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6,569 results for “Long-term”
Fig. 1 in Long-term memory in the parasitoid Trichogramma telengai Sorokina, 1987 (Hymenoptera: Trichogrammatidae)
Fig. 1. The performance of T. telengai in each of three tests: a — the percentage (%) of time spent by T. telengai in the target sector in each of the three tests (M ± SE), t-test: *p <0.05. b — the learning index in each of the three tests (M ± SE), t-test: *p <0.05. The column headers indicate the sample sizes. Рис. 1. ПокаЗатели перемеЩениЯ T. telengai в каждом иЗ трех тестов: a — процент (%) времени, проведенного T. telengai в целевом секторе в каждом иЗ трех тестов (M ± SE), t-test: *p <0.05. b — индекс обучениЯ в каждом иЗ трех тестов (M ± SE), t-test: *p <0.05. В основании столбцов укаЗаны объемы выборок.
Fig. 2 in Long-term dynamics of microplastic accumulation in the intestinal tract of terrestrial insects on the example of Vespula vulgaris (Linnaeus, 1758) (Hymenoptera: Vespidae)
Fig. 2. Occurrence of different types of MPs inside wasps, %. Рис. 2. Встречаемость раЗнотипных частиц МП в осах, %.
Fig. 1 in Long-term dynamics of microplastic accumulation in the intestinal tract of terrestrial insects on the example of Vespula vulgaris (Linnaeus, 1758) (Hymenoptera: Vespidae)
Fig. 1. Box and whisker plot showing average content of MP particles inside wasps from natural populations sampled in the vicinity of the village of Kireevsk (five samples/year; Tomsk region, Russia). Рис. 1. Диаграмма, демонстрируюЩаЯ среднее содержание частиц МП в осах иЗ природных популЯций (5 выборок/год) в окрестностЯх с. Киреевск (ТомскаЯ область, РоссиЯ).
FIG. 4 in Long-Term Turtle Declines: Protected Is a Verb, Not an Outcome
FIG. 4. (A) Population Viability Analysis for Spotted Turtles for the historic North Wetland Complex (NWC) site. (B) Population Viability Analysis for Spotted Turtles for the historic South Wetland Complex (SWC) site. In both graphs, the line represents the average number of individuals starting with the estimated population size from 1987–1992 and projected into the future using Program VORTEX (error bars are SDs). The black circle represents the estimated mean population size from Program MARK for 2014–2017. In both cases, the estimated population size projected from VORTEX was within ten individuals from the estimated population size from MARK during 2014–2017.
FIG. 3 in Long-Term Turtle Declines: Protected Is a Verb, Not an Outcome
FIG. 3. Estimates of annual survivorship rates for Spotted Turtles from the historic (1987–1992) and contemporary (2014–2017) sampling periods. Estimates are broken down by population (NWC, North Wetland Complex; SWC, South Wetland Complex) and sex (M, males; F, females). There were no significant changes in survivorship rates between sampling periods within any population. Error bars are 95% confidence intervals of survivorship estimates generated in Program MARK.
FIG. 1 in Long-Term Turtle Declines: Protected Is a Verb, Not an Outcome
FIG. 1. Estimated population size of Spotted Turtles over time (historic population sizes are from 1987–1992 and contemporary population sizes are from 2014–2017). The estimated mean number of individuals shown separately for males (solid black bars) and females (diagonally striped bars), and the North Wetland Complex (NWC) and the South Wetland Complex (SWC). Error bars are standard error estimates of population size from Program MARK. Significant differences between the estimated mean number of males and females are denoted with an *.
