Phenotypic flexibility and the evolution of organismal design

Evolutionary biologists often use phenotypic differences between species and between individuals to gain an understanding of organismal design. The focus of much recent attention has been on developmental plasticity – the environmentally induced variability during development within a single genotype. The phenotypic variation expressed by single reproductively mature organisms throughout their life, traditionally the subject of many physiological studies, has remained underexploited in evolutionary biology. Phenotypic flexibility, the reversible within-individual variation, is a function of environmental conditions varying predictably (e.g. with season), or of more stochastic fluctuations in the environment. Here, we provide a common framework to bring the different categories of phenotypic plasticity together, and emphasize perspectives on adaptation that reversible types of plasticity might provide. We argue that better recognition and use of the various levels of phenotypic variation will increase the scope for phenotypic experimentation, comparison and integration.

Optimal data pooling for shared learning in maintenance operations

This paper addresses the benefits of pooling data for shared learning in maintenance operations. We consider a set of systems subject to Poisson degradation that are coupled through an a-priori unknown rate. Decision problems involving these systems are high-dimensional Markov decision processes (MDPs). We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. We leverage this decomposition to demonstrate that pooling data can lead to significant cost reductions compared to not pooling

Optimal data pooling for shared learning in maintenance operations

We study optimal data pooling for shared learning in two common maintenance operations: condition-based maintenance and spare parts management. We consider systems subject to Poisson input – the degradation or demand process – that are coupled through an unknown rate. Decision problems for these systems are high-dimensional Markov decision processes (MDPs) and are thus notoriously difficult to solve. We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. Leveraging this decomposition, we (i) show that pooling data can lead to significant cost reductions compared to not pooling, and (ii) prove that the optimal policy for the condition-based maintenance problem is a control limit policy, while for the spare parts management problem, it is an order-up-to level policy, both dependent on the pooled data

Optimal data pooling for shared learning in maintenance operations

We study optimal data pooling for shared learning in two common maintenance operations: condition-based maintenance and spare parts management. We consider systems subject to Poisson input – the degradation or demand process – that are coupled through an unknown rate. Decision problems for these systems are high-dimensional Markov decision processes (MDPs) and are thus notoriously difficult to solve. We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. Leveraging this decomposition, we (i) show that pooling data can lead to significant cost reductions compared to not pooling, and (ii) prove that the optimal policy for the condition-based maintenance problem is a control limit policy, while for the spare parts management problem, it is an order-up-to level policy, both dependent on the pooled data

The GH/IGF-I Axis and Cognitive Changes across a 4-Year Period in Healthy Adults

After the age of 40, the amount of growth hormone in humans decreases. The reduced activity of the GH-IGF axis may play a role in age-related cognitive impairments. In the present study, mood and cognition of 30 healthy subjects (7 males, 23 females, aged 41–76 yr, mean age 60.9 ± 9.0) were examined twice. At baseline, we determined fasting blood levels of GH and IGF-I. Mood and cognitive status were assessed at baseline and after, on the average, 3 years and 9 months of followup. Working memory performance decreased over the years in the low IGF-group (P = .007), but not the high IGF-I group. Higher levels of GH were related with a better working memory at the second test (r = 0.42, P = .01) while higher levels of IGF-I tended to be related with a better working memory (r = 0.3, P = .06). The results suggest that higher serum levels of GH and IGF-I preserve the quality of working memory functions over the years