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33,754 results for “Enhancers”
Fig. 1 in Exposure to tea tree oil enhances the mating success of male Mediterranean fruit flies (Diptera: Tephritidae)
Fig. 1. Number of matings (mean ± SE) obtained per replicate by wild-like medfly males exposed to TTO (treated) or not exposed (control) for the 3 different TTO concentrations provided to the treated males (the 5% and 50% doses represent TTO concentration [v/v] in a hexane solution). Bar heights represent averages; whiskers represent 1 SE. Results of t-tests: 5% solution: t = 4.8, P <0.001; 50% solution: t = 3.4, P = 0.003; pure (100%): t = 3.8, P <0.001; N = 12 replicates at each concentration.
Draft version of poposed images and plotting code of manuscript: Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite
<p>This documentation provides a detailed description of the folder structure and image contents uploaded to the website. It aims to help users understand the purpose and organization of the files. The corresponding manuscript is titled,<strong><em> Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite</em></strong>.</p>
ASIC2 Deletion in Medial Prefrontal Cortex Enhances Social Dominance in Mice
<p>Social dominance is essential for maintaining a stable social society and has well-established positive and negative impacts on sociable animals, including humans. However, the regulatory mechanisms governing social dominance, as well as the crucial regulators and biomarkers involved, remain poorly understood. We discover that mice lacking acid-sensing ion channel 2 (ASIC2) exhibit a persistent higher social dominance ranking compared to their wild-type cagemates. Conversely, the overexpression of ASIC2 in the medial prefrontal cortex (mPFC) reverses the dominance hierarchy observed in ASIC2 knockout mice. ASIC2 deletion prolongs the inactivation time of ASICs, resulting in enhanced ASIC-dependent synaptic transmission and plasticity in the mPFC through the protein kinase A signaling pathway. Furthermore, ASIC2 exhibits distinct functional roles in excitatory and inhibitory neurons, thereby modulating the balance of neuronal activities underlying social dominance behaviors—a phenomenon suggestive of a cell-subtype-specific mechanism. Finally, this research establishes a foundational understanding of the mechanisms governing social dominance formation, offering potential insights for the management or prevention of social disorders, such as depression and anxiety.</p>
Fig. 2 in Enhanced understanding of ectoparasite-host trophic linkages on coral reefs through stable isotope analysis
Fig. 2. Stable carbon and nitrogen isotope data for Haemulon flavolineatum blood vs. P3 (A and B), blood vs. adult gnathiids (C and D), and P3 vs. adult gnathiids (E and F). Solid triangles represent males and P3s, open triangles represent females. Error bars represent 1 SE of N = 5 individual P3 gnathiids analysed from each fish. Dashed line represents 1:1 linear relationship.
Fig. 1 in Enhanced understanding of ectoparasite-host trophic linkages on coral reefs through stable isotope analysis
Fig. 1. Mean δ13C and δ15N (±1 Standard Error) values of gnathiids (open squares), (A) Haemulon flavolineatum heart (grey diamond), blood (white diamond), and muscle (black diamond), Pederson shrimp (Ancylomenes pedersoni, open triangle), and Anilocra isopods (cross), (B) Stegastes diencaeus heart (grey diamond), and muscle (black diamond), and Pederson shrimp (open triangle), and (c) Holocentrus adscenscionis heart (grey diamond), and muscle (black diamond), and Anilocra isopods (cross).
Rawdata for publication: Observation of Transient Prenucleation Species of Calcium Carbonate by DNP-Enhanced NMR
<p>This is the raw data set for the publication:</p> <p>Observation of Transient Prenucleation Species of Calcium Carbonate by DNP-Enhanced NMR</p> <p>by</p> <p>Martins Balodis, Yu Rao, Gabriele Stevanato, Matthias Kellner, Josephine Meibom, Mattia Negroni, Bradley F. Chmelka, Lyndon Emsley .</p> <p>https://pubs.acs.org/doi/10.1021/acs.jpclett.4c01588</p>
Gamification for Enhancing Soft Skills Development: a Systematic Mapping Study (supplementary material) - Temporary Repository
<p>This TEMPORARY repository contains supplementary material for the manuscript "Gamification for Enhancing Soft Skills Development: a Systematic Mapping Study":</p> <ul> <li>Gamification for Enhancing Soft Skills Development.CSV: Meta-data and raw data extracted from papers inlcuding in the mapping study</li> </ul>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 3:(a) Input MR Image (b) Enhanced Image (c) Segmented Tumor (d) Located brain tumor
<p>Figure 3 shows three different original brain MR images, contrast enhancement of the<br> images, segmented images using K-means algorithm and finally located tumor. Fig 1.4 shows the<br> performance of the unsupervised clustering methods with the no. of tumor pixels and execution<br> time to locate the brain tumor.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 8. Performance in 1st Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 4. Principal stages of image classification system
<p>In computer vision, images or objects are recognised by machine going through two phases<br> shown in the figure 4. First the system is trained with features extracted from sample images in<br> training stage then they are tested on input images in testing stage. The performance of the classifier<br> depends on features extracted from the image. This research work is carried out in three different<br> experiments, first experiment is performed on data set containing the original images of sixteen<br> categories, noisy images are classified in second experiment, and third experiment detects the type<br> of noise affected the image followed by filtering through appropriate filter, then filtered images are<br> classified. The performance of each of the experiment is measured with two approaches. First<br> approach extracts the statistical texture features of the whole image, and the original image of size<br> 128x128 is divided into sixteen blocks of size 32x32 pixels in second approach. Then six statistical<br> texture features discussed in second section are extracted from each of the block producing 96<br> features from each of the images are used for training and testing stage.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 1. Sample Images of sixteen categories
