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253 results for “functional organization”
Supplementary data for "The subgenual organ complex in stick insects: Functional morphology and mechanical coupling of a complex mechanosensory organ"
<p>µCT-scans of the upper tibial regions of the foreleg (T1) and the midleg (T2) of <em>Ramulus artemis</em> (Westwood, 1859), <em>Carausius morosus</em> (Sinéty, 1901), and <em>Sipyloidea sipylus</em> (Westwood, 1859). For use of scans, please cite the following publication:</p> <p>Strauß, J., Moritz, L. & Rühr, P.T. (<strong>2021</strong>): The subgenual organ complex in stick insects: Functional morphology and mechanical coupling of a complex mechanosensory organ. <em>Frontiers in Ecology and Evolution (Research Topic “Evolutionary Biomechanics of Sound Production and Reception”)</em>. doi: <a href="https://doi.org/10.3389/fevo.2021.632493">10.3389/fevo.2021.632493</a>.</p> <p>All scans were performed with a commercial μCT desktop system (Skyscan 1272, Bruker microCT, Kontich, Belgium) at the Zoological Research Museum Alexander Koenig, Leibniz Institute for Animal Biodiversity, Bonn, Germany.</p> <p><strong>µCT scan settings of all samples:</strong></p> <p><em>Ramulus artemis:</em></p> <ul> <li>tube voltage = 30 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1980 ms</li> <li>binning = 1x1</li> <li>averaging = 8</li> <li>random movement = 15 px</li> <li>voxel size = 1.8 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Ramulus_artemis_T1.tif; Ramulus_artemis_T2.tif</li> </ul> <p><em>Carausius morosus:</em></p> <ul> <li>tube voltage = 29 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1900 ms</li> <li>binning = 1x1</li> <li>averaging = 5</li> <li>random movement = 15 px</li> <li>voxel size = 1.0 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Carausius_morosus_T1.tif; Carausius_morosus_T2.tif</li> </ul> <p><em>Sipyloidea sipylus:</em></p> <ul> <li>tube voltage = 29 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1900 ms</li> <li>binning = 1x1</li> <li>averaging = 7</li> <li>random movement = 15 px</li> <li>voxel size = 1.8 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Sipyloidea_sipylus_T1.tif; Sipyloidea_sipylus_T2.tif</li> </ul>
Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic
<p>Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance:<br><code>Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)</code><br>There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes).</p> <p>The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (<a href="https://hermes.acri.fr/">https://hermes.acri.fr/</a>) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B).</p> <p>The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W.</p> <h2><strong>Storage</strong></h2> <p>All variables are stored in the same daily file: <code>PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc</code>. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions.</p> <p>The PFT variables are stored using linear packing (see <a href="https://nco.sourceforge.net/nco.html#Linear-Packing">https://nco.sourceforge.net/nco.html#Linear-Packing</a>) as 16-bits unsigned integers (<code>NC_USHORT</code>), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (<code>=(2**16 - 2) * 1.54e-3</code>), with a discretization step of 1.54e-3.<br>As for the PFT variables, these values are in percent points.</p> <h2><strong>References</strong></h2> <ul> <li>El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S.<br>“Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”,<br><em>J. Geophys. Res. Oceans</em> 124, 1357–1378, <a href="https://doi.org/10.1029/2018jc014450">https://doi.org/10.1029/2018jc014450</a>, <strong>2019</strong></li> <li> <div>El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C.<br>“Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”<em>,<br>Ocean Science</em> 20 no. 1, 217−39. <a href="https://doi.org/10.5194/os-20-217-2024">https://doi.org/10.5194/os-20-217-2024</a>, <strong>2024</strong></div> </li> </ul> <h2><strong>Changelog</strong></h2> <h3>v1.2</h3> <ul> <li>[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).</li> <li>[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.</li> </ul> <h3>v1.1</h3> <ul> <li>[2023-08-30] Store PFT data using linear packing. </li> </ul> <h3>v1.0</h3> <ul> <li>[2023-08-03] those data are reorganised, and metadata is added, using the script <a href="https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads">https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads</a></li> <li>[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`</li> </ul>
