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7,515 results for “screenings”
Figure 3. H 2O2 in Phytochemical screening and evaluation of antioxidant, total phenolic and flavonoid contents in various weed plants associated with wheat crops
Figure 3. H 2O2 Scavenging assay for Convolvulus arvensis, Chenopodium murale, Avena fatua, Phalaris minor extracts in different solvents.
Fig. 5. The Mantel test compares a in Application of allozyme markers for screening of turbot populations along Western Black Sea coast
Fig. 5. The Mantel test compares a genetic distance (Y matrix) with a geographical distance (X matrix) in kilometers to test correlation between genetics and geographical location.
Fig. 3 in Application of allozyme markers for screening of turbot populations along Western Black Sea coast
Fig. 3. Enzymograms of general unspecified esterases (EST) from muscle tissue of turbot (1) and haemoglobin (2), EST-2* and EST-3* - polymorphic loci, 0 – origin.
Fig. 2. A in Application of allozyme markers for screening of turbot populations along Western Black Sea coast
Fig. 2. A. Electrophoregrams on PROT from turbot (Bulgarian and Romanian coast), using different tissues:1-3 – haemoglobins, 4-5- eye (retina) and 6 – muscle, 0 – origin. B. Electrophoregrams on PROT from turbot Bulgarian and Rumanian coast haemoglobin tissue. PROT-1* and PROT-2* were polymorphic, 0 – origin.
Fig. 1 in Screening of essential oil antifeedants in the elm pest Ambrostoma quadriimpressum (Coleoptera: Chrysomelidae)
Fig. 1. Arena design for use in (A) Exp. 2 (screening for behaviorally active odorants) and (B & C) for Exp. 4 (choice test for beetle foraging).
Fig. 4 in Screening of essential oil antifeedants in the elm pest Ambrostoma quadriimpressum (Coleoptera: Chrysomelidae)
Fig. 4. (A & B) Response of female and male Ambrostoma quadriimpressum beetles to 3 concentrations of odorant 8 in the Y-tube olfactometer (A: female, B: male, n = 30). (C) The results of the choice foraging test (n = 10). * indicates significant difference by χ2-analysis (Asymp. Sig. <0.05).
Fig. 3 in Screening of essential oil antifeedants in the elm pest Ambrostoma quadriimpressum (Coleoptera: Chrysomelidae)
Fig. 3. Dose-response curves of stimuli. The x-axis represents stimulus concentration and the y-axis represents EAG response relative values. (A) Female doseresponse to odorant 11. (B) Female dose-response to odorant 12. (C) Male dose-response to odorant 6. (D) Male dose-response to odorant 5. (E) Male and female dose-responses to odorant 8.
Figure 4. (a) Therapy player software screen, where a) is the stimuli time, b) is the total therapy time, c) is the file path, d) displays the numeric values of each sequence of the therapy, e) shows the current value, and f) shows the current lag angle for zenith and azimuth values; (b) USB mechanism for conversion, where a) USB-UART converter, and b) USB-Zigbee converter.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>Where tt time expended by the servomotors to point the laser to a given position and execute<br> a laser beam sequence; tspin is the time that a servomotor needs to spin one degree; ttol is a given the<br> tolerance time; θservo is the addition of degrees that both servos in a laser driver need to spin point<br> the laser in a given position; tstimuli is the time expended in execute a laser beam, between 250 and<br> 605 ms (Weiskrantz et al., 1991); T is the total time of all repetitions in a therapy, suggested<br> between 20 and 60 minutes and N is the number of repetitions in a therapy.</p>
Figure 3. The 'Welcome Screen' of our implementation-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>Figure 3 shows an avatar ready to start the game. The user has the option to click the button called ‘Go Cardinals! Start’ Button. Once that happens, the avatar gets to choose going to one of the buildings of interest mentioned above.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 4. Screen shot of the C# Windows form app
<p>The screen shot of the C# Windows form app is shown in figure 4. The text field on the left side includes numbers from 2 to 7, which are commands to control the states of relays. </p>
Optimal modes for wavefront sensorless adaptive optics. Turbulence- and oocyte-induced phase screens and Mathematica notebooks.
