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5,573 results for “optimality”
Data from: Optimization of nitrogen and carbon removal with simultaneous partial nitrification, anammox, and denitrification (SNAD) in membrane bioreactor (MBR)
<p>In this study, a membrane bioreactor (MBR) was used to achieve both nitrogen and carbon removal by a simultaneous partial nitrification, anammox, and denitrification (SNAD) process. During the entire experiment, the optimal operating parameters as determined by response surface methodology (RSM) were a C/N value of 1.16, a DO value of 0.84 mg·L<sup><span>-1</span></sup>, and an aerobic time (<i><span>T</span></i><i><sub><span><span>ae</span></span></sub></i>) of 15.75 min. Under these conditions, the SNAD process achieved efficient and stable nitrogen and carbon removal; the total inorganic nitrogen (TIN) removal efficiency and chemical oxygen demand (COD) removal efficiency were 92.31% and 95.67%, respectively. With the formation of granular sludge, the membrane fouling rate (MFR) decreased significantly from 35.0 Pa·h<sup><span>-1</span></sup> at SNAD start-up to 19.9 Pa·h<sup><span>-1</span></sup> during stable operation. Fluorescence in situ hybrid (FISH) analyse confirmed the structural characteristics and the relative ratio of AerAOB, AnAOB, and DNB in the SNAD system.</p>
Optimizing whole-genomic prediction for autotetraploid blueberry breeding
<p><span><span><span><span><span><span><span><span><span><span><span><span><span>Blueberry (<em>Vaccinium</em> spp.) is an important autopolyploid crop with significant benefits for human health. Apart from its genetic complexity, the feasibility of genomic prediction has been proven for blueberry, </span></span><span><span>enabling a reduction in the breeding cycle time and increasing genetic gain. </span></span>However, as for other polyploid crops<span><span>, </span></span>sequencing <span><span>costs still hinder the implementation of genome-based breeding methods for blueberry.</span></span> This motivated us to evaluate the effect of training population sizes and composition, as well as the impact of marker density and sequencing depth on phenotype prediction for the species. For this, data from a large real breeding population of 1 804 individuals was used. Genotypic data from 86 930 markers and three traits with different genetic architecture (fruit firmness, fruit weight, and total yield) were evaluated. Herein, we suggested that marker density, sequencing depth, and training population size can be substantially reduced with no significant impact on model accuracy. Our results can help guide decisions towards resource allocation (e.g., genotyping and phenotyping) in order to maximize prediction accuracy. These findings have the potential to allow for a faster and more accurate release of varieties with a substantial reduction of resources for the application of genomic prediction in blueberry. We anticipate that the benefits and pipeline described in our study <span><span>can be applied to optimize genomic prediction for other diploid and polyploid species.</span></span></span></span></span></span></span></span></span></span></span></span></span></p>
Research data supporting "Expanding and Optimizing 3D Bioprinting Capabilities Using Complementary Network Bioinks"
<p>Raw research data supporting:</p> <p>Ouyang L. et al., 2020, Science Advances, 6:eabc5529.</p>
Pedestrian simulation results used for choosing optimal location for crosswalks
<p>Pedestrian simulation results generated by AntRoadPlanner simulation system for 2 areas in Saint-Petersburg. Used in article "Using Multi-Agent Simulation to Predict Natural Crossing Points for Pedestrians and Choose Locations for Mid-Block Crosswalks"</p> <p> </p>
Cancer cells optimize elasticity for efficient migration
<p>Cancer progression is associated with alternations in the cytoskeletal architecture of cells and, consequently, their mechanical properties such as stiffness. Changing the mechanics of cells enables cancer cells to migrate and invade to distant organ sites. This process, metastasis, is the main reason for cancer-related mortality. Cell migration is an essential step toward increasing the invasive potential of cells. Although many studies have shown that the migratory speed and the invasion of cells can be inversely correlated to the stiffness of cells, some other investigations indicate exactly opposing results. In the current work, based on the strain energy stored in cells due to the contractile forces, we defined an energy-dependent term, migratory index, to approximate how changes in the mechanical properties of cells influence cell migration required for cancer progression. Cell migration involves both cell deformation and force transmission within cells. The effects of these two parameters can be represented equally by the migratory index. Our mechanical modeling and computational study show that cells depending on their shape, size, and other physical parameters, have a maximum migratory index taking place at a specific range of cell bulk stiffness, indicating the most favorable conditions for invasive mobility. This approximate model can be used to explain why the stiffness of cells varies during cancer progression. We believe that the stiffness of invasive cells depending on the stiffness of their non-invasive counterparts is either decreased or increased to reach the critical condition in which the mobility potential of cells is approximated to be maximum.</p>
