Decoding transcriptional identity in developing human sensory neurons and organoid modeling.

Dorsal root ganglia (DRGs) play a crucial role in processing sensory information, making it essential to understand their development. Here, we construct a single-cell spatiotemporal transcriptomic atlas of human embryonic DRG. This atlas reveals the diversity of cell types and highlights the extrinsic signaling cascades and intrinsic regulatory hierarchies that guide cell fate decisions, including neuronal/glial lineage restriction, sensory neuron differentiation and specification, and the formation of neuron-satellite glial cell (SGC) units.

DeepMulticut: Deep Learning of Multicut Problem for Neuron Segmentation From Electron Microscopy Volume.

Superpixel aggregation is a powerful tool for automated neuron segmentation from electron microscopy (EM) volume. However, existing graph partitioning methods for superpixel aggregation still involve two separate stages-model estimation and model solving, and therefore model error is inherent. To address this issue, we integrate the two stages and propose an end-to-end aggregation framework based on deep learning of the minimum cost multicut problem called DeepMulticut.

Multi-consensus formation control by artificial potential field based on velocity threshold.

This study proposes a multi-consensus formation control algorithm by artificial potential field (APF) method based on velocity threshold. The algorithm improves the multi-consensus technique. This algorithm can split a group of agents into multiple agent groups.

An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography.

Background: As an extension of electron tomography (ET), serial section electron tomography (serial section ET) aims to align the tomographic images of multiple thick tissue sections together, to break through the volume limitation of the single section and preserve the sub-nanoscale voxel size. It could be applied to reconstruct the intact synapse, which expands about one micrometer and contains nanoscale vesicles. However, there are several drawbacks of the existing serial section ET methods.

Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes.

Background: Nanoscale connectomics, which aims to map the fine connections between neurons with synaptic-level detail, has attracted increasing attention in recent years. Currently, the automated reconstruction algorithms in electron microscope volumes are in great demand. Most existing reconstruction methodologies for cellular and subcellular structures are independent, and exploring the inter-relationships between structures will contribute to image analysis.

Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.

Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demanding but still highly informative. We thus developed a region-CNN-based deep learning method to identify, segment, and reconstruct synapses and mitochondria to explore the structural plasticity of synapses and mitochondria in the auditory cortex of mice subjected to fear conditioning.

Deep residual contextual and subpixel convolution network for automated neuronal structure segmentation in micro-connectomics.

Background And Objective: The goal of micro-connectomics research is to reconstruct the connectome and elucidate the mechanisms and functions of the nervous system via electron microscopy (EM). Due to the enormous variety of neuronal structures, neuron segmentation is among most difficult tasks in connectome reconstruction, and neuroanatomists desperately need a reliable neuronal structure segmentation method to reduce the burden of manual labeling and validation.

Methods: In this article, we proposed an effective deep learning method based on a deep residual contextual and subpixel convolution network to obtain the neuronal structure segmentation in anisotropic EM image stacks.

Automatic Reconstruction of Mitochondria and Endoplasmic Reticulum in Electron Microscopy Volumes by Deep Learning.

Together, mitochondria and the endoplasmic reticulum (ER) occupy more than 20% of a cell's volume, and morphological abnormality may lead to cellular function disorders. With the rapid development of large-scale electron microscopy (EM), manual contouring and three-dimensional (3D) reconstruction of these organelles has previously been accomplished in biological studies. However, manual segmentation of mitochondria and ER from EM images is time consuming and thus unable to meet the demands of large data analysis.

An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms.

Current methods of evaluating the degree of diabetic retinopathy are highly subjective and have no quantitative standard. To objectively evaluate the slight changes in tissue structure during the early stage of retinal diseases, a subjective interpretation and qualitative analysis of the pathological sections of retinal HE in diabetic animals is required for screening and evaluating the degree of diabetic retinopathy and drug efficacy. To develop an innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms.

Automatic Classification for the Type of Multiple Synapse Based on Deep Learning.

Recent studies have shown that the synaptic plasticity induced by development and learning can promote the formation of multiple synapse. With the rapid development of electron microscopy (EM) technology, we can closely observe the multiple synapse structure with high resolution. Although the multiple synapse has been widely researched by recent researchers, the classification accuracy for the type of multiple synapse has not been documented.

Automatic Detection and Segmentation of Mitochondria from SEM Images using Deep Neural Network.

Investigating the link between mitochondrial function and its physical structure is a hot topic in neurobiology research. With the rapid development of Scanning Electron Microscope (SEM), we can look closely into the fine mitochondrial structure with high resolution. Consequently, many meaningful researches have focused on how to detect and segment the mitochondria from EM images.

The Energy Density and Treatment Times Are the Main Factors That Affect the Efficacy of Long-Pulsed 1,064-nm Nd:YAG Laser Treatment for Onychomycosis Caused by Trichophyton rubrum.

Background: No optimal regimen exists for the LPNYL (long-pulsed 1,064-nm neodymium:yttrium-aluminum-garnet laser) for treating onychomycosis.

Objective: To establish an optimal LPNYL treatment regimen for onychomycosis caused by Trichophyton rubrum (OCTr).

Patients And Methods: First, 511 infected nails of 177 patients were treated using LPNYL with orthogonally designed regimens according to various energy densities, spot sizes, pulse widths, and treatment times.

Treatment of a patient with multiple cutaneous piloleiomyoma-related pain with a local injection of triamcinolone acetonide.

Background: Multiple cutaneous piloleiomyomas are rare, frequently painful and difficult-to-treat benign tumours originating in the arrectores pilorum muscles of the hair follicles.

Objective: The aim of this study was to determine the efficacy of a local injection of triamcinolone acetonide in relieving cutaneous piloleiomyoma-related pain.

Methods: A patient with multiple painful piloleiomyomas was treated with weekly local injections of triamcinolone acetonide into the nodules and papules for 3 weeks.