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13,343 results for “Primary”
Figs 35–37 in Primary chaetotaxy and larval morphometry of Phaenonotum exstriatum and Dactylosternum cacti (Coleoptera: Hydrophilidae)
Figs 35–37. Phaenonotum exstriatum (Say, 1835), chaetotaxy of first instar larva. 35 – detail of clypeolabrum; 36 – left antenna, dorsal view; 37 – right mandible, dorsal view. Scale bars = 0.05 mm.
Figs 25–28 in Primary chaetotaxy and larval morphometry of Phaenonotum exstriatum and Dactylosternum cacti (Coleoptera: Hydrophilidae)
Figs 25–28. Dactylosternum cacti (LeConte, 1855), chaetotaxy of third instar larva. 25 – detail of clypeolabrum; 26 – left antenna, dorsal view; 27 – left mandible, dorsal view; 28 – right mandible, dorsal view. Scale bars = 0.1 mm.
Figs 13–16 in Primary chaetotaxy and larval morphometry of Phaenonotum exstriatum and Dactylosternum cacti (Coleoptera: Hydrophilidae)
Figs 13–16. Dactylosternum cacti (LeConte, 1855), chaetotaxy of first instar larva. 13 – detail of clypeolabrum; 14 – left antenna, dorsal view; 15 – left mandible, dorsal view; 16 – right mandible, dorsal view. Scale bars: Fig. 13 = 0.1 mm, Figs 14–16 = 0.05 mm.
Figs 17–20 in Primary chaetotaxy and larval morphometry of Phaenonotum exstriatum and Dactylosternum cacti (Coleoptera: Hydrophilidae)
Figs 17–20. Dactylosternum cacti (LeConte, 1855), chaetotaxy of first instar larva. 17 – left maxilla, dorsal view; 18 – maxilla, ventral view; 19 – labium, ventral view; 20 – labium, dorsal view. Scale bars = 0.05 mm.
Figs 11–12 in Primary chaetotaxy and larval morphometry of Phaenonotum exstriatum and Dactylosternum cacti (Coleoptera: Hydrophilidae)
Figs 11–12. Dactylosternum cacti (LeConte, 1855), chaetotaxy of the head capsule of the first instar larva. 11 – dorsal view; 12 – ventral view. Scale bar = 0.1 mm.
Figs 38–41 in Primary chaetotaxy and larval morphometry of Phaenonotum exstriatum and Dactylosternum cacti (Coleoptera: Hydrophilidae)
Figs 38–41. Phaenonotum exstriatum (Say, 1835), chaetotaxy of first instar larva. 38–39 – maxilla (38– dorsal view; 39 – ventral view). 40–41 – labium (40 – dorsal view; 41 – labium, ventral view). Scale bars = 0.05 mm.
Global estimates of marine gross primary production based on machine‐learning upscaling of field observations
<p>4 variables (excluding dimension variables):</p> <p>double GPP_LD_MLD_RF[Lon,Lat,Month] <br> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly mixed-layer integration of gross primary production trained from the<br> dataset determined by the light-dark bottle incubation using Random Forest<br> algorithm<br> coordinates: [Longitude, Latitude Month]</p> <p>double GPP_LD_ZEU_RF[Lon,Lat,Month] </p> <p> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly euphotic-zone integration of gross primary production trained from<br> the dataset determined by the light-dark bottle incubation using Random<br> Forest algorithm<br> coordinates: [Longitude, Latitude Month]</p> <p>double GPP_Triple_MLD_RF[Lon,Lat,Month] <br> units: mmol mmol O2 m-2 d-1<br> fillvalue: NaN<br> long_name: Monthly mixed-layer integration of gross primary production trained from<br> the dataset determined by the triple isotopes of dissolved oxygen using<br> Random Forest algorithm<br> coordinates: [Longitude, Latitude Month]<br> <br> double GPP_Triple_ZEU_RF[Lon,Lat,Month] <br> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly euphotic-zone integration of gross primary production trained from<br> the dataset determined by the triple isotopes of dissolved oxygen using<br> Random Forest algorithm</p> <p>3 dimensions:</p> <p> Lon Size:181<br> units: degree_north<br> long_name: Longitude</p> <p> Lat Size:91<br> units: degree_east<br> long_name: Latitude</p> <p> Month Size:13<br> units: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Annuual_mean<br> long_name: Month</p> <p><br> Author: Yibin Huang & Nicolas Cassar<br> Correspond: nicolas.cassar@duke.edu<br> <br> Request_for_citation: If you use these data in publications or presentations, please cite: Huang,<br> Y., Nicholson, D., Huang, B., & Cassar, N. (2021). Global estimates of<br> marine gross primary production based on machine‐learning upscaling of<br> field observations. Global Biogeochemical Cycles, 35, e2020GB006718.<br> https://doi.org/10.1029/2020GB006718<br> <br> Creation date: Dec/6th/2021</p>
Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production. Reproducible workflow
<p>Dataset for manuscript entitled "Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production" accepted for publication in New Phytologist. The dataset was obtained for the site Majadas del Tietar, Spain, between June/2018 and August/2018. It consists of eddy covariance data, sun-induced fluorescence data and active fluorescence data. </p>
Normal Retinotopy in Primary Visual Cortex in a Congenital Complete Unilateral Lesion of Lateral Geniculate Nucleus in Human: A Case Study
<p>The data set contains .nii files for each condition of retinotopic mapping in fMRI. (Meridians, Wedges and concentric rings). It also contains DTI data files with .bvec and .bval files. Psychophysics data is in two excel files for motion and orientation discrimination. </p>
Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982-2018)
<p><strong>Aim:</strong> Drought has broad and deep impacts on vegetation. Studies on the effects of drought on vegetation have been conducted over years. However, global-scale and long-term (>30 years) studies on the cumulative<strong> </strong>effect of drought are still lacking. Thus, combining a new satellite based gross primary productivity (GPP) and multi-timescale Standardized Precipitation Evapotranspiration Index datasets, we investigated the cumulative effect of drought on global vegetation GPP.</p> <p><strong>Location: </strong>Global.</p> <p><strong>Time period: </strong>1982 – 2018 (37 years).</p> <p><strong>Major taxa studied: </strong>Forests and grasslands.</p> <p><strong>Method: </strong>Based on correlation analysis framework, we investigated the cumulative effect duration of drought on global vegetation GPP. Meanwhile, the variability of this cumulative effect across different elevation gradients and climatic zones was analyzed using variance analysis.</p>
Dataset for "Detailed cartography of Cotopaxi's 1877 primary lahar deposits obtained by drone-imagery and field surveys in the proximal northern drainage"
<p>Contains the following datasets supporting the results in "Detailed cartography of Cotopaxi’s 1877 primary lahar deposits obtained by drone-imagery and field surveys in the proximal northern drainage": 1) Drone-NDVI 25cm/pixel imagery for the four surveyed plains. 2) Geospatial vector layer for the geological fieldwork control points. 3) Geospatial vector layer for the 1877 deposit contact lines. 4) Geospatial vector layer for the 1877 deposit polygons.</p>
Dependence of MeV TOF SIMS secondary molecular ion yield from phthalocyanine blue on primary ion stopping power
<p>Time-of-flight Secondary Ion Mass Spectrometry (TOF SIMS) is a well-established mass spectrometry technique used for the chemical analysis of both organic and inorganic materials. In the last ten years, many advances have been made to improve the yield of secondary molecular ions, especially those desorbed from the surfaces of organic samples. For that, cluster ion beams with keV energies for the excitation were mostly used. Alternatively, single-ion beams with MeV energies can be applied, as done in the present work. It is well known that secondary molecular/ion yield depends strongly on the primary ion stopping power, but the nature of this dependence is not completely clear. Therefore, in the present work secondary ion yield from the phthalocyanine blue (C<sub>32</sub>H<sub>16</sub>CuN<sub>8</sub>, organic pigment) was measured for the various combinations of ion masses, energies and charge states. Measured values were compared with the existing models for ion sputtering. An increase of the secondary yield with the primary ion energy, electronic stopping, velocity and charge state was found for different types of primary ions. Although this general behavior is valid for all primary ions, there is no single parameter that can describe the measured results for all primary ions at once. </p> <p>- measured (calibrated) spectra are uploaded </p> <p> </p> <p> </p>
Digital Phase Contrast on Primary Dermal Human Fibroblasts cells
