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820 results for “Orchestration”
Accumbal calcium-permeable AMPA receptors orchestrate neuronal ensembles underlying social attachment
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Simulation Data for "How GPCR phosphorylation patterns orchestrate arrestin-mediated signaling"
<p>Simulation data and analysis code for Latorraca, Masureel et al., How GPCR Phosphorylation Patterns Orchestrate Arrestin-Mediated Signaling, Cell (2020), https://doi.org/10.1016/j.cell.2020.11.014. Please see included readme files for more information. </p>
Natural toxic impact and thyroid signaling interplay orchestrates riverine adaptive divergence of salmonid fish
<p>Abstract 1. Adaptive radiation in fishes has been actively investigated over the last decades. Along with numerous well-studied cases of lacustrine radiation, some examples of riverine sympatric divergence have been recently discovered. In contrast to the lakes, the riverine conditions do not provide evident stability in the ecological gradients. Consequently, external factors triggering the radiation, as well as developmental mechanisms underpinning it, remain unclear. 2. Herein, we present the comprehensive study of external and internal drivers of the riverine adaptive divergence of the salmonid fish Salvelinus malma. In the Kamchatka River, N-E Asia, this species splits in the reproductively isolated morphs that drastically differ in ecology and morphology: the benthivorous Dolly Varden (DV) and the piscivorous Stone charr (SC). 3. To understand why and how these morphs originated, we performed a series of field and experimental work, including common-garden rearing, comparative ontogenetic, physiological and endocrinological analyses, hormonal "engineering" of phenotypes, and acute toxicological tests. 4. We revealed that the type of spawning ground acts as the main external factor driving the radiation of S. malma. In contrast to DV spawning in the leaf krummholz zone, SC reproduces in the zone of coniferous forest, which litter has a toxic impact on developing fishes. SC enhances resistance to the toxicants via metabolism acceleration provided by the elevated thyroid hormone content. These physiological changes lead to the multiple heterochronies resulting in a specific morphology and SC's expansion into a piscivorous niche. 5. Salvelinus malma represents a notable example of how the thyroid axis contributes to the generation of diverse phenotypic outcomes underlying the riverine sympatric divergence. Our findings, along with the paleoecology data concerning spruce forest distribution during the Pleistocene, provide an opportunity to reconstruct a scenario of S. malma divergence. Taken together, obtained results with the data of the role of thyroid hormones in the ontogeny and diversification of fishes contribute a resource to consider the thyroid axis as a prime director orchestrating the phenotypic plasticity promoting evolutionary diversification under the changing environmental conditions. 06-Jan-2021</p>
Data from: Teleconnections and local weather orchestrate the reproduction of tit species in the Carpathian Basin
<p>Variation in climatic conditions is an important driving force of ecological processes. Populations are under selection to respond to climatic changes with respect to phenology of the annual cycle (e.g. breeding, migration) and life-history. As teleconnections can reflect climate on a global scale, the responses of terrestrial animals are often investigated in relation to the El Niño-Southern Oscillation and North Atlantic Oscillation. However, investigation of other teleconnections and local climate is often neglected. In this study, we examined over a 33-year period the relationships between four teleconnections (El Niño-Southern Oscillation, North Atlantic Oscillation, Arctic Oscillation, East Atlantic Pattern), local weather parameters (temperature and precipitation) and reproduction in great tits <i>Parus major</i> and blue tits <i>Cyanistes caeruleus</i> in the Carpathian Basin, Hungary. Furthermore, we explored how annual variations in the timing of food availability were correlated with breeding performance. In both species, annual laying date was negatively associated with the Arctic Oscillation. The date of peak abundance of caterpillars was negatively associated with local temperatures in December-January, while laying date was negatively related to January-March temperature. We found that date of peak