The olfactory bulb (OB) contains various interneuron types that play key roles in processing olfactory information via synaptic contacts. Many previous studies have reported synaptic connections of heterogeneous interneurons in superficial OB layers. In contrast, few studies have examined synaptic connections in deep layers because of the lack of a selective marker for intrinsic neurons located in the deeper layers, including the mitral cell layer, internal plexiform layer (IPL) and granule cell layer. However, neural circuits in the deep layers are likely to have a strong effect on the output of the OB because of the cellular composition of these regions. Here, we analyzed the calbindin-immunoreactive neurons in the IPL, one of the clearly neurochemically defined interneuron types in the deep layers, using multiple immunolabeling and confocal laser scanning microscopy combined with electron microscopic three-dimensional serial-section reconstruction, enabling correlated laser and volume electron microscopy (EM). Despite a resemblance to the morphological features of deep short axon cells, IPL calbindin-immunoreactive (IPL-CB-ir) neurons lacked axons. Furthermore, multiple immunolabeling for plural neurochemicals indicated that IPL-CB-ir neurons differed from any interneuron types reported previously. We identified symmetrical synapses formed by IPL-CB-ir neurons on granule cells (GCs) using correlated laser and volume EM. These synapses might inhibit GCs and thus disinhibit mitral and tufted cells. Our present findings indicate, for the first time, that IPL-CB-ir neurons are involved in regulating the activities of projection neurons, further suggesting their involvement in synaptic circuitry for output from the deeper layers of the OB, which has not previously been clarified.
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http://dx.doi.org/10.1093/jmicro/dfz019 | DOI Listing |
Anal Chem
January 2025
State Key Laboratory of Cellular Stress Biology, Institute of Artificial Intelligence, School of Life Sciences, Faculty of Medicine and Life Sciences, National Institute for Data Science in Health and Medicine, XMU-HBN skin biomedical research center, Xiamen University, Xiamen, Fujian 361102, China.
In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address these challenges, we developed QuanFormer, a deep learning method based on object detection designed to accurately quantify peak signals.
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December 2025
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, TamilNadu India.
Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
Faculty of Psychology, Naval Medical University (Second Military Medical University), No. 800 Xiangyin Road, Yangpu District, Shanghai, 200433 China.
Fatigue-induced incidents in transportation, aerospace, military, and other areas have been on the rise, posing a threat to human life and safety. The determination of fatigue states holds significant importance, especially through reliable and conveniently available physiological indicators. Here, a portable custom-built fNIRS system was used to monitor the fatigue state caused by nap deprivation.
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January 2025
School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.
Introduction: With the advent of technologies such as deep learning in agriculture, a novel approach to classifying wheat seed varieties has emerged. However, some existing deep learning models encounter challenges, including long processing times, high computational demands, and low classification accuracy when analyzing wheat seed images, which can hinder their ability to meet real-time requirements.
Methods: To address these challenges, we propose a lightweight wheat seed classification model called LWheatNet.
MethodsX
June 2025
Department of Networking & Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Forecasting student performance with precision in the educational space is paramount for creating tailor-made interventions capable to boost learning effectiveness. It means most of the traditional student performance prediction models have difficulty in dealing with multi-dimensional academic data, can cause sub-optimal classification and generate a simple generalized insight. To address these challenges of the existing system, in this research we propose a new model Multi-dimensional Student Performance Prediction Model (MSPP) that is inspired by advanced data preprocessing and feature engineering techniques using deep learning.
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