Optimization data for determination of montelukast and bambuterol as a combined medication
<p>Sensitive, simple and green analytical methodology for simultaneous estimation of bambuterol and montelukast as a combined medication based on their native fluorescence character was developed. The method relies on synchronous spectrofluorimety to solve the problem of the overlapping emission spectra of the studied drugs. Where, utilizing second derivative synchronous spectra enabled the simultaneous quantitation of bambuterol and montelukast without interference. The peak amplitudes of the aqueous solutions at Δλ = 20 nm were estimated at 284 nm & 304 nm for bambuterol and at 374 nm & 384 nm for montelukast. A linear relationship was achieved over the concentration range of 0.2–1.00 μg/mL for bambuterol and 0.4–2.00 μg/mL for montelukast. All factors and parameters were carefully studied to obtain the highest sensitivity and good precision of the proposed method. Additionally, the validation criteria were assessed in accordance with ICH guidelines. The method was utilized for the estimation of both drugs in their raw materials, synthetic mixtures as well their combined tablets with good agreement between its results and those from the comparison method.</p>
Multi-objective optimization data (large scale truss)
<p>The file contains data obtained from the multi-objective optimization of a large scale truss</p>
Data from: Conserving the genetic diversity of condemned populations: optimizing collections and translocation
<p>We generated SNP genotype data from two endangered plant species, <em>Pimelea spicata</em> and <em>Eucalyptus</em> sp. Cattai. For each, we genotyped plants from a population that was 'condemned,' or that would soon be destoyed. We used the genotype data to design ex situ germplasm collections that preserved the diversity in the condemned populations, in ways that were optimized. Here, we provide an archive of these SNP genotype data, and the code that was used to analyse these data, including the optimizations.</p>
Data from: Using optimal control to disambiguate the effect of depression on sensorimotor, motivational and goal-setting functions
Differentiating the ability from the motivation to act is of central importance to psychiatric disorders in general and depression in particular. However, it has been difficult to develop quantitative approaches to relate depression to poor motor performance in goal-directed tasks. Here, we use an inverse optimal control approach to provide a computational framework that can be used to infer and factorize performance deficits into three components: sensorimotor speed, goal setting and motivation. Using a novel computer-simulated driving experiment, we found that (1) severity of depression is associated with both altered sensorimotor speed and motivational function; (2) moderately to severely depressed individuals show an increased distance from the stop sign indicating aversive learning affecting goal setting functions. Taken together, the inverse optimal control framework can disambiguate on an individual basis the sensorimotor from the motivational dysfunctions in depression, which may help to develop more precisely targeted interventions.
Data from: Optimizing techniques to capture and extract environmental DNA for detection and quantification of fish
Few studies have examined capture and extraction methods for environmental DNA (eDNA) to identify techniques optimal for detection and quantification. In this study, precipitation, centrifugation and filtration eDNA capture methods and six commercially available DNA extraction kits were evaluated for their ability to detect and quantify common carp (Cyprinus carpio) mitochondrial DNA using quantitative PCR in a series of laboratory experiments. Filtration methods yielded the most carp eDNA, and a glass fibre (GF) filter performed better than a similar pore size polycarbonate (PC) filter. Smaller pore sized filters had higher regression slopes of biomass to eDNA, indicating that they were potentially more sensitive to changes in biomass. Comparison of DNA extraction kits showed that the MP Biomedicals FastDNA SPIN Kit yielded the most carp eDNA and was the most sensitive for detection purposes, despite minor inhibition. The MoBio PowerSoil DNA Isolation Kit had the lowest coefficient of variation in extraction efficiency between lake and well water and had no detectable inhibition, making it most suitable for comparisons across aquatic environments. Of the methods tested, we recommend using a 1.5 μm GF filter, followed by extraction with the MP Biomedicals FastDNA SPIN Kit for detection. For quantification of eDNA, filtration through a 0.2–0.6 μm pore size PC filter, followed by extraction with MoBio PowerSoil DNA Isolation Kit was optimal. These results are broadly applicable for laboratory studies on carps and potentially other cyprinids. The recommendations can also be used to inform choice of methodology for field studies.
