Blended Biogeography-based Optimization for Constrained Optimization

Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm optimization 2007 (SPSO 07). The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained single-objective optimization problems

Analysis of Migration Models of Biogeography-based Optimization Using Markov Theory

Biogeography-based optimization (BBO) is a new evolutionary algorithm inspired by biogeography, which involves the study of the migration of biological species between habitats. Previous work has shown that various migration models of BBO result in significant changes in performance. Sinusoidal migration models have been shown to provide the best performance so far. Motivated by biogeography theory and previous results, in this paper a generalized sinusoidal migration model curve is proposed. A previously derived BBO Markov model is used to analyze the effect of migration models on optimization performance, and new theoretical results which are confirmed with simulation results are obtained. The results show that the generalized sinusoidal migration model is significantly better than other models for simple but representative problems, including a unimodal one-max problem, a multimodal problem, and a deceptive problem. In addition, performance comparison is further investigated through 23 benchmark functions with a wide range of dimensions and diverse complexities, to verify the superiority of the generalized sinusoidal migration model

RGS10 shapes the hemostatic response to injury through its differential effects on intracellular signaling by platelet agonists.

Platelets express ≥2 members of the regulators of G protein signaling (RGS) family. Here, we have focused on the most abundant, RGS10, examining its impact on the hemostatic response in vivo and the mechanisms involved. We have previously shown that the hemostatic thrombi formed in response to penetrating injuries consist of a core of fully activated densely packed platelets overlaid by a shell of less-activated platelets responding to adenosine 5\u27-diphosphate (ADP) and thromboxane A2 (TxA2). Hemostatic thrombi formed in RGS10-/- mice were larger than in controls, with the increase due to expansion of the shell but not the core. Clot retraction was slower, and average packing density was reduced. Deleting RGS10 had agonist-specific effects on signaling. There was a leftward shift in the dose/response curve for the thrombin receptor (PAR4) agonist peptide AYPGKF but no increase in the maximum response. This contrasted with ADP and TxA2, both of which evoked considerably greater maximum responses in RGS10-/- platelets with enhanced Gq- and Gi-mediated signaling. Shape change, which is G13-mediated, was unaffected. Finally, we found that free RGS10 levels in platelets are actively regulated. In resting platelets, RGS10 was bound to 2 scaffold proteins: spinophilin and 14-3-3γ. Platelet activation caused an increase in free RGS10, as did the endothelium-derived platelet antagonist prostacyclin. Collectively, these observations show that RGS10 serves as an actively regulated node on the platelet signaling network, helping to produce smaller and more densely packed hemostatic thrombi with a greater proportion of fully activated platelets

Model selection and prediction of outcomes in recent onset schizophrenia patients who undergo cognitive training.

Predicting treatment outcomes in psychiatric populations remains a challenge, but is increasingly important in the pursuit of personalized medicine. Patients with schizophrenia have deficits in cognition, and targeted cognitive training (TCT) of auditory processing and working memory has been shown to improve some of these impairments; but little is known about the baseline patient characteristics predictive of cognitive improvement. Here we use a model selection and regression approach called least absolute shrinkage and selection operator (LASSO) to examine predictors of cognitive improvement in response to TCT for patients with recent onset schizophrenia. Forty-three individuals with recent onset schizophrenia randomized to undergo TCT were assessed at baseline on measures of cognition, symptoms, functioning, illness duration, and demographic variables. We carried out 10-fold cross-validation of LASSO for model selection and regression. We followed up on these results using linear models for statistical inference. No individual variable was found to correlate with improvement in global cognition using a Pearson correlation approach, and a linear model including all variables was also found not to be significant. However, the LASSO model identified baseline global cognition, education, and gender in a model predictive of improvement on global cognition following TCT. These findings offer guidelines for personalized approaches to cognitive training for patients with schizophrenia