Deterministic Global Attitude Estimation

A deterministic attitude estimation problem for a rigid body in an attitude dependent potential field with bounded measurement errors is studied. An attitude estimation scheme that does not use generalized coordinate representations of the attitude is presented here. Assuming that the initial attitude, angular velocity and measurement noise lie within given ellipsoidal bounds, an uncertainty ellipsoid that bounds the attitude and the angular velocity of the rigid body is obtained. The center of the uncertainty ellipsoid provides point estimates, and its size gives the accuracy of the estimates. The point estimates and the uncertainty ellipsoids are propagated using a Lie group variational integrator and its linearization, respectively. The estimation scheme is optimal in the sense that the attitude estimation error and the size of the uncertainty ellipsoid is minimized at each measurement instant, and it is global since the attitude is represented by a rotation matrix.Comment: IEEE Conference on Decision and Control, 2006. 6 pages, 6 figure

Identification of differentially expressed subnetworks based on multivariate ANOVA

<p>Abstract</p> <p>Background</p> <p>Since high-throughput protein-protein interaction (PPI) data has recently become available for humans, there has been a growing interest in combining PPI data with other genome-wide data. In particular, the identification of phenotype-related PPI subnetworks using gene expression data has been of great concern. Successful integration for the identification of significant subnetworks requires the use of a search algorithm with a proper scoring method. Here we propose a multivariate analysis of variance (MANOVA)-based scoring method with a greedy search for identifying differentially expressed PPI subnetworks.</p> <p>Results</p> <p>Given the MANOVA-based scoring method, we performed a greedy search to identify the subnetworks with the maximum scores in the PPI network. Our approach was successfully applied to human microarray datasets. Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex. We also compared these results with those of other scoring methods such as <it>t </it>statistic- and mutual information-based scoring methods. The MANOVA-based method produced subnetworks with a larger number of proteins than the other methods. Furthermore, the subnetworks identified by the MANOVA-based method tended to consist of highly correlated proteins.</p> <p>Conclusion</p> <p>This article proposes a MANOVA-based scoring method to combine PPI data with expression data using a greedy search. This method is recommended for the highly sensitive detection of large subnetworks.</p

Non-contrast cardiac computed tomography can accurately detect chronic myocardial infarction: Validation study

BackgroundThis study evaluates whether non-contrast cardiac computed tomography (CCT) can detect chronic myocardial infarction (MI) in patients with irreversible perfusion defects on nuclear myocardial perfusion imaging (MPI).MethodsOne hundred twenty-two symptomatic patients with irreversible perfusion defect (N = 62) or normal MPI (N = 60) underwent coronary artery calcium (CAC) scanning. MI on these non-contrast CCTs was visually detected based on the hypo-attenuation areas (dark) in the myocardium and corresponding Hounsfield units (HU) were measured.ResultsNon-contrast CCT accurately detected MI in 57 patients with irreversible perfusion defect on MPI, yielding a sensitivity of 92%, specificity of 72%, negative predictive value (NPV) of 90%, and a positive predictive value (PPV) of 77%. On a per myocardial region analysis, non-contrast CT showed a sensitivity of 70%, specificity of 85%, NPV of 91%, and a PPV of 57%. The ROC curve showed that the optimal cutoff value of LV myocardium HU to predict MI on non-contrast CCT was 21.7 with a sensitivity of 97.4% and specificity of 99.7%.ConclusionNon-contrast CCT has an excellent agreement with MPI in detecting chronic MI. This study highlights a novel clinical utility of non-contrast CCT in addition to assessment of overall burden of atherosclerosis measured by CAC

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly