76 research outputs found
Building instance knowledge network for word sense disambiguation
No full textIn this paper, a new high precision focused word sense disambiguation (WSD) approach is proposed, which not only attempts to identify the proper sense for a word but also provides the probabilistic evaluation for the identification confidence at the same time. A novel Instance Knowledge Network (IKN) is built to generate and maintain semantic knowledge at the word, type synonym set and instance levels. Related algorithms based on graph matching are developed to train IKN with probabilistic knowledge and to use IKN for probabilistic word sense disambiguation. Based on the Senseval-3 all-words task, we run extensive experiments to show the performance enhancements in different precision ranges and the rationality of probabilistic based automatic confidence evaluation of disambiguation. We combine our WSD algorithm with five best WSD algorithms in senseval-3 all words tasks. The results show that the combined algorithms all outperform the corresponding algorithms
Analysis of the functional relationship between NCAM2 and BACE1
No full textThe neural cell adhesion molecule 2 (NCAM2) is a cell adhesion molecule (CAM) of the immunoglobulin superfamily (IgCAMs). NCAM2 plays an important role in the developing and mature nervous system by regulating neurite outgrowth and synapse formation and maintenance. NCAM2 is also involved in neurodevelopmental and neurodegenerative brain disorders, such as autism spectrum disorder (ASD) and Alzheimer’s disease (AD), respectively. β-site amyloid precursor protein cleaving enzyme 1 (BACE1) is an aspartic protease, which plays a key role in AD by cleaving the amyloid precursor protein (APP) to produce the toxic Aβ peptide accumulating in brains of AD patients. Synaptic levels of NCAM2 are reduced in the hippocampus of AD patients and Aβ treated cultured hippocampal neurons. Since BACE1 activity is increased in brains of AD patients, we investigated whether there is a functional interaction between NCAM2 and BACE1.
Our study shows that NCAM2 can be cleaved by BACE1 and BACE2, a homolog of BACE1. The cleavage site of NCAM2 by BACE1 and BACE2 is within the extracellular domain region, which is close to the transmembrane domain of NCAM2. The intracellular domain of NCAM2 is not required for the cleavage. Our study also shows that BACE-dependent cleavage of NCAM2 is not enhanced by Aβ. The BACE-dependent turnover of NCAM2 is higher in hippocampal than in cortical neurons possibly due to higher exo- and endocytosis of BACE1 in hippocampal neurons when compared to cortical neurons. We also found that the cell surface levels of BACE1 and the shedding of BACE1 are increased in NCAM2 knock-out neurons, whereas the total levels and endocytosis of BACE1 are not affected by NCAM2 deficiency.
Taken together, our study demonstrates that NCAM2 is cleaved by BACE1 and BACE2 and regulates cell surface levels of BACE1 most likely by regulating the delivery of BACE1 to the cell surface
Analysis of Zhejiang Business Fluctuations with Dynamic Stochastic General Equilibrium Model
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Instance knowledge network and its application to word sense disambiguation
No full textNatural language processing has been a classical topic of computer science since the early stage of computer. In 1950, Alan Turing proposed the Turing test as a criterion of intelligence to test the ability of a computer program to impersonate a human in a real-time written conversation with a human judge on the basis of the conversational content alone between the program and a real human. In the past 60 years, many researchers have worked in this area and have made many important contributions. Unfortunately, until today this classical problem has not been solved well. This thesis addresses some fundamental issues of natural language processing. After careful analysis of the current state of the art in natural language processing, we find that several fundamental yet challenging problems still exist that hinder the advancement of natural language processing. In this thesis, we aim to make several initial attempts towards solving these challenging problems. One of the bottlenecks of current natural language processing research is that different knowledge sources are used for different tasks. To provide an integrated knowledge source for multiple natural language processing tasks and capture more knowledge, especially context sensitive semantic relationships between each pair of concepts, we propose a new knowledge representation model called instance knowledge network. In an instance knowledge network, both type level features and instance level context sensitive features can be modeled in a single knowledge representation model and processed using a single learning algorithm. The instance graph matching algorithm for the instance knowledge network is also developed. Word sense disambiguation is an important task for natural language processing. A serious problem with existing word sense disambiguation approaches is the precision they can achieve. This causes the 'Garbage in, garbage out' phenomenon. To deal with this issue, we propose a probabilistic word sense disambiguation approach based on the