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13 results for “population switching”
Supporting Data Ferguson, Camenzind, et al., "Measurement-induced induced population switching", Phys. Rev. Research 5, 023028 (2023)
<p>This repository contains data for the publication "Measurement-induced population switching", Phys. Rev. Research 5, 023028 (2023) by Ferguson, Camenzind, <em>et al</em>.</p> <p><strong>Abstract</strong></p> <p>Quantum information processing is a key technology in the ongoing second quantum revolution, with a wide variety of hardware platforms competing toward its realization. An indispensable component of such hardware is a measurement device, i.e., a quantum detector that is used to determine the outcome of a computation. The act of measurement in quantum mechanics, however, is naturally invasive as the measurement apparatus becomes entangled with the system that it observes. This always leads to a disturbance in the observed system, a phenomenon called quantum measurement backaction, which should solely lead to the collapse of the quantum wave function and the physical realization of the measurement postulate of quantum mechanics. Here we demonstrate that backaction can fundamentally change the quantum system through the detection process. For quantum information processing, this means that the readout alters the system in such a way that a faulty measurement outcome is obtained. Specifically, we report a backaction-induced population switching, where the bare presence of weak, nonprojective measurements by an adjacent charge sensor inverts the electronic charge configuration of a semiconductor double quantum dot system. The transition region grows with measurement strength and is suppressed by temperature, in excellent agreement with our coherent quantum backaction model. Our result exposes backaction channels that appear at the interplay between the detector and the system environments, and opens new avenues for controlling and mitigating backaction effects in future quantum technologies.</p>
Linking diet switching to reproductive performance across populations of two Critically Endangered mammalian herbivores
<p>Data associated with Harvey Sky, N., Britnell, J., Antwis, R. <em>et al.</em> Linking diet switching to reproductive performance across populations of two critically endangered mammalian herbivores. <em>Commun Biol</em> <strong>7</strong>, 333 (2024). https://doi.org/10.1038/s42003-024-05983-3</p> <p>The data deposited here includes raw metabarcoding output fasta files and some processed metabarcoding and sample data in xslx files. We include a more detailed description of each file below.</p> <p>Data regarding Kenyan black rhino and Grevy’s zebra are treated as sensitive and confidential. There are therefore restrictions on the data that we can make available. Due to these confidentiality considerations, the sample data stored here does not include locations of sample collection within each reserve for either species, or the identity or breeding data for black rhino. It also only includes the final processed values for NDVI and rainfall. The remote sensing data is available from the repositories cited in the methods, but we cannot provide the shapefiles or other data used to calculate the final values for each sample. </p> <p><em><strong>Raw fasta files_plants.zip</strong></em></p> <p>A zipped folder containing the raw fasta files which were the output from the MiSeq sequencing of dietary plants in the faecal samples for both black rhino and Grevy's zebra. Within the zipped folder, the first part of the title of each fasta.gz file is the sample code (S1, S2, S3 etc), which allows you to cross reference these files with the sample data and processed sequencing data in the xslx files. Files with R1 in the title are foward reads, and R2 are reverse reads. </p> <p><em><strong>Raw fasta files_bacteria.zip</strong></em></p> <p>A zipped folder containing the raw fasta files which were the output from the MiSeq sequencing of microbiome bateria in the faecal samples for both black rhino and Grevy's zebra. Within the zipped folder, the first part of the title of each fasta.gz file is the sample code (S1, S2, S3 etc), which allows you to cross reference these files with the sample data and processed sequencing data in the xslx files. Files with R1 in the title are foward reads, and R2 are reverse reads. </p> <p><em><strong>Sample data and processed metabarcoding data_Black rhino.xlsx</strong></em></p> <p><em>Sample data tab</em></p> <p>The data that we are able to share that is associated with each black rhino sample.</p> <p>SampleID - The code used to identiy each sample which allows it be cross-referenced with other tabs and the fasta files. </p> <p>IndividualID - We are not able to share rhino names or other identifiers, but we have given each individual a unique number so that it can be seen which samples came from the same individuals. </p> <p>NDVI - Mean NDVI of each individual's area of utilisation in the 10-day period within which the sample was collected. The method used to calculate this is described in the methods of the article. </p> <p>Rainfall - Cumulative rainfall over the 30 days previous to sample collection for the 0.05 degree pixel under the sample. The method used to calculate this is described in the methods of the article. </p> <p>Season - Post is the post-rain sampling season June-July 2018. Pre is the pre-rain sampling season January-March 2019. </p> <p>Reserve - The reserve that the sample was collected on. </p> <p>Date - The date of sample collection. </p> <p>Dietary breadth - Shannon-Wiener index of dietary alpha diversity. The method used to calculate this is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p>Poaceae, Fabaceae, Ebenaceae - The relative abundance of each of these three dietary plant families that were the focus of our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p><em>Bacteria numbers of reads</em></p> <p>The number of reads assigned to each bacterial ASV found in each sample. </p> <p><em>Bacteria sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the microbiome metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p><em>Plant numbers of reads</em></p> <p>The number of reads assigned to each dietary plant ASV found in each sample. </p> <p><em>Plant sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the dietary plant metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p> </p> <p><em><strong>Sample data and processed metabarcoding data_Grevy's zebra.xlsx</strong></em></p> <p><em>Sample data tab</em></p> <p>The data that we are able to share that is associated with each Grevy's zebra sample.