Concept: Operations research
Many choice situations require imagining potential outcomes, a capacity that was shown to involve memory brain regions such as the hippocampus. We reasoned that the quality of hippocampus-mediated simulation might therefore condition the subjective value assigned to imagined outcomes. We developed a novel paradigm to assess the impact of hippocampus structure and function on the propensity to favor imagined outcomes in the context of intertemporal choices. The ecological condition opposed immediate options presented as pictures (hence directly observable) to delayed options presented as texts (hence requiring mental stimulation). To avoid confounding simulation process with delay discounting, we compared this ecological condition to control conditions using the same temporal labels while keeping constant the presentation mode. Behavioral data showed that participants who imagined future options with greater details rated them as more likeable. Functional MRI data confirmed that hippocampus activity could account for subjects assigning higher values to simulated options. Structural MRI data suggested that grey matter density was a significant predictor of hippocampus activation, and therefore of the propensity to favor simulated options. Conversely, patients with hippocampus atrophy due to Alzheimer’s disease, but not patients with Fronto-Temporal Dementia, were less inclined to favor options that required mental simulation. We conclude that hippocampus-mediated simulation plays a critical role in providing the motivation to pursue goals that are not present to our senses.
Myxococcus xanthus cells self-organize into periodic bands of traveling waves, termed ripples, during multicellular fruiting body development and predation on other bacteria. To investigate the mechanistic basis of rippling behavior and its physiological role during predation by this Gram-negative soil bacterium, we have used an approach that combines mathematical modeling with experimental observations. Specifically, we developed an agent-based model (ABM) to simulate rippling behavior that employs a new signaling mechanism to trigger cellular reversals. The ABM has demonstrated that three ingredients are sufficient to generate rippling behavior: (i) side-to-side signaling between two cells that causes one of the cells to reverse, (ii) a minimal refractory time period after each reversal during which cells cannot reverse again, and (iii) physical interactions that cause the cells to locally align. To explain why rippling behavior appears as a consequence of the presence of prey, we postulate that prey-associated macromolecules indirectly induce ripples by stimulating side-to-side contact-mediated signaling. In parallel to the simulations, M. xanthus predatory rippling behavior was experimentally observed and analyzed using time-lapse microscopy. A formalized relationship between the wavelength, reversal time, and cell velocity has been predicted by the simulations and confirmed by the experimental data. Furthermore, the results suggest that the physiological role of rippling behavior during M. xanthus predation is to increase the rate of spreading over prey cells due to increased side-to-side contact-mediated signaling and to allow predatory cells to remain on the prey longer as a result of more periodic cell motility.
Computational Neuroscience is an emerging field that provides unique opportunities to study complex brain structures through realistic neural simulations. However, as biological details are added to models, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs) are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they have shown significant improvement in execution time compared to Central Processing Units (CPUs). Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks, the NeoCortical Simulator version 6 (NCS6). NCS6 is a free, open-source, parallelizable, and scalable simulator, designed to run on clusters of multiple machines, potentially with high performance computing devices in each of them. It has built-in leaky-integrate-and-fire (LIF) and Izhikevich (IZH) neuron models, but users also have the capability to design their own plug-in interface for different neuron types as desired. NCS6 is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing data across eight machines with each having two video cards.
Power and performance management problem in large scale computing systems like data centers has attracted a lot of interests from both enterprises and academic researchers as power saving has become more and more important in many fields. Because of the multiple objectives, multiple influential factors and hierarchical structure in the system, the problem is indeed complex and hard. In this paper, the problem will be investigated in a virtualized computing system. Specifically, it is formulated as a power optimization problem with some constraints on performance. Then, the adaptive controller based on least-square self-tuning regulator(LS-STR) is designed to track performance in the first step; and the resource solved by the controller is allocated in order to minimize the power consumption as the second step. Some simulations are designed to test the effectiveness of this method and to compare it with some other controllers. The simulation results show that the adaptive controller is generally effective: it is applicable for different performance metrics, for different workloads, and for single and multiple workloads; it can track the performance requirement effectively and save the power consumption significantly.
Computer modeling, simulation and optimization are powerful tools that have seen increased use in biomechanics research. Dynamic optimizations can be categorized as either data-tracking or predictive problems. The data-tracking approach has been used extensively to address human movement problems of clinical relevance. The predictive approach also holds great promise, but has seen limited use in clinical applications. Enhanced software tools would facilitate the application of predictive musculoskeletal simulations to clinically-relevant research. The open-source software OpenSim provides tools for generating tracking simulations but not predictive simulations. However, OpenSim includes an extensive application programming interface that permits extending its capabilities with scripting languages such as MATLAB. In the work presented here, we combine the computational tools provided by MATLAB with the musculoskeletal modeling capabilities of OpenSim to create a framework for generating predictive simulations of musculoskeletal movement based on direct collocation optimal control techniques. In many cases, the direct collocation approach can be used to solve optimal control problems considerably faster than traditional shooting methods. Cyclical and discrete movement problems were solved using a simple 1 degree of freedom musculoskeletal model and a model of the human lower limb, respectively. The problems could be solved in reasonable amounts of time (several seconds to 1-2 hours) using the open-source IPOPT solver. The problems could also be solved using the fmincon solver that is included with MATLAB, but the computation times were excessively long for all but the smallest of problems. The performance advantage for IPOPT was derived primarily by exploiting sparsity in the constraints Jacobian. The framework presented here provides a powerful and flexible approach for generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB. This should allow researchers to more readily use predictive simulation as a tool to address clinical conditions that limit human mobility.