Data from: Optimized modelling and application of exhaust gas recirculation performance evaluation of turbocharged diesel engine
Aimed at the problem of turbocharged diesel engine EGR (exhaust gas recirculation) performance evaluation and optimal EGR rate determination, especially in some cases with poor data because of the limit of conditions, an optimized multi-objective grey decision method based on grey theory and entropy weight is proposed. The internal combustion pressure ,fuel consumption rate, NOX ,CO and smoke degree were selected as the decision target, and the initial decision-making model was established based on traditional grey situation theory. According to the characteristics and optimization requirements of EGR, The optimal compromise between combustion and emission performance is proposed to transform into the decision-making target weight problem , then a subjective weight optimization method based on expert scoring and grey relational analysis is proposed. In addition, the entropy weight method was used to solve the subjective and objective weight vector and the optimized multi-objective grey situation decision making model was established, which can not weaken the human error of subjective empowerment, but also fully explore the intrinsic objective relationship of the evaluation index parameters. At last, an optimization simulation platform for EGR performance evaluation based MATLB/GUIDE is designed and established. The results show that the optimization simulation platform can effectively improve the efficiency of simulation calculation, which is more convenient for practical engineering applications; the optimized method can successfully solve the EGR performance evaluation and optimal EGR rate determination under different working conditions, the decision result is reasonable and consistent with the prevailing principle of the EGR determining, which provide a new research ideas for EGR performance optimization.
Data from: Optimal numbers of matings: the conditional balance between benefits and costs of mating for females of a nuptial gift-giving spider
In species where females gain a nutritious nuptial gift during mating, the balance between benefits and costs of mating may depend on access to food. This means that there is not one optimal number of matings for the female but a range of optimal mating numbers. With increasing food availability, the optimal number of matings for a female should vary from the number necessary only for fertilization of her eggs to the number needed also for producing these eggs. In three experimental series, the average number of matings for females of the nuptial gift-giving spider Pisaura mirabilis before egg sac construction varied from 2 to 16 with food-limited females generally accepting more matings than well-fed females. Minimal level of optimal mating number for females at satiation feeding conditions was predicted to be 2–3; in an experimental test, the median number was 2 (range 0–4). Multiple mating gave benefits in terms of increased fecundity and increased egg hatching success up to the third mating, and it had costs in terms of reduced fecundity, reduced egg hatching success after the third mating, and lower offspring size. The level of polyandry seems to vary with the female optimum, regulated by a satiation-dependent resistance to mating, potentially leaving satiated females in lifelong virginity.
Data from: Memory and fitness optimization of bacteria under fluctuating environments
Bacteria prudently regulate their metabolic phenotypes by sensing the availability of specific nutrients, expressing the required genes for their metabolism, and repressing them after specific metabolites are depleted. It is unclear, however, how genetic networks maintain and transmit phenotypic states between generations under rapidly fluctuating environments. By subjecting bacteria to fluctuating carbon sources (glucose and lactose) using microfluidics, we discover two types of non-genetic memory in Escherichia coli and analyze their benefits. First, phenotypic memory conferred by transmission of stable intracellular lac proteins dramatically reduces lag phases under cyclical fluctuations with intermediate timescales (1–10 generations). Second, response memory, a hysteretic behavior in which gene expression persists after removal of its external inducer, enhances adaptation when environments fluctuate over short timescales (<1 generation). Using a mathematical model we analyze the benefits of memory across environmental fluctuation timescales. We show that memory mechanisms provide an important class of survival strategies in biology that improve long-term fitness under fluctuating environments. These results can be used to understand how organisms adapt to fluctuating levels of nutrients, antibiotics, and other environmental stresses.
