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601 results for “crossovers”
Figure 3. The crossover operation between two HMM chromosomes Figure-Genetic Algorithms Principles Towards Hidden Markov Model
<p>Crossover<br> In this genetic operator, we choose two chromosomes at random and apply crossover between<br> them. Figure 3 shows the proposed crossover. We choose a crossing cut site at random. It is to be<br> noted that the crossing cut site should be even number. We should have two crossing cut sites. If<br> we make crossing cut site odd number, the resultant child will not have a correct value of<br> probability. The incorrect crossover is shown in Figure 4.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 11. Crossover with two cut points
<p>Then next generation are produced by combination of the elites (15%), crossover (55%) and mutation (30%) of the initial population. Elites are the best chromosomes which are directly transferred to the next generation. Because of long chromosome length, for crossover, five points are randomly chosen in each parent as cut points. Figure 11 shows a typical crossover with two cut points and Figure 12 illustrates a flowchart for the proposed evolutionary model. Selections are based on Roulette Wheel selection, more detailed information can be found in.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 4. Structural Crossover
<p>Guided crossover operator is based on the two point separation from parents are selected<br> Left and right parts of them are related to each other by the condition to be meaningful With this<br> new child of his parents is that. But a new generation of the random choice to have reached this<br> stage. The crossover rate is fixed for our algorithm.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 5. Crossover process
<p>In the selection mechanism, the individuals passed down from the previous generation occasionally cannot produce a better individual. In that case, the compatibility of the individuals might worsen, while producing the exact opposite is what is expected. To avoid this, the elitism operator is used and it is ensured that the best individual of the previous generation is passed down to the next generation, even though the current population is diminishing on an overall basis as a result of the production operators (Goldberg, 1989). In the current study, the consecutive selection method and elitism selection were preferred. For this aim, after calculating the compatibility function, the population was ranked according to the population function values (total route length). In case the crossover possibility is realized, the number of individuals to select will be determined according to the parameter related to the crossover size. To ensure a high level of variability in the generation, it is suggested that this possibility is taken as 50% and 95% (Goldberg, 1989). The crossover process allows the production of a new individual using the genes taken from two individuals, based on the selected crossover method. In this study, a single point crossover method was selected. During the crossing over, limitations that were previously mentioned regarding the formation of a new initial population were taken into consideration. The identified initial or final point was fixed and kept out of the context of crossing over. An example of crossover can be seen in Figure 5.</p>
Dynamic and thermodynamic crossover scenarios in the Kob-Andersen mixture: Insights from multi-CPU and multi-GPU simulations
<p>This dataset is associated with "Dynamic and thermodynamic crossover scenarios in the Kob-Andersen mixture: Insights from multi-CPU and multi-GPU simulations", Daniele Coslovich, Misaki Ozawa, and Walter Kob, Eur. Phys. J. E 62, 41 (2018) [<a href="https://doi.org/10.1140/epje/i2018-11671-2">doi:10.1140/epje/i2018-11671-2</a> <a href="https://arxiv.org/abs/1804.04559">arXiv:1804.04559</a>]</p> <p>It includes scripts and data files to allow for the replication of the figures. EPS figures were generated using gnuplot version 5.0.</p> <p>Notes:</p> <ul> <li>Small differences in the dynamic data for the N=3600 dataset obtained with the MD protocol reflect additional statistics gathered since acceptance of the paper.</li> <li>Figure 6(b) in the published version of the manuscript was obtained using slightly incorrect values of the parameters J, T_0 entering equation 10. This minor issue has been fixed in this dataset.</li> </ul>
Dataset for "Seismological Expression of the Iron Spin Crossover in Ferropericlase in the Earth's Lower Mantle"
<p>The folder contains data related to the publication: Shephard, Houser, Hernlund, Trønnes, Valencia-Cardona, Wentzkovitch (2021) Seismological Expression of the Iron Spin Crossover in Ferropericlase in the Earth’s Lower Mantle. Accepted for publication in Nature Communications.</p> <p>It include numerical grids, a simple plotting script, colour maps, and images (jpg and ps files). There is a README file explaining the content and uses of the these additional data files.</p>
