Systems for energy control, management.
Energy management and control systems can effect substantial energy savings and optimization of energy use, but they require careful planning, evaluation, installation, and maintenance.
Computers↗
SEARCH · PubMed Health
Results for “energy optimization”
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
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[Optimization of energy-dependent processes in mitochondria from rat liver and brain after inhalation of negative air ions].
The effect of negative air ions (NAI) inhalation by rats on energy reactions of mitochondria in homogenates of liver and brain was studied. The influence of NAI was investigated under activation of animals by administration of physiological dose of adrenaline. Adrenaline administration induced hyperactivation of the rate of phosphorylating oxidation of succinic acid in liver and brain mitochondria which was accompanied by oxalacetate inhibition of succinate dehydrogenase (SDH) as well as excessive Ca2+ accumulation in liver mitochondria. These changes connected with a decrease of coefficient of phosphorylation efficiency ADP/O and uncoupler stimulation of respiration evidenced decrease of energy control of respiration in mitochondria. NAI inhalation diminished the rate of hyperactivated respiration and abolished excessive Ca2+ accumulation. These changes together with ADP/O coefficient and DNP stimulation increase evidence improvement of energy control of respiration which provide more moderate function of the respiratory chain under activation by adrenaline. Animals were excited by adrenaline administration. This effect was abolished by NAI inhalation, animals relaxed, some fell asleep. The data evidence sensitivity of mitochondrial processes in internal organs to inhalation of NAI and show participation of mitochondria in realization of physiological effects of NAI.
Adenosine Diphosphate↗
Preferential codon usage in prokaryotic genes: the optimal codon-anticodon interaction energy and the selective codon usage in efficiently expressed genes.
By considering the nucleotide sequence of several highly expressed coding regions in bacteriophage MS2 and mRNAs from Escherichia coli, it is possible to deduce some rules which govern the selection of the most appropriate synonymous codons NNU or NNC read by tRNAs having GNN, QNN or INN as anticodon. The rules fit with the general hypothesis that an efficient in-phase translation is facilitated by proper choice of degenerate codewords promoting a codon-anticodon interaction with intermediate strength (optimal energy) over those with very strong or very weak interaction energy. Moreover, codons corresponding to minor tRNAs are clearly avoided in these efficiently expressed genes. These correlations are clearcut in the normal reading frame but not in the corresponding frameshift sequences +1 and +2. We hypothesize that both the optimization of codon-anticodon interaction energy and the adaptation of the population to codon frequency or vice versa in highly expressed mRNAs of E. coli are part of a strategy that optimizes the efficiency of translation. Conversely, codon usage in weakly expressed genes such as repressor genes follows exactly the opposite rules. It may be concluded that, in addition to the need for coding an amino acid sequence, the energetic consideration for codon-anticodon pairing, as well as the adaptation of codons to the tRNA population, may have been important evolutionary constraints on the selection of the optimal nucleotide sequence.
Anticodon↗
Are automated molecular dynamics simulations and binding free energy calculations realistic tools in lead optimization? An evaluation of the linear interaction energy (LIE) method.
An extensive evaluation of the linear interaction energy (LIE) method for the prediction of binding affinity of docked compounds has been performed, with an emphasis on its applicability in lead optimization. An automated setup is presented, which allows for the use of the method in an industrial setting. Calculations are performed for four realistic examples, retinoic acid receptor gamma, matrix metalloprotease 3, estrogen receptor alpha, and dihydrofolate reductase, focusing on different aspects of the procedure. The obtained LIE models are evaluated in terms of the root-mean-square (RMS) errors from experimental binding free energies and the ability to rank compounds appropriately. The results are compared to the best empirical scoring function, selected from a set of 10 scoring functions. In all cases, good LIE models can be obtained in terms of free-energy RMS errors, although reasonable ranking of the ligands of dihydrofolate reductase proves difficult for both the LIE method and scoring functions. For the other proteins, the LIE model results in better predictions than the best performing scoring function. These results indicate that the LIE approach, as a tool to evaluate docking results, can be a valuable asset in computational lead optimization programs.
Automation↗
Optimal body size and energy expenditure during winter: why are voles smaller in declining populations?
