Biomimetic models for redox enzyme systems

Supramolecular chemistry involves the study of noncovalent interactions that take place between molecules. A supramolecule or host-guest complex is formed when a noncovalent binding or complexation event occurs between two such molecules. Hydrogen bonds, electrostatics, pi-stacking, hydrophobic effects, solvatophobic effects and van der Waals forces are all types of noncovalent interactions. Biological systems have provided much of the inspiration for the development of supramolecular chemistry, and many synthetic supramolecular systems have been designed to mimic biological and enzymatic processes. Biomimetic modelling involves the synthesis of compounds containing similar functional groups to that of the specific enzyme’s protein and cofactor. Subsequent analysis using chemical, physical or computational techniques can be used to gain a better understanding of the interactions taking place. This study involves the investigation of various biomimetic redox enzyme systems. Firstly, model systems containing the 1- and 5-deazaflavin cofactor have been synthesised and studied to probe how their redox behaviour compares to that of riboflavin in a supramolecular environment using physical, electrochemical and computational techniques. Secondly, this study has focussed on the flavin cofactor but has expanded upon what factors influence its redox behaviour, and ability to noncovalently interact with other molecules, by examining how the presence of different dendritic architectures can affect its redox properties and noncovalent behaviour. A series of dendrons have been synthesised and studied that have a water-soluble dendron architecture attached to the flavin moiety, as well as a series of dendrons with branching designed to encapsulate the flavin unit. Finally, a biomimetic model of the pyrroloquinoline quinone cofactor has also been synthesised and studies carried out to investigate its redox behaviour in a supramolecular environment, and ability to noncovalently interact with other molecules. The results of this study will hopefully contribute significantly to the body of chemical research in the area of supramolecular chemistry and biomimetics. Of particular interest will be the results from the flavin-based dendron research, as the prospect of purpose-built synthetic enzymes, designed and synthesised for whatever role is required, would surely be of great significance

Maximum Likelihood Detection for Cooperative Molecular Communication

In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, a fusion center (FC) chooses the transmitter's symbol that is more likely, given the likelihood of the observations from multiple receivers (RXs). We propose three different ML detection variants according to different constraints on the information available to the FC, which enables us to demonstrate trade-offs in their performance versus the information available. The system error probability for one variant is derived in closed form. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of the simpler cooperative variants and demonstrate that majority rule detection has performance comparable to ML detection when the reporting is noisy.Comment: 7 pages, 4 figurs. This work has been accepted by the IEEE ICC 201

Measurement of retinal vessel widths from fundus images based on 2-D modeling

Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel

Optic nerve head segmentation

Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 /spl mu//pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred image

Optimization of postbuckled stiffened panels with multiple stiffener sizes

The panel analysis and optimization code VICONOPT, based on exact strip theory, is utilized to investigate the optimum design of stiffened panels with multiple stiffener sizes or substiffeners. The optimization ensures that the buckling stability of the panel includes an allowance for postbuckling reserve of strength. The adoption of this approach necessarily results in the local buckling stress being lower than the overall buckling stress and with the introduction of substiffeners introduces extra buckling modes. This complicates the post buckling behavior of the panel which is investigated by examining the case when the smaller stiffeners lose stiffness, i.e. there is a change from a local to a torsional mode. The panels are loaded in axial compression with a sinusoidal imperfection. It is found that small mass savings are achieved by using stiffeners of more than one size and there is an increase in the spacing of the major stiffeners and transverse supports. The optimum panel designs obtained by VICONOPT are evaluated by comparison with the optimum designs produced with one size of stiffener

Nonlinear aspects of the EEG during sleep in children

Electroencephalograph (EEG) analysis enables the neuronal behavior of a section of the brain to be examined. If the behavior is nonlinear then nonlinear tools can be used to glean information on brain behavior, and aid in the diagnosis of sleep abnormalities such as obstructive sleep apnea syndrome (OSAS). In this paper the sleep EEGs of a set of normal and mild OSAS children are evaluated for nonlinear behaviour. We consider how the behaviour of the brain changes with sleep stage and between normal and OSAS children.Comment: 9 pages, 2 figures, 4 table

Convex optimization of distributed cooperative detection in multi-receiver molecular communication

In this paper, the error performance achieved by cooperative detection among K distributed receivers in a diffusion-based molecular communication system is analyzed and optimized. In this system, the receivers first make local hard decisions on the transmitted symbol and then report these decisions to a fusion center (FC). The FC combines the local hard decisions to make a global decision using an N -out-of- K fusion rule. Two reporting scenarios, namely, perfect reporting and noisy reporting, are considered. Closed-form expressions are derived for the expected global error probability of the system for both reporting scenarios. New approximated expressions are also derived for the expected error probability. Convex constraints are then found to make the approximated expressions jointly convex with respect to the decision thresholds at the receivers and the FC. Based on such constraints, suboptimal convex optimization problems are formulated and solved to determine the optimal decision thresholds which minimize the expected error probability of the system. Numerical and simulation results reveal that the system error performance is greatly improved by combining the detection information of distributed receivers. They also reveal that the solutions to the formulated suboptimal convex optimization problems achieve near-optimal global error performance

Development of an Intersection Assessment Protocol in Boston, Massachusetts

This report, prepared for the consulting firm Nelson-Nygaard, analyzes conventional protocols for assessing intersections. Through our analysis, we developed a new protocol which sought to provide a traffic engineer with more descriptive data on an intersection. Our protocol broke up an intersection timeline into phases and then quantified pedestrian behavior through those phases. The intersections of Boylston and Tremont in Boston, MA and Prospect and Mass Ave in Cambridge, MA were used to develop and test our protocol

Convex optimization of distributed cooperative detection in multi-receiver molecular communication

In this paper, the error performance achieved by cooperative detection among K distributed receivers in a diffusion-based molecular communication system is analyzed and optimized. In this system, the receivers first make local hard decisions on the transmitted symbol and then report these decisions to a fusion center (FC). The FC combines the local hard decisions to make a global decision using an N -out-of- K fusion rule. Two reporting scenarios, namely, perfect reporting and noisy reporting, are considered. Closed-form expressions are derived for the expected global error probability of the system for both reporting scenarios. New approximated expressions are also derived for the expected error probability. Convex constraints are then found to make the approximated expressions jointly convex with respect to the decision thresholds at the receivers and the FC. Based on such constraints, suboptimal convex optimization problems are formulated and solved to determine the optimal decision thresholds which minimize the expected error probability of the system. Numerical and simulation results reveal that the system error performance is greatly improved by combining the detection information of distributed receivers. They also reveal that the solutions to the formulated suboptimal convex optimization problems achieve near-optimal global error performance