abundance of caterpillars and laying date of great tits advanced, while in blue tits clutch size decreased over the decades but laying date did not advance. The results suggest that weather conditions during the months that preceded the breeding season, as well as temporally more distant winter conditions, were connected to breeding date. Our results highlight that phenological synchronization to food availability was different between the two tit species, namely it was disrupted in blue tits only. Additionally, the results suggest that in order to find the climatic drivers of the phenological changes of organisms, we should analyze a broader range of global meteorological parameters.</p>
Data for the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'
<p>The deposited data were employed to generate the figures concerning single-cell RNA (scRNA) and spatial transcriptomic analyses in the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'.</p>
D1.3.2 Lab demonstrator of 6GSMART Cloud continuum Orchestrator-MNO
<p><em>D1.3.2 Lab demonstrator of 6GSMART Cloud continuum Orchestrator-MNO</em></p> <p>Video</p> <p>Developed by TELEFÓNICA-MINSAIT</p>
Demonstrating a Bayesian Online Learning forEnergy-Aware Resource Orchestration in vRANs
<p>Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration problems in energy-constrained vRANs. Specifically, we demonstrate two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, converge an order of magnitude faster than other machine learning methods-and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the advantages of our approach in a testbed comprised of fully-fledged LTE stacks and a power meter, and implemented our approach into O-RAN's non-real-time RAN Intelligent Controller (RIC).</p>
Estimating Orchestration Load in Collaborative Learning Situations Using EDA - Activity 6
<p>Skin conductivity of the teacher while orchestrating a Pyramid activity. The green highlight indicates an SCR concurred with the teacher report: "When students told me that after increasing time in the "improving phase" they could not continue editing their improved answer.".</p>
Estimating Orchestration Load in Collaborative Learning Situations Using EDA - Activity 2
<p>Skin conductivity of the teacher while orchestrating a Pyramid activity. During this activity, the teacher reported: "I noticed that the scenario for the task that I shared with student was not the one I planned (I have several ... and was confused with the one I picked) so I had to read the scenario as well .. while students where completing the Pyramid activity. In any case I know all scenarios very well and was quick for me to remember it.".</p>
Estimating Orchestration Load in Collaborative Learning Situations Using EDA - Activity 3
<p>Skin conductivity of the teacher while orchestrating a Pyramid activity. The teacher did not report any stressful situation during this activity.</p>
Estimating Orchestration Load in Collaborative Learning Situations Using EDA - Activity 1
<p>Skin conductivity of the teacher while orchestrating a Pyramid activity. The green highlight indicates an SCR concurred with the teacher report: "I was running out of time, and needed to reduce time in the XXX activity. However I'm used to this kind of situations, and was not highly stressed."</p>
Connecting Intelligence and Smart Orchestration for B5G/6G Networks
<p>Hexa-X project will pave the way towards future 6G system concepts by explorative research. Hexa-X is working out a set of 6G technology enablers to interconnect human, physical, and digital worlds. An essential tool to empower this 6G vision is the application of Artificial Intelligence (AI)/Machine Learning (ML) technologies to significantly improve efficiency and service experience by the research on "Connecting Intelligence". Wide exploration on the potential of AI/ML is necessary to monetise generated data and optimize network management. This can substantially enhance the cost, energy consumption, trust level and service efficiency of network infrastructure. AI allows networks to faster and more precisely adapt to scenarios and traffic demand by acting on predictive orchestration mechanisms in future 6G. Network orchestration should incorporate a complete end-to-end perspective in its decisions and enforcing actions from far edge/devices to RAN disaggregated functions, edge computing, core and cloud elements. This presentation will give an overview on the research and application of AI/ML algorithms to a holistic network orchestration and smart service management.</p>
