Background: Prior research has shown preliminary evidence that calligraphy activity improves various body functions and decreases severity of psychotic symptoms in individuals with schizophrenia. However, major limitations of earlier studies include small and heterogeneous samples. The current large-scale randomized controlled trial examined effects of calligraphy activity on cognition (including attention), emotions, psychotic symptoms, quality of life, and mood in people with schizophrenia. Methods: One-hundred-and-fifty patients with schizophrenia were randomly allocated to the treatment group (receiving calligraphy activity) or the control group (receiving general activity), both of which lasted for 24 weeks (70 minutes per session; one session per week). Assessments were conducted at pretest, posttest, and three-month follow-up. The Montreal Cognitive Assessment, Chu’s Attention Test, Depression, Anxiety, and Stress Scale, Positive and Negative Syndrome Scale, World Health Questionnaire on the Quality of Life-Brief Form, and Visual Analogue Scale were used. Results: Improved cognition and attention were found in both groups, although no group effects were shown. The treatment group appeared to show lower severity of positive symptoms at follow-up than posttest, whereas the control group appeared to show the opposite pattern. Improved mood was found in the treatment group. Conclusion: This study provides evidence regarding effects of calligraphy activity on increasing cognition and potentially decreasing severity of positive symptoms in patients with schizophrenia. Calligraphy activity can be incorporated in clinical occupational therapy and may be provided to supplement medication treatment. Trial Registration: ClinicalTrials.gov NCT03882619; https://clinicaltrials.gov/ct2/show/NCT03882619
In this paper, we propose an algorithm that detects overlapping communities in networks (graphs) based on a simple node behavior model. The key idea in the proposed algorithm is to find communities in an agglomerative manner such that every detected community S has the following property: For each node i ∈ S, we have (i) the fraction of nodes in S \ {i} that are neighbors of node i is greater than a given threshold, or (ii) the fraction of neighbors of node i that are in S \ {i} is greater than another given threshold. Through computer simulations of random graphs with built-in overlapping community structure, including LFR benchmark random graphs and Erdös-Rényi type random graphs, we show that our algorithm has excellent performance. Furthermore, we apply our algorithm to several real-world networks and show that the overlapping communities detected by our algorithm are very close to the known communities in these networks.
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