Massive Data Management and Sharing Module for Connectome Reconstruction

Recently, with the rapid development of electron microscopy (EM) technology and the increasing demand of neuron circuit reconstruction, the scale of reconstruction data grows significantly. This brings many challenges, one of which is how to effectively manage large-scale data so that researchers can mine valuable information. For this purpose, we developed a data management module equipped with two parts, a storage and retrieval module on the server-side and an image cache module on the client-side. On the server-side, Hadoop and HBase are introduced to resolve massive data storage and retrieval. The pyramid model is adopted to store electron microscope images, which represent multiresolution data of the image. A block storage method is proposed to store volume segmentation results. We design a spatial location-based retrieval method for fast obtaining images and segments by layers rapidly, which achieves a constant time complexity. On the client-side, a three-level image cache module is designed to reduce latency when acquiring data. Through theoretical analysis and practical tests, our tool shows excellent real-time performance when handling large-scale data. Additionally, the server-side can be used as a backend of other similar software or a public database to manage shared datasets, showing strong scalability.

Comparative study on nanostructured order-disorder in the wing eyespots of the giant owl butterfly, Caligo memnon

A characteristic feature of the giant owl butterfly, i.e. Caligo memnon, is its big wing eyespot. This feature could serve as deceiving functionality for the butterfly against predators. As evidenced by scanning electron microscope (SEM) image on black part of eyespot, the scales on wing eyespot contain nanostructured ridges and cross-ribs. Applying direct measurement, statistical method, and Fourier analysis, the authors evidence that these nanostructures display order-disorder in their shape and position. The autocorrelation of SEM image provides average values of characteristic periods of the order-disorder nanostructures together with an estimation of corresponding correlation lengths. Linecuts obtained from the Fourier transform of SEM image were also analysed with the Hosemann function to extract similar information. These analyses indicate that the nanostructured order-disorder may contribute to blackness on wing eyespot. The authors thus conclude that the blackness on wing eyespot of C. memnon could be attributed to contributions from both the nanostructured order-disorder and melanin pigment.