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5,573 results for “optimality”
Tree seedling trait optimization and growth in response to local-scale soil and light variability
At local scales, it has been suggested that high levels of resources lead to increased tree growth via trait optimization (highly peaked trait distribution). However, this contrasts with (i) theories that suggest that trait optimization and high growth occur in the most common resource level and (ii) empirical evidence showing that high trait optimization can be also found at low resource levels. This raises the question of how are traits and growth optimized in highly diverse plant communities? Here, we propose a series of hypotheses about how traits and growth are expected to be maximized under different resource levels (low, the most common, and high) in tree seedling communities from a subtropical forest in Puerto Rico. We studied the variation in the distribution of biomass allocation and leaf traits and seedlings growth rate along four resource gradients: light availability (canopy openness) and soil K, Mg, and N contents. Our analyses consisted of comparing community trait means, trait kurtosis (a measurement of trait optimization), and relative growth rates at three resource levels (low, common, and high). Trait optimization varied across the three resource levels depending on the type of resource and trait, with leaf traits being optimized under high N and in the most common K and Mg conditions, but not at any of the light levels. Also, seedling growth increased at high light conditions and high N and K but was not related to trait kurtosis. Our results indicate that local-scale variability of soil fertility and understory light conditions result in shifts in species ecological strategies that increase growth despite a weak trait optimization, suggesting the existence of alternative phenotypes that achieve similar high performance. Uncovering the links between abiotic factors, functional trait diversity and performance is necessary to better predict tree responses to future changes in abiotic conditions.
Optimization-based upscaling for gravity segregation with 3D capillary heterogeneity effects
<p>Fine-scale and upscaled simulation input files and MATLAB codes for optimization</p>
Dataset of the paper: "POLICIER: Pareto-OptimaL allocatIon of Continuous IntEgration Resources"
<p>This repository contains the dataset of the paper "POLICIER: Pareto-OptimaL allocatIon of Continuous IntEgration Resources"</p>
Green fluorescent protein-like pigments optimize the internal light environment in symbiotic reef building corals
<p>Pigments homologous to the Green Fluorescent Protein (GFP) have been proposed to fine-tune the internal light microclimate of corals, facilitating photoacclimation of photosynthetic coral symbionts (Symbiodiniaceae) to life in different reef habitats and environmental conditions. However, direct measurements of the <i>in vivo</i> light conditions inside the coral tissue supporting this conclusion are lacking. Here, we quantified the intra-tissue spectral light environment of corals expressing GFP-like proteins from widely different light regimes. We focus on (1) photoconvertible red fluorescent proteins (pcRFPs), thought to enhance photosynthesis in mesophotic habitats via wavelength conversion, and (2) chromoproteins (CPs), which provide photoprotection to the symbionts in shallow water via light absorption. Optical microsensor measurements indicated that both pigment groups strongly alter the<i> </i>coral tissue light environment. Estimates derived from light spectra measured in pcRFP-containing corals showed that fluorescence emission can contribute to >50% of orange-red light available to the photosynthetic symbionts at mesophotic depths. We further show that upregulation of pink CPs in shallow-water corals during bleaching leads to a reduction of orange light by 10-20% compared to low-CP tissue. Thus, screening by CPs has an important role in mitigating the light-enhancing effect of coral tissue scattering during bleaching. Our results provide the first experimental quantification of the importance of GFP-like proteins in fine-tuning the light microclimate of corals during photoacclimation.</p>
Artifact for paper "Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?" published at IEEE SANER 2022
<p>The zip file contains all the source code and data for the experiments.</p>
Optimizing Continuous Integration by Dynamic Test Selection
<p>Replication package include source code and dataset.</p>
Data set for reliability-based lift-to-power consumption optimization with an accelerated Kriging model for clapping-wing micro air vehicles
<p>Procedures of the reliability-based lift-to-power consumption optimization with an accelerated Kriging model</p> <p>Step 1: Run the file “LHS.m” to generate initial samples.</p> <p>Step 2: Modify the aerodynamic model according to initial samples (e.g. flapping1_Def.xml, flapping1.bat), and then run the “.bat file” to obtain the original force data.</p> <p>Step 3: Run the file “Kriging.m” to obtain the average lift using a filter.</p> <p>Step 4: Run the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result.</p> <p>Step 5: Find the new training sample and obtain the eigenvalue of the new training sample.</p> <p>Step 6: Rerun the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result by reloading the new “.mat” files (e.g. FW_2_41.mat, FW_2_P_20.mat).</p> <p>Step 7: Go to Step 4 until the convergence criteria are satisfied.</p> <p>Step 8: Obtain the optimal result. PS: Other files are function files.</p>
