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299
datasets available to search
ShareScore release 0.9.0
Dataset results
299 results for “Active networks”
Integrative genomic and epigenomic profiling reveals cell-type specific signaling networks in activated lung mononuclear phagocytes [scRNA-seq]
GEO Series GSE136915. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Noise exposures causing hearing loss generate proteotoxic stress and activate the proteostasis network
GEO Series GSE160639. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Escalated oxycodone self-administration is associated with activation of specific gene networks in the rat dorsal striatum
GEO Series GSE280582. Rattus norvegicus. 24 samples. Type: Expression profiling by high throughput sequencing.
Gene-regulatory networks activated by pattern-specific generation of action potentials in dorsal root ganglia neurons
GEO Series GSE84872. Mus musculus. 20 samples. Type: Expression profiling by array.
Tip60 complex binds to active Pol II promoters and a subset of enhancers and co-regulates the c-Myc network in mouse embryonic stem cells
GEO Series GSE69671. Mus musculus. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Symbiont-host interactome mapping reveals effector-targeted modulation of hormone networks and activation of host benefits
GEO Series GSE222356. Arabidopsis thaliana. 8 samples. Type: Expression profiling by high throughput sequencing.
Interleukin-4 receptor signaling modulates neuronal network activity [mouse]
GEO Series GSE200569. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome-based network analysis reveals a spectrum model of human macrophage activation [miRNA-seq]
GEO Series GSE51307. Homo sapiens. 20 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Patient-derived hiPSC neurons with heterozygous CNTNAP2 deletions display altered neuronal gene expression and network activity
GEO Series GSE102838. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Histone lactylation couples cellular metabolism with the activation of developmental gene regulatory networks
GEO Series GSE228343. Gallus gallus. 25 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Anemia Activates a Network of Cis-Regulatory Elements in Red Blood Cell Regeneration
GEO Series GSE212224. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Electrophysiological recordings from: Effects of Acute Stress on the Oscillatory Activity of the Hippocampus-Amygdala-Prefrontal Cortex Network. Merino et al. 2021
<p>Electrophysiological recordings of local field potentials (LFP) from infralimbic cortex (IL), basolateral amygdala (BLA) and dorsal hippocampus (dHPC). These data are collected from urethane-anesthetized rats. </p><p><strong>Abstract:</strong></p><p>Displaying a stress response to threatening stimuli is essential for survival. These reactions must be adjusted to be adaptive. Otherwise, even mental illnesses may develop. Describing the physiological stress response may contribute to distinguishing the abnormal responses that accompany the pathology, which may help to improve the development of both diagnoses and treatments. Recent advances have elucidated many of the processes and structures involved in stress response management; however, there is still much to unravel regarding this phenomenon. The main aim of the present research is to characterize the response of three brain areas deeply involved in the stress response (i.e., to an acute stressful experience). Specifically, the electrophysiological activity of the infralimbic division of the medial prefrontal cortex (IL), the basolateral nucleus of the amygdala (BLA), and the dorsal hippocampus (dHPC) was recorded after the infusion of 0.5 µl of corticosterone-releasing factor into the dorsal raphe nucleus (DRN), a procedure which has been validated as a paradigm to cause acute stress. This procedure induced a delayed reduction in slow waves in the three structures, and an increase in faster oscillations, such as those in theta, beta, and gamma bands. The mutual information at low theta frequencies between the BLA and the IL increased, and the delta and slow wave mutual information decreased. The low theta-mid gamma phase-amplitude coupling increased within BLA, as well as between BLA and IL. This electrical pattern may facilitate the activation of these structures, in response to the stressor, and memory consolidation. </p>
Neural Network for Determination of the Substrate Activation in Enzymes
<p>1. Multiwfn_lap.sh -- a Linux script to run calculation of the Laplacian of electron density grid in the plain of a nucleophile atom and a carbonyl group. </p> <p>2. lapZero150DPI.py - a Python script for visualization of the 2D Laplacian of the electron density map</p> <p>3. crop_image.py - a Python script that crops the image </p> <p>4. 2500_crop_dataset_MolInf.ipynb - a Python notebook that trains the CNN with the dataset_crop_2500-MolInf </p> <p>5. dataset_crop_2500-MolInf.7z - a dataset to train the CNN</p> <p>6. model_crop_2500-MolInf.h5 - trained CNN, ready for utilization </p> <p>7. validation_datasets.zip -- an archive that includes additional datasets for the neural network validation (complexes of the Mpro with substrates containing Ser, Thr and Pro at P2 and a complex of the NDM-1 and imipenem)</p>
Med-ReLU: A Hybrid Activation Function Tailored for Deep Artificial Neural Networks in Medical Image Segmentation without Parameter Tuning
<p>Background: Deep learning (DL) is derived from the domain of Artificial Neural Network (ANN). It makes one of the most important elements of deep learning algorithms. Deep learning segmentation models are based on layer-by-layer convolution learning attribute representation directed by forward and backward propagation. Throughout the process vital role is played by appropriately chosen activation function (AF) in order to guarantee the robustness of the model learning. However, the existing activation functions are either ineffective in addressing the vanishing gradient problem or get burdened with multiple parameters that need to be manually tuned. Moreover, the current research on activation function design mainly focuses on classification tasks using natural images from the MNIST, CIFAR-10 and CIFAR-100 datasets. Therefore,Med-ReLU as a novel activation function for medical image segmentation, is proposed. The proposed activation function avoids deep learning models from the attacks of dead neurons or from the vanishing gradient problems. Method: Med-ReLU is a hybrid activation function that combines the property of two activation functions of ReLU and Softsign. For positive inputs, Med-ReLU utilizes the linear property just like ReLU to produce an output without vanishing gradient. The negative inputs converge in polynomial ways towards their asymptotes as property of the softsign AF that ensures robust training processing without the problem of dead neurons that rarely activate across the entire training dataset. Results: The training performance and segmentation accuracy of Med-ReLU have been investigated. The proposed function has demonstrated stable training and does not suffer from over-fitting. Hence, Med-ReLU has consistently outperformed the existing state-of-art activation functions in medical image segmentation tasks. Conclusion: Med-ReLU has been designed as a parameter-free activation function for DL image segmentation tasks. This activation function is easy-to-implement on complex and deep learning models. The utility of this research lies in affirming the impact of Med-ReLU on different Artificial Neural Network architectures and for various kinds of anomaly addressing tasks.</p>
Using Social Networks to Promote Physical Activity in African American and Hispanic Women
ClinicalTrials.gov study NCT03199196. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Investigator Initiated Protocol for the: Evaluation of the Effectiveness and Clinical Utility of Brain Network Activation (BNA™) Technology in the Management of Sport Related Concussion and the Establ
ClinicalTrials.gov study NCT02105935. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Normative Data Base of Brain Network Activation (BNA) Using Evoked Response Potentials
ClinicalTrials.gov study NCT02418208. IPD Sharing: Not stated. Countries: 2. Publications: 0.
Motivational Encouragement With Networks (MEN) for Healthy Eating Activity Resting Together (HEART) Health Study
ClinicalTrials.gov study NCT03693768. IPD Sharing: NO. Countries: 1. Publications: 0.
Synaptic Imaging and Network Activity in Treatment Resistant Depression
ClinicalTrials.gov study NCT05870501. IPD Sharing: NO. Countries: 1. Publications: 0.
Plasticity of Neonatal Neuronal Networks Temporal Theta Activity, the First Endogenous
ClinicalTrials.gov study NCT03677908. IPD Sharing: NO. Countries: 0. Publications: 0.
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.