946 research outputs found
Utilizing biomarkers in colorectal cancer: an interview with Ajay Goel
No full text Ajay Goel speaks to Rachel Jenkins, Commissioning Editor. Ajay Goel, PhD, is a Professor and Director, Center for Gastrointestinal Research, and Director, Center for Translational Genomics and Oncology, at the Baylor Scott & White Research Institute, Baylor University Medical Center in Dallas, Texas. Dr Goel has spent more than 20 years researching cancer and has been the lead author or contributor to over 240 scientific articles published in peer-reviewed international journals and several book chapters. He is also a primary inventor on more than 15 international patents aimed at developing various biomarkers for the diagnosis, prognosis and prediction of gastrointestinal cancers. He is currently using advanced genomic and transcriptomic approaches to develop novel DNA- and miRNA-based biomarkers for the early detection of colorectal cancers. In addition, he is researching the prevention of gastrointestinal cancers using integrative and alternative approaches, including botanical products such as curcumin (from turmeric) and boswellia. Dr Goel is a member of the American Association for Cancer Research (AACR) and the American Gastroenterology Association (AGA) and is on the international editorial boards of several journals including Gastroenterology, Clinical Cancer Research, Carcinogenesis, PLoS ONE, Scientific Reports, Epigenomics, Future Medicine, Alternative Therapies in Heath and Medicine and World Journal of Gastroenterology. He is also actively involved in peer-reviewing activities for more than 100 international scientific journals and various grant review panels of various national and international funding organizations. His research has been actively funded by various private and federal organizations, including funding from the National Cancer Institute (NCI) at the NIH, American Cancer Society (ACS) and other state organizations. He has won more than dozen awards and honors, including the Union of European Gastroenterology Federation's Distinguished Researcher Award, multiple Poster of Distinction Awards from the AGA, and Visiting Professorships from various national and international academic institutions and academic bodies. Some of his key research interests include: Understanding the basic genetics and epigenetic basis of gastrointestinal cancers; Use of epigenetic markers, both DNA and RNA, for the early detection of colorectal, pancreatic and other gastrointestinal cancers; Personalized medicine and treatment of gastrointestinal cancers; Chemoprevention, using complementary and alternative approaches using nutraceuticals such as curcumin, green tea, resveratrol and other botanicals. </jats:p
Experimental evaluation of long term evolution-based NC OFDM secondary-to-secondary interference
No full textScarcity of spectrum resources, inefficient spectrum usage and the inflexibility of the current spectrum assignment are few of the major roadblocks in the development of new wireless communication standards. Secondary spectrum sharing has become a viable solution to alleviate this problem. Secondary users are unlicensed devices that use opportunistic spectrum access to identify vacant frequency bins and thereby utilize the spectrum. For advanced wireless communication standards like the Long Term Evolution (LTE) which primarily calls for higher data rates, evaluation of design parameters for ensuring efficient coexistence of heterogeneous secondary users and guaranteeing acceptable minimum level of performance becomes essential. Additionally, the understanding of the interference between secondary users occupying adjacent frequency bands for their transmission is imperative. This thesis focuses on the coexistence of secondary users in the same band assuming that the primary spectrum is found available. By Implementing two Non Contiguous Orthogonal Frequency Division Multiplexing ( NC-OFDM) based secondary transmitters on a real time platform, the design parameters that need to be considered to ensure efficient coexistence have been identified and investigated. The performance degradations observed at a particular secondary link due to presence of another interfering secondary link occupying adjacent frequency bands for its transmission have also been studied. This thesis also focuses on implementation of algorithms to modify the existing NC-OFDM transmission at the secondary transmitter end to reduce its Interference effects on the other secondary links operating within the same band. The focus is on an LTE-based Secondary Non Contiguous Orthogonal Frequency Division Multiplexing Transceiver on a Real Time Platform developed by National Instruments. The various blocks needed to design a real time LTE based communications links are discussed. An experimental LTE-to-LTE interference analysis based on the Real Time Platform and the designed system is presented.M.S.Includes bibliographical referencesby Ajay Ramkumar Iye
Automated image-based detection and grading of lymphocytic infiltration in breast cancer histopathology:
No full textThe identification of phenotypic changes in breast cancer (BC) histopathology is of significant clinical importance in predicting disease outcome and prescribing appropriate therapy. One such example is the presence of lymphocytic infiltration (LI) in histopathology, which has been correlated with a variety of prognoses and theragnoses (i.e. response to treatment) in BC patients. In this thesis work a computer-aided diagnosis (CADx) system is detailed for quantitatively measuring the extent of LI from hematoxylin and eosin (H & E) stained histopathology. The CADx system is subsequently applied to BC patients expressing the HER2 gene (HER2+ BC), where LI extent has been found to correlate with nodal metastasis and distant recurrence. Although LI may be graded qualitatively by BC pathologists, there is currently no quantitative and reproducible method for measuring LI extent in HER2+ BC histopathology. Hence, a CADx system that performs this task will potentially help clinicians predict disease outcome and allow them to make better therapy recommendations for HER2+ BC patients. The CADx methodology comprises three key steps. First, a combination of region-growing and Markov Random Field algorithms is used to detect individual lymphocyte nuclei and isolate areas of LI in digitized H & E stained histopathology images. The centers of individual detected lymphocytes are used as vertices to construct a series of graphs (Voronoi Diagram, Delaunay Triangulation, and Minimum Spanning Tree) and a total of 50 architectural features describing the spatial arrangement of lymphocytes are extracted from each image. By using Graph Embedding, a non-linear dimensionality reduction method, to project the high-dimensional feature vectors into a reduced 3D embedding space, it is possible to visualize the underlying manifold that represents the continuous nature of the LI phenotype. Over a set of 100 randomized cross-validation trials, a Support Vector Machine classifier shows that the architectural feature set distinguishes HER2+ BC histopathology samples containing high and low levels of LI with a classification accuracy greater than 90%.M.S.Includes bibliographical references (p. 35-35)by Ajay Basavanhall
Investigation in Prolog-based Machine Translation with English-Hungarian Case Study
No full textabstract: This undergraduate thesis explores the efficacy of developing a translator generator in the Prolog programming language using Lexical Functional Grammars. A bidirectional machine translator between English and Hungarian, developed as a proof-of-concept case study, is discussed and assessed. The benefits and drawbacks of this approach as generalized to Machine Translation systems are also discussed, along with possible areas of future work
Hydrodynamic Studies on a Trickle Bed Reactor for Foaming Liquids
No full textHydrodynamic studies of trickle bed reactors (TBRs) are essential for the design and prediction of their performance. The hydrodynamic characteristics involving pressure drop and dynamic liquid saturation are greatly affected by the physical properties of the liquids. In the present study experiments have been carried out in a concurrent downflow air - liquid trickle bed reactor to investigate the dynamic liquid saturation and pressure drop for the water (non-foaming) and 3% polyethylene glycol and 4% polyethylene glycol foaming liquids in the gas continuous regime (GCF) and foaming pulsing regime (FP). In the GCF regime the dynamic liquid saturation was found to increase with increase in liquid flow rate for non-foaming and foaming liquids. While for 3% and 4% polyethylene glycol solutions the severe foaming was observed in the high interaction regime and the regime is referred to as foaming pulsing (FP) regime. The decrease in dynamic liquid saturation followed by a sharp rise in the pressure drop was observed during transition from gas GCF to FP regime. However in the FP regime, a dip in the dynamic liquid saturation was observed. The pressure drop for foaming liquids is observed to be manifolds higher compared to non-foaming liquid in the GCF regime. ©2010 BCREC UNDIP. All rights reserved(Received: 16th January 2010, Revised: 10th February 2010, Accepted: 21st Feberuary 2010)[How to Cite: R. Gupta, A. Bansal. (2010). Hydrodynamic Studies on a Trickle Bed Reactor for Foaming Liquids. Bulletin of Chemical Reaction Engineering & Catalysis, 5 (1): 31-37. doi:10.9767/bcrec.5.1.7127.31-37][How to Link / DOI: http://dx.doi.org/10.9767/bcrec.5.1.7127.31-37 || or local: http://ejournal.undip.ac.id/index.php/bcrec/article/view/7127][Cited by: Scopus 1 | ] </p
A Composite Natural Language Processing and Information Retrieval Approach to Question Answering Against a Structured Knowledge Base
Get PDFabstract: With the inception of World Wide Web, the amount of data present on the internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. The aim of this thesis is to develop a Question Answering system to ease the process of information retrieval.
Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. Text REtrieval Conference (TREC) is a yearly conference which provides large - scale infrastructure and resources to support research in information retrieval domain. TREC has a question answering track since 1999 where the questions dataset contains a list of factual questions (Vorhees & Tice, 1999). DBpedia (Bizer et al., 2009) is a community driven effort to extract and structure the data present in Wikipedia.
The research objective of this thesis is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval and Natural Language processing. The focus is also on exploring the use of a structured and annotated knowledge base as opposed to an unstructured knowledge base. The knowledge base used here is DBpedia and the final system is evaluated on the TREC 2004 questions dataset.Dissertation/ThesisMasters Thesis Computer Science 201
Distributed SPARQL over Big RDF Data - A Comparative Analysis using Presto and MapReduce
Get PDFabstract: The processing of large volumes of RDF data require an efficient storage and query processing engine that can scale well with the volume of data. The initial attempts to address this issue focused on optimizing native RDF stores as well as conventional relational databases management systems. But as the volume of RDF data grew to exponential proportions, the limitations of these systems became apparent and researchers began to focus on using big data analysis tools, most notably Hadoop, to process RDF data. Various studies and benchmarks that evaluate these tools for RDF data processing have been published. In the past two and half years, however, heavy users of big data systems, like Facebook, noted limitations with the query performance of these big data systems and began to develop new distributed query engines for big data that do not rely on map-reduce. Facebook's Presto is one such example.