Optimality theory predicts the maximization of productivity in social insect colonies, but many inactive workers are found in ant colonies. Indeed, the low short-term productivity of ant colonies is often the consequence of high variation among workers in the threshold to respond to task-related stimuli. Why is such an inefficient strategy among colonies maintained by natural selection? Here, we show that inactive workers are necessary for the long-term sustainability of a colony. Our simulation shows that colonies with variable thresholds persist longer than those with invariable thresholds because inactive workers perform the critical function of replacing active workers when they become fatigued. Evidence of the replacement of active workers by inactive workers has been found in ant colonies. Thus, the presence of inactive workers increases the long-term persistence of the colony at the expense of decreasing short-term productivity. Inactive workers may represent a bet-hedging strategy in response to environmental stochasticity.
- Proceedings of the National Academy of Sciences of the United States of America
- Published 9 months ago
Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.
Ice-free cryopreservation, known as vitrification, is an appealing approach for banking of adherent cells and tissues because it prevents dissociation and morphological damage that may result from ice crystal formation. However, current vitrification methods are often limited by the cytotoxicity of the concentrated cryoprotective agent (CPA) solutions that are required to suppress ice formation. Recently, we described a mathematical strategy for identifying minimally toxic CPA equilibration procedures based on the minimization of a toxicity cost function. Here we provide direct experimental support for the feasibility of these methods when applied to adherent endothelial cells. We first developed a concentration- and temperature-dependent toxicity cost function by exposing the cells to a range of glycerol concentrations at 21°C and 37°C, and fitting the resulting viability data to a first order cell death model. This cost function was then numerically minimized in our state constrained optimization routine to determine addition and removal procedures for 17 molal (mol/kg water) glycerol solutions. Using these predicted optimal procedures, we obtained 81% recovery after exposure to vitrification solutions, as well as successful vitrification with the relatively slow cooling and warming rates of 50°C/min and 130°C/min. In comparison, conventional multistep CPA equilibration procedures resulted in much lower cell yields of about 10%. Our results demonstrate the potential for rational design of minimally toxic vitrification procedures and pave the way for extension of our optimization approach to other adherent cell types as well as more complex systems such as tissues and organs.
The aim of this work was to prepare L-DOPA loaded poly(d,l-lactide-co-glycolide) (PLGA) nanoparticles by a modified water-in-oil-in-water (W(1)/O/W(2)) emulsification solvent evaporation method. A central composite design was applied for optimization of the formulation parameters and for studying the effects of three independent variables: PLGA concentration, polyvinyl alcohol (PVA) concentration and organic solvent removal rate on the particle size and the entrapment efficiency (response variables). Second-order models were obtained to adequately describe the influence of the independent variables on the selected responses. The analysis of variance showed that the three independent variables had significant effects (p < 0.05) on the responses. The experimental results were in perfect accordance with the predictions estimated by the models. Using the desirability approach and overlay contour plots, the optimal preparation area can be highlighted. It was found that the optimum values of the responses could be obtained at higher concentration of PLGA (5%, w/v) and PVA (6%, w/v); and faster organic solvent removal rate (700 rpm). The corresponding particle size was 256.2 nm and the entrapment efficiency was 62.19%. FTIR investigation confirmed that the L-DOPA and PLGA polymer maintained its backbone structure in the fabrication of nanoparticles. The scanning electron microscopic images of nanoparticles showed that all particles had spherical shape with porous outer skin. The results suggested that PLGA nanoparticles might represent a promising formulation for brain delivery of L-DOPA. The preparation of L-DOPA loaded PLGA nanoparticles can be optimized by the central composite design.
Flood spreading is a suitable strategy for controlling and benefiting from floods. Selecting suitable areas for flood spreading and directing the floodwater into permeable formations are amongst the most effective strategies in flood spreading projects. Having combined geographic information systems (GIS) and multi-criteria decision analysis approaches, the present study sought to locate the most suitable areas for flood spreading operation in the Garabaygan Basin of Iran. To this end, the data layers relating to the eight effective factors were prepared in GIS environment. This stage was followed by elimination of the exclusionary areas for flood spreading while determining the potentially suitable ones. Having closely examined the potentially suitable areas using the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) II and analytic hierarchy process (AHP) methods, the land suitability map for flood spreading was produced. The PROMETHEE II and AHP were used for ranking all the alternatives and weighting the criteria involved, respectively. The results of the study showed that most suitable areas for the artificial groundwater recharge are located in Quaternary Q(g) and Q(gsc) geologic units and in geomorphological units of pediment and Alluvial fans with slopes not exceeding 3 %. Furthermore, significant correspondence between the produced map and the control areas, where the flood spreading projects were successfully performed, provided further evidence for the acceptable efficiency of the integrated PROMETHEE II-AHP method in locating suitable flood spreading areas.