Topological kinetic crossover in a nanomagnet array
<p>Common explanations of equilibrium thermodynamics depend critically on ergodicity, i.e., the ability to explore all available states, but ergodic kinetics can be constrained by a system's topology. We report an experimental study of a model nanomagnetic array, in which such constraints visibly affect the behavior. In this system, magnetic excitations are connected in thermally active one-dimensional strings whose motion can be imaged in real-time. At high temperatures, the kinetics include the merging, breaking, and reconnecting of strings, resulting in the system transitioning between topologically distinct configurations. Below a crossover temperature, however, the string motion is dominated by simple changes in length and shape. In this low-temperature regime, the system is energetically stable because of the inability to explore the full set of possible topological configurations. This kinetic crossover suggests a generalizable conception of topologically broken ergodicity and limited equilibration.</p>
A quantum engine in the BEC-BCS crossover
<p>This data set contains all values plotted in the publication "A quantum engine in the BEC-BCS crossover". The chart represents the experimental values and the theoretical calculations.<br> All values are summarized in one file. Within the file, the values are sorted by figures.</p> <p> </p>
Datasets and R code associated with: Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires
<p>The attached code and csv files accompany the manuscript titled: Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires by Duncan et al. accepted for publication in the journal GeoHealth in September 2023. </p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Project%20wildfire%20data.csv">Project wildfire data.csv</a> contains a subset wildfire data obtained from:</p> <p>Short, Karen C. 2022. Spatial wildfire occurrence data for the United States, 1992-2020 [FPA_FOD_20221014]. 6th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2013-0009.6</p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Modeled%20PM2.5%20data_clean.csv">Modeled PM2.5 data_clean.csv</a> includes modeled PM<sub>2.5</sub> concentrations used to make Figure 2. Model results can be obtained from <a href="https://www.epa.gov/hesc/rsig-related-downloadable-data-files">EPA's Fused Air Quality Surfaces Using Downscaling Tool</a>. <a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/plot_Modeled.R">plot_Modeled.R</a> contains the R code to generate this figure. </p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/ORs%20all%20Counties.csv">ORs all Counties.csv</a> contains the odds ratios, confidence intervals, and p-values used to make Figures 3, 4, and 5.<a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/PM2.5%2035ug-m3%20ORs.csv">PM2.5 35ug-m3 ORs.csv</a> contains the odds ratios, confidence intervals, and p-values used to make Figure S1. <a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Wildfire_Forest.R">Wildfire_Forest.R</a> contains the R code to generate these figures. </p>
Topological kinetic crossover in a nanomagnet array
Open the record for dataset details and reuse information.
Experimental Data Sets for the study "Benchmarking a $(\mu+\lambda)$ Genetic Algorithm with Configurable Crossover Probability"
<p>This is the experimental result of the study "Benchmarking a (μ+λ) Genetic Algorithm with Configurable Crossover Probability". A novel (μ+λ) GA is proposed and benchmarked, in which we stochastically determine whether to apply the crossover operator either for each individual or generation with a crossover probability <span class="math-tex">\(p_c\)</span>. This data set consists of two parts:</p> <ol> <li>The results of (μ+λ) GA on 25 pseudo-Boolean problems defined in <em>IOHprofiler </em>(<a href="https://iohprofiler.github.io/">https://iohprofiler.github.io/</a>) with the following setup: <span class="math-tex">\(\mu \in \{10, 50, 100\}, \lambda \in \{1, \lceil\mu/2\rceil, \mu\}, p_c\in\{0, 0.5\}.\)</span> <ul> <li>'IOHprofiler_Problems_standard_bit_mutation.csv' --> the (μ+λ) GA with standard bit mutation.</li> <li>'IOHprofiler_Problems_fast_mutation.csv' --> the (μ+λ) GA with fast mutation.</li> </ul> </li> <li>The results of (μ+λ) GA on OneMax and LeadingOnes problems with the following setup: <span class="math-tex">\(n \in \{64,100,150,200,250,500\}, \mu \in \{2,3,5,8,10,20,30,...,100\}, \\ \lambda \in \{1, \lceil \mu/2 \rceil, \mu\}, \text{and }p_c \in \{0.1 k \mid k \in [0..9]\}\cup\{0.95\}.\)</span> <ul> <li>'OneMax_raw.csv' --> the fixed-target running time/first hitting time from 100 independent runs for target values in <span class="math-tex">\([1..n]\)</span>.</li> <li>'OneMax_summary.csv' --> the mean, median, standard deviation, some quantiles, expected running time (ERT), the number of successful runs, and the success rate from 100 independent runs for target values in <span class="math-tex">\([1..n]\)</span>.</li> <li>'LeadingOnes_raw.csv' --> the same with 'OneMax_raw.csv' for LeadingOnes.</li> <li>'LeadingOnes_summary.csv' --> the same with 'OneMax_summary.csv' for LeadingOnes.</li> </ul> </li> </ol> <p><strong>Contact</strong>: if you have any questions or suggestions, please feel free to contact <a href="https://www.universiteitleiden.nl/en/staffmembers/furong-ye#tab-1">Furong Ye</a> or <a href="http://www-ia.lip6.fr/~doerr/">Carola Doerr</a>.</p>
