17 research outputs found
Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter
Three or more copies of the proteolipid protein gene PLP1 cause severe Pelizaeus-Merzbacher disease
We describe five boys from different families with an atypically severe form of Pelizaeus-Merzbacher disease (PMD) who have three, and in one case, five copies of the proteolipid protein (PLP1) gene. This is the first report of more than two copies of PLP1 in PMD patients and clearly demonstrates that severe clinical symptoms are associated with increased PLP1 gene dosage. Previously, duplications, deletions and mutations of the PLP1 gene were reported to give rise to this X-linked disorder. Patients with PLP1 duplication are usually classified as having either classical or transitional PMD rather than the more rare severe connatal form. The clinical symptoms of the five patients in this study included lack of stable head control and severe mental retardation, with three having severe paroxysmal disorder and two dying before the first year of life. Gene dosage was determined using interphase FISH (fluorescence in situ hybridization) and the novel approach of multiple ligation probe amplification (MLPA). We found FISH unreliable for dosage detection above the level of a duplication and MLPA to be more accurate in determination of specific copy number. Our finding that three or more copies of the gene give rise to a more severe phenotype is in agreement with observations in transgenic mice where severity of disease increased with Plp1 gene dosage and level of overexpression. The patient with five copies of PLP1 was not more affected than those with a triplication, suggesting that there is possibly a limit to the level of severity or that other genetic factors influence the phenotype. It highlights the significance of PLP1 dosage in CNS myelinogenesis as well as the importance of accurate determination of PLP1 gene copy number in the diagnosis of PMD and carrier detection.</p
Feeding in Snakes: Form, Function, and Evolution of the Feeding System
International audienc
The transition from spark to arc discharge and its implications with respect to nanoparticle production
A discrete element implementation for concrete: particle generation, contact calculations, packing under gravity and modeling material response
Do lizards and snakes really differ in their ability to take large prey? A study of relative prey mass and feeding tactics in lizards
A mechanistic Individual-based Model of microbial communities
Accurate predictive modelling of the growth of microbial communities requires the credible representation of the interactions of biological, chemical and mechanical processes. How-ever, although biological and chemical processes are represented in a number of Individual-based Models (IbMs) the interaction of growth and mechanics is limited. Conversely, there are mechanically sophisticated IbMs with only elementary biology and chemistry. This study focuses on addressing these limitations by developing a flexible IbM that can robustly com-bine the biological, chemical and physical processes that dictate the emergent properties of a wide range of bacterial communities. This IbM is developed by creating a microbiological adaptation of the open source Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). This innovation should provide the basis for " bottom up " prediction of the emer-gent behaviour of entire microbial systems. In the model presented here, bacterial growth, division, decay, mechanical contact among bacterial cells, and adhesion between the bacte-ria and extracellular polymeric substances are incorporated. In addition, fluid-bacteria inter-action is implemented to simulate biofilm deformation and erosion. The model predicts that the surface morphology of biofilms becomes smoother with increased nutrient concentra-tion, which agrees well with previous literature. In addition, the results show that increased shear rate results in smoother and more compact biofilms. The model can also predict shear rate dependent biofilm deformation, erosion, streamer formation and breakup