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86 results for “resource allocation”
Resource allocation decision-making in dementia care with and without budget constraints: a qualitative analysis
<p>Vignettes and COREQ checklist for Applied Partnership Award project: Resource Allocation, Priority-Setting and Consensus in Dementia Care. See Keogh F, Pierse T, O'Shea E <em>et al.</em> Resource allocation decision-making in dementia care with and without budget constraints: a qualitative analysis [version 1; peer review: awaiting peer review]. <em>HRB Open Res</em> 2020, <strong>3</strong>:69 (<a href="https://doi.org/10.12688/hrbopenres.13147.1">https://doi.org/10.12688/hrbopenres.13147.1</a>)</p>
Resource allocation underlies parental decision-making during incubation in the Manx shearwater
<p>Examining resource allocation is fundamental to understanding the relationships between a species' behaviour and its life history. Furthermore, for biparentally-caring animals, examining the relative investment decisions made by members of a breeding pair can give insight into the extent and nature of cooperative care. As a key measure of resource availability, examining body mass changes can help elucidate the ways in which parents balance their allocation. In birds, these trade-offs become particularly stark during incubation, as maintaining constant egg warming usually requires one parent to fast. This period therefore represents a key opportunity to investigate investment decisions. We took daily measurements of body mass from breeding Manx shearwaters, a biparentally-caring seabird, during incubation, and related this to measures of nest attendance and behaviour collected using field observations and miniaturised biologgers. We investigated how changes in body mass related to the decisions made at the nest and at sea, whether this differed between the sexes, and whether pair experience influenced incubation behaviour. We found that while body mass predicted the probability that incubating birds would choose to temporarily desert the nest, incubation shift duration was ultimately set by return of the foraging bird. The trip durations of foraging birds in turn were primarily dictated by their body mass reserves on departure from the nest. However, foragers appeared to account for the condition of the incubating partner, returning from sea earlier when their partner was in poor condition. Our results contribute to understanding the mechanisms by which individuals regulate both their own and their partner's incubation behaviour, with implications for interacting with fine-scale resource availability.</p> <p> </p>
Data from: Pleiotropy alleviates the fitness costs associated with resource allocation trade-offs in immune signaling networks
<p>Many genes and signaling pathways within plant and animal taxa drive the expression of multiple organismal traits. This form of genetic pleiotropy instigates trade-offs among life-history traits if a mutation in the pleiotropic gene improves the fitness contribution of one trait at the expense of another. Whether or not pleiotropy gives rise to conflict among traits, however, likely depends on the resource costs and timing of trait deployment during organismal development. To investigate factors that could influence the evolutionary maintenance of pleiotropy in gene networks, we developed an agent-based model of co-evolution between parasites and hosts. Hosts comprise signaling networks that must faithfully complete a developmental program while also defending against parasites, and trait signaling networks could be independent or share a pleiotropic component as they evolved to improve host fitness. We found that hosts with independent developmental and immune networks were significantly more fit than hosts with pleiotropic networks when traits were deployed asynchronously during development. When host genotypes directly competed against each other, however, pleiotropic hosts were victorious regardless of trait synchrony because the pleiotropic networks were more robust to parasite manipulation, potentially explaining the abundance of pleiotropy in immune systems despite its contribution to life history trade-offs.</p>
Datasets for input and output of INFORM Severity-based SMAA study of resource allocation in humanitarian aid and disaster management under climatic losses and damages
<p>The landscape of climate change and extreme events will remain a wicked problem for equitable and forward-looking resource prioritisation. The question of how to couple climate and multi-risk information remains. IPCC has considered that multi-criteria decision analysis (MCDA) can help.</p> <p>We use stochastic multi-attribute analysis (SMAA), a variant of MCDA, to compute prioritisations of climatic losses & damages (l&d) for fragile countries with a humanitarian response plan. SMAA is combined with the INFORM Severity index, measuring the status of crises and disasters, and preferences gathered from stakeholders (e.g., United Nations, European Union, World Bank, the research and public sector, civil society).</p> <ul> <li><strong>Dataset S1. </strong>XLS-file with all the input data compiled from sources, concurrent data manipulation, and descriptions of steps taken until ready for the SMAA.</li> <li><strong>Dataset S2.</strong> XLS-file with results of the SMAA for all weight schemes and concurrent analysis, such as sensitivity heat mapping, correlations, regressions, and Tukey mean-difference plot.</li> </ul>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 6. Comparison on resource utilization.