Data_Schönauer et al. (2023)_Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest
<p>Data used in</p> <p>Schönauer, M., Hietz, P., Schuldt, B., and Rewald, B. (2023). Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest. Frontiers in plant science 14, 1127292. doi: 10.3389/fpls.2023.1127292</p>
Functional organization of the mouse lemur Primate Microcebus murinus : from multilevel validation to comparison with humans
<p>Brain network organization in the mouse lemur (Microcebus murinus) Primate.<br> (comparison with humans)<br> Archives contain:<br> <br> - Dictionary learning analysis in mouse lemurs and humans showing networks identified in these two species.<br> <br> - Cerebral templates from mouse lemurs and humans (MNI template). They can be used to localize networks.<br> <br> - A functional atlas of the mouse lemur brain issued from resting fMRI. Resting-state functional MR images were recorded from 14 mouse lemurs at 11.7 Tesla (2 time point per animal).<br> - An atlas from human brain (issued from <a href="http://www.gin.cnrs.fr/fr/outils/aal-aal2/">http://www.gin.cnrs.fr/fr/outils/aal-aal...</a>) that can be used to attribute human cerebral networks.<br> <br> - Templates, atlases and networks can be easily observed together using ITK-SNAP (<a href="http://www.itksnap.org/">http://www.itksnap.org/</a>).</p> <p>if used for publication please cite: </p> <p><strong>Resting state functional atlas and cerebral networks in mouse lemur primates at 11.7 Tesla</strong><br> <strong>Clément M Garin</strong>, Nachiket A Nadkarni, Brigitte Landeau, Gaël Chételat, Jean-Luc Picq, Salma Bougacha, Marc Dhenain<br> Feb 2021<br> <strong>NeuroImage</strong> 226, 117589<br> DOI: 10.1016/J.NEUROIMAGE.2020.117589<br> <a href="https://www.sciencedirect.com/science/article/pii/S1053811920310740">https://www.sciencedirect.com/science/article/pii/S1053811920310740</a></p>
The Editors-in-Chief of SOIL ORGANISMS: Prof. Dr. Willi Xylander (Görlitz) and Prof. Dr. Nico Eisenhauer (Leipzig). in SOIL ORGANISMS - an international open access journal on the taxonomic and functional biodiversity in the soil
The Editors-in-Chief of SOIL ORGANISMS: Prof. Dr. Willi Xylander (Görlitz) and Prof. Dr. Nico Eisenhauer (Leipzig).
Glacier shrinkage will accelerate downstream decomposition of organic matter and alters microbiome structure and function
<p>Two datasets supporting the publication, "Glacier shrinkage will accelerate downstream decomposition of organic matter and alters microbiome structure and function" in Global Change Biology.</p> <p><strong>DATA S1 </strong>Detailed metadata for sampled glacier-fed streams, including sample date and time, GPS coordinates, elevation, physical streamwater measurements, glacier characteristics, nutrient chemistry, and a column indicating samples used in metagenomics analyses.</p> <p><strong>DATA S2 </strong>Full patch-level dataset of extracellular enzyme activities (nmol h<sup>-1</sup> g<sup>-1</sup> DM sediment) and chlorophyll <em>a </em>(µg chlorophyll <em>a</em> g<sup>-1</sup> DM).</p>
Raw data for the article "Organic Dye Photocatalyzed Synthesis of Functionalized Lactones and Lactams via a Cyclization-Alkynylation Cascade"
<p>Raw NMR and MS data for the article "Organic Dye Photocatalyzed Synthesis of Functionalized Lactones and Lactams via a Cyclization-Alkynylation Cascade" published in Organic Letters, DOI: </p> <p><a title="DOI URL" href="https://doi.org/10.1021/acs.orglett.3c03603">https://doi.org/10.1021/acs.orglett.4c01078</a></p> <p>The number of the folders either correspond to compounds numbers in the article or the name of the folder is self-describing. All details concerning conditions and equipment for measurements can be found in the supporting information of the article. For convenience, the word file version of the supporting information can be found on the top of the raw data folder.</p>
Functionalization of PEDOT:PSS for aptamer-based sensing of IL6 using organic electrochemical transistors
