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1,773 results for “Supplementary data,”
Supplementary data for article: Central insulin modulates dopamine signaling in the human striatum
<p><b>Objective:</b> Activity in the dopaminergic pathways of the brain is highly sensitive to body weight and metabolic states. Animal studies show that dopamine neurons are important targets for the metabolic hormone insulin with abolished effects in the insulin resistant state, leading to increases in body weight and food intake. In humans, the influence of central acting insulin on dopamine and effects of their interplay are still elusive.</p> <p><b>Research Design and Methods:</b> We investigated whether central administered insulin influences dopaminergic activity in striatal regions and whole-brain neural activity. Using a PET/MRI hybrid scanner, we simultaneously performed [<sup>11</sup>C]-raclopride-PET and resting state fMRI in 10 healthy normal weight men after application of intranasal insulin or placebo on two separate days in a randomized, placebo-controlled, blinded, crossover trial.</p> <p><b>Results:</b> In response to central insulin compared to placebo administration, we observed greater [<sup>11</sup>C]-raclopride binding potential (BP<sub>nd</sub>) in the bilateral ventral and dorsal striatum. This suggests an insulin-induced reduction in synaptic dopamine levels. Resting-state striatal activity was lower 15 and 30 min after nasal insulin compared to placebo. Functional connectivity of the mesocorticolimbic circuitry associated with differences in dopamine levels: individuals with a stronger insulin-induced effect on dopamine levels showed a stronger increase in functional connectivity 45 min after intranasal insulin.</p> <p><b>Conclusions</b>: This study indicates that central insulin modulates dopaminergic tone in the striatum, which may affect regional brain activity and connectivity. Our results deepen the understanding of the insulin-dopamine interaction and the complex network that underlies the regulation of whole-body metabolism.</p>
Supplementary material 1 from: Jordaens K, Goergen G, Skevington JH, Kelso S, De Meyer M (2021) Revision of the Afrotropical species of the hover fly genus Mesembrius Rondani (Diptera, Syrphidae) using morphological and molecular data. ZooKeys 1046: 1-141. https://doi.org/10.3897/zookeys.1046.57052
Table S1. List of specimens, collection information, DNA voucher codes and GenBank/Barcode of Life Datasystems (BOLD) accession numbers used for the DNA barcode and phylogenetic analyses
Supplementary data for diversification in the Arctic: biogeography and systematics of the North American Micranthes (Saxifragaceae)
<p>Flora endemic to the cold habitats of the Northern Hemisphere provide important models for investigating diversification and disjunctions, given both the intense climatic fluctuations of these areas in the recent past and the fascinating biogeographic patterns of today's Arctic-alpine plant communities. Micranthes Haw. (Saxifragaceae), a clade of small-flowered herbaceous flowering plants comprising 80 species, is an ideal group for investigating the evolution and diversification of plants in montane and Arctic ecosystems. Micranthes has proven to be a particularly challenging clade to unravel taxonomically due in part to rampant auto- and allopolyploidy, hybridization, and cryptic speciation. With the goal of providing an updated conspectus for this group, we build upon a recent large phylogenomic analysis, to help elucidate the evolution of Micranthes . Here, we present new downstream analyses including diversification analyses, biogeographical reconstructions, and a comparison of methods for dating phylogenomic analyses. To complement these latest analyses, we also synthesize chromosomal variation, new observations regarding morphology and species identification, comprehensive field studies, and an extensive review of the literature for Saxifragaceae and Micranthes. A new perspective on the systematics and taxonomy of Micranthes is provided.</p>
Supplementary data: "Revealing drivers and risks for power grid frequency stability with explainable AI"