<p>An image is often corrupted by noise in its acquisition or transmission. Noise is any<br> undesired information that degrades the image and appears in images from a variety of sources.</p> <p>Basically, there are three standard noise models [17], which model the types of noise<br> encountered in most images; they are additive noise, multiplicative noise and impulse noise. In this<br> work we have considered the occurrence of additive noise. An image function is given by f (x, y)<br> where (x, y) is spatial coordinate and f is intensity at point(x, y). Let f (x, y) be the original image,<br> g(x, y) be the noisy version and η(x, y) be the noise function, which returns random values coming<br> from an arbitrary distribution.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 7. Performance in experiment 3 for both approaches
<p>Classification of noisy images starts with detection of type of noise followed by appropriate<br> filtering operation. Then similar approaches are followed for feature extraction as discussed in<br> above experiments.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 4. Feed Forward Back propagation Neural Network
<p>This BPNN provides a computationally efficient<br> method for changing the weights in feed forward network, with differentiable activation function<br> units, to learn a training set of input-output data. Being a gradient descent method it minimizes the<br> total squared error of the output computed by the net. The aim is to train the network to achieve a<br> balance between the ability to respond correctly to the input patterns that are used for training and<br> the ability to provide good response to the input that are similar. A typical back propagation<br> network of input layer, one hidden layer and output layer is shown in figure 4.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 6. Performance in experiment 2 for both approaches
<p>Second experiment is performed on noisy data set with 50 images each class for training of<br> feed forward neural network, and 100 images each class are used for testing phase. This experiment<br> is also carried out with two approaches as discussed in first experiment.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 9. Performance in 2nd Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise. This is because the<br> texture feature of the original images consists Gaussian pattern also. Filtering of the noise from the<br> second data set improves the result. The table also shows that feature extraction using blocking of<br> the image enhance the average classification rate in all the case.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 5. Performance in experiment 1 for both approaches
<p>The first experiment is carried out on data set containing 16 class and 50 images of each<br> class for training and 100 images of each class for testing. Initially the original images are resized to<br> 128X128 pixels and six texture features are extracted from 800 training images in the first<br> approach. It produces a feature matrix of size 6X800. Similarly 6X1600 feature matrix is produced<br> for testing stage. Then a neural classifier is designed with ten hidden layers and 16 output layers.<br> Feature matrix of training images are used to train the neural network. After that they are tested on feature matrix of test image. In second approach, the original images are divided into 16 blocks of<br> size 32X32 pixel each block. So it produces 96 features (16 blocksX6 features) for each image and<br> feature matrix of 96X800 for training image and 96X1600 feature matrix for testing samples.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 3. Applying Wiener Filter on noisy image
<p>The Wiener filter is the mean square error-optimal stationary linear filter for images<br> degraded by additive noise and blurring. It removes the additive noise and inverts the blurring<br> simultaneously. The Wiener filtering is a linear estimation of the original image. The approach is<br> based on a stochastic framework.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 2. Original image affected by Gaussian noise
<p>In this research work we have detected Gaussian noise (fig 2) pattern in our images based on<br> methodology discussed in [18]. Where statistical moments features are extracted from the noise<br> patterns for noise class detection. This experiment detects the Gaussian noise patterns from images.<br> This leads to applying of wiener filter on noisy images, which gives the best noise removal.</p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset1
<p>This dataset contains the data underlying the following publication: Mouna Mahjoubi, Simone Cappello, Yasmine Souissi, Atef Jaouani and Ameur Cherif (February 7th 2018). Microbial Bioremediation of Petroleum Hydrocarbon– Contaminated Marine Environments, Recent Insights in Petroleum Science and Engineering Mansoor Zoveidavianpoor, IntechOpen, DOI: 10.5772/intechopen.72207</p> <p> </p>
Data from Figures in "Selection rules for cavity-enhanced Brillouin light scattering from magnetostatic modes"
<p>Data from figures in our paper "Selection rules for cavity-enhanced Brillouin light scattering from magnetostatic modes" in Physical Review B. The figures are in an Origin file (OriginPro 2016). Matlab code (R2016b) that can be used to generate plots of the magneto-static modes is also included.</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.