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The referenced article [Appl. Opt.61, 6241 (2022)APOPAI0003-693510.1364/AO.460977] has been retracted by the authors.
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In this paper, we propose a method to automatically generate design starting points for free-form three-mirror imaging systems with different folding configurations using deep neural networks. For a given range of system parameters, a large number of datasets are automatically generated using the double seed extended curve algorithm and coded optimization. Deep neural networks are then trained using a supervised learning approach and can be used to generate good design starting points directly. The feasibility of the method is verified by designing a free-form three-mirror system with three different folding configurations. This method can significantly reduce the design time and effort for free-form imaging systems, and can be extended to complex optical systems with more optical surfaces.
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In this paper, we use Gaussian brackets and a particle swarm optimization (PSO) algorithm to design a 20× four-group zoom lens with a focus tunable lens and two moving groups. This method uses Gaussian brackets to derive the paraxial design equations of the zoom lens and determine the lens parameters. In the optimization stage, we define an objective function as a performance indicator to optimize its first-order design and implement the PSO algorithm in MATLAB to find its global optimal first-order design. The optimized 20× zoom lens has a focal length of 4.5-90 mm and a total length of 145 mm. This method solves the difficulty of solving the initial structure of the zoom. Compared with traditional trial and error, the calculation speed is faster, the accuracy is higher, and it does not rely on the initial value. The results show that this method is suitable for the first-order design of complex optical systems.
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BACKGROUND: In vivo confocal scanning laser microscopy (CSLM) is a recently developed non-invasive technique for visualizing microscopic structures with the skin. CSLM has been used to characterize proliferative and inflammatory skin diseases, neoplastic skin lesions and pigmented lesions. OBJECTIVE: Here, we assessed the ability of CSLM to evaluate the formation of neogenic hair follicles after a full-thickness wound in mice. METHODS: Full-thickness wounds were made on the dorsal skin of 3-week-old mice. After scab detachment (SD), the number, width, length, space and volume of neogenic hair follicles were analyzed using CSLM. The results were compared with those from conventional methods, including staining for alkaline phosphatase (AP) and keratin 17 (K17) as well as histology. RESULTS: Quantification of neogenic hair follicles using CSLM compared favorably with the results from direct measurements on isolated epidermal tissue after immunostaining for K17, a marker for the epithelial portion of new hair follicles. CSLM detected 89% of K17-stained follicles. CSLM more accurately quantified the number of new follicles compared with AP staining, which detects the dermal portion of the new follicle. The width and length measurement from CSLM and histology were very close and correlated with each other. The minimum length of a neogenic hair follicle that could be detected by CSLM was 21 µm. The space between neogenic hair follicles was decreased in histological sections compared with CSLM. CONCLUSION: CSLM is an accurate and valuable method for counting and measuring neogenic hair follicles non-invasively. CSLM produces images similar to histology in mice. Measurements of microstructures using CSLM more accurately reflect actual sizes as this technique avoids fixation artifacts. In vivo visualization of developing follicles with CSLM allows the detection of serial changes in hair follicle formation, thus conserving the numbers of mice required for studies and improving the detection of temporal changes in developing hair follicles.