Traditional Chinese Medicine syndrome patterns and Qi-regulating, chest-relaxing and blood-activating therapy on cardiac syndrome X

AbstractObjectiveTo master the syndrome patterns characteristics and explore the effective therapy methods of Traditional Chinese Medicine (TCM) for cardiac syndrome X (CSX).MethodsThe TCM syndrome characteristics were mastered and the TCM intervention programs were determined by clinical investigations for TCM syndrome patterns characteristics of CSX patients. Then, the clinical efficacy studies on TCM intervention for CSX were carried out through randomized controlled trials.ResultsCSX is a clinical syndrome with the main manifestations of chest pain and chest stuffiness, and Qi stagnation, phlegm retention and blood stasis are the basic symptoms of CSX. As a result, the Qi-regulating, chest-relaxing and blood-activating therapy integrated with some Western Medicines was adopted for treatment. The effect of Qi-regulating, chest-relaxing and blood-activating therapy can reduce the frequency and degree of angina, improve the symptoms and exercise the tolerance of patients, inhibit the inflammatory response of vascular walls and protect the function of vascular endothelial cells, which is better than that of the simple and conventional Western Medicine alone.ConclusionA good effect was achieved in the integration of Chinese and Western Medicines for CSX. The therapy is worthy to be applied further in clinical practice. On the other hand, more long-term and randomised controlled studies with large samples are still required to further determine the clinical efficacy and safety of the therapy

A Grammar-Based Structural CNN Decoder for Code Generation

Code generation maps a program description to executable source code in a programming language. Existing approaches mainly rely on a recurrent neural network (RNN) as the decoder. However, we find that a program contains significantly more tokens than a natural language sentence, and thus it may be inappropriate for RNN to capture such a long sequence. In this paper, we propose a grammar-based structural convolutional neural network (CNN) for code generation. Our model generates a program by predicting the grammar rules of the programming language; we design several CNN modules, including the tree-based convolution and pre-order convolution, whose information is further aggregated by dedicated attentive pooling layers. Experimental results on the HearthStone benchmark dataset show that our CNN code generator significantly outperforms the previous state-of-the-art method by 5 percentage points; additional experiments on several semantic parsing tasks demonstrate the robustness of our model. We also conduct in-depth ablation test to better understand each component of our model