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30,948 results for “Profile”
Open access publishers: the DOAJ Seal profile
<p>The DOAJ database was created in 2003 and includes almost 14,000 peer-reviewed open access journals covering all knowledge areas, published in 130 countries. There is a selective process to be followed to assure the quality of the titles. DOAJ is maintained by Infrastructure Services for Open Access (IS4OA) and its funding is derived from donations (40% from publishers and 60% from the public sector). DOAJ introduced a quality distinction, called the DOAJ Seal, to identify the most prominent journals. There are 1354 journals (around 10% of the total) that have been awarded the Seal.</p> <p>The search strategy involved using the Seal option, then ranking the journals to identify the biggest publishers, the number of journals and the number of articles. We have extracted the following indicators from DOAJ: publisher, title, ISSN, country, number of articles, knowledge area (according to the DOAJ classification), value of article processing charges in USD, time for publication in weeks, and year of indexing in DOAJ.</p>
Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></p>
Stellar Density Profiles of Dwarf Spheroidal Galaxies - Posterior Distributions
<p>This archive contains samples from the posterior distributions of our 3-plummer, 1-plummer, 3-steeper and 1-steeper fits for 40 dwarf spheroidal galaxies as described in "Stellar Density Profiles of Dwarf Spheroidal Galaxies" (<a href="https://arxiv.org/abs/1910.10134">https://arxiv.org/abs/1910.10134</a>)</p>
Raw Fast5 data for "Microbiota profiling with long amplicons using Nanopore sequencing: full-length 16S rRNA gene and the 16S-ITS-23S of the rrn operon" - PART I
<p>Raw Fast5 data for "Microbiota profiling with long amplicons using Nanopore sequencing: full-length 16S rRNA gene and the 16S-ITS-23S of the rrn operon". See Supplementary Table 2 for associating each sample to its barcode.</p> <p>- FC1_1 includes data for the HM mock community from BEI resources and skin microbiome of the chin in dogs.</p> <p>- FC1_2 includes data for the dorsal skin samples</p> <p>- FC2 includes data for the Zymobiomics mock community and Staphylococcus pseudintermedius isolate</p> <p> </p> <p> </p>
Particulate organic carbon concentration in seawater profiles collected on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Seawater samples were collected from a Conductivity, Temperature, Depth (CTD) rosette, which was deployed from the R/V Akademik Tryoshnikov. The samples were collected at a number of locations in the Southern Ocean as part of the Antarctic Circumnavigation Expedition (ACE) during the expedition that took place in the Austral summer of 2016/2017. Particulate organic matter (POC) is inclusive of all living and detrital material. It is the most common form of organic carbon matter that results from photosynthesis by phytoplankton. Seawater samples were filtered during the expedition and analysed post-cruise to provide this dataset of POC concentration profiles.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_ctd_particulate_organic_carbon_concentration.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This particulate organic carbon concentration in seawater dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/</p>
RNASeq profiling of Foxg1-GOF neocortical neurons.v2
<p>The dataset uploaded in this entry includes data referred to by the manuscript "<em>Foxg1</em> upregulation enhances neocortical projection neuron activity", by Wendalina Tigani, Moira Pinzan Rossi et al. (submitted).</p> <p> </p>
Ammonium concentrations in surface seawater profiles in the Southern Ocean collected on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Seawater samples were collected from the ship's underway system or from the surface niskin bottle from the CTD rosette. Some incidental samples were collected from the surface using a bucket from the side of the ship or a zodiac. The samples were collected every 6 hours from the underway and from every CTD cast as part of the Antarctic Circumnavigation Expedition (ACE) which took place in the Austral summer of 2016/2017.</p> <p>Ammonium is representative of the recycled nitrogen pool in the upper mixed layer where inputs include marine production and terrestrial runoff. Seawater samples were collected and frozen during the expedition and analysed post-cruise and quality controlled to produce this dataset, which presents ammonium concentration in surface seawater from the Southern Ocean.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_ammonium_concentration_surface_seawater.csv file, data file, comma-separated values</li> <li>ace_ammonium_concentration_surface_seawater_chnage_log.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - Added detection limit to README.txt. Data points formatted to 2 decimal places.</p> <p><strong>v1.0</strong> - Initial release of dataset</p>