<p>Figure 6 shows resource utilization in different system loads and as shown in it, in ICDA<br> resource utilization is more efficient than other methods especially in higher system load which is<br> due to tradeoff and sharing factors.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 4. Sharing and tradeoff factor effect on resource utilization
<p>In Figure 4 we consider tradeoff and sharing factors in providers. The result illustrates that<br> by using these factors providers improve resource utilization. Higher resource utilization motivates<br> more providers to participate in the cloud and also enables the cloud market to handle more<br> consumers which influences market efficiency.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 1. Resource allocation schema in proposed method
<p>We assume that the resources allocation satisfies the following conditions:<br> • The quantity of a resource can be measured in arbitrary units (e.g. 60 units of resource<br> A).<br> • A resource can be divided into an arbitrary fraction (e.g. a resource of 60 units is divided<br> into 20 units for consumer 1 and 40 units for consumer 2).<br> • A resource request of a service can be divided into sub-requests and acquired from<br> multiple providers (e.g. a resource request of 40 units utilized as 10 units from provider<br> 1 and 30 units from provider 2).<br> Figure 1 shows a cloud computing environment with the proposed mechanism.</p>
Lack of pollinators selects for increased selfing, restricted gene flow and resource allocation in the rare Mediterranean sage Salvia brachyodon
<p>Salvia brachyodon (Lamiaceae): raw data on flower morphometry, nectar concentration and volume and seed weight according various pollination treatments.</p>
Data from: Neural mechanisms of resource allocation in working memory
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Energy allocation explains how protozoan phenotypic traits change in response to temperature and resource supply
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Resource allocation to a structural biomaterial: induced production of byssal threads decreases growth of a marine mussel
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Resource allocation underlies parental decision-making during incubation in the Manx shearwater
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Data from: Pleiotropy alleviates the fitness costs associated with resource allocation trade-offs in immune signaling networks
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Data for: Relatively rare root endophytic bacteria drive plant resource allocation patterns and tissue nutrient concentration in unpredictable ways
<p><span><span><span><span><span><span><span><span><span><span><span><b>Premise of Study</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Plant endophytic bacterial strains can influence plant traits such as leaf area and root length. Yet, the influence of more complex bacterial communities in regulating overall plant phenotype is less explored. Here, we conducted two complementary experiments to test if we can predict plant phenotype response to changes in microbial community composition. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>In the first study, we inoculated a single genotype of <i>Populus deltoides</i>with individual root endophytic bacteria and measured plant phenotype. Next, single inoculation data were used to predict phenotypic traits in mixed three-member community inoculations, which we tested in the second experiment. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Key Results</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>When in isolation, each bacterial endophyte significantly but weakly altered plant phenotype relative to non-inoculated plants. In mixture, bacterial strain <i>Burkholderia</i>BT03, constituted at least 98% of community relative abundance. Yet, plant resource allocation and tissue nutrient concentrationswere disproportionately influenced by <i>Pseudomonas </i>sp.GM17, GM30, and GM41. We found a 10% increase in leaf mass fraction and a 11% decrease in root mass fraction when replacing<i>Pseudomonas </i>GM17 with GM41 in communities containing both <i>Pseudomonas </i>GM30 and <i>Burkholderia</i>BT03. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Our results indicate that interactions among endophytic bacteria may drive plant phenotype over the contribution of each strain individually. Additionally, we have shown that low-abundant strains contribute to plant phenotype challenging the assumption that the dominant strains will drive plant function.</span></span></span></span></span></span></span></span></span></span></span></p>
Optimal resource allocation and prolonged dormancy strategies in herbaceous plants
<p>1. Understanding the fitness consequences of different life histories is critical for explaining their diversity and for predicting effects of changing environmental conditions. However, current theory on plant life histories relies on phenomenological, rather than mechanistic, models of resource production.</p> <p>2. We combined a well-supported mechanistic model of ontogenetic growth that incorporates differences in the size-dependent scaling of gross resource production and maintenance costs with a dynamic optimization model to predict schedules of reproduction and prolonged dormancy (plants staying below ground for ≥ 1 growing season) that maximize lifetime offspring production.</p> <p>3. Our model makes three novel predictions: First, maintenance costs strongly influence the conditions under which a monocarpic or polycarpic life history evolves and how resources should be allocated to reproduction by polycarpic plants. Second, in contrast to previous theory, our model allows plants to compensate for low survival conditions by allocating a larger proportion of resources to storage and thereby improving overwinter survival. Incorporating this ecological mechanism in the model is critically important because without it our model never predicts significant investment into storage, which is inconsistent with empirical observations. Third, our model predicts that prolonged dormancy may evolve solely in response to resource allocation tradeoffs.</p> <p>4. Significance: Our findings reveal that maintenance costs and the effects of resource allocation on survival are primary determinants of the fitness consequences of different life history strategies, yet previous theory on plant life history evolution has largely ignored these factors. Our findings also validate recent arguments that prolonged dormancy may be an optimal response to costs of sprouting. These findings have broad implications for understanding patterns of plant life history variation and predicting plant responses to changing environments.</p>
