To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here is a computationally intensive reconstruction of the ultrastructure of a cubic millimeter of human temporal cortex that was surgically removed to gain access to an underlying epileptic focus. It contains about 57,000 cells, about 230 millimeters of blood vessels, and about 150 million synapses and comprises 1.4 petabytes. Our analysis showed that glia outnumber neurons 2:1, oligodendrocytes were the most common cell, deep layer excitatory neurons could be classified on the basis of dendritic orientation, and among thousands of weak connections to each neuron, there exist rare powerful axonal inputs of up to 50 synapses. Further studies using this resource may bring valuable insights into the mysteries of the human brain.

Regular inspection of sewer pipes can detect serious defects in time, which is significant to ensure the healthy operation of sewer systems and urban safety. Currently, the widely used closed-circuit television (CCTV) inspection system relies mainly on manual assessment, which is labor intensive and inefficient. Therefore, it is urgent to develop an efficient and accurate automatic defect detection method. In this paper, an improved method based on YOLOv4 is proposed for the detection of sewer defects. A significant improvement of this method is using the spatial pyramid pooling (SPP) module to expand the receptive field and improve the ability of the model to fuse context features in different receptive fields. Meanwhile, the influence of three bounding box loss functions on model performance are compared based on their processing speed and detection accuracy, and the effectiveness of the combination of DIoU loss function and SPP module is verified. In addition, to address the lack of datasets for sewer defect detection, a dataset that contains 2700 images and 4 types of defects was created, which provides useful help for the application of computer vision techniques in this field. Experimental results show that, compared with the YOLOv4 model, the mean average precision (mAP) of the improved model for sewer defect detection are improved by 4.6%, the mAP can reach 92.3% and the recall can reach 89.0%. The improved model can effectively improve the detection and classification accuracy of sewer defects, and has significant advantages compared with other methods.

We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory ( vglut2a ) and inhibitory ( gad1b ) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing >180,000 segmented soma, >40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the dataset’s utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain.

The study of the intricate flight patterns and behaviors of swarm insects, such as drosophilas, has long been a subject of interest in both the biological and computational realms. Tracking drosophilas is an essential and indispensable method for researching drosophilas’ behaviors. Still, it remains a challenging task due to the highly dynamic nature of these drosophilas and their partial occlusion in multi-target environments. To address these challenges, particularly in environments where multiple targets (drosophilas) interact and overlap, we have developed a long-term Trajectory 3D Position and Orientation Tracking Method (Long 3D-POT) that combines deep learning with particle filtering. Our approach employs a detection model based on an improved Mask-RCNN to accurately detect the position and state of drosophilas from frames, even when they are partially occluded. Following detection, improved particle filtering is used to predict and update the motion of the drosophilas. To further enhance accuracy, we have introduced a prediction module based on the self-attention backbone that predicts the drosophila’s next state and updates the particles’ weights accordingly. Compared with previous methods by Ameni, Cheng, and Wang, our method has demonstrated a higher degree of accuracy and robustness in tracking the long-term trajectories of drosophilas, even those that are partially occluded. Specifically, Ameni employs the Interacting Multiple Model (IMM) combined with the Global Nearest Neighbor (GNN) assignment algorithm, primarily designed for tracking larger, more predictable targets like aircraft, which tends to perform poorly with small, fast-moving objects like drosophilas. The method by Cheng then integrates particle filtering with LSTM networks to predict particle weights, enhancing trajectory prediction under kinetic uncertainties. Wang’s approach builds on Cheng’s by incorporating an estimation of the orientation of drosophilas in order to refine tracking further. Compared with those methods, our method performs with higher accuracy on detection, which increases by more than 10% on the F1 Score, and tracks more long-term trajectories, showing stability.

In response to the issues of the existing sewer defect detection models, which are not applicable to small computing platforms due to their complex structure and large computational volume, as well as the low detection accuracy, a lightweight detection model based on YOLOv5, named YOLOv5-GBC, is proposed. Firstly, to address the computational redundancy problem of the traditional convolutional approach, GhostNet, which is composed of Ghost modules, is used to replace the original backbone network. Secondly, aiming at the problem of low detection accuracy of small defects, more detailed spatial information is introduced by fusing shallow features in the neck network, and weighted feature fusion is used to improve the feature fusion efficiency. Finally, to improve the sensitivity of the model to key feature information, the coordinate attention mechanism is introduced into the Ghost module and replaced the traditional convolution approach in the neck network. Experimental results show that compared with the YOLOv5 model, the model size and floating point of operations (FLOPs) of YOLOv5-GBC are reduced by 74.01% and 74.78%, respectively; the mean average precision (MAP) and recall are improved by 0.88% and 1.51%, respectively; the detection speed is increased by 63.64%; and the model size and computational volume are significantly reduced under the premise of ensuring the detection accuracy, which can effectively meet the needs of sewer defect detection on small computing platforms.