608 research outputs found
Highlighting the effect of heterogeneous blood perfusion on radio-frequency ablation of human brain tumors: An image-based numerical investigation
No full textAjay Bhandari would like to acknowledge the support received by a grant from the Science and Engineering Research Board (Grant Number: SRG/2021/000053). Wenbo Zhan acknowledges the support received from the Royal Society of Edinburgh (Grant Number: SAPHIRE-2999). Both authors would like to acknowledge the support received from the Royal Society (Grant Number: IES\R1\221015). Anup Singh acknowledges the support received from Science and Engineering Research Board (Grant Number: CRG/2019/005032).Peer reviewe
sj-docx-1-car-10.1177_19476035231154530 – Supplemental material for Intra-articular Hyaluronic Acid Treatments for Knee Osteoarthritis: A Systematic Review of Product Properties
No full textSupplemental material, sj-docx-1-car-10.1177_19476035231154530 for Intra-articular Hyaluronic Acid Treatments for Knee Osteoarthritis: A Systematic Review of Product Properties by Eric Ferkel, Ajay Manjoo, Damion Martins, Mohit Bhandari, Paul Sethi and Mathew Nicholls in CARTILAGE</p
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
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
A Ground Control Station for Multi-Vehicular Data Visualization and Control
No full textA vehicle’s autonomy allows an operator to perform other tasks, but it also poses the significant challenge of monitoring the vehicle’s actions. When multiple vehicles are involved, maintaining interoperability becomes tedious and complicated. As the role of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) increases, it is important to provide an effective method of monitoring and controlling multiple unmanned vehicles. We propose the development of an operating system agnostic Ground Control Station (GCS) with a specialized, scalable communication structure for communicating with multiple vehicles and between different control stations. The software aspect consists of data storage, transmission, and visualization. Data storage is handled by SQLite, a highly scalable object-relational database system that guarantees reliable processing of database transactions. It is responsible for managing incoming data from vehicles and responding to queries from the displays. Data transmission and visualization are managed by Qt, a cross-platform application framework that provides packet and serial communication support as well as an easy-to-use graphical user interface (GUI) designer. The interface of the GCS adaptively displays information based on what the user selects, rather than displaying all the data it can retrieve from the remote database. In addition, users may interact with the Situational Awareness Map (SAM) to monitor and maneuver the UAVs and UGVs with real-time data. The hardware aspect of the GCS provides interaction with XBee-PRO XSC S3B radios and Wi-Fi capable hardware. The XBee radio is used for vehicle-to-vehicle, vehicle-to-GCS, and GCS-to-vehicle communication to transmit data. The Wi-Fi capable hardware is for GCS-to-GCS and transmitting visual data from vehicles to the GCS
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
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