A database for using machine learning and data mining techniques for coronary artery disease diagnosis.

We present the coronary artery disease (CAD) database, a comprehensive resource, comprising 126 papers and 68 datasets relevant to CAD diagnosis, extracted from the scientific literature from 1992 and 2018. These data were collected to help advance research on CAD-related machine learning and data mining algorithms, and hopefully to ultimately advance clinical diagnosis and early treatment. To aid users, we have also built a web application that presents the database through various reports.

At a glance: cellular biology for engineers.

Engineering contributions have played an important role in the rise and evolution of cellular biology. Engineering technologies have helped biologists to explore the living organisms at cellular and molecular levels, and have created new opportunities to tackle the unsolved biological problems. There is now a growing demand to further expand the role of engineering in cellular biology research. For an engineer to play an effective role in cellular biology, the first essential step is to understand the cells and their components. However, the stumbling block of this step is to comprehend the information given in the cellular biology literature because it best suits the readers with a biological background. This paper aims to overcome this bottleneck by describing the human cell components as micro-plants that form cells as micro-bio-factories. This concept can accelerate the engineers' comprehension of the subject. In this paper, first the structure and function of different cell components are described. In addition, the engineering attempts to mimic various cell components through numerical modelling or physical implementation are highlighted. Next, the interaction of different cell components that facilitate complicated chemical processes, such as energy generation and protein synthesis, are described. These complex interactions are translated into simple flow diagrams, generally used by engineers to represent multi-component processes.

An ocular biomechanic model for dynamic simulation of different eye movements.

Simulating and analysing eye movement is useful for assessing visual system contribution to discomfort with respect to body movements, especially in virtual environments where simulation sickness might occur. It can also be used in the design of eye prosthesis or humanoid robot eye. In this paper, we present two biomechanic ocular models that are easily integrated into the available musculoskeletal models. The model was previously used to simulate eye-head coordination. The models are used to simulate and analyse eye movements. The proposed models are based on physiological and kinematic properties of the human eye. They incorporate an eye-globe, orbital suspension tissues and six muscles with their connective tissues (pulleys). Pulleys were incorporated in rectus and inferior oblique muscles. The two proposed models are the passive pulleys and the active pulleys models. Dynamic simulations of different eye movements, including fixation, saccade and smooth pursuit, are performed to validate both models. The resultant force-length curves of the models were similar to the experimental data. The simulation results show that the proposed models are suitable to generate eye movement simulations with results comparable to other musculoskeletal models. The maximum kinematic root mean square error (RMSE) is 5.68° and 4.35° for the passive and active pulley models, respectively. The analysis of the muscle forces showed realistic muscle activation with increased muscle synergy in the active pulley model.

Dynamical Analysis of the Hindmarsh-Rose Neuron With Time Delays.

This brief is mainly concerned with a series of dynamical analyses of the Hindmarsh-Rose (HR) neuron with state-dependent time delays. The dynamical analyses focus on stability, Hopf bifurcation, as well as chaos and chaos control. Through the stability and bifurcation analysis, we determine that increasing the external current causes the excitable HR neuron to exhibit periodic or chaotic bursting/spiking behaviors and emit subcritical Hopf bifurcation. Furthermore, by choosing a fixed external current and varying the time delay, the stability of the HR neuron is affected. We analyze the chaotic behaviors of the HR neuron under a fixed external current through time series, bifurcation diagram, Lyapunov exponents, and Lyapunov dimension. We also analyze the synchronization of the chaotic time-delayed HR neuron through nonlinear control. Based on an appropriate Lyapunov-Krasovskii functional with triple integral terms, a nonlinear feedback control scheme is designed to achieve synchronization between the uncontrolled and controlled models. The proposed synchronization criteria are derived in terms of linear matrix inequalities to achieve the global asymptotical stability of the considered error model under the designed control scheme. Finally, numerical simulations pertaining to stability, Hopf bifurcation, periodic, chaotic, and synchronized models are provided to demonstrate the effectiveness of the derived theoretical results.

Enhancement of electro-chemical properties of TiO2 nanotubes for biological interfacing.

Micro/nano electrodes employing nanotubes has attracted paramount attention in recent years due to their inherent superior mechanical and structural properties. Electrical interfaces with different geometries and sizes have been developed as electrodes for measuring action potentials and investigating neural information processing in neural networks. In this work, we investigated the possibility of using TiO2 nanotube arrays that were grown using electrochemical anodization technique, as a micro/nano electrode for neural interfacing. The morphology of fabricated nanotube arrays were found to be significantly affected by the applied voltage. Annealing and doping of TiO2 nanotube arrays has been performed to improve the structural and electrical properties of the nanotube arrays. It was found that the annealing and doping with nitrogen improve the electrical conductivity of the nanotube arrays. Moreover, the tube diameter and length can be controlled by changing the applied voltage and that can significantly affect the biocompatibility of the nanotube arrays. It was observed that nitrogen doped nanotubes with morphology consisting of 61nm diameter, 25nm wall thickness and tube length of 2.25µm could be good candidate to be used as electrodes for biological interfacing. This is due to the fact that the nitrogen doped nanotubes with aforementioned morphology possess great properties necessary for effective biological interfacing such as low impedance, high capacitance and good biocompatibility.

Anodization parameters influencing the morphology and electrical properties of TiO2 nanotubes for living cell interfacing and investigations.

Nanotube structures have attracted tremendous attention in recent years in many applications. Among such nanotube structures, titania nanotubes (TiO2) have received paramount attention in the medical domain due to their unique properties, represented by high corrosion resistance, good mechanical properties, high specific surface area, as well as great cell proliferation, adhesion and mineralization. Although lot of research has been reported in developing optimized titanium nanotube structures for different medical applications, however there is a lack of unified literature source that could provide information about the key parameters and experimental conditions required to develop such optimized structure. This paper addresses this gap, by focussing on the fabrication of TiO2 nanotubes through anodization process on both pure titanium and titanium alloys substrates to exploit the biocompatibility and electrical conductivity aspects, critical factors for many medical applications from implants to in-vivo and in-vitro living cell studies. It is shown that the morphology of TiO2 directly impacts the biocompatibility aspects of the titanium in terms of cell proliferation, adhesion and mineralization. Similarly, TiO2 nanotube wall thickness of 30-40nm has shown to exhibit improved electrical behaviour, a critical factor in brain mapping and behaviour investigations if such nanotubes are employed as micro-nano-electrodes.

Unified selective sorting approach to analyse multi-electrode extracellular data.

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators.