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54,099 results for “Expression”
Figure 1. The AGLO Workflow-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>E-learning is a key area of research with a great influence on the developments of several<br> industries. For example, the nowadays ITC industry is in a continuous growth because of its<br> applications in almost all industrial domains. Companies tend to lack qualified human resources and<br> because of that they reject high economical value projects. In response to this lack of human<br> resource problem, universities started to develop several alternative study programs, many of them<br> are based on e-learning technology and namely on electronic learning materials. Learning objects<br> (LO) are considered to be digital resources that support learning and can be delivered across<br> networks in large or small sizes (Wiley, 2000). In order to increase the reusability and<br> interoperability of LOs, standards were developed by several organizations (IEEE Learning<br> Technology Standards Committee) (e.g. LOM http://ltsc.ieee.org/doc/wg12/LOMv4.1.htm.)</p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 2. Result of the implementation of EVM method.
<p>In our research, we focus on the parameter values in the second case. Then we convert the obtained video to the images sequence. The number of images is decreased after the implementation of the EVM method. The reason of this problem is the integration of multiple pixels in the images. The resulting output is a generic extract of all motions created in the face.</p> <p>In this study, two experiments are performed as follows: Phase1: investigating the emotional/unemotional detection on face in micro expressions. Phase2: investigating facial micro expressions recognition </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 5.The chart of run time in emotional/unemotional detection in two databases.
<p>The processing time of our proposed method is of 0.420468 fps and in regular data is of 5.27 fps. As the results show, the EVM method has good effect in processing. Of course, the creation time of the magnification image sequence was not considered in the result. In other words, the processing time on a magnified images sequence is less than the non-magnification state. Figure 5 shows the running time in emotional/unemotional detection. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 4. The chart of average percent of emotional/unemotional detection in SMIC
<p>In Figure 4 the blue color curve represents the average percent of emotional/unemotional detection using the EVM method. The red color curve also represents the average percent of emotional/unemotional detection without this method. Table 1 shows the running time and speed in this experiment. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 1. Overall structure of micro expression recognition
<p>In this study we investigate the emotional/unemotional detection on the face and micro expression recognition. As expressed in Murthy & Jadon (2007) and Frank & Nöth (2003) the eigenface method is not able to perform micro expression recognition. So, we use the EVM method for micro expression recognition based on the eigenface method. Using this magnification method, the subtle motions are retrieved from the face during micro expression and those are not visible. In order to do this work, we need a dataset including the spontaneous subtle facial motions. Therefore, in this study we use two databases, SMIC and CASME. According to the obvious features in the EVM method for retrieving and displaying subtle motions, first we test these databases using this method. Then we evaluate the rate of emotional/unemotional detection in the face and micro expression recognition using the eigenface method. An overall structure of our research is shown in Figure 1. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 3.The chart of emotional/unemotional detection on the face in negative, positive and surprise states (Regular and magnified data)
<p>To evaluate the emotional/unemotional detection on the face, 328 tests were performed: 164 tests on the magnified data and 164 tests on the regular data. For this purpose, the train set includes the neutral state and only one of the emotional states (negativism, positivism and surprise) according to the test set. So that the train set includes regular data in 328 experiments. The experimental results are shown in Figure 3. </p>
Full-length HTT purification from HEK293T suspension culture using baculovirus transduction protocol for over-expression 2018/04/02
<p>Structure-function open lab notebook project. Full-length HTT purification from HEK293T suspension culture using baculovirus transduction protocol for over-expression 2018/04/02.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 8. 3D model of some facial expressions
