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Inhibition of epithelial cell YAP-TEAD/LOX signaling attenuates pulmonary fibrosis "
<table> <tbody> <tr> <td> <p>Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal disease characterized by excessive extracellular matrix (ECM) deposition. Current IPF therapies slow disease progression but do not stop or reverse it. The (myo)fibroblasts are thought to be the main cellular contributors to excessive ECM production in IPF. Here we report that fibrotic AT2 cells regulate production and crosslinking of ECM via the co-transcriptional activator YAP. YAP leads to increase expression of Lysyloxidase (LOX) and subsequent LOX mediated crosslinking by fibrotic AT2 cells. Pharmacological YAP inhibition reverses fibrotic AT2 cell reprogramming and LOX expression in experimental lung fibrosis <span>in vivo</span><span> and in human fibrotic </span><span>tissue ex vivo</span><span>. We thus identify YAP-TEAD/LOX inhibition in AT2 cells as a promising potential new therapy for IPF patients.<span> </span></span></p> <p><span><span>In</span></span></p> </td> </tr> </tbody> </table>
Model output used in the manuscript "Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency"
<p>This *.zip file contains the model output from seasonal variability experiments using the NPZD-DOP GEOMAR biogeochemical model (<a href="https://doi.org/10.1016/j.pocean.2010.05.002" target="_blank" rel="noopener">Kriest et al., 2010</a>) coupled with the MITgcm 2.8deg ocean circulation via the transport matrix method (<a href="https://doi.org/10.1016/j.ocemod.2004.04.002" target="_blank" rel="noopener">Khatiwala et al., 2005</a>; <a href="https://doi.org/10.1029/2007GB002923" target="_blank" rel="noopener">Khatiwala, 2007</a>; <a href="https://doi.org/10.5281/zenodo.1246300" target="_blank" rel="noopener">Khatiwala, 2018</a>).</p> <p>These model outputs are presented and discussed in the Preprint "<em>Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency</em>", published by Geophysical Research Letters (<a href="https://doi.org/10.1029/2023GL107050" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original model. For this matter, we also refer you to <a href="https://doi.org/10.1029/2021GB007101" target="_blank" rel="noopener">de Melo Viríssimo et al. (2022)</a>.</p> <p>All files uploaded were generated from simulations run by the authors, except: the grid file, the salinity field, and the temperature field, which came with the model; and the density fields, who were computed from the MITgcm 2.8deg transport matrix by Dr Rafaelle Bernadello, using a TEOS-10 Matlab routine (<a href="http://www.teos-10.org/">http://www.teos-10.org/</a>).</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p>
Dataset: Mapping intrinsic and scattering attenuation in the southern Aegean crust using S-wave envelope inversion and sensitivity kernels derived from perturbation theory
<p><strong>Data Set S1: </strong>File “ds01.csv” contains the catalogue of relocated events used in this study. The columns in the file represent origin time (in year-month-day’H’hour’M’minute’S’seconds format), event longitude, event latitude, event depth in a sequential manner.</p> <p><strong>Data Set S2: </strong>File “ds02.zip” contains four ASCII data files (ray_prmtrs12.txt, ray_prmtrs24.txt, ray_prmtrs48.txt and ray_prmtrs816.txt). The data files contain scattering coefficient (<em>g<sup>*</sup></em>) and intrinsic coefficient (<em>b</em>) values in 1-2, 2-4 Hz, 4-8 Hz and 8-16 Hz bands respectively. The columns in the text files represent event latitude, event longitude, event depth, station latitude, station longitude, station velocity, envelope duration, <em>g<sup>*</sup></em>, <em>b</em>, early-S window length, percentage error in early-S window, percentage error for full envelope, and event origin time in a sequential manner.</p> <p><strong>Data Set S3: </strong>File “ds03.zip” contains four data files (envnodes15g_3_3_1-2.txt, envnodes15g_3_3_2-4.txt, envnodes15g_3_3_4-8.txt, and envnodes15g_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qs_envg.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S4: </strong>File “ds04.zip” contains four data files (envnodes15b_3_3_1-2.txt, envnodes15b_3_3_2-4.txt, envnodes15b_3_3_4-8.txt, and envnodes15b_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qi_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S5: </strong>File “ds05.zip” contains four data files (envnodes15a_3_3_1-2.txt, envnodes15a_3_3_2-4.txt, envnodes15a_3_3_4-8.txt, and envnodes15a_3_3_8-16.txt), one BASH script containing GMT and Octave commands (albd_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain Albedo (<em>B<sub>o</sub></em>) as % values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and <em>B<sub>o</sub></em> value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of <em>B<sub>o</sub></em> using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above. </p>
Corrected IODP Gamma Ray Attenuation (GRA) densities and calculated porosities derived from the LILY Database
