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Background: To quantitatively evaluate the effectiveness of the Noise2Noise (N2N) model, a deep learning (DL)-based noise reduction algorithm, on enhanced depth imaging-optical coherence tomography (EDI-OCT) images with different noise levels. Methods: The study included 30 subfoveal EDI-OCT images averaged with 100 frames from 30 healthy participants. Artificial Gaussian noise at 25.00, 50.00, and 75.00 standard deviations were added to the averaged (original) images, and the images were grouped as 25N, 50N, and 75N. Afterward, noise-added images were denoised with the N2N model and grouped as 25dN, 50dN, and 75dN, according to previous noise levels. The choroidal vascularity index (CVI) and deep choroidal contrast-to-noise ratio (CNR) were calculated for all images, and noise-added and denoised images were compared with the original images. The structural similarity of the noise-added and denoised images to the original images was assessed by the Multi-Scale Structural Similarity Index (MS-SSI). Results: The CVI and CNR parameters of the original images (68.08 ± 2.47 %, and 9.71 ± 2.80) did not differ from the only 25dN images (67.97 ± 2.34 % and 8.50 ± 2.43) (p:1.000, and p:0.062, respectively). Noise reduction improved the MS-SSI at each noise level (p

We record Optical coherence tomography (OCT) images of various textile fabrics. Each textile fabric consisted of one material only: wool, cotton or polyester. We took OCT images from three different fabrics for each material type giving a total of 9 different fabrics. We scan each material at least a hundred times at different places on each surface. In order to have approximately consistent data between samples, the scans for each image were fixed to 2 mm scan length and saved in a portable network format. We divide the material data into three categories. Groups 1, 2, and 3 consisted only of cotton, wool, and polyester fabrics, respectively. These were placed in folders, becoming the labelled dataset for deep learning training classes. We publish this OCT fabric image dataset publicly. Researchers can utilize the data to train deep learning networks, test existing machine learning algorithms, or develop new systems for automated material classification and recycling.

Background/purpose: The aim of this study is to investigate the effects of systemically administered boric acid on osteoporosis-related bone alterations, alveolar bone loss, receptor activator of nuclear factor kappa-b ligand (RANKL) expressions, and mandibular bone density in experimental periodontitis model in osteoporotic rats. Materials and methods: Thirty-six male Wistar rats were separated into five study groups: nonligated control (C, n = 6) group; periodontitis (P, n = 6) group; osteoporosis (O, n = 8) group; osteoporosis + periodontitis (O+P, n = 8) group, and osteoporosis + periodontitis with 50 mg/kg/d boric acid (BA50, n = 8) group for 15 days. Osteoporosis was created with intraperitoneal injection of 80 mg/kg retinoic acid for 15 days. Silk ligatures (4/0) were placed around the mandibular right first molar teeth to induce experimental periodontitis. After induction of osteoporosis and periodontitis, rats were sacrificed at Day 15. Alveolar bone loss was evaluated with a stereomicroscope by measuring the distance from the cement-enamel junction to the alveolar crest. Density measurements were performed on radiographs. RANKL and tartrate-resistant acid phosphatase (TRAP) staining were performed on histological slides. Results: Alveolar bone loss was significantly higher in the O+P group than those of the other groups (P 0.05). Conclusion: Within limitations of this study, we conclude that boric acid may decrease alveolar bone loss in a rat model with periodontitis and osteoporosis.

In this study, the numerical investigation of ship motions and added resistance at constant forward velocity of KVLCC2 model is presented. Finite volume CFD code is used to calculate three dimensional, incompressible, unsteady RANS equations. Numerical computations show that reliable numerical results can be obtained in head waves. In the numerical analyses, body attached mesh method is used to simulate the ship motions. Free surface is simulated by using VOF method. The relationship between the turbulence viscosity and the velocities are obtained through the standard k − ε turbulence model. The numerical results are examined in terms of ship resistance, ship motions and added resistance. The validation studies are carried out by comparing the present results obtained for the KVLCC2 hull from the literature. It is shown that, ship resistance, pitch and heave motions in regular head waves can be estimated accurately, although, added resistance can be predicted with some error.