Mechanistic and pathological study of the genesis, growth, and rupture of abdominal aortic aneurysms

Postprint (published version

CFD modelling of abdominal aortic aneurysm on hemodynamic loads using a realistic geometry with CT

The objective of this study is to find a correlation between the abdominal aortic aneurysm (AAA) geometric parameters, wall stress shear (WSS), abdominal flow patterns, intraluminal thrombus (ILT), and AAA arterial wall rupture using computational fluid dynamics (CFD). Real AAA 3D models were created by three-dimensional (3D) reconstruction of in vivo acquired computed tomography (CT) images from 5 patients. Based on 3D AAA models, high quality volume meshes were created using an optimal tetrahedral aspect ratio for the whole domain. In order to quantify the WSS and the recirculation inside the AAA, a 3D CFD using finite elements analysis was used. The CFD computation was performed assuming that the arterial wall is rigid and the blood is considered a homogeneous Newtonian fluid with a density of 1050¿kg/m3 and a kinematic viscosity of Pa·s. Parallelization procedures were used in order to increase the performance of the CFD calculations. A relation between AAA geometric parameters (asymmetry index (ß), saccular index (¿), deformation diameter ratio (¿), and tortuosity index (e)) and hemodynamic loads was observed, and it could be used as a potential predictor of AAA arterial wall rupture and potential ILT formation

EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG) is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL) based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches

New high-speed centre of mass method incorporating background subtraction for accurate determination of fluorescence lifetime

We demonstrate an implementation of a centre-of-mass method (CMM) incorporating background subtraction for use in multifocal fluorescence lifetime imaging microscopy to accurately determine fluorescence lifetime in live cell imaging using the Megaframe camera. The inclusion of background subtraction solves one of the major issues associated with centre-of-mass approaches, namely the sensitivity of the algorithm to background signal. The algorithm, which is predominantly implemented in hardware, provides real-time lifetime output and allows the user to effectively condense large amounts of photon data. Instead of requiring the transfer of thousands of photon arrival times, the lifetime is simply represented by one value which allows the system to collect data up to limit of pulse pile-up without any limitations on data transfer rates. In order to evaluate the performance of this new CMM algorithm with existing techniques (i.e. Rapid lifetime determination and Levenburg-Marquardt), we imaged live MCF-7 human breast carcinoma cells transiently transfected with FRET standards. We show that, it offers significant advantages in terms of lifetime accuracy and insensitivity to variability in dark count rate (DCR) between Megaframe camera pixels. Unlike other algorithms no prior knowledge of the expected lifetime is required to perform lifetime determination. The ability of this technique to provide real-time lifetime readout makes it extremely useful for a number of applications

Configurable cellular automata for pseudorandom number generation

This paper proposes a generalized structure of cellular automata (CA) – the configurable cellular automata (CoCA). With selected properties from programmable CA (PCA) and controllable CA (CCA), a new approach to cellular automata is developed. In CoCA, the cells are dynamically reconfigured at run-time via a control CA. Reconfiguration of a cell simply means varying the properties of that cell with time. Some examples of properties to be reconfigured are rule selection, boundary condition, and radius. While the objective of this paper is to propose CoCA as a new CA method, the main focus is to design a CoCA that can function as a good pseudorandom number generator (PRNG). As a PRNG, CoCA can be a suitable candidate as it can pass 17 out of 18 Diehard tests with 31 cells. CoCA PRNG’s performance based on Diehard test is considered superior over other CA PRNG works. Moreover, CoCA opens new rooms for research not only in the field of random number generation, but in modeling complex systems as well

Compliant curved-crease origami-inspired metamaterials with a programmable force-displacement response

Origami-inspired metamaterials utilise geometric sheet transformations to generate and control novel material mechanical properties. The majority of research effort has been devoted to straight-crease origami-inspired metamaterials, however curved-crease origami, which allows compliant folding and bending behaviours, has significant potential for adoption. This study proposes a new type of compliant curved-crease origami-inspired metamaterial, constructed with an ‘elastica’ non-zero principal surface curvature. Construction parameters for the new metamaterial are shown to influence a range of non-linear force–displacement response characteristics, including response shape, response duration, and response magnitude. A concise analytical curved-crease bending translation (CCBT) method is developed for rapid response prediction from metamaterial geometric parameters. The CCBT method also then enables the direct design and specification of a metamaterial with a fully programmable compliant force–displacement response

EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG) is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL) based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches

EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG) is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL) based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches

CFD Modelling of Abdominal Aortic Aneurysm on Hemodynamic Loads Using a Realistic Geometry with CT

The objective of this study is to find a correlation between the abdominal aortic aneurysm (AAA) geometric parameters, wall stress shear (WSS), abdominal flow patterns, intraluminal thrombus (ILT), and AAA arterial wall rupture using computational fluid dynamics (CFD). Real AAA 3D models were created by three-dimensional (3D) reconstruction of in vivo acquired computed tomography (CT) images from 5 patients. Based on 3D AAA models, high quality volume meshes were created using an optimal tetrahedral aspect ratio for the whole domain. In order to quantify the WSS and the recirculation inside the AAA, a 3D CFD using finite elements analysis was used. The CFD computation was performed assuming that the arterial wall is rigid and the blood is considered a homogeneous Newtonian fluid with a density of 1050 kg/m3 and a kinematic viscosity of 4×10-3 Pa·s. Parallelization procedures were used in order to increase the performance of the CFD calculations. A relation between AAA geometric parameters (asymmetry index (β), saccular index (γ), deformation diameter ratio (χ), and tortuosity index (ε)) and hemodynamic loads was observed, and it could be used as a potential predictor of AAA arterial wall rupture and potential ILT formation

CFD modelling of abdominal aortic aneurysm on hemodynamic loads using a realistic geometry with CT

The objective of this study is to find a correlation between the abdominal aortic aneurysm (AAA) geometric parameters, wall stress shear (WSS), abdominal flow patterns, intraluminal thrombus (ILT), and AAA arterial wall rupture using computational fluid dynamics (CFD). Real AAA 3D models were created by three-dimensional (3D) reconstruction of in vivo acquired computed tomography (CT) images from 5 patients. Based on 3D AAA models, high quality volume meshes were created using an optimal tetrahedral aspect ratio for the whole domain. In order to quantify the WSS and the recirculation inside the AAA, a 3D CFD using finite elements analysis was used. The CFD computation was performed assuming that the arterial wall is rigid and the blood is considered a homogeneous Newtonian fluid with a density of 1050¿kg/m3 and a kinematic viscosity of Pa·s. Parallelization procedures were used in order to increase the performance of the CFD calculations. A relation between AAA geometric parameters (asymmetry index (ß), saccular index (¿), deformation diameter ratio (¿), and tortuosity index (e)) and hemodynamic loads was observed, and it could be used as a potential predictor of AAA arterial wall rupture and potential ILT formation.Peer Reviewe