Beaks of Birds & Squids: Profile Traces of 260 Specimens
<p>Two-dimensional coordinates for lines traced onto images of beaks.</p> <ul> <li>This is a .zip archive xy coordinates (250 files, .txt); and a list of specimen names (1 file, .csv).</li> <li>All images traced in FIJI.</li> <li>For each specimen, there is a trace of the beak rostrum and a separate trace of the beak bite surface.</li> <li>Each trace file should be a list of xy coordinates that ends at the beak tip. This must be checked/verified/corrected for all files before running any analyses! I recommend visual inspection by plotting each beak dataset as a scatterplot in a color spectrum (rainbow, etc.).</li> <li>These were traced over pixel images, so each file has a different number of xy coordinates (depending on the pixel resolution/image size that was traced).</li> <li>All bird specimen images were downloaded from Phenome10k.org</li> <li>All cephalopod specimens were traced from images published in: <ul> <li>Xavier, J. C. & Cherel, Y. 2009 Cephalopod beak guide for the Southern Ocean. British Antarctic Survey.</li> </ul> </li> </ul>
Carbon-Phosphorous Lyase Enzyme Activity Profiles in the North Pacific Subtropical Gyre
<p>These datasets contain oceanic enzyme activity measurements of Carbon-Phosphorus (C-P) lyase, a catalytic enzyme pathway, which cleaves C-P bonds, allowing microbial utilization of phosphonates as a phosphorous source. These activity measurements were made in the North Pacific Subtropical Gyre. Samples were collected in the top 1000 m of the water column on KM1717, KM1821, and KM2001 cruises and in the top 225 m of the water column on FK180310-2.</p>
PIPD2020 - Pinterest Interests Profiling Dataset 2020
<p>This dataset contains features extracted with EfficientNet-B3 from over 60000 images classified in 15 interests categories 4 of which are further analyzed in subcategories, features from the images of 12 users with hand-labeled Pinboards, the text extracted from the previous images and over 200000 features from the images of 422 Pinterest users along with their follower-followee relationships.</p>
FCR second test Testing protocol frequency or power profile
<p>Data files for performing FCR second test Testing protocol as described in annex B of QualyGridS publication "Finalized testing protocol" DOI: <a href="https://doi.org/10.5281/zenodo.3912063">https://doi.org/10.5281/zenodo.3912063</a>. Based on real grid Frequency profile from Fingrid. Files FCR_Secondtest_Frequency_... contain the grid frequency input for the test with each line a frequency value in 0.1 second steps, total duration 4 hours.</p> <p>File FCR_secondtest:Power… contain the power vs. time profile derived from the grid frequency profile for a range of 0-100% with 0% being the lowest power to be used in the test and 100% the highest power. The file contain the power percentage with each line a percentage value in 0.1 second steps, total duration 4 hours.</p> <p>Further explanations in QualyGridS publication "Finalized testing protocol" <a href="https://doi.org/10.5281/zenodo.3912063">https://doi.org/10.5281/zenodo.3912063</a></p>
Vertical profiles of Doppler spectra of hydrometeors from a Micro Rain Radar recorded during the austral summer of 2016/2017 in the Southern Ocean on the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This dataset includes vertical profiles of the Doppler spectra and derived quantities of hydrometeors using a Micro Rain Radar (MRR-2) during the Antarctic Circumnavigation Expedition in 2016/2017. The MRR is a frequency-modulated-continuous-wave (FMCW) Doppler radar working at 24 GHz (K band). It measures the Doppler spectrum at vertical incidence with 31 range gates of 100 m. The data are processed to calculate rainfall rates and standard radar moments. For rain events, the drop size distribution and the rainfall rate are derived using the method by Peters et al. (2005). For snow events, the moments are computed from the Doppler spectrum as described in Maahn & Kollias (2012).</p> <p><strong>Dataset contents</strong></p> <ul> <li>RawSpectra/${year}${month}/MRR_ACE_${year}${month}${day}_RAW.nc: raw data of Doppler spectra for rainfall events. Null values are denoted as -99900.</li> <li>ProcessedData/${year}${month}/MRR_ACE_${year}${month}${day}_PRO.nc: 10-second averages (highest resolution) for specific rain events, using the standard products from Metek (MRR physical basics, 2009).</li> <li>AveData/${year}${month}/MRR_ACE_${year}${month}${day}_AVE.nc: one-minute averages, using the standard products from Metek (MRR physical basics, 2009).