Nonlinear complexity analysis of brain fMRI signals in schizophrenia

Peer reviewedPublisher PD

Profiling bacterial communities associated with sediment-based aquaculture bioremediation systems under contrasting redox regimes

Deposit-feeding invertebrates are proposed bioremediators in microbial-driven sediment-based aquaculture effluent treatment systems. We elucidate the role of the sediment reduction-oxidation (redox) regime in structuring benthic bacterial communities, having direct implications for bioremediation potential and deposit-feeder nutrition. The sea cucumber Holothuria scabra was cultured on sediments under contrasting redox regimes; fully oxygenated (oxic) and redox stratified (oxic-anoxic). Taxonomically, metabolically and functionally distinct bacterial communities developed between the redox treatments with the oxic treatment supporting the greater diversity; redox regime and dissolved oxygen levels were the main environmental drivers. Oxic sediments were colonised by nitrifying bacteria with the potential to remediate nitrogenous wastes. Percolation of oxygenated water prevented the proliferation of anaerobic sulphate-reducing bacteria, which were prevalent in the oxic-anoxic sediments. At the predictive functional level, bacteria within the oxic treatment were enriched with genes associated with xenobiotics metabolism. Oxic sediments showed the greater bioremediation potential; however, the oxic-anoxic sediments supported a greater sea cucumber biomass. Overall, the results indicate that bacterial communities present in fully oxic sediments may enhance the metabolic capacity and bioremediation potential of deposit-feeder microbial systems. This study highlights the benefits of incorporating deposit-feeding invertebrates into effluent treatment systems, particularly when the sediment is oxygenated

Variance in brain volume with advancing age: implications for defining the limits of normality

Background: Statistical models of normal ageing brain tissue volumes may support earlier diagnosis of increasingly common, yet still fatal, neurodegenerative diseases. For example, the statistically defined distribution of normal ageing brain tissue volumes may be used as a reference to assess patient volumes. To date, such models were often derived from mean values which were assumed to represent the distributions and boundaries, i.e. percentile ranks, of brain tissue volume. Since it was previously unknown, the objective of the present study was to determine if this assumption was robust, i.e. whether regression models derived from mean values accurately represented the distributions and boundaries of brain tissue volume at older ages. Materials and Methods: We acquired T1-w magnetic resonance (MR) brain images of 227 normal and 219 Alzheimer’s disease (AD) subjects (aged 55-89 years) from publicly available databanks. Using nonlinear regression within both samples, we compared mean and percentile rank estimates of whole brain tissue volume by age. Results: In both the normal and AD sample, mean regression estimates of brain tissue volume often did not accurately represent percentile rank estimates (errors=-74% to 75%). In the normal sample, mean estimates generally underestimated differences in brain volume at percentile ranks below the mean. Conversely, in the AD sample, mean estimates generally underestimated differences in brain volume at percentile ranks above the mean. Differences between ages at the 5th percentile rank of normal subjects were ~39% greater than mean differences in the AD subjects. Conclusions: While more data are required to make true population inferences, our results indicate that mean regression estimates may not accurately represent the distributions of ageing brain tissue volumes. This suggests that percentile rank estimates will be required to robustly define the limits of brain tissue volume in normal ageing and neurodegenerative disease