In the so-called surface microscopy, serial block-face imaging is combined with mechanic sectioning to obtain volumetric imaging. While mapping a resin-embedded green fluorescent protein (GFP)-labeled specimen, it has been recently reported that an alkaline buffer is used to chemically reactivate the protonated GFP molecules, and thus improve the signal-to-noise ratio. In such a procedure, the image quality is highly affected by the penetration rate of a solution. We propose a reliable penetration model to describe the penetration process of the solution into the resin. The experimental results are consistent with the parameters predicted using this model. Thus, this model provides a valuable theoretical explanation and aids in optimizing the system parameters for mapping resin-embedded GFP biological samples.
The brain-wide reconstruction of neuronal population is an indispensible step towards exploring the complete structure of neuronal circuits, a central task that underlies the structure-function relation in neuroscience. Recent advances in molecular labeling and imaging techniques enable us to collect the whole mouse brain imaging dataset at cellular resolution, including the morphological information of neurons across different brain region or even the whole brain. Reconstruction of these neurons poses substantial challenges, and at presents there is no tool for high-speed achieving this reconstruction close to human performance. Here, we presented a tool for filling in the blanks. The tool mainly contains the following function modules: 3D visualization of large-scale imaging dataset, automated reconstruction of neurons, manual editing of the reconstructions at local and global scale. In this tool, in the framework of our previous tools (NeuroGPS-Tree and SparseTracer), the two identifying models were constructed for boosting the automatic level of the reconstruction. One is used to identify the weak signals from inhomogeneous backgrounds and the other is used to identify closely packed neurites. This tool can be suitable for the different big-data formats and can make the dataset be fastly read into memory for the reconstruction. The manual editing module in this tool can correct the errors drawn from above automated algorithms. And thus helps to achieve the reconstruction closer to human performance. We demonstrated the features of our tool on various kinds of sparsely labelled datasets. The results indicated that without loss of the reconstruction accuracy, our tool has a 7-10 folds speed gain over the commercial software that provides the manual reconstruction.
Compared with wavelet, framelet has good time frequency analysis ability and redundant characteristic. SVD (Singular Value Decomposition) can obtain stable feature of images which is not easily destroyed. To further improve the watermarking technique, a robust digital watermarking algorithm based on framelet and SVD is proposed. Firstly, Arnold transform is implemented to the grayscale watermark image. Secondly perform framelet transform to each host block which is divided according to the size of the watermark. Then embed the scrambled watermark into the biggest singular values produced in SVD transform to each coarse band gained from framelet transform to host image block. At last inverse framelet transform after inverse SVD transform to obtain embedded coarse band. Experimental results show that the proposed method gains good performance in robustness and security compared with traditional image processing including noise attack, cropping, filtering and JPEG compression etc. Moreover, the watermark imperceptibility of our method is better than that of wavelet and has stronger robustness than pure framelet without SVD.
Spatiotemporal activity patterns in local neural networks are fundamental to understanding how information is processed and stored in brain microcircuits. Currently, imaging techniques are able to map the directional activation of macronetworks across brain areas; however, these strategies still fail to resolve the activation direction for fine microcircuits with cellular spatial resolution. Here, we show the capability to identify the activation direction of a multicell network with a cellular resolution and millisecond precision by using fast two-photon microscopy and cross correlation procedures. As an example, we characterized a directional neuronal network in an epilepsy brain slice to provide different initiation delay among multiple neurons defined at a millisecond scale.
Localization of a single fluorescent molecule is required in a number of superresolution imaging techniques for visualizing biological structures at cellular and subcellular levels. The localization capability and limitation of low-light detectors are critical for such a purpose. We present an updated evaluation on the performance of three typical low-light detectors, including a popular electron-multiplying CCD (EMCCD), a newly developed scientific CMOS (sCMOS), and a representative cooled CCD, for superresolution imaging. We find that under some experimental accessible conditions, the sCMOS camera shows a competitive and even better performance than the EMCCD camera, which has long been considered the detector of choice in the field of superresolution imaging.
KEYWORDS: Calcium, Luminescence, Reconstruction algorithms, Action potentials, Neurons, Deconvolution, Signal to noise ratio, In vivo imaging, Convolution, Linear filtering
Identification of a small population of neuronal action potentials (APs) firing is considered essential to discover the operating principles of neuronal circuits. A promising method is to indirectly monitor the AP discharges in neurons from the recordings their intracellular calcium fluorescence transients. However, it is hard to reveal the nonlinear relationship between neuronal calcium fluorescence transients and the corresponding AP burst discharging. We propose a method to reconstruct the neuronal AP train from calcium fluorescence diversifications based on a multiscale filter and a convolution operation. Results of experimental data processing show that the false-positive rate and the event detection rate are about 10 and 90%, respectively. Meanwhile, the APs firing at a high frequency up to 40 Hz can also be successfully identified. From the results, it can be concluded that the method has strong power to reconstruct a neuronal AP train from a burst firing.
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