Integral imaging reconstruction system based on the human eye viewing mechanism.

For integral stereo imaging systems based on lens arrays, the cross-mixing of erroneous light rays between adjacent lenses seriously affects the quality of the reconstructed light field. In this paper, we proposed a light field reconstruction method based on the human eye viewing mechanism, which incorporates simplified human eye imaging into the integral imaging system. First, the light field model for specified viewpoint is established, and the distribution of the light source for each viewpoint is accurately calculated for the EIA generation algorithm of fixed viewpoint. Second, according to the ray tracing algorithm in this paper, non-overlapping EIA based on the human eye viewing mechanism is designed to suppress the amount of crosstalk rays fundamentally. The actual viewing clarity is improved with the same reconstructed resolution. Experimental results verify the effectiveness of the proposed method. The SSIM value is higher than 0.93, which verifies that the viewing angle range is increased to 62°.

Longitudinal symptom network structure in first-episode psychosis: a possible marker for remission.

BACKGROUND: Network approach has been applied to a wide variety of psychiatric disorders. The aim of the present study was to identify network structures of remitters and non-remitters in patients with first-episode psychosis (FEP) at baseline and the 6-month follow-up. METHODS: Participants (n = 252) from the Korean Early Psychosis Study (KEPS) were enrolled. They were classified as remitters or non-remitters using Andreasen's criteria. We estimated network structure with 10 symptoms (three symptoms from the Positive and Negative Syndrome Scale, one depressive symptom, and six symptoms related to schema and rumination) as nodes using a Gaussian graphical model. Global and local network metrics were compared within and between the networks over time. RESULTS: Global network metrics did not differ between the remitters and non-remitters at baseline or 6 months. However, the network structure and nodal strengths associated with positive-self and positive-others scores changed significantly in the remitters over time. Unique central symptoms for remitters and non-remitters were cognitive brooding and negative-self, respectively. The correlation stability coefficients for nodal strength were within the acceptable range. CONCLUSION: Our findings indicate that network structure and some nodal strengths were more flexible in remitters. Negative-self could be an important target for therapeutic intervention.

Anti-Toxoplasma gondii agent isolated from Orostachys malacophylla (Pallas) Fischer.

Botanical medicinal plants have aroused our interest to deal with Toxoplasmosis which can causes serious public health problems. Nipagic acid, gallic acid, ethyl gallate, phloretic acid, protocatechuic acid, methyl p-coumarate, arbutin, and homoprotocatechuic acid are first isolated from Orostachys malacophylla (Pallas) Fischer, their inhibition rate, survival rate, biochemical and viscera index are evaluated using gastric epithelia strain-1(GES-1). Among them, arbutin can effectively prolong the survival time of mice acutely infected with T. gondii, and exhibit the same curative effect as Spiramycin (Spi) group in terms of the glutathione (GSH) and malondialdehyde (MDA) content, alleviate hepatomegaly and splenomegaly. Structure-activity relationship (SAR) and molecular docking implies that phenolic hydroxyl group would be preferred for improvement of activity. In a summary, arbutin is a potential anti-T. gondii candidate for clinical application.

Video Person Re-Identification with Frame Sampling-Random Erasure and Mutual Information-Temporal Weight Aggregation.

Partial occlusion and background clutter in camera video surveillance affect the accuracy of video-based person re-identification (re-ID). To address these problems, we propose a person re-ID method based on random erasure of frame sampling and temporal weight aggregation of mutual information of partial and global features. First, for the case in which the target person is interfered or partially occluded, the frame sampling-random erasure (FSE) method is used for data enhancement to effectively alleviate the occlusion problem, improve the generalization ability of the model, and match persons more accurately. Second, to further improve the re-ID accuracy of video-based persons and learn more discriminative feature representations, we use a ResNet-50 network to extract global and partial features and fuse these features to obtain frame-level features. In the time dimension, based on a mutual information-temporal weight aggregation (MI-TWA) module, the partial features are added according to different weights and the global features are added according to equal weights and connected to output sequence features. The proposed method is extensively experimented on three public video datasets, MARS, DukeMTMC-VideoReID, and PRID-2011; the mean average precision (mAP) values are 82.4%, 94.1%, and 95.3% and Rank-1 values are 86.4%, 94.8%, and 95.2%, respectively.

