Medicine and Health Sciences Commons^™

Framework For Hyperspectral Image Processing And Quantification For Cancer Detection During Animal Tumor Surgery, Guolan Lu, Dongsheng Wang, Xulei Qin, Luma Halig, Susan Muller, Hongzheng Zhang, Amy Chen, Brian W. Pogue, Zhuo G. Chen

Dartmouth Scholarship

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, …

Logic Regression-Derived Algorithms For Syndromic Management Of Vaginal Infections, Sujit D. Rathod, Tan Li, Jeffery D. Klausner, Alan Hubbard, Arthur L. Reingold, Purnima Madhivanan

Department of Biostatistics Faculty Publications

Background: Syndromic management of vaginal infections is known to have poor diagnostic accuracy. Logic regression is a machine-learning procedure which allows for the identification of combinations of variables to predict an outcome, such as the presence of a vaginal infection.

Methods: We used logic regression to develop predictive models for syndromic management of vaginal infection among symptomatic, reproductive-age women in south India. We assessed the positive predictive values, negative predictive values, sensitivities and specificities of the logic regression procedure and a standard WHO algorithm against laboratory-confirmed diagnoses of two conditions: metronidazole-sensitive vaginitis [bacterial vaginosis or trichomoniasis (BV/TV)], and vulvovaginal candidiasis …

Leveraging Global Gene Expression Patterns To Predict Expression Of Unmeasured Genes, James Rudd, René A. Zelaya, Eugene Demidenko, Ellen L. Goode, Casey S. Greene S. Greene, Jennifer A. Doherty

Dartmouth Scholarship

BackgroundLarge collections of paraffin-embedded tissue represent a rich resource to test hypotheses based on gene expression patterns; however, measurement of genome-wide expression is cost-prohibitive on a large scale. Using the known expression correlation structure within a given disease type (in this case, high grade serous ovarian cancer; HGSC), we sought to identify reduced sets of directly measured (DM) genes which could accurately predict the expression of a maximized number of unmeasured genes.

Applying Osteopathic Principles To Formulate Treatment For Patients With Chronic Pain, Michael Kuchera

Michael Kuchera

Osteopathic manipulative medicine (OMM) is a physician-directed approach to patient care that incorporates diagnostic and therapeutic strategies to address body unity issues, enhance homeostatic mechanisms, and maximize structure-function interrelationships. Osteopathic physicians integrate a thorough medical history with palpatory examination of a patient to ascertain distinctive characteristics and origins of the patient's pain, to evaluate how pain uniquely affects the patient, and to determine whether segmental, reflex, or triggered pain phenomena coexist in the patient. Osteopathic manipulative medicine expands differential diagnoses by allowing the physician to consider somatic dysfunction and implement treatment options via integration of specific aspects of complementary care …

Osteopathic Manipulative Medicine Considerations In Patients With Chronic Pain, Michael Kuchera

Michael Kuchera

Osteopathic manipulative medicine (OMM) incorporates diagnostic and therapeutic strategies that address body unity, homeostatic mechanisms, and structure-function interrelationships. In regard to pain, osteopathic physicians take thorough histories guided by palpatory examination to determine the quality, duration, and origin of this condition, how it uniquely affects the individual, and whether segmental, reflex, or triggered pain phenomena coexist. Osteopathic manipulative medicine expands differential diagnoses by considering somatic dysfunction and treatment options by integrating specific aspects of complementary care into state-of-the-art pain management practices. Prescriptions formulated through an OMM algorithm integrate each osteopathic tenet with biopsychosocial and patient education models and medication, rehabilitation, …

Segment And Fit Thresholding: A New Method For Image Analysis Applied To Microarray And Immunofluorescence Data., Elliot Ensink, Jessica Sinha, Arkadeep Sinha, Huiyuan Tang, Heather M. Calderone, Galen Hostetter, Jordan M. Winter, David Cherba, Randall E. Brand, Peter J. Allen, Lorenzo F. Sempere, Brian B. Haab

Department of Surgery Faculty Papers

Experiments involving the high-throughput quantification of image data require algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to …