"Field integration of shoot gas exchange and leaf chlorophyll fluorescence measurements to study the long-term regulation of photosynthesis in situ" data set
<p>The main data used in the "Field integration of shoot gas exchange and leaf chlorophyll fluorescence measurements to study the long-term regulation of photosynthesis in situ" publication, as well as the readme-file for the dataset. Supplementary figures are available at the Tree Physiology website.</p>
Dataset_Performance of different wheat varieties and their associated microbiome under contrasting tillage and fertilization intensities: Insights from a Swiss Long-Term Field Experiment
<p>Data collected in the frame of the<span> SolACE (</span><span><a href="https://www.solaceeu.net/"><span>https://www.solaceeu.net/</span></a></span><span>) project which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 727247 and the State Secretariat for Education, Research and Innovation SERI under no. 17.00094. Data table includes all data reported in the manuscript entitled "</span></p> <p><span>Performance of different wheat varieties and their associated microbiome under contrasting tillage and fertilization intensities: Insights from a Swiss Long-Term Field Experiment</span>"</p> <p>and all additional data which had been collected during the field sampling campaign on additional agronomic, soil and microbial data, which were not analysed as part of the above mentioned manuscript. </p>
AntiSMASH results of the genome sequences of four novel Endozoicomonas strains associated with the octocoral Litophyton in a long-term aquarium facility
<p>Secondary Metabolite-Encoding Biosynthetic Gene Cluster (SM-BGC) annotation files from antiSMASH bacterial version 7.1.0 for four <em>Endozoicomonas</em> strains associated with the tropical octocoral Litophyton in a long-term aquarium facility. Data corresponds to the assemblies of NE35, NE40, NE41 and NE43, available under the BioProject accession numbers <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075803">PRJNA1075803</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075804">PRJNA1075804</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075805">PRJNA1075805</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075806">PRJNA1075806</a>, respectively.<u> </u></p> <p>To easily and interactively review the results, please download the genome you wish to examine, extract the entire contents of the folder, and open the HTML file named "Results".</p> <p> </p> <p>This dataset is part of the following study:</p> <p>Marques M, da Silva DMG, Santos E, Baylina N, Peixoto R, Kyrpides NC, Woyke T, Whitman WB, Keller-Costa T, Costa R. 2024. Genome sequences of four novel <em>Endozoicomonas </em>strains associated with a tropical octocoral in a long-term aquarium facility. Microbiology Resource Announcements</p>
COG and Pfam annotation results of the genome sequences of four novel Endozoicomonas strains associated with the octocoral Litophyton in a long-term aquarium facility
<p>COG and Pfam annotation files from DOE-JGI Microbial Genome Annotation Pipeline (MGAP) version 4(1), for four <em>Endozoicomonas</em> strains associated with the tropical octocoral Litophyton in a long-term aquarium facility. Data correspond to the assemblies of NE35, NE40, NE41, and NE43, available under the BioProject accession numbers <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075803">PRJNA1075803</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075804">PRJNA1075804</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075805">PRJNA1075805</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075806">PRJNA1075806</a>, respectively. Results were submitted to the Integrated Microbial Genomes and Microbiomes system v7 (IMG/M) (2) for comparative analysis. The genome annotations can be interactively accessed on IMG/M (https://img.jgi.doe.gov/cgi-bin/m/main.cgi) using the following identifiers: 8036267134 (strain NE35), 8036277142 (strain NE40), 8045494135 (strain NE41); 8036272146 (strain NE43). </p> <p>This dataset is part of the following study:</p> <p>Marques M, da Silva DMG, Santos E, Baylina N, Peixoto R, Kyrpides NC, Woyke T, Whitman WB, Keller-Costa T, Costa R. 2024. Genome sequences of four novel <em>Endozoicomonas </em>strains associated with a tropical octocoral in a long-term aquarium facility. Microbiology Resource Announcements</p> <p> </p> <p>Other reference sources:</p> <p>(1) Huntemann M, Ivanova NN, Mavromatis K, James Tripp H, Paez-Espino D, Palaniappan K, Szeto E, Pillay M, Chen IMA, Pati A, Nielsen T, Markowitz VM, Kyrpides NC. 2015. The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4). Stand Genomic Sci 10:1–6.</p> <p>(2) Chen IMA, Chu K, Palaniappan K, Ratner A, Huang J, Huntemann M, Hajek P, Ritter SJ, Webb C, Wu D, Varghese NJ, Reddy TBK, Mukherjee S, Ovchinnikova G, Nolan M, Seshadri R, Roux S, Visel A, Woyke T, Eloe-Fadrosh EA, Kyrpides NC, Ivanova NN. 2023. The IMG/M data management and analysis system v.7: content updates and new features. Nucleic Acids Res 51:D723–D732.</p>
No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity
<p>This is the EEG and behavioral data for the long-term memory ping study by Sander van Bree, Abbie Sarah Mackenzie, and Maria Wimber.</p> <p>Paper title: No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity</p> <p>The behavioral data is named "behav_res_pp", which is the output of script 1 (s0_extractdata.m) on Github. The EEG data is named "pp_reorder" and it is the output of script 5 (s5_correctdata.m); i.e., it is both preprocessed and correctly formatted for the main analyses.</p> <p>Move the behavioral data to folder /data/behav_data/ and the EEG data to folder /data/eeg_data/</p> <p>For the analysis scripts and more information, check Github: https://github.com/sandervanbree/MemPing</p>
D3.10 - Users document for methods and models of the long-term coastline evolution.