Deep Multi-Feature Transfer Network for Fourier Ptychographic Microscopy Imaging Reconstruction.

Fourier ptychographic microscopy (FPM) is a potential imaging technique, which is used to achieve wide field-of-view (FOV), high-resolution and quantitative phase information. The LED array is used to irradiate the samples from different angles to obtain the corresponding low-resolution intensity images. However, the performance of reconstruction still suffers from noise and image data redundancy, which needs to be considered. In this paper, we present a novel Fourier ptychographic microscopy imaging reconstruction method based on a deep multi-feature transfer network, which can achieve good anti-noise performance and realize high-resolution reconstruction with reduced image data. First, in this paper, the image features are deeply extracted through transfer learning ResNet50, Xception and DenseNet121 networks, and utilize the complementarity of deep multiple features and adopt cascaded feature fusion strategy for channel merging to improve the quality of image reconstruction; then the pre-upsampling is used to reconstruct the network to improve the texture details of the high-resolution reconstructed image. We validate the performance of the reported method via both simulation and experiment. The model has good robustness to noise and blurred images. Better reconstruction results are obtained under the conditions of short time and low resolution. We hope that the end-to-end mapping method of neural network can provide a neural-network perspective to solve the FPM reconstruction.

Diffraction efficiency enhancement and optimization in full-color HOE using the inhibition characteristics of the photopolymer.

A novel and effective simultaneous recording method, to the best of our knowledge, is proposed for improving the diffraction efficiency and uniformity of full-color holographic optical elements (HOE) using the Bayfol HX102 photopolymer. To improve the diffraction efficiency of a full-color HOE, it is important to find the optimal recording beam intensity taking into account the initial and late responses of the medium. The range of optimal beam intensity for recording full-color HOE can be found experimentally by analyzing the inhibition period and response characteristics of the recording medium for three wavelengths. Through this method, a full-color HOE with an average diffraction efficiency of about 56.81% and a standard deviation of about 1.7% was implemented in a single layer photopolymer.

Simplified digital content generation based on an inverse-directed propagation algorithm for holographic stereogram printing.

Holographic stereogram (HS) printing requires extensive memory capacity and long computation time during perspective acquisition and implementation of the pixel re-arrangement algorithm. Hogels contain very weak depth information of the object. We propose a HS printing system that uses simplified digital content generation based on the inverse-directed propagation (IDP) algorithm for hogel generation. Specifically, the IDP algorithm generates an array of hogels using a simple process that acquires the full three-dimensional (3D) information of the object, including parallax, depth, color, and shading, via a computer-generated integral imaging technique. This technique requires a short computation time and is capable of accounting for occlusion and accommodation effects of the object points via the IDP algorithm. Parallel computing is utilized to produce a high-resolution hologram based on the properties of independent hogels. To demonstrate the proposed approach, optical experiments are conducted in which the natural 3D visualizations of real and virtual objects are printed on holographic material. Experimental results demonstrate the simplified computation involved in content generation using the proposed IDP-based HS printing system and the improved image quality of the holograms.

Max-depth-range technique for faster full-color hologram generation.

In this paper, a max-depth-range method is proposed to determine the optimum length of depth range for faster generation of full-color holograms. For each color channel, objects are divided by a fixed length to create a temporary depth range, and the wavefront recording plane (WRP) is placed in the middle of all layers within the temporary depth range. The proposed method is used to calculate full-color holograms significantly faster than a conventional multiple-WRP method but with almost the same reconstructed image quality. The feasibility of the proposed method was confirmed using numerical and optical experiments for various scenes containing multiple real objects.

Improving the quality of full-color holographic three-dimensional displays using depth-related multiple wavefront recording planes with uniform active areas.

In this paper, a depth-related uniform multiple wavefront recording plane (UM-WRP) method is proposed for enhancing the image quality of point cloud-based holograms. Conventional multiple WRP methods, based on full-color computer-generated holograms, experience a color uniformity problem caused by intensity distributions. To solve this problem, the proposed method generates depth-related WRPs to enhance color uniformity, thereby accelerating hologram generation using a uniform active area. The aim is to calculate depth-related WRPs with designed active area sizes that then propagate to the hologram. Compared with conventional multiple WRP methods, reconstructed images have significantly improved quality, as confirmed by numerical simulations and optical experiments.

