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We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification. We first explore and implement expert features from statistical area, signal processing area and medical area. Then, we build DNNs to automatically extract deep features. Besides, we propose a new algorithm to find the most representative wave (called centerwave) among long ECG record, and extract features from centerwave. Finally, we combine these features together and put them into ensemble classifiers. Experiment on 4-class ECG data classification reports 0.84 F 1 score, which is much better than any of the single model.

Gastrointestinal microbiome plays as a symbiont which provides protection effect against invading pathogens, aids in the immune system development, nutrient reclamation and absorption as well as molecule breakdown. And it may avert carcinogenesis through these biological activities. By now, studies have been carried out to elaborate the association between gastrointestinal microbiome and breast cancer. It has been implicated that breast cancer was substantially associated with estrogen-dependent and estrogen-independent functions of gastrointestinal microbiome. Evidence from animal experiments also confirmed mammary tumor-related changes in microbial community. The possible mechanisms involve estrogen metabolism, immune regulation, obese status and so forth. Based on the current evidence, cues on future management strategies of breast cancer such as antibiotics and dietary interventions are proposed. In conclusion, large-scale clinical studies and bench-based researches are needed to validate the associations and elaborate the mechanisms, so as to reduce the risk of breast cancer and improve the outcomes of those already diagnosed.

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