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13,343 results for “Primary”
Linked collectors and determiners for: Catalogue of the primary types of marine molluscan taxa described by Tommaso Allery Di Maria, Marquis of Monterosato, deposited in the Museo Civico di Zoologia, Roma.
Natural history specimen data linked to collectors and determiners held within, "Catalogue of the primary types of marine molluscan taxa described by Tommaso Allery Di Maria, Marquis of Monterosato, deposited in the Museo Civico di Zoologia, Roma". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/5846b72f-37ca-4033-af93-c20fb297add9">https://bionomia.net/dataset/5846b72f-37ca-4033-af93-c20fb297add9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5846b72f-37ca-4033-af93-c20fb297add9">https://gbif.org/dataset/5846b72f-37ca-4033-af93-c20fb297add9</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Tropical epiphyte diversity under human impact ? Comparing primary forests, secondary forests, and forest fragments in Ecuador - Bilsa.
Natural history specimen data linked to collectors and determiners held within, "Tropical epiphyte diversity under human impact ? Comparing primary forests, secondary forests, and forest fragments in Ecuador - Bilsa". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0">https://bionomia.net/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0">https://gbif.org/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: The description of a new species of gall-inducing wasp: a learning activity for primary school students.
Natural history specimen data linked to collectors and determiners held within, "The description of a new species of gall-inducing wasp: a learning activity for primary school students". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/8fa4af52-5a18-43b0-9cd7-f9887f71a1f9">https://bionomia.net/dataset/8fa4af52-5a18-43b0-9cd7-f9887f71a1f9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/8fa4af52-5a18-43b0-9cd7-f9887f71a1f9">https://gbif.org/dataset/8fa4af52-5a18-43b0-9cd7-f9887f71a1f9</a>. Formatted as a Frictionless Data package.
Primary MS data and secondary data for: "Post-proline cleaving enzymes also show specificity to reduced cysteine" Part2
<p>raw LC-MS/MS data (Bruker Daltonics - tims TOF SCP) and related PEAKS<em> search results (*</em>.csv) plus corresponding custom database (*.fasta). Protein identifications were performed using PEAKS programs.</p> <p>Data were used to:</p> <ul> <li>extract cleavage preferences of Clarity Ferm AnPEP. The target sample was HEK cell lysate.</li> </ul> <p>These data are related to https://doi.org/10.5281/zenodo.13938580.</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 1
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: unused productive wilderness areas (WILD-core); productive wilderness areas that are sporadically used at very low intensity (WILD-periphery); unused unproductive wilderness areas (WILD-nps); forestry areas, mainly coniferous (FO-con); forestry areas, mainly non-coniferous (FO-ncon); settlements, urban areas and infrastructure (BU-builtup)</p> <p> </p>
Data for "Temporal and vertical variability in phytoplankton primary production and microbial community respiration in the North Pacific Subtropical Gyre"
<p>This ALOHA_GOP&R.xslx data set provides measurements of biological rates conducted between April 2015 and July 2020 at different depths in the euphotic zone at or in the vicinity of Station ALOHA (22° 45' N, 158° W), the long-term sampling site of the Hawaii Ocean Time-series (HOT) program, within the North Pacific Subtropical Gyre. </p> <p>The file ALOHA_GOP&R.xslx contains incubation-based measurements of gross oxygen production and community respiration that were measured in the same incubation bottles by applying the <sup>18</sup>O-water method and tracking net changes in oxygen to argon ratios during dawn to dusk in situ incubations, following Ferrón et al. (2016). The samples were measured using membrane inlet mass spectrometry. Rates were measured at 6 depths within the euphotic zone: 5, 25, 45, 75, 100, 125 m, except in a few occasions in which there were no measurements made at 125 m.</p> <p>Data description</p> <table> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Description</p> </td> <td> <p>Units</p> </td> </tr> <tr> <td> <p>Date </p> </td> <td> <p>Date of sampling and start of incubation (UTC -10 hours)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Cruise ID</p> </td> <td> <p>Cruise identification</p> </td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Latitude</p> </td> <td> <p>Latitude</p> </td> <td> <p>degrees N</p> </td> </tr> <tr> <td> <p>Longitude</p> </td> <td> <p>Longitude</p> </td> <td> <p>degrees E</p> </td> </tr> <tr> <td>Stn ALOHA </td> <td>Whether the data are from Station ALOHA (yes/no)</td> <td> </td> </tr> <tr> <td>IncT</td> <td> <p>Incubation time</p> </td> <td> <p>hours</p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td>Nominal depth of