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1,457 results for “feedback”
Faa1 membrane binding drives positive feedback in autophagosome biogenesis via fatty acid activation
<p>Autophagy serves as a stress response pathway by mediating the degradation of cellular material within lysosomes. In autophagy, this material is encapsulated in double-membrane vesicles termed autophagosomes, which form from precursors referred to as phagophores. Phagophores grow by lipid influx from the endoplasmic reticulum into Atg9-positive compartments and local lipid synthesis provides lipids for their expansion. How phagophore nucleation and expansion are coordinated with lipid synthesis is unclear. Here, we show that Faa1, an enzyme activating fatty acids, is recruited to Atg9 vesicles by directly binding to negatively charged membranes with a preference for phosphoinositides such as PI3P and PI4P. We define the membrane-binding surface of Faa1 and show that its direct interaction with the membrane is required for its recruitment to phagophores. Furthermore, the physiological localization of Faa1 is key for its efficient catalysis and promotes phagophore expansion. Our results suggest a positive feedback loop coupling phagophore nucleation and expansion to lipid synthesis.</p>
Replication Data for figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s
<p>Supporting data to reproduce figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s</p>
PALOMERA Feedback Form and Dataset
<p>As part of the engagement strategy and feedback collection for the PALOMERA project, a survey was designed and distributed on various outputs the project was working on at the time (April - June 2024). The form provides the list of questions used while the dataset provides the raw data collected. </p> <div><em>Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Agency. Neither the European Union nor the granting authority can be held responsible for them.</em> </div>
Accessibility Rank: A Machine Learning Approach for Prioritising Accessibility User Feedback
<p>This repository serves as a comprehensive collection of datasets, code scripts, and associated data used in my master's research conducted at the University of Auckland on accessibility-related reviews. The research findings and methodology are described in detail in our paper titled "Accessibility Rank: A Machine Learning Approach for Prioritising Accessibility User Feedback". By making these resources openly available, we aim to foster collaboration, reproducibility, and advancement in the field of accessibility research. Researchers and developers can leverage these datasets, associated data, and code scripts to gain insights, validate findings, and explore novel approaches to addressing accessibility challenges.</p> <p>We encourage users to refer to our paper for a comprehensive understanding of our research methodology, experimental setup, and results. Proper attribution and citation of our paper are appreciated when utilizing any part of this repository in further research or publications.</p>
Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2021). Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. <em>Clim. Past</em>, <em>17</em>(1), 253–267. <a href="https://doi.org/10.5194/cp-17-253-2021">https://doi.org/10.5194/cp-17-253-2021</a></p> <p>Casename:</p> <ul> <li>FCM_PI: b.e12.B1850C5.f19_g16.iPI.01</li> <li>FCM_LGM: b.e12.B1850C5.f19_g16.i21ka.03</li> <li>SOM_PI: e.e12.E1850C5.f19_g16.PI.02</li> <li>SOM_GHG: e.e12.E1850C5.f19_g16.PI.21kaGHG.02</li> <li>SOM_ICE: e.e12.E1850C5.f19_g16.PI.21kaICE.02</li> <li>SOM_2CO2: e.e12.E1850C5.f19_g16.PIx2.02</li> <li>ATM_PI: f.e12.F1850C5.f19_g16.iPI.01</li> <li>ATM_GHG: f.e12.F1850C5.f19_g16.iPI.21kaGHG_ERF</li> <li>ATM_ICE: f.e12.F1850C5.f19_g16.iPI.21kaICE_ERF</li> <li>ATM_2CO2: f.e12.F1850C5.f19_g16.iPI.01.x2</li> </ul> <p><strong>Boundary condition files and the restart files are also provided as .zip files (bc.zip & rest.zip).</strong></p> <p><strong>Check out the Github repository for the setup of the LGM simulation</strong> (i.e., the entire CESM case folder): <a href="https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne">https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne</a></p> <p><strong>[NEW IN V3] More monthly data for PMIP4 (cmorized) are provided (files starting with `PMIP4.NCAR.CESM1.2-FV2`).</strong></p>
Cloud Feedbacks and Organized Convection in Radiative-Convective Equilibrium
