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Figure 4 from: Al-Mahadeen MM, Jaber AM, Al-Najjar BO (2024) Design, synthesis and biological evaluation of novel 2-hydroxy-1 H-indene-1,3(2 H)-dione derivatives as FGFR1 inhibitors. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e122127
Figure 4 Dose-response curves of compounds 7b, 9a, 9b and 9c FGFR1 inhibitor. A. Dose-response curve of 7b with IC50 = 3.1 μM; B. Dose-response curve of 9a with IC50 = 5.7 μM; C. Dose-response curve of 9b with IC50 = 3.3 μM; D. Dose-response curve of 9c with IC50 = 4.1 μM.
Pre-analytical validity of arterial blood gas samples: A prospective experimental study on changes derived from time delay and mechanical stress
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The velocity difference derived from GPS and well data in the North China Plain and Shaanxi-Gansu region
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DERIVATION IN DER DEUTSCHEN GEGENWARTSSPRACHE
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5-bromoindole carbothioamide derivative
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indole carbohydrazide derivative
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ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from 4DVARNET8 geostrophic velocities (1/24°)
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/8°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10908416) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
Figure 6 in A new classification of Callianassidae and related families (Crustacea: Decapoda: Axiidea) derived from a molecular phylogeny with morphological support
Figure 6. Diagnostic characters for genera of Callianassidae. Anterior carapace, eyestalks, antennules, antennae: a, Aqaballianassa; b, Arenallianassa; c, Caviallianassa; d, Coriollianassa; e, f, Filhollianassa; g, Jocullianassa; h, Notiax i, Praedatrypaea; j, Pugnatrypaea; k, l, Rayllianassa; m, Rudisullianassa; n, Trypaea. Mandible, mesial and lower views: o, Callianassa; p, Rayllianassa. Original illustrations: a, Aqaballianassa lewtonae, MNHN-IU-2016-8152; c, Caviallianassa FP-11, UF 29204; j, Pugnatrypaea GMX, USNM 1559553 (ULLZ 17962); o, Callianassa subterranea, NMV J16779; p, Rayllianassa amboinensis, MNHN-IU-2014-2778.
Human iPSC-derived liver co-culture spheroids to model liver fibrosis
<div> <p><span>The lack of adequate human <em>in vitro</em> models that recapitulate the cellular composition and response of the human liver to injury hampers the development of anti-fibrotic drugs. The goal of this study was to develop a human spheroid culture model to study liver fibrosis by using induced pluripotent stem cell (iPSC)-derived liver cells. iPSCs were independently differentiated towards hepatoblasts (iHepatoblasts), hepatic stellate cells (iHSCs), endothelial cells (iECs) and macrophages (iMΦ), before assembly into free floating spheroids by culturing cells in 96-well U-bottom plates and orbital shaking for up to 21 days to allow further maturation. Through transcriptome analysis, we show further maturation of iECs and iMΦ, the differentiation of the iHepatoblasts towards hepatocyte-like cells <span> </span>(iHeps) and the inactivation of the iHSCs by the end of the 3D culture. Moreover, these cultures display a similar expression of cell-specific marker genes (<em>CYP3A4</em>, <em>PDGFRβ</em>, <em>CD31</em> and <em>CD68</em>) and sensitivity to hepatotoxicity as spheroids made using freshly isolated primary human liver cells. Furthermore, we show the functionality of the iHeps and the iHSCs by mimicking liver fibrosis through iHep-induced iHSC activation, using acetaminophen.<span> </span><br>In conclusion, we have established a reproducible human iPSC-derived liver culture model that can be used to mimic fibrosis <em>in vitro</em> as a replacement of primary human liver derived 3D models. The model can be used to investigate pathways involved in fibrosis development and to identify new targets for chronic liver disease therapy.</span></p> </div>
Carbazole derivatives binding to Bcl-2 promoter sequence G-quadruplex
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Dataset related to article "Neonicotinoid pesticides affect developing neurons in experi-mental mouse models and in human induced pluripotent stem cell (iPSC)-derived neural cultures and organoids"
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Neonicotinoid pesticides affect developing neurons in experi-mental mouse models and in human induced pluripotent stem cell (iPSC)-derived neural cultures and organoids
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Fig. 2 in External Sex Specific Signs In The Structure Of Derivatives Of Sterlet (Acipenser Ruthenus, Linnaeus, 1758) Corium
Fig. 2. Typical dorsal scutes of males (upper) and females (lower) of young sterlet. Age - 1 year. The average length is 24.8 ± 1.5 cm.