<p><strong>Name</strong>: Digital Phase Contrast on Primary Dermal Human Fibroblasts cells </p> <p><strong>Data type</strong>: Paired microscopy images (Digital Phase Contrast, <em>square rooted</em>) and corresponding labels/masks used for cellpose training (the corresponding Brightfield images are also present), organized as recommended by <a href="https://cellpose.readthedocs.io/en/latest/train.html">cellpose documentation</a>.</p> <p><strong>Microscopy data type</strong>: Light microscopy (Digital Phase Contrast and Brighfield )</p> <p><strong>Manual annotations</strong>: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects (Dust, fused cells) were left unannotated, so that the cellpose model (10.5281/zenodo.6023317) may mimic the same user bias during prediction. This was particularly necessary due to the accumulation of floating debris in the center of the well.</p> <p><strong>Microscope</strong>: Perkin Elmer Operetta microscope with a 10x 0.35 NA objective</p> <p><strong>Cell type</strong>: Primary Dermal Human Fibroblasts cells</p> <p><strong>File format</strong>: .tif (16-bit for DPC and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p> <p>NOTE : This dataset was used to train cellpose model ( 10.5281/zenodo.6023317 )</p> <p> </p>
Strain-dependent induction of primary bile acid 7-dehydroxylation by cholic acid.
<p>Raw data files for the publication <em>Strain-dependent induction of primary bile acid 7-dehydroxylation by cholic acid.</em></p>
Avian botulism is a primary, year-round threat to adult survival in the endangered Hawaiian Duck (Anas wyvilliana) on Kaua'i, Hawai'i, USA
<p>Adult survival is the most important demographic parameter influencing population dynamics for many bird taxa. Thus, understanding how survival probabilities and causes of mortality vary throughout the annual cycle is critical for developing informed and effective management strategies. In this study, we used radio-telemetry data to evaluate the effects of biotic (e.g., sex, peak [September–April] vs. off-peak [May–August] nesting seasons) and abiotic factors (e.g., rainfall, year, bi-monthly interval) on adult survival, estimate annual survival probabilities, and identify primary sources of mortality for Hawaiian Ducks (<em>Anas wyvilliana</em>), an endangered, non-migratory dabbling duck, on the island of Kaua'i, Hawai'i, USA over 2013 and 2014. Additionally, we used contemporaneous Hawaiian Duck carcass recovery and surveillance data to examine temporal and climatic associations with avian botulism outbreaks. Our results suggested bi-monthly survival decreased with total rainfall during the preceding two-month interval. Survival did not vary with sex, between peak and off-peak nesting seasons, or between the two years of this study. Annual survival probabilities (62–80%) were relatively low compared to the closely related Laysan Duck (<em>Anas laysanensis</em>) on Laysan Island. Primary causes of mortality included avian botulism and presumed predation by cats (<em>Felis catus</em>). The botulism surveillance dataset revealed support for the effect of rainfall on the number of sick and dead birds recovered (<em>n</em> = 216), with generally a greater number of recoveries during months with middle-range total rainfall during the concurrent and preceding months. Our study provides critical baseline demographic data for population monitoring and highlights the importance of managing botulism risk and non-native mammalian predators for the recovery of the endangered Hawaiian Duck.</p>
Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)
<p>This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX).</p> <p>The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France); Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015.</p> <p>Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant.</p> <p>The objectives of CLIMAX include better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate.</p> <ol> <li> <p><strong>EXPERIMENT DESIGN</strong></p> </li> </ol> <p>We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005).</p> <p>We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022).</p> <p>We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named <strong>historical CO</strong><strong>2</strong> (<strong>hist CO</strong><strong>2</strong>); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named <strong>constant CO</strong><strong>2</strong><strong> (const CO</strong><strong>2</strong><strong>)</strong>. We ran both CO2 experiments under <strong>Land Use Change</strong> (<strong>LUC</strong>) and <strong>Potential Natural Vegetation </strong>(<strong>PNV</strong>) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. <strong>LUC historical CO</strong><strong>2</strong>; 2. <strong>LUC constant CO</strong><strong>2</strong>; 3. <strong>PNV historical CO</strong><strong>2</strong>; 4. <strong>PNV constant CO</strong><strong>2 </strong> (Rezende et al., 2022).