Coordinated optimization of the polymerization and transportation processes to enhance the yield of exopolysaccharide heparosan.

Heparosan as the precursor for heparin biosynthesis has attracted intensive attention while Escherichia coli Nissle 1917 (EcN) has been applied as a chassis for heparosan biosynthesis. Here, after uncovering the pivotal role of KfiB in heparosan biosynthesis, we further demonstrate KfiB is involved in facilitating KpsT to translocate the nascent heparosan polysaccharide chain. As a result, an artificial expression cassette KfiACB was constructed with optimized RBS elements, resulting in 0.77 g/L heparosan in shake flask culture. Moreover, in view of the intracellular accumulation of heparosan, we further investigated the effects of overexpression of the ABC transport system proteins on heparosan biosynthesis. By co-overexpressing KfiACB with KpsTME, the heparosan production in flask cultures was increased to 1.03 g/L with an extracellular concentration of 0.96 g/L. Eventually, the engineered strain EcN/pET-kfiACB3-galU-kfiD-glmM/pCDF-kpsTME produced 12.2 g/L heparosan in 5-L fed-batch cultures while the extracellular heparosan was about 11.2 g/L. The results demonstrate the high-efficiency of the strategy for co-optimizing the polymerization and transportation for heparosan biosynthesis. Moreover, this strategy should be also available for enhancing the production of other polysaccharides.

A Meta-Transfer Learning Approach to ECG Arrhythmia Detection.

Automatic classification of cardiac abnormalities is becoming increasingly popular with the prevalence of ECG recordings. Many signal processing and machine learning algorithms have shown the potential to identify cardiac ab-normalities accurately. However, most of these methods heavily rely on a large amount of relatively homogeneous datasets. In real life, chances are that there is not enough data for a specific category, and regular deep learning may fail in this scenario. A straightforward intuition is to use the knowledge learned from previous data to solve the problem. This idea leads to learning-to-learn: extrapolating the knowledge accumulated from the old dataset and using it in a different but somewhat related dataset. In this way, we do not need to have considerable data to learn the new task because the underlying features of the old and new datasets resemble one another. In this paper, a novel machine learning method is introduced to solve the ECG arrhythmia detection problem with a limited amount of data. The proposed method combines the popular techniques of meta-learning and transfer learning. It is shown that our method achieves much higher accuracy in ECG arrhythmia classification with a few data and learns the new task faster than regular deep learning.