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Aim: The main aim of this study was to investigate the additional effects of L‐theanine, an amino acid in tea and an analog of glutamate with neuroprotective and anti‐depressant properties, on obsessive–compulsive disorder (OCD) symptoms in combination with fluvoxamine. Methods: Patients from either sex aged between 18 and 60 years diagnosed with OCD, based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM‐5), who had a Yale‐Brown Obsessive Compulsive Scale (Y‐BOCS) score of more than 21 were enrolled in a double‐blinded, parallel‐group, placebo‐controlled, clinical trial of 10 weeks to receive either L‐theanine (100 mg twice daily) and fluvoxamine (100 mg daily initially followed by 200 mg daily after week 5) or placebo and fluvoxamine. The primary outcome of interest in this study was the Y‐BOCS total score decrease from baseline. Results: From a total of 95 evaluated patients, 50 completed our study; 30 were randomly assigned to each group. Multivariate analysis (ANOVA) showed a significant effect of time ×$$ \times $$ treatment for L‐theanine in obsession subscale (F = 5.51, P = 0.008) of the Y‐BOCS score but not in the total and compulsion scores. Our results showed significantly more improvement in obsession subscale scores in L‐theanine compared to placebo group (P = 0.007, Cohen's d = 0.82). Also, total Y‐BOCS scores were lower in L‐theanine compared to placebo group at week 5 (P = 0.039, Cohen's d = 0.60) and 10 (P = 0.008, Cohen's d = 0.80). However, there was no significant between‐group differences in compulsion subscale scores. Complete response was also more frequent in the L‐theanine group (P = 0.0001). Conclusion: Findings in this study suggest L‐theanine as a relatively safe and effective adjuvant therapy for moderate to severe OCD. [ABSTRACT FROM AUTHOR]

Recently network embedding has gained increasing attention due to its advantages in facilitating network computation tasks such as link prediction, node classification and node clustering. The objective of network embedding is to represent network nodes in a low-dimensional vector space while retaining as much information as possible from the original network including structural, relational, and semantic information. However, asymmetric nature of directed networks poses many challenges as how to best preserve edge directions during the embedding process. Here, we propose a novel deep asymmetric attributed network embedding model based on the convolutional graph neural network, called AAGCN. The main idea is to maximally preserve the asymmetric proximity and asymmetric similarity of directed attributed networks. AAGCN introduces two neighbourhood feature aggregation schemes to separately aggregate the features of a node with the features of its in- and out- neighbours. Then, it learns two embedding vectors for each node, one source embedding vector and one target embedding vector. The final representations are the results of concatenating source and target embedding vectors. We test the performance of AAGCN on four real-world networks for network reconstruction, link prediction, node classification and visualization downstream tasks and investigate the impact of hyperparameters of the proposed method on the performance of the tasks. The experimental results show the superiority of AAGCN against state-of-the-art embedding methods. [ABSTRACT FROM AUTHOR]