miR-29a-3p, a new myokine orchestrating resistance exercise via coordinated metabolic responses
<p>NGS extracelular vesicles miRNA total reads of three pooled plasma samples from sedentary(C), endurance (E) and resitance (R) 4-week trained mice.</p>
April 7, 2024 (v1) Image Open Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 5 relative to Figure 7 - Figure Supplement 4
<p>Source data file relative to <strong><span>Figure 7 – figure supplement 4 Panel A</span></strong></p> <p><span>Raw image of agarose gel showing the 2 alternative mRNAs encoding for ArhGEF11 in control animals (left track, control) and after injection of the MO at the one cell stage (right track, +MO at 2 and 5ng). The source data includes the raw files (native format .scn and open source format .tiff) as well as a pdf file showing both the full scale image and the cropped image selected for the figure.<br></span></p>
PHENICX-Anechoic: note annotations for Aalto anechoic orchestral database
<p>This dataset includes audio and annotations useful for tasks as score-informed source separation, score following, multi-pitch estimation, transcription or instrument detection, in the context of symphonic music.</p> <p>This dataset was presented and used in the evaluation of:</p> <blockquote> <p>M. Miron, J. Carabias-Orti, J. J. Bosch, E. Gómez and J. Janer, "Score-informed source separation for multi-channel orchestral recordings", Journal of Electrical and Computer Engineering (2016))"</p> </blockquote> <p>On this web page we do not provide the original audio files, which can be found at the <a href="http://research.cs.aalto.fi/acoustics/virtual-acoustics/research/acoustic-measurement-and-analysis/85-anechoic-recordings.html">web page</a> hosted by Aalto University. However, with their permission we distribute the denoised versions for some of the anechoic orchestral recordings:</p> <blockquote> <p>Pätynen, J., Pulkki, V., and Lokki, T., "Anechoic recording system for symphony orchestra," Acta Acustica united with Acustica, vol. 94, nr. 6, pp. 856-865, November/December 2008.</p> </blockquote> <p>For the intellectual rights and the distribution policy of the audio recordings in this dataset contact Aalto University, Jukka Pätynen and Tapio Lokki. For more information about the original anechoic recordings we refer to the <a href="http://research.cs.aalto.fi/acoustics/virtual-acoustics/research/acoustic-measurement-and-analysis/85-anechoic-recordings.html">web page</a> and the associated publication [2]</p> <p>We provide the associated musical note onset and offset annotations, and the Roomsim[3] configuration files used to generate the <a href="http://repovizz.upf.edu/phenicx/anechoic_multi/">multi-microphone recordings</a> [1].</p> <p>The anechoic dataset in [2] consists of four passages of symphonic music from the Classical and Romantic periods. This work presented a set of anechoic recordings for each of the instruments, which were then synchronized between them so that they could later be combined to a mix of the orchestra. In order to keep the evaluation setup consistent between the four pieces, we selected the following instruments: violin, viola, cello, double bass, oboe, flute, clarinet, horn, trumpet and bassoon.</p> <p>We created a ground truth score, by hand annotating the notes played by the instruments. The annotation process involved gathering the original scores in MIDI format, performing an initial automatic audio-to-score alignment, then manually aligning each instrument track separately with the guidance of a monophonic pitch estimation.</p> <p>During the recording process detailed in [2], the gain of the microphone amplifiers was fixed to the same value for the whole process, which reduced the dynamic range of the recordings of the quieter instruments. This lead to problems with which we had to deal, in order to reduce the noise. In the paper we described the score-informed denoising procedure we applied to each track.</p> <p>A complete description of the dataset and the creation methodology, including the generation of the <a href="http://repovizz.upf.edu/phenicx/anechoic_multi/">multi-microphone recordings</a>, is presented in [1].</p> <p>Please Acknowledge PHENICX-Anechoic in Academic Research</p> <p><strong>Using this dataset</strong></p> <p>When the present dataset is used for academic research, we would highly appreciate if scientific publications of works partly based on the PHENICX-Anechoic dataset quote the publications above.</p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p> </p> <p><a href="https://www.upf.edu/web/mtg/phenicx-anechoic">https://www.upf.edu/web/mtg/phenicx-anechoic</a></p>