<p>Derived data and scripts used in Stauffer and Wing (2024).</p> <p>The standardized RCEMIP output, including time and domain mean profiles, is hosted by the German Climate Computing Center (DKRZ) and is publicly available at <a href="http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e">http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e</a>.</p> <p>Descriptions of each of the data files and the variables contained within them are discussed in a README file. The jupyter notebook contains the scripts used to plot the figures used in Stauffer and Wing (2024).</p> <p>Updated 01 April 2025 to include corrected Index of Organization values.</p>
InnoRate_Feedback_on_InnoRate's_pilots_Dataset11_2021.11.25_v1
<p>This dataset has been collected in the context of monitoring and assessing the pilot operation of the technology assessment and rating platform of the InnoRate project (H2020 GA 821518). It represents the outcome of an online survey targeting pilot participants, including the stakeholder groups of innovators, innovation intermediaries, and investors. To deploy the online survey, we used the EUSurvey tool.</p>
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Dataset]
<p>Dataset used for the paper submitted to RO-MAN 2022</p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p>
Experimental data on the effects of an azimuthal mean flow on the (thermo)acoustic modes in the annular electroacoustic feedback setup at TU Berlin
<p>Experimental data obtained in the presence of an azimuthal mean flow on the acoustic/thermoacoustic response in the annular electroacoustic feedback setup at TU Berlin. This dataset was used for the published article<br> S. C. Humbert, J. P. Moeck, A. Orchini, C. O. Paschereit, "Effect of an Azimuthal Mean Flow on the Structure and Stability of Thermoacoustic Modes in an Annular Combustor Model With Electroacoustic Feedback", J. Eng. Gas Turbines Power. June 2021, 143(6): 061026. Experimental data as well as Matlab scripts to use them are provided. Useful information is contained in "readme" files.</p>
Data from: Recovering wetland biogeomorphic feedbacks to restore the world's biotic carbon hotspots
<p>These datafiles are part of a study that reviews how feedbacks between geomorphology and landscape-building vegetation underlie carbon storage and sequestration, and how feedback disruption can switch wetlands from carbon sinks into sources carbon stocks. Included ecosystems: ocean, forests, peatlands, mangrove forests, salt marshes, and seagrass meadows. Data were collected from other studies.</p> <p>Additional information regarding the methods, references or results of these datasets can be found in: Ralph J.M. Temmink, Leon P.M. Lamers, Christine Angelini, Tjeerd J. Bouma, Christian Fritz, Johan van de Koppel, Robin Lexmond, Max Rietkerk, Brian R. Silliman, Hans Joosten, Tjisse van der Heide. 2022. Recovering wetland biogeomorphic feedbacks to restore the world’s biotic carbon hotspots. <em>Science</em>.</p> <p> </p>
Data - Coherent combining of low-power optical signals based on optically amplified error feedback
<p>This dataset contains measurement data and processing code for the results published in "Coherent combining of low-power optical signals based on optically amplified error feedback". Code for the Micro-controllers used in the work is also attached.</p> <p>This work was funded by the Swedish Research Council (grant VR-2015-00535).</p>
Spatial structure within root systems moderates stability of Arbuscular Mycorrhizal mutualism and plant-soil feedbacks
<p class="RealLife">The persistence of mutualisms is paradoxical, as there are fitness incentives for exploitation. This is particularly true for plant-microbe mutualisms like arbuscular mycorrhizae (AM), which are promiscuously, horizontally-transmitted. Preferential allocation by hosts to the best mutualist can stabilize horizontal mutualisms, however preferential allocation is imperfect, with its fidelity likely depending upon the spatial structure of symbionts in plant roots. In this study, we tested AM mutualisms' dependence on two dimensions of spatial structure: the initial dispersion of fungi and the ease of fungal dispersal, through three complementary experiments. We found that fitness of the beneficial AM fungus increased when fungi were initially separate, while initial spatial mixing benefited the fitness of the non-beneficial fungus. These effects were strongest when dispersal was limited, and hosts could discriminate. Additionally, we found that spatial structure moderated changes in AM fungal composition produced differential feedbacks on plant growth. Our results identify symbiont spatial structure within plant roots as an important modifier of plant preferential allocation and the dynamics of mycorrhizal mutualisms, with cascading effects on plant communities.</p>
