Oxygen transport in soil and the vertical distribution of roots

An analytical solution for steady-state oxygen transport in soils including 2 sink terms, viz roots and microbes with the corresponding vertical distribution scaling lengths forming a ratio p, showed p governed the critical air-filled porosity, θ_c, needed by most plants. For low temperature and p, θ_c was <0.1 but at higher temperatures and p = 1, θ_c was >0.15 m³/m³. When root length density at the surface was 10⁴ m/m³ and p > 3, θ_c was 0.25 m³/m³, more than half the pore space. Few combinations of soil and climate regularly meet this condition. However, for sandy soils and seasonally warm, arid regions, the theory is consistent with observation, in that plants may have some deep roots. Critical θ_c values are used to formulate theoretical solutions in a forward mode, so different levels of oxygen uptake by roots may be compared to microbial activity. The proportion of respiration by plant roots increases rapidly with p up to p ≈2.

Some novel techniques of parameter estimation for the dynamical models in biological systems

Inverse problems based on using experimental data to estimate unknown parameters of a system often arise in biological and chaotic systems. In this paper, we consider parameter estimation in systems biology involving linear and non-linear complex dynamical models, including the Michaelis–Menten enzyme kinetic system, a dynamical model of competence induction in Bacillus subtilis bacteria and a model of feedback bypass in B. subtilis bacteria. We propose some novel techniques for inverse problems. Firstly, we establish an approximation of a non-linear differential algebraic equation that corresponds to the given biological systems. Secondly, we use the Picard contraction mapping, collage methods and numerical integration techniques to convert the parameter estimation into a minimization problem of the parameters. We propose two optimization techniques: a grid approximation method and a modified hybrid Nelder–Mead simplex search and particle swarm optimization (MH-NMSS-PSO) for non-linear parameter estimation. The two techniques are used for parameter estimation in a model of competence induction in B. subtilis bacteria with noisy data. The MH-NMSS-PSO scheme is applied to a dynamical model of competence induction in B. subtilis bacteria based on experimental data and the model for feedback bypass. Numerical results demonstrate the effectiveness of our approach.

A Krylov-based finite state projection algorithm for solving the chemical master equation arising in the discrete modelling of biological systems

Biochemical reactions underlying genetic regulation are often modelled as a continuous-time, discrete-state, Markov process, and the evolution of the associated probability density is described by the so-called chemical master equation (CME). However the CME is typically difficult to solve, since the state-space involved can be very large or even countably infinite. Recently a finite state projection method (FSP) that truncates the state-space was suggested and shown to be effective in an example of a model of the Pap-pili epigenetic switch. However in this example, both the model and the final time at which the solution was computed, were relatively small. Presented here is a Krylov FSP algorithm based on a combination of state-space truncation and inexact matrix-vector product routines. This allows larger-scale models to be studied and solutions for larger final times to be computed in a realistic execution time. Additionally the new method computes the solution at intermediate times at virtually no extra cost, since it is derived from Krylov-type methods for computing matrix exponentials. For the purpose of comparison the new algorithm is applied to the model of the Pap-pili epigenetic switch, where the original FSP was first demonstrated. Also the method is applied to a more sophisticated model of regulated transcription. Numerical results indicate that the new approach is significantly faster and extendable to larger biological models.

A software tool for simulating spatial separation and kinetics in biological membranes

Self-segregation and compartimentalisation are observed experimentally to occur spontaneously on live membranes as well as reconstructed model membranes. It is believed that many of these processes are caused or supported by anomalous diffusive behaviours of biomolecules on membranes due to the complex and heterogeneous nature of these environments. These phenomena are on the one hand of great interest in biology, since they may be an important way for biological systems to selectively localize receptors, regulate signaling or modulate kinetics; and on the other, they provide an inspiration for engineering designs that mimick natural systems. We present an interactive software package we are developing for the purpose of simulating such processes numerically using a fundamental Monte Carlo approach. This program includes the ability to simulate kinetics and mass transport in the presence of either mobile or immobile obstacles and other relevant structures such as liquid-ordered lipid microdomains. We also present preliminary simulation results regarding the selective spatial localization and chemical kinetics modulating power of immobile obstacles on the membrane, obtained using the program.

But does it work? Effectiveness of scientific visualisations in high school chemistry and physics instruction.

Scientific visualisations such as computer-based animations and simulations are increasingly a feature of high school science instruction. Visualisations are adopted enthusiastically by teachers and embraced by students, and there is good evidence that they are popular and well received. There is limited evidence, however, of how effective they are in enabling students to learn key scientific concepts. This paper reports the results of a quantitative study conducted in Australian physics and chemistry classrooms. In general there was no statistically significant difference between teaching with and without visualisations, however there were intriguing differences around student sex and academic ability.