</li> <li>Processed_IMProToo/${year}${month}/mrr_improtoo_0.101_ACE_${year}${month}${day}.nc: processed data for snow events using IMProToo (Maahn & Kollias 2012) in one-minute averages.</li> <li>RR_timeline/${year}${month}/RR_MRR_ACE_${year}${month}${day}.csv: 10-minute running mean with one-minute resolution of rain rate for [100-200]m and [200-300]m range gate. Null values are denoted as ‘nan’.</li> <li>RR_timeline/${year}${month}/RR_MRR_ACE_${year}${month}${day}.png: daily plots of rain rates.</li> <li>PrecipitationEvents.txt: text file classifying the precipitation events according to hydrometeors (rain, snow or hail).</li> <li>Graphs_Metek/${year}${month}/moments_MRR_ACE_${year}${month}${day}_AVEnc.png: daily plots of Radar reflectivity and Doppler velocity for rain events.</li> <li>Graphs_IMProToo/${year}${month}/moments_mrr_improtoo_0101_ACE_${year}${month}${day}.png: daily plots of Radar reflectivity and Doppler velocity for snow events.</li> <li>RawSpectra_data_file_header.txt, metadata, text format</li> <li>Metek_ProcessedData_data_file_header.txt, metadata, text format</li> <li>IMProToo_ProcessedData_data_file_header.txt, metadata, text format</li> <li>AveData_data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>change_log.txt, metadata, text format</li> </ul> <p><strong>Change log</strong></p> <ul> <li>v1.2 - Amended abstract text. Changed descriptions of files in dataset contents. Added dataset license to README. Updated README and change_log files.</li> <li>v1.1 - Added missing PrecipitationEvents.txt file. Added change_log.txt file.</li> <li>v1.0 - Initial version of dataset.</li> </ul> <p><strong>Dataset license</strong></p> <p>This vertical Doppler spectra profile dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Refraction Microtremor Vs(z) Profile Archive
<p>The PDF is a representation of a public website available at <a href="https://sites.google.com/view/vs-profile-archive">https://sites.google.com/view/vs-profile-archive</a></p> <p>The time-averaged seismic shear-wave velocity from the surface to 30 m (100 ft) depth, defined in the Building Code as Vs30, is in the United States one of the principal determinants of earthquake site-hazard classification. Over the past 20 years the Nevada Seismological Lab and the Applied Geophysics class at the University of Nevada, Reno; and Optim Earth have made shallow (<1 km deep) shear-wave velocity measurements at hundreds of sites in Nevada, California, and New Zealand using refraction microtremor technology. Many of these measurements were made at stations in regional earthquake-monitoring networks, and sponsored by the US Geological Survey. The <a href="https://drive.google.com/open?id=15VSI4vhzPfy_GVoB_XYKsWfrhPM9u5E9">Google Drive link</a> leads to a directory structure grouping the measurements by region, and the files are often named with the monitoring network station name. Each file is a self-explanatory, plain-text list of the data and results from the measurement. Where multiple files are given for a particular site, measurements were made at slightly different refraction microtremor array locations, at different times, and by different interpreters; thus expressing both the aleatory variation of velocity in the ground and the epistemic variability of the measurement technique (+/- 15% according to <a href="https://drive.google.com/file/d/1hM3Ows2z2a_xzwriodQT-aKP-UlPubuB/view?usp=sharing">Louie, 2001</a>). Each measurement file includes refraction microtremor array location data, a summary Vs30 value, and a modeled shear-wave-velocity-versus-depth profile. Efforts are underway to add the picked refraction microtremor p-f image and the picked fundamental-mode Rayleigh-wave dispersion-curve data to each file. Many of these measurements have been published in peer-reviewed journal papers and project reports (available in the Preprint Archive from <a href="https://www.google.com/url?q=https%3A%2F%2Flouie.pub&sa=D&sntz=1&usg=AFQjCNFFK9_R0AxlZlKRpWgeFFhzxMON0A">Louie.pub</a>). As well, these archives give additional details on refraction microtremor measurements found in the US Geological Survey's Vs30 archive at <a href="https://www.google.com/url?q=https%3A%2F%2Fearthquake.usgs.gov%2Fdata%2Fvs30%2Fus%2F&sa=D&sntz=1&usg=AFQjCNEOXhj6fRvlr7ftsB-yN_DaDGcE8Q">https://earthquake.usgs.gov/data/vs30/us/</a> . All data in this archive are in the public domain, distributed under a <a href="https://www.google.com/url?q=https%3A%2F%2Fcreativecommons.org%2Flicenses%2Fby%2F4.0%2F&sa=D&sntz=1&usg=AFQjCNHR4439nM4_Ar7-II5_R_iZxNxJdg">Creative Commons CC BY</a> license.</p>
Output of Tax4Fun of predicted functional profiles. KO IDs and a KO abundance in every samples after a process of normalization.