Current nest box designs may not be optimal for the larger forest dormice; pre-hibernation increase in body mass might lead to sampling bias in ecological data
<p>Biologists commonly use nest boxes to study small arboreal mammals, including forest dormouse (Dryomys nitedula). Hibernating dormouse species often experience pronounced seasonal variations in body mass, which might lead to sampling biases if it is not taken into account when designing nest boxes. In our study of forest dormouse, we noticed that the entrance hole of nest boxes had been gnawed on. We hypothesized that this behavior was exhibited by individual dormice who had higher body mass and, therefore, were unable to pass through the entrance holes. To test our hypothesis, we categorized individual dormice present inside nest boxes based on their body mass; then compared the seasonal body mass dynamics with the timing of the gnawing behavior. We also compared nest box occupancy by forest dormouse before and after the gnawing behavior. Interestingly, we found that the gnawing behavior was displayed exclusively when part of the dormouse population increased considerably in body mass, which supports our hypothesis. Additionally, nest box occupancy decreased significantly from 20% before to 4.6% after the gnawing behavior. We suggest that researchers use nest boxes with entrance holes larger than 4 cm in future studies of forest dormouse to prevent the possible exclusion of the conspecifics that have higher body mass before hibernation. This type of sampling bias can probably happen in studies of other species, such as fat dormouse, that similarly show pronounced seasonal variations in body mass. We recommend that biologists consider the seasonal body mass dynamics of the target species when designing nest boxes to minimize bias in ecological data and improve management actions.</p>
Improving results from touch DNA evidence with optimized direct PCR methods
<p>Improved methods to generate high-quality DNA profiles from touch DNA samples are of considerable interest to forensic DNA laboratories. Direct polymerase chain reaction (PCR) amplification, a sample processing method in which samples are added directly to amplification reactions without prior purification or quantification, has been identified as a method that may improve the generation of genotyping data from such samples; however, laboratories in the United States are required to use standard DNA processing methods to process low-level sample types. In part, this is due to FBI Quality Assurance Standard (QAS) 9.4, which requires all unknown forensic samples to undergo human-specific DNA quantification prior to amplification of short tandem repeat (STR) loci. The goal of this 3-year study was to generate data in support of a reevaluation of QAS 9.4.</p> <p>In two phases, this study examined the following: direct PCR-compatible collection methods in conjunction with mock touch DNA evidence samples on a variety of substrates (Phase I), direct PCR of touch DNA samples that were stored at room temperature for up to six months after collection with the optimum methods identified in Phase I, and direct PCR of touch DNA samples that were re-sampled after initial processing. Nine collection methods and various substrates were examined in Phase I, and three time points were examined in Phase II. In both phases, two processing methods were used: standard processing samples were extracted, quantified, and amplified in accordance with the QAS; and direct PCR samples were directly amplified. All STR profiles were assessed for overall profile quality.</p> <p>This project was supported by Award No. 2019-DU-BX-0009, awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect those of the Department of Justice.</p>
Materials Science Optimization Benchmark Dataset for Multi-fidelity Hard-sphere Packing Simulations
Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a "Turing test" of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 438371 random hard-sphere packing simulations representing 279 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets can be used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.
Molecular nanomagnets with competing interactions as optimal units for qudit-based quantum computation. Open data set
<p>Data supporting the original figures 1, 3, 4, 5, 6 and 7 of the related publication.</p>
Optimizing Performance and Energy Across Problem Sizes Through a Search Space Exploration and Machine Learning - dataset
<p>Dataset used for a journal submission at JPDC.</p>
TinyTLA: Topological Landscape Analysis for Optimization Problem Classification in a Limited Sample Setting
<p>Topological features as well as the AutoGluon classification accuracies</p>
Pharmacokinetics and pharmacodynamics of imatinib for optimal drug repurposing from cancer to COVID-19
