Maternal and paternal growth regulatory factor expression in hybrid sunfish.

Unidirectional hybridization between bluegill (Lepomis macrochirus) and pumpkinseed (L. gibbosus) sunfish enables researchers to explore the relative expression of paternal and maternal alleles in hybrids. Past studies have found that the metabolic dysfunction in bluegill-pumpkinseed hybrids may be due to incompatibilities between nuclear and mitochondrial genomes. However, the consequences of hybridization on body size and muscle growth have not been examined. This topic is particularly interesting because hybrids grow larger than parentals despite the fact that they are often sired by smaller, precociously mature bluegills. In order to improve our understanding of growth dynamics in hybrid sunfish, I conducted real-time quantitative PCR using species-specific primers on the white muscle tissue of bluegills, pumpkinseeds, and hybrids collected from Lake Opinicon, ON. Five growth factors that have been linked to muscle growth and body size demonstrated similar expression for maternal and paternal alleles. While about half of the hybrids showed the same pattern with myogenin, about half showed very low levels of mRNA for the paternal (bluegill) gene. While this did not explain the heterosis seen in hybrids, it may explain the small body phenotype of the cuckholding bluegill males. I explored the upstream genetic structure of bluegill myogenin and established that four alleles exist within the population. Furthermore, I uncovered a relationship in hybrids between the proximal promoter/ 5’ UTR of myogenin and its transcript level. I found that the hybrids demonstrating low paternal myogenin expression unfailingly possessed A3 or A4 alleles, but future studies will be needed to reveal the molecular links between the genotype and the growth phenotype. A similar genotype-phenotype association was not obvious in parentals, even those that were homozygous for these alleles. Whether this relationship can provide insight into the genetic determinants of bluegill alternative mating strategies has yet to be determined.

Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty

A scenario-based two-stage stochastic programming model for gas production network planning under uncertainty is usually a large-scale nonconvex mixed-integer nonlinear programme (MINLP), which can be efficiently solved to global optimality with nonconvex generalized Benders decomposition (NGBD). This paper is concerned with the parallelization of NGBD to exploit multiple available computing resources. Three parallelization strategies are proposed, namely, naive scenario parallelization, adaptive scenario parallelization, and adaptive scenario and bounding parallelization. Case study of two industrial natural gas production network planning problems shows that, while the NGBD without parallelization is already faster than a state-of-the-art global optimization solver by an order of magnitude, the parallelization can improve the efficiency by several times on computers with multicore processors. The adaptive scenario and bounding parallelization achieves the best overall performance among the three proposed parallelization strategies.

Robust scenario formulations for strategic supply chain optimization under uncertainty

Strategic supply chain optimization (SCO) problems are often modelled as a two-stage optimization problem, in which the first-stage variables represent decisions on the development of the supply chain and the second-stage variables represent decisions on the operations of the supply chain. When uncertainty is explicitly considered, the problem becomes an intractable infinite-dimensional optimization problem, which is usually solved approximately via a scenario or a robust approach. This paper proposes a novel synergy of the scenario and robust approaches for strategic SCO under uncertainty. Two formulations are developed, namely, naïve robust scenario formulation and affinely adjustable robust scenario formulation. It is shown that both formulations can be reformulated into tractable deterministic optimization problems if the uncertainty is bounded with the infinity-norm, and the uncertain equality constraints can be reformulated into deterministic constraints without assumption of the uncertainty region. Case studies of a classical farm planning problem and an energy and bioproduct SCO problem demonstrate the advantages of the proposed formulations over the classical scenario formulation. The proposed formulations not only can generate solutions with guaranteed feasibility or indicate infeasibility of a problem, but also can achieve optimal expected economic performance with smaller numbers of scenarios.

Novel Formulation and Efficient Solution Strategy for Strategic Optimization of an Industrial Chemical Supply Chain Under Demand Uncertainty

This paper is concerned with strategic optimization of a typical industrial chemical supply chain, which involves a material purchase and transportation network, several manufacturing plants with on-site material and product inventories, a product transportation network and several regional markets. In order to address large uncertainties in customer demands at the different regional markets, a novel robust scenario formulation, which has been developed by the authors recently, is tailored and applied for the strategic optimization. Case study results show that the robust scenario formulation works well for this real industrial supply chain system, and it outperforms the deterministic formulation and the classical scenario-based stochastic programming formulation by generating better expected economic performance and solutions that are guaranteed to be feasible for all uncertainty realizations. The robust scenario problem exhibits a decomposable structure that can be taken advantage of by Benders decomposition for efficient solution, so the application of Benders decomposition to the solution of the strategic optimization is also discussed. The case study results show that Benders decomposition can reduce the solution time by almost an order of magnitude when the number of scenarios in the problem is large.