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opencc-by-4.0May 2024View details →

ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from MIOST geostrophic velocities (1/72°)

This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the MIOST algorithm (Ubelmann et al., 2019; https://doi.org/10.1029/2020JC016560) to altimetry L3-data (https://doi.org/10.5281/zenodo.10648981) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022.  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). ---------------------------------------------------------------------------------------- Geographical coverage: Mediterranean Sea Grid and horizontal spatial resolution: Evenly spaced 1/72º grid Vertical levels: Only surface level Temporal resolution: Daily (April 2016 - July 2022) ----------------------------------------------------------------------------------------- Variables: lon (1D) lat (1D) time (1D) fsle (2D)

opencc-by-4.0Dec 2022View details →

Figure 6 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol

Figure 6. Treatment Group with resveratrol standard after induced by Beta-Amyloid (10 x10).

opencc-by-4.0Dec 2022View details →

Figure 3 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol

Figure 3. Treatment group: 2- ME + Resveratrol isolated from Tempeh (10 x10).

ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from 4DVARNET20 geostrophic velocities (1/24°)

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 1/20°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10912777) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022.  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). ---------------------------------------------------------------------------------------- Geographical coverage: Mediterranean Sea Grid and horizontal spatial resolution: Evenly spaced 1/24º grid Vertical levels: Only surface level Temporal resolution: Daily (April 2016 - July 2022) ----------------------------------------------------------------------------------------- Variables: lon (1D) lat (1D) time (1D) fsle (2D)

ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from 4DVARNET20 geostrophic velocities (1/72°)

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 1/20°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10912777) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022.  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). ---------------------------------------------------------------------------------------- Geographical coverage: Mediterranean Sea Grid and horizontal spatial resolution: Evenly spaced 1/72º grid Vertical levels: Only surface level Temporal resolution: Daily (April 2016 - July 2022) ----------------------------------------------------------------------------------------- Variables: lon (1D) lat (1D) time (1D) fsle (2D)

ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from 4DVARNET8 geostrophic velocities (1/72°)

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 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022.  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). ---------------------------------------------------------------------------------------- Geographical coverage: Mediterranean Sea Grid and horizontal spatial resolution: Evenly spaced 1/72º grid Vertical levels: Only surface level Temporal resolution: Daily (April 2016 - July 2022) ----------------------------------------------------------------------------------------- Variables: lon (1D) lat (1D) time (1D) fsle (2D)

opencc-zeroJun 2024View details →

dryad36/100

Data from: Trends in plant cover derived from vegetation-plot data using ordinal zero-augmented beta regression

Questions. Plant cover values in vegetation-plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non-equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is if we can detect species trends in vegetation-plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners. Location. The Netherlands, Western Europe. Methods. We used vegetation-plot data collected from 10.000 fixed plots which were surveyed once every four years during 1999-2022. We used the ordinal zero-augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four-year period. Thereafter we assessed trends in cover and in occurrence across all periods. Results. We found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence. Conclusions. Our model enables researchers to detect trends in cover using longitudinal vegetation-plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring datasets.

Infection of mice by the enteroaggregative E. coli strain 042 and two mutant derivatives overexpressing virulence factors: impact on disease markers, gut microbiota and concentration of SCFAs in feces

This dataset contains raw sequencing data from the microbiota analysis conducted in the enteroaggregative E. coli strain 042 study. The data includes FASTQ files generated from Illumina sequencing, along with metadata describing the sample collection and processing methods.

Lipidomics of exosomes derived from granulosa cells

The mapped data files generated from the analysis of the pathway in MetExploreViz. Lipid content categorized by saturation level.

The effect of extracellular vesicles derived from oral squamous cell carcinoma on the metabolic profile of oral fibroblasts

Oral cancer is one of the most common forms of head and neck cancers. Oral squamous cell carcinoma (OSCC) accounts for more than 90% of the oral malignancies. The molecular pathogenesis of OSCC is complex as it involves altered expression of specific genes and proteins, but also comprises changes in metabolic processes. It is suggested that extracellular vesicles (EVs) released by cancer cells may contribute to cancer development and metastasis by recruiting and changing phenotype of normal cells that surround the tumor. The aim of the project was to characterize the effect of OSCC EVs on the metabolic profile of normal oral fibroblasts (NOFs). Targeted liquid chromatography-mass spectrometry metabolic profiling was performed on control cells and NOFs exposed to OSCC EVs for 24 and 48 h. Analysis of detected metabolites revealed that OSCC EVs affected NOFs the most after 24 h of exposure. Among metabolites that were significantly altered at 24 h, pyruvate, ATP, UTP, coenzyme A, and dihydroxyacetone phosphate were upregulated, while fatty acids such as nervonic acid, linoleate, oleate, palmitoleic acid, and docosahexaenoic acid were downregulated. These findings were supported by Western blotting of pyruvate kinase M2 (PKM2). The metabolic pathways of glycolysis, citric acid cycle, and amino acid metabolism were enriched, suggesting that OSCC EVs cause phenotype switch in NOFs that may contribute to acquiring a pro-tumorigenic phenotype.

