Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets

To propose the determination of the macromolecular baseline (MMBL) in clinical 1H MR spectra based on T(1) and T(2) differentiation using 2D fitting in FiTAID, a general Fitting Tool for Arrays of Interrelated Datasets.

Quantitative measurements in 3-dimensional datasets of mouse lymph nodes resolve organ-wide functional dependencies

Deep tissue imaging has become state of the art in biology, but now the problem is to quantify spatial information in a global, organ-wide context. Although access to the raw data is no longer a limitation, the computational tools to extract biologically useful information out of these large data sets is still catching up. In many cases, to understand the mechanism behind a biological process, where molecules or cells interact with each other, it is mandatory to know their mutual positions. We illustrate this principle here with the immune system. Although the general functions of lymph nodes as immune sentinels are well described, many cellular and molecular details governing the interactions of lymphocytes and dendritic cells remain unclear to date and prevent an in-depth mechanistic understanding of the immune system. We imaged ex vivo lymph nodes isolated from both wild-type and transgenic mice lacking key factors for dendritic cell positioning and used software written in MATLAB to determine the spatial distances between the dendritic cells and the internal high endothelial vascular network. This allowed us to quantify the spatial localization of the dendritic cells in the lymph node, which is a critical parameter determining the effectiveness of an adaptive immune response.

Object-Oriented Legacy System Trace-based Logic Testing

When reengineering legacy systems, it is crucial to assess if the legacy behavior has been preserved or how it changed due to the reengineering effort. Ideally if a legacy system is covered by tests, running the tests on the new version can identify potential differences or discrepancies. However, writing tests for an unknown and large system is difficult due to the lack of internal knowledge. It is especially difficult to bring the system to an appropriate state. Our solution is based on the acknowledgment that one of the few trustable piece of information available when approaching a legacy system is the running system itself. Our approach reifies the execution traces and uses logic programming to express tests on them. Thereby it eliminates the need to programatically bring the system in a particular state, and handles the test-writer a high-level abstraction mechanism to query the trace. The resulting system, called TESTLOG, was used on several real-world case studies to validate our claims.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity.

Analysis of independent microarray datasets of renal biopsies identifies a robust transcript signature of acute allograft rejection

Transcriptomics could contribute significantly to the early and specific diagnosis of rejection episodes by defining 'molecular Banff' signatures. Recently, the description of pathogenesis-based transcript sets offered a new opportunity for objective and quantitative diagnosis. Generating high-quality transcript panels is thus critical to define high-performance diagnostic classifier. In this study, a comparative analysis was performed across four different microarray datasets of heterogeneous sample collections from two published clinical datasets and two own datasets including biopsies for clinical indication, and samples from nonhuman primates. We characterized a common transcriptional profile of 70 genes, defined as acute rejection transcript set (ARTS). ARTS expression is significantly up-regulated in all AR samples as compared with stable allografts or healthy kidneys, and strongly correlates with the severity of Banff AR types. Similarly, ARTS were tested as a classifier in a large collection of 143 independent biopsies recently published by the University of Alberta. Results demonstrate that the 'in silico' approach applied in this study is able to identify a robust and reliable molecular signature for AR, supporting a specific and sensitive molecular diagnostic approach for renal transplant monitoring.

Analyzing PL/1 Legacy Ecosystems: An Experience Report

This paper presents a case study of analyzing a legacy PL/1 ecosystem that has grown for 40 years to support the business needs of a large banking company. In order to support the stakeholders in analyzing it we developed St1-PL/1 — a tool that parses the code for association data and computes structural metrics which it then visualizes using top-down interactive exploration. Before building the tool and after demonstrating it to stakeholders we conducted several interviews to learn about legacy ecosystem analysis requirements. We briefly introduce the tool and then present results of analysing the case study. We show that although the vision for the future is to have an ecosystem architecture in which systems are as decoupled as possible the current state of the ecosystem is still removed from this. We also present some of the lessons learned during our experience discussions with stakeholders which include their interests in automatically assessing the quality of the legacy code.