<div>The dataset <strong>GRA_Densities_Corrected_and_Porosities_2023-12-26.csv</strong> is derived from an analysis of data from the LILY Database (<a href="https://doi.org/10.5281/zenodo.8408296">https://doi.org/10.5281/zenodo.8408296</a>) as described in Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). The file contains over 3.7 million corrected gamma ray attenuation (GRA) bulk density data derived from the LILY database file GRA_DataLITH.csv. It also contains over 3.7 million porosity estimates that are computed from the corrected GRA bulk density using grain densities computed for each lithology from Moisture and Density (MAD) grain densities (derived from LILY file MAD_DataLITH.csv).</div> <div> </div> <div><strong>Citation: </strong>Please cite Childress et al. (2024) when using these data:</div> <div>Childress, L.B., Acton, G.D., Percuoco, V.P., Hastedt, M., 2024. The LILY Database: Linking Lithology to IODP Physical, Chemical, and Magnetic Properties Data, <em>Geochemistry, Geophysics, Geosystems, 25</em>, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>.</div> <div> </div> <div><strong>GRA_Densities_Corrected_and_Porosities_2023-12-26.csv</strong> file size uncompressed is 950 Mb.</div> <div> </div> <div><strong>Data File format:</strong></div> <ul> <li>Exp: expedition number</li> <li>Site: site number</li> <li>Hole: hole number</li> <li>Core: core number</li> <li>Type: Type indicates the coring tool used to recover the core (typical types are F, H, R, X; see Table S3 in Childress et al., 2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>).</li> <li>Sect: section number</li> <li>Offset (cm): position of the observation, measured relative to the top of a section.</li> <li>Depth CSF-A (m): location of the observation expressed relative to the top of a hole.</li> <li>Bulk density (GRA): bulk GRA density measured on whole core sections in g/cm^3.</li> <li>Timestamp (UTC): date and time the observation was made.</li> <li>Instrument: abbreviation or mnemonic for the GRA sensing device used to make this observation (GRA1 or GRA2).</li> <li>Instrument group: abbreviation or mnemonic for the data collection device (logger) used to acquire this observation (WRMSL).</li> <li>Text ID: automatically generated unique database identifier for a sample, visible on printed labels.</li> <li>Prefix: Prefix of the lithology</li> <li>Principal: Principal lithology</li> <li>Suffix: Suffix of the lithology</li> <li>Full Lithology: full lithologic name = Prefix + Principal + Suffix</li> <li>Simplified Lithology: categorization of lithologies (see Supporting Information in Childress et al., 2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>)</li> <li>Lithology Type: Sedimentary, Igneous, or Metamorphic</li> <li>Degree of Consolidation: consolidation state of the lithology.</li> <li>Lithology Subtype: categorization of lithologies (see Supporting Information in Childress et al., 2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>).</li> <li>Expanded Core Type: the actual coring type used, because some coring types were incorrectly grouped in the "Type" column (see Childress et al., 2024 for an explanation)</li> <li>Latitude (DD): Latitude in decimal degrees</li> <li>Longitude (DD): Longitude in decimal degrees</li> <li>Water Depth (mbsl): water depth in meters below sea level</li> <li>Grain Density: grain density associated with the Principal lithology, computed from MAD data</li> <li>Mean MAD Bulk Density: mean MAD bulk density associated with the Principal lithology.</li> <li>Std MAD Bulk Density: standard deviation in the MAD bulk densities for each Principal lithology.</li> <li>Correction Basis: the GRA bulk densities are corrected based on coring tool used. If the RCB was used, then the lithology cored by the RCB is used in determining the size of the correction.</li> <li>Median Difference: The correction that will be applied based on the median difference between the raw GRA bulk density and the colocated MAD bulk density for a specific Correction Basis.</li> <li>GRA Bulk Density Corrected: The corrected GRA bulk density in g/cm^3.</li> <li>Porosity: porosity computed from the corrected GRA bulk densities and grain density.</li> <li>Deviation: difference between "GRA Bulk Density Corrected" and "Mean MAD Bulk Density", which is the deviation the corrected density has from that expected for its Principal lithology.</li> <li>N Deviations: The number of standard deviations by which the observation differs from the expected value (= Deviation/(Std MAD Bulk Density)), which is useful for identifying outliers.</li> </ul> <h3>GitHub Repository:</h3> <ul> <li>Contains a few notebooks to demonstrate how to work with the LILY database</li> <li><a title="IODP LILY GitHub Repository" href="https://github.com/IODP?tab=repositories">IODP LILY GitHub Repository</a> </li> </ul>
Output tomographic models for "The attenuation and scattering signature of fluids and tectonic interactions in Central-Southern Apennine."