Annex B – Occurrence data on TBBPA and TBBPA derivatives in food submitted to EFSA, dietary surveys per country and age group available in the EFSA Comprehensive Database considered in the exposure assessment, and chronic dietary exposure to TBBPA and the contribution of different food groups to the dietary exposure

This Annex contains the occurrence data submitted to EFSA, the dietary surveys per country and age group, and the chronic dietary exposure to PBDEs and the contribution of different food groups to the dietary exposure related to the Update of the risk assessment of TBBPA and its derivatives in food.

GOES-16 Derived 1-km resolution Daily Solar Insolation (2022)

Dataset Description 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. 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. 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). 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. Data Quality 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. 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. References 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. J. Atmos. Ocean Tech., 37, 553–571. 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). Remote Sens. Environ., 195, 79–95, doi:10.1016/j.rse. 2017.04.002. 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. http://fl.water.usgs.gov/et/publications/GOES_FinalReport.pdf. 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. J. Irrig. Drain. Eng., 144(9): 04018025. 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. Evapotranspiration. ISBN 978-953-307-251-7, Editor Leszek Labedzki, Chapter 10, pgs. 229-254. 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. J. Amer. Water Resources Assoc., 45, 1328-1342.

Observed changes in significant wave heights derived from long-term homogenized measurements offshore mainland Portugal

The data set contains statistical estimators of significant height acquired over the last 40 years by wave stations operating on the Portuguese coast; Monthly Mean, Monthly Maximum and the monthly 99, 95, 90, 50 , 10 percentiles The data was calculated based on long-term homogenized 3h series of in-situ significant wave height These buoys have been maintained by the Instituto Hidrografico and constitute one of the longest time series of wave data. Each file correspond to a particular wave station. All files are in ASCII and have the same 9 column format. The heading identify the variables Leixoes_corr_monthly_percentiles.txt  - Data acquired by Leixoes buoy: Location 41º 19.00' N  8º 59.00' W, depth app 100 m Sines_corr_monthly_percentiles.txt - Data acquired by Sines buoy: Location 37º 55.27' N  8º 55.73 ' W, depth app 100 m Faro_coor_monthly_percentiles.txt - Data acquired by Faro buoy: Location 36º 54.28' N 7º 53.90' W, depth app 100 m

Taxonomic and functional annotations of transcripts and proteins derived from a Metatranscriptomic study of microbial eukaryotes from Lake Pavin

These data were obtain as part of a metatranscriptomic study (Monjot et al., 2023, 2024). All scripts to obtain these annotations are available at https://github.com/amonjot/SSN_Monjot_2024. The sequencing data (i.e. metatranscriptomic) used to obtain this taxonomic and functional information are archived at ENA under accession number PRJEB61515. This repository also contains various protein sequence similarity networks (Lagoon_output.zip). As these files are very time-consuming to produce, we have provided them to complete all the steps in Monjot et al, 2024. All procedures to produce them are present on the following github repository : https://github.com/amonjot/SSN_Monjot_2024.

opencc-by-4.0Feb 2008View details →

Fig. 1 in Effect of folate derivatives on the activity of antifolate drugs used against malaria and cancer

Fig. 1 Chemical structures of folic acid and of the antifolate drugs we analyzed

Dataset for Modelling of infiltration rates in soils derived from contrasting parent materials using Horton's model

This dataset was initially generated as part of a broader project investigating infiltration and various soil properties in soils derived from three parent materials in Akwa Ibom State, Nigeria: Alluvial Clay, Coastal Plain Sands, and Sandstone. Utin and Oguike (2018) initially utilized this data to evaluate Philip's and Kostiakov's models in these soils. Subsequently, the infiltration data were employed as secondary data to assess the performance of Horton's model in predicting infiltration rates for the three soil types.

Fluorescence images acquisition on cultures of human-derived cardiomyocytes

These data are used to evaluate the performance of the SiMulTox platform in terms of performance of live-cell fluorescence imaging.

Modeling Lewy Body Disease with SNCA Triplication iPSC-Derived Cortical Organoids and Identifying Therapeutic Drugs

This repository contains the source code for the single-cell and single-nuclei RNA sequencing data analysis for the study Modeling Lewy Body Disease with SNCA Triplication iPSC-Derived Cortical Organoids and Identifying Therapeutic Drugs by Yunjung Jin et al.

The Impact of Structural Modification on Electrochromic and Electroluminescent Properties of D-A-D Benzothiadiazole Derivatives with a Fluorene Linker and (Bi)Thiophene Units

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