sampling and incubation </td> <td> <p>meters</p> </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>CR</td> <td>Estimate of community respiration </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td> <p>Flag GOP</p> </td> <td>Flag identification for gross oxygen production (good=1,questionable=2)</td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Flag CR</p> </td> <td> <p>Flag identification for community respiration (good=1,questionable=2)</p> </td> <td> <p>#</p> </td> </tr> </tbody> </table> <p> </p> <p>The Light-dark_ALOHA_rates.xlsx file contains metabolic rates measured by the ligth-dark oxygen method between June 2005 and June 2007 at different depths in the euphotic zone at Station ALOHA (22° 45' N, 158° W). Rates of net community production, communnity respiration, and gross oxygen production were measured at 6 depths within the euphotic zone (5, 25, 45, 75, 100, 125 m) in dawn to dawn incubations, following Williams et al. (2004). </p> <p>Data description</p> <table> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Description</p> </td> <td> <p>Units</p> </td> </tr> <tr> <td> <p>HOT</p> </td> <td>HOT cruise number</td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td>Date of sampling and start of incubation (UTC -10)</td> <td> <p> </p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td>Nominal depth of sampling and incubation </td> <td> <p>meters</p> </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production, average of 8 replicates</td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>GOP SE</td> <td>Gross oxygen production standard error </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>CR</td> <td> <p>Dark community respiration, average of 8 replicates</p> </td> <td> <p>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></p> </td> </tr> <tr> <td> <p>CR SE</p> </td> <td>Dark community respiration standard error</td> <td> <p>meters</p> </td> </tr> <tr> <td>NCP</td> <td>Net community production,average of 8 replicates </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>NCP SE</td> <td>Net community production standard error </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> </tbody> </table> <p> </p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 6
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of potatoes (CL-POTA); sweet potatoes and yams (CL-SWPY); and rest of crops (CL-REST)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 4
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. </p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of wheat (CL-WHEA); maize (CL-MAIZ); soybean (CL-SOYB)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 5
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of millet (CL-MILL); barley (CL-BARL); sorghum (CL-SORG); rice (CL-RICE)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 7
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of cassava (CL-CASS); sugarcane (CL-SUGC); sugarbeet (CL-SUGB); cotton (CL-COTT); fruits and vegetables (CL-VEFR)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 8
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of beans (CL-BEAN); other pulses (CL-OPUL); groundnuts (CL-GROU); bananas and plantains (CL-BANP)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 9
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of other oilcrops (CL-OOIL); coffee (CL-COFF); fodder crops (CL-FODD)</p>
A dataset of estimated net primary production of Japanese cedar plantations (ver.1)
<p># Abbreviations in this text<br> NPP: Net Primary Production, average of stand ages 36-40<br> GCM: Global Climate Model<br> HT: Historical Trend, average of five years 1996-2000<br> FP2050: Future Prediction, average of five years 2046-2050<br> FP2100: Future Prediction, average of five years 2096-2100 (except for a GCM HadGEM2-ES of 2095-2099)</p> <p># Description<br> The data is prepared as four files in tab-delimited text format or ten netcdf files in a zip file. The annual NPP of cedar plantations under current and future climates are calculated in 196928 meshes in Japan. The climate scenarios used are owned and distributed by the third party (National Agriculture and Food Research Organization, Japan). For the methodology on modeling, parameterization and nation-wide calculation, please check the paper below.