<p>Output ASCII file for the seismic attenuation tomography in Central-Southern Apennines. The output format is the one from MuRAT software (De Siena et al., 2014). Q and Peak-Delay models in 1.5 Hz, 3 Hz and 6 Hz frequencies are reported as specificated by the files name. The output points of a grid with coordinates available in WGS84 degrees ("Degrees" suffix) or already projected in kilometric UTM coordinates ("UTM" suffix).</p> <p>All other information can be found in the main and supplementary text.</p>
High-frequency light attenuation measurements in 35 lakes: companion data from "Coefficients in Taylor’s Law increase with the time scale of water clarity measurements in a global suite of lakes"
Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor’s Law of fluctuation scaling, which states that the variance of a population is proportional to a positive power of the mean, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. This dataset uses light attenuation observations from 10,531 days of high-frequency measurements in 35 globally distributed lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor’s Law, and that model coefficients increase in a predictable yet non-linear manner with decreasing observation frequency.
Repository: Rayleigh-wave attenuation and phase velocity maps of the greater Alpine region from ambient noise
<p><br>Repository organized by Henrique Berger Roisenberg for the paper Roisenberg et al. (2024). The files are organized as follows:</p> <p><strong>Folders:</strong></p> <p><strong>-dispersion_curves:</strong><br>inside this folder there is a .zip file that contains all the dispersion curves calculated;</p> <p><strong>-attenuation:</strong><br>comprising three files with the results of attenuation calculations, i.e., the attenuation values, the grid, and the periods;</p> <p><strong>-c:</strong><br>comprising three files with the results of phase velocity calculations, i.e., the phase velocity values, the grid, and the periods;</p> <p><strong>-scripts: </strong><br>contains two python scripts, one called 'figures' to plot the figure 1, 4, and 6 of the paper, and another called 'alparray_computations' to perform the computations with the original alparray data, using seislib, resulting on the figures 2, 3, and 5 of the paper.</p> <p>Inside the folder '<strong>inputs</strong>' there are three folders that serve as input for the figures of the paper, to be used in the scripts. These are:</p> <p><strong>-raster: </strong><br>contains the topography raster used to plot the map of the study area;</p> <p><strong>-shapefiles:</strong><br>contains the shapefiles used in the regionalization analysis;</p> <p><strong>-station locations: </strong><br>contains the latitudes and longitudes of the stations used in this study.</p> <p> </p>
Dataset: A Labeled Dataset for Osteoporosis Screening Based on Electromagnetic Attenuation
<p><strong>README</strong></p> <p><strong>Dataset name:</strong> osseus_dataset.csv </p> <p><strong>Version:</strong> 1.0 </p> <p><strong>Dataset period:</strong> 07/01/2021 - 09/31/2023</p> <p><strong>Dataset Characteristics:</strong> Multivalued </p> <p><strong>Number of Instances:</strong> 669</p> <p><strong>Number of Attributes:</strong> 31</p> <p><strong>Missing Values:</strong> yes</p> <p><strong>Area(s):</strong> Health and technology </p> <p><strong>Sources:</strong> </p> <ul> <li> <p>Electronic Patient Record (EPR) - University Hospital Onofre Lopes of Federal University of Rio Grande do Norte (HUOL/UFRN), Brazil;</p> </li> <li> <p>OSSEUS (Osteoporosis screening based on electromagnetic waves); and,</p> </li> <li> <p>DXA (Dual-energy x-ray absorptiometry). </p> </li> </ul> <p> </p> <p><strong>Description</strong>: The dataset “osseus_dataset.csv” (Table 1) contains elementary data related to risk factors and examinations performed by individuals in Rio Grande do Norte, Brazil, to investigate bone mineral density. Data were collected using the EPR of HUOL/UFRN, DXA, and OSSEUS, a low-cost device based on electromagnetic waves, which measures the attenuation of the signal when crossing the medial phalanx of the middle finger (PINHEIRO et al., 2021, ALBUQUERQUE et al., 2022).