A5006501975- Advanced Fluorescence Microscopy Techniques
- Neuroinflammation and Neurodegeneration Mechanisms
- Neural dynamics and brain function
- Image Processing Techniques and Applications
- Robotics and Sensor-Based Localization
- Neuroscience and Neuropharmacology Research
- Distributed Control Multi-Agent Systems
- Advanced Electron Microscopy Techniques and Applications
- Visual Attention and Saliency Detection
- Infrastructure Maintenance and Monitoring
- Cell Image Analysis Techniques
- Water Systems and Optimization
- Single-cell and spatial transcriptomics
- Biotin and Related Studies
- Glycosylation and Glycoproteins Research
- Neurological Disease Mechanisms and Treatments
- Smart Agriculture and AI
- Spectroscopy Techniques in Biomedical and Chemical Research
- Guidance and Control Systems
- Zebrafish Biomedical Research Applications
- Advanced Memory and Neural Computing
- Network Security and Intrusion Detection
- Remote Sensing and LiDAR Applications
- Wood and Agarwood Research
- Liver Disease Diagnosis and Treatment
Harvard University
2020-2025
Harvard University Press
2021-2022
Fudan University
2015-2018
Abstract We acquired a rapidly preserved human surgical sample from the temporal lobe of cerebral cortex. stained 1 mm 3 volume with heavy metals, embedded it in resin, cut more than 5000 slices at ∼30 nm and imaged these sections using high-speed multibeam scanning electron microscope. used computational methods to render three-dimensional structure containing 57,216 cells, hundreds millions neurites 133.7 million synaptic connections. The 1.4 petabyte microscopy volume, segmented cell...
To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here a computationally intensive reconstruction ultrastructure cubic millimeter temporal cortex that was surgically removed to gain access an underlying epileptic focus. It contains about 57,000 cells, 230 millimeters blood vessels, and 150 million synapses comprises 1.4 petabytes. Our analysis showed glia outnumber neurons 2:1, oligodendrocytes were most common cell, deep layer...
Regular inspection of sewer pipes can detect serious defects in time, which is significant to ensure the healthy operation systems and urban safety. Currently, widely used closed-circuit television (CCTV) system relies mainly on manual assessment, labor intensive inefficient. Therefore, it urgent develop an efficient accurate automatic defect detection method. In this paper, improved method based YOLOv4 proposed for defects. A improvement using spatial pyramid pooling (SPP) module expand...
The analysis of complex flight patterns and collective behaviors in swarming insects has emerged as a significant focus across biological computational fields. Tracking these insects, like fruit fly, presents persistent challenges due to their rapid motion frequent occlusions densely populated environments. To address challenges, we propose tracking method using particle filter framework combined with Kolmogorov–Arnold Network (KAN)-Transformer model extract the global features fine-grained...
Abstract Mapping neuronal networks is a central focus in neuroscience. While volume electron microscopy (vEM) can reveal the fine structure of (connectomics), it does not provide molecular information to identify cell types or functions. We developed an approach that uses fluorescent single-chain variable fragments (scFvs) perform multiplexed detergent-free immunolabeling and volumetric-correlated-light-and-electron-microscopy on same sample. generated eight scFvs targeting brain markers....
Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of (connectomics), it does not provide molecular information helps identify cell types or their functional properties. Volumetric correlated light and (vCLEM) combines ssEM volumetric fluorescence to incorporate labeling into datasets. We developed an approach uses small fluorescent single-chain variable fragment (scFv)...
The study of the intricate flight patterns and behaviors swarm insects, such as drosophilas, has long been a subject interest in both biological computational realms. Tracking drosophilas is an essential indispensable method for researching drosophilas’ behaviors. Still, it remains challenging task due to highly dynamic nature these their partial occlusion multi-target environments. To address challenges, particularly environments where multiple targets (drosophilas) interact overlap, we...
Abstract The sense of smell is generated by electrical currents that are influenced the concentration ions in olfactory sensory neurons and mucus. In contrast to extensive morphological molecular characterization neurons, there has been little description cells control ion concentrations zebrafish system. Here, we report ultrastructural ionocytes. Transcriptome analysis suggests epithelium contains at least three different ionocyte types, which resemble Na + /K -ATPase-rich (NaR), /Cl -...
In response to the issues of existing sewer defect detection models, which are not applicable small computing platforms due their complex structure and large computational volume, as well low accuracy, a lightweight model based on YOLOv5, named YOLOv5-GBC, is proposed. Firstly, address redundancy problem traditional convolutional approach, GhostNet, composed Ghost modules, used replace original backbone network. Secondly, aiming at accuracy defects, more detailed spatial information...
Connectomics is a nascent neuroscience field to map and analyze neuronal networks. It provides new way investigate abnormalities in brain tissue, including models of Alzheimer's disease (AD). This age-related associated with alterations amyloid-β (Aβ) phosphorylated tau (pTau). These correlate AD's clinical manifestations, but causal links remain unclear. Therefore, studying these molecular within the context local glial milieu may provide insight into mechanisms. Volume electron microscopy...
Fish, birds, insects and robots frequently swim or fly in groups. During their three dimensional collective motion, these agents do not stop, they avoid collisions by strong short-range repulsion, achieve group cohesion weak long-range attraction. In a minimal model that is isotropic, continuous both space time, we demonstrate (i) adjusting speed to preferred value, combined with (ii) radial repulsion an (iii) effective attraction are sufficient for the stable ordering of autonomously moving...
Android occupies the absolute dominant position in mobile operating system and has largest market share.Meanwhile, faces risk of malicious insiders leaking sensitive information.In this paper, we present DefDroid, a repackaging tool for enforcing security policies by modifying applications without root privilege.The main advantages DefDroid are that it provides user-friendly interface to configure fine-grained supplies multiple deployment methods.We have implemented aimed at three types...
The deep learning method automatically extracts advanced features from a large amount of data, avoiding cumbersome manual feature screening, and using digital pathology artificial intelligence technology to build computer-aided diagnosis system help pathologists quickly make objective reliable diagnoses improve work efficiency. Because pathological images are limited by factors such as sample size, labeling expertise, complexity, algorithms have not been extensively in-depth researched on...
Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of (connectomics), it does not provide molecular information helps identify cell types or their functional properties. Volumetric correlated light and (vCLEM) combines ssEM volumetric fluorescence to incorporate labeling into datasets. We developed an approach uses small fluorescent single-chain variable fragment (scFv)...
Potential competing interests: The author(s) declared that no potential interests exist.This work proposes a convolutional neural network framework to identify tree species using Xray fluorescence (XRF).Rapid, non-invasive and non-destructive analysis is thus enabled with satisfying accuracy.The proposed could also be used in other fields.