</p> <p># Reference<br> Toriyama J, Hashimoto S, Osone Y, Yamashita N, Tsurita T, Shimizu T, Saitoh TM, Sawano S, Lehtonen A, Ishizuka S (2021) Estimating spatial variation in the effects of climate change on the net primary production of Japanese cedar plantations based on modeled carbon dynamics. PLoS ONE 16(2): e0247165. https://doi.org/10.1371/journal.pone.0247165</p> <p># List of variables in text files</p> <p># site.txt (6.8 MB)<br> mesh: Number of third-mesh order in the Japanese grid square system<br> lat: Latitude (degree) of center point of third-mesh order<br> long: Longitude (degree) of center point of third-mesh order<br> block: Block number in Toriyama et al. (2021), 1, 2 and 3 for SW, CT and NW, respectively<br> pref: Prefecture number in the Japanese administrative system</p> <p># npp_2000.txt (8.7 MB)<br> mesh: Number of third-mesh order in the Japanese grid square system<br> npp_2000_cgcm: Annual NPP (kgC m-2 year-1) in HT, MRI-CGCM3<br> npp_2000_csiro: Annual NPP (kgC m-2 year-1) in HT, CSIRO-Mk3-6-0<br> npp_2000_gfdl: Annual NPP (kgC m-2 year-1) in HT, GFDL-CM3<br> npp_2000_hadgem: Annual NPP (kgC m-2 year-1) in HT, HadGEM2-ES<br> npp_2000_miroc: Annual NPP (kgC m-2 year-1) in HT, MIROC5</p> <p># npp_2050.txt (15.4 MB)<br> mesh: Number of third-mesh order in the Japanese grid square system<br> npp_rcp26_2050_cgcm: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, MRI-CGCM3<br> npp_rcp26_2050_csiro: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, CSIRO-Mk3-6-0<br> npp_rcp26_2050_gfdl: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, GFDL-CM3<br> npp_rcp26_2050_hadgem: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, HadGEM2-ES<br> npp_rcp26_2050_miroc: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, MIROC5<br> npp_rcp85_2050_cgcm: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, MRI-CGCM3<br> npp_rcp85_2050_csiro: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, CSIRO-Mk3-6-0<br> npp_rcp85_2050_gfdl: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, GFDL-CM3<br> npp_rcp85_2050_hadgem: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, HadGEM2-ES<br> npp_rcp85_2050_miroc: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, MIROC5</p> <p># npp_2100.txt (15.4 MB)<br> mesh: Number of third-mesh order in the Japanese grid square system<br> npp_rcp26_2100_cgcm: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, MRI-CGCM3<br> npp_rcp26_2100_csiro: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, CSIRO-Mk3-6-0<br> npp_rcp26_2100_gfdl: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, GFDL-CM3<br> npp_rcp26_2100_hadgem: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, HadGEM2-ES<br> npp_rcp26_2100_miroc: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, MIROC5<br> npp_rcp85_2100_cgcm: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, MRI-CGCM3<br> npp_rcp85_2100_csiro: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, CSIRO-Mk3-6-0<br> npp_rcp85_2100_gfdl: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, GFDL-CM3<br> npp_rcp85_2100_hadgem: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, HadGEM2-ES<br> npp_rcp85_2100_miroc: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, MIROC5</p> <p># npp_netcdf.zip (594 MB)<br> The 10 files in netcdf format are compiled in a zip file for NPP data of different RCP scenarios and GCMs.<br> Please check the content of each file by following command.<br> ncdump -h filename</p> <p># summary_map.zip (5.8 MB)<br> The 20 files in png format are compiled in a zip file for maps of NPP and its change. The maps were created using the Generic Mapping Tools version 5 (http://gmt.soest.hawaii.edu/). The average values of five GCMs are used for mapping.</p> <p># Acknowledgement<br> This dataset was funded by the Agriculture, Forestry and Fisheries Research Council in Japan, under the project “Research on adaptation to climate change for agriculture, forestry and fisheries”.</p>
Timeseries of simulated glacier meltwater runoff for primary hydrological regions in Svalbard
<p>Timeseries of annual cumulative glacier meltwater runoff for 14 primary hydrological regions of Svalbard, as well as one subregion, for the period September 2003 to September 2013. Regional glacier meltwater runoff are extracted from climatic mass balance simulations for all glaciers in Svalbard published by Aas et al., 2016, "The climatic mass balance of Svalbard glaciers: a 10-year simulation with a coupled atmosphere–glacier mass balance model", doi:10.5194/tc-10-1089-2016) and used in a manuscript by Dunse et al., 2021, "Regional-scale phytoplankton dynamics and their association with glacier meltwater runoff in Svalbard", submitted to the EGU journal Biogeosciences in July 2021</p>
Global ubiquitylation analysis of mitochondria in primary neurons identifies endogenous Parkin targets following activation of PINK1
<p> How activation of PINK1 and Parkin leads to elimination of damaged mitochondria by mitophagy is largely based on cell lines with few studies in neurons. Herein we have undertaken proteomic analysis of mitochondria from mouse neurons to identify ubiquitylated substrates of endogenous Parkin. Comparative analysis with human iNeuron datasets revealed a subset of 49 PINK1 activation-dependent diGLY sites in 22 proteins conserved across mouse and human systems. We employ reconstitution assays to demonstrate direct ubiquitylation by Parkin in vitro. We also identified a subset of cytoplasmic proteins recruited to mitochondria that undergo PINK1 and Parkin independent ubiquitylation indicating the presence of alternate ubiquitin E3 ligase pathways that are activated by mitochondrial depolarisation in neurons. Finally we have developed an online resource to search for ubiquitin sites and enzymes in mitochondria of neurons, MitoNUb. These findings will aid future studies to understand Parkin activation in neuronal subtypes.