<p>This repository contains processed data and result files for the paper <a href="https://arxiv.org/abs/2106.04341">Revealing drivers and risks for power grid frequency stability with explainable AI</a>. The code for producing the processed data and the results is <a href="https://github.com/johkruse/XAI-for-grid-frequency-stability">available at github</a>.</p> <p><strong>Data</strong></p> <p>For each area, the data folder contains the feature and target data used to train the ML model.</p> <ul> <li><em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features.</li> <li><em>input_forecast.h5 </em>and<em> input_actual.h5: </em>The day-ahead available (forecast) and ex-post available (actual) data of external features including the engineered features.</li> <li><em>outputs.h5 </em>: The grid frequency stability indicators.</li> <li><em>version_2021-07-01</em>: Folder containing the training and test sets used for the results.</li> <li><em>documentation_of_data_download</em><strong>: </strong>Plots and information files concerning the ENTSO-E raw data and its aggregation.</li> </ul> <p><strong>Data sources</strong></p> <p>The data for input features (<em>raw_input_data.h5, input_forecast.h5 </em>and<em> input_actual.h5</em>) is derived from ENTSO-E Transparency Platform data [1]. The target data (<em>outputs.h5</em>) is based on power grid frequency recordings from the German Transmission System Operator TransnetBW [2].</p> <p><strong>Results</strong></p> <p>For each area and each target, the result folder comprises the results of hyper-parameter optimization, model prediction and interpretation via SHAP. The results refer to the full model and the restricted model (containing only day-ahead features).</p> <ul> <li><em>cv_results_gtb_full.csv</em> and <em>cv_results_gtb_day_ahead.csv</em> : Performance results for each combination in the hyper-parameter grid search.</li> <li><em>cv_best_params_gtb_full.csv</em> and <em>cv_best_params_gtb_day_ahead.csv</em> : Hyper-parameters used in the final (optimized) model.</li> <li><em>shap_values_gtb_full.npy</em> and <em>shap_interaction_values_gtb_full.npy </em>: First-order SHAP values and second-order SHAP interaction values for the full model.</li> <li><em>y_pred.h5</em> : Predictions of daily profile predictor, full model and day-ahead model.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The data might be subject to copyright or related rights. Please consult the primary data owner.</p>
Supplementary data for "Beyond radical-rebound: Methane oxidation to methanol catalyzed by iron species in metal–organic framework nodes"
<p>Cartesian coordinates in the *.XYZ format for all the structures optimized at the M06-L/def2-TZVP in the reactivity study as part of the article "Beyond radical-rebound: Methane oxidation to methanol catalyzed by iron species in metal-organic framework nodes" (<a href="https://doi.org/10.1021/jacs.1c04766">https://doi.org/10.1021/jacs.1c04766</a>)</p>
Data from: The genetic landscape of axonal neuropathies in the middle aged and elderly: focus on MME (supplementary information)
<p class="BarbaraStandard">OBJECTIVE: To test the hypothesis that monogenic neuropathies such as Charcot-Marie-Tooth disease (CMT) contribute to frequent but often unexplained neuropathies in the elderly, we performed genetic analysis of 230 patients with unexplained axonal neuropathies and disease onset <u>></u>35 years.</p> <p class="BarbaraStandard">METHODS: We recruited patients, collected clinical data and conducted whole-exome sequencing (WES, n=126) and <i>MME</i> single-gene sequencing (n=104). We further queried WES repositories for <i>MME</i> variants and measured blood levels of the <i>MME</i>-encoded protein neprilysin.</p> <p class="BarbaraStandard">RESULTS: In the WES cohort, the overall detection rate for assumed disease-causing variants in genes for CMT or other conditions associated with neuropathies was 18.3% (familial cases 26.4%, apparently sporadic cases 12.3%). <i>MME</i> was most frequently involved and accounted for 34.8% of genetically solved cases. The relevance of <i>MME</i> for late-onset neuropathies was further supported by detection of a comparable proportion of cases in an independent patient sample, preponderance of <i>MME</i> variants among patients compared to population frequencies, retrieval of additional late-onset neuropathy patients with <i>MME</i> variants from WES repositories, and low neprilysin levels in patients' blood samples. Transmission of <i>MME</i> variants was often consistent with an incompletely penetrant autosomal dominant trait and less frequently with autosomal recessive inheritance.</p> <p class="BarbaraStandard">CONCLUSIONS: A detectable fraction of unexplained late-onset axonal neuropathies is genetically determined, either by variants in CMT genes or genes involved in other conditions that affect the peripheral nerves and can mimic a CMT phenotype. <i>MME</i> variants can act as completely penetrant recessive alleles but also confer dominantly inherited susceptibility to axonal neuropathies in an aging population.</p> <p> </p>
