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64 results for “Dynamic optimization”
Scripts to perform dynamic optimization of a model of invasive aspergillosis
<p>Matlab scripts to perform optimization and analyses of a dynamic optimization model of invasive aspergillosis:<br> "Dynamic optimization reveals alveolar epithelial cells as key mediators of host defense in invasive aspergillosis"<br> Jan Ewald, Flora Rivieccio, Lukáš Radosa, Stefan Schuster, Axel A. Brakhage, Christoph Kaleta<br> https://doi.org/10.1101/2021.05.12.443764</p> <p>File structure:</p> <p>--- Core optimization files ---<br> -- simulation.cpp --<br> Description of ODE model in 'C', used by solver to calculate the time course given the parameters, initial states and control variables</p> <p>-- simulation.mexa64 --<br> Compiled mex-File from simulation.cpp used as interface by MATLAB to 'C' files.</p> <p>-- optFun.m --<br> Definition of the objective function. Uses a control vector 'u' to calculate the, objective function value F, the time course of state variables 'x' and gradients used by the solver to determine the optimal control of 'u'.</p> <p>-- single_opt.m --<br> Function which performs an optimization run for a given parameter set and a randomized initial solution u0. Optimization can be conducted by SNOPT or in conjunction with IPOPT. Both solver are not provided here (proprietary).</p> <p><br> --- Additional files ---<br> -- solve_with_ipopt.m --<br> Configuration file and interface to call the solver IPOPT.</p> <p>-- lognorm_mu.m --<br> Function to provide log-normal distributed samples given the standard deviation, reference value (mode of distribution) and number of samples which should be calculated.</p> <p>-- mice_param_alv.m --<br> Reference parameter set of the model for mice as host organism and cell numbers normalized per alveolus.</p> <p>-- parameter_model.m --<br> Definition of parameter container to handle interface to optFun-call by solvers which do not allow parameter in function call.</p> <p>--- Optimization Runs ---<br> -- rand_params.m --<br> Performs optimization runs for multiple randomized parameter sets and several scenarios to determine parameter sensitivity.</p> <p>-- doseresponse.m --<br> Performs optimization runs for a sequnce of initial conidial dosages to determine the dose response of the system.</p> <p>--- Scripts for post-analysis ---<br> -- rand_analysis.m --<br> Loads the data created by the optimization runs (rand_params.m) and performs parameter sensitivity analysis as well as produces plots for analysis related to parameter influence.</p> <p>-- doseanalysis.m --<br> Loads the data created by the optimization runs (doseresponse.m ) and produces dose response curves as well as additional plots and analyses.</p> <p>--- Optimization output files ---<br> -- rand_param_out/ --<br> Folder contains optimization results as MATLAB session-file for randomized parameters used for parameter sensitivity analysis.</p> <p>-- dose_out/ --<br> Folder contains optimization results as MATLAB session-file for dose response curve calculation.</p>
Data for: Optimal inference of molecular interaction dynamics in FRET microscopy
<p>Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible—a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise.</p>
Optimal prey for red fox cubs – an example of dual optimizing foraging strategy in foxes from a dynamic wetland habitat
<p>The red fox (<em>Vulpes</em> <em>vulpes</em>) is the most abundant mesopredator in the Central-European region. Detailed knowledge about their feeding behavior is important both from ecological and wildlife management reasons. Food choices of foxes are poorly predictable in high-biodiversity marshlands. The main aim of our study was to sample parallel the main food-type abundances in the study area and analyze the diet of fox cubs and cohabiting adults across three years during the period of maternal dependence of the cubs. According to the optimal foraging theory, we predicted that the cubs' diet would show higher energy content, would be more varied, and the individual prey species fed to the young would be larger. We analyzed the composition of adult fox and cub fecal samples collected separately around dens in a marshland of western Hungary, May 2014, 2017 and 2020, when the abundance values of main food sources differed. Rodents and waterfowl dominated the diet, but their relative occurrence in the samples showed yearly variations. We found that vixens follow a dual optimizing foraging strategy regarding their provisioning of the cubs and their own diet. Adult foxes optimized their diet according to the actual yearly abundances of their main food sources. Additionally, they preferred prey items that can be consumed at the site of capture (large carrion and small individual prey items). Cubs on the other hand were provisioned with optimal high-energy food, even if those in question became less abundant in that year. Vixens mostly fed to their young either larger rodents and waterfowl, or multiple small rodents at a time – these types of prey are both optimal for transportation as a single load. Providing optimal prey at an early age in a changing environment may contribute to the ecological success of the red fox.</p>
Dynamic ctDNA Assessment in Cervical and Anal Canal Tumors: Optimizing Follow-up and Clinical Outcomes
ClinicalTrials.gov study NCT06640283. IPD Sharing: NO. Countries: 1. Publications: 6.
