Correlative study on retinal microvascular changes and sex hormones in male patients with central serous chorioretinopathy

Central serous chorioretinopathy (CSC) is a disease in which the outer retinal barrier is damaged with high incidence in young adult males. We aimed to analyze the correlations between retinal microvascular changes and sex hormone levels. The vascular density of the superficial retinal capillary plexus (SCP), deep retinal capillary plexus (DCP), foveal avascular zone (FAZ) area, choriocapillary blood flow area, and the subfoveal choroidal thickness (SCT) were investigated by optical coherence tomography angiography (OCTA). We also determined the levels of sex hormones (adrenaline (AD), norepinephrine (NE), dopamine (DA), corticosteroids (Cor), aldosterone (ALD), estradiol (E2) and total testosterone (TT)). The relationship between sex hormone levels and OCTA parameters was then determined. We detected significantly higher levels of NE, Cor and TT in serum from the observation group than in the control group (p < 0.05). Significant correlations were identified between SCT and choriocapillary blood flow area in the affected eyes, contralateral eyes and healthy eyes in the control group (p < 0.05). SCT levels of both eyes in the observation group were higher and the choriocapillary blood flow area was smaller than in the control group. The SCT in affected eyes from the observation group were higher than the contralateral eyes (p < 0.05). The choriocapillary blood flow area was significantly smaller than in the contralateral eyes (p < 0.05). Correlation analysis unveiled that NE, Cor and TT levels were positively correlated with SCT in CSC patients and negatively correlated with choriocapillary blood flow area (p < 0.05). The serum levels of sex hormone levels in male CSC patients were different from those in healthy men of the same age. Our findings suggest that the serum levels of NE, Cor and TT levels may influence the pathogenesis of CSC by affecting SCT thickness and choriocapillary blood flow

A Recommendation Algorithm Combining Local and Global Interest Features

Due to the ability of knowledge graph to effectively solve the sparsity problem of collaborative filtering, knowledge graph (KG) has been widely studied and applied as auxiliary information in the field of recommendation systems. However, existing KG-based recommendation methods mainly focus on learning its representation from the neighborhood of target items, ignoring the influence of other items on the target item. The learning focuses on the local feature representation of the target item, which is not sufficient to effectively explore the user’s preference degree for the target item. To address the above issues, in this paper, an approach combining users’ local interest features with global interest features (KGG) is proposed to efficiently explore the user’s preference level for the target item, which learns the user’s local interest features and global interest features for target item through Knowledge Graph Convolutional Network and Generative Adversarial Network (GAN). Specifically, this paper first utilizes the Knowledge Graph Convolutional Network to mine related attributes on the knowledge graph to effectively capture item correlations and obtain the local feature representation of the target item, then uses the matrix factorization method to learn the user’s local interest features for target items. Secondly, it uses GAN to learn the user’s global interest features for target items from the implicit interaction matrix. Finally, a linear fusion layer is designed to effectively fuse the user’s local and global interests towards target items to obtain the final click prediction. Experimental results on three real datasets show that the proposed method not only effectively integrates the user’s local and global interests but also further alleviates the problem of data sparsity. Compared with the current baselines for knowledge graph-based systems, the KGG method achieves a maximum improvement of 8.1% and 7.6% in AUC and ACC, respectively

Deep Collaborative Recommendation Algorithm Based on Attention Mechanism

Representation learning-based collaborative filtering (CF) methods address the linear relationship of user-items with dot products and cannot study the latent nonlinear relationship applied to implicit feedback. Matching function learning-based CF methods directly learn the complicated mapping functions that map user-item pairs to matching scores, which has limitations in identifying low-rank relationships. To this end, we propose a deep collaborative recommendation algorithm based on attention mechanism (DACR). First, before the user-item representations are input into the DNNs, we utilize the attention mechanism to adaptively assign different weights to the user-item representations, which captures the hidden information in implicit feedback. After that, we input the user-item representations with corresponding weights into the representation learning and matching function learning modules. Finally, we concatenate the prediction vectors learned from different dimensions to predict the matching scores. The results show that we can improve the expression ability of the model while taking into account not only the nonlinear information hidden in implicit feedback, but also the low-rank relationships of user-item pairs to obtain more accurate predictions. Through detailed experiments on two datasets, we find that the ranking capability of the DACR model is enhanced compared with other baseline models, and the evaluation metrics HR and NDCG of DACR are increased by 0.88–1.19% and 0.65–1.15%, respectively