Development And Validation Of An Epitope Prediction Tool For Swine (Pigmatrix) Based On The Pocket Profile Method, Andres H. Gutiérrez, William D. Martin, Chris Bailey-Kellogg, Frances Terry, Leonard Moise, Anee S. De Groot

Dartmouth Scholarship

Background: T cell epitope prediction tools and associated vaccine design algorithms have accelerated the development of vaccines for humans. Predictive tools for swine and other food animals are not as well developed, primarily because the data required to develop the tools are lacking. Here, we overcome a lack of T cell epitope data to construct swine epitope predictors by systematically leveraging available human information. Applying the “pocket profile method ”, we use sequence and structural similarities in the binding pockets of human and swine major histocompatibility complex proteins to infer Swine Leukocyte Antigen (SLA) peptide binding preferences. We developed epitope-prediction …

Prediction Of Relevant Biomedical Documents: A Human Microbiome Case Study, Paul Thompson, Juliette C. Madan, Jason H. Moore

Dartmouth Scholarship

Background:

Retrieving relevant biomedical literature has become increasingly difficult due to the large volume and rapid growth of biomedical publication. A query to a biomedical retrieval system often retrieves hundreds of results. Since the searcher will not likely consider all of these documents, ranking the documents is important. Ranking by recency, as PubMed does, takes into account only one factor indicating potential relevance. This study explores the use of the searcher’s relevance feedback judgments to support relevance ranking based on features more general than recency.

Results:

It was found that the researcher’s relevance judgments could be used to accurately predict …

Logarithmic Intensity Compression In Fluorescence Guided Surgery Applications, Alisha V. Dsouza, Huiyun Lin, Jason Gunn, Brian W. Pogue

Dartmouth Scholarship

The use of fluorescence video imaging to guide surgery is rapidly expanding, and improvements in camera readout dynamic range have not matched display capabilities. Logarithmic intensity compression is a fast, single-step mapping technique that can map the useable dynamic range of high-bit fluorescence images onto the typical 8-bit display and potentially be a variable dynamic contrast enhancement tool. We demonstrate a ∼4.6  times improvement in image quality quantified by image entropy and a dynamic range reduction by a factor of ∼380 by the use of log-compression tools in processing in vivo fluorescence images.

Principal Component Gene Set Enrichment (Pcgse), H. Robert Frost, Zhigang Li, Jason H. Moore

Dartmouth Scholarship

Background:

Although principal component analysis (PCA) is widely used for the dimensional reduction of biomedical data, interpretation of PCA results remains daunting. Most existing interpretation methods attempt to explain each principal component (PC) in terms of a small number of variables by generating approximate PCs with mainly zero loadings. Although useful when just a few variables dominate the population PCs, these methods can perform poorly on genomic data, where interesting biological features are frequently represented by the combined signal of functionally related sets of genes. While gene set testing methods have been widely used in supervised settings to quantify the …

Confident Difference Criterion: A New Bayesian Differentially Expressed Gene Selection Algorithm With Applications., Fang Yu, Ming-Hui Chen, Lynn Kuo, Heather Talbott, John S. Davis

Journal Articles: Obstetrics & Gynecology

BACKGROUND: Recently, the Bayesian method becomes more popular for analyzing high dimensional gene expression data as it allows us to borrow information across different genes and provides powerful estimators for evaluating gene expression levels. It is crucial to develop a simple but efficient gene selection algorithm for detecting differentially expressed (DE) genes based on the Bayesian estimators.

RESULTS: In this paper, by extending the two-criterion idea of Chen et al. (Chen M-H, Ibrahim JG, Chi Y-Y. A new class of mixture models for differential gene expression in DNA microarray data. J Stat Plan Inference. 2008;138:387-404), we propose two new gene …

Classification Of Dengue Illness Based On Readily Available Laboratory Data, James Potts, Stephen Thomas, Anon Srikiatkhachorn, Pra-On Supradish, Wenjun Li, Ananda Nisalak, Suchitra Nimmannitya, Timothy Endy, Daniel Libraty, Robert Gibbons, Sharone Green, Alan Rothman, Siripen Kalayanarooj