<p>Supplementary Material Chapter 1.</p> <p> </p> <p>Table # 3: Output of Tax4Fun of predicted functional profiles. KO IDs and a KO abundance in every samples after a process of normalization.</p>
Power profiles of loads, DERs and MGs' internal units
<p>Dataset of power profiles used in the Simulation section of the paper </p> <blockquote> <p>Bonassi, F., La Bella, A., Lazzari, R., Sandroni, C., & Scattolini, R. (2021). Supervised control of hybrid AC-DC grids for power balance restoration. <em>Electric Power Systems Research</em>, <em>196</em>, 107107.</p> </blockquote> <p><br> Please cite the paper if you use this data.</p> <p><strong>The dataset is made of the following files:</strong></p> <ul> <li>'cluster_references.xls': Table of the reference power programs of the four clusters. <ul> <li>Column 'Cluster_i_ref': Power program of the i-th cluster [kW]<br> </li> </ul> </li> <li>'converter_schedules.xls': Table of power converters' scheduled power profiles. <ul> <li>Column 'Converter_i_P': Active power program of the i-th power converter [kW]</li> <li>Column 'Converter_i_Q': Reactive power program of the i-th power converter [kVAR]<br> </li> </ul> </li> <li>'load_actual.xls': Table of the actual power profiles of the loads connected to the grid. <ul> <li>Column 'Load_i_P': Active power absorbed by the i-th load [kW]</li> <li>Column 'Load_i_Q': Reactive power absorbed by the i-th load [kVAR]<br> </li> </ul> </li> <li>'load_forecasts.xls': Table of the power profiles' forecasts of the loads connected to the grid. <ul> <li>Column 'Load_i_P': Active power forecast of the i-th load [kW]</li> <li>Column 'Load_i_Q': Reactive power forecast of the i-th load [kVAR]<br> </li> </ul> </li> <li>'MG_schedules.xls': Table of MGs' schedules for dispatchable units. <ul> <li>Column 'MG_i_Gen_P': Active power schedule for the dispatchable generator of the i-th MG [kW] </li> <li>Column 'MG_i_Gen_Q': Reactive power schedule for the dispatchable generator of the i-th MG [kVAR]</li> <li>Column 'MG_i_Batt_P': Active power schedule for the battery of MG_i [kW] </li> <li>Column 'MG_i_Batt_Q': Reactive power schedule for the battery of MG_i [kVAR]<br> </li> </ul> </li> <li>'pv_actual.xls': Table of the actual power profiles of the PV panels. <ul> <li>Column 'MG_i_PV_P': Active power injected by the PV panel of MG_i [kW] <br> </li> </ul> </li> <li>'pv_forecasts.xls': Table of the power profiles' forecasts of the PV panels. <ul> <li>Column 'MG_i_PV_P': Active power forecast of the PV panel of MG_i [kW]<br> </li> </ul> </li> </ul> <ul> <li>'units_network_data.xls': Table of units' and network data. <ul> <li>Sheet 'AC nodes': Data concerning the AC buses.</li> <li>Sheet 'AC branches': Data concerning the AC branches connecting the AC nodes.</li> <li>Sheet 'AC-DC interfaces': Data of the AC-DC interfacing power converters.</li> <li>Sheet 'DC links': Data concerning the DC links.</li> <li>Sheet 'MGs Generators': Nominal data of MGs' internal generators.</li> <li>Sheet 'MGs Batteries': Nominal data of MGs' internal batteries.</li> </ul> </li> </ul>
Data from: Whole-organism behavioral profiling reveals a role for dopamine in state-dependent motor program coupling in C. elegans.
