Unsupervised Extraction and Clustering of Key Phrases from Scientific Publications

Mapping a research domain can be of great significance for understanding and structuring the state-of-art of a research area. Standard techniques for systematically reviewing scientific literature entail extensive selection and intensive reading of manuscripts, a laborious and time consuming process performed by human experts. Researchers have spent efforts on automating methods in one or more sub-tasks of reviewing process. The main challenge of this work lies in the gap in semantic understanding of text and background domain knowledge. In this thesis we investigate the possibility of extracting keywords from scientific abstracts in an automated way. We intended to use the categories of these keywords to form a basis of a classification scheme in the context of systematically mapping studies. We propose a framework by joint unsupervised keyphrase extraction and semantic keyphrase clustering. Specifically, we (1) explore the effect of domain relevance and phrase quality measures in keyphrase extraction; (2) explore the effect of knowledge graph based word embedding in embedding representation of phrase semantics; (3) explore the effect of clustering for grouping semantically related keyphrases. Experiments are conducted on a dataset of publications pertaining the domain of "Explainable Artificial Intelligence (XAI)”. We further test the performance of clustering using terms and labels from publicly available academic taxonomies and keyword databases. Experiment results shows that: (1) Extended ranking score does improve the keyphrase extraction performance. Adapting pre-processing and candidate selection method to target document type would be more important. (2) Semantic network based word embeddings (ConceptNet) has fairly good performance, with less computational complexity. (3) Term-level semantic keyphrase clustering does not generate ideal categories for terms, however it is shown that clustering can group semantically similar terms together. Finally, we conclude that it is considered particularly challenging to find semantic related, but not morphologically similar terms

Unsupervised Extraction and Clustering of Key Phrases from Scientific Publications

Mapping a research domain can be of great significance for understanding and structuring the state-of-art of a research area. Standard techniques for systematically reviewing scientific literature entail extensive selection and intensive reading of manuscripts, a laborious and time consuming process performed by human experts. Researchers have spent efforts on automating methods in one or more sub-tasks of reviewing process. The main challenge of this work lies in the gap in semantic understanding of text and background domain knowledge. In this thesis we investigate the possibility of extracting keywords from scientific abstracts in an automated way. We intended to use the categories of these keywords to form a basis of a classification scheme in the context of systematically mapping studies. We propose a framework by joint unsupervised keyphrase extraction and semantic keyphrase clustering. Specifically, we (1) explore the effect of domain relevance and phrase quality measures in keyphrase extraction; (2) explore the effect of knowledge graph based word embedding in embedding representation of phrase semantics; (3) explore the effect of clustering for grouping semantically related keyphrases. Experiments are conducted on a dataset of publications pertaining the domain of "Explainable Artificial Intelligence (XAI)”. We further test the performance of clustering using terms and labels from publicly available academic taxonomies and keyword databases. Experiment results shows that: (1) Extended ranking score does improve the keyphrase extraction performance. Adapting pre-processing and candidate selection method to target document type would be more important. (2) Semantic network based word embeddings (ConceptNet) has fairly good performance, with less computational complexity. (3) Term-level semantic keyphrase clustering does not generate ideal categories for terms, however it is shown that clustering can group semantically similar terms together. Finally, we conclude that it is considered particularly challenging to find semantic related, but not morphologically similar terms

Decentralized Collision Avoidance of Multi-Agent Systems in 3-Dimensional Space

Multi-Agent Systems, often referred to a network of loosely connected autonomous units, are widely used to model the dynamics of crowds, vehicles, robots and swarms in traffic management, biological environment, distributed control and communication technologies. Recently, the study of multi-agent systems is rapidly growing due to the beneficial advantages of using a team of agents in logistics, mapping, search and rescue, etc.In this thesis, we focus on the problem of decentralized collision avoidance among multiple intelligent moving agents. Imaging each agent to be a human, a moving car, or an aircraft, it is supposed to make its decision independently based on its perception of the local environment through sensors only. Two different collision avoidance protocols are presented to generate updated reference velocity continuously for each agent that leads to no future collision. The first method, the rotation based method, is adopted by a geometric based algorithm introduced for 2-Dimensional space and the second method, the potential fields based method, could be categorized as an example of Harmonic Potential Fields (HPF) discussed by Masoud. Both methods employing position information of obstacles result in collision-free paths for each agent under the assumption that all other agents following similar maneuvers. Through simulations in MATLAB, satisfied performances are achieved for method 2 in all challenging scenarios we set while method 1 faced some difficulties dealing with multi-obstacle simultaneously as well as 3D scenarios. Mechanical Engineering | Systems and Contro

Increased utilization of fructose has a positive effect on the development of breast cancer

Rapid proliferation and Warburg effect make cancer cells consume plenty of glucose, which induces a low glucose micro-environment within the tumor. Up to date, how cancer cells keep proliferating in the condition of glucose insufficiency still remains to be explored. Recent studies have revealed a close correlation between excessive fructose consumption and breast cancer genesis and progression, but there is no convincing evidence showing that fructose could directly promote breast cancer development. Herein, we found that fructose, not amino acids, could functionally replace glucose to support proliferation of breast cancer cells. Fructose endowed breast cancer cells with the colony formation ability and migratory capacity as effective as glucose. Interestingly, although fructose was readily used by breast cancer cells, it failed to restore proliferation of non-tumor cells in the absence of glucose. These results suggest that fructose could be relatively selectively employed by breast cancer cells. Indeed, we observed that a main transporter of fructose, GLUT5, was highly expressed in breast cancer cells and tumor tissues but not in their normal counterparts. Furthermore, we demonstrated that the fructose diet promoted metastasis of 4T1 cells in the mouse models. Taken together, our data show that fructose can be used by breast cancer cells specifically in glucose-deficiency, and suggest that the high-fructose diet could accelerate the progress of breast cancer in vivo

Tibial fracture surgery in elderly mice caused postoperative neurocognitive disorder via SOX2OT lncRNA in the hippocampus

Abstract Increasing evidence indicates the major role of mitochondrial function in neurodegenerative disease. However, it is unclear whether mitochondrial dynamics directly affect postoperative neurocognitive disorder (PND). This study aimed to analyze the underlying mechanisms of mitochondrial dynamics in the pathogenesis of PND. Tibial fracture surgery was performed in elderly mice to generate a PND model in vivo. Cognitive behavior was evaluated 3 days post-surgery using novel object recognition and fear conditioning. A gradual increase in the SOX2OT mRNA level and decrease in the SOX2 mRNA level were noted, with impaired cognitive function, in the mice 3 days after tibial surgery compared with mice in the sham group. To evaluate the role of SOX2OT in PND, SOX2OT knockdown was performed in vitro and in vivo using lentivirus transfection in HT22 cells and via brain stereotactic injection of lentivirus, respectively. SOX2OT knockdown reduced apoptosis, inhibited oxidative stress, suppressed mitochondrial hyperdivision, attenuated surgery-induced cognitive dysfunction, and promoted downstream SOX2 expression in elderly mice. Furthermore, Sox2 alleviated mitochondrial functional damage by inhibiting the transcription of mitochondrial division protein Drp1. Our study findings indicate that SOX2OT knockout alleviates surgery-induced mitochondrial fission and cognitive function defects by upregulating the expression of Sox2 in mice, resulting in the inhibition of drp1 transcription. Therefore, regulation of the SOX2/Drp1 pathway may be a potential mechanism for the treatment of patients with PND