Supercoiling-mediated feedback simulation dataset
<p><strong>Supercoiling-mediated feedback simulation dataset</strong></p> <p><strong>Background</strong><br> These files represent simulation datasets generated for the publication<br> "Supercoiling-mediated feedback rapidly couples and tunes transcription"<br> by Christopher Johnstone and Kate E. Galloway.</p> <p>All figures in the paper can be replicated by using the code available at <a href="https://github.com/GallowayLabMIT/tangles_model">https://github.com/GallowayLabMIT/tangles_model</a> (<a href="https://doi.org/10.5281/zenodo.7054394">permalink</a>) and these datasets.</p> <p><strong>File summary</strong></p> <ul> <li>`unprocessed_datasets.zip` contains the merged Julia simulation files.</li> <li>`preprocessed_datasets.zip` contains the smaller, preprocessed datasets used for actuall plotting of data figures.</li> </ul> <p><strong>File format</strong></p> <ul> <li>The preprocessed datasets are serialized Pandas dataframes (gzipped Parquet files).</li> <li>The unprocessed datasets are self-describing HDF/H5 files.</li> </ul> <p><strong>Usage</strong><br> The main figure-plotting notebook, `notebooks/modeling_paper_figures.ipynb`, contained in the code repository mentioned above can use either the unprocessed or preprocessed datasets. If the preprocessed datasets are present, it will load them directly. If the preprocessed datasets are not present, that Python notebook will preprocess the data.</p> <p><br> <strong>License</strong><br> This data is available under a CC-BY 4.0 International License.<br> Please attribute:</p> <ul> <li>Christopher Johnstone (cjohnsto@mit.edu)</li> <li>Kate E. Galloway (katiegal@mit.edu)</li> </ul>
Livestock management promotes bush encroachment in savanna systems by altering plant-herbivore feedback
<p>This repository contains all code to reproduce the analysis in Koch et al. 2022 "Livestock management promotes bush encroachment in savanna systems by altering plant-herbivore feedback".</p> <p>We use a set of coupled differential equations to describe competition between shrubs and grasses, as well as plant biomass consumption via grazing and browsing. Grazers were assumed to receive a certain level of care from farmers, so that grazer densities emerge dynamically from the combined effect of vegetation abundance and farmer<br>support. Our main goal was to understand how critical transitions from grass-dominated to shrub-dominated system states were affected by the dynamic role of grazing.</p> <p>Our results show that bistability emerges for intermediate levels of farmer support due to positive feedback that arises from competition between shrubs and grasses and from herbivory. We furthermore demonstrate that disturbances, such as drought events, trigger abrupt transitions from the grass dominated to the shrub dominated state and that the system becomes more susceptible to disturbances with increasing farmer support.</p>
Dataset for study: Adaptive Hip Exoskeleton Control using Heart Rate Feedback Reduces Oxygen Cost during Ecological Locomotion
<p>This record contains the dataset for a manuscript currently under preparation and submission. See the description PDF file for more details. The information here will be updated according to the progress in the peer review and publication procedure.</p>
Infection-nutrition feedbacks: fat supports pathogen clearance but pathogens reduce fat in a wild mammal