Data from: The Shortlist Method for fast computation of the Earth Mover's Distance and finding optimal solutions to transportation problems
Finding solutions to the classical transportation problem is of great importance, since this optimization problem arises in many engineering and computer science applications. Especially the Earth Mover's Distance is used in a plethora of applications ranging from content-based image retrieval, shape matching, fingerprint recognition, object tracking and phishing web page detection to computing color differences in linguistics and biology. Our starting point is the well-known revised simplex algorithm, which iteratively improves a feasible solution to optimality. The Shortlist Method that we propose substantially reduces the number of candidates inspected for improving the solution, while at the same time balancing the number of pivots required. Tests on simulated benchmarks demonstrate a considerable reduction in computation time for the new method as compared to the usual revised simplex algorithm implemented with state-of-the-art initialization and pivot strategies. As a consequence, the Shortlist Method facilitates the computation of large scale transportation problems in viable time. In addition we describe a novel method for finding an initial feasible solution which we coin Modified Russell's Method.
Data from: Data for evaluation of fast kurtosis imaging, b-value optimization and exploration of diffusion MRI contrast
Here we describe and provide diffusion magnetic resonance imaging (dMRI) data that was acquired in neural tissue and a physical phantom. Data acquired in biological tissue includes: fixed rat brain (acquired at 9.4T) and spinal cord (acquired at 16.4T) and in normal human brain (acquired at 3T). This data was recently used for evaluation of diffusion kurtosis imaging (DKI) contrasts and for comparison to diffusion tensor imaging (DTI) parameter contrast. The data has also been used to optimize b-values for ex vivo and in vivo fast kurtosis imaging. The remaining data was obtained in a physical phantom with three orthogonal fiber orientations (fresh asparagus stems) for exploration of the kurtosis fractional anisotropy. However, the data may have broader interest and, collectively, may form the basis for image contrast exploration and simulations based on a wide range of dMRI analysis strategies.
Data from: Optimizing the spatial planning of prescribed burns to achieve multiple objectives in a fire-dependent ecosystem.
1. There is potential for negative consequences for the ecological integrity of fire-dependent ecosystems as a result of inappropriate fire regimes. This can occur when asset (property) protection is prioritised over conservation objectives in burn programs. 2. Optimisation of fire management for multiple objectives is rarely undertaken. Here, we use integer linear programming to identify burn scheduling solutions that will cost-effectively achieve asset protection and conservation objectives. 3. An approach to burn scheduling that favours a risk-averse asset protection strategy results in poor conservation outcomes. Conversely, a conservation-focused approach achieves only modest asset protection benefits. However, when formulated as a multi-objective problem, good conservation outcomes can be realised with only a small reduction in potential benefits for asset protection. 4. A conservation-focused approach resulted in substantially more heterogeneity in burns at multiple spatial scales and a marked reduction in mean time since fire among all forest patches relative to an asset protection scenario. This increase in heterogeneity improves ecological integrity, while the resulting reduction in fuel load is beneficial for asset protection. 5. Synthesis and applications. Mathematical optimisation is a powerful framework for informing fire management that improves the prioritisation and scheduling of controlled burns to efficiently achieve management objectives. By quantifying the trade-offs that exist between the two competing objectives of conservation and asset protection we demonstrate that compromise solutions can be identified that achieve good outcomes for both objectives. In a transparent and equitable manner, we show that conservation value may be improved within a fire-dependent ecosystem with only modest concession to asset protection performance. Explicitly evaluating trade-offs among competing objectives enables managers to identify potentially undesirable outcomes, and facilitate development of preferred solutions. Heterogeneous burning under the auspices of conservation also has the potential to reduce overall fuel loads within the ecosystem and thus its value for asset protection is likely underappreciated.