</p> <p><strong>Descrição</strong>: O conjunto de dados “osseus_dataset.csv” (Tabela 1) contém dados elementares relacionados a fatores de risco e exames realizados por indivíduos no Estado do Rio Grande do Norte, Brasil, para a investigação da densidade mineral óssea. Os dados foram coletados por meio do EPR do HUOL/UFRN, DXA e OSSEUS, um dispositivo de baixo custo baseado em ondas eletromagnéticas, que mede a atenuação do sinal ao atravessar a falange medial do dedo médio (PINHEIRO et al., 2021, ALBUQUERQUE et al., 2022).</p> <p> </p> <p><strong><strong>Table 1: </strong></strong>Description of Dataset Features.</p> <div> <table> <tbody> <tr> <td> <p><strong>Attributes</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>Electronic Patient Record (EPR)</strong></p> </td> </tr> <tr> <td> <p><strong>id</strong></p> </td> <td> <p>Unique identifier for a person (anonymous).</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Person unique identifier.</p> </td> </tr> <tr> <td> <p><strong>gender</strong></p> </td> <td> <p>It informs the person's gender.</p> </td> <td> <p>Categorical.</p> </td> <td> <ul> <li> <p>female</p> </li> <li> <p>male</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>age</strong></p> </td> <td> <p>It informs the person's age.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Integer value for age</p> </td> </tr> <tr> <td> <p><strong>weight</strong></p> </td> <td> <p>Informs the value referring to the person's weight—the unit of mass in kilogram (kg).</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Integer value for weight</p> </td> </tr> <tr> <td> <p><strong>height</strong></p> </td> <td> <p>Informs the value relating to the person's height—the unit of measurement for size in centimeters (cm).</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Integer value for height</p> </td> </tr> <tr> <td> <p><strong>ethnicity</strong></p> </td> <td> <p>Informs the person's ethnicity.</p> </td> <td> <p>Categorical.</p> </td> <td> <ul> <li> <p>black</p> </li> <li> <p>brown</p> </li> <li> <p>white</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>target</strong></p> </td> <td> <p>Describe the person's diagnosis or medical report.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>normal</p> </li> <li> <p>osteoporosis</p> </li> <li> <p>low bone mineral density</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>alcohol</strong></p> </td> <td> <p>It informs whether the person consumes alcoholic beverages.</p> </td> <td> <p>Categorical.</p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>smoking</strong></p> </td> <td> <p>Informs whether the person is a smoker.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>activity</strong></p> </td> <td> <p>It informs whether the person practices physical activities.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>milk</strong></p> </td> <td> <p>It informs whether the person consumes dairy drinks.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>calcium</strong></p> </td> <td> <p>It informs whether the person uses calcium.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>vitamin_d</strong></p> </td> <td> <p>It informs whether the person uses Vitamin D.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>fall</strong></p> </td> <td> <p>It informs whether the person has a history of falling.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>parents_osteoporosis</strong></p> </td> <td> <p>It informs whether the person has a family history of osteoporosis.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>parents_curved</strong></p> </td> <td> <p>It informs whether the person has a family history of "parents curved."</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>corticosteroids</strong></p> </td> <td> <p>It informs whether the person uses corticosteroid-type medications for three months or longer.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>arthritis</strong></p> </td> <td> <p>Informs if the person has arthritis.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>diseases</strong></p> </td> <td> <p>Informs if the person has comorbidities.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>menopause</strong></p> </td> <td> <p>Informs if the person has menopause.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>testosterone</strong></p> </td> <td> <p>It informs whether the person uses testosterone.