Sharone Green

The aim of this study was to examine retrospective dengue-illness classification using only clinical laboratory data, without relying on X-ray, ultrasound, or percent hemoconcentration. We analyzed data from a study of children who presented with acute febrile illness to two hospitals in Thailand. Multivariable logistic regression models were used to distinguish: (1) dengue hemorrhagic fever (DHF) versus dengue fever (DF), (2) DHF versus DF + other febrile illness (OFI), (3) dengue versus OFI, and (4) severe dengue versus non-severe dengue + OFI. Data from the second hospital served as a validation set. There were 1,227 patients in the analysis. The …

3d Thoracoscopic Ultrasound Volume Measurement Validation In An Ex Vivo And In Vivo Porcine Model Of Lung Tumours, V. Hornblower, E. Yu, A. Fenster, J. Battista, R. Malthaner

Richard A. Malthaner

The purpose of this study was to validate the accuracy and reliability of volume measurements obtained using three-dimensional (3D) thoracoscopic ultrasound (US) imaging. Artificial "tumours" were created by injecting a liquid agar mixture into spherical moulds of known volume. Once solidified, the "tumours" were implanted into the lung tissue in both a porcine lung sample ex vivo and a surgical porcine model in vivo. 3D US images were created by mechanically rotating the thoracoscopic ultrasound probe about its long axis while the transducer was maintained in close contact with the tissue. Volume measurements were made by one observer using the …

3d Thoracoscopic Ultrasound Volume Measurement Validation In An Ex Vivo And In Vivo Porcine Model Of Lung Tumours, V. Hornblower, E. Yu, A. Fenster, J. Battista, R. Malthaner

Richard A. Malthaner

The purpose of this study was to validate the accuracy and reliability of volume measurements obtained using three-dimensional (3D) thoracoscopic ultrasound (US) imaging. Artificial "tumours" were created by injecting a liquid agar mixture into spherical moulds of known volume. Once solidified, the "tumours" were implanted into the lung tissue in both a porcine lung sample ex vivo and a surgical porcine model in vivo. 3D US images were created by mechanically rotating the thoracoscopic ultrasound probe about its long axis while the transducer was maintained in close contact with the tissue. Volume measurements were made by one observer using the …

Testing Multiple Hypotheses Through Imp Weighted Fdr Based On A Genetic Functional Network With Application To A New Zebrafish Transcriptome Study, Jiang Gui, Casey S. Greene, Con Sullivan, Walter Taylor, Jason H. Moore, Carol Kim

Dartmouth Scholarship

In genome-wide studies, hundreds of thousands of hypothesis tests are performed simultaneously. Bonferroni correction and False Discovery Rate (FDR) can effectively control type I error but often yield a high false negative rate. We aim to develop a more powerful method to detect differentially expressed genes. We present a Weighted False Discovery Rate (WFDR) method that incorporate biological knowledge from genetic networks. We first identify weights using Integrative Multi-species Prediction (IMP) and then apply the weights in WFDR to identify differentially expressed genes through an IMP-WFDR algorithm. We performed a gene expression experiment to identify zebrafish genes that change expression …

Network-Based Stratification Analysis Of 13 Major Cancer Types Using Mutations In Panels Of Cancer Genes., Xue Zhong, Hushan Yang, Shuyang Zhao, Yu Shyr, Bingshan Li

Department of Medical Oncology Faculty Papers

BACKGROUND: Cancers are complex diseases with heterogeneous genetic causes and clinical outcomes. It is critical to classify patients into subtypes and associate the subtypes with clinical outcomes for better prognosis and treatment. Large-scale studies have comprehensively identified somatic mutations across multiple tumor types, providing rich datasets for classifying patients based on genomic mutations. One challenge associated with this task is that mutations are rarely shared across patients. Network-based stratification (NBS) approaches have been proposed to overcome this challenge and used to classify tumors based on exome-level mutations. In routine research and clinical applications, however, usually only a small panel of …

The Reproducibility And Absolute Values Of Echocardiographic Measurements Of Left Ventricular Size And Function In Children Are Algorithm Dependent., Renee Margossian, Shan Chen, Lynn A. Sleeper, Lloyd Y. Tani, Girish S. Shirali, Fraser Golding, Elif Seda Selamet Tierney, Karen Altmann, Michael J. Campbell, Anita Szwast, Angela Sharkey, Elizabeth Radojewski, Steven D. Colan, Pediatric Heart Network Investigators

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: Several quantification algorithms for measuring left ventricular (LV) size and function are used in clinical and research settings. The aims of this study were to investigate the effects of measurement algorithm and beat averaging on the reproducibility of measurements of the left ventricle and to assess the magnitude of agreement among the algorithms in children with dilated cardiomyopathy.

