Muscle membrane repair and inflammatory attack in dysferlinopathy

Repair of plasma membrane tears is an important normal physiological process that enables the cells to survive a variety of physiological and pathological membrane lesions. Dysferlin was the first protein reported to play a crucial role in this repair process in muscle, and recently, several other proteins including Mitsugumin 53 (MG53), annexin and calpain were also found to participate. These findings have now established the framework of the membrane repair mechanism. Defective membrane repair in dysferlin-deficient muscle leads to the development of muscular dystrophy associated with remarkable muscle inflammation. Recent studies have demonstrated a crosstalk between defective membrane repair and immunological attack, thus unveiling a new pathophysiological mechanism of dysferlinopathy. Here I summarize and discuss the latest progress in the molecular mechanisms of membrane repair and the pathogenesis of dysferlinopathy. Discussion about potential therapeutic applications of these findings is also provided

Improving English Listening Self-efficacy Of Chinese University Students ----- Influences of Learning Strategy Training with Feedback on Strategy Use and Performance

Self-efficacy which is people’s evaluation of their capabilities of performing certain tasks affects students’ persistence, effort, and academic performance in academic settings. This present study aimed at exploring how to improve English listening self-efficacy and performance of Chinese university students as English listening comprehension is the most difficult part of English acquisition perceived by Chinese university students. Based on Graham’s work in 2007, the study examined the impacts of strategy training and feedback on strategy use and performance on English listening self-efficacy, English listening performance and attributions of Chinese university students. 96 first year non-English majored Chinese university students were invited to participate in the study and they were divided into three groups with 32 in each group. One group of students received both strategy training and feedback on strategy use and performance. They were also asked to keep a strategy use diary, for which feedback was also given. At the end of study, they were required to comment on feedback they received. The other group received only strategy training. A control group was involved receiving no intervention at all. The findings of the study suggested that strategy training and feedback on strategy use and performance improved self-efficacy in English listening and English listening performance of the participants significantly. Their attributions however, were not changed significantly after the training. The reasons for the findings were discussed. Pedagogical implications were recommended to help improve self-efficacy and performance in English listening of Chinese university students

Genome data analysis, protein function and structure prediction by machine learning techniques

Dissertation supervisor: Professor Jianlin Cheng.Includes vita.The raw information of a typical human genome has been generated at 2001 by Human Genome Project. However, since there are a huge amount of data, it is still a big challenge for people to understand them, and extract useful structure and function information, such as the function of genes, the structure of proteins encoded by gene, and the function of proteins. Understanding these information is crucial for us to improve longevity and quality of life, and has a lot of applications, such as genomic medicine, drug design, and etc. In the meantime, machine learning techniques are growing rapidly and are good at processing large datasets, but many of them are limited for the impact on larger real world problems. In this thesis, three major contributions are described. First of all, we generate gene-gene interaction network from human genome conformation data by Hi-C technique, and the relationship of gene function and gene-gene interaction has been discovered. Second, we introduce a novel framework SMISS, which uses new source of information from gene-gene interaction network and uses a new way to integrate difference sources of information for protein function prediction. Finally, we introduce a tool called DeepQA which use machine learning technique to evaluate how well is the predicted protein structure, and a method MULTICOM for protein structure prediction. All of these protein structure and function prediction methods are available as software and web servers which are freely available to the scientific communities.Includes bibliographical references (pages 150-168)

ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network

With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.Comment: 13 pages, 5 figure

GOGGLES: Automatic Image Labeling with Affinity Coding

Generating large labeled training data is becoming the biggest bottleneck in building and deploying supervised machine learning models. Recently, the data programming paradigm has been proposed to reduce the human cost in labeling training data. However, data programming relies on designing labeling functions which still requires significant domain expertise. Also, it is prohibitively difficult to write labeling functions for image datasets as it is hard to express domain knowledge using raw features for images (pixels). We propose affinity coding, a new domain-agnostic paradigm for automated training data labeling. The core premise of affinity coding is that the affinity scores of instance pairs belonging to the same class on average should be higher than those of pairs belonging to different classes, according to some affinity functions. We build the GOGGLES system that implements affinity coding for labeling image datasets by designing a novel set of reusable affinity functions for images, and propose a novel hierarchical generative model for class inference using a small development set. We compare GOGGLES with existing data programming systems on 5 image labeling tasks from diverse domains. GOGGLES achieves labeling accuracies ranging from a minimum of 71% to a maximum of 98% without requiring any extensive human annotation. In terms of end-to-end performance, GOGGLES outperforms the state-of-the-art data programming system Snuba by 21% and a state-of-the-art few-shot learning technique by 5%, and is only 7% away from the fully supervised upper bound.Comment: Published at 2020 ACM SIGMOD International Conference on Management of Dat