Pharmacodynamics parameters underlying the manuscript: The effect of morning versus evening administration of empagliflozin on its pharmacokinetics and pharmacodynamics characteristics in healthy adults: a two-way crossover, non-randomised trial
<p><b>Background</b>: Empagliflozin is an SGLT2 inhibitor approved for use in patients with Diabetes Mellitus type 2 (DMT2) with- or without other cardiovascular disease. Empagliflozin is taken once daily without rationale on the optimal timing for administration. This study aimed<b> </b>to determine the chronopharmacological effects of morning vs evening administration of empagliflozin 10 mg in Healthy Egyptian adults, by investigating the pharmacokinetics and pharmacodynamics parameters of empagliflozin depending on the intake time. </p> <p><b>Methods: </b>An open label, sequential, two‐way crossover trial comprised two periods with a washout period of 7 days. Pharmacokinetics parameters (t<sub>max</sub> (h), C<sub>max</sub> (ng/ml), AUC <sub>0-t</sub> (ng.h/ml)) as primary endpoints, and (AUC <sub>0 to ∞</sub>(ng.h/ml)) as secondary endpoint were assessed. Method validation was done prior to injection in LC/MS/MS and samples were processed by Liquid-Liquid extraction. The pharmacodynamic profile (UGE <sub>0-24</sub>) was determined after method validation (glucose hexokinase method).</p> <p><b>Results: </b>T<sub>max</sub> increased by (35%) in the evening phase compared to the morning phase, while C<sub>max</sub> decreased by (-6.5%)in the evening dose compared to the morning dose. Besides, AUC<sub>0 to ∞</sub> increased in the evening phase by (8.25%) compared to the morning phase. The mean cumulative amount of glucose excreted; UGE (<sub>0-24</sub>) increased by (43%) in the evening dose compared to the morning dose</p> <p><strong>Conclusion: </strong>Despite there was a significant difference between morning and evening doses, it didn't reach the significant level, thus, it can be concluded that there is no difference between the morning and evening doses.</p>
Data from: Combined fluorescent and electron microscopic imaging unveils the specific properties of two classes of meiotic crossovers
<p>Crossovers (COs) shuffle genetic information and allow balanced segregation of homologous chromosomes during the first division of meiosis. Recombination nodules (RNs) are closely correlated with crossing over, and, because they are observed by electron microscopy of synaptonemal complexes (SCs) in extended pachytene chromosomes, RNs provide the highest-resolution cytological marker currently available for defining the frequency and distribution of crossovers along the length of chromosomes. In several organisms, mutants demonstrate that two molecularly distinct pathways produce COs. One pathway produces class I COs that exhibit interference (lowered probability of nearby COs), and the other pathway produces class II COs with little or no interference. However, the relative contributions, genomic distributions, and interactions of these two pathways are essentially unknown in nonmutant organisms because marker segregation only indicates that a CO has occurred, not its class type. Here, we combine the efficiency of light microscopy for revealing cellular functions using a fluorescent probe to MLH1 proteins to mark class I COs with the high resolution of electron microscopy to localize and characterize all COs (RNs) in the same sample of meiotic pachytene chromosomes from wild-type tomato. To our knowledge, for the first time, every CO along each chromosome can be identified by class to unveil specific characteristics of each pathway. We find that class I (MLH1-positive) and class II (MLH1-negative) COs have different recombination profiles along chromosomes. In particular, class II COs, which represent about 18% of all COs, exhibit no interference and are disproportionately represented in pericentric heterochromatin, a feature potentially exploitable in plant breeding. Finally, our results demonstrate that the two pathways are not independent because there is interference between class I and II COs.</p>
Data from: Use of the lung flute ECO to assist in sputum collection for tuberculosis testing: a randomized crossover trial
<p>The Lung Flute ECO, a self-powered, low cost, oscillatory positive expiratory pressure (OPEP) device, assisted people with presumptive tuberculosis to produce an adequate sputum volume for diagnostic testing and was well-tolerated.</p>
