predicts no statistically-significant tendency (p=0.77) for the low modulus device to perform better. It is concluded that population-based simulations of implant performance–simulating a clinical trial–present a very valuable opportunity for more realistic computational pre-clinical testing of medical devices.