Metabolic engineering of Corynebacterium glutamicum for efficient production of optically pure (2R,3R)-2,3-butanediol

Background: 2,3-butanediol is an important platform compound which has a wide range of applications, involving in medicine, chemical industry, food and other fields. Especially the optically pure (2R,3R)-2,3-butanediol can be employed as an antifreeze agent and as the precursor for producing chiral compounds. However, some (2R,3R)-2,3-butanediol overproducing strains are pathogenic such as Enterobacter cloacae and Klebsiella oxytoca. Results: In this study, a (3R)-acetoin overproducing C. glutamicum strain, CGS9, was engineered to produce optically pure (2R,3R)-2,3-butanediol efficiently. Firstly, the gene bdhA from B. subtilis 168 was integrated into strain CGS9 and its expression level was further enhanced by using a strong promoter Psod and ribosome binding site (RBS) with high translation initiation rate, and the (2R,3R)-2,3-butanediol titer of the resulting strain was increased by 33.9%. Then the transhydrogenase gene udhA from E. coli was expressed to provide more NADH for 2,3-butanediol synthesis, which reduced the accumulation of the main byproduct acetoin by 57.2%. Next, a mutant atpG was integrated into strain CGK3, which increased the glucose consumption rate by 10.5% and the 2,3-butanediol productivity by 10.9% in shake-flask fermentation. Through fermentation engineering, the most promising strain CGK4 produced a titer of 144.9\ua0g/L (2R,3R)-2,3-butanediol with a yield of 0.429\ua0g/g glucose and a productivity of 1.10\ua0g/L/h in fed-batch fermentation. The optical purity of the resulting (2R,3R)-2,3-butanediol surpassed 98%. Conclusions: To the best of our knowledge, this is the highest titer of optically pure (2R,3R)-2,3-butanediol achieved by GRAS strains, and the result has demonstrated that C. glutamicum is a competitive candidate for (2R,3R)-2,3-butanediol production

Fix it where it fails: Pronunciation learning by mining error corrections from speech logs

The pronunciation dictionary, or lexicon, is an essential component in an automatic speech recognition (ASR) system in that incorrect pronunciations cause systematic misrecognitions. It typically con-sists of a list of word-pronunciation pairs written by linguists, and a grapheme-to-phoneme (G2P) engine to generate pronunciations for words not in the list. The hand-generated list can never keep pace with the growing vocabulary of a live speech recognition sys-tem, and the G2P is usually of limited accuracy. This is especially true for proper names whose pronunciations may be influenced by various historical or foreign-origin factors. In this paper, we pro-pose a language-independent approach to detect misrecognitions and their corrections from voice search logs. We learn previously un-known pronunciations from this data, and demonstrate that they sig-nificantly improve the quality of a production-quality speech recog-nition system. Index Terms — speech recognition, pronunciation learning, data extraction, logistic regression 1

Stacked graphical models for efficient inference in markov random fields

In collective classification, classes are predicted simultaneously for a group of related instances, rather than predicting a class for each instance separately. Collective classification has been widely used for classification on relational datasets. However, the inference procedure used in collective classification usually requires many iterations and thus is expensive. We propose stacked graphical learning, a meta-learning scheme in which a base learner is augmented by expanding one instance’s features with predictions on other related instances. Stacked graphical learning is efficient, especially during inference, capable of capturing dependencies easily, and can be implemented with any kind of base learner. In experiments on eight datasets, stacked graphical learning is 40 to 80 times faster than Gibbs sampling during inference.

ABSTRACT Online Stacked Graphical Learning

Collective classification has been widely studied to predict class labels simultaneously for relational data, such as hyperlinked webpages, social networks, and data in a relational database. The existing collective classification methods are usually expensive due to the iterative inference in graphical models and their learning procedures based on iterative optimization. When the dataset is large, the cost of maintaining large graphs or related instances in memory becomes a problem as well. Stacked graphical learning has been proposed for collective classification with efficient inference. However, the memory and time cost of standard stacked graphical learning is still expensive since it requires cross-validation-like predictions to be constructed during training. In this paper, we proposed a new scheme to integrate recently-developed single-pass online learning with stacked learning, to save training time and to handle large streaming datasets with minimal memory overhead. Experimentally we showed that online stacked graphical learning gives accurate and reliable results on eleven sample problems from three domains, with much less time and memory cost. With competitive accuracy, high efficiency and low memory cost, online stacked graphical learning is very promising in real world large-scale applications. Also, with the online learning scheme, stacked graphical learning is able to be applied to streaming data. 1

Notes on Stacked Graphical Learning for Efficient Inference

In collective classification, classes are predicted simultaneously for a group of related instances, rather than predicting a class for each instance separately. Collective classification has been widely used for classification on relational datasets. However, the inference procedure used in collective classification usually requires many iterations and thus is expensive. We propose stacked graphical learning, a meta-learning scheme in which a base learner is augmented by expanding one instance’s features with predictions on other related instances. Stacked graphical learning is efficient, especially during inference, capable of capturing dependencies easily, and can be implemented with any kind of base learner. In experiments on eight datasets, stacked graphical learning is 40 to 80 times faster than Gibbs sampling during inference. We also give theoretical analysis to better understand the algorithm

Stacked Graphical Learning: Learning in Markov Random Fields using Very Short Inhomogeneous Markov Chains

We described stacked graphical learning, a meta-learning scheme in which a base learner is augmented by expanding one instance’s features with predictions on other related instances. The stacked graphical learning is efficient, especially during inference, capable of capturing dependencies easily, and can be constructed based on any kind of base learner. In experiments on two classification problems, stacked graphical learning generally achieved comparable accuracy to other graphical models via much faster inference.