Supermultipotency and unpredictability in the developing superior colliculus.

Trends Neurosci

Department of Ophthalmology and Visual Science, Yale University School of Medicine, New Haven, CT, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA. Electronic address:

Published: April 2024

A recent study by Cheung, Pauler, Koppensteiner et al. combining lineage tracing with single-cell RNA sequencing (scRNA-seq) has revealed unexpected features of the developing superior colliculus (SC). Extremely multipotent individual progenitors generate all types of SC neurons and glial cells that were found to localize in a non-predetermined pattern, demonstrating a remarkable degree of unpredictability in SC development.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11047761PMC
http://dx.doi.org/10.1016/j.tins.2024.03.001DOI Listing

Publication Analysis

Top Keywords

developing superior
8
superior colliculus
8
supermultipotency unpredictability
4
unpredictability developing
4
colliculus study
4
study cheung
4
cheung pauler
4
pauler koppensteiner
4
koppensteiner combining
4
combining lineage
4

Similar Publications

Breast cancer remains one of the most prevalent malignancies among women globally. Despite advances in therapeutic options, the prognosis often remains challenging. Breast cancer typically originates in the epithelial lining of glandular tissue ducts (85%) or lobules (15%).

View Article and Find Full Text PDF

Metal chalcogenides have been extensively studied for thermoelectric applications. Among other metal chalcogenides, silver selenide (AgSe) is considered one of the most promising n-type semiconducting materials for thermoelectric applications due to its low band gap value, Seebeck coefficient, and superior power factor (PF) rendered at room temperature. However, one of the main drawbacks of using AgSe as a thermoelectric material on a large scale is the time-consuming physical methods to obtain them, and the need for high vacuum synthesis conditions as well as high-cost.

View Article and Find Full Text PDF

Introduction: Musical instrument recognition is a critical component of music information retrieval (MIR), aimed at identifying and classifying instruments from audio recordings. This task poses significant challenges due to the complexity and variability of musical signals.

Methods: In this study, we employed convolutional neural networks (CNNs) to analyze the contributions of various spectrogram representations-STFT, Log-Mel, MFCC, Chroma, Spectral Contrast, and Tonnetz-to the classification of ten different musical instruments.

View Article and Find Full Text PDF

TCSRNet: a lightweight tobacco leaf curing stage recognition network model.

Front Plant Sci

December 2024

Jiangxi Branch of China National Tobacco Corporation, Nanchang, China.

Due to the constraints of the tobacco leaf curing environment and computational resources, current image classification models struggle to balance recognition accuracy and computational efficiency, making practical deployment challenging. To address this issue, this study proposes the development of a lightweight classification network model for recognizing tobacco leaf curing stages (TCSRNet). Firstly, the model utilizes an Inception structure with parallel convolutional branches to capture features at different receptive fields, thereby better adapting to the appearance variations of tobacco leaves at different curing stages.

View Article and Find Full Text PDF

Background: Although schizophrenia and autism spectrum disorder (ASD) are currently conceptualized as distinct disorders, the similarity in their symptoms often makes differential diagnosis difficult. This study aimed to identify similarities and differences in the symptoms of schizophrenia and ASD to establish a more useful and objective differential diagnostic method and to identify ASD traits in participants with schizophrenia.

Methods: A total of 40 participants with schizophrenia (13 females, mean age: 34 ± 11 years) and 50 participants with ASD (15 females, mean age: 34 ± 8 years) were evaluated using the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) and other clinical measures.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!