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 3. Feature vectors of facial expression in database
<p> Figure 3 shows feature vectors of facial expression of our database. Matrices ‘U’ and ‘V’ values that are obtained from this algorithm are used as feature vectors. The ‘U’ matrix represents the position and the ‘V’ matrix represents the change of direction. In the following, the proposed method is combined with some other feature extraction methods (LBP uniform approach and LBP circular approach) and the obtained results will be mentioned.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 2. Facial expression recognition in proposed method
<p>In this stage, a video is prepared using the color data captured from Kinect camera. The face region in each frame is obtained from the video using Viola-Jones algorithm (Figure 2). Because of different distance from the Kinect camera, the obtained images from the face must be re-sized, in order to have the same size. At the end, the colored images are converted to gray-scaled images.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 1. Peak of facial expression in database
<p>In this study, data are obtained from the Kinect camera that benefits from colorful images and depth data. Kinect can record colorful and depth data simultaneously at 30 frames per second. The data are collected from the person who initially pose in front of the camera with normal face mode and then the various modes are represented. It should be noted that data are obtained at different distances from the Kinect camera and in different lighting conditions. Figure 1 shows various facial modes in our database.</p>
Reciprocal F1 hybrids of two inbred mouse strains reveal parent-of-origin and perinatal diet effects on behavior and expression
<p>Raw data and statistical analyses from an experiment to study parent-of-origin and diet-by-parent-of-origin effects on expression and behavior. </p> <p>In this experiment, female NOD/ShiLtJ x C57Bl/6J and C57Bl/6J x NOD/ShiLtJ mice were exposed in utero to one of four diets. After weaning, their whole-brain gene expression, as well as a set of behaviors that model psychiatric disease, were recorded and analyzed.</p> <p>File_S1_README contains detailed descriptions of all included files.</p>
Cell specificity of human regulatory annotations and their genetic effects on gene expression
<p>Processed data for the manuscript: Cell specificty of regulatory annotations and their genetics effects on gene expression.</p> <p>The code to generate these data is included in the zip-file: regulatoryAnnotations_comparisons-master.zip</p> <p>and at GitHub: https://github.com/ParkerLab/regulatoryAnnotations_comparisons</p> <p>Individual dataset information:</p> <p>numberOfSegments.dat.gz: Number of segments in each annotation</p> <p>lengthDistributionOfSegments.dat.gz: Length distribution of segments in each annnotation</p> <p>genomeCoverage.dat.gz: Total genome coverage of regions in each annoation</p> <p>overlapFraction.dat.gz: Basepair level overlap between two pairs of annotations</p> <p>annotations_chromatinStateOverlap.dat.gz: Overlap of annotations with chromatin states</p> <p>information.fourcells_enhancer.dat.gz: Information content of the mean enhancer chromatin posterior probability for annotation segments across four cell types</p> <p>information.fourcells_promoter.dat.gz: Information content of the mean promoter chromatin posterior probability for annotation segments across four cell types</p> <p>gwas_enrichment_stats.txt.gz: Enrichment of GWAS SNPs in annotations</p> <p>GTEx_v7.ESI_medianTPM_min0.15.dat.gz : ESI for genes across 50 tissues</p> <p>gtexv7_lcleqtl.enrichment.dat.gz: Enrichment of GTEx v7 LCL eQTL in annotations</p> <p>gtexv7_lcleqtl.binned_lclESI.enrichment.dat.gz: Enrichment of GTEx v7 LCL eQTL binned by lclESI in annotations</p> <p>K562.gtexv7_bloodeqtl.fdr0.1.prune0.8.maf0.2.ld0.99.annotations.dat.gz: GTEx v7 blood eQTL effect sizes in K562 annotations </p> <p>GM12878.gtexv7_lcleqtl.fdr0.1.prune0.8.maf0.2.ld0.99.annotations.dat.gz: GTEx v7 LCL eQTL effect sizes in GM12878 annotations</p> <p>GM12878.dsqtl.prune0.8.maf0.2.ld0.99.annotations.dat.gz : GM12878 DNase QTL effect sizes in GM12878 annotations</p> <p>GM12878.allelicBiasResults.FracRef.downsampled30.annotations.withMAF.dat.gz: GM12878 ATAC-seq allelic bias effect sizes in GM12878 annotations </p> <p> <br> <br> <br> </p>
Characterizing Novel Olfactory Receptors Expressed in the Murine Renal Cortex: Supplemental Table S1
<p><strong>Ligand screening of Olfr90, Olfr461, Olfr558, Olfr1034, and Olfr1396.</strong> The complete screening results of all compounds tested on all 5 murine ORs. Compounds were tested at 0.5 mM unless otherwise specified. “+” indicates robust statistically signification activation, while “–” indicates no response. ORs listed in the Classification column are siblings of tested ORs. The term “general” is used for compounds in our library that are commonly used to screen ORs, and “biofluids” refers to compounds listed in the Human Metabolome Database which are detected in biofluids such as blood, urine, etc.</p> <p> </p> <p> </p>