FIGURE 10 in Body length estimation of Neogene macrophagous lamniform sharks (Carcharodon and Otodus) derived from associated fossil dentitions
FIGURE 10. Re-calculation of Otodus megalodon body lengths from Pimiento et al. (2010). (A) Re-calculation using the correct equations from Shimada (2002a). (B) Re-calculation using the SCW direct proportions method, showing results based on CH-31-46P and UF- VP-311000 as analogs separately.
FIGURE 11 in Body length estimation of Neogene macrophagous lamniform sharks (Carcharodon and Otodus) derived from associated fossil dentitions
FIGURE 11. Maximum body length estimation of Otodus megalodon. (A) Widest known tooth of O. megalodon (GHC 6), lingual view. The tooth enamel has been repaired inside the red polygon. Scale bar equals 5 cm. (B) Body length calculation for specimen GHC 6, using the associated dentition UF-VP-311000 as an analog and assuming the tooth represents position L1. SCWc = summed crown width corrected and TL = total body length. (C) Illustration depicting the mathematical equation used to solve for body length. Otodus megalodon artwork by Tim Scheirer, used with permission from the Calvert Marine Museum.
FIGURE 7 in Body length estimation of Neogene macrophagous lamniform sharks (Carcharodon and Otodus) derived from associated fossil dentitions
FIGURE 7. Comparison of body length estimates based on SCW direct proportions and Shimada (2002a) CH linear regression. (A–B) Otodus megalodon; (C–D) Otodus chubutensis; and (E–F) Carcharodon spp. (A, C, E) SCW direct proportions. (B, D, F) Shimada (2002a) CH linear regression. U = upper and L = lower.
FIGURE 5 in Body length estimation of Neogene macrophagous lamniform sharks (Carcharodon and Otodus) derived from associated fossil dentitions
FIGURE 5. Associated dentitions of Carcharodon spp. in lingual view. (A) Carcharodon carcharias, GHC 4. (B) Carcharodon hastalis, GHC 5. Scale bars equal 5 cm. See Ehret et al. (2009) for images of the Carcharodon hubbelli dentition, UF-VP-226255.
GOES-16 Derived 1-km resolution Daily Solar Insolation (2023)
<p><strong>Dataset Description</strong></p> <p>This is the University of Alabama in Huntsville (UAH) GOES satellite-derived solar insolation dataset. The dataset and method have been described in previous documents, including Jacobs et al. (2008), Paech et al. (2009), Mecikalski et al. (2011, 2018), Diak (2017) and Cheng et al. (2020). The data are in separate files for Julian days 1-365 (or 366). The dataset extends for the period 1 January 1985 through 31 December 2023.</p> <p>Prior to 2022, the dataset domain was on a 474 x 407 grid at 2 km resolution centered on the State of Florida. From 2022 onward the data domain is on a 1668 x 1668 grid of 1 km resolution that includes several Southeastern U. S. states in addition to Florida. The daily solar insolation is in units of MegaJoules per square meter (MJ/m^2/day), and the data themselves are in compressed ASCII format with the latitude (degrees) and longitude (degrees) of a given point listed. These solar insolation data are used to produce an evapotranspiration (ET) product that covers all watersheds that flow onto the extent of the Floridan aquifer system, which will allow water managers in north Florida to use the dataset when conducting water budget analyses that necessarily cross state borders. The ET data are subsequently used for climatological studies and water resources management.</p> <p>From 1985 to 2021, the solar insolation data was developed using native 1-km resolution GOES-East visible channel data. For each grid point, an averaging of 4 x 4 1-km resolution pixels was done to define a 2 km resolution product. For the 2022 to current datasets, native 500-meter, 10-minute resolution channel 2 (0.64 µm) data are used rom the GOES-16/-18 satellite's Advanced Baseline Imager. For each grid point from 2022 onward, an averaging of 2 x 2 500-meter resolution pixels occurred to define a 1 km resolution product. The GOES data were gathered from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).</p> <p>Prior to 2022, a simple climatological or daily model reanalysis based total precipitable water (TPW) correction was implemented to the solar insolation data. From 2022 onward, the TPW correction for solar insolation is performed using the NESDIS Blended Hydrometeorological Product suite (BLENDHYDRO) “blended total precipitable water” data. These data, as obtained from NOAA CLASS, represents a merging of microwave derived TPW products from multiple polar-orbiting and geostationary satellite sensors including: AMU/MHS onboard the NOAA and MetOp satellite series; SSMIS onboard the DMSP satellite series; ATMS onboard S-NPP and NOAA-20; Sounder onboard the GOES satellite series; and GPS Met onboard Orbview-1, and have a spatial resolution of 16 km at the Equator. From 24 hourly files per day, a daily average TPW grid was created and then mapped onto the 1 km x 1 km grid that spans the domain of solar insolation coverage.