</p> <p> </p> <p><strong>FILE DECRIPTIONS</strong></p> <p><strong>Figure 1C: Immunoblots for PINK1 signaling in PINK1 WT and KO mouse cortical neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>Figure1C_GAPDH.tif</strong></li> <li>Parkin shown in <strong>Figure1C_Parkin.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>Figure1C_ParkinPSer65.tif</strong></li> <li>PINK1 shown in <strong>Figure1C_PINK1.tif </strong>(immunoprecipitation)</li> <li>Rab8A shown in <strong>Figure1C_Rab8A.tif</strong></li> <li>Phospho-Ser111 Rab8A shown in <strong>Figure1C_Rab8APSer111.tif</strong></li> <li>Ubiquitin shown in <strong>Figure1C_ S10A_Ubiquitin.tif </strong>(same blot used for Figure_S10A, Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure1C_S10A_UbiquitinPSer65.tif </strong>(same blot used for Figure_S10A, Halo-multiDSK pull-down)</li> </ul> <p> </p> <p><strong>Figure 4A: Immunoblots for time-course of Parkin-dependent substrates in C57BL/6J neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1 shown in <strong>Figure4A_CISD1.tif </strong>(Halo-multiDSK pull-down) <ul> <li><strong> Figure4A_INPUT_CISD1.tif</strong> (INPUT)</li> </ul> </li> <li>CPT1α shown in <strong>Figure4A_ CPT1</strong><strong>a</strong><strong>.tif</strong> (Halo-multiDSK pull-down) <ul> <li><strong> Figure4A_INPUT_CPT1</strong><strong>a</strong><strong>.tif</strong> (INPUT)</li> </ul> </li> <li>Ubiquitin shown in <strong>Figure4A_Ubiquitin.tif </strong>(Halo-multiDSK pull-down) <ul> <li><strong> Figure4A_INPUT_Ubiquitin.tif</strong> (INPUT)</li> </ul> </li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure4_UbiquitinPSer65.tif </strong>(Halo-multiDSK pull-down)</li> <li>GAPDH Shown in <strong>Figure4A_INPUT_GAPDH.tif </strong>(INPUT)</li> </ul> <p><strong>Figure 4B: Immunoblots for validation of Parkin-dependent substrates in PARKIN WT and KO neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1 shown in <strong>Figure4B_CISD1.tif </strong>(Halo-multiDSK pull-down) <ul> <li>(bottom blot)<strong> Figure4B_INPUT_CPT1a_CISD1.tif</strong> (INPUT)</li> </ul> </li> <li>CPT1α shown in <strong>Figure4B_ CPT1a.tif</strong> (Halo-multiDSK pull-down) <ul> <li>(top blot)<strong> Figure4B_INPUT_CPT1a_CISD1.tif</strong> ( (INPUT)</li> </ul> </li> <li>Ubiquitin shown in <strong>Figure4B_Ubiquitin.tif </strong>(Halo-multiDSK pull-down) <ul> <li><strong> Figure4B_INPUT_Ubiquitin.tif</strong> (INPUT)</li> </ul> </li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure4B_UbiquitinPSer65.tif </strong>(Halo-multiDSK pull-down)</li> <li>GAPDH Shown in <strong>Figure4B_INPUT_GAPDH.tif </strong>(INPUT)</li> </ul> <p> </p> <p><strong>Figure 5A: Immunoblots for <em>in vitro</em> reconstitution assay in PINK1 WT and KO mouse embryonic fibroblasts.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1shown in <strong>Figure5A_CISD1.tif</strong></li> <li>CPT1α shown in <strong>Figure5A_ CPT1a.tif</strong></li> <li>CYB5B shown in <strong>Figure5A_CYB5B.tif</strong></li> <li>HK1 shown in <strong>Figure5A_HK1.tif</strong></li> <li>MFN2 shown in <strong>Figure5A_MFN2.tif</strong></li> <li>VDAC shown in <strong>Figure5A_VDAC.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>Figure5A_ParkinPSer65.tif</strong></li> <li>Parkin shown in <strong>Figure5A_Parkin.tif</strong></li> <li>Ubiquitin shown in <strong>Figure5A_Ubiquitin.tif</strong></li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure5A_UbiquitinPSer65.tif </strong></li> </ul> <p> </p> <p><strong>Figure 5B: Immunoblots for validation of Parkin-dependent substrates in<em> in vitro </em>studies.</strong></p> <p>Scans of Western blots (LI-COR):</p> <ul> <li>CPT1α shown in <strong>Figure5B_ CPT1 </strong><strong>α_800nm</strong><strong>.tif</strong></li> <li>Flag-Ub (CPT1α) shown in <strong>Figure5B_ CPT1 </strong><strong>α_Flag_800nm</strong><strong>.tif</strong></li> <li>His (CPT1α) shown in <strong>Figure5B_ CPT1 </strong><strong>α_His_800nm</strong><strong>.tif</strong></li> </ul> <p> </p> <ul> <li>Miro1 shown in <strong>Figure5B_Miro1_800nm.tif</strong></li> <li>Flag-Ub (Miro1) shown in <strong>Figure5B_ Miro1</strong><strong>_Flag_800nm</strong><strong>.tif</strong></li> <li>His (Miro1) shown in <strong>Figure5B_ Miro1</strong><strong>_His_800nm</strong><strong>.tif</strong></li> </ul> <p> </p> <p><strong>Figure 5C: Immunoblots for time-course of CPT1</strong><strong>α</strong><strong> ubiquitylation in<em> in vitro </em>studies.</strong></p> <p>Scans of Western blots (LI-COR):</p> <ul> <li>CPT1α shown in top blot: <strong>Figure5C_Flag_CPT1</strong><strong>α_800nm</strong><strong>.tif</strong></li> <li>Flag-Ub shown in bottom blot: <strong>Figure5C_Flag_CPT1</strong><strong>α_800nm</strong><strong>.tif</strong></li> <li>His shown in <strong>Figure5C_ CPT1 </strong><strong>α_His_800nm</strong><strong>.tif</strong></li> </ul> <p> </p> <p><strong>Figure S3A: Immunoblots for PINK1 signaling in C57BL/6J neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>FigureS3A_GAPDH.tif</strong></li> <li>Parkin shown in <strong>FigureS3A_Parkin.