Creating Object-Based Stimuli to Explore Media Device Orchestration Reproduction Techniques
<p>Dataset containing Object-based versions and rendered out MDO loudspeaker feeds of two programme items, adapted from existing material to explore Media Device Orchestration reproduction techniques. </p> <p>This forms part of the PhD research of Craig Cieciura. This was experiment-based research to determine how to render object-based audio in the domestic environment using ad-hoc, audio-capable devices.</p> <p><strong>References</strong></p> <p>Cieciura, C., Mason, R., Coleman, P. and Paradis, M. 2018. Creating Object-Based Stimuli to Explore Media Device Orchestration Reproduction Techniques, Audio Engineering Society Preprint, 145th Convention, Engineering Brief 463.</p> <p> </p>
Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization
<p>Data resulting from the experiments described in "Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization" (https://doi.org/10.1016/j.swevo.2018.12.010). The contents of the files is the following:</p> <ul> <li>CAMO-AIS_SWEVO.zip: All of the data below in a single file</li> <li>CAMO_Iowa.zip: Audio and Data for the orchestrations with the Iowa sound database found at http://theremin.music.uiowa.edu/MIS.html</li> <li>CAMO_Phil.zip: Audio and Data for the orchestrations with the Philharmonia sound database found at https://www.philharmonia.co.uk/explore/sound_samples</li> <li>CAMO_RWC.zip: Audio and Data for the orchestrations with the RWC sound database found at https://staff.aist.go.jp/m.goto/RWC-MDB/rwc-mdb-i.html</li> <li>CAMO_SOL.zip: Audio and Data for the orchestrations with the Studio Online sound database available with Orchids http://forumnet.ircam.fr/product/orchids-en/</li> <li>Listening_Test.zip: Raw data (i.e., perceptual similarity ratings) from the listening test found at http://http://camo.inesctec.pt/. This data is anonymous (each participant is assigned a reference number) so the participants cannot be identified.</li> </ul> <p>See the README.txt file for a detailed description of the contents of each file.</p>
Assessing Orchestration Load in Teacher-Facing Dashboards
<p>Figures</p>
Dataset 2: The plant response to high CO2 levels is heritable and orchestrated by DNA methylation
<p>This is a supporting dataset from the manuscript “The plant response to high CO<sub>2</sub> levels is heritable and orchestrated by DNA methylation” by Panda et al. This dataset was used to study trans-generational growth responses to environments with contrasting CO<sub>2</sub> levels.</p> <p>Raspberry Pi computers and cameras were used to image <em>Arabidopsis thaliana</em> wild-type (Columbia) and mutant (<em>ago1-27</em>, <em>cmt2-7/cmt3-11t</em>, <em>ddm1-2</em>, <em>met1</em>, <em>pol IV</em>, <em>suvh4/suvh5/suvh6</em>, and <em>ubp1b</em>) plants growing under ambient (450-500 ppm) and high (1000 ppm) CO<sub>2</sub> conditions. Imaging was done hourly from overhead beginning on the 8th day after sowing and continued until the 25th day after sowing.</p> <p>Dataset 2 contains images of plants grown in the second round of the experiment under ambient and high CO<sub>2</sub> conditions.</p>
Dataset 1: The plant response to high CO2 levels is heritable and orchestrated by DNA methylation
<p>This is a supporting dataset from the manuscript “The plant response to high CO<sub>2</sub> levels is heritable and orchestrated by DNA methylation” by Panda et al. This dataset was used to study trans-generational growth responses to environments with contrasting CO<sub>2</sub> levels.</p> <p>Raspberry Pi computers and cameras were used to image <em>Arabidopsis thaliana</em> wild-type (Columbia) and mutant (<em>ago1-27</em>, <em>cmt2-7/cmt3-11t</em>, <em>ddm1-2</em>, <em>met1</em>, <em>pol IV</em>, <em>suvh4/suvh5/suvh6</em>, and <em>ubp1b</em>) plants growing under ambient (450-500 ppm) and high (1000 ppm) CO<sub>2</sub> conditions. Imaging was done hourly from overhead beginning on the 8th day after sowing and continued until the 25th day after sowing.</p> <p>Dataset 1 contains images of plants grown in the first round of the experiment under ambient and high CO<sub>2</sub> conditions.</p>
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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.