One approach to finding evidence for the effectiveness of scientific visualisations in high school physics and chemistry education

Enormous amounts of money and energy are being devoted to the development, use and organisation of computer-based scientific visualisations (e.g. animations and simulations) in science education. It seems plausible that visualisations that enable students to gain visual access to scientific phenomena that are too large, too small or occur too quickly or too slowly to be seen by the naked eye, or to scientific concepts and models, would yield enhanced conceptual learning. When the literature is searched, however, it quickly becomes apparent that there is a dearth of quantitative evidence for the effectiveness of scientific visualisations in enhancing students’ learning of science concepts. This paper outlines an Australian project that is using innovative research methodology to gather evidence on this question in physics and chemistry classrooms.

Velocity-jump models with crowding effects

Velocity jump processes are discrete random walk models that have many applications including the study of biological and ecological collective motion. In particular, velocity jump models are often used to represent a type of persistent motion, known as a “run and tumble”, which is exhibited by some isolated bacteria cells. All previous velocity jump processes are non-interacting, which means that crowding effects and agent-to-agent interactions are neglected. By neglecting these agent-to-agent interactions, traditional velocity jump models are only applicable to very dilute systems. Our work is motivated by the fact that many applications in cell biology, such as wound healing, cancer invasion and development, often involve tissues that are densely packed with cells where cell-to-cell contact and crowding effects can be important. To describe these kinds of high cell density problems using a velocity jump process we introduce three different classes of crowding interactions into a one-dimensional model. Simulation data and averaging arguments lead to a suite of continuum descriptions of the interacting velocity jump processes. We show that the resulting systems of hyperbolic partial differential equations predict the mean behavior of the stochastic simulations very well.

Biological and environmental determinants

- describe the complex web of determinants as part of broad causal pathways that affect health - identify and discuss the range of physical, biological and environmental determinants that impact on health - suggest why it is important to the practice of public health that you understand how determinants contribute to health - understand the complexity of health and illness and the multifaceted role of health determinants - relate determinants of health to public health activity and realise the need for multisectoral action and multiple approaches when working to improve health

Vacuum cleaner emissions as a source of indoor exposure to airborne particles and bacteria

Vacuuming can be a source of indoor exposure to biological and non-biological aerosols, although there is little data that describes the magnitude of emissions from the vacuum cleaner itself. We therefore sought to quantify emission rates of particles and bacteria from a large group of vacuum cleaners and investigate their potential determinants, including temperature, dust bags, exhaust filters, price and age. Emissions of particles between 0.009 and 20 µm and bacteria were measured from 21 vacuums. Ultrafine (<100 nm) particle emission rates ranged from 4.0 × 10^6 to 1.1 × 10^11 particles min-1. Emission of 0.54 to 20 µm particles ranged from 4.0 × 10^4 to 1.2 × 10^9 particles min-1. PM2.5 emissions were between 2.4 × 10-1 and 5.4 × 10^3 µg min-1. Bacteria emissions ranged from 0 to 7.4 × 10^5 bacteria min-1 and were poorly correlated with dust bag bacteria content and particle emissions. Large variability in emission of all parameters was observed across the 21 vacuums we assessed, which was largely not attributable to the range of determinant factors we assessed. Vacuum cleaner emissions contribute to indoor exposure to non-biological and biological aerosols when vacuuming, and this may vary markedly depending on the vacuum used.

Neuroprotective effects of low-dose lithium in individuals at ultra-high risk for psychosis. A longitudinal MRI/MRS study

Objectives: To investigate if low-dose lithium may counteract the microstructural and metabolic brain changes proposed to occur in individuals at ultra-high risk (UHR) for psychosis. Methods: Hippocampal T2 relaxation time (HT2RT) and proton magnetic resonance spectroscopy (1H-MRS) measurements were performed prior to initiation and following three months of treatment in 11 UHR patients receiving low-dose lithium and 10 UHR patients receiving treatment as usual (TAU). HT2RT and 1H-MRS percentage change scores between scans were compared using one-way ANOVA and correlated with behavioural change scores. Results: Low-dose lithium significantly reduced HT2RT compared to TAU (p=0.018). No significant group by time effects were seen for any brain metabolites as measured with 1H-MRS, although myo-inositol, creatine, choline-containing compounds and NAA increased in the group receiving low-dose lithium and decreased or remained unchanged in subjects receiving TAU. Conclusions: This pilot study suggests that low-dose lithium may protect the microstructure of the hippocampus in UHR states as reflected by significantly decreasing HT2RT. Larger scale replication studies in UHR states using T2 relaxation time as a proxy for emerging brain pathology seem a feasible mean to test neuroprotective strategies such as low-dose lithium as potential treatments to delay or even prevent the progression to full-blown disorder.