Winter is energetically challenging for small herbivores because of greater energy requirements for thermogenesis at a time when little energy is available. We formulated a model predicting optimal wintering body size, accounting for the scaling of both energy expenditure and assimilation to body size, and the trade-off between survival benefits of a large size and avoiding survival costs of foraging. The model predicts that if the energy cost of maintaining a given body mass differs between environments, animals should be smaller in the more demanding environments, and there should be a negative correlation between body mass and daily energy expenditure (DEE) across environments. In contrast, if animals adjust their energy intake according to variation in survival costs of foraging, there should be a positive correlation between body mass and DEE. Decreasing temperature always increases equilibrium DEE, but optimal body mass may either increase or decrease in colder climates depending on the exact effects of temperature on mass-specific survival and energy demands. Measuring DEE with doubly labeled water on wintering Microtus agrestis at four field sites, we found that DEE was highest at the sites where voles were smallest despite a positive correlation between DEE and body mass within sites. This suggests that variation in wintering body mass between sites was due to variation in food quality/availability and not adjustments in foraging activity to varying risks of predation.
Animals↗
Optimization of quantitative electron energy loss spectroscopy in the low loss region: phosphorus L-edge.
The purpose of this study was to optimize quantitative electron energy loss spectroscopy (EELS) of elements that have characteristic edges in the low energy loss region and are components of organic matrices. The optimum parameters for phosphorus L2,3-edge (at 135 eV) detection were determined by numerical analysis of computer-generated, Poisson-noisy spectra and by experimental measurements (at 80 keV) of films of the phosphoprotein, phosvitin. When the first, second and third valence electron/plasmon scatterings are included in the multiple least-squares (MLS) fit, the background subtraction of (first-difference) spectra is significantly more accurate than that obtained with the "inverse power law" method, even for a specimen thickness of only 0.25 lambda. Taking into account the effects of plural scattering, the optimal thickness for P quantitation is approximately 0.3 lambda. Signal-to-noise (S/N) ratio decreases rapidly with thickness, and at 1.0 lambda, it is only about 60% of the optimum S/N. The combined effects of the statistical uncertainty of measurements and of the systematic error due to gain variations of the parallel detector were evaluated, and the relative sensitivities of the no-difference (raw spectrum), first-difference and second-difference methods were compared. For channel-to-channel gain variations greater than 0.1% and up to 0.8%, the first-difference method results in the lowest uncertainty of P measurements. In the absence of gain variations, direct fitting provides the greatest sensitivity (least uncertainty), whereas at larger gain variations it may be necessary to use the second-difference method. The optimum energy shift for collecting a first-difference spectrum, approximately 15 eV, did not show any great variation between 5 and 25 eV, and is, in general, specimen dependent. Quantitation with EELS showed excellent correlation with simultaneous electron probe X-ray microanalysis, but, for the detection of P in a 0.25 lambda thick specimen, EELS was approximately five to six times more sensitive than X-ray. The minimal detectable P concentration, with 0.5 nA beam current for 100 s in a 0.25 lambda thick specimen, was 8.4 mmol/kg (0.01 at%) at the 99% confidence level, equivalent to 34 phosphorus atoms for a 15 nm probe. This value is close to the theoretical prediction of 7.5 mmol/kg, and can be improved only by further reducing the gain variation and directly fitting the non-difference spectrum. Appropriate reduction of the gain variations to less than 0.1% would result in a further, approximately two-fold, improvement in the parallel EELS detection system.(ABSTRACT TRUNCATED AT 400 WORDS)
Computer Simulation↗
A paradigm for viewing biologic systems as scale-free networks based on energy efficiency: implications for present therapies and the future of evolution.
A network constitutes an abstract description of the relationships among entities, respectively termed links and nodes. If a power law describes the probability distribution of the number of links per node, the network is said to be scale-free. Scale-free networks feature link clustering around certain hubs based on preferential attachments that emerge due either to merit or legacy. Biologic systems ranging from sub-atomic to ecosystems represent scale-free networks in which energy efficiency forms the basis of preferential attachments. This paradigm engenders a novel scale-free network theory of evolution based on energy efficiency. As environmental flux induces fitness dislocations and compels a new meritocracy, new merit-based hubs emerge, previously merit-based hubs become legacy hubs, and network recalibration occurs to achieve system optimization. To date, Darwinian evolution, characterized by innovation sampling, variation, and selection through filtered termination, has enabled biologic progress through optimization of energy efficiency. However, as humans remodel their environment, increasing the level of unanticipated fitness dislocations and inducing evolutionary stress, the tendency of networks to exhibit inertia and retain legacy hubs engender maladaptations. Many modern diseases may fundamentally derive from these evolutionary displacements. Death itself may constitute a programmed adaptation, terminating individuals who represent legacy hubs and recalibrating the network. As memes replace genes as the basis of innovation, death itself has become a legacy hub. Post-Darwinian evolution may favor indefinite persistence to optimize energy efficiency. We describe strategies to reprogram or decommission legacy hubs that participate in human disease and death.