<p>Though far less obvious than direct effects (clinical disease or mortality), the indirect influences of pathogens are difficult to estimate but may hold fitness consequences. Here, we disentangle the directional relationships between infection and energetic reserves, evaluating the hypotheses that energetic reserves influence infection status of the host and that infection elicits costs to energetic reserves. Using repeated measures of fat reserves and infection status in individual bighorn sheep (<em>Ovis canadensis</em>) in the Greater Yellowstone Ecosystem, we documented that fat influenced ability to clear pathogens (<em>Mycoplasma ovipneumoniae</em>) and infection with respiratory pathogens was costly to fat reserves. Costs of infection approached, and in some instances exceeded, costs of rearing offspring to independence in terms of reductions to fat reserves. Fat influenced probability of clearing pathogens, pregnancy, and over-winter survival; from an energetic perspective, an animal could survive for up to 23 days on the amount of fat that was lost to high levels of infection. Cost of pathogens may amplify tradeoffs between reproduction and survival. In the absence of an active outbreak, the influence of resident pathogens often is overlooked. Nevertheless, the energetic burden of pathogens likely has consequences for fitness and population dynamics, especially when food resources are insufficient.</p> <p><span> </span></p>
Data files for "Quantifying the global climate feedback from energy-based adaptation"
<p>Data files for "Quantifying the global climate feedback from energy-based adaptation".</p> <p>Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/ACDM_Climate_Adaptation_Feedback.</p> <p>Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.</p> <p> </p> <p><strong>Attribution:</strong></p> <p><br>Some processed data contain excerpts of Non-Creative Commons Material as defined by the International Energy Agency (IEA -- see their terms of use at `https://www.iea.org/terms/terms-of-use-for-non-cc-material'). The emissions factors we use in our analysis are generated using IEA datasets. These data are aggregates of the underlying country-by-fuel level emissions factors and as presented contain only insubstantial amounts of the Non-CC Material. We attest they cannot be used to reconstruct individual data points in the original dataset. The factors we produce are attributable to the following two sources: </p> <p>IEA. Emissions factors. Tech. Rep., International Energy Agency (IEA 2021). URL https://www.iea.org/data-and-statistics/data-product/910emissions-factors-2021. All Rights Reserved.</p> <p>IEA. World energy balances 2021. Tech. Rep., International Energy Agency (IEA) (2022). URL https://www.iea.org/data-and-statistics/data-product/world-energy-balances. All Rights Reserved.</p> <p> </p>
CESM1.2 simulation data for "Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks"
<p>CESM1.2 simulation data for Early Eocene</p> <p><strong>Citations:</strong></p> <p>Zhu, J., Poulsen, C. J., & Tierney, J. E. (2019). Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks. <em>Science Advances</em>, 5(9), eaax1874. <a href="https://doi.org/10.1126/sciadv.aax1874">https://doi.org/10.1126/sciadv.aax1874</a></p> <p>Zhu, J., Poulsen, C. J., Otto-Bliesner, B. L., Liu, Z., Brady, E. C., & Noone, D. C. (2020). Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction. Earth and Planetary Science Letters, 537, 116164. <a href="https://doi.org/10.1016/j.epsl.2020.116164" rel="nofollow">https://doi.org/10.1016/j.epsl.2020.116164</a></p> <p> </p> <ul> <li>Data set includes climatology (12 months) sea-surface temperature (TEMP), surface temperature (TS) and surface temperature at reference height (TREFHT) from four Eocene simulations with 1×, 3×, 6× and 9× preindustrial level of CO2 (284.7 ppmv), and a preindustrial simulation.</li> <li>Climatology was calculated from averaging data over the last 100 years of each simulation.</li> <li>TS and TREFHT are on the atmosphere grid of 1.9 × 2.5° (latitude × longitude).</li> <li>TEMP is on the POP ocean grid (~1°; see here: http://www.cesm.ucar.edu/models/cesm1.2/pop2/).</li> <li>NEW on July 09, 2024: restart files for the Eocene simulations.</li> </ul> <p>A case folder is available on GitHub: <a href="https://github.com/jiang-zhu/icesm1.2_eocene_cheyenne">https://github.com/jiang-zhu/icesm1.2_eocene_cheyenne</a></p> <p> </p>
Data for: Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use
<p>This dataset contains the code and the data files needed to create the figures shown in the paper titled "Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use".</p>
Figure2. Generation of negative feedbacks gets tuned once a TCR completes stimulation beyond the threshold l. A TCell generates activation signal to BCell once it gets stimulation of its k-TCRs.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
ScienceDex guides
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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.