Search

We propose a light-field microscopy display system that provides improved image quality and realistic three-dimensional (3D) measurement information. Our approach acquires both high-resolution two-dimensional (2D) and light-field images of the specimen sequentially. We put forward a matting Laplacian-based depth estimation algorithm to obtain nearly realistic 3D surface data, allowing the calculation of depth data, which is relatively close to the actual surface, and measurement information from the light-field images of specimens. High-reliability area data of the focus measure map and spatial affinity information of the matting Laplacian are used to estimate nearly realistic depths. This process represents a reference value for the light-field microscopy depth range that was not previously available. A 3D model is regenerated by combining the depth data and the high-resolution 2D image. The element image array is rendered through a simplified direction-reversal calculation method, which depends on user interaction from the 3D model and is displayed on the 3D display device. We confirm that the proposed system increases the accuracy of depth estimation and measurement and improves the quality of visualization and 3D display images.

This study proposes a robust depth map framework based on a convolutional neural network (CNN) to calculate disparities using multi-direction epipolar plane images (EPIs). A combination of three-dimensional (3D) and two-dimensional (2D) CNN-based deep learning networks is used to extract the features from each input stream separately. The 3D convolutional blocks are adapted according to the disparity of different directions of epipolar images, and 2D-CNNs are employed to minimize data loss. Finally, the multi-stream networks are merged to restore the depth information. A fully convolutional approach is scalable, which can handle any size of input and is less prone to overfitting. However, there is some noise in the direction of the edge. A weighted median filtering (WMF) is used to acquire the boundary information and improve the accuracy of the results to overcome this issue. Experimental results indicate that the suggested deep learning network architecture outperforms other architectures in terms of depth estimation accuracy.

Trajectory-based writing system refers to writing a linguistic character or word in free space by moving a finger, marker, or handheld device. It is widely applicable where traditional pen-up and pen-down writing systems are troublesome. Due to the simple writing style, it has a great advantage over the gesture-based system. However, it is a challenging task because of the non-uniform characters and different writing styles. In this research, we developed an air-writing recognition system using three-dimensional (3D) trajectories collected by a depth camera that tracks the fingertip. For better feature selection, the nearest neighbor and root point translation was used to normalize the trajectory. We employed the long short-term memory (LSTM) and a convolutional neural network (CNN) as a recognizer. The model was tested and verified by the self-collected dataset. To evaluate the robustness of our model, we also employed the 6D motion gesture (6DMG) alphanumeric character dataset and achieved 99.32% accuracy which is the highest to date. Hence, it verifies that the proposed model is invariant for digits and characters. Moreover, we publish a dataset containing 21,000 digits; which solves the lack of dataset in the current research.

Background : Tamarix dioica is traditionally used to manage various disorders related to smooth muscle in the gastrointestinal, respiratory, and cardiovascular systems. This study was planned to establish a pharmacological basis for the uses of Tamarix dioica in certain medical conditions related to the digestive, respiratory, and cardiovascular systems, and to explore the underlying mechanisms. Methods : A phytochemical study was performed by preliminary methods, followed by HPLC-DAD and spectrometric methods. In vivo evaluation of a crude hydromethanolic extract of T. dioica (TdCr) was done with a castor-oil-provoked diarrheal model in rats to determine its antidiarrheal effect. Ex vivo experiments were done by using isolated tissues to determine the effects on smooth and cardiac muscles and explore the possible mechanisms. Results : TdCr tested positive for flavonoids, saponins, phenols, and tannins as methanolic solvable constituents in a preliminary study. The maximum quantity of gallic acid equivalent (GAE), phenolic, and quercetin equivalent (QE) flavonoid content found was 146 ± 0.001 μg GAE/mg extract and 36.17 ± 2.35 μg QE/mg extract. Quantification based on HPLC-DAD (reverse phase) exposed the presence of rutin at the highest concentration, followed by catechin, gallic acid, myricetin, kaempferol, and apigenin in TdCr. In vivo experiments showed the significant antidiarrheal effect of TdCr (100, 200, and 400 mg/kg) in the diarrheal (castor-oil-provoked) model. Ex vivo experiments revealed spasmolytic, bronchodilatory, and vasorelaxant activities as well as partial cardiac depressant activity, which may be potentiated by a potassium channel opener mechanism, similar to that of cromakalim. The potassium channel (K ATP channel)-opening activity was further confirmed by repeating the experiments in glibenclamide-pretreated tissues. Conclusions : In vivo and ex vivo studies of T. dioica provided evidence of the antidiarrheal, spasmolytic, bronchodilator, vasorelaxant, and partial cardiodepressant properties facilitated through the opening of the K ATP channel., Competing Interests: The authors declare no conflict of interest.