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  • Resveratrol is a natural product associated with wide-ranging effects in animal and cellular models including lifespan extension. To identify the genetic target of resveratrol in human cells, we conducted genome-wide CRISPR-Cas9 screens to pinpoint genes that confer sensitivity or resistance to resveratrol. An extensive network of DNA damage response and replicative stress genes exhibited genetic interactions with resveratrol and its analog pterostilbene. These genetic profiles showed similarity to the response to hydroxyurea, an inhibitor of ribonucleotide reductase that causes replicative stress. Resveratrol, pterostilbene and hydroxyurea caused similar depletion of nucleotide pools, inhibition of replication fork progression and induction of replicative stress. The ability of resveratrol to inhibit cell proliferation and S phase transit was independent of the histone deacetylase Sirtuin 1, which has been implicated in lifespan extension by resveratrol. These results establish that a primary impact of resveratrol on human cell proliferation is the induction of low-level replicative stress.
    Data Types:
    • Sequencing Data
    • Image
    • Video
    • Tabular Data
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  • Title of related paper: SHORT TERM FORECASTING THE ELECTRICAL CONSUMPTION BY USING NEURAL NETWORK: JOINT APPROXIMATE DIAGONAL EIGENVALUE The data is taken from incoming data from utility provider in Malaysia Suggested citation: Please reference the associated publication above when using this datasets/or any papers below: [1] M. Mohd Hussain, Z. H. Zakaria, and S. Serwan, “Voltage estimation using ICA on distribution system,” 2013, doi: 10.1109/PEOCO.2013.6564555. [2] M. M. Hussain, Z. H. Zakaria, and S. Serwan, “FastICA techniques for load profiles estimation,” in ISIEA 2012 - 2012 IEEE Symposium on Industrial Electronics and Applications, 2012, pp. 161–166, doi: 10.1109/ISIEA.2012.6496620. [3] M. M. Hussain et al., “Prediction of time series based on load profile using JADE technique,” 2017 IEEE 8th Control Syst. Grad. Res. Colloquium, ICSGRC 2017 - Proc., no. August, pp. 33–36, 2017, doi: 10.1109/ICSGRC.2017.8070563. [4] M. M. Hussain, “POWER LOSS ESTIMATION DUE TO DIFFERENCE TRANSFORMER TAP OWER LOSS ESTIMATION DUE TO DIFFERENCE TRANSFORMER TAP CHANGER POSITION AT INTERFACE,” J. Fundam. Appl. Sci., vol. 4, no. 1, pp. 685–696, 2017. [5] M. M. Hussain, S. Serwan, and Z. H. Zakaria, “Nodal Load Profiles Estimation for Time Series Load Flow Using Independent Component Analysis,” 2012, pp. 1050–1055. Contact information: Mashitah Mohd Hussain, Universiti Teknologi MARA, Malaysia, mashitah1116@gmail.com or mashitah1116@uitm.edu.mhy
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  • Data result from careful measurements with hot wires in an aerodynamic channel and from flow visualization in a hydraulic channel to study the wake behavior after pairs of finite cylinders with 25 mm diameter and pitch-to-diameter ratio p/d = 1.26, at several angular positions to the flow, 0o (side-by-side), 2.5o, 5o, 15o, 25o, 35o, 45o, 75o and 90o (tandem). The aerodynamic channel was made of acrylic, with a cross section of 0.147 m x 0.193 m. The flow velocity was 10 m/s, turbulence intensity of about 1%, Reynolds number 1.66x104. The two circular cylinders were made of PVC with a diameter of 25.1 mm, and a blockage ratio of 13% - 26%, depending on the angular position of the cylinders. Wake flow velocity was measured with a DANTEC StreamLine CTA system with two DANTEC 55P11 probes. Data acquisition was performed by a USB 16-bit NI USB-9162 A/D board, sampling frequency 1000 Hz, low pass filter 300 Hz, 131,072 samples, time series 131.072 s long. The cylinders and the probes were numbered as 1 and 2. Cylinder number one is on the left side of the flow stream. At positions different of 0o, cylinder number 2 faced the flow firstly. Flow visualizations were made in a closed-circuit hydraulic channel with the same dimensions as the aerodynamic channel. The Reynolds number was 1.97x10³ (kinematic viscosity of the water at 15 ºC was 1.14 x 106 m²/s), turbulence intensity about 4%. Two ink tanks provided dye, injected by gravity in the flow through 3 mm diameter flexible PVC tubes inside