<p>The D3.10, related to Task 3.5 and entitled "User Document for Methods and Models of the Long-term Coastline Evolution," is a WP3 deliverable, specifically a report describing the methods and models used to study long-term morpho-dynamic processes in a climate-change scenario. </p>
Long-term Changes in Salinity in the South China Sea due to Anthropogenic Forcing
Open the record for dataset details and reuse information.
Results and plotting scripts for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'
<p><br>This archives the results for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'</p> <p>For the model version used to create these results, please see: https://doi.org/10.5281/zenodo.13981520</p> <p>Files to reproduce the figures, in R language:<br>SuCCESs validation.R - Reads GDX files and produces plots for energy and emissions.<br>SuCCESs validation MC.R - The same, but with the Monte Carlo GDXs.</p> <p>External data sources:</p> <p>***<br>GHG emissions are from IGCC and PRIMAP</p> <p>IGCC: https://climatechangetracker.org/igcc (CC-BY license)</p> <p>PRIMAP:<br>Gütschow, Johannes; Jeffery, M. Louise; Gieseke, Robert; Gebel, Ronja; Stevens, David; Krapp, Mario; Rocha, Marcia (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, https://doi.org/10.5194/essd-8-571-2016<br>Gütschow, Johannes ; Busch, Daniel ; Pflüger, Mika (2024): The PRIMAP-hist national historical emissions time series (1750-2023) v2.6. Zenodo. https://doi.org/10.5281/zenodo.13752654<br>https://primap.org/primap-hist/ (CC-BY-4.0 license)</p> <p>***<br>Historical energy production and use data are from IEA Energy Statistics Data Browser (CC BY 4.0 licence).<br>https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=CO2%20emissions&indicator=CO2BySource</p> <p>***<br>IAM results are from the SSP database: https://tntcat.iiasa.ac.at/SspDb </p> <p>Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlík, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni.<br>The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environmental Change, Volume 42, Pages 153-168, 2017,<br>DOI:110.1016/j.gloenvcha.2016.05.009</p> <p>Rogelj, J., Popp, A., Calvin, K.V., Luderer, G., Emmerling, J., Gernaat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G., Krey, V., Kriegler, E., Riahi, K., van Vuuren, D.P., Doelman, J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlik, P., Humpenöder, F., Stehfest, E., Tavoni, M., Scenarios towards limiting global mean temperature increase below 1.5 °C. Nature Climate Change 8, 2018, 325-332.<br>DOI:10.1038/s41558-018-0091-3</p>
Long-term adaptation of lymphoma cell lines to hypoxia is mediated by diverse molecular mechanisms that are targetable with specific inhibitors - Supplemental Data Tables
<p><strong>Supplemental Data Table - Metabolome.xlsx</strong> - a complete list of measured metabolites for Ramos and HBL2 cell lines. Peak area values for normoxic and hypoxia-adapted cells in triplicates. Mean log 2 fold change and adjusted P values are provided for all metabolites except those under the limits of detection.</p> <p><strong>Supplemental Data Table - Transcriptome Differential Expression.xlsx</strong> - the tables of all transcripts detected in hypoxia-adapted HBL2 and Ramos lymphoma cell lines compared to normoxic controls. Fold change and log 2 fold change, log 2 CPM, F statistic, P value and False Discovery Rate (FDR) were calculated using EdgeR package. Last column shows if a transcript was significantly <em>up</em>- or <em>down</em>regulated or not (<em>ns</em>).</p> <p><strong>Supplemental Data Table - Transcriptome Gene Set Analysis.xlsx</strong> - the tables of the gene set enrichment analysis of Reactome pathways in hypoxia-adapted HBL2 and Ramos lymphoma cell lines compared to normoxic controls.</p> <p><strong>Supplemental Data Table - Proteome Differential Expression.xlsx</strong> - the tables of proteins detected and differentially expressed in hypoxia-adapted HBL2 and Ramos lymphoma cell lines compared to normoxic controls.