</p> <p><strong>2. DATA DESCRIPTION</strong></p> <p>The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs.</p> <p><strong>2.1 SOFTWARE </strong></p> <p><br> The data were manipulated, worked, standardized, converted using the software: <strong>Climate Data Operators (cdo) version 1.7.0</strong>, <strong>Grid Analysis and Display System (Grads) (</strong><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a><strong>) version 2.0.2, and RStudio Desktop version</strong> 1.3.1093 <strong>(R Core Team, 2020)</strong>, through command lines and several scripts developed for this purpose. The figures were generated with <strong>Grads,</strong> and <strong>RStudio</strong>, and some images were enhanced with <strong>Gimp version 2.8.22</strong>. All the software used is freeware.</p> <p><strong> 2.2 PRIMARY DATA FROM SIMULATIONS</strong></p> <p>Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010.</p> <p><strong>Variables</strong>: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p>The naming of the files is according to the following rules:</p> <p><strong>DGVM_forcing_vegetation cover_CO2 concentration_attribute</strong></p> <p><strong>DGVMs</strong>;</p> <p> InLand (INLAND)</p> <p> LPJ-G (LPJ-GUESS)</p> <p> LPJmL (LPJmL4)</p> <p> ORCHI (ORCHIDEE)</p> <p>forcings: </p> <p> gld - GLDAS</p> <p> gsw – GSWP3</p> <p> wat – WATCH+WFDEI</p> <p> </p> <p>vegetation cover:</p> <p> LU – Land Use Change</p> <p> PNV – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration:</p> <p> CO2 – historical CO2</p> <p> noCO2 – constant CO2 = 278 ppm</p> <p> </p> <p>variables:</p> <p> E – evaporation</p> <p> Et – transpiration</p> <p> gpp – Gross Primary Productivity</p> <p> npp - Net Primary Productivity (not used in the experiment)</p> <p> </p> <p><strong>Example</strong>:</p> <p> inLand_gld_LU_noCO2_E.nc</p> <p> </p> <p><strong> 2.3 SUPPLEMENTARY DATA </strong></p> <p>These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).</p> <p>Files are named according to the following rules:</p> <p>variable_season_forcing_DGVM_vegetation cover CO2 concentration_region</p> <p>variable:</p> <p> inter – loss by interception</p> <p> </p> <p>season:</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>forcings: </p> <p> gl - GLDAS</p> <p> gs – GSWP3</p> <p> wa – WATCH+WFDEI</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or – ORCHIDEE</p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p> inter_D_gl_in_LC_SA.nc</p> <p><strong>2.4 PROCESSED DATA</strong></p> <p>Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p> </p> <p>Files are named according to the following rules:</p> <p> </p> <p><strong>season_DGVM_forcing_vegetation cover CO2 concentration_region</strong></p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>Example:</p> <p>D_in_Gs_LN.nc</p> <p><strong>2.5 DERIVED DATA</strong></p> <p> </p> <p>The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1). </p> <p> </p> <table> <tbody> <tr> <td> <p> WUE = GPP / Tr</p> </td> <td> <p>(Eq. 1)</p> </td> </tr> </tbody> </table> <p> </p> <p>These data refer to the study region: southern Amazon and apply to only one scenario: Land Use Change and historic CO2. Files are named naming of according to the following rules:</p> <p> </p> <p>Variable:</p> <p> WUE – Water Use Efficiency</p> <p> </p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p>wue_D_lm_Gl_LC_SA.nc</p> <p> </p> <p><strong>How to cite this work</strong>:</p> <p>Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p><strong>References</strong></p> <p><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015.</p> <p>Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a></p> <p>Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199.</p> <p>R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. </p> <p>Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg.</p> <p>Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6.</p> <p>Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p>Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>.</p> <p>Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637</p> <p>Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36</p> <p><br> </p> <p> </p>
