We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral cortex. We stained a 1 mm3 volume with heavy metals, embedded it in resin, cut more than 5000 slices at ~30 nm and imaged these sections using a high-speed multibeam scanning electron microscope. We used computational methods to render the three-dimensional structure of 50,000 cells, hundreds of millions of neurites and 130 million synaptic connections. The 1.3 petabyte electron microscopy volume, the segmented cells, cell parts, blood vessels, myelin, inhibitory and excitatory synapses, and 100 manually proofread cells are available to peruse online. Despite the incompleteness of the automated segmentation caused by split and merge errors, many interesting features were evident. Glia outnumbered neurons 2:1 and oligodendrocytes were the most common cell type in the volume. The E:I balance of neurons was 69:31%, as was the ratio of excitatory versus inhibitory synapses in the volume. The E:I ratio of synapses was significantly higher on pyramidal neurons than inhibitory interneurons. We found that deep layer excitatory cell types can be classified into subsets based on structural and connectivity differences, that chandelier interneurons not only innervate excitatory neuron initial segments as previously described, but also each others initial segments, and that among the thousands of weak connections established on each neuron, there exist rarer highly powerful axonal inputs that establish multi-synaptic contacts (up to ~20 synapses) with target neurons. Our analysis indicates that these strong inputs are specific, and allow small numbers of axons to have an outsized role in the activity of some of their postsynaptic partners.

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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.