The island of Ireland comprises the Republic of Ireland and Northern Ireland which forms part of the United Kingdom. It is situated in the North Atlantic Ocean in North-Western Europe (Fig. 1). The census population of Ireland in 2022 reported a growing population of more than 7 million for the entire island and 5.1 million in the Republic, the highest population since 1841.1

RNA-directed antisense and interference therapeutics are a promising treatment option for cancer. The demonstration of depletion of target proteins within human tumors in vivo using validated methodology will be a key to the application of this technology. Here, we present a flow cytometric-based approach to quantitatively determine protein levels in solid tumor material derived by fiber optic brushing (FOB) of non-small cell lung cancer (NSCLC) patients. Focusing upon the survivin protein, and its depletion by an antisense oligonucleotide (ASO) (LY2181308), we show that we can robustly identify a subpopulation of survivin positive tumor cells in FOB samples, and, moreover, detect survivin depletion in tumor samples from a patient treated with LY2181308. Survivin depletion appears to be a result of treatment with this ASO, because a tumor treated with conventional cytotoxic chemotherapy did not exhibit a decreased percentage of survivin positive cells. Our approach is likely to be broadly applicable to, and useful for, the quantification of protein levels in tumor samples obtained as part of clinical trials and studies, facilitating the proof-of-principle testing of novel targeted therapies.

Project Goal. The purpose of the project is to develop computational techniques for analyzing histology images of breast cancer tumors in order to ascertain the metastasis status of the tumor. The computational techniques will derive shape and color information from the images that will enable automated evaluation of the tumor. Specifically, the techniques will be used to determine if a patient’s breast cancer has spread to nearby lymph nodes by examining a primary tumor that has been removed from the patient. This image analysis capability will eliminate the need for exploratory surgical removal of lymph nodes; thus eliminating the associated side effects, e.g. pain, swelling and morbidity, and costs.

Understanding trends in ground water contaminant concentrations is complicated by the fact that ground water quality presents complex three-dimensional trends over multiple spatial scales. To address this issue we have developed a method to jointly display three-dimensional ground water quality and topographic data. This approach allows the user to: 1) identify local areas of elevated contaminant concentrations, 2) identify particular topographic features (river valleys, plateaus, etc) associated with elevated concentrations, 3) assess differences among aquifers and well use types, and 4) identify associations among different constituents. The approach used here links geospatially referenced concentration data with elevation data contained in Digital Terrain Elevation Data (DTED) files within an easyto-use MATLAB-based visualization system. The method was applied to visualize information on nitrate and arsenic occurrence in a national ground water quality database. The method shows that high arsenic is associated with the transition from plains to piedmont in New Jersey. Nitrate in Iowa is shown to be associated with shallow wells in the southeastern portion of the state. The approach developed here is compatible with any Microsoft Excel Spreadsheet database which follows specific format conventions.

Mesenteric panniculitis is a rare, complex disorder characterized by a chronic, idiopathic fibro-inflammatory process of mesenteric adipose tissue. Due to its protean clinical presentation, mesenteric panniculitis poses a significant diagnostic challenge to the clinician, radiologist and pathologist alike.

To better understand geographic trends in arsenic occurrence, researchers recently used an interactive visualization technique to link geospatially referenced arsenic concentration information from a water quality database with data contained in digital terrain elevation data (DTED) files. DTED files allow users to develop 3‐D plots of arsenic concentration and topography. The study used the U.S. Geological Survey arsenic point database, which contains water quality data for the United States and its territories. Researchers used technical computing software that allows users to specify subset columns within a graphical user interface.

Segmentation of anatomical regions of the brain is one of the fundamental problems in medical image analysis. It is traditionally solved by iso-surfacing or through the use of active contours/deformable models on a gray-scale MRI data. In this paper we develop a technique that uses anisotropic di usion properties of brain tissue available from DTMRI to segment out brain structures. We develop a computational pipeline starting from raw di usion tensor data, through computation of invariant anisotropy measures to construction of geometric models of the brain structures. This provides an environment for user-controlled 3D segmentation of DT-MRI datasets. We use a level set approach to remove noise from the data and to produce smooth, geometric models. We apply our technique to DT-MRI data of a human subject and build models of the isotropic and strongly anisotropic regions of the brain. Once geometric models have been constructed they may be combined to study spatial relationships and quantitatively analyzed to produce the volume and surface area of the segmented regions.

This paper describes an application of the second generation Sepia architecture implementation (Sepia-2) to support interactive visualization of scalar fields represented as very large 3D rectilinear grids. By employing pipelined sort-last associative blending operators a demonstration system yields scalable interactivity at 30 frames per second. We believe these results can be extended to support other types of structured and unstructured grids and a variety of GL rendering techniques. We show how to extend our single-stage demonstration system to larger multi-stage networks. This requires solving a dynamic mapping problem for blending operators that are similar to Porter-Duff compositing operators. We conclude by discussing technical and fundamental issues to address in future work.

Describes an application of a second generation implementation of the Sepia architecture (Sepia-2) to interactive volumetric visualization of large rectilinear scalar fields. By employing pipelined associative blending operators in a sort-last configuration a demonstration system with 8 rendering computers sustains 24 to 28 frames per second while interactively rendering large data volumes (1024/spl times/256/spl times/256 voxels, and 512/spl times/512/spl times/512 voxels). We believe interactive performance at these frame rates and data sizes is unprecedented. We also believe these results can be extended to other types of structured and unstructured grids and a variety of GL rendering techniques including surface rendering and shadow mapping. We show how to extend our single-stage crossbar demonstration system to multi-stage networks in order to support much larger data sizes and higher image resolutions. This requires solving a dynamic mapping problem for a class of blending operators that includes Porter-Duff compositing operators.

Di usion weighted magnetic resonance imaging (DW MRI) is sensitive to random thermal movement of water molecules known as Brownian motion. Consequently, DWI can be used to detect the di usion of water molecules in tissues. Because water molecules can di use more easily along ber tracts, for example in the brain, rather than across them, di usion is anisotropic and can be used for segmentation. Segmentation requires the identi cation of regions with di erent di usion properties. In this paper we propose a set of rotationally invariant di usion measures which may be used to map the tensor data into a scalar representation. Proposed invariants are faster to compute and they are more stable with respect to noise than eigenvalue based methods. We use these invariants to analyze a 3D DW MRI scan of a human head and build geometric models corresponding to isotropic and anisotropic regions. We then utilize the models to perform quantitative analysis of these regions calculating their surface areas and volumes.