Data from: Linking in vivo muscle dynamics to in situ force-length and force-velocity reveals that guinea fowl lateral gastrocnemius operates at shorter than optimal lengths
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Data from: Shifting precipitation regimes influence optimal germination strategies and population dynamics in bet-hedging desert annuals
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Optimal prey for red fox cubs – an example of dual optimizing foraging strategy in foxes from a dynamic wetland habitat
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Comparative analysis of RNN versus IIR digital filtering to optimize resilience to dynamic perturbations in pH sensing for vertical farming
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Data from: Precise control of neural activity using dynamically optimized electrical stimulation
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Optimal foraging can drive emergent initiator-follower dynamics in social groups
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Data for: Optimal inference of molecular interaction dynamics in FRET microscopy
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When and How to Transfer Knowledge in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments of the paper below:</p> <p>Ruan, G., Minku, L.L., Menzel, S., Sendhoff, B., Yao, X., "When and How to Transfer Knowledge in Dynamic Multi-objective Optimization," 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 2019, pp. 2034-2041. </p> <p> </p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving another related problem. This paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective benchmark problems, we show that transfer learning fails on problems with fixed Pareto optimal solution sets and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always adequate. Therefore, transfer learning should be avoided when dealing with problems for which transfer learning fails and other kernel functions should be used when the Gaussian kernel is inadequate. This paper proposes novel strategies and kernel functions that can be used in such cases. Experimental studies have demonstrated the superiority of our proposed techniques to state-of-the-art methods, on a number of dynamic bi- and tri-objective test problems.</p>
Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments from the paper below:</p> <p>Ruan, G., Minku, L., Menzel, S., Sendhoff, B., Yao., “Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization” <em>2020 IEEE Congress on Evolutionary Computation</em></p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the ‘inner’ optimization problem introduced by transfer learning is very time-consuming. In order to enhance the efficiency, two alternatives are computationally investigated on a number of dynamic bi- and tri-objective test problems. Experimental results have shown that the greatly enhanced efficiency does not result in much degeneration on the performance of transfer learning. Considering the high computational cost of transfer learning, it is likely that the original purpose of using transfer learning in DMO might be negated. In other words, the computation time saved in optimization is eaten up by computationally expensive transfer learning. As a result, there is less gain than expected in the overall computational efficiency. To verify this, experiments have been conducted, regarding using computational cost of transfer learning to optimize randomly generated solutions. The results have demonstrated that the convergence and diversity of final solutions generated from the random solutions are significantly better than those generated from transferred solutions under the same total computational budget.</p>
Optimizing agents with genetic programming - An evaluation of hyper-heuristics in dynamic real-time logistics - datasets and results
<p>This directory contains the data and results that we used and obtained during the realization of the following paper:</p> <blockquote> <p>Optimizing agents with genetic programming - An evaluation of hyper-heuristics in dynamic real-time logistics. Rinde R.S. van Lon, Juergen Branke, and Tom Holvoet. Genetic Programming and Evolvable Machines (2017).</p> </blockquote> <p>The code that has been used can be found on GitHub (https://github.com/rinde/vanLon17-GPEM-code), an archive is available at https://doi.org/10.5281/zenodo.260130.</p> <p>This repository contains the following files:</p> <ul> <li>evo.zip (expands to 21.2 GB) A zip file containing all results of the training experiment and tuning experiment.</li> <li>JAAMAS.zip (expands to 227.6 MB) A zip file containing the main results of the JAAMAS experiment, also available at https://doi.org/10.5281/zenodo.209760</li> <li>realtime.zip (expands to 913.5 MB) A zip file containing the main results</li> <li>dataset10k.zip (expands to 4.9 GB) Contains the scenarios used for the training and tuning experiments.</li> <li>overview.zip (expands to 42 MB) Contains the following files: <ul> <li>overview/experiment-overview.csv - table containing basic overview of the 40 main evolutionary runs that were performed for this paper.</li> <li>overview/heuristics/ - folder with a tree visualization for every evolved heuristic.</li> <li>overview/makefile - executes the analysis scripts that were used in the paper (requires the R programming language, https://www.r-project.org/). All tables and figures will be placed in results/generated/.</li> <li>readme.txt - this file.</li> <li>overview/results/ - contains the scripts</li> <li>overview/results/data/ - empty directory in which evo.zip, JAAMAS.zip, and realtime.zip have to be unpacked for the analysis scripts to work.</li> </ul> </li> </ul>
Machine Learning Guided Optimization of Dynamic Peptide Organocatalysts
<p>Associated data for publication "<strong>Machine Learning Guided Optimization of Dynamic Peptide Organocatalysts". </strong>Full description of the dataset is in the SI of the associated manuscript. </p>
Simulation input files and analysis scripts for "Optimal bond-constraint topology for molecular dynamics simulations of cholesterol"
<p>Simulation input files and analysis scripts for "Optimal bond-constraint topology for molecular dynamics simulations of cholesterol".</p> <p>See ... for details.</p>
Effects of Different Intra-arterial Catheter Size on Frequency of Optimal Dynamic Response in Radial Arterial Waveform
ClinicalTrials.gov study NCT03642756. IPD Sharing: NO. Countries: 1. Publications: 1.
Hemodynamic Optimization Based on a Dynamic Elastance Protocol
ClinicalTrials.gov study NCT05187273. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Adjuvant Dynamic Marker-Adjusted Personalized Therapy Trial Optimizing Risk Assessment and Therapy Response Prediction in Early Breast Cancer
ClinicalTrials.gov study NCT01781338. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Encouraging Abstinence Behavior in a Drug Epidemic: Optimizing Dynamic Incentives
ClinicalTrials.gov study NCT04927143. IPD Sharing: NO. Countries: 1. Publications: 22.
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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.