Intercellular communication between vascular and nerve cells mediated by diffusible proteins has recently emerged as a critical intrinsic program for neural development. However, whether the vascular smooth muscle cell (VSMC) secretome regulates the connectivity of neural circuits remains unknown. Here, we show that conditioned medium from brain VSMC cultures enhances multiple neuronal functions, such as neuritogenesis, neuronal maturation, and survival, thereby improving circuit connectivity. However, protein denaturation by heating compromised these effects. Combined omics analyses of donor VSMC secretomes and recipient neuron transcriptomes revealed that overlapping pathways of extracellular matrix receptor signaling and adhesion molecule integrin binding mediate VSMC-dependent neuronal development. Furthermore, we found that human arterial VSMCs promote neuronal development in multiple ways, including expanding the time window for nascent neurite initiation, increasing neuronal density, and promoting synchronized firing, whereas human umbilical vein VSMCs lack this capability. These in vitro data indicate that brain arteriolar VSMCs may carry direct instructive information for neural development through intercellular communication in vivo.
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http://dx.doi.org/10.1186/s13041-022-00983-y | DOI Listing |
J Am Coll Cardiol
December 2024
UCL MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom; UCL Institute of Cardiovascular Science, University College London, London, United Kingdom; Centre for Inherited Heart Muscle Conditions, Cardiology Department, Royal Free Hospital, London, United Kingdom. Electronic address:
Background: Aircraft noise is a growing concern for communities living near airports.
Objectives: This study aimed to explore the impact of aircraft noise on heart structure and function.
Methods: Nighttime aircraft noise levels (L) and weighted 24-hour day-evening-night aircraft noise levels (L) were provided by the UK Civil Aviation Authority for 2011.
Sensors (Basel)
December 2024
Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA.
Multi-modal systems extract information about the environment using specialized sensors that are optimized based on the wavelength of the phenomenology and material interactions. To maximize the entropy, complementary systems operating in regions of non-overlapping wavelengths are optimal. VIS-IR (Visible-Infrared) systems have been at the forefront of multi-modal fusion research and are used extensively to represent information in all-day all-weather applications.
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December 2024
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China.
Addressing the issue of excessive manual intervention in discharging fermented grains from underground tanks in traditional brewing technology, this paper proposes an intelligent grains-out strategy based on a multi-degree-of-freedom hybrid robot. The robot's structure and control system are introduced, along with analyses of kinematics solutions for its parallel components and end-effector speeds. According to its structural characteristics and working conditions, a visual-perception-based motion control method of discharging fermented grains is determined.
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December 2024
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
With the advancement of service robot technology, the demand for higher boundary precision in indoor semantic segmentation has increased. Traditional methods of extracting Euclidean features using point cloud and voxel data often neglect geodesic information, reducing boundary accuracy for adjacent objects and consuming significant computational resources. This study proposes a novel network, the Euclidean-geodesic network (EGNet), which uses point cloud-voxel-mesh data to characterize detail, contour, and geodesic features, respectively.
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December 2024
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance.
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