Chromatic-dispersion-corrected full-color holographic display using directional-view image scaling method.

A novel directional-view image scaling method that corrects chromatic dispersion and enhances the quality of three-dimensional (3D) images reconstructed by a full-color holographic display system is proposed. When the 3D information of the real scene is acquired through the integral imaging pickup method, the orthographic projection image is reconstructed. Then, each directional-view image is separated and synthesized onto the computer-generated hologram. To correct the chromatic dispersion of the full-color holographic 3D display, each directional-view image is scaled depending on the relation between the different wavelengths of single-channel holograms and resolutions of the sub-holograms. According to the optical experimental results, it can be concluded that the proposed method is an effective way of producing full-color holographic images from an orthographic projection image through a simple process.

Multi-depth three-dimensional image encryption based on the phase retrieval algorithm in the Fresnel and fractional Fourier transform domains.

We propose a multi-depth three-dimensional (3D) image cryptosystem by employing the phase retrieval algorithm in the Fresnel and fractional Fourier (Fr-FrF) domains. Encryption was realized by applying the phase retrieval algorithm based on the double-random-phase-encoding architecture in which two encryption keys will be incessantly updated in each iteration loop. The phase-only functions (POFs) are generated in two cascaded Fr-FrF transforms (Fr-FrFT), serving as decryption keys to efficiently reduce the speckle noise and crosstalk between encrypted 3D image depths. The use of Fr-FrFT position parameters and fractional order as decryption keys further extended the key space, enhancing the cryptosystem's security level. Numerical simulations demonstrated the feasibility and robustness of our proposed scheme.

Depth-layer weighted prediction method for a full-color polygon-based holographic system with real objects.

We propose a full-color polygon-based holographic system for real three-dimensional (3D) objects using a depth-layer weighted prediction method. The proposed system is composed of four main stages: acquisition, preprocessing, hologram generation, and reconstruction. In the preprocessing stage, the point cloud model is separated into red, green, and blue channels with depth-layer weighted prediction. The color component values are characterized based on the depth information of the real object, then color prediction is derived from the measurement data. The computer-generated holograms reconstruct 3D full-color images with a strong sensation of depth resulting from the polygon approach. The feasibility of the proposed method was confirmed by numerical and optical reconstruction.

Three-dimensional image acquisition and reconstruction system on a mobile device based on computer-generated integral imaging.

A mobile three-dimensional image acquisition and reconstruction system using a computer-generated integral imaging technique is proposed. A depth camera connected to the mobile device acquires the color and depth data of a real object simultaneously, and an elemental image array is generated based on the original three-dimensional information for the object, with lens array specifications input into the mobile device. The three-dimensional visualization of the real object is reconstructed on the mobile display through optical or digital reconstruction methods. The proposed system is implemented successfully and the experimental results certify that the system is an effective and interesting method of displaying real three-dimensional content on a mobile device.

A multi-task fusion model based on a residual-Multi-layer perceptron network for mammographic breast cancer screening.

BACKGROUND AND OBJECTIVE: Deep learning approaches are being increasingly applied for medical computer-aided diagnosis (CAD). However, these methods generally target only specific image-processing tasks, such as lesion segmentation or benign state prediction. For the breast cancer screening task, single feature extraction models are generally used, which directly extract only those potential features from the input mammogram that are relevant to the target task. This can lead to the neglect of other important morphological features of the lesion as well as other auxiliary information from the internal breast tissue. To obtain more comprehensive and objective diagnostic results, in this study, we developed a multi-task fusion model that combines multiple specific tasks for CAD of mammograms. METHODS: We first trained a set of separate, task-specific models, including a density classification model, a mass segmentation model, and a lesion benignity-malignancy classification model, and then developed a multi-task fusion model that incorporates all of the mammographic features from these different tasks to yield comprehensive and refined prediction results for breast cancer diagnosis. RESULTS: The experimental results showed that our proposed multi-task fusion model outperformed other related state-of-the-art models in both breast cancer screening tasks in the publicly available datasets CBIS-DDSM and INbreast, achieving a competitive screening performance with area-under-the-curve scores of 0.92 and 0.95, respectively. CONCLUSIONS: Our model not only allows an overall assessment of lesion types in mammography but also provides intermediate results related to radiological features and potential cancer risk factors, indicating its potential to offer comprehensive workflow support to radiologists.