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>OSSEUS (Osteoporosis screening based on electromagnetic waves)</strong></p> </td> </tr> <tr> <td> <p><strong>medial_length</strong></p> </td> <td> <p>Length of the medial phalanx in mm.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Integer value for length.</p> </td> </tr> <tr> <td> <p><strong>medial_height</strong></p> </td> <td> <p>Height of the medial phalanx in mm.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Integer value for height.</p> </td> </tr> <tr> <td> <p><strong>medial_width</strong></p> </td> <td> <p>Width of the medial phalanx in mm.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Integer value for width.</p> </td> </tr> <tr> <td> <p><strong>calibration</strong></p> </td> <td> <p>Osseus signal strength with no obstacle between the antennas.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Float value for calibration.</p> </td> </tr> <tr> <td> <p><strong>attenuation</strong></p> </td> <td> <p>Osseus signal strength with obstacles between antennas.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Float value for attenuation.</p> </td> </tr> <tr> <td> <p><strong>DXA (Dual-energy x-ray absorptiometry)</strong></p> </td> </tr> <tr> <td> <p><strong>spine_deviation</strong></p> </td> <td> <p>Reports the spinal standard deviation score that represents the difference between bone density and the expected value.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Float value for deviation.</p> </td> </tr> <tr> <td> <p><strong>femur_deviation</strong></p> </td> <td> <p>Reports the femur standard deviation score that represents the difference between bone density and the expected value.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Float value for deviation.</p> </td> </tr> <tr> <td> <p><strong>body_deviation</strong></p> </td> <td> <p>Reports the full body standard deviation score that represents the difference between bone density and the expected value.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Float value for deviation.</p> </td> </tr> <tr> <td> <p><strong>forearm_deviation</strong></p> </td> <td> <p>Reports the forearm standard deviation score that represents the difference between bone density and the expected value.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Float value for deviation.</p> </td> </tr> <tr> <td> <p><strong>worst_deviation</strong></p> </td> <td> <p>Reports the worst standard deviation score among all deviations obtained from the record.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Float value for deviation.</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> </div> <p><strong>REFERENCES</strong><br>Albuquerque, G. et al. A method based on non-ionizing microwave radiation for ancillary diagnosis of osteoporosis: a pilot study. BioMedical Eng. OnLine 21, 70, https://doi.org/10.1186/s12938-022-01038-y (2022).</p> <p>Pinheiro, B. d. M. et al. The influence of antenna gain and beamwidth used in osseus in the screening process for osteoporosis. Sci. Reports 11, 19148, https://doi.org/10.1038/s41598-021-98204-4 (2021).</p> <div> <p> </p> </div>
Data set of Reversing anterior insular cortex neuronal hypoexcitability attenuates compulsive behavior in juvenile rats
<p>Development of self-regulatory competencies during adolescence is partially dependent on normative brain maturation. Here we report that adolescent rats as compared to adults exhibit impulsive and compulsive-like behavioral traits, the latter being associated with lower expression of mRNA levels of the immediate early gene zif268 in the anterior insula cortex (AIC). This suggests that underdeveloped AIC function in adolescent rats could contribute to an immature pattern of interoceptive cue integration in decision-making and a compulsive phenotype. In support of this, we report that layer 5 pyramidal neurons in the adolescent rat AIC are hypoexcitable and receive fewer glutamatergic synaptic inputs compared to adults. Chemogenetic activation of the AIC attenuated compulsive traits in adolescent rats supporting the idea that in early stages of AIC maturity there exists a suboptimal integration of sensory and cognitive information that contributes to inflexible behaviors in specific conditions of reward availability.</p>