instance knowledge network model. A probabilistic training algorithm is proposed to the instance knowledge networks with context sensitive conditional probability value associated with a pair of concepts. An iterative probabilistic reasoning framework is developed for word sense disambiguation. The probabilistic result provides not only which sense is the proper sense of an ambiguous word, but also the confidence or the self-evaluation value for the precision of disambiguation. This self-evaluation functionality is important for the system to judge which parts can be understood in a high precision from a raw text corpus. We believe that the self-evaluation functionality can be used for the semi-supervised learning method in the future. Based on the Senseval-3 all-words task, we run extensive experiments to show the performance enhancements of our word sense disambiguation algorithm in different precision ranges. We also combine our word sense disambiguation algorithm with five best word sense disambiguation algorithms in senseval-3 all words tasks. The results show that the combined algorithms all outperform the corresponding algorithms. To demonstrate that an instance knowledge network can be served for different tasks of natural language processing and to further improve the performance of word sense disambiguation, we incorporate the coreference resolution results into word sense disambiguation. Our work shows that the results of coreference resolution can be used for enlarging the size of context in an instance knowledge network and the performance of word sense disambiguation can be improved accordingly. This work is the first attempt to integrate two main natural language processing tasks together and to make use of coreference resolution to help word sense disambiguation. In the future, we plan to incorporate more natural language processing tasks in a coherent process, with the help of our instance knowledge network
Regulation of Neuroinflammation through Programed Death-1/Programed Death Ligand Signaling in Neurological Disorders
No full textImmune responses in the central nervous system (CNS), which involve both resident glial cells and infiltrating peripheral immune cells, play critical roles in the progress of brain injuries and neurodegeneration. To avoid inflammatory damage to the compromised brain, the immune cell activities in the CNS are controlled by a plethora of chemical mediators and signal transduction cascades, such as inhibitory signaling through programed death-1 (PD-1) and programed death ligand (PD-L) interactions. An increasing number of recent studies have highlighted the importance of PD-1/PD-L pathway in immune regulation in CNS disorders such as ischemic stroke, multiple sclerosis, and Alzheimer\u27s disease. Here, we review the current knowledge of the impact of PD-1/PD-L signaling on brain injury and neurodegeneration. An improved understanding of the function of PD-1/PD-L in the cross-talk between peripheral immune cells, CNS glial cells, and non-immune CNS cells is expected to shed further light on immunomodulation and help develop effective and safe immunotherapies for CNS disorders. © 2014 Zhao, Li, Leak, Chen and Hu
Surface Modification of TiO<sub>2</sub> for Perovskite Solar Cells
No full textTitanium oxide (TiO2) is commonly used as an electron transport layer (ETL) of regular-structure perovskite solar cells (PSCs); however, it suffers from inherent drawbacks such as low electron mobility and a high density of trap states. Modifying the surface chemistry of TiO2 has proved facile and efficient in enhancing key electron-transport properties, thereby improving device performance. In this review, we summarize recent progress on the surface modification of TiO2 in planar PSCs. The functions of different modifiers in improving device performance are elucidated, revealing the influence of modifier chemical and electronic structure on the properties of TiO2. This offers new opportunities to exploit novel materials for modifying TiO2 toward high-efficiency PSCs.</p
Incorporating coreference resolution into word sense disambiguation
No full textWord sense disambiguation (WSD) and coreference resolution are two fundamental tasks for natural language processing. Unfortunately, they are seldom studied together. In this paper, we propose to incorporate the coreference resolution technique into a word sense disambiguation system for improving disambiguation precision. Our work is based on the existing instance knowledge network (IKN) based approach for WSD. With the help of coreference resolution, we are able to connect related candidate dependency graphs at the candidate level and similarly the related instance graph patterns at the instance level in IKN together. Consequently, the contexts which can be considered for WSD are expanded and precision for WSD is improved. Based on Senseval-3 all-words task, we run extensive experiments by following the same experimental approach as the IKN based WSD. It turns out that each combined algorithm between the extended IKN WSD algorithm and one of the best five existing algorithms consistently outperforms the corresponding combined algorithm between the IKN WSD algorithm and the existing algorithm
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