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>FigureS3A_ParkinPSer65.tif</strong></li> <li>PINK1 shown in <strong>FigureS3A_PINK1.tif </strong>(immunoprecipitation)</li> <li>Rab8A shown in <strong>FigureS3A_Rab8A.tif</strong></li> <li>Phospho-Ser111 Rab8A shown in <strong>FigureS3A_Rab8APSer111.tif</strong></li> <li>Ubiquitin shown in <strong>FigureS3A_Ubiquitin.tif </strong>(same blot used for Figure_4A, Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS3A_UbiquitinPSer65.tif </strong>(same blot used for Figure_4A, Halo-multiDSK pull-down)</li> <li>Phospho-Ser111 Rab8A and Phospho-Ser65 Parkin also shown in <strong>FigureS3A_ParkinPSer65_Rab8APSer111.tif</strong></li> </ul> <p> </p> <p><strong>Figure S3B: Immunoblots for comparison of Halo-multiDSK and Halo-TUBE in C57BL/6J neurons.</strong></p> <ul> <li>Ubiquitin shown in <strong>FigureS9_S3_Ubiquitin.tif </strong>(same blot used for Figure_S9)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS9_S3_UbiquitinPSer65.tif </strong>(same blot used for Figure_S9)</li> </ul> <p> </p> <p><strong>Figure S3C: Immunoblots for PINK1 signaling in Parkin WT and KO mouse cortical neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>FigureS3C_GAPDH.tif</strong></li> <li>Parkin shown in <strong>FigureS3C_Parkin.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>FigureS3C_ParkinPSer65.tif</strong></li> <li>PINK1 shown in <strong>FigureS3C_PINK1.tif </strong>(immunoprecipitation)</li> <li>Rab8A shown in <strong>FigureS3C_Rab8A.tif</strong></li> <li>Phospho-Ser111 Rab8A shown in <strong>FigureS3C_Rab8APSer111.tif</strong></li> <li>Ubiquitin shown in <strong>FigureS3C_ Ubiquitin.tif </strong>(same blot used for Figure_4B, Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS3C_ UbiquitinPSer65.tif </strong>(same blot used for Figure_4B, Halo-multiDSK pull-down)</li> </ul> <p> </p> <p><strong>Figure S4: Immunoblots for PINK-Parkin signaling in VPS35 D620N mouse cortical neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>FigureS4_GAPDH.tif</strong></li> <li>Parkin shown in <strong>FigureS4_Parkin.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>FigureS4_ParkinPSer65.tif</strong></li> <li>Rab8A shown in <strong>FigureS4_Rab8A.tif</strong></li> <li>Phospho-Ser111 Rab8A shown in <strong>FigureS4_Rab8APSer111.tif</strong></li> <li>CISD1 shown in <strong>FigureS4_ CISD1.tif </strong>(Halo-UBQLN1 pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS4_ UbiquitinPSer65.tif</strong> (Halo-UBQLN1 pull-down)</li> <li>VPS35 shown in <strong>FigureS4_VPS35.tif</strong></li> </ul> <p>Scan of Memcode shown in <strong>FigureS4_Memcode.tif</strong></p> <p> </p> <p><strong>Figure S6: Immunoblots for biochemical analysis in C56BL/6J mouse cortical neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>FigureS6_GAPDH.tif</strong></li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS6_ UbiquitinPSer65.tif </strong></li> </ul> <p> </p> <p><strong>Figure S8: Immunoblots for biochemical analysis in PINK1 WT and KO mouse cortical neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>FigureS8_GAPDH.tif</strong></li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS8_ UbiquitinPSer65.tif</strong></li> </ul> <p> </p> <p><strong>Figure S9: Immunoblots for biochemical analysis of ubiquitylated target in C56BL/6J mouse cortical neurons.</strong></p> <p>Membrane-enriched lysate subjected to ubiquitin capture using UBQLN1(TUBE), multiDSK and mutant multiDSK pull-down (Illustration of sample loading in figures FigureS9_ACSL6,_MFN2, _UbiquitinPSer65, _NAV1.7).</p> <p>Scans of X-ray film:</p> <ul> <li>Ubiquitin shown in <strong>FigureS9_S3_Ubiquitin.tif </strong>(same blot used for Figure_S3B)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS9_S3_UbiquitinPSer65.tif </strong>(same blot used for Figure_S3B)</li> <li>ABCD3 shown in <strong>FigureS9_ABCD3.tif</strong></li> <li>ACSL1shown in <strong>FigureS9_ ACSL1.tif</strong></li> <li>ACSL6 shown in <strong>FigureS9_ACSL6.tif</strong></li> <li>AGPAT5 shown in <strong>FigureS9_AGPAT5.tif</strong></li> <li>ARHGAP33 shown in <strong>FigureS9_ARHGAP33.tif</strong></li> <li>ATAD1 shown in <strong>FigureS9_ATAD1.tif</strong></li> <li>CAD shown in <strong>FigureS9_ CAD.tif</strong></li> <li>CAMK2A shown in <strong>FigureS9_CAMK2A.tif</strong></li> <li>CAMK2B shown in <strong>FigureS9_CAMK2B.tif</strong></li> <li>CDK16 shown in <strong>FigureS9_CDK16.tif</strong></li> <li>CISD1shown in <strong>FigureS9_CISD1.tif</strong></li> <li>CNN3 shown in<strong> FigureS9_CNN3.tif</strong></li> <li>CPT1α shown in <strong>FigureS9_ CPT1A.tif</strong></li> <li>CYB5B shown in <strong>FigureS9_CYB5B.tif</strong></li> <li>CYB5R3 shown in <strong>FigureS9_CYB5R3.tif</strong></li> <li>DCAKD shown in <strong>FigureS9_DCAKD.tif</strong></li> <li>DCAMKL2 shown in <strong>FigureS9_DCAMKL2.tif</strong></li> <li>FAM213A shown in <strong>FigureS9_FAM213A.tif</strong></li> <li>FBXO41 shown in <strong>FigureS9_FBXO41.tif</strong></li> <li>GK shown in <strong>FigureS9_GK.tif</strong></li> <li>HK1 shown in <strong>FigureS9_HK1.tif</strong></li> <li>HSDL1 shown in <strong>FigureS9_HSDL1.tif</strong></li> <li>MAO-A shown in <strong>FigureS9_MAOA.tif</strong></li> <li>MAO-B shown in <strong>FigureS9_MAOB.tif</strong></li> <li>MAPRE2 shown in <strong>FigureS9_MAPRE2.tif</strong></li> <li>MARC2 shown in <strong>FigureS9_MARC2.tif</strong></li> <li>MFN1 shown in <strong>FigureS9_MFN1.tif</strong></li> <li>MFN2 shown in <strong>FigureS9_MFN2.tif</strong></li> <li>NAV1.7 shown in <strong>FigureS9_NAV1.7.tif</strong></li> <li>P23 shown in <strong>FigureS9_p23.tif</strong></li> <li>PRKCG shown in <strong>FigureS9_PRKCG.tif</strong></li> <li>RAB5C shown in <strong>FigureS9_Rab5c.tif</strong></li> <li>RHOT2 shown in <strong>FigureS9_RHOT2.tif</strong></li> <li>RIMS4 shown in <strong>FigureS9_RIMS4.tif</strong></li> <li>RUFY3 shown in <strong>FigureS9_RUFY3.tif</strong></li> <li>SH3BP4 shown in <strong>FigureS9_SH3BP4.tif</strong></li> <li>SNX3 shown in <strong>FigureS9_SNX3.tif</strong></li> <li>TDRKH shown in <strong>FigureS9_TDRKH.tif</strong></li> <li>TOMM70 shown in <strong>FigureS9_TOMM70.tif</strong></li> </ul> <p> </p> <p><strong>Figure S10A: Immunoblots for validation of Parkin-dependent substrates in PINK1 WT and KO neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1 shown in <strong>Figure S10A _CISD1.tif </strong>(Halo-multiDSK pull-down) <ul> <li><strong> Figure S10A _INPUT_CISD1.tif </strong>(INPUT)</li> </ul> </li> <li>CPT1α shown in <strong>Figure S10A _ CPT1A.tif</strong> (Halo-multiDSK pull-down) <ul> <li><strong> Figure S10A _INPUT_ CPT1A.tif</strong> (INPUT)</li> </ul> </li> <li>Ubiquitin shown in <strong>Figure1C_ S10A _Ubiquitin.tif </strong>(same blot used for FigureS1C, Halo-multiDSK pull-down) <ul> <li><strong> Figure S10A _INPUT_Ubiquitin.tif</strong> (INPUT)</li> </ul> </li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure1C_ S10A_UbiquitinPSer65.tif </strong>(same blot used for FigureS1C, Halo-multiDSK pull-down)</li> <li>GAPDH Shown in <strong>Figure S10A _INPUT_GAPDH.tif </strong>(INPUT)</li> </ul> <p> </p> <p><strong>Figure S10B: Immunoblots for time-course of Parkin-dependent substrates in SH-SY5Y cells.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1 shown in <strong>Figure S10B _CISD1.tif </strong>(Halo-multiDSK pull-down)</li> <li>CPT1α shown in <strong>Figure S10B _ CPT1A.tif</strong> (Halo-multiDSK pull-down)</li> <li>Ubiquitin shown in <strong>Figure S10B _Ubiquitin.tif </strong>(Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure S10B _UbiquitinPSer65.tif </strong>(Halo-multiDSK pull-down)</li> <li>GAPDH Shown in <strong>Figure S10B _GAPDH.tif </strong></li> <li>Parkin shown in <strong>FigureS10B_Parkin.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>FigureS10B_ParkinPSer65.tif</strong></li> <li>PINK1 shown in <strong>FigureS10B_PINK1.tif</strong></li> <li>OPA1 shown in <strong>FigureS10B_OPA1.tif</strong></li> </ul> <p> </p> <p><strong>Figure S11: Immunoblots for <em>in vitro</em> reconstitution assay in HeLa cells.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1shown in <strong>FigureS11_CISD1.tif</strong></li> <li>CPT1α shown in <strong>FigureS11_ CPT1</strong><strong>a</strong><strong>.tif</strong></li> <li>CYB5B shown in <strong>FigureS11_CYB5B.tif</strong></li> <li>HK1 shown in <strong>FigureS11_HK1.tif</strong></li> <li>MFN2 shown in <strong>FigureS11_MFN2.tif</strong></li> <li>VDAC shown in <strong>FigureS11_VDAC.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>FigureS11_ParkinPSer65.tif</strong></li> <li>Parkin shown in <strong>FigureS11_Parkin.tif</strong></li> <li>Ubiquitin shown in <strong>FigureS11_Ubiquitin .tif</strong></li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS11_UbiquitinPSer65 .tif</strong></li> </ul> <p> </p> <p><strong>FigureS12A: Purification of CPT1</strong><strong>α protein.</strong></p> <ul> <li><strong>FigureS12A_</strong><strong> Akta_Purifier_Curves.txt</strong>.</li> </ul> <p> Tab delimited text of data from the AKTA system, plotted in figure S12A. Columns are laid out as volume/measured parameter for each parameter from the Akta. The columns plotted in figure S12A were mAU (columns A and B in excel) and fractions (columns M and N in excel).</p> <ul> <li><strong>Figure_S12A_Coomassie_Gel.tif</strong>. </li> </ul> <p>Coomassie stained gels of fractions from the AKTA system in figure S12A.</p> <p>Top: Left hand Coomassie gel, figure S12A</p> <p>Bottom: Right hand Coomassie gel, figure S12A</p> <p> </p> <p><strong>FigureS12B-C: Purification of recombinant Parkin targets </strong></p> <p>Blots shown in the paper are highlighted in bold.