Rapid assessment of lipidomics sample quality and quantity using attenuated total reflectance Fourier-transform infrared spectroscopy
<p>In this work, we aimed to develop a simple lipid quality and quantification method for biological lipid extracts, as a step in lipidomics workflows, with minimal sample requirement. We chose FTIR spectroscopy with an Attenuate Total Reflectance (ATR) sampling method as it requires just 1 microliter of MS-ready sample without additional sample preparation. We validated the proposed lipidomics sample quality control workflow using a set of plasma samples (n=107, with 3-4 technical replicates) with comparison to LC-MS-based lipidomics. The following file contains the resulting spectra acquired by ATR-FTIR spectrometry for these plasma samples, standard curves and contaminated samples used for method development. Spectrometer was ambient blanked and detector cleaned between each measurement. Lipid samples were extracted by butanol-methanol (3:1) precipitation, and dried directly onto the ATR-FTIR detector. Absorbance was measured between 4,000 and 650 cm-1 wavenumbers, at a resolution of 8cm-1. Each spectra has been baseline corrected (whole spectra).</p>
Waveforms and results of seismic attenuation in Sumatra subduction zone, Indonesia
<p>Datasets for the manuscript:</p> <p>Styawan, Y., Kuo, C.-H., Huang, B.-S., Wen, K.-L., Haridhi, H. A., Sianipar, D., Characteristics of seismic attenuation in Sumatra subduction zone, Indonesia (submitted)</p> <p>The attached files include:</p> <p>1) 0.2 Hz Highpass filtered waveforms for Z and T components.</p> <p>2) Results (α, event, station, t*, Q, corner frequency, Ω0, SNR, component (Z or T), category (forearc, mountain, or backarc), and Qp/Qs).</p> <p>3) Additional data (information on events and stations).</p> <p>4) site amplification factors (P and S of all stations in different α).</p>
Database of Panama Region to determine intrinsic and scattering attenuation
<p><strong>Database of Panama Region to determine intrinsic and scattering attenuation.</strong></p> <p>Sagel Aguilar, Daphne (2); Prudencio, Janire (1,2); Del Pezzo, Edoardo (3); Ibáñez, Jesús (1,2), Ligdamis Gutierrez (1,2)</p> <p>Database of Panama Region to determine intrinsic and scattering attenuation</p> <p>by Sagel Aguilar, Daphne (2); Prudencio, Janire (1,2); Del Pezzo, Edoardo (3); Ibáñez, Jesús (1,2) and Ligdamis Gutierrez (1,2)</p> <p><strong>Institutions associated:</strong></p> <p>(1) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain.</p> <p>(2) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain.</p> <p>(3) INGV Observatory vesiviano. Via Diocleziano, 328. 80124 Napoli, Italy.</p> <p><strong>Acknowledgment:</strong></p> <p>This study was partially supported by the National Secretariat of Science, Technology, and Innovation-SENACYT and the Institute for the Training and Use of Human Resources-IFARHU, through the Program: Research Doctorate BBIDP-II-2019-03, for granting me the scholarship to be able to carry out the Doctorate in Earth Sciences at the University of Granada, Spain.</p> <p>To the Institute of Geoscience of Panama UGC and University of Panama UP, for providing us with the seismic data to carry out this research.</p> <p>Analysis of Spectral Characteristics System of Seismic Records, Versión 1.0. Ligdamis Gutierrez (1,2). (2022). Analysis of Spectral Characteristics System of Seismic Records (1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7008075">https://doi.org/10.5281/zenodo.7008075</a> Department of Theoretical Physics and the Cosmos, Science Faculty. Instituto Andaluz de Geofísica y Prevención de Desastres Sísmicos. Granada University (Ugr), Granada, Spain</p> <p><br> <strong>Data availability statement:</strong></p> <p>The database was provided by the <em>Institute of Geosciences of Panama</em> in “<strong>. MSEED</strong>” format, processed in “<strong>. SAC</strong>” format and executed in MATLAB to work with a “<strong>.txt</strong>” extension. Analyzed in MATHEMATICA software.</p>
Figure 1 in Methods of application of salicylic acid as attenuator of salt stress in cherry tomato
Figure 1. Air temperature (maximum and minimum) and mean relative air humidity observed in the internal area of the greenhouse during the experimental period.
