To investigate the influence of continuous near-infrared (CNI) laser to potassium ion channels on retinal ganglion cell (RGC).Experiment study. Porcine RGC was cultured with enzymatic digestion method in vitro by taking off the retina from the piglets. Whole-cell patch clamp mode recordings were obtained from primary cultured porcine RGC. Whole-cell currents of porcine RGC irradiated with a single-mode CNI laser of 845 nm wavelength and 30 mW power were also recorded. The primary cultured porcine RGC were divided into laser irradiation group and control group according to whether they accepted CNI laser of 845 nm wavelength. t test was used to analyze the average peak amplitude between the two groups.The cells had the morphological characteristics of typical neurons after one week cultured observed by inverted phase contrast microscope. The body and bumps with yellow-green fluorescence was positive cell which observed by immune cell fluorescence chemical testing. CNI laser had a regulation on outward potassium current of porcine RGC in a voltage-dependent manner. There was significant difference on the average peak amplitude of potassium current between the laser irradiation group [(634.4 ± 86.8) Pa] and the control group [(580.5 ± 116.4) Pa], respectively (n = 30, t = 7.923, P = 0.000).CNI laser can change the properties of outward K(+) channel. Therefore, formation and releasing of action potential is affected. Further, physiological functions of RGC are regulated, which might contribute to the protection and restoration of injured RGC. It can be provided a new scholar direction for the protection of the RGC, which are injured by glaucoma.
Optical coherence tomography (OCT) is a promising non-invasive non-contact 3D imaging technique that can be used to evaluate and inspect material surfaces, multilayer polymer films, fiber coils, and coatings. OCT can be used for the examination of cultural heritage objects and 3D imaging of microstructures. With subsurface 3D fingerprint imaging capability, OCT could be a valuable tool for enhancing security in biometric applications. OCT can also be used for the evaluation of fastener flushness for improving aerodynamic performance of high-speed aircraft. More and more OCT non-medical applications are emerging. In this book, we present some recent advancements in OCT technology and non-medical applications.
In spectral-domain optical coherence tomography (SDOCT), traditional spectrometers with a grating and line-scan camera yield nonlinear wavenumber responses, affecting OCT signal sensitivity and resolution. This necessitates post-processing for spectral interferogram remapping, but it's limited in short-wavelength ranges due to uneven pixel frequency spacing.
To overcome these challenges, we introduce a cost-effective, simple linear-wavenumber spectrometer using a dual-prism and reflector setup, significantly enhancing spectral dispersion linearity, vital for ultra-high resolution SDOCT. Our method employs iterative calculations with global stochastic gradient descent for higher-order dispersion linearization. This results in a substantial increase in wavenumber linearity, from 99.9714% to 99.9998% for 80 nm at 850 nm wavelength, and 99.6828% to 99.9861% for 260 nm bandwidth. Our design eliminates resampling needs for up to 260 nm bandwidth, with nonlinearity-induced wavenumber mismatch under one pixel.
This innovation marks a significant advancement in SDOCT spectrometer design, enhancing performance and resolution beyond traditional system limitations.
Real-time Fourier domain optical coherence tomography (FDOCT) has been widely used in clinical applications. In order to accelerate the imaging processing and display of FDOCT, an alternative lookup table-based strategy for logarithmic transformation was presented. In this paper, real-time and high-quality FDOCT imaging display of biological tissues at an A-line rate of 62 kHz was demonstrated by optimizing the logarithmic calculation.
The ventrolateral periaqueductal gray (VLPAG) is thought to be the main PAG column for bladder control. PAG neurons (especially VLPAG neurons) and neurons in the pontine micturition center (PMC) innervating the bladder detrusor have anatomical and functional synaptic connections. The prevailing viewpoint on neural control of the bladder is that PAG neurons receive information on the decision to void made by upstream brain regions, and consequently activate the PMC through their direct projections to initiate urination reflex. However, the exact location of the PMC-projecting VLPAG neurons, their activity in response to urination, and their whole-brain inputs remain unclear. Here, we identified the distribution of VLPAG neurons that may participate in control of the bladder or project to the PMC through retrograde neural tracing. Population Ca2+ signals of PMC-projecting VLPAG neurons highly correlated with bladder contractions and urination as shown by in vivo recording in freely moving animals. Using a RV-based retrograde trans-synaptic tracing strategy, morphological results showed that urination-related PMC-projecting VLPAG neurons received dense inputs from multiple urination-related higher brain areas, such as the medial preoptic area, medial prefrontal cortex, and lateral hypothalamus. Thus, our findings reveal a novel insight into the VLPAG for control of bladder function and provide a potential therapeutic midbrain node for neurogenic bladder dysfunction.
With the rapid development of the marine industry, intelligent ship detection plays a very important role in the marine traffic safety and the port management. Current detection methods mainly focus on synthetic aperture radar (SAR) images, which is of great significance to the field of ship detection. However, these methods sometimes cannot meet the real‐time requirement. To solve the problems, a novel ship detection network based on SSD (Single Shot Detector), named NSD‐SSD, is proposed in this paper. Nowadays, the surveillance system is widely used in the indoor and outdoor environment, and its combination with deep learning greatly promotes the development of intelligent object detection and recognition. The NSD‐SSD uses visual images captured by surveillance cameras to achieve real‐time detection and further improves detection performance. First, dilated convolution and multiscale feature fusion are combined to improve the small objects’ performance and detection accuracy. Second, an improved prediction module is introduced to enhance deeper feature extraction ability of the model, and the mean Average Precision (mAP) and recall are significant improved. Finally, the prior boxes are reconstructed by using the K ‐means clustering algorithm, the Intersection‐over‐Union (IoU) is higher, and the visual effect is better. The experimental results based on ship images show that the mAP and recall can reach 89.3% and 93.6%, respectively, which outperforms the representative model (Faster R‐CNN, SSD, and YOLOv3). Moreover, our model’s FPS is 45, which can meet real‐time detection acquirement well. Hence, the proposed method has the better overall performance and achieves higher detection efficiency and better robustness.
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