Progress on deep learning in genomics.

With the rapid growth of data driven by high-throughput sequencing technologies, genomics has entered an era characterized by big data, which presents significant challenges for traditional bioinformatics methods in handling complex data patterns. At this critical juncture of technological progress, deep learning-an advanced artificial intelligence technology-offers powerful capabilities for data analysis and pattern recognition, revitalizing genomic research. In this review, we focus on four major deep learning models: Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), and Generative Adversarial Network(GAN).

[Cannabinoid receptor system regulates ion channels and synaptic transmission in retinal cells].

Endocannabinoid receptor system is extensively expressed in the vertebrate retina. There are two types of cannabinoid receptors, CB1 and CB2. Activation of these two receptors by endocannabinoids N-arachidonoylethanolamide (anandamine, AEA) and 2-arachidonyl glycerol (2-AG) regulates multiple neuronal and glial ion channels, thus getting involved in retinal visual information processing.

Cannabinoid CB1 receptor signaling dichotomously modulates inhibitory and excitatory synaptic transmission in rat inner retina.

In the inner retina, ganglion cells (RGCs) integrate and process excitatory signal from bipolar cells (BCs) and inhibitory signal from amacrine cells (ACs). Using multiple labeling immunohistochemistry, we first revealed the expression of the cannabinoid CB1 receptor (CB1R) at the terminals of ACs and BCs in rat retina. By patch-clamp techniques, we then showed how the activation of this receptor dichotomously regulated miniature inhibitory postsynaptic currents (mIPSCs), mediated by GABAA receptors and glycine receptors, and miniature excitatory postsynaptic currents (mEPSCs), mediated by AMPA receptors, of RGCs in rat retinal slices.