</p> <p><strong>Supplemental Data Table - Cell lines SNV characterization.xlsx</strong> - the tables of mutations (single nucleotide variations and short indels) detected in four tested lymphpoma cell lines and corresponding primary tumor (PT) samples. AF_CTRL - allelic frequency (0..1) in healthy control DNA from the patient, AF_PT - allelic frequency in primary tumor, AF_PDCL - allelic frequency in patient-derived cell line. DP - total sequence depth, TLOD - Log odds that the variant is present in the tumor sample relative to the expected noise. Gencode and HGNC annotation columns are output of the Funcotator analysis. SNVs were detected using Mutect2 as a part of GATK 4.6.0.0 somatic variat calling pipeline.</p>
Supplements for Inferring Long-Term Tectonic Uplift Patterns from Bayesian Inversion of Fluvially-Incised Landscapes paper
<p><strong>Data and File Organization:</strong></p> <ol> <li><strong>Natural Landscapes (DEM):</strong> <ul> <li>Look for <code>.tif</code> files containing DEMs of natural landscapes. These files are in latitude-longitude coordinates; convert them to UTM if needed.</li> </ul> </li> <li><strong>Synthetic Landscapes (DEM):</strong> <ul> <li>DEM files for synthetic landscapes, ready for use in inversion schemes, are labeled with a <code>syn_</code> prefix.</li> </ul> </li> <li><strong>Climatic Data:</strong> <ul> <li>Climatic data for the Himalayas is available in <code>climate_data_him.zip</code>.</li> </ul> </li> </ol> <p><strong>Running the Code:</strong></p> <ol> <li> <p><strong>Loading DEMs:</strong></p> <ul> <li>Use the <code>loadDEM</code> package to load your DEM file.</li> <li>Specify <code>Z0</code> and <code>A0</code> values, then plot the landscape and <code>basinID</code> for reference.</li> </ul> </li> <li> <p><strong>Identifying Basins of Interest:</strong></p> <ul> <li>Determine which <code>basinID</code>s are of interest, then save them as forward objects. The functions for this process are available within the relevant packages.</li> </ul> </li> <li> <p><strong>Loading the Forward Model:</strong></p> <ul> <li>Load the forward model from the saved file using the appropriate function in the <code>frd</code> package.</li> <li>Choose the number of knots and specify if you prefer a 1D or 2D inversion.</li> </ul> </li> <li> <p><strong>Running and Plotting Inversion Results:</strong></p> <ul> <li>After running the inversion, view results in <code>inversion.step</code>.</li> <li>Plot these results using the plotting functions in the <code>frdplotting</code> package.</li> </ul> </li> </ol> <p> </p> <p> </p> <p><strong>Setup and Installation:</strong></p> <ul> <li>Install the package <code>scabbard</code> with: <div> <div> </div> <div><code>pip install pyscabbard </code></div> </div> </li> <li>All other Python dependencies are standard and can be installed via <code>pip</code> or <code>conda</code> as needed.</li> </ul>
Optimisation of RCA Content in Mortar Mixture Based on the Long-term Fracture and Fatigue Tests
<p>Data for the experimental study presented in: Optimisation of RCA Content in Mortar Mixture Based on the Long-term Fracture and Fatigue Tests</p>
Data for: Epigenetic Age and Long-Term Cancer Risk Following a Stroke
<p><strong>Data for Manuscript: "Epigenetic Age and Long-Term Cancer Risk Following a Stroke"</strong></p> <p>In this study, we investigated whether stroke patients who later developed cancer exhibited an accelerated epigenetic age compared to those who did not. A total of 648 stroke patients were followed for 15 years, during which 83 individuals were diagnosed with cancer. Biological age (B-age) was estimated from DNA methylation data derived from whole blood samples collected within 24 hours of stroke onset. These samples were analyzed using the 450K or EPIC Illumina Beadchip arrays. We employed several epigenetic clocks to calculate B-age, including the Hannum, Horvath, PhenoAge, ZhangBLUP, ZhangEN, and the mitotic epiTOC clocks.</p> <p>After adjusting for multiple testing, competing risks, and key confounders, we found that patients who developed cancer were biologically older, as measured by the Hannum clock.</p> <p>This repository provides the associated phenotypic data, estimated white cell counts and B-age estimations, including both raw data and age acceleration values.</p>