</p> <p>Sample ID - The code used to identiy each sample which allows it be cross-referenced with other tabs and the fasta files. </p> <p>NDVI - Mean NDVI of each individual's area of utilisation in the 10-day period within which the sample was collected. The method used to calculate this is described in the methods of the article. </p> <p>Rainfall - Cumulative rainfall over the 30 days previous to sample collection for the 0.05 degree pixel under the sample. The method used to calculate this is described in the methods of the article. </p> <p>Reserve - The reserve that the sample was collected on. </p> <p>Season - Post is the post-rain sampling season July-August 2018. Pre is the pre-rain sampling season January-February 2019. </p> <p>Date - The date of sample collection. </p> <p>Dietary breadth - Shannon-Wiener index of dietary alpha diversity. The method used to calculate this is described in the methods of the article. </p> <p>Poaceae, Fabaceae - The relative abundance of each of these two dietary plant families that were the focus of our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p>Indigofera - The relative abundance of each of this Fabaceae genus was included in our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p><em>Bacteria numbers of reads</em></p> <p>The number of reads assigned to each bacterial ASV found in each sample. </p> <p><em>Bacteria sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the microbiome metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p><em>Plant numbers of reads</em></p> <p>The number of reads assigned to each dietary plant ASV found in each sample. </p> <p><em>Plant sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the dietary plant metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p> </p>
Investigating the Effectiveness of Tresiba® (Insulin Degludec) After Switching Basal Insulin in a Population With Type 1 or Type 2 Diabetes Mellitus
ClinicalTrials.gov study NCT02662114. IPD Sharing: Not stated. Countries: 6. Publications: 2.
A Canadian Study of the Effectiveness of Tresiba® (Insulin Degludec) After Switching Basal Insulin in a Population With Type 1 or Type 2 Diabetes Mellitus
ClinicalTrials.gov study NCT03674866. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Slowly switching between environments facilitates reverse evolution in small populations
Natural populations must constantly adapt to ever-changing environmental conditions. A particularly interesting question is whether such adaptations can be reversed by returning the population to an ancestral environment. Such evolutionary reversals have been observed in both natural and laboratory populations. However, the factors that determine the reversibility of evolution are still under debate. The timescales of environmental change vary over a wide range, but little is known about how the rate of environmental change influences the reversibility of evolution. Here we demonstrate computationally that slowly switching between environments increases the reversibility of evolution for small populations, which are subject to only modest clonal interference. For small populations, slow switching reduces the mean number of mutations acquired in a new environment and also increases the probability of reverse evolution at each of these "genetic distances." As the population size increases, slow switching no longer reduces the genetic distance, thus decreasing the evolutionary reversibility. We confirm this effect using both a phenomenological model of clonal interference and also a Wright-Fisher stochastic simulation that incorporates genetic diversity. Our results suggest that the rate of environmental change is a key determinant of the reversibility of evolution, and provides testable hypotheses for experimental evolution.
Lowering the switching cost related to the activation of burdensome gene circuits promotes cell population homogeneity and productivity
Open the record for dataset details and reuse information.
Data from: Slowly switching between environments facilitates reverse evolution in small populations
Open the record for dataset details and reuse information.
Transnuclear mice reveal a population of Peyer’s patch iNKT cells that regulate B cell class switching to IgG1
GEO Series GSE129366. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Two distinct durable human switched memory B cell populations are induced by vaccination and infection
GEO Series GSE278378. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Efficient detection and purification of cell populations using synthetic microRNA switches
GEO Series GSE60633. Homo sapiens. 43 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
A mesenchymal to epithelial switch in Fgf10 expression specifies an evolutionary-conserved population of ionocytes in salivary gland
GEO Series GSE188904. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Evaluation of Switching From Current cART to Triumeq With Adherence Support Will Enhance HIV Control in Vulnerable Populations
ClinicalTrials.gov study NCT02354053. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Switching to a Fixed Dose Combination of Bictegravir/Emtricitabine/Tenofovir Alafenamide (B/F/TAF) in HIV-1 Infected Marginalized Populations Who Are Virologically Suppressed
ClinicalTrials.gov study NCT04132674. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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)
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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.