Seizure prediction based on improved vision transformer model for EEG channel optimization.

Epileptic seizures are unpredictable events caused by abnormal discharges of a patient's brain cells. Extensive research has been conducted to develop seizure prediction algorithms based on long-term continuous electroencephalogram (EEG) signals. This paper describes a patient-specific seizure prediction method that can serve as a basis for the design of lightweight, wearable and effective seizure-prediction devices. We aim to achieve two objectives using this method. The first aim is to extract robust feature representations from multichannel EEG signals, and the second aim is to reduce the number of channels used for prediction by selecting an optimal set of channels from multichannel EEG signals while ensuring good prediction performance. We design a seizure-prediction algorithm based on a vision transformer (ViT) model. The algorithm selects channels that play a key role in seizure prediction from 22 channels of EEG signals. First, we perform a time-frequency analysis of processed time-series signals to obtain EEG spectrograms. We then segment the spectrograms of multiple channels into many non-overlapping patches of the same size, which are input into the channel selection layer of the proposed model, named Sel-JPM-ViT, enabling it to select channels. Application of the Sel-JPM-ViT model to the Boston Children's Hospital-Massachusetts Institute of Technology scalp EEG dataset yields results using only three to six channels of EEG signals that are slightly better that the results obtained using 22 channels of EEG signals. Overall, the Sel-JPM-ViT model exhibits an average classification accuracy of 93.65%, an average sensitivity of 94.70% and an average specificity of 92.78%.

Xanthophyllomyces dendrorhous Is a Safe Dietary Supplement with Potent Antioxidant Defense Enhancing Activity.

Xanthophyllomyces dendrorhous (X. dendrorhous), previously known as Phaffia rhodozyma, is a red yeast that is widely recognized as a rich source of carotenoids, particularly astaxanthin, which exhibits potent antioxidant activity and other health-promoting functions. However, there is currently a lack of research on the safety of consuming X. dendrorhous. To address this, we conducted an acute toxicity study followed by a 90-day subchronic toxicity trial to evaluate the safety of X. dendrorhous and investigate its in vivo antioxidant activity. In the acute toxicity study, Sprague-Dawley rats were administered a maximum of 12 g/kg body weight of X. dendrorhous powder by gavage and survived without any adverse effects for 14 days. In the subsequent subchronic toxicity test, the rats were randomly divided into five groups, each with free access to their diet adulterated with 0% (control), 2.5% (low), 5% (middle), 10% (high), and 20% (extreme high) X. dendrorhous powder. The rats' behavior, body weight, and food intake were monitored during the 90-day experiment. At the end of the experiment, urine, blood, and organs were collected from the rats for biochemical testing. Additionally, the antioxidant activity in rat sera was evaluated. The results of the acute toxicity test demonstrated that the LD50 of X. dendrorhous was greater than 12 g/kg body weight, indicating that the substance was not toxic. Throughout the 90-day period of subchronic toxicity, the triglyceride levels of male rats fed with 10 and 20% X. dendrorhous increased to 1.54 ± 0.17 and 1.55 ± 0.25 mmol/L (P < 0.05), respectively. This may be attributed to the elevated fat content of the diet in the high-dose and extreme high-dose groups, which was 5.5 and 2.5% higher than that in the control, respectively. Additionally, the white pulp in the spleen exhibited an increase, and the number of white blood cells in the extreme high-dose group increased by 2.41 × 109/L (P < 0.05), which may contribute to enhanced immunity. Finally, the body weight, food intake, blood and urine indexes, and histopathological examination results of the organs of the rats did not demonstrate any regular toxic effects. With the adulteration of X. dendrorhous, the activity of GSH-Px in male rats increased by 16-36.32%. The activity of GSH-Px in female rats of the extreme high-dose group increased by 14.70% (P < 0.05). The free radical scavenging ability of ABTS in male rats in the two high-dose groups exhibited an increase of 6.5 and 11.41% (P < 0.05). In contrast, the MDA content of male rats in the extreme high-dose group demonstrated a reduction of 2.73 nmol/mL (P < 0.05). These findings indicate that X. dendrorhous has no toxic effects, can be taken in high doses, and has a beneficial antioxidant effect that may enhance the body's immunity.