</p> <p> </p> <p><strong>Data Quality</strong></p> <p>For the solar insolation dataset, between 15 and 22 pyranometer stations from across the state of Florida are utilized, with ~30% used for calibration of the satellite-estimated model product and the remaining used for validation of model performance. Every effort was made to screen for data quality, with the highest quality data being reserved for calibration. These data were provided by three State of Florida Water Management District (WMD) weather station networks (South Florida (SF), Saint John’s River (SJR) and Southwest Florida (SWF), the University of Florida (UF) Institute of Food and Agricultural Sciences (IFAS) Florida Automated Weather Network (FAWN), and the United States Geological Survey (USGS) network.</p> <p>The following uncalibrated and calibrated pyranometer station-averaged statistics were developed for comparison of satellite-estimated and pyranometer-measured daily-integrated insolation at the ten verification pyranometer station locations: mean bias error (MBE), root mean square error (RMSE, and as a percentage of the mean pyranometer-measured value given in parentheses), and coefficient of determination (R2). As an example for 2023, the uncalibrated values are: MBE = –0.59 MJ/m^2/day, RMSE = 1.38 MJ/m^2/day (8%), and R2 = 0.96. The calibrated values are: MBE = 0.19 MJ/m^2/day, RMSE = 1.20 MJ/m^2/day (7%), and R2 = 0.97.</p> <p> </p> <p><strong>References</strong></p> <p>Cheng, P., A. Pour-Biazar, R. T. McNider, and J. R. Mecikalski, 2020: Validation of GOES-based surface insolation retrievals and its utility for model evaluation. <em>J. Atmos. Ocean Tech</em>., <strong>37</strong>, 553–571.</p> <p>Diak, G. R., 2017: Investigations of improvements to an operational GOES-satellite-data-based insolation system using pyranometer data from the U. S. Climate Reference Network (USCRN). <em>Remote Sens. Environ</em>., <strong>195</strong>, 79–95, doi:10.1016/j.rse. 2017.04.002.</p> <p>Jacobs, J., J. Mecikalski, and S. Paech, 2008: Satellite-based solar radiation, net radiation, and potential and reference evapotranspiration estimates over Florida. Technical Report. July 2008, 138 pp. <a href="http://fl.water.usgs.gov/et/publications/GOES_FinalReport.pdf">http://fl.water.usgs.gov/et/publications/GOES_FinalReport.pdf</a>.</p> <p>Mecikalski, J. R., W. B. Shoemaker, Q. Wu, M. A. Holmes, S. J. Paech, and D. M. Sumner, 2018: A 20-Year high-resolution GOES insolation–evapotranspiration dataset for water resource management over the State of Florida. <em>J. Irrig. Drain. Eng</em>., <strong>144</strong>(9): 04018025.</p> <p>Mecikalski, J. R., D. M. Sumner, J. M. Jacobs, C. S. Pathak, S. J. Paech, and E. M. Douglas, 2011: Use of visible Geostationary Operational Meteorological Satellite imagery in mapping reference and potential evapotranspiration over Florida. <em>Evapotranspiration</em>. ISBN 978-953-307-251-7, Editor Leszek Labedzki, Chapter 10, pgs. 229-254.</p> <p>Paech, S. J., J. R. Mecikalski, D. M. Sumner, C. S. Pathak, Q. Wu, S. Islam, and T. Sangoyomi, 2009: A calibrated, high-resolution GOES satellite solar insolation product for a climatology of Florida evapotranspiration. <em>J. Amer. Water Resources Assoc</em>., <strong>45</strong>, 1328-1342.</p>
Figure 2 from: Ávila MP, Carvalho RN, Casatti L, Simião-Ferreira J, de Morais LF, Teresa FB (2018) Metrics derived from fish assemblages as indicators of environmental degradation in Cerrado streams. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e12895
Figure 2 Stream sites grouped by their environmental characteristics by using UPGMA (Unweighted Pair Group Method with Arithmetic Mean). Height at y-axis refers to Euclidean distance.
Figure 3 from: Ávila MP, Carvalho RN, Casatti L, Simião-Ferreira J, de Morais LF, Teresa FB (2018) Metrics derived from fish assemblages as indicators of environmental degradation in Cerrado streams. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e12895
Figure 3 Box-and-Whisker plots of the three sensitive metrics. Rectangles represent the 1st and 3rd quartiles, small squares are medians, bars are maximum and minimum values. Different letters indicate statistically significant differences.
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.