Alterations in Patterns of Gene Expression and Perturbed Pathways Associated With Chemotherapy-Induced Nausea
<p>This dataset contains the supplementary materials describing in detail the methods and the results of the analyses. The manuscript has been submitted for publication. Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
Classification of Gene Mutations and Cancer-Types with BioBombe Compressed Gene Expression Features
<p>BioBombe analysis applied to gene expression data from The Cancer Genome Atlas (TCGA) PanCanAtlas.</p> <p>Then, the compressed features learned through the serial compression are fed into several machine learning algorithms.</p> <p>The algorithms are used to predict mutation status (for the top 50 most mutated genes in TCGA) and cancer-type using the compressed gene expression features.</p> <p>Method and results described in https://github.com/greenelab/BioBombe. We use the classification approach outlined in https://github.com/greenelab/pancancer</p>
Interspecific competition affects the expression of personality-traits in natural populations
<p>Interspecific competition can cause niche partitioning among species in a community and shape an individual’s phenotype, including its behaviour. However, little is known about how interspecific competition affects the expression of animal personality. We investigated whether the occurrence of competing alien grey squirrels (<em>Sciurus carolinensis</em>) altered personality traits in native Eurasian red squirrels (<em>Sciurus vulgaris</em>). We compared personality traits of red squirrels in red-only (no interspecific competition) and red-grey (with interspecific competition) sites, using arena tests. Open Field Test returned repeatable estimates of activity and shyness, Mirror Image Stimulation test of sociability and avoidance of a conspecific. Red squirrels competing with the alien species had higher sociability and slightly lower avoidance than in red-only sites. Moreover, an individual’s probability to survive was not affected by any of the personality traits, but more active females were more likely to wean a litter than shy ones. This relationship was not affected by the presence or absence of the alien competitor. Our findings suggest that the occurrence of certain personality traits in red squirrels was affected by interspecific competition not as a consequence of short-term selection, but more likely as a result of context-related advantages of sociability or avoidance.</p>
GTEx gene-level expression summary
<p>GTEx gene-level expression summary from the Snaptron collection. Format is a tab-separated text file compressed and indexed using BGZip. The accompanying Tabix index is also available. Uses GENCODE v25 annotation for quantification. Source data for the quantification are the bigWig files produced as part of recount2. More information at http://snaptron.cs.jhu.edu.</p>
GTEx exon-level expression summary
<p>GTEx exon-level expression summary from the Snaptron collection. Format is a tab-separated text file compressed and indexed using BGZip. The accompanying Tabix index is also available. Uses GENCODE v25 annotation for quantification. Source data for the quantification are the bigWig files produced as part of recount2. More information at http://snaptron.cs.jhu.edu.</p>
Gene interaction networks from donor and failing heart gene expression
<p>These are the gene-gene interaction networks obtained from gene expression microarray data in the paper "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure" by Cordero, Parikh, et al. The dataset contains the following items:</p> <p>* networks.zip: Compressed weighted matrices, in numpy format, for various methods in heart failure and donor cohorts, which are in the file name (ARACNE, JGL [Joint Graphical Lasso], Z-score, Pearson correlation, CLR [Context Likelihood of Relatedness])</p> <p>* wgcna.zip: Compressed weighted matrices, in numpy format, for WGCNA. These are the networks used in the paper.</p> <p>* network_gene_names.txt: Gene names for rows and columns of the gene networks in networks.zip</p> <p>* wgcna</p> <p>Construction of the networks were done as follows for each method:</p> <p>* For Pearson, Z-score, and CLR, we simply computed each relevant statistic in a pairwise manner, no thresholds were applied.</p> <p>* For ARACNE, we performed 500 bootstrapped iterations and searched the MI cutoff automatically using default parameters.</p> <p>* For JGL we performed an Akaike Information Criteria guided model selection by scanning the sparsity and group difference L1 penalty strengths.</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.