Nitric oxide donation lowers blood pressure in adrenocorticotrophic hormone-induced hypertensive rats.

Adrenocorticotrophic hormone (ACTH) elevates systolic blood pressure (SBP) and lowers plasma reactive nitrogen intermediates in rats. We assessed the ability of NO donation from isosorbide dinitrate (ISDN) to prevent or reverse the hypertension caused by ACTH. In the prevention study, male Sprague Dawley rats were treated with ACTH (0.2 mg/kg/day) or saline control for 8 days, with either concurrent ISDN (100 mg/kg/day) via the drinking water or water alone. Animals receiving ISDN via the drinking water were provided with nitrate-free water for 8 hours every day. In the reversal study ISDN (100 mg/kg) or vehicle was given as a single oral dose on day 8. SBP was measured daily by the indirect tail-cuff method in conscious, restrained rats. ACTH caused a significant increase in SBP compared with saline (P < 0.0015). In the prevention study, chronic administration of ISDN (100 mg/kg/day) did not affect the SBP in either group. In the reversal study, ISDN significantly lowered SBP in ACTH-treated rats at 1 and 2.5 hours (132 +/- 3 mmHg (1 h) and 131 +/- 2 mmHg (2.5 h) versus 143 +/- 3 mmHg (0 h), P < 0.002), but not to control levels. It had no effect in control (saline treated) rats. In conclusion, the lowering of SBP by NO donation is consistent with the notion that ACTH-induced hypertension involves an impaired bioavailability or action of NO in vivo

Finding High-Dimensional D-OptimalDesigns for Logistic Models via Differential Evolution

D-optimal designs are frequently used in controlled experiments to obtain the most accurateestimate of model parameters at minimal cost. Finding them can be a challenging task, especially whenthere are many factors in a nonlinear model. As the number of factors becomes large and interact withone another, there are many more variables to optimize and the D-optimal design problem becomes highdimensionaland non-separable. Consequently, premature convergence issues arise. Candidate solutions gettrapped in local optima and the classical gradient-based optimization approaches to search for the D-optimaldesigns rarely succeed. We propose a specially designed version of differential evolution (DE) which is arepresentative gradient-free optimization approach to solve such high-dimensional optimization problems.The proposed specially designed DE uses a new novelty-based mutation strategy to explore the variousregions in the search space. The exploration of the regions will be carried out differently from the previouslyexplored regions and the diversity of the population can be preserved. The proposed novelty-based mutationstrategy is collaborated with two common DE mutation strategies to balance exploration and exploitationat the early or medium stage of the evolution. Additionally, we adapt the control parameters of DE as theevolution proceeds. Using logistic models with several factors on various design spaces as examples, oursimulation results show our algorithm can find D-optimal designs efficiently and the algorithm outperformsits competitors. As an application, we apply our algorithm and re-design a 10-factor car refueling experimentwith discrete and continuous factors and selected pairwise interactions. Our proposed algorithm was able toconsistently outperform the other algorithms and find a more efficient D-optimal design for the problem

Two-Stage Eagle Strategy with Differential Evolution

Efficiency of an optimization process is largely determined by the search algorithm and its fundamental characteristics. In a given optimization, a single type of algorithm is used in most applications. In this paper, we will investigate the Eagle Strategy recently developed for global optimization, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimization problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to 10 in many applications

Differential evolution with an evolution path: a DEEP evolutionary algorithm

Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs

Real-time dynamics in spin-1/2 chains with adaptive time-dependent DMRG

We investigate the influence of different interaction strengths and dimerizations on the magnetization transport in antiferromagnetic spin-1/2 XXZ-chains. We focus on the real-time evolution of the inhomogeneous initial state with all spins pointing up along the z axis in the left half and down in the right half of the chain, using the adaptive time-dependent density-matrix renormalization group (adaptive t-DMRG). We find on time-scales accessible to us ballistic magnetization transport for small Sz-Sz-interaction and arbitrary dimerization, but almost no transport for stronger Sz-Sz-interaction, with a sharp crossover at Jz=1. At Jz=1 results indicate superdiffusive transport. Additionally, we perform a detailed analysis of the error made by the adaptive time-dependent DMRG using the fact that the evolution in the XX-model is known exactly. We find that the error at small times is dominated by the error made by the Trotter decomposition, whereas for longer times the DMRG truncation error becomes the most important, with a very sharp crossover at some "runaway" time.Comment: 13 pages, 20 figure