N6-methyladenosine (m6A) is the most prevalent, abundant and conserved internal cotranscriptional modification in eukaryotic RNAs, especially within higher eukaryotic cells. m6A modification is modified by the m6A methyltransferases, or writers, such as METTL3/14/16, RBM15/15B, ZC3H3, VIRMA, CBLL1, WTAP, and KIAA1429, and, removed by the demethylases, or erasers, including FTO and ALKBH5. It is recognized by m6A-binding proteins YTHDF1/2/3, YTHDC1/2 IGF2BP1/2/3 and HNRNPA2B1, also known as “readers”. Recent studies have shown that m6A RNA modification plays essential role in both physiological and pathological conditions, especially in the initiation and progression of different types of human cancers. In this review, we discuss how m6A RNA methylation influences both the physiological and pathological progressions of hematopoietic, central nervous and reproductive systems. We will mainly focus on recent progress in identifying the biological functions and the underlying molecular mechanisms of m6A RNA methylation, its regulators and downstream target genes, during cancer progression in above systems. We propose that m6A RNA methylation process offer potential targets for cancer therapy in the future.

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Spoken language understanding system is traditionally designed as a pipeline of a number of components. First, the audio signal is processed by an automatic speech recognizer for transcription or n-best hypotheses. With the recognition results, a natural language understanding system classifies the text to structured data as domain, intent and slots for downstreaming consumers, such as dialog system, hands-free applications. These components are usually developed and optimized independently. In this paper, we present our study on an end-to-end learning system for spoken language understanding. With this unified approach, we can infer the semantic meaning directly from audio features without the intermediate text representation. This study showed that the trained model can achieve reasonable good result and demonstrated that the model can capture the semantic attention directly from the audio features.Index Terms-Spoken language understanding, end-toend training, recurrent neural networks

The sparse representation has been widely used in many areas and utilized for visual tracking. Tracking with sparse representation is formulated as searching for samples with minimal reconstruction errors from learned template subspace. However, the computational cost makes it unsuitable to utilize high dimensional advanced features which are often important for robust tracking under dynamic environment. Based on the observations that a target can be reconstructed from several templates, and only some of the features with discriminative power are significant to separate the target from the background, we propose a novel online tracking algorithm with two stage sparse optimization to jointly minimize the target reconstruction error and maximize the discriminative power. As the target template and discriminative features usually have temporal and spatial relationship, dynamic group sparsity (DGS) is utilized in our algorithm. The proposed method is compared with three state-of-art trackers using five public challenging sequences, which exhibit appearance changes, heavy occlusions, and pose variations. Our algorithm is shown to outperform these methods.

Stance classification, which aims at detecting the stance expressed in text towards a specific target, is an emerging problem in sentiment analysis. A major difference between stance classification and traditional aspect-level sentiment classification is that the identification of stance is dependent on target which might not be explicitly mentioned in text. This indicates that apart from text content, the target information is important to stance detection. To this end, we propose a neural network-based model, which incorporates target-specific information into stance classification by following a novel attention mechanism. In specific, the attention mechanism is expected to locate the critical parts of text which are related to target. Our evaluations on both the English and Chinese Stance Detection datasets show that the proposed model achieves the state-of-theart performance.

Online learned tracking is widely used for its adaptive ability to handle appearance changes. However, it introduces potential drifting problems due to the accumulation of errors during the self-updating, especially for occluded scenarios. The recent literature demonstrates that appropriate combinations of trackers can help balance the stability and flexibility requirements. We have developed a robust tracking algorithm using a local sparse appearance model (SPT) and K-Selection. A static sparse dictionary and a dynamically updated online dictionary basis distribution are used to model the target appearance. A novel sparse representation-based voting map and a sparse constraint regularized mean shift are proposed to track the object robustly. Besides these contributions, we also introduce a new selection-based dictionary learning algorithm with a locally constrained sparse representation, called K-Selection. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the recent literature.

Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01% in F-measure.

Triterpenoids are a highly diverse group of natural products and used particularly as medicine. Here, a strategy combining stepwise metabolic engineering and transcriptional control was developed to strengthen triterpenoid biosynthesis in Saccharomyces cerevisiae. Consequently, an efficient biosynthetic pathway for producing β‐amyrin, a commercially valuable compound and precursor of triterpenoids, was constructed through expressing a plant‐derived β‐amyrin synthase. Introducing a heterologous squalene monooxygenase greatly dragged intermediate metabolite squalene toward β‐amyrin. Increasing squalene pool by overexpressing IPP isomerase, FPP, and squalene synthase further enhanced β‐amyrin synthesis of 49‐folds. Through reconstructing the promoters with the binding site of transcription factor UPC2, directed transcriptional regulation on engineered pathway was availably achieved, resulting in β‐amyrin titer increased by 65‐folds. Using ethanol fed‐batch fermentation, β‐amyrin titer was finally improved up to 138.80 mg/L with a yield of 16.30 mg/g dry cell, almost 185 and 232 and folds of the initially engineered strain, respectively. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3172–3179, 2015