<div>Explanation of included data for following publication:</div> <div>-------------------------------------------------------------------------------------</div> <div> </div> <div>Functionalization of PEDOT:PSS for aptamer-based sensing of IL6 using organic electrochemical transistors</div> <div> <div>DOI: 10.1038/s44328-024-00007-w</div> </div> <div> </div> <div>Bernhard Burtscher (1), Chiara Diacci (1), Anatolii Makhinia (1,2), Marios Savvakis (1), Erik O Gabrielsson (1), Lothar Veith (3), Xianjie Liu (1), Xenofon Strakosas (1), Daniel T Simon (1)</div> <div> </div> <div>1. Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, 60174 Norrköping, Sweden</div> <div>2. RISE Research Institutes of Sweden, Digital Systems, Smart, Hardware, Printed, Bio- and Organic Electronics, 60221 Norrköping, Sweden</div> <div>3. Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany</div> <div> </div> <div>-------------------------------------------------------------------------------------</div> <div>The data is structured according to the first figure they appear in the manuscript.</div> <div> </div> <div>Figure 2:</div> <div>a) Transfer curves data as .txt at various stages of the functionalization process with information of</div> <div>(Time / s; Gate Voltage / V; Drain Voltage / V; Drain Current / A; Gate Current / A; VR (measured voltage at the reference electrode) / V)</div> <div>1) PEDOT:PSS</div> <div>2) 4-ABA + PEDOT:PSS</div> <div>3) Aptamer + 4-ABA + PEDOT:PSS</div> <div> </div> <div>b) Measurement data of constant sensing experiments of either IL6 (6 meas.), BSA (3 meas.) or a control without aptamers (4ABA-ETA_Control_IL6-measurement) with explanation of when analyte was changed to which concentration.</div> <div> </div> <div>c) Data of constant measurement of fully functionalized OECT in bovine plasma after incubation over night in bovine plasma and information regarding time and concentration of changes</div> <div> </div> <div> </div> <div>Figure 3: Raw image files of fluorescent test of AL4083 PEDOT:PSS formulation with PLL-FITC as .nd2 files:</div> <div>- 0.25% Gops with PLL-FITC (name: AL4083-Gops025_sample5_PLLFITC_FITC)</div> <div>- 0.25% Gops without PLL-FITC (name: AL4083-Gops025_sample3_4ABA_noPLLFITC_FITC_5s)</div> <div>- 1% Gops and 1% PSS with PLL-FITC (name: AL4083-Gops1_PSS1_sample4_PLLFITC_FITC)</div> <div>- 1% Gops and 1% PSS without PLL-FITC (name: AL4083-Gops1_PSS1_sample3_4ABA_noPLLFITC_FITC_5s)</div> <div> </div> <div> </div> <div>Figure 4: Frequency dependent OECT measurements of IL6 with PBS and different concentration of IL6. Each folder contains the nominal applied voltage (as .csv file) and the measurement voltage (in V) from the DAQ (as .tdms file in the ao1 column for each frequency). Data can then be processed with Python to fit the sine wave for each frequency to observe changes in the amplitude and phase.</div> <div> </div> <div>Figure 5: Data taken from Figure 2b and Figure 4</div>
Fig. 6 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 6. Four examples of the tissue expression profiling of unknown distinct unigenes (>500 bp) expressed highly in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis. Relative expression levels were determined as described in Fig. 5.
Fig. 4 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 4. Kyoto encyclopedia of gene and genomes (KEGG) analysis of unigenes expressed highly in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis. Each category contains more than 1 unigene sequences.
Fig. 2 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 2. Clusters of orthologous groups (COG) functional classification of unigenes expressed highly and specifically in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis.
Fig. 1 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 1. Statistics of sequences expressed specifically in each analyzed tissue of Bactrocera dorsalis.
Fig. 5 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 5. Six examples of the tissue expression profiling of predicted distinct unigenes (>500 bp) expressed highly in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis. Relative expression levels were determined by qRT-PCR in head (HE), thorax (TH), abdomen (AB), midgut (MG), fat body (FB), Malpighian tubules (MT), testes (TE), and male accessory glands and ejaculatory duct (MAG) samples from B. dorsalis males. Relative expression levels were calculated based on the value in head, which was ascribed an arbitrary value of 1. Different letters above the bars indicate significant differences based on Tukey's test (P ≤ 0.05).