</p> <ul> <li><strong>FigureS12B_Ub_Targets_High_800nm.tif</strong></li> </ul> <p>Coomassie stained gels of the parkin targets alongside a BSA curve.</p> <p>Left: Lane: 1: Mwt Marker, 2: Fam213A, 3: MAO-B, 4: CAMK2α, <strong>5: MAO-A (figure S12B)</strong>, 6: GST-MAPRE2, 7: MBP-CYB5R3, 8: MBP-CYB5B, 9: MBP-CYB5B, 10: SNX3, 11: His-SUMO-MAO-B, 12: GST-MAO-B, 13: 0.03125 ug BSA, 14: 0.0625 ug BSA, 15: 0.125 ug BSA, 16: 0.25 ug BSA, 17: 0.5 ug BSA, 18: 1 ug BSA</p> <p>Right: Lane: 1: Mwt Marker, 2: GST-MAO-A, 3: GST-FAM213A, <strong>4: MBP-CAMK2</strong><strong>α</strong><strong> (figure S12B)</strong>, 5: GST-TDRKH, 6: MBP-CPT1α, 7: MBP-CYB5B, 8: CAMK2β, 9:, 10: His-SUMO-MAO-B, 11: GST-MAO-B, 12: 0.03125 ug BSA, 13: 0.0625 ug BSA, 14: 0.125 ug BSA, 15: 0.25 ug BSA, 16: 0.5 ug BSA, 17: 1 ug BSA</p> <p> </p> <ul> <li><strong>Figure_S12B_Ub_Targets_Low_800nm.tif</strong></li> </ul> <p>Coomassie stained gels of the parkin targets alongside a BSA curve.</p> <p>Lane: 1: Mwt Marker, <strong>2: GST-Miro1 (figure S12B</strong>), 3: His-SUMO-Fam213A, 4: His-SUMO-MAPRE2, 5: His-SUMO-MAO-A, 6: His-SUMO-MAO-B, 7: His-SUMO-SNX26, 8: His-SNX3, 9: His-MAPRE2, 10: GST-FAM213A, 11: His-SUMO-CAMK2α, 12: His-SUMO-CAMK2β, 13: GST-FAM213A, 14: 0.03125 ug BSA, 15: 0.0625 ug BSA, 16: 0.125 ug BSA, 17: 0.25 ug BSA, 18: 0.5 ug BSA, 19: 1 ug BSA</p> <p> </p> <ul> <li><strong>Figure_S12C_Ub_Targets_800nm.tif</strong></li> </ul> <p>Coomassie stained gels of the parkin targets in duplicate alongside a BSA curve.</p> <p>Top: Lane: 1: Mwt Marker, <strong>2+3: ACSL1 45-end (Figure S12C)</strong>, <strong>4+5: SNX3 (Figure S12C),</strong> 6+7: MFN1, 8+9: MFN2, 10+11: 0.125 ug BSA, 12+13: 0.25 ug BSA, 14+15: 0.5 ug BSA, 16+17: 1 ug BSA</p> <p>Middle: Lane: 1: Mwt Marker, 2+3: CAMK2α, <strong>4+5: CAMK2</strong><strong>β</strong><strong> (Figure S12C)</strong>, 6+7: MAO-A, <strong>8+9: MAO-B (Figure S12C)</strong>, 10+11: 0.125 ug BSA, 12+13: 0.25 ug BSA, 14+15: 0.5 ug BSA, 16+17: 1 ug BSA</p> <p>Bottom: Lane: 1: Mwt Marker, 2+3: SRCIN, 4+5: His-MAPRE2, 6+7: <strong>FAM213A (Figure S12C)</strong>, 8+9: CISD1, 10+11: 0.125 ug BSA, 12+13: 0.25 ug BSA, 14+15: 0.5 ug BSA, 16+17: 1 ug BSA</p> <p> </p> <p><strong>FigureS13-S14: Immunoblots for validation of Parkin-dependent substrates in<em> in vitro </em>studies.</strong></p> <p>Blots shown in the paper are highlighted in bold.</p> <p>Scans of X-ray film:</p> <ul> <li><strong>FigureS13A-B_S14C-D_a6His_Film.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes from left to right:</p> <p>Top: 1: <strong>FAM213A (Figure S13B</strong>), 2: <strong>MAO-B (Figure S13A</strong>), 3: MAPRE2, 4: <strong>CAMK2</strong><strong>β</strong><strong> (Figure S14D)</strong></p> <p>Top: 1: <strong>CAMK2</strong><strong>α</strong><strong> (Figure S14C)</strong>, 2: Miro1, 3: <strong>MAO-A (Figure S13A)</strong></p> <p> </p> <ul> <li><strong>FigureS13A-B_S14C-D_aFLAG_Film.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes from left to right:</p> <p>Top: 1: <strong>FAM213A (Figure S13B)</strong>, 2: MAPRE2, 3: <strong>MAO-A (Figure S13A)</strong></p> <p>Top: 1: <strong>MAO-B (Figure S13A)</strong>, 2: <strong>CAMK2</strong><strong>α</strong><strong> (Figure S14C),</strong> 3: <strong>CAMK2</strong><strong>β</strong><strong> (Figure S14D)</strong>, 4: Miro1 </p> <p> </p> <ul> <li><strong>FigureS13A-B_S14C-D_aTarget_05m00s_Film.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes from left to right:</p> <p>Top: 1: FAM213A, 2: MAPRE2, 3: MAO-A</p> <p>Top: 1: MAO-B, 2<strong>: CAMK2</strong><strong>α</strong><strong> (Figure S14C)</strong>, 3: <strong>CAMK2</strong><strong>β</strong><strong> (Figure S14D</strong>), 4: Miro1 </p> <p> </p> <ul> <li><strong>FigureS13A-B_S14C-D_aTarget_06m00s_Film.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes from left to right:</p> <p>Top: 1: <strong>FAM213A (Figure S13B)</strong>, 2: MAPRE2, 3: <strong>MAO-A (Figure S13A)</strong></p> <p>Top: 1: <strong>MAO-B (Figure S13A)</strong>, 2: CAMK2α, 3: CAMK2β, 4: Miro1 </p> <p> </p> <p><strong>FigureS13C: Immunoblots for validation of Parkin-dependent substrates in<em> in vitro </em>studies.</strong></p> <p>Scans of Western blots (LI-COR):</p> <ul> <li>ACSL1 shown in <strong>FigureS13C_ACSL1_800nm.tif</strong></li> <li>Flag-Ub (ACSL1) shown in <strong>Figure_S13C_ACSL1_Flag_800nm.tif</strong></li> <li>His (ACSL1) shown in <strong>Figure_S13C_ACSL1_His_800nm.tif</strong></li> </ul> <p> </p> <p><strong>FigureS14B: Immunoblots for validation of Parkin-dependent substrates in<em> in vitro </em>studies.</strong></p> <p>Blots shown in the paper are highlighted in bold.</p> <p>Scans of Western blots (LI-COR):</p> <ul> <li>SNX3 shown in <strong>FigureS14B_SNX3_800nm.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes, left to right: <strong>SNX3 (Figure S14B</strong>), FAM213A, Miro1, Miro1</p> <ul> <li>Flag-Ub (SNX3) shown in <strong>Figure_S14B_Flag_SNX3_800nm.