the cylinders. Video resolution was 1080 x 720 pixels at 30 FPS. Each movie was 25 minutes long. A mirror at 45° with the camera allowed simultaneous top and side view. From top view images, a time series of the angle formed between the tangent to the cylinder wall and the deflected flow from the gap between the cylinders was generated with Tracker and Matlab software. Angles were measured at each 200 frames (6.67 s), sampling frequency of 0.15 Hz. Data are in .txt format to be read by almost all software. Excerpts from the movies from the flow visualizations are in .mkv format. Velocity series files: Velocity signals from the hot wire anemometers (m/s) – Time (s); velocity Probe 1; velocity Probe 2 06 Vel 0 deg.txt: Side-by-side cylinders 0o. 11 Vel 2.5 deg.txt: 2.5o. 12 Vel 5 deg.txt: 5o. 13a Vel 15 deg.txt: 15o. 13b Vel 25 deg.txt: 25o. 13c Vel 35 deg.txt: 35o. 15a Vel 45 deg.txt: 45o. 15b Vel 60 deg.txt: 60o. 15c Vel 75 deg.txt: 75o. 15d Vel 90 deg.txt: Tandem 90o. Angular deflection file: 16 Flow deflection angle.txt: Deflection angles from flow visualizations respectively for 0° (side-by-side cylinders), 2.5°, 5°, 15°, 25°, 35°, 45°, 60°, 75° and 90° (tandem). Flow visualization files: Top and side view of flow visualization. Cylinder position is inverted on top view by a mirror. 0deg.mkv: 0° (side-by-side). 2.5deg.mkv: 2.5o. 5deg.mkv: 5o. 15deg.mkv 15o. 25deg.mkv: 25o. 35deg.mkv: 35o. 45deg.mkv: 45o. 60deg.mkv: 60o. 75deg.mkv: 75o. 90deg.mkv: 90o (tandem).
    Data Types:
    • Video
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  • The Mars General Circulation Model MarsWRF has been run with fully interactive dust lifting parametrization, assuming an inexhaustible surface dust reservoir. Changing the horizontal resolution from 5°×5° to 2°×2° produced different dust cycle variability and dust source regions. That is because the higher spatial resolution allows for a better representation of the surface topography and other surface properties. Global Dust Storm Events are likely to occur if regional dust storm activity at the northern Hellas Basin connects with that south of Chryse Planitia.
    Data Types:
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  • data used for our stdudy
    Data Types:
    • Tabular Data
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    • Document
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  • It is a dataset for ten algae species (Chlamydomonas, Cladophora, Nostoc, Oedogonium, Oscillatoria, Pithophora, Spirogyra, Ulothrix, Vaucheria, and Volvox). It has been created for building classification model for automatic identification. Following are the features along with their descriptions which are extracted from the microscopic images of the algae. 1. Solidity: It is the ratio of area of an object to the area of a convex hull of the object. Computed as Area/ConvexArea. 2. Eccentricity: The eccentricity is the ratio of length of major to minor axis of an object. 3. EquivDiameter: Diameter of a circle with the same area as the region. 4. Extrema: Extrema points in the region. The format of the vector is [top-left top-right right-top right-bottom bottom-right bottom-left left-bottom left-top]. 5. Filled Area: Number of on pixels in FilledImage, returned as a scalar. 6. Extent: Ratio of the pixel area of a region with respect to the bounding box area of an object. 7. Orientation: The overall direction of the shape. The value ranges from -90 degrees to 90 degrees. 8. Euler number: Number of objects in the region minus the number of holes in those objects. 9. Bounding box: Position and size of the smallest box (rectangle) which bounds the object. 10. Convex hull: Smallest convex shape/polygon that contains the object. 11. Major axis: The major axis is the endpoints of the longest line that can be drawn through the object. Length (in pixels) of the major axis is the largest dimension of the object. 12. Minor axis: The axis perpendicular to the major axis is called the minor axis. Length (in pixels) of the minor axis is the smallest line connecting a pair of points on the contour. 13. Perimeter: Number of pixels around the border of the region. 14. Centroid: Centre of mass of the region. It is a measure of object’s location in the image. 15. Area: Total number of pixels in a region/shape.