Figure 2 in Methods of application of salicylic acid as attenuator of salt stress in cherry tomato
Figure 2. Two-dimensional projection of the scores of the principal components for the factors salinity levels (S) and methods of application of salicylic acid (M) (A) and the variables analyzed (B) in the first two principal components (PC and PC ).
Figure 2 in Osmoprotection in Salvia hispanica L. seeds under water stress attenuators
Figure 2. Shoot length- SL (A), root length- RL (B) and total dry mass (C) of Salvia hispanica L. seedlings subjected to different attenuators and water potentials.
Figure 3 in Osmoprotection in Salvia hispanica L. seeds under water stress attenuators
Figure 3. Contents of amino acids (A), proline (B) and total soluble sugars (C) in Salvia hispanica L. seedlings subjected to different attenuators and water potentials.
Figure 1 in Osmoprotection in Salvia hispanica L. seeds under water stress attenuators
Figure 1. Germination (A) and germination speed index- GSI (B) of Salvia hispanica L. seeds subjected to different attenuators and water potentials.
Tomographic X-ray data of a lotus root filled with attenuating objects
<p>This is an open-access dataset of tomographic X-ray data of a slice of a lotus root. The dataset consists of</p> <ul> <li>the X-ray sinogram of a single 2D slice of the lotus root with two different resolutions, and</li> <li>the corresponding measurement matrices modeling the linear operation of the X-ray transform.</li> </ul> <p>Each of these sinograms was obtained from a measured 360-projection fan-beam sinogram by down-sampling and taking logarithms. The original (measured) sinogram is also provided in its original form and resolution.</p> <p>Also MATLAB code for reconstructions using filtered back-projection, Landweber iteration, and Tikhonov regularization are provided.</p> <p>Documentation of the dataset is available at <a href="https://arxiv.org/abs/1609.07299">arxiv.org/abs/1609.07299</a>. See also <a href="https://www.fips.fi/dataset.php">www.fips.fi/dataset.php</a>.</p>
Influence of Vegetation Flexibility on Hydrodynamics and Wave Attenuation, Hydralab+ Experiment Dataset
<p>This dataset provides the raw data from a series of experiments investigating the influence of submerged aquatic vegetation blade flexibility on wave hydrodynamics. The experiments were conducted as part of collaborative research between the University of Hull and the University of Aberdeen as part of Hydralab+ (www.hydralab.eu), and were completed within the Aberdeen University Random Wave Flume (AURWF). The dataset is supported by a data storage report titled "HYPLUS-HULL-ABDN-01: Influence of Vegetation Flexibility on Hydrodynamics and Wave Attenuation". Due to storage capacity restrictions, the photograph and video media has been removed from this dataset, but can be provided upon request form the authors.</p> <p>The experimental campaign is distinguished into two experimental series:</p> <p>Series 1 - Velocity Measurements:</p> <p>Velocity measurements were conducted for three regular wave conditions, for nine surrogate aquatic vegetation canopies: two canopy heights, two canopy densities, and four vegetation flexibilities. High-resolution non-intrusive velocity measurements were made with a two-component Laser-Doppler Anemometer (LDA), measuring a vertical profile in the centre of each canopy from the flume baseboard top to slightly below the free surface. These measurements were coupled with wave gauge recordings.</p> <p>Series 2 - Wave Attenuation Measurements</p> <p>Wave attenuation measurements were obtained for the nine surrogate vegetation canopies explained in series 1, plus one additional canopy with a greater submergence ratio, for a total of 2 irregular and 12 regular wave conditions.</p>
Figure 4 in Evaluation of nickel-induced brain injuries in rats via oxidative stress and apoptosis: attenuating effects of hyperoside
Figure 4. Morphology and location of PAS granules of brain tissues of control (A), Hyp-treated (B), Ni-treated (C), and Ni + Hyp-treated (D) rats. Homogeneous PAS staining was observed in the control group (A) and the Hyp-treated (B) groups. Clusters of granules shown inside square (C) and by black arrow (D), respectively (200× magnification).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.