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4:WT PINK1, 5+6: KD PINK1</p> <p>Membranes, left to right: Miro1, FAM213A, Miro1, <strong>SNX3 (Figure S14B)</strong></p> <ul> <li>His (SNX3) shown in <strong>Figure_S14B_His_SNX3_700nm.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes, left to right: CPT1α, MFN1, <strong>SNX3 (Figure S14B)</strong>, FAM213A</p> <p> </p> <p><strong>FigureS1: Immunocytochemistry of mouse cortical neurons.</strong></p> <ul> <li><strong>FigureS1_TILE_maximun intensity projection.tif</strong></li> </ul> <p>Original acquisition: MAP2 neuronal marker (green-488); GFAP astrocytic marker (red-594); Hoechst nuclear marker (blue 361/497). </p> <ul> <li><strong>FigureS1MGB (RGB).tif</strong></li> </ul> <p>Modified from FigureS1_TILE_maximun intensity projection.tif: MAP2 (green); GFAP (magenta); Hoechst (blue).</p> <p> </p> <p><strong>Table for Fig1DE.xlsx</strong></p> <p>Numerical data for the charts shown in Figure1D and Figure1E.</p> <p> </p> <p><strong>Table for FigS1A.xlsx</strong></p> <p> Numerical data for quantification of Map2 and GFAP in mouse cortical neurons shown in FigureS1A.</p> <p> </p> <p><strong>Table for FigS1B (Cortical neurons_DIA_Report).xlsx</strong></p> <p> Numerical data for DIA proteomic analysis of AO and DMSO treated cortical neurons shown in FigureS1B.</p> <p> </p> <p><strong>Table for FigS2B_C_Oxygraph.xlsx</strong></p> <p>Numerical data for the charts shown in FigureS2B and FigureS2C.</p> <p> </p> <p><strong>Table for Fig3A.xlsx</strong></p> <p>Numerical data for total protein abundance in PINK1 WT and KO shown in Figure3A.</p> <p> </p> <p><strong>Table for Fig3B.xlsx</strong></p> <p>Numerical data of abundance of phosphorylated-Ser65 or Ser57 shown in Figure3B.</p>
Data files for manuscript "Prenatal phenotype of PNKP-related primary microcephaly associated with variants in the FHA and Phosphatase domain"
<p>#2021-08-26<br> #Summary<br> This ZIP-file contains the Excel files used for the clinical and variant analyses of the PNKP protein/gene for the manuscript "Prenatal phenotype of PNKP-related primary microcephaly associated with variants in the FHA and Phosphatase domain".</p> <p>#Folder structure<br> ./ (parent directory containing this README file and all subfolders)<br> ./Clinical/ (contains an Excel sheet with complete clinical data of 4 affected individuals)<br> ./Variants/ (contains an Excel sheet with all variant annotation and domain information used in 6 sheets)</p> <p>#Files and checksums<br> 4B5BE914E77EB6511DF3DB94C6918E45 ./Clinical/FileS2_PNKP_Clinical.xlsx<br> 0228C6EF543D597C1C800B8D78E3097D ./Variants/FileS3_PNKP_Variants.xlsx</p>
Figs 1–4. Bolboceras excavatum R. A. Philippi, 1859. 1 in Rediscovery of primary type specimens and lectotype designation for Bolboceras excavatum R. A. Philippi, 1859 (Coleoptera: Geotrupidae)
Figs 1–4. Bolboceras excavatum R. A. Philippi, 1859. 1 – lectotype, oblique view; 2 – lectotype, dorsal view; 3 – lectotype, labels; 4 – paralectotype, labels.
Figure 3 in Aedeagus evolution promotes speciation? A primary pattern in rove beetle phylogeny
Figure 3. Plotting Paramere-to-Median-lobe Index (PM Index) and the number of species of the selected genera (Y axis) with the divergence time (X axis) estimated. PM Index variations along with divergence time (a, c); species number variations along with evolutionary time (b, d). The abbreviation of geological ages are as follows: Jur—Jurassic; Cre—Cretaceous; Pal—Paleocene; Eoc— Eocene; Oli—Oligocene; Mio—Miocene; Pli—Pliocene.
Figure 2. Figure 2 in Aedeagus evolution promotes speciation? A primary pattern in rove beetle phylogeny
Figure 2. Figure 2. Phylogeny of the subfamily Staphylininae with divergence time estimates based on the concatenated sequence. Blue bars at each node show 95% highest posterior density interval for the main nodes. Colored branches and circled capital letters are as in Figure 1. Circled numbers refer to three main sub-clades within Clade A discussed in text. Circled roman letters represent the calibration points. Capital letter 'A', 'P' and 'N' represent aedeagus, paramere and species number of each corresponding genus respectively. Colored arrows refer to phylogenetic patterns of aedeagus morphology discussed in text. Dot lines with lower case letters refer to divergence times discussed in text. The abbreviation of geological ages are as follows: Jur—Jurassic; Cre—Cretaceous; Pal— Paleocene; Eoc—Eocene; Oli—Oligocene; Mio—Miocene; Pli—Pliocene.
Figure 1 in Aedeagus evolution promotes speciation? A primary pattern in rove beetle phylogeny
Figure 1. Bayesian phylogenetic tree for the partitioned combined analysis of four genes. Only posterior probabilities above 0.60 are shown. Colored branches represent the mainly monophyletic groups resolved in the phylogenetic inference. Verticle bars: thick bars denote the tribes of Staphylinini, thin bars the subtribes, grey bar the outgroup. The capital letters "A" and "B" refer to two main clades discussed in the text.
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