Simulating local deformations in the human cortex due to blood flow-induced changes in mechanical tissue properties: Impact on functional magnetic resonance imaging

Investigating human brain tissue is challenging due to the complexity and the manifold interactions between structures across different scales. Increasing evidence suggests that brain function and microstructural features including biomechanical features are related. More importantly, the relationship between tissue mechanics and its influence on brain imaging results remains poorly understood. As an important example, the study of the brain tissue response to blood flow could have important theoretical and experimental consequences for functional magnetic resonance imaging (fMRI) at high spatial resolutions. Computational simulations, using realistic mechanical models can predict and characterize the brain tissue behavior and give us insights into the consequent potential biases or limitations of in vivo, high-resolution fMRI. In this manuscript, we used a two dimensional biomechanical simulation of an exemplary human gyrus to investigate the relationship between mechanical tissue properties and the respective changes induced by focal blood flow changes. The model is based on the changes in the brain’s stiffness and volume due to the vasodilation evoked by neural activity. Modeling an exemplary gyrus from a brain atlas we assessed the influence of different potential mechanisms: (i) a local increase in tissue stiffness (at the level of a single anatomical layer), (ii) an increase in local volume, and (iii) a combination of both effects. Our simulation results showed considerable tissue displacement because of these temporary changes in mechanical properties. We found that the local volume increase causes more deformation and consequently higher displacement of the gyrus. These displacements introduced considerable artifacts in our simulated fMRI measurements. Our results underline the necessity to consider and characterize the tissue displacement which could be responsible for fMRI artifacts

Allosteric pyruvate kinase-based "logic gate" synergistically senses energy and sugar levels in <i>Mycobacterium tuberculosis</i>

Pyruvate kinase (PYK) is an essential glycolytic enzyme that controls glycolytic flux and is critical for ATP production in all organisms, with tight regulation by multiple metabolites. Yet the allosteric mechanisms governing PYK activity in bacterial pathogens are poorly understood. Here we report biochemical, structural and metabolomic evidence that Mycobacterium tuberculosis (Mtb) PYK uses AMP and glucose-6-phosphate (G6P) as synergistic allosteric activators that function as a molecular "OR logic gate" to tightly regulate energy and glucose metabolism. G6P was found to bind to a previously unknown site adjacent to the canonical site for AMP. Kinetic data and structural network analysis further show that AMP and G6P work synergistically as allosteric activators. Importantly, metabolome profiling in the Mtb surrogate, Mycobacterium bovis BCG, reveals significant changes in AMP and G6P levels during nutrient deprivation, which provides insights into how a PYK OR gate would function during the stress of Mtb infection

Integration of resource investment problem with quantity discount problem in material ordering for minimizing resource costs of projects

Minimizing the costs in a project is highly tied with the way the required resources are provided. The resource investment problem deals with how to employ the renewable resources such that the related costs are minimized. Furthermore, the material ordering problem alludes to outlining a proper plan for supplying the nonrenewable resources (materials) to minimize the associated costs. The present paper studies the integration of the resource investment problem with the quantity discount problem in material ordering to thoroughly investigate the resource costs of projects in a single circumstance. The integrated model is presented and mathematically formulated. Three hybrid procedures are proposed for the model, each of which includes a genetic algorithm combined with a dynamic programming, a simulated annealing or a particle swarm optimization algorithm. The mathematical formulations of some small instances are solved to be the subject of an exact comparison with hybrid procedures. The proposed procedures are tested on a set of 810 benchmarks known in the literature. The computational experiments reported by algorithms validate the efficiency of the hybrid genetic algorithm and dynamic programming for the model more than other hybrid approaches

Multi-mode resource-constrained project scheduling problem with material ordering under bonus-penalty policies

This study emphasizes that project scheduling and material ordering (time and quantity of an order) must be considered simultaneously to minimize the total cost, as setting the material ordering decisions after the project scheduling phase leads to non-optimal solutions. Hence, this paper mathematically formulates the model for the multi-mode resource-constrained project scheduling with material ordering (MRCPSMO) problem. In order to be more realistic, bonus and penalty policies are included for the project. The objective function of the model consists of four elements: the material holding cost, the material ordering cost, the bonus paid by the client and the cost of delay in the project completion. Since MRCPSMO is NP-hard, the paper proposes three hybrid meta-heuristic algorithms called PSO-GA, GA-GA and SA-GA to obtain near-optimal solutions. In addition, the design of experiments and Taguchi method is used to tune the algorithms&#039; parameters. The proposed algorithms consist of two components: an outside search, in which the algorithm searches for the best schedule and mode assignment, and the inside search, which determines the time and quantity of orders of the nonrenewable resources. First, a comparison is made for each individual component with the exact or best solutions available in the literature. Then, a set of standard PROGEN test problems is solved by the proposed hybrid algorithms under fixed CPU time. The results reveal that the PSO-GA algorithm outperforms both GA-GA and SA-GA algorithms and provides good solutions in a reasonable time

Estimating the four parameters of the Burr III distribution using a hybrid method of variable neighborhood search and iterated local search algorithms

The Burr III distribution properly approximates many familiar distributions such as Normal, Lognormal, Gamma, Weibull, and Exponential distributions. It plays an important role in reliability engineering, statistical quality control, and risk analysis models