<p>Animal behaviors are commonly organized into long-lasting states that coordinately impact the generation of diverse motor outputs such as feeding, locomotion, and grooming. However, the neural mechanisms that coordinate these diverse motor programs remain poorly understood. Here, we examine how the distinct motor programs of the nematode <i>C. elegans </i>are coupled together across behavioral states. We describe a new imaging platform that permits automated, simultaneous quantification of each of the main <i>C. elegans</i> motor programs over hours or days. Analysis of these whole-organism behavioral profiles shows that the motor programs coordinately change as animals switch behavioral states. Utilizing genetics, optogenetics, and calcium imaging, we identify a new role for dopamine in coupling locomotion and egg-laying together across states. These results provide new insights into how the diverse motor programs throughout an organism are coordinated and suggest that neuromodulators like dopamine can couple motor circuits together in a state-dependent manner. </p>
Surveyed terrace profiles and terrace radiocarbon ages, western Jumgal basin, Tien Shan, Kyrgyz Republic
<p>This archive contains survey data and radiocarbon ages from deformed terraces in the western Jumgal basin, Kyrgyz Republic. These data support analysis by Coddington and Burgette (2020) to be published in <em>Tectonics</em>: “Fold kinematics and fault slip rates from progressively deformed terraces: Implications for structural evolution of basins in the Kyrgyz Tien Shan” </p>
The Ubiquitin Ligase WWP1 Contributes to Shifts in Matrix Proteolytic Profiles and a Myocardial Aging Phenotype with Diastolic Heart Failure
<p><strong><em>Aims</em></strong>. Ubiquitylation is a key event that regulates protein turnover, and induction of the ubiquitin ligase E3 WWP1 has been associated with age. Left ventricular hypertrophy (LVH) commonly occurs as a function of age and can cause heart failure with a preserved ejection fraction (EF; HFpEF). We hypothesized that overexpression (O/E) of WWP1 in the heart would cause LVH as well as functional and structural changes consistent with the aging HFpEF phenotype.</p> <p><strong><em>Methods and Results.</em></strong> Global WWP1 O/E was achieved in mice (n=11) and echocardiography (40 MHz) performed to measure LV mass, EF, Doppler velocities (early-E, late/atrial-A), myocardial relaxation (E’), and isovolumetric relaxation time (IVRT) at 4, 6, and 8 weeks. Age matched wild type animals (n=15) were included as referent controls. LV EF was identical (60+1% vs 60+1%, p>0.90) with no difference in LV mass (67+3 vs 75+5, p>0.25) at 4 weeks. However, at 8 weeks of age, LV mass increased by over two-fold, E/A fell (impaired passive filling), and E/E’ was lower and IVRT prolonged (impaired LV relaxation) - all p<0.05. Collagen percent area increased by over two-fold and fibrillar collagen expression (rtPCR) by over 1.5 fold (p<0.05) with WWP1 O/E. WWP1 with an anti-WWP1 antibody could be identified in isolated cardiac fibroblasts with WWP1 increased by over two-fold in O/E fibroblasts (p<0.05).</p> <p><strong><em>Conclusion.</em></strong> Inducing WWP1 expression caused LVH, preserved systolic function, but impaired diastolic dysfunction, consistent with the HFpEF phenotype. Targeting the WWP1 pathway may be a novel therapeutic target for this intractable form of HF associated with aging.</p>
Supplementary data (simulated metagenome set 2) to accompany "phyloFlash – Rapid SSU rRNA profiling and targeted assembly from metagenomes"
<p>Comparison of SSU rRNA read extraction and targeted assembly from simulated shotgun metagenome of closely related Bacteorides strains.</p> <p>The phyloFlash software is available from https://github.com/HRGV/phyloFlash. Examples were generated with phyloFlash v3.3b.</p>
Epigenetic Profiling of Social Communication Trajectories and Co-occurring Mental Health Problems: A Prospective, Methylome-wide Association Study
<p>While previous studies suggest that both genetic and environmental factors play an important role in the development of autism-related traits, little is known about potential biological mechanisms underlying these associations. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), we examined prospective associations between DNA methylation (DNAm: N-birth=804, N-age7=877) and trajectories of social communication deficits (8-17 years). Methylomic variation at three loci across the genome (false discovery rate=0.048) differentiated children following high (n=80) versus low (n=724) trajectories of social communication deficits. This differential DNAm was specific to the neonatal period and not observed at age 7. Associations between DNAm and trajectory membership remained robust after controlling for co-occurring mental health problems (i.e., hyperactivity/inattention, conduct problems). The three loci identified at birth were not replicated in the Generation R Study. However, to the best of our knowledge, ALSPAC is the only study to date that is prospective enough to examine DNAm in relation to longitudinal trajectories of social communication deficits from late childhood to late adolescence. Although the present findings might point to potentially novel sites that differentiate between a high versus low trajectory of social communication deficits, the results should be considered tentative until further replicated.</p> <p>This dataset contains summary statistics for the methylome-wide association study using DNAm data collected from individuals at birth.</p> <p>Upload of this dataset was completed by The EWAS Catalog team. The data can be queried along with hundreds of other EWAS at ewascatalog.org. To upload your EWAS summary statistics and have a zenodo DOI generated for you go to ewascatalog.org/upload</p>
ScienceDex guides
Understand access before you commit
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.