Supplementary material 4 from: Haubrock PJ, Cuthbert RN, Yeo DCJ, Banerjee AK, Liu C, Diagne C, Courchamp F (2021) Biological invasions in Singapore and Southeast Asia: data gaps fail to mask potentially massive economic costs. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 131-152. https://doi.org/10.3897/neobiota.67.64560
Extrapolated annual average costs for those invasive species known to be in Singapore with recorded costs in InvaCost
Supplementary material 1 from: Haubrock PJ, Cuthbert RN, Yeo DCJ, Banerjee AK, Liu C, Diagne C, Courchamp F (2021) Biological invasions in Singapore and Southeast Asia: data gaps fail to mask potentially massive economic costs. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 131-152. https://doi.org/10.3897/neobiota.67.64560
Description of the procedure used for collecting and describing cost data in the InvaCost database (adapted from Diagne et al. 2020)
Supplementary material 5 from: Haubrock PJ, Cuthbert RN, Yeo DCJ, Banerjee AK, Liu C, Diagne C, Courchamp F (2021) Biological invasions in Singapore and Southeast Asia: data gaps fail to mask potentially massive economic costs. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 131-152. https://doi.org/10.3897/neobiota.67.64560
Relationships between trade value and recorded cost entries per country in InvaCost, as well as land area and human population with total cost
Supplementary Data for "Hydrate Formation on Marine Seep Bubbles and the Implications for Water Column Methane Dissolution"
<p>This data deposit contains the data necessary to reproduce results presented in the paper "Hydrate Formation on Marine Seep Bubbles and the Implications for Water Column Methane Dissolution", to appear in JGR Oceans. </p> <p>The files "Fu_AGUSupplementary_S11.xlsx" and "Fu_AGUSupplementary_S12.xlsx" are referenced in the supplement of the original paper. </p> <p>The file "Fu_AGUSupplementary_additionalDATA.xlsx" contains all the data and inputs used to create Figure 14, 15 and 16 of the original manuscript.</p>
Supplementary Data: sampedro-etal_2021_Applied_Energy
<p>Supplementary Information for the following publication:</p> <p>Sampedro, J., Kyle, P., Ramig, C.W., Tanner, D., Huster, J.E., and Wise, M.A. "<em>Dynamic linking of upstream energy and freight demands for bio and fossil energy pathways in the Global Change Analysis Model</em>". Applied Energy. Accepted Manuscript (Aug 2021)</p>
Supplementary data: What drives grassland-forest boundaries? Assessing fire and frost effects on tree seedling survival and architecture
<ol> <li>Fire and frost represent two major hurdles for the persistence of trees in open grassy biomes and have both been proposed as drivers of grassland-forest boundaries in Africa.</li> <li>We assess the response of young tree seedlings, which represent a vulnerable stage in tree recruitment, to traumatic fire and frost disturbances.</li> <li>In a greenhouse experiment, we investigated how seedling traits predicted survival and resprouting ability in response to fire <i>vs</i> frost; we characterised survival strategies of seedlings in response to the two disturbances, and we documented how the architecture of surviving seedlings is affected by fire <i>vs</i> frost injury.</li> <li>Survival rates were similar under both treatments. However, different species displayed different levels of sensitivity to fire and frost. Seedling survival was higher for older seedlings and seedlings with more basal leaves. Survivors of a fire event lost more biomass than the survivors of a frost event. However, the architecture of recovered fire and frost treated seedlings were mostly similar. Seedlings that recovered from fire and frost treatments were often shorter than those that had not been exposed to any disturbance, with multiple thin branches, which may increase vulnerability to the next frost or fire event.</li> <li> <i>Synthesis</i>. Fire caused more severe aboveground damage compared to a single frost event, suggesting that fire is an important driver of tree distribution in these open grassland systems. However, the impact of repeated frost events may be equally severe, and needs to be investigated. Also, woody species composition may be influenced by phenomena that affect the timing and frequency of seedling exposure to damage, as mortality was found to be dependent on seedling age. Therefore, changes in fire regime and climate (esp. changes that bring about less frost and reduced fire intensity and frequency) are likely to result in changes in the composition and the structure of the woody components of these systems.</li> </ol>
Supplementary data: Adaptation and persistence of a low diversity microbiome in a high salinity offshore oil reservoir.
