Sending Your Grandparents to University Increases Cognitive Reserve: The Tasmanian Healthy Brain Project.

Increasing an individual’s level of cognitive reserve (CR) has been suggested as a nonpharmacological approach to reducing the risk for Alzheimer’s disease. We examined changes in CR in older adults participating over 4 years in the Tasmanian Healthy Brain Project. Method: A sample of 459 healthy older adults between 50 and 79 years of age underwent a comprehensive annual assessment of current CR, neuropsychological function, and psychosocial factors over a 4-year period. The intervention group of 359 older adults (M � 59.61 years, SD � 6.67) having completed a minimum of 12 months part-time university study were compared against a control reference group of 100 adults (M � 62.49 years, SD � 6.24) who did not engage in further education. Results: Growth mixture modeling demonstrated that 44.3% of the control sample showed no change in CR, whereas 92.5% of the further education participants displayed a significant linear increase in CR over the 4 years of the study. These results indicate that older adults engaging in high-level mental stimulation display an increase in CR over a 4-year period. Conclusion: Increasing mental activity in older adulthood may be a viable strategy to improve cognitive function and offset cognitive decline associated with normal aging

Beyond clustering: mean-field dynamics on networks with arbitrary subgraph composition

Clustering is the propensity of nodes that share a common neighbour to be connected. It is ubiquitous in many networks but poses many modelling challenges. Clustering typically manifests itself by a higher than expected frequency of triangles, and this has led to the principle of constructing networks from such building blocks. This approach has been generalised to networks being constructed from a set of more exotic subgraphs. As long as these are fully connected, it is then possible to derive mean-field models that approximate epidemic dynamics well. However, there are virtually no results for non-fully connected subgraphs. In this paper, we provide a general and automated approach to deriving a set of ordinary differential equations, or mean-field model, that describes, to a high degree of accuracy, the expected values of system-level quantities, such as the prevalence of infection. Our approach offers a previously unattainable degree of control over the arrangement of subgraphs and network characteristics such as classical node degree, variance and clustering. The combination of these features makes it possible to generate families of networks with different subgraph compositions while keeping classical network metrics constant. Using our approach, we show that higher-order structure realised either through the introduction of loops of different sizes or by generating networks based on different subgraphs but with identical degree distribution and clustering, leads to non-negligible differences in epidemic dynamics

Animating the Carbon Cycle

This a post-print, author-produced version of an article accepted for publication in Ecosystems. Copyright © 2013 Springer Science+Business Media New York. The final publication is available at Springer via http://dx.doi.org/10.1007/s10021-013-9715-7Understanding the biogeochemical processes regulating carbon cycling is central to mitigating atmospheric CO2 emissions. The role of living organisms has been accounted for, but the focus has traditionally been on contributions of plants and microbes. We develop the case that fully “animating” the carbon cycle requires broader consideration of the functional role of animals in mediating biogeochemical processes and quantification of their effects on carbon storage and exchange among terrestrial and aquatic reservoirs and the atmosphere. To encourage more hypothesis-driven experimental research that quantifies animal effects we discuss the mechanisms by which animals may affect carbon exchanges and storage within and among ecosystems and the atmosphere. We illustrate how those mechanisms lead to multiplier effects whose magnitudes may rival those of more traditional carbon storage and exchange rate estimates currently used in the carbon budget. Many animal species are already directly managed. Thus improved quantitative understanding of their influence on carbon budgets may create opportunity for management and policy to identify and implement new options for mitigating CO2 release at regional scales.US National Science FoundationNERCBBSRCNippon Foundatio

Mapping structural diversity in networks sharing a given degree distribution and global clustering: Adaptive resolution grid search evolution with Diophantine equation-based mutations

Methods that generate networks sharing a given degree distribution and global clustering can induce changes in structural properties other than that controlled for. Diversity in structural properties, in turn, can affect the outcomes of dynamical processes operating on those networks. Since exhaustive sampling is not possible, we propose a novel evolutionary framework for mapping this structural diversity. The three main features of this framework are: (a) subgraph-based encoding of networks, (b) exact mutations based on solving systems of Diophantine equations, and (c) heuristic diversity-driven mechanism to drive resolution changes in the MapElite algorithm.We show that our framework can elicit networks with diversity in their higher-order structure and that this diversity affects the behaviour of the complex contagion model. Through a comparison with state of the art clustered network generation methods, we demonstrate that our approach can uncover a comparably diverse range of networks without needing computationally unfeasible mixing times. Further, we suggest that the subgraph-based encoding provides greater confidence in the diversity of higher-order network structure for low numbers of samples and is the basis for explaining our results with complex contagion model. We believe that this framework could be applied to other complex landscapes that cannot be practically mapped via exhaustive sampling

Tyrosine Phosphorylation of Tau by the Src Family Kinases Lck and Fyn

<p>Abstract</p> <p>Background</p> <p>Tau protein is the principal component of the neurofibrillary tangles found in Alzheimer's disease, where it is hyperphosphorylated on serine and threonine residues, and recently phosphotyrosine has been demonstrated. The Src-family kinase Fyn has been linked circumstantially to the pathology of Alzheimer's disease, and shown to phosphorylate Tyr18. Recently another Src-family kinase, Lck, has been identified as a genetic risk factor for this disease.</p> <p>Results</p> <p>In this study we show that Lck is a tau kinase. <it>In vitro</it>, comparison of Lck and Fyn showed that while both kinases phosphorylated Tyr18 preferentially, Lck phosphorylated other tyrosines somewhat better than Fyn. In co-transfected COS-7 cells, mutating any one of the five tyrosines in tau to phenylalanine reduced the apparent level of tau tyrosine phosphorylation to 25-40% of that given by wild-type tau. Consistent with this, tau mutants with only one remaining tyrosine gave poor phosphorylation; however, Tyr18 was phosphorylated better than the others.</p> <p>Conclusions</p> <p>Fyn and Lck have subtle differences in their properties as tau kinases, and the phosphorylation of tau is one mechanism by which the genetic risk associated with Lck might be expressed pathogenically.</p

Microbial regulation of the L cell transcriptome.

L cells are an important class of enteroendocrine cells secreting hormones such as glucagon like peptide-1 and peptide YY that have several metabolic and physiological effects. The gut is home to trillions of bacteria affecting host physiology, but there has been limited understanding about how the microbiota affects gene expression in L cells. Thus, we rederived the reporter mouse strain, GLU-Venus expressing yellow fluorescent protein under the control of the proglucagon gene, as germ-free (GF). Lpos cells from ileum and colon of GF and conventionally raised (CONV-R) GLU-Venus mice were isolated and subjected to transcriptomic profiling. We observed that the microbiota exerted major effects on ileal L cells. Gene Ontology enrichment analysis revealed that microbiota suppressed biological processes related to vesicle localization and synaptic vesicle cycling in Lpos cells from ileum. This finding was corroborated by electron microscopy of Lpos cells showing reduced numbers of vesicles as well as by demonstrating decreased intracellular GLP-1 content in primary cultures from ileum of CONV-R compared with GF GLU-Venus mice. By analysing Lpos cells following colonization of GF mice we observed that the greatest transcriptional regulation was evident within 1 day of colonization. Thus, the microbiota has a rapid and pronounced effect on the L cell transcriptome, predominantly in the ileum

Brain age predicts mortality

Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, ‘brain-predicted age’, derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death