Data from: Carrier density crossover and quasiparticle mass enhancement in a doped 5d Mott insulator
<p>High-temperature superconductivity in cuprates emerges upon doping the parent Mott insulator. Key features of the low-doped cuprate superconductors include an effective carrier density that tracks the number of doped holes, the emergence of an anisotropic pseudogap that is characterized by disconnected Fermi arcs, and the closure of the gap at a critical doping level. In Sr<sub>2</sub>IrO<sub>4</sub>, a spin–orbit-coupled Mott insulator often regarded as a 5<em>d </em>analog of the cuprates, surface probes have also revealed the emergence of an anisotropic pseudogap and Fermi arcs under electron doping. However, neither the corresponding critical doping nor the bulk signatures of pseudogap closure have yet been observed. Here we demonstrate that electron-doped Sr<sub>2</sub>IrO<sub>4</sub> exhibits a critical doping level with a marked crossover in the effective carrier density at low temperatures. This is accompanied by a five-orders-of-magnitude increase in conductivity and a sixfold enhancement in the electronic-specific heat. These collective findings resemble the bulk pseudogap phenomenology in cuprates. However, given that electron-doped Sr<sub>2</sub>IrO<sub>4</sub> is non-superconducting, it suggests that the pseudogap may not be a state of precursor pairing. Therefore, our results narrow the search for the key ingredient underpinning the formation of the superconducting condensate in doped Mott insulators.</p>
Data from: Gardner-like crossover from variable to persistent force contacts in granular crystals
<p>We report experimental evidence of a Gardner-like crossover from variable to persistent force contacts in a two-dimensional, bidisperse granular crystal by analyzing the variability of both particle positions and force networks formed under uniaxial compression. Starting from densities just above the freezing transition, and for variable amounts of additional compression, we compare configurations to both their own initial state, and to an ensemble of equivalent, reinitialized states. This protocol shows that force contacts are largely undetermined when the density is below a Gardner-like crossover, after which they gradually transition to being persistent, being fully so only above the jamming point. We associate the disorder that underlies this effect to the size of the microscopic asperities of the photoelastic disks used, by analogy to other mechanisms that have been previously predicted theoretically.</p>
Video supplement to "A randomized, controlled, crossover trial of median nerve stimulation for treatment of Tourette syndrome"
<p>Video from the last 60 s of Block 5 (stimulation off) and the first 60 s of Block 6 (stimulation on) from the rhythmic <span class="math-tex">\(\pm \)</span> arrhythmic visits of three study participants who reported substantial improvement in tic severity on at least one visit.</p>
Data from: High-resolution crossover maps for each bivalent of Zea mays using recombination nodules
<p>Recombination nodules (RNs) mark sites of crossing over along pachytene synaptonemal complexes (SCs). Thus, RNs provide the highest resolution cytological marker currently available for defining the frequency and distribution of crossovers along the length of chromosomes because they are observed by electron microscopy. Using the maize inbred line KYS, we have prepared an SC karyotype in which each SC was identified by relative length and arm ratio and related to the proper linkage group using inversion heterozygotes. We mapped 4272 RNs on 2080 identified SCs to produce high-resolution maps of RN frequency and distribution on each bivalent. Average RN frequency per bivalent is closely correlated with SC length. The total length of the RN map is about two-fold shorter than most linkage maps, but there is good correspondence between the relative lengths of the different maps when individual bivalents are considered. Each bivalent has a unique distribution of crossing over, but all bivalents share a high frequency of distal RNs and a severe reduction of RNs at and near kinetochores. The frequency of RNs at knobs is either similar to or higher than the average frequency of RNs along the SCs. These RN maps represent an independent measure of crossing over along maize bivalents.</p>
A 2-Period Crossover Study of BPN14770 in Adults Males With Fragile X Syndrome
ClinicalTrials.gov study NCT03569631. IPD Sharing: NO. Countries: 1. Publications: 2.
CES1 Crossover Trial of Clopidogrel and Ticagrelor
ClinicalTrials.gov study NCT03161678. IPD Sharing: YES. Countries: 1. Publications: 0.
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Allen Brain Atlas
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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.