Animals↗
Calculation of protein conformation by global optimization of a potential energy function.
A novel hierarchical approach to protein folding has been applied to compute the unknown structures of seven target proteins provided by CASP3. The approach is based exclusively on the global optimization of a potential energy function for a united-residue model by conformational space annealing, followed by energy refinement using an all-atom potential. Comparison of the submitted models for five globular proteins with the experimental structures shows that the conformations of large fragments (approximately 60 aa) were predicted with rmsds of 4.2-6.8 A for the C alpha atoms. Our lowest-energy models for targets T0056 and T0061 were particularly successful, producing the correct fold of approximately 52% and 80% of the structures, respectively. These results support the thermodynamic hypothesis that protein structure can be computed solely by global optimization of a potential energy function for a given amino acid sequence.
Algorithms↗
Prediction of peptide conformation using a scale-transformed entropy sampling algorithm.
An algorithm for predicting the lowest energy structure of a peptide has been developed. High-energy barriers on an energy surface can be easily overcome by logarithmically transforming the energy space. For efficient optimizations, the energy space is searched using a scale-transformed entropy sampling method, and conformations specific to a primary structure of the peptide are sampled with large weights. The efficiency of the present method is demonstrated by calculations on cholecystokinin octapeptide (CCK-8).
Algorithms↗
Dose distribution of narrow beam irradiation for small lung tumor.
PURPOSE: To aid in the selection of incident X-ray energy for stereotactic irradiation (STI) of lung tumor, dose distribution was investigated in a model of a thorax embedded with a tumor. METHODS AND MATERIALS: The dose distribution in a thorax model was calculated using the EGS4 Monte Carlo simulation; it was also measured with dosimetric film of a tentative thorax phantom. Uniformity of dose distribution in a tumor region was compared among the results of irradiation for several X-ray energies, and optimal X-ray energy for STI of a lung tumor was discussed. RESULTS: Dose distributions in the thorax were obtained. An increase in X-ray energy led not only to an increased dose delivered to the tumor, but also to an increased dose to surrounding normal lung tissue. CONCLUSIONS: The flat range in dose distribution along the beam axis and in the beam profiles of the tumor increases with decreasing X-ray energy. Consequently, lower energy, rather than higher energy, is recommended for STI of a lung tumor in terms of higher uniformity in the target volume.
Computer Simulation↗
Optimization of measurement conditions of an energy dispersive X-ray fluorescence spectrometer with high-energy polarized beam excitation for analysis of aerosol filters.
A new commercial energy dispersive X-ray fluorescence spectrometer (EDXRF), applying a three-dimensional geometry and high-energy excitation, was optimized for the quantitative analysis of aerosols deposited on filters. The preliminary results are presented here. First-order calibration curves were obtained for 20 elements deposited on the filters. The accuracy of the applied method and of the obtained calibration curves was checked by the measurement of a standard reference material from NIST. The precision of the analysis for the majority of the analytes was better than 10%. Due to the obtained low detection limits, it is possible to determine the analytes usually present at very low concentrations in ambient air, such as, e.g., Cd, Sb, Cr, and V. It is also possible to decrease significantly the time of analysis or the time of the sampling.
Aerosols↗
Energy-speed relation and optimal speed during level walking.
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Energy Metabolism↗
How optimization of potential functions affects protein folding.
The relationship between the optimization of the potential function and the foldability of theoretical protein models is studied based on investigations of a 27-mer cubic-lattice protein model and a more realistic lattice model for the protein crambin. In both the simple and the more complicated systems, optimization of the energy parameters achieves significant improvements in the statistical-mechanical characteristics of the systems and leads to foldable protein models in simulation experiments. The foldability of the protein models is characterized by their statistical-mechanical properties--e.g., by the density of states and by Monte Carlo folding simulations of the models. With optimized energy parameters, a high level of consistency exists among different interactions in the native structures of the protein models, as revealed by a correlation function between the optimized energy parameters and the native structure of the model proteins. The results of this work are relevant to the design of a general potential function for folding proteins by theoretical simulations.