Microstructure Formation and Characterization of Long-Acting Injectable Microspheres: The Gateway to Fully Controlled Drug Release Pattern.

Long-acting injectable microspheres have been on the market for more than three decades, but if calculated on the brand name, only 12 products have been approved by the FDA due to numerous challenges in achieving a fully controllable drug release pattern. Recently, more and more researches on the critical factors that determine the release kinetics of microspheres shifted from evaluating the typical physicochemical properties to exploring the microstructure. The microstructure of microspheres mainly includes the spatial distribution and the dispersed state of drug, PLGA and pores, which has been considered as one of the most important characteristics of microspheres, especially when comparative characterization of the microstructure (Q3) has been recommended by the FDA for the bioequivalence assessment. This review extracted the main variables affecting the microstructure formation from microsphere formulation compositions and preparation processes and highlighted the latest advances in microstructure characterization techniques. The further understanding of the microsphere microstructure has significant reference value for the development of long-acting injectable microspheres, particularly for the development of the generic microspheres.

Multi-view fusion-based local-global dynamic pyramid convolutional cross-tansformer network for density classification in mammography.

Object.Breast density is an important indicator of breast cancer risk. However, existing methods for breast density classification do not fully utilise the multi-view information produced by mammography and thus have limited classification accuracy.Method.In this paper, we propose a multi-view fusion network, denoted local-global dynamic pyramidal-convolution transformer network (LG-DPTNet), for breast density classification in mammography. First, for single-view feature extraction, we develop a dynamic pyramid convolutional network to enable the network to adaptively learn global and local features. Second, we address the problem exhibited by traditional multi-view fusion methods, this is based on a cross-transformer that integrates fine-grained information and global contextual information from different views and thereby provides accurate predictions for the network. Finally, we use an asymmetric focal loss function instead of traditional cross-entropy loss during network training to solve the problem of class imbalance in public datasets, thereby further improving the performance of the model.Results.We evaluated the effectiveness of our method on two publicly available mammography datasets, CBIS-DDSM and INbreast, and achieved areas under the curve (AUC) of 96.73% and 91.12%, respectively.Conclusion.Our experiments demonstrated that the devised fusion model can more effectively utilise the information contained in multiple views than existing models and exhibits classification performance that is superior to that of baseline and state-of-the-art methods.

Predicting epileptic seizures based on EEG signals using spatial depth features of a 3D-2D hybrid CNN.

Epilepsy is a recurrent chronic brain disease that affects nearly 75 million people around the world. Therefore, the ability to reliably predict epileptic seizures would be instrumental for implementing interventions to reduce brain injury and improve patients' quality of life. In addition to classical machine learning algorithms and feature engineering methods, the use of electroencephalography (EEG) to predict seizures has gradually become a mainstream trend. Here, we propose a patient-specific method to predict epileptic seizures based on EEG data acquired using spatial depth features of a three-dimensional-two-dimensional hybrid convolutional neural network (3D-2D HyCNN) model. This method facilitates the acquisition of abundant and reliable deep features from multi-channel EEG signals. We first developed a reliable data preprocessing method to reconstruct time-series EEG signals into 3D feature images. Then, the 3D-2D HyCNN model was used to extract correlation features between multiple channels of EEG signals, which are automatically exploited by the network to improve seizure prediction. The method achieved accuracy of 98.43% and 93.11%, sensitivity of 98.58% and 90.98%, and specificity of 96.86% and 92.39% on the CHB-MIT Scalp EEG dataset and the American Epilepsy Society Epilepsy Prediction Challenge dataset, respectively. The results revealed that the new algorithm is reliable. Graphical Abstract A new patient-specific epilepsy prediction approach.

Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification.

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.