Genotoxicity of inorganic lead salts and disturbance of microtubule function

Lead compounds are known genotoxicants, principally affecting the integrity of chromosomes. Lead chloride and lead acetate induced concentration-dependent increases in micronucleus frequency in V79 cells, starting at 1.1 μM lead chloride and 0.05 μM lead acetate. The difference between the lead salts, which was expected based on their relative abilities to form complex acetato-cations, was confirmed in an independent experiment. CREST analyses of the micronuclei verified that lead chloride and acetate were predominantly aneugenic (CREST-positive response), which was consistent with the morphology of the micronuclei (larger micronuclei, compared with micronuclei induced by a clastogenic mechanism). The effects of high concentrations of lead salts on the microtubule network of V79 cells were also examined using immunofluorescence staining. The dose effects of these responses were consistent with the cytotoxicity of lead(II), as visualized in the neutral-red uptake assay. In a cell-free system, 20-60 μM lead salts inhibited tubulin assembly dose-dependently. The no-observed-effect concentration of lead(II) in this assay was 10 μM. This inhibitory effect was interpreted as a shift of the assembly/disassembly steady-state toward disassembly, e.g., by reducing the concentration of assembly-competent tubulin dimers. The effects of lead salts on microtubule-associated motor-protein functions were studied using a kinesin-gliding assay that mimics intracellular transport processes in vitro by quantifying the movement of paclitaxel-stabilized microtubules across a kinesin-coated glass surface. There was a dose-dependent effect of lead nitrate on microtubule motility. Lead nitrate affected the gliding velocities of microtubules starting at concentrations above 10 μM and reached half-maximal inhibition of motility at about 50 μM. The processes reported here point to relevant interactions of lead with tubulin and kinesin at low dose levels.

Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1–28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.

Epigenetics and migraine; complex mitochondrial interactions contributing to disease susceptibility

Migraine is a common neurological disorder classified by the World Health Organisation (WHO) as one of the top twenty most debilitating diseases in the developed world. Current therapies are only effective for a proportion of sufferers and new therapeutic targets are desperately needed to alleviate this burden. Recently the role of epigenetics in the development of many complex diseases including migraine has become an emerging topic. By understanding the importance of acetylation, methylation and other epigenetic modifications, it then follows that this modification process is a potential target to manipulate epigenetic status with the goal of treating disease. Bisulphite sequencing and methylated DNA immunoprecipitation have been used to demonstrate the presence of methylated cytosines in the human D-loop of mitochondrial DNA (mtDNA), proving that the mitochondrial genome is methylated. For the first time, it has been shown that there is a difference in mtDNA epigenetic status between healthy controls and those with disease, especially for neurodegenerative and age related conditions. Given co-morbidities with migraine and the suggestive link between mitochondrial dysfunction and the lowered threshold for triggering a migraine attack, mitochondrial methylation may be a new avenue to pursue. Creative thinking and new approaches are needed to solve complex problems and a systems biology approach, where multiple layers of information are integrated is becoming more important in complex disease modelling.

A frontier functions approach to optimal scales of sustainable production

This paper translates the concepts of sustainable production to three dimensions of economic, environmental and ecological sustainability to analyze optimal production scales by solving optimizing problems. Economic optimization seeks input-output combinations to maximize profits. Environmental optimization searches for input-output combinations that minimize the polluting effects of materials balance on the surrounding environment. Ecological optimization looks for input-output combinations that minimize the cumulative destruction of the entire ecosystem. Using an aggregate space, the framework illustrates that these optimal scales are often not identical because markets fail to account for all negative externalities. Profit-maximizing firms normally operate at the scales which are larger than optimal scales from the viewpoints of environmental and ecological sustainability; hence policy interventions are favoured. The framework offers a useful tool for efficiency studies and policy implication analysis. The paper provides an empirical investigation using a data set of rice farms in South Korea.

Functions and therapeutic roles of exosomes in cancer

The role of exosomes in cancer development has become the focus of much research, due to the many emerging roles possessed by exosomes. These micro-vesicles that are ubiquitously released in to the extracellular milieu, have been found to regulate immune system function, particularly in tumorigenesis, as well as conditioning future metastatic sites for the attachment and growth of tumor tissue. Through an interaction with a range of host tissue, exosomes are able to generate a pro-tumor environment that is essential for carcinogenesis. Herein, we discuss the contents of exosomes and their contribution to tumorigenesis, as well as their role in chemotherapeutic resistance and the development of novel cancer treatments and the identification of cancer biomarkers.

Critical linguistics in the early years : exploring functions of language through sophisticated picture books and process drama strategies

In this chapter we describe a critical fairytales unit taught to 4.5 to 5.5 year olds in a context of intensifying pressure to raise literacy achievement. The unit was infused with lessons on reinterpreted fairytales followed by process drama activities built around a sophisticated picture book, Beware of the Bears (MacDonald, 2004). The latter entailed a text analytic approach to critical literacy derived from systemic functional linguistics (Halliday, 1978; Halliday & Matthiessen, 2004). This approach provides a way of analysing how words and discourse are used to represent the world in a particular way and shape reader relations with the author in a particular field (Janks, 2010).

Computation of Mathematical Models for Complex Industrial Processes

Designed for undergraduate and postgraduate students, academic researchers and industrial practitioners, this book provides comprehensive case studies on numerical computing of industrial processes and step-by-step procedures for conducting industrial computing. It assumes minimal knowledge in numerical computing and computer programming, making it easy to read, understand and follow. Topics discussed include fundamentals of industrial computing, finite difference methods, the Wavelet-Collocation Method, the Wavelet-Galerkin Method, High Resolution Methods, and comparative studies of various methods. These are discussed using examples of carefully selected models from real processes of industrial significance. The step-by-step procedures in all these case studies can be easily applied to other industrial processes without a need for major changes and thus provide readers with useful frameworks for the applications of engineering computing in fundamental research problems and practical development scenarios.