<p>Supplementary material S1: Cation and anion concentrations from the formation water from the Gulf of Mexico oil reservoir. </p> <p>Supplementary material S2: Bioinformatic related information on metabolic pathways and adaptations found within MAGs of the Gulf of Mexico formation water.</p>
Supplementary Data for Calculated Electron Impact Ionization Fragmentation Patterns
<p>Supplementary Data for the paper: Calculated Electron Impact Ionization Fragmentation Patterns</p> <p> </p> <p>Data includes:</p> <p> - Total and partial cross sections.</p> <p> - Computed Dissociation Thresholds, before and after corrected to Koopman's Theorem. </p> <p> </p> <p> </p>
Supplementary data: "Secondary control activation analysed and predicted with explainable AI"
<p>This repository contains processed data and result files for the paper <a href="https://arxiv.org/abs/2109.04802">Secondary control activation analysed and predicted with explainable AI</a> . The code for producing the processed data and the results is <a href="https://github.com/johkruse/XAI-for-aFRR-activation">available at github</a>.</p> <p><strong>Data</strong></p> <p>The data folder contains the feature and target data used to train the ML model. The data for Germany comprises the following folders and files:</p> <ul> <li><em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features.</li> <li><em>inputs_<model_type>.h5 </em><em>:</em> The input features for the different model types used in the paper including the engineered features. Depending on the model type, the input files also contain the IGCC features.</li> <li><em>outputs.h5 </em>: The activated aFRR volumes in Germany.</li> <li><em>version_2021-08-20</em>: Folder containing the training and test sets used for the results.</li> <li><em>documentation_of_data_download</em>: Information files concerning the ENTSO-E raw data and its aggregation.</li> </ul> <p>In addition to the German time series, the data folder contains the raw input data for the remaining IGCC states. Note that the results contain more model types as actually discussed in the paper.</p> <p><strong>Data sources</strong></p> <p>The data for input features (<em>raw_input_data.h5</em> and <em>input_<model_type>.h5</em>) is derived from ENTSO-E Transparency Platform data [1]. The target data (<em>outputs.h5</em>) is based on publicly available data from the German Transmission System Operators (TSOs) [2].</p> <p><strong>Results</strong></p> <p>The result folder comprises the results of hyper-parameter optimization, model prediction and interpretation via SHAP. The model type, the loss function to train the model and the data set for prediction/interpretation were varied.</p> <ul> <li><em>cv_results_<model_type>_<loss_function>.csv</em> : Performance results for each combination in the hyper-parameter grid search.</li> <li><em>cv_best_params_<model_type>_<loss_function>.csv</em> : Hyper-parameters used in the final (optimized) model.</li> <li><em>shap_values_<data_set>_<model_type>_<loss_function>.npy</em> : First-order SHAP values calculated on different data sets: The train set, the randomized test set and the continuous test set.</li> <li><em>y_pred_<data_set>.h5</em> : Predictions of daily profile predictor and Machine Learning models.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The data might be subject to copyright or related rights. Please consult the primary data owner.</p>
Supplementary data to "Geometric morphometrics shows a close relationship between the shape features, position on thalli, and CaCO3 content of segments in Halimeda tuna (Bryopsidales, Ulvophyceae)"
<p>zenodo-landmarks: Landmark coordinates of segment outlines of Halimeda tuna used in the geometric morphometric analyses.</p> <p>zenodo-designation: Designation of individual segments in the order corresponding to the landmark data file.</p> <p>zenodo-script-shape: The script executing the multivariate linear Procrustes ANOVA model decomposing the shape data of Halimeda tuna segments into different sources.</p> <p>zenodo-script-areas: The script computing the areas of Halimeda tuna segments based on their outline morphometric data.</p> <p> </p>
Supplementary data for "Recent increase in global land evapotranspiration is overestimated"
<p>Supplementary data for "Recent increase in global land evapotranspiration is overestimated"</p>
Deep learning for early warning signals of tipping points : supplementary data
<p>This data accompanies the publication by Bury et al. “Deep learning for early warning signals of tipping points” published in PNAS and the Github repository <a href="https://github.com/ThomasMBury/deep-early-warnings-pnas">https://github.com/ThomasMBury/deep-early-warnings-pnas</a>. It contains the model time series data that are used to train the deep learning algorithm. The directory ts_500 contains 500k time series used to train the 500-classifier. The directory ts_1500 contains 200k time series used to train the 1500-classifier. Both directories contain files <em>labels.csv </em>and <em>groups.csv </em>which provide numbers corresponding to the labels (Fold, Hopf, Branch, Null) and groups (Training, Validation, Test) for each time series respectively.</p>
Data and supplementary information for "A multiple time step algorithm for trajectory surface hopping simulations"
<p>This repository contains the supplementary information as well as the data necessary to reproduce the content of the article "A multiple time step algorithm for trajectory surface hopping simulations".</p>
Supplementary data to publication "Cellular detection of the chemokine receptor CXCR4 in bovine mammary glands and its distribution and regulation on bovine leukocytes"
<p>Supplementary data to publication "Cellular detection of the chemokine receptor CXCR4 in bovine mammary glands and its distribution and regulation on bovine leukocytes" in Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20799</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.