Biophysical Phenomena↗
Recent improvements in prediction of protein structure by global optimization of a potential energy function.
Recent improvements of a hierarchical ab initio or de novo approach for predicting both alpha and beta structures of proteins are described. The united-residue energy function used in this procedure includes multibody interactions from a cumulant expansion of the free energy of polypeptide chains, with their relative weights determined by Z-score optimization. The critical initial stage of the hierarchical procedure involves a search of conformational space by the conformational space annealing (CSA) method, followed by optimization of an all-atom model. The procedure was assessed in a recent blind test of protein structure prediction (CASP4). The resulting lowest-energy structures of the target proteins (ranging in size from 70 to 244 residues) agreed with the experimental structures in many respects. The entire experimental structure of a cyclic alpha-helical protein of 70 residues was predicted to within 4.3 A alpha-carbon (C(alpha)) rms deviation (rmsd) whereas, for other alpha-helical proteins, fragments of roughly 60 residues were predicted to within 6.0 A C(alpha) rmsd. Whereas beta structures can now be predicted with the new procedure, the success rate for alpha/beta- and beta-proteins is lower than that for alpha-proteins at present. For the beta portions of alpha/beta structures, the C(alpha) rmsd's are less than 6.0 A for contiguous fragments of 30-40 residues; for one target, three fragments (of length 10, 23, and 28 residues, respectively) formed a compact part of the tertiary structure with a C(alpha) rmsd less than 6.0 A. Overall, these results constitute an important step toward the ab initio prediction of protein structure solely from the amino acid sequence.
Models, Molecular↗
Learning to optimize speed, accuracy, and energy expenditure: a framework for understanding speed-accuracy relations in goal-directed aiming.
Over the last century, investigators have developed a number of models to explain the relation between speed and accuracy in target-directed manual aiming. The models vary in the extent to which they stress the importance of feedforward processes and the online use of sensory information (see D. Elliott, W. F. Helsen, & R. Chua, 2001, for a recent review). A common feature of those models is that the role of practice in optimizing speed, accuracy, and energy expenditure in goal-directed aiming is either ignored or minimized. The authors present a theoretical framework for understanding speed-accuracy tradeoffs that takes into account the strategic, trial-to-trial behavior of the performer. The strategic behavior enables individuals to maximize movement speed while minimizing error and energy expenditure.
Cognition↗
Molecular tailoring approach for geometry optimization of large molecules: energy evaluation and parallelization strategies.
A linear-scaling scheme for estimating the electronic energy, gradients, and Hessian of a large molecule at ab initio level of theory based on fragment set cardinality is presented. With this proposition, a general, cardinality-guided molecular tailoring approach (CG-MTA) for ab initio geometry optimization of large molecules is implemented. The method employs energy gradients extracted from fragment wave functions, enabling computations otherwise impractical on PC hardware. Further, the method is readily amenable to large scale coarse-grain parallelization with minimal communication among nodes, resulting in a near-linear speedup. CG-MTA is applied for density-functional-theory-based geometry optimization of a variety of molecules including alpha-tocopherol, taxol, gamma-cyclodextrin, and two conformations of polyglycine. In the tests performed, energy and gradient estimates obtained from CG-MTA during optimization runs show an excellent agreement with those obtained from actual computation. Accuracy of the Hessian obtained employing CG-MTA provides good hope for the application of Hessian-based geometry optimization to large molecules.
Journal Article↗
Protein structure prediction by global optimization of a potential energy function.
An approach based exclusively on finding the global minimum of an appropriate potential energy function has been used to predict the unknown structures of five globular proteins with sizes ranging from 89 to 140 amino acid residues. Comparison of the computed lowest-energy structures of two of them (HDEA and MarA) with the crystal structures, released by the Protein Data Bank after the predictions were made, shows that large fragments (61 residues) of both proteins were predicted with rms deviations of 4.2 and 6.0 A for the Calpha atoms, for HDEA and MarA, respectively. This represents 80% and 53% of the observed structures of HDEA and MarA, respectively. Similar rms deviations were obtained for approximately 60-residue fragments of the other three proteins. These results constitute an important step toward the prediction of protein structure based solely on global optimization of a potential energy function for a given amino acid sequence.
Bacterial Proteins↗
An energy-based model of optimal feeding-territory size.
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Aggression↗