Two-photon Ca2+ imaging is a leading technique for recording neuronal activities in vivo with cellular or subcellular resolution. However, during experiments, the images often suffer from corruption due to complex noises. Therefore, the analysis of Ca2+ imaging data requires preprocessing steps, such as denoising, to extract biologically relevant information. We present an approach that facilitates imaging data restoration through image denoising performed by a neural network combining spatiotemporal filtering and model blind learning. Tests with synthetic and real two-photon Ca2+ imaging datasets demonstrate that the proposed approach enables efficient restoration of imaging data. In addition, we demonstrate that the proposed approach outperforms the current state-of-the-art methods by evaluating the qualities of the denoising performance of the models quantitatively. Therefore, our method provides an invaluable tool for denoising two-photon Ca2+ imaging data by model blind spatiotemporal processing.

AHCAs an important member of the cyclooxygenase isoenzymes, cyclooxygenase-2 (COX-2) mainly catalyzes the first two steps in prostanoid synthesis. In mammalian animals, although COX-2 was thought to be rarely expressed in most normal tissues and was usually upregulated in a variety of epithelial tumors and inflammatory reactions, recently it was reported that COX-2 could localize in the epidermis as well as the pilosebaceous unit of the normal human and mouse skin. Until now, the function of COX-2 in normal skin has remained unknown. To investigate the possible roles of COX-2 in normal skin by RT-PCR and immunochemistry, we studied the expression pattern of COX-2 in hair cycle of the normal rat skin. The expression of COX-2 mRNA was detected in normal rat skin sample and was related to the hair follicle cycle. When the hair cycle entered catagen and telogen, COX-2 mRNA transcription in skin increased significantly. Furthermore, the location of COX-2 immunoreactivity showed that COX-2 protein is mainly concentrated in the epidermis and pilosebaceous unit. In the stratified epidermis, the strong COX-2 protein expression was detected in the suprabasal layers of epidermis in anagen and declined in catagen and telogen. In hair follicle, COX-2 protein was obviously expressed in the outer root sheath of the anagen hair follicle, and was barely detectable in catagen as well as telogen. In the sebaceous gland, the COX-2 protein expression became more intense in catagen and telogen, with an increase in sebaceous gland size. Our results suggested that COX-2 was not specific to some abnormal tissues and was indeed involved in the normal physiology of rat skin, such as the differentiation of epidermis, the morphogenesis of the hair follicle, the transformation of hair cycle stages, and the lipid production of the sebaceous gland.

The implementation of a high-speed pulse swallow frequency divider for a PLL (Phase Locked Loop) frequency synthesizer, using a 0.18,.m CMOS technology and operating with a 1.8V power supply, is described. The frequency divider is used as a scalable programmable divide-by-N frequency divider.It employs a divide-by-8/9 dual-modulus prescaler, two programmable counters, and a control circuit necessary for the time sequence and operation of the division. In the pulse swallow frequency divider, the divider circuit is attractive for the large range of programmable divide ratio from 120 to 400 since the architecture is based on using an original design of D-type Flip-Flop (DFF) with synchronous number-set and clear.Post-simulated results show that the programmable divider's operation frequency is from 0.75 GHz to 2.2 GHz with steep pulse output wave-form, providing a stable clock edge of the required frequency for the PLL frequency synthesizer.Keywords-pulse swallow frequency divider, the large range of programmable divide ratio, original design of D-type Flip-Flop, PLL

Two-photon Ca2+ imaging is a widely used technique for investigating brain functions across multiple spatial scales. However, the recording of neuronal activities is affected by movement of the brain during tasks in which the animal is behaving normally. Although post-hoc image registration is the commonly used approach, the recent developments of online neuroscience experiments require real-time image processing with efficient motion correction performance, posing new challenges in neuroinformatics. We propose a fast and accurate image density feature-based motion correction method to address the problem of imaging animal during behaviors. This method is implemented by first robustly estimating and clustering the density features from two-photon images. Then, it takes advantage of the temporal correlation in imaging data to update features of consecutive imaging frames with efficient calculations. Thus, motion artifacts can be quickly and accurately corrected by matching the features and obtaining the transformation parameters for the raw images. Based on this efficient motion correction strategy, our algorithm yields promising computational efficiency on imaging datasets with scales ranging from dendritic spines to neuronal populations. Furthermore, we show that the proposed motion correction method outperforms other methods by evaluating not only computational speed but also the quality of the correction performance. Specifically, we provide a powerful tool to perform motion correction for two-photon Ca2+ imaging data, which may facilitate online imaging experiments in the future.