    Data Types:
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    • Document
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  • With this dataset, we investigated the ability of 4-year-old children and adults to discriminate between relevant and different types of irrelevant actions (pseudo-instrumental actions, noncontact actions, see Schleihauf & Hoehl, 2020). We applied a visual paired preference eye-tracking paradigm in a within-subject design. In each trial, we presented two video clips simultaneously showing actions of different action types. These actions were either functionally irrelevant or relevant for reaching a prior specified goal, i.e. extracting a golden marble from a puzzle box. Participants saw either (a) two nonidentical actions of the same action type (two irrelevant noncontact actions, two irrelevant pseudo-instrumental actions, or two relevant actions), (b) a relevant action paired with an irrelevant pseudo-instrumental action, (c) a relevant action paired with an irrelevant noncontact irrelevant action, or (d) an irrelevant pseudo-instrumental action paired with an irrelevant noncontact irrelevant action. Participants were instructed that after watching the videos they would be asked to extract a marble themselves. Therefore, we expected participants to direct their attention to the action which seemed most likely to be task-relevant. We measured their looking times to both simultaneously demonstrated actions, which allowed us to calculate a preference score that indicated whether they looked longer at one or the other action (for more details see the word document "Variable description"). We hypothesized that looking preferences are stronger when it is easy for participants to identify the task-relevant action, whereas we thought looking preferences are lower when it is harder for participants to identify the relevant action (for more details see the word document "Hypotheses").
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  • This is a corpus of 40k (40,001) open access (OA) CC-BY articles from across Elsevier’s journals represent the first cross-discipline research of data at this scale to support NLP and ML research. This dataset was released to support the development of ML and NLP models targeting science articles from across all research domains. While the release builds on other datasets designed for specific domains and tasks, it will allow for similar datasets to be derived or for the development of models which can be applied and tested across domains.
    Data Types:
    • Tabular Data
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  • This work introduces a newly developed reaction mechanism for the oxidation of ammonia in freely propagating and burner-stabilized premixed flames as well as shock-tube, jet-stirred reactor, and plug-flow reactor experiments. The paper mainly focuses on pure ammonia and ammonia–hydrogen fuel blends. The reaction mechanism also considers the formation of nitrogen oxides as well as the reduction of nitrogen oxides depending upon the conditions of the surrounding gas phase. Doping of the fuel blend with NO2 can result in acceleration of H2 autoignition via the reaction NO2 + HO2 ⇋ HONO + O2, followed by the thermal decomposition of HONO, or deceleration of H2 oxidation via NO2 + OH ⇋ NO + HO2. The concentration of HO2 is decisive for the active reaction pathway. The formation of NO in burner-stabilized premixed flames is shown to demonstrate the capability of the mechanism to be integrated into a mechanism for hydrocarbon oxidation. This mechanism is the supplementary material to the work: Detailed Kinetic Mechanism for the Oxidation of Ammonia Including the Formation and Reduction of Nitrogen Oxides. https://pubs.acs.org/doi/10.1021/acs.energyfuels.8b01056 https://doi.org/10.1021/acs.energyfuels.8b01056 The above (link) mentioned article should be properly cited when used for research work and publication.
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  • 1) This file contains the dataset for each main figure in "Processing of motion-boundary orientation in macaque V2". 2) Each file to each figure contains all the raw data(.mat file or sheet file mostly) and code that can be used to reproduce the results. The code is based on matlab-2018b.
    Data Types:
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    • File Set