Background Syndrome differentiation aims at dividing patients into several types according to their clinical symptoms and signs, which is essential for traditional Chinese medicine (TCM). Several previous works were devoted to employing the classical algorithms to classify the syndrome and achieved delightful results. However, the presence of ambiguous symptoms substantially disturbed the performance of syndrome differentiation, This disturbance is always due to the diversity and complexity of the patients’ symptoms. Methods To alleviate this issue, we proposed an algorithm based on the multilayer perceptron model with an attention mechanism (ATT-MLP). In particular, we first introduced an attention mechanism to assign different weights for different symptoms among the symptomatic features. In this manner, the symptoms of major significance were highlighted and ambiguous symptoms were restrained. Subsequently, those weighted features were further fed into an MLP to predict the syndrome type of AIDS. Results Experimental results for a real-world AIDS dataset show that our framework achieves significant and consistent improvements compared to other methods. Besides, our model can also capture the key symptoms corresponding to each type of syndrome. Conclusion In conclusion, our proposed method can learn these intrinsic correlations between symptoms and types of syndromes. Our model is able to learn the core cluster of symptoms for each type of syndrome from limited data, while assisting medical doctors to diagnose patients efficiently.

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BackgroundDepression is widespread global problem that not only severely impacts individuals’ physical and mental health but also imposes a heavy disease burden on nations and societies. The role of inflammation in the pathogenesis and pathophysiology of depression has received much attention, but the precise relationship between the two remains unclear. This study aims to investigate the correlation between depression and inflammation using a network medicine approach.MethodsWe utilized a degree-preserving approach to identify the large connected component (LCC) of all depression-related proteins in the human interactome. The LCC was deemed as the disease module for depression. To measure the association between depression and other diseases, we calculated the overlap between these disease protein modules using the Sab algorithm. A smaller Sab value indicates a stronger association between diseases. Building on the results of this analysis, we further explored the correlation between inflammation and depression by conducting enrichment and pathway analyses of critical targets. Finally, we used a network proximity approach to calculate drug-disease proximity to predict the efficacy of drugs for the treatment of depression. We calculated and ranked the distances between depression disease modules and 6,100 drugs. The top-ranked drugs were selected to explore their potential for treating depression based on the hypothesis that their antidepressant effects are related to reducing inflammation.ResultsIn the human interactome, all depression-related proteins are clustered into a large connected component (LCC) consisting of 202 proteins and multiple small subgraphs. This indicates that depression-related proteins tend to form clusters within the same network. We used the 202 LCC proteins as the key disease module for depression. Next, we investigated the potential relationships between depression and 299 other diseases. Our analysis identified over 18 diseases that exhibited significant overlap with the depression module. Where SAB = −0.075 for the vascular disease and depressive disorders module, SAB = −0.070 for the gastrointestinal disease and depressive disorders module, and SAB = −0.062 for the endocrine system disease and depressive disorders module. The distance between them SAB < 0 implies that the pathogenesis of depression is likely to be related to the pathogenesis of its co-morbidities of depression and that potential therapeutic approaches may be derived from the disease treatment libraries of these co-morbidities. Further, considering that the inflammation is ubiquitous in some disease, we calculate the overlap between the collected inflammation module (236 proteins) and the depression module (202 proteins), finding that they are closely related (Sdi = −0.358) in the human protein interaction network. After enrichment and pathway analysis of key genes, we identified the HIF-1 signaling pathway, PI3K-Akt signaling pathway, Th17 cell differentiation, hepatitis B, and inflammatory bowel disease as key to the inflammatory response in depression. Finally, we calculated the Z-score to determine the proximity of 6,100 drugs to the depression disease module. Among the top three drugs identified by drug-disease proximity analysis were Perphenazine, Clomipramine, and Amitriptyline, all of which had a greater number of targets in the network associated with the depression disease module. Notably, these drugs have been shown to exert both anti-inflammatory and antidepressant effects, suggesting that they may modulate depression through an anti-inflammatory mechanism. These findings demonstrate a correlation between depression and inflammation at the network medicine level, which has important implications for future elucidation of the etiology of depression and improved treatment outcomes.ConclusionNeuroimmune signaling pathways play an important role in the pathogenesis of depression, and many classes of antidepressants exhibiting anti-inflammatory properties. The pathogenesis of depression is closely related to inflammation.

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Pattern-driven Programming in Scala by Huaxin Pang This is an experimental exploration of the pattern-driven programming paradigm-the sole use of pattern matching to determine the next instruction or execute. We define a pure pattern-driven programming language named PA-Scala by defining a subset of the Scala programming language, which restricts sequence control to the powerful pattern matching facilities in Scala. We use PA-Scala to explore the strengths and limitations of pattern-driven programming. By implementing a phrase structure grammar solver in PA-Scala, we show that pattern-driven programming can be used to solve general computation problems. We then implement a Prolog interpreter in PA-Scala, which demonstrates how resolution and unification can be implemented in PA-Scala. Finally we analyzed the possibility of parallel execution for PA-Scala, and show that pattern-driven programming also has the potential to achieve performance improvements by running pattern matching operations in parallel. ACKNOWLEDGMENTS First I would like to sincerely thank my advisor, Dr. Jon Pearce, for his continuous support and technical guidance of my master's study. Dr. Jon Pearce gave me so many detailed suggestions and instructions when I needed them, which lead to the key ideas and main structure of this thesis. Also I'm grateful for his support of my ideas and his trust in my ability. I want to thank Dr. Thomas Austin, for his great lectures on advanced programming languages and advices on the topic, which provided great helps on my thesis. I want to thank Dr. Thomas Howell for his strong support and great advices regarding to my study and research. Last but not the least, I want to say great thanks to my dear wife. My wife supported me to pursue my dream and gave me strengths for conquer any difficulty. v