<p><strong>Introduction:</strong> In the randomized double-blind placebo-controlled CounterCOVID study, oral imatinib treatment<br> conferred a positive clinical outcome and a signal for reduced mortality in COVID-19 patients. High concen-<br> trations of alpha-1 acid glycoprotein (AAG) were observed in these patients and were associated with increased<br> total imatinib concentrations.<br> <strong>Aims:</strong> This post-hoc study aimed to compare the difference in exposure following oral imatinib administration in<br> COVID-19 patients to cancer patients and assess assocations between pharmacokinetic (PK) parameters and<br> pharmacodynamic (PD) outcomes of imatinib in COVID-19 patients. We hypothesize that a relatively higher drug<br> exposure of imatinib in severe COVID-19 patients leads to improved pharmacodynamic outcome parameters.<br> <strong>Methods:</strong> 648 total concentration plasma samples obtained from 168 COVID-19 patients were compared to 475<br> samples of 105 cancer patients, using an AAG-binding model. Total trough concentration at steady state (Cttrough)<br> and total average area under the concentration-time curve (AUCtave) were associated with ratio between partial<br> oxygen pressure and fraction of inspired oxygen (P/F), WHO ordinal scale (WHO-score) and liberation of oxygen<br> supplementation (O2lib). Linear regression, linear mixed effects models and time-to-event analysis were adjusted<br> for possible confounders.<br> <strong>Results:</strong> AUCtave and Cttrough were respectively 2.21-fold (95%CI 2.07–2.37) and 1.53-fold (95%CI 1.44–1.63)<br> lower for cancer compared to COVID-19 patients. Cttrough, not AUCtave, associated significantly with P/F<br> (β=-19,64; p-value=0.014) and O2lib (HR 0.78; p-value= 0.032), after adjusting for sex, age, neutrophil-<br> lymphocyte ratio, dexamethasone concomitant treatment, AAG and baseline P/F-and WHO-score. Cttrough, but<br> not AUCtave associated significantly with WHO-score. These results suggest an inverse relationship between PK-<br> parameters, Cttrough and AUCtave, and PD outcomes.<br> <strong>Conclusion:</strong> COVID-19 patients exhibit higher total imatinib exposure compared to cancer patients, attributed to<br> differences in plasma protein concentrations. Higher imatinib exposure in COVID-19 patients did not associate<br> with improved clinical outcomes. Cttrough and AUCtave inversely associated with some PD-outcomes, which may be biased by disease course, variability in metabolic rate and protein binding. Therefore, additional PKPD an-<br> alyses into unbound imatinib and its main metabolite may better explain exposure-response.</p>
Particle tracking algorithm and additional data for "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)"
<p>This repository provides additional files for in the publication "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)" by Stefan Dittmar, Aki Sebastian Ruhl and Martin Jekel (DOI: <a href="https://www.doi.org/10.1021/acsestwater.3c00457">10.1021/acsestwater.3c00457</a>) </p><p>It contains:</p><p>- image processing routine for particle tracking written in Python (<strong>1_particle_tracking_algorithm.zip</strong>)<br>- single particle raw data from settling experiments (<strong>2_settling_data.zip</strong>)<br>- single particle data from appyling empirical model for interactions between settling particles (<strong>3_model_results_data.zip</strong>)<br>- additional video & animated graph referenced in publication or SI (<strong>4_videos.zip</strong>)</p>
Enhancing Accessibility of Web-based SVG Buttons: An Optimization Method and Best Practices
<p>Here is the relevant dataset and open-source code for the article titled "Enhancing Accessibility of Web-based SVG Buttons: An Optimization Method and Best Practices"</p> <p><strong>Introduction</strong></p> <p>In this paper, we propose a novel approach, named SAM, to classify SVG buttons, create feature templates within these buttons, and then automatically fill their desired alternative texts.</p>
Model output after parameter optimization for NEMO4.0-SI3
<p>Using the default configuration of NEMO4.0-SI3 (version 4.0.7 revision 15731), the 2003-2009 seasonal mean Antarctic sea ice concentration budget was obtained after adjusting the following three parameters: rn_pstar=3.298e+04, rn_cio=0.006094, Cd_ice=0.0009563.</p> <p>Both files are in npz format and can be read using python. ''Benchmark_budget_1to12_NEMO4-SI3-optim.npz'' file contains 4 fields: 'dadt_seasonal', 'adv_seasonal', 'div_seasonal' and 'res_seasonal', each of size [145, 145, 4], corresponding to lon, lat and season. The meaning of each field can be found in Nie et al. (2023), noting that here is the seasonal average for the years 2003-2009.</p> <p>The ''Klonlat.npz'' file contains 3 fields, 'Klon' (longitude), 'Klat' (latitude) and 'K_A' (grid area), all of size [145, 145].</p>
Fig. 5 in Sesquiterpenoids and their quaternary ammonium hybrids from the mycelium of mushroom Stereum hirsutum by medium optimization
Fig. 5. Experimental CD spectra of 3–5 and TDDFT-calculated CD spectra of 5.
Fig. 4 in Sesquiterpenoids and their quaternary ammonium hybrids from the mycelium of mushroom Stereum hirsutum by medium optimization
Fig. 4. Key ROESY correlations of 2–5.
Fig. 1 in Sesquiterpenoids and their quaternary ammonium hybrids from the mycelium of mushroom Stereum hirsutum by medium optimization
Fig. 1. The LC-MS profiles of extracts for S. hirsutum FP-91666 cultures on five media.
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.