FSS7B - Inhibiting human aversive memory by transcranial theta-burst stimulation to primary sensory cortex: Supplementary fMRI data
<p>Functional magnetic resonance imaging (fMRI) data supplementing a publication on inhibiting somatosensory fear memory in humans with transcranial magnetic stimulation (TMS). Contains 1) individual regions-of-interest (ROIs) masks in the bilateral primary somatosensory cortex (S1) to target with TMS, 2) S1 masks for left and right hemisphere used in restricting the ROIs to a priori expected area, 3) sum and probability maps of the ROIs over participants, and 4) summary group level fMRI NIFTI images including beta images and T-maps. Individual SPMs/beta images can be requested from the authors for academic research purposes (k.ojala@uke.de). Details on the methods are found in the Supplement of the publication (see linked DOI). </p>
Local chromatin context dictates the genetic determinants of the heterochromatin spreading reaction. Analysis Code, Numerical and Primary data.
<p>Uploaded under this Zenodo DOI is the following:</p> <p>1. the Analysis Code used for Flow Cytometry analysis in the paper, GO complex analysis (Figure 3) and Hit visualization (Figure 1, 2 S1, S4 Figs).</p> <p>2. The primary Flow Cytometry data from both the initial screen (ScreenFlowFCS) and validation experiments (ValidationFlowFCS) are included as .zip files.</p> <p>3. a .zip folder is uploaded that contains all the analysis code for the ChIP-Seq experiments. </p> <p>4. Excel worksheets that contain the numerical source data for all qPCR bar plots.</p>
Reconstruction of Natural Visual Scenes from Primary Visual Cortical Neural Activity Using Adversarial Learning
<p>This data set and code goes with "Reconstruction of Natural Visual Scenes from Primary Visual Cortical Neural Activity Using Adversarial Learning"</p>
Edge effects and vertical stratification of aerial insectivorous bats across the interface of primary-secondary Amazonian rainforest
<p><span>Edge effects - abiotic and biotic changes associated with habitat boundaries - are key drivers of community change in fragmented landscapes. Their influence is heavily modulated by matrix composition. With over half of the world's tropical forests predicted to become forest edge by the end of the </span><span>century, it is paramount that conservationists gain a better understanding of how tropical biota is impacted by edge gradients. Bats comprise a large fraction of tropical mammalian fauna and are demonstrably sensitive to habitat modification. Yet, </span><span>knowledge about how bat assemblages are affected by edge effects remains scarce</span><span>. Capitalizing on a whole-ecosystem manipulation in the Central Amazon, the aims of this study were to i) assess the consequences of edge effects for twelve aerial insectivorous bat species across the interface of primary and secondary forest and ii) investigate if the activity levels of these species differed between the understory and canopy and if they were modulated by distance from the edge</span><span>. Acoustic surveys were conducted along four 2-km transects each traversing equal parts of primary and ca. 30-year-old secondary forest. Five models were used to assess the changes in the relative activity of forest specialists (three species), flexible forest foragers (three species), and edge foragers (six species). Modelling results revealed no evidence of edge effects, except for forest specialists in the understory. No significant differences in activity were found between the secondary or primary forest but most species exhibited pronounced vertical stratification. Our study highlights that forest specialist bats are more edge-sensitive than both flexible forest and edge foraging bats and suggests that the influence of edge effects on aerial insectivorous bats may exceed 2 km. The absence of pronounced edge effects and the comparable activity levels between primary and old secondary forests indicates that old secondary forest can help ameliorate the consequences of fragmentation on tropical aerial insectivorous bats. </span></p>
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