Allocating resources for land protection using continuous optimization: an application to US biodiversity conservation
Spatial optimization approaches that were originally developed to help conservation organizations determine protection decisions over small spatial scales are now used to inform global or continental scale priority setting. However, the different decision contexts involved in large-scale resource allocation need to be considered. We present a continuous optimization approach in which a decision-maker allocates funding to regional offices. Local decision-makers then use these funds to implement habitat protection efforts with varying effectiveness when evaluated in terms of the funder's goals. We illustrate this continuous formulation by examining the relative priority that should be given to different counties in the coterminous United States (US) when acquiring land to establish new protected areas. If weighting all species equally, counties in the southwest US, where large areas can be bought cheaply, are priorities for protection. If focusing only on species of conservation concern, priorities shift to locations rich in such species, particularly near expanding exurban areas facing high rates of future habitat conversion (e.g., south-central Texas). Priorities for protection are sensitive to what is assumed about local ecological and decision-making processes. For example, decision-makers who doubt the efficacy of local land protection efforts should focus on a few key areas, while optimistic decision-makers should disperse funding more widely. Efforts to inform large-scale conservation priorities should reflect better the types of choice that decision-makers actually face when working over these scales. They also need to report the sensitivity of recommended priorities to what are often unstated assumptions about local processes affecting conservation outcomes.
Data from: Don't put all your eggs in one basket: a cost-effective and powerful method to optimize primer choice for rRNA environmental community analyses using the Fluidigm Access Array
With the increasing democratization of high-throughout sequencing (HTS) technologies, along with the concomitant increase in sequence yield per dollar, many researchers are exploring HTS for microbial community ecology. Many elements of experimental design can drastically affect the final observed community structure, notably the choice of primers for amplification prior to sequencing. Some targeted microbes can fail to amplify due to primer-targeted sequence divergence and be omitted from obtained sequences, leading to differences among primer pairs in the sequenced organisms even when targeting the same community. This potential source of taxonomic bias in HTS makes it prudent to investigate how primer choice will affect the sequenced community prior to investing in a costly community-wide sequencing effort. Here we use Fluidigm's micro-fluidic Access Arrays (IFC) followed by Illumina® MiSeq Nano sequencing on a culture-derived local mock community to demonstrate how this approach allows for a low-cost combinatorial investigation of primer pairs and experimental samples (up to 48 primer pairs and 48 samples) to determine the most effective primers that maximize obtained communities whilst minimizing taxonomic biases.
Data from: Optimized flocking of autonomous drones in confined environments
We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous existing flocking models are rarely tested on actual hardware because they typically neglect some crucial aspects of multirobot systems. Constrained motion and communication capabilities, delays, perturbations, or the presence of barriers should be modeled and treated explicitly because they have large effects on collective behavior during the cooperation of real agents. Handling these issues properly results in additional model complexity and a natural increase in the number of tunable parameters, which calls for appropriate optimization methods to be coupled tightly to model development. In this paper, we propose such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 30 drones. This is the largest of such aerial outdoor systems without central control reported to date exhibiting flocking with collective collision and object avoidance. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.
Data from: Using branch-and-bound algorithms to optimize selection of a fixed-size breeding population under a relatedness constraint
Tree breeders often face the challenge of conserving genetic diversity, while at the same time maximizing response to selection. When selecting advanced-generation breeding populations, the best-performing candidates will quite often be closely related and selecting them without consideration of their relatedness will very quickly erode genetic diversity. Optimal selection will not completely avoid kinship, but rather maximize gain while imposing a constraint on average relatedness. Genetic contributions are most easily optimized if breeders can manage a real, continuous distribution of contributions from parents. While generally possible when establishing seed orchards, unequal contributions to a breeding population may present difficult and time-consuming operational constraints. In these situations, a specified number of parents contributing equally may be a preferred configuration for the breeding population. Here we formulate the selection of a fixed-size breeding population while imposing a constraint on relatedness of the population members. The problem is expressed as a Mixed Integer Quadratically Constrained Optimization (MIQCO) and solved using branch-and-bound techniques (BB). An open-source solver, dsOpt, was developed and embedded into a user-friendly tool, OPSEL, designed to simplify the process of optimizing selection of breeding populations. Case studies optimizing selection of breeding populations for Scots pine and loblolly pine illustrate the superiority of the BB solution compared with selection from ranked lists with restrictions on numbers of genotypes contributed by each full-sib family, and with solutions from GENCONT, a publically available optimum selection program using an algorithm with Lagrangian multipliers. The case studies also illustrate the extreme differences that can occur with respect to time required to confirm the optimality of solutions found by BB.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.