This thesis deals with evaluating the performance of Presto in processing big RDF data against Apache Hive. A comparative analysis was also conducted against 4store, a native RDF store. To evaluate the performance Presto for big RDF data processing, a map-reduce program and a compiler, based on Flex and Bison, were implemented. The map-reduce program loads RDF data into HDFS while the compiler translates SPARQL queries into a subset of SQL that Presto (and Hive) can understand. The evaluation was done on four and eight node Linux clusters installed on Microsoft Windows Azure platform with RDF datasets of size 10, 20, and 30 million triples. The results of the experiment show that Presto has a much higher performance than Hive can be used to process big RDF data. The thesis also proposes an architecture based on Presto, Presto-RDF, that can be used to process big RDF data.Dissertation/ThesisMasters Thesis Computing Studies 201
MOOCLink: Linking and Maintaining Quality of Data Provided by Various MOOC Providers
Get PDFabstract: The concept of Linked Data is gaining widespread popularity and importance. The method of publishing and linking structured data on the web is called Linked Data. Emergence of Linked Data has made it possible to make sense of huge data, which is scattered all over the web, and link multiple heterogeneous sources. This leads to the challenge of maintaining the quality of Linked Data, i.e., ensuring outdated data is removed and new data is included. The focus of this thesis is devising strategies to effectively integrate data from multiple sources, publish it as Linked Data, and maintain the quality of Linked Data. The domain used in the study is online education. With so many online courses offered by Massive Open Online Courses (MOOC), it is becoming increasingly difficult for an end user to gauge which course best fits his/her needs.
Users are spoilt for choices. It would be very helpful for them to make a choice if there is a single place where they can visually compare the offerings of various MOOC providers for the course they are interested in. Previous work has been done in this area through the MOOCLink project that involved integrating data from Coursera, EdX, and Udacity and generation of linked data, i.e. Resource Description Framework (RDF) triples.
The research objective of this thesis is to determine a methodology by which the quality
of data available through the MOOCLink application is maintained, as there are lots of new courses being constantly added and old courses being removed by data providers. This thesis presents the integration of data from various MOOC providers and algorithms for incrementally updating linked data to maintain their quality and compare it against a naïve approach in order to constantly keep the users engaged with up-to-date data. A master threshold value was determined through experiments and analysis that quantifies one algorithm being better than the other in terms of time efficiency. An evaluation of the tool shows the effectiveness of the algorithms presented in this thesis.Dissertation/ThesisMasters Thesis Computer Science 201
All Purpose Textual Data Information Extraction, Visualization and Querying
Get PDFabstract: Since the advent of the internet and even more after social media platforms, the explosive growth of textual data and its availability has made analysis a tedious task. Information extraction systems are available but are generally too specific and often only extract certain kinds of information they deem necessary and extraction worthy. Using data visualization theory and fast, interactive querying methods, leaving out information might not really be necessary. This thesis explores textual data visualization techniques, intuitive querying, and a novel approach to all-purpose textual information extraction to encode large text corpus to improve human understanding of the information present in textual data.
This thesis presents a modified traversal algorithm on dependency parse output of text to extract all subject predicate object pairs from text while ensuring that no information is missed out. To support full scale, all-purpose information extraction from large text corpuses, a data preprocessing pipeline is recommended to be used before the extraction is run. The output format is designed specifically to fit on a node-edge-node model and form the building blocks of a network which makes understanding of the text and querying of information from corpus quick and intuitive. It attempts to reduce reading time and enhancing understanding of the text using interactive graph and timeline.Dissertation/ThesisMasters Thesis Software Engineering 201
A Semantic Framework for Integrating and Publishing Linked Data on the Web
Get PDFabstract: Semantic web is the web of data that provides a common framework and technologies for sharing and reusing data in various applications. In semantic web terminology, linked data is the term used to describe a method of exposing and connecting data on the web from different sources. The purpose of linked data and semantic web is to publish data in an open and standard format and to link this data with existing data on the Linked Open Data Cloud. The goal of this thesis to come up with a semantic framework for integrating and publishing linked data on the web. Traditionally integrating data from multiple sources usually involves an Extract-Transform-Load (ETL) framework to generate datasets for analytics and visualization. The thesis proposes introducing a semantic component in the ETL framework to semi-automate the generation and publishing of linked data. In this thesis, various existing ETL tools and data integration techniques have been analyzed and deficiencies have been identified. This thesis proposes a set of requirements for the semantic ETL framework by conducting a manual process to integrate data from various sources such as weather, holidays, airports, flight arrival, departure and delays. The research questions that are addressed are: (i) to what extent can the integration, generation, and publishing of linked data to the cloud using a semantic ETL framework be automated; (ii) does use of semantic technologies produce a richer data model and integrated data. Details of the methodology, data collection, and application that uses the linked data generated are presented. Evaluation is done by comparing traditional data integration approach with semantic ETL approach in terms of effort involved in integration, data model generated and querying the data generated.Dissertation/ThesisMasters Thesis Computer Science 201
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