Fig. 3 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 3. Gene ontology (GO) classification of unigenes expressed highly in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis.
(DATASET) (10,0) carbon nanotubes functionalized with carboxyl and hydroxyl organic groups
<p>Starting from a (10,0) carbon nanotube, 10 000 structures where randomly generated for both functionalizations (carboxyl, -COOH, and hydroxyl, -OH) and for 5 concentrations of the surface being funcionalized (5%, 10%, 15%, 20% and 25%). Then, the entropy of all system was calculated. The structures with highest entropy on each group/percentage where selected as representative of each functionalization.</p> <p>Here are the structures of functionalized (10,0) carbon nanotubes in MOL2 and XYZ formats.</p> <p>These systems were used in the following publications:</p> <ul> <li>M.S. Ribeiro, A.L. Pascoini, W.G. Knupp, I. Camps. <em>Effects of surface functionalization on the electronic and structural properties of carbon nanotubes: A computational approach</em>. Applied Surface Science 426 (2017) 781–787. DOI: <a href="http://dx.doi.org/10.1016/j.apsusc.2017.07.162">10.1016/j.apsusc.2017.07.162</a></li> <li>W.G. Knupp, M.S. Ribeiro, M. Mir, I. Camps. <em>Dynamics of hydroxyapatite and carbon nanotubes interaction</em>. Applied Surface Science 495 (2019) 143493. DOI: <a href="https://doi.org/10.1016/j.apsusc.2019.07.235">10.1016/j.apsusc.2019.07.235</a></li> </ul>
Data for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
<p>This repository contains the data and external data used by teams in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body" and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".</p> <p>The directories contain:</p> <p><strong>data.zip:</strong> The training and test data, including metadata, used in the Kaggle competition "HuBMAP + HPA - Hacking the Human Body".</p> <p><strong>Team_1.zip: </strong>External data used by the first place winning solution.</p> <p><strong>Team_2.zip: </strong>External data used by the second place winning solution.</p>
Trained Models for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
<p>This repository contains the trained model weights for the baseline model and the winning solutions in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body", and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".</p> <p>The directory contains:</p> <p><strong>trained_model_1_weights.zip: </strong>Trained model weights for first place solution (Team 1).</p> <p><strong>trained_model_2_weights.zip:</strong> Trained model weights for second place solution (Team 2).</p> <p><strong>trained_model_3_weights.zip: </strong>Trained model weights for third place solution (Team 3).</p> <p><strong>trained_model_weights_baseline.zip:</strong> Trained model weights for the baseline model.</p>
Contributions of mirror-image hair cell orientation to mouse otolith organ and zebrafish neuromast function: Part 2/2, Hair cell and afferent physiology from mouse utricle
Open the record for dataset details and reuse information.
Data from: Contributions of mirror-image hair cell orientation to mouse otolith organ and zebrafish neuromast function: Part 1/2, Zebrafish data
Open the record for dataset details and reuse information.
Plant and soil organic matter responses to ten years of nutrient enrichment in the Nutrient Network:Nutrient Network. A cross-site investigation of bottom-up control over herbaceous plant community dynamics and ecosystem function
This experiment is one implementation of a globally distributed experiment, known as the Nutrient Network. At Cedar Creek, as in over 70 other sites in grasslands around the world, the experiment aims to describe impacts of increased nutrients (nitrogen, phosphorus, potassium, sulfur and other metals) and decreased herbivory (removal of mammals by fencing). Two overarching questions are being explored with these manipulations: 1. To what extent are plant production and diversity co-limited by multiple nutrients in herbaceous-dominated communities? 2. Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? By utilizing identical protocols at diverse grassland sites around the world, NutNet aims to uncover both the generalities in ecosystem functioning, and the contingencies or differences which can obscure those common mechanisms. In addition to the standard NutNet protocol, e247 includes an additional low Nitrogen gradient (1 gram Nitrogen per meter squared per year and 5 grams Nitrogen per meter squared per year in addition to the standard 10 grams Nitrogen per meter squared per year).
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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.