603 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
Optimization of smart traffic management system using machine vision-based functionalities with CNN model
No full textThe rapid developing volume of urban activity has made an urgent need for smart arrangements to oversee congestion and improve road safety. This paper presents a Smart Traffic Management System (STMS) that utilizes computer vision to optimize real-time traffic flow. By analyzing live video streams from various traffic cameras, the system is able to evaluate vehicle congestion, classify different types of vehicles, and detect traffic violations. From the various application of methods such as object detection, motion tracking, and deep learning, the STMS alters traffic signals dynamically, making a difference to ease traffic congestion and improve traffic flow. Moreover the system can identify accidents and alarm the appropriate authorities to encourage convenient intervention. The system’s execution was tested over various urban conditions showing impressive advancements in both traffic management and traffic congestion reduction
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
Tabish Khair in Conversation with Ajay K Chaubey
No full textBorn in Ranchi and educated up to his MA in Gaya, Tabish Khair, PhD (Copenhagen), DPhil (Aarhus), is a Professor of English in Denmark and the author of a number of acclaimed books. Winner of the All India Poetry Prize, Khair’s novels – The Bus Stopped (2004), Filming (2007) and The Thing About Thugs (2010) – have been shortlisted for awards including the Hindu Prize, Man Asian Prize, DSC Prize for South Asia. His last novel, How to Fight Islamist Terror from the Missionary Position, was dubbed the ‘best 9/11 novel’ by the New Republic and ‘unmissable’ by the Times. A study by Khair, The New Xenophobia, will be published by Oxford University Press in January 2016. Professor Khair, while being in Denmark, spoke to me through email promptly and positively on several aspects of diaspora, narratives of migration and rationale of ‘brain-drain’ and the theoretical contours of the Indian diaspora in the wake of multiple terrorist attacks in the West
Quantitative histomorphometry of digital pathology as a companion diagnostic: predicting outcome for ER+ breast cancers
No full textThis work involves the creation of an image-based companion diagnostic framework that employs quantitative features extracted from whole-slide, H & E stained digital pathology (DP) images to distinguish patients based on disease outcome, with a clinical application aimed at distinguishing estrogen receptor-positive (ER+) breast cancer (BCa) patients with good and poor outcomes. Quantitative histomorphometry (QH) -- the conversion of a digitized histopathology slide into a series of quantitative measurements of tumor morphology -- is a rapidly growing field aimed at introducing advanced image analytics into the histopathological workflow. The thrust towards personalized medicine has led to the development of companion diagnostic tools that measure gene expression, yielding quantitative outcome predictions for improved disease stratification and customized therapies, e.g. Oncotype DX (Genomic Health, Inc.) for ER+ BCa. Yet, tumor morphology is often correlated with genomic assays, suggesting that genotypic variations in biologically distinct classes of tumors lead to distinct patterns of tumor cell morphology and tissue architecture in histopathology. The application of this work to ER+ BCa is highly relevant to current clinical needs. Current treatment guidelines recommend that the majority of women with ER+ BCa receive chemotherapy in addition to hormonal therapy; yet, approximately half will not benefit from chemotherapy while still enduring its harmful side effects. Hence, there is a clear need for the development of automated prognostic tools to identify women with poorer outcomes who will likely benefit from chemotherapy. The primary novel contributions of this work are (1) a color standardization system for improving the consistency in appearance of tissue structures across images, (2) the identification of tissue structures and corresponding QH signatures with prognostic value in ER+ BCa, (3) a multi-field-of-view framework for robust integration of prognostic information across whole-slide DP images, and (4) a method for predicting classifier performance for a large data cohort based on the availability of limited training data. This work will pave the way for the development of novel companion diagnostic systems capable of producing quantitative and reproducible image-based risk scores. These risk scores will play a vital role in decision support by helping clinicians predict patient outcome and prescribing appropriate therapies.Ph.D.Includes bibliographical referencesby Ajay Nagesh Basavanhall
Killing Same and Different Location Multiple Mutants
No full textM.E. (Software Engineering)Software testing is an important technique for assurance of software quality and
mutation testing is the White -box, fault -based testing technique for Unit testing. For
mutation testing usually we generate test data according to one mutant at one time, so
for killing all the mutants by this technique large size of test suite required. But in this
thesis work we propose a new approach to generate one test data according to
multiple mutants that are mutated at the same location or mutated at different
locations and this test data can kill multiple mutants at one time. Reachability
condition, necessity condition and sufficiency condition are three conditions which
must be satisfied for a test data to kill a mutant and the reachability condition and the
necessity condition of a mutant can be acquired when the mutant is generated Mutants
mutated at the same location have the same reachability conditions and their necessity
conditions are of similar structure. For killing multiple mutants we will combine the
necessity conditions of some same-location mutants and different-location mutants
into one necessity condition and generate one test data to satisfy the shared
reachability conditions and the combined necessity condition. We can find the lesser
number of test data inputs than all the test data inputs which are used to kill all same
location mutants by acquiring , combining, reusing , minimizing and defining the
range of all the test data inputs used to kill same-location or different-location
mutants.
Thus our proposed approach can generate smaller test suite of very less cost that can
achieve the same mutation testing score
ANSWER: A Semantic Approach to Film Direction
No full textIn this paper we present ANSWER, an innovative approach to film direction. Here we describe a methodology to semantically model the film domain in a way which is coherent with the director’s intent during film production. To achieve this, we are developing a system architecture which will provide the director with the necessary tools and services to author a scene description through intuitive gesture based graphical user interfaces, which will in turn populate the underlying model with a rich set of semantic descriptions. These semantic descriptions will be used to render the scene graphically through animated previsualizations. A director using the ANSWER methodology will be able to understand and assert certain film making decisions before film production begin
A tactical traffic management solution for smart cities using reinforcement learning
No full textAs urbanization accelerates, traffic congestion presents a significant challenge for smart cities, impacting mobility and air quality. The paper is all about traffic management solution using reinforcement learning (RL) for real-time traffic control. Our system is set to change the signal timings based on traffic conditions, pedestrian movements, and environmental factors learning it lively and accurately. Deploying a multi-layered architecture, where the medium manages their role unitedly to enhance traffic flow. Feedback mechanisms are there to process the model for effective intermediation. Simulations project that the approach is noticeable in reducing travel times and crowding. This research helps in the advancement of smarter, more flexible urban transportation systems, assisting ability to move and making the urban life easier
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