Data from: Differences in the regulation of growth and biomineralization genes revealed through long-term common garden acclimation and experimental genomics in the purple sea urchin
Across heterogeneous landscapes, populations may have adaptive differences in gene regulation that adjust their physiologies to match local environments. Such differences could have origins in acclimation or in genetically fixed variation between habitats. Here we use common garden experiments to evaluate differences in gene expression between populations of the purple sea urchin, Strongylocentrotus purpuratus, spanning 1700 km and average temperature differences of 5 °C to 8 °C. Across expression profiles from 18,883 genes after three years of common conditions, we find highly correlated expression patterns (Pearson's r = 0.992) among most genes. However, sixty-six genes were differentially expressed, including many ribosomal protein and biomineralization genes, all of which had higher expression in urchins originally from the southern population. Gene function analyses revealed slight but pervasive expression differences in genes related to ribosomal function, metabolism, transport, "bone" development, and response to stimuli. In accord with gene expression patterns, a post-hoc spine re-growth experiment revealed that urchins of southern origin re-grew spines at a faster rate than northern urchins. These results suggest that there may be genetically controlled, potentially adaptive differences in gene regulation across habitats and that gene expression differences may be under strong enough selection to overcome high dispersal-mediated gene flow in this marine species.
Data from: Population signatures of large-scale, long-term disjunction and small-scale, short-term habitat fragmentation in an Afromontane forest bird
The Eastern Afromontane cloud forests occur as geographically distinct mountain exclaves. The conditions of these forests range from large to small and from fairly intact to strongly degraded. For this study, we sampled individuals of the forest bird species, the Montane White-eye Zosterops poliogaster from 16 sites and four mountain archipelagos. We analysed 12 polymorphic microsatellites and three phenotypic traits, and calculated Species Distribution Models (SDMs) to project past distributions and predict potential future range shifts under a scenario of climate warming. We found well-supported genetic and morphologic clusters corresponding to the mountain ranges where populations were sampled, with 43% of all alleles being restricted to single mountains. Our data suggest that large-scale and long-term geographic isolation on mountain islands caused genetically and morphologically distinct population clusters in Z. poliogaster. However, major genetic and biometric splits were not correlated to the geographic distances among populations. This heterogeneous pattern can be explained by past climatic shifts, as highlighted by our SDM projections. Anthropogenically fragmented populations showed lower genetic diversity and a lower mean body mass, possibly in response to suboptimal habitat conditions. On the basis of these findings and the results from our SDM analysis we predict further loss of genotypic and phenotypic uniqueness in the wake of climate change, due to the contraction of the species' climatic niche and subsequent decline in population size.
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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)
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.