Deep Collaborative Recommendation Algorithm Based on Attention Mechanism

Representation learning-based collaborative filtering (CF) methods address the linear relationship of user-items with dot products and cannot study the latent nonlinear relationship applied to implicit feedback. Matching function learning-based CF methods directly learn the complicated mapping functions that map user-item pairs to matching scores, which has limitations in identifying low-rank relationships. To this end, we propose a deep collaborative recommendation algorithm based on attention mechanism (DACR). First, before the user-item representations are input into the DNNs, we utilize the attention mechanism to adaptively assign different weights to the user-item representations, which captures the hidden information in implicit feedback. After that, we input the user-item representations with corresponding weights into the representation learning and matching function learning modules. Finally, we concatenate the prediction vectors learned from different dimensions to predict the matching scores. The results show that we can improve the expression ability of the model while taking into account not only the nonlinear information hidden in implicit feedback, but also the low-rank relationships of user-item pairs to obtain more accurate predictions. Through detailed experiments on two datasets, we find that the ranking capability of the DACR model is enhanced compared with other baseline models, and the evaluation metrics HR and NDCG of DACR are increased by 0.88&ndash;1.19% and 0.65&ndash;1.15%, respectively

A Ranking Recommendation Algorithm Based on Dynamic User Preference

In recent years, hybrid recommendation techniques based on feature fusion have gained extensive attention in the field of list ranking. Most of them fuse linear and nonlinear models to simultaneously learn the linear and nonlinear features of entities and jointly fit user-item interactions. These methods are based on implicit feedback, which can reduce the difficulty of data collection and the time of data preprocessing, but will lead to the lack of entity interaction depth information due to the lack of user satisfaction. This is equivalent to artificially reducing the entity interaction features, limiting the overall performance of the model. To address this problem, we propose a two-stage recommendation model named A-DNR, short for Attention-based Deep Neural Ranking. In the first stage, user short-term preferences are modeled through an attention mechanism network. Then the user short-term preferences and user long-term preferences are fused into dynamic user preferences. In the second stage, the high-order and low-order feature interactions are modeled by a matrix factorization (MF) model and a multi-layer perceptron (MLP) model, respectively. Then, the features are fused through a fully connected layer, and the vectors are mapped to scores. Finally, a ranking list is output through the scores. Experiments on three real-world datasets (Movielens100K, Movielens1M and Yahoo Movies) show that our proposed model achieves significant improvements compared to existing methods

Nanoarchitectonics of Illite-Based Materials: Effect of Metal Oxides Intercalation on the Mechanical Properties

Clay minerals inevitably interact with colloidal oxides (mainly iron and aluminum oxides) in the evolution of natural geomaterials. However, the interaction between the clay minerals and the colloidal oxides affecting the stability and the strength of geotechnical materials remains poorly understood. In the present work, the interaction between the clay minerals and the colloidal oxides was investigated by reaction molecular dynamics simulations to explore the mechanical properties of illite-based materials. It was found that the metal atoms of the intercalated amorphous iron and aluminum oxides interact with oxygen atoms of the silica tetrahedron at the interface generating chemical bonds to enhance the strength of the illite-based materials considerably. The deformation and failure processes of the hybrid illite-based structures illustrated that the Al–O bonds were more favorable to the mechanical properties’ improvement of the hybrid system compared with Fe–O bonds. Moreover, the anisotropy of illite was greatly improved with metal oxide intercalation. This study provides new insight into the mechanical properties’ improvement of clay-based materials through metal oxides intercalation

Intelligent Control/Operational Strategies in WWTPs through an Integrated Q-Learning Algorithm with ASM2d-Guided Reward

The operation of a wastewater treatment plant (WWTP) is a typical complex control problem, with nonlinear dynamics and coupling effects among the variables, which renders the implementation of real-time optimal control an enormous challenge. In this study, a Q-learning algorithm with activated sludge model No. 2d-guided (ASM2d-guided) reward setting (an integrated ASM2d-QL algorithm) is proposed, and the widely applied anaerobic-anoxic-oxic (AAO) system is chosen as the research paradigm. The integrated ASM2d-QL algorithms equipped with a self-learning mechanism are derived for optimizing the control strategies (hydraulic retention time (HRT) and internal recycling ratio (IRR)) of the AAO system. To optimize the control strategies of the AAO system under varying influent loads, Q matrixes were built for both HRTs and IRR optimization through the pair of &lt;max reward-action&gt; based on the integrated ASM2d-QL algorithm. 8 days of actual influent qualities of a certain municipal AAO wastewater treatment plant in June were arbitrarily chosen as the influent concentrations for model verification. Good agreement between the values of the model simulations and experimental results indicated that this proposed integrated ASM2d-QL algorithm performed properly and successfully realized intelligent modeling and stable optimal control strategies under fluctuating influent loads during wastewater treatment