Supplemental data to AALE 2024 publication "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"
<p>Release as supplementary material for our contribution at AALE 2024: "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"<br><br>The evaluation datasets stored in this collection are used to compare the performance of multi-agent reinforcement learning. In addition, the performance of other methods such as (meta-) heuristic algorithms or single agent reinforcement learning algorithms or novel methods of search space reduction can also be compared.</p>
Diet influences resource allocation in chemical defence in an aposematic moth
<p>For animals that synthesise their chemical compounds de novo, resources, particularly proteins, can influence investment in chemical defences and nitrogen-based wing colouration such as melanin. Competing for the same resources often leads to trade-offs in resource allocation. We manipulated protein availability in the larval diet of the wood tiger moth, <em>Arctia plantaginis</em>, to test how early life resource availability influences relevant life history traits, melanin production, and chemical defences. We expected higher dietary protein to result in more effective chemical defences in adult moths and a higher amount of melanin in the wings. According to the resource allocation hypothesis, we also expected individuals with less melanin to have more resources to allocate to chemical defences. We found that protein-deprived moths had a slower larval development, and their chemical defences were less unpalatable for bird predators, but the expression of melanin in their wings did not differ from that of moths raised on a high-protein diet. The amount of melanin in the wings, however, unexpectedly correlated positively with chemical defences. Our findings demonstrate that the resources available in early life have an important role in the efficacy of chemical defences, but melanin-based warning colours are less sensitive to resource variability than other fitness-related traits.</p>
Preferential allocation of benefits and resource competition among recipients allows coexistence of symbionts within hosts
<p>Functionally variable symbionts commonly co-occur including within the roots of individual plants, in spite of arguments from simple models of the stability of mutualism that predict competitive exclusion among symbionts. We explore this paradox by evaluating the dynamics generated by symbiont competition for plant resources, and the plant's preferential allocation to the most beneficial symbiont, using a system of differential equations representing the densities of mutualistic and non-mutualistic symbionts and the level of preferentially allocated and non-preferentially allocated resources for which the symbionts compete. We find that host preferential allocation and costs of mutualism generate resource specialization that makes the coexistence of beneficial and non-beneficial symbionts possible. Furthermore, coexistence becomes likely due to negative physiological feedbacks in host preferential allocation. We find that biologically realistic models of plant physiology and symbiont competition predict that the coexistence of beneficial and non-beneficial symbionts should be common in root symbioses, and that the density and relative abundance of mutualists should increase in proportion to the needs of the host.</p>
Exploratory pilot study on resource allocation along the dementia continuum under constrained and unconstrained budget scenarios
<p>Supporting quantitative data for a pilot longitudinal balance of care study. </p>
A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast
<p>Computational models and figure data for the publication "A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast" (preprint on <a href="https://doi.org/10.1101/2022.05.04.490403"><em>bioRxiv</em></a>). Data put together by Pranas Grigaitis, p.grigaitis [at] vu.nl.</p> <p> </p> <p><em>Abstract</em></p> <p>The fission yeast <em>Schizosaccharomyces pombe</em> is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, <em>S. pombe</em> also holds promise for use in metabolism research and industrial applications. However, unlike the baker’s yeast <em>Saccharomyces cerevisiae</em>, a major workhorse in these areas, cell physiology and metabolism of <em>S. pombe</em> remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of <em>S. cerevisiae</em> to generate a manually-curated high-quality reconstruction of <em>S. pombe’s</em> metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in <em>S. pombe</em>. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes, and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of <em>S. pombe</em> with <em>S. cerevisiae</em>, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these use cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behaviour and the role of resource allocation in driving different strategies in fission yeast.</p>
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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.