<p>This notebook and phase screens constitute a numerical experiment to calculate the error of the wavefront approximation using first N modes of the a) Zernike and b) Lukosz-Braat polynomials, c) SVD modes obtained with respect to the gradient-dot product, and the d) eigenfunctions of the Laplace operator with the Neumann boundary conditions. It's a complementary material to a paper submitted to Optics Express.</p>
Compound profiling matrices extracted from screening data
<p>Compound profiling matrices record assay results for compound libraries tested against panels of targets. In addition to their relevance for exploring structure-activity relationships, such matrices are of considerable interest for chemoinformatic and chemogenomic applications. For example, profiling matrices provide a valuable data resource for the development and evaluation of machine learning approaches for multi-task activity prediction. However, experimental compound profiling matrices are rare in the public domain. Although they are generated in pharmaceutical settings, they are typically not disclosed. Herein, we present an algorithm for the generation of large profiling matrices, for example, containing more than 100,000 compounds exhaustively tested against 50 to 100 targets. The new methodology is a variant of bi-clustering algorithms originally introduced for large-scale analysis of genomics data. Our approach is applied here to assays from the PubChem BioAssay database and generates profiling matrices of increasing assay or compound coverage by iterative removal of entities that limit coverage. Weight settings control final matrix size by preferentially retaining assays or compounds. In addition, the methodology can also be applied to generate matrices enriched with active entries representing above-average assay hit rates.</p>
Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 3. Screen showing the agent finding the gold.
<p>The novelty of a map, that is, an 8x8 board added to the world, is introduced in the MCWW design, as opposed to the CWW version. This map is responsible for showing on the screen the agent's knowledge base or the inferences she makes as she moves and receives the different perceptions on the board. In this way, it is easier for the experimental subjects to predict the possible movements of the agent. As an example, observe a sequence of screens of the board of our version MCWW in which it is seen that the agent kills the Wumpus (the goal of Intention 2). See the figures 1-3.</p>
Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 2. Screen showing the agent hunting the Wumpus.
<p>The novelty of a map, that is, an 8x8 board added to the world, is introduced in the MCWW design, as opposed to the CWW version. This map is responsible for showing on the screen the agent's knowledge base or the inferences she makes as she moves and receives the different perceptions on the board. In this way, it is easier for the experimental subjects to predict the possible movements of the agent. As an example, observe a sequence of screens of the board of our version MCWW in which it is seen that the agent kills the Wumpus (the goal of Intention 2). See the figures 1-3.</p>
Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 1. Screen showing the agent in the cave.
<p>The novelty of a map, that is, an 8x8 board added to the world, is introduced in the MCWW design, as opposed to the CWW version. This map is responsible for showing on the screen the agent's knowledge base or the inferences she makes as she moves and receives the different perceptions on the board. In this way, it is easier for the experimental subjects to predict the possible movements of the agent. As an example, observe a sequence of screens of the board of our version MCWW in which it is seen that the agent kills the Wumpus (the goal of Intention 2). See the figures 1-3.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 10. Room model generated with Autodesk 123D Catch - the 3D model (screen capture from GLC Player)
<p>Structure from motion was used for rapid modeling of a small room with all its objects. Two files were generated, a Wavefront obj and mtl (corresponding to the texture). The 3D model was post-processed with MeshLab, during which several filters were applied to clean up the model. The mesh model was also connected with the scanned model, by choosing at least 4 connection points. The 2D and 3D results are shown in Figures 9, 10. A post-processing could also be performed using the Autodesk 123D Catch web application.</p>
Enhanced Dataset of Digitized Screen-film Mammograms of African Descent
<p>This dataset presents the enhanced version of digitized Screen-film Mammograms of African Descent. It contains mamographic images of 78 African cancer patients</p>
Hit Expansion using Substructure Search, Virtual Screening & Free Energy Perturbation
<p>Identification of commercially available chemical analogs of primary hits previously crystallized in complex with the zinc finger ubiquitin binding domain (Zf-UBD) of USP5 and prioritization of chemical analogues by free energy perturbation (FEP). </p>
Virtual Screening with Molecular Forecaster
<p>Commercially available compounds for USP5 zinc-finger ubiquitin binding domain (ZnF-UBD) were identified with Molecular Forecasters (MFI) FITTED docking platform. Preliminary assessment of docking for USP5 ZnF-UBD with FITTED can be found <a href="https://zenodo.org/record/2620208#.XQO2pYhKjIV">here</a>.</p>
Test data set for macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins
<p>This a bundle of test data can be used to run the macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins.</p> <p>These data sets can be used to run the following macros that can be found on GitHub:</p> <ol> <li><a href="https://github.com/molcyto/MC-Ratio-96-wells">https://github.com/molcyto/MC-Ratio-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Ratio-Petri-dish">https://github.com/molcyto/MC-Ratio-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-Petri-dish">https://github.com/molcyto/MC-FLIM-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-Bleach-96-wells">https://github.com/molcyto/MC-Bleach-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Scatter5D">https://github.com/molcyto/MC-Scatter5D</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-96-wells">https://github.com/molcyto/MC-FLIM-96-wells</a></li> </ol> <p>Funding:<br> This work was supported by the NWO CW-Echo grant 711.011.018 (M.A.H. and T.W.J.G.), grant 12149 (T.W.J.G.) from the Foundation for Technological Sciences (STW) from the Netherlands</p> <p> </p>
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.