METHODS: Echocardiograms were obtained in 169 children from eight clinical centers. Inter- and intrareader reproducibility was assessed on measurements of LV volumes using the biplane Simpson, modified Simpson, and 5/6 × area × length (5/6AL) algorithms. Percentage error was calculated as …

Sparcoc: A New Framework For Molecular Pattern Discovery And Cancer Gene Identification, Shiqian Ma, Daniel Johnson, Cody Ashby, Donghai Xiong, Carole L. Cramer, Jason H. Moore, Shuzhong Zhang, Xiuzhen Huang

Dartmouth Scholarship

It is challenging to cluster cancer patients of a certain histopathological type into molecular subtypes of clinical importance and identify gene signatures directly relevant to the subtypes. Current clustering approaches have inherent limitations, which prevent them from gauging the subtle heterogeneity of the molecular subtypes. In this paper we present a new framework: SPARCoC (Sparse-CoClust), which is based on a novel Common-background and Sparse-foreground Decomposition (CSD) model and the Maximum Block Improvement (MBI) co-clustering technique. SPARCoC has clear advantages compared with widely-used alternative approaches: hierarchical clustering (Hclust) and nonnegative matrix factorization (NMF). We apply SPARCoC to the study of lung …

Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore

Dartmouth Scholarship

Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes …

Microarray Enriched Gene Rank, Eugene Demidenko

Dartmouth Scholarship

We develop a new concept that reflects how genes are connected based on microarray data using the coefficient of determination (the squared Pearson correlation coefficient). Our gene rank combines a priori knowledge about gene connectivity, say, from the Gene Ontology (GO) database, and the microarray expression data at hand, called the microarray enriched gene rank, or simply gene rank (GR). GR, similarly to Google PageRank, is defined in a recursive fashion and is computed as the left maximum eigenvector of a stochastic matrix derived from microarray expression data. An efficient algorithm is devised that allows computation of GR for 50 …

Automatic Segmentation Of The Hippocampus For Preterm Neonates From Early-In-Life To Term-Equivalent Age., Ting Guo, Julie L Winterburn, Jon Pipitone, Emma G Duerden, Min Tae M Park, Vann Chau, Kenneth J Poskitt, Ruth E Grunau, Anne Synnes, Steven P Miller, M Mallar Chakravarty

Brain and Mind Institute Researchers' Publications

INTRODUCTION: The hippocampus, a medial temporal lobe structure central to learning and memory, is particularly vulnerable in preterm-born neonates. To date, segmentation of the hippocampus for preterm-born neonates has not yet been performed early-in-life (shortly after birth when clinically stable). The present study focuses on the development and validation of an automatic segmentation protocol that is based on the MAGeT-Brain (Multiple Automatically Generated Templates) algorithm to delineate the hippocampi of preterm neonates on their brain MRIs acquired at not only term-equivalent age but also early-in-life.

METHODS: First, we present a three-step manual segmentation protocol to delineate the hippocampus for preterm …

A Dynamic Programming Algorithm For Finding The Optimal Placement Of A Secondary Structure Topology In Cryo-Em Data, Abhishek Biswas, Desh Ranjan, Mohammad Zubair, Jing He

Computer Science Faculty Publications

The determination of secondary structure topology is a critical step in deriving the atomic structures from the protein density maps obtained from electron cryomicroscopy technique. This step often relies on matching the secondary structure traces detected from the protein density map to the secondary structure sequence segments predicted from the amino acid sequence. Due to inaccuracies in both sources of information, a pool of possible secondary structure positions needs to be sampled. One way to approach the problem is to first derive a small number of possible topologies using existing matching algorithms, and then find the optimal placement for each …