Biologically Inspired Components in Embedded Vision Systems

Biological vision components like visual attention (VA) algorithms aim to mimic the mechanism of the human vision system. Often VA algorithms are complex and require high computational and memory requirements to be realized. In biologically-inspired vision and embedded systems, the computational capacity and memory resources are of a primary concern. This paper presents a discussion for implementing VA algorithms in embedded vision systems in a resource constrained environment. The authors survey various types of VA algorithms and identify potential techniques which can be implemented in embedded vision systems. Then, they propose a low complexity and low memory VA model based on a well-established mainstream VA model. The proposed model addresses critical factors in terms of algorithm complexity, memory requirements, computational speed, and salience prediction performance to ensure the reliability of the VA in a resource constrained environment. Finally a custom softcore microprocessor-based hardware implementation on a Field-Programmable Gate Array (FPGA) is used to verify the implementation feasibility of the presented model.

Polarization-based Robot Orientation and Navigation

From insects in your garden to creatures in the sea, inspiration can be drawn from nature to design a whole new class of smart robotic devices. These smart machines may move like living creatures. They can be launched toward a specific target for a pre-defined task. Bio-inspiration is developing to meet the needs of many challenges particularly in machine vision. Some species in the animal kingdom like cephalopods, crustaceans and insects are distinguished with their visual capabilities which are strongly improved by means of polarization. This work surveys the most recent research in the area of bio-inspired polarization based robot orientation and navigation. Firstly, the authors will briefly discuss the polarization based orientation and navigation behavior in the animal kingdom. Secondly, a comprehensive cover of its mapping into robotics navigation and orientation estimation will be given. Finally, the future research directions will be discussed.

Biomedical Instrumentation

This paper covers the measurement of biopotentials for diagnosis: the electrical voltages that can be measured from electrodes placed on the skin or within the body. Biopotentials include: the electrocardiogram (ECG), electroencephalogram (EEG), electrocortogram (ECoG), electromyogram (EMG), electroneurogram (ENG), electrogastrogram (EGG), action potential (AP), electroretinogram (ERG), electro-oculogram (EOG). This paper also covers skin conductance, pulse oximeters, urology, wearable systems and important therapeutic devices such as: the artificial cardiac pacemaker, defibrillator, cochlear implant, hemodialysis, lithotripsy, ventilator, anesthesia machine, heart-lung machine, infant incubator, infusion pumps, electrosurgery, tissue ablation, and medical imaging. It concludes by covering electrical safety. It provides future subjects for research such as a blood glucose sensor and a permanently implanted intracranial pressure sensor.

Evolutionary Search for Cellular Automata with Self-Organizing Properties toward Controlling Decentralized Pervasive Systems and Its Applications

Cellular Automata (CAs) have been investigated extensively as abstract models of the decentralized systems composed of autonomous entities characterized by local interactions. However, it is poorly understood how CAs can interact with their external environment, which would be useful for implementing pervasive systems that consist of billions of components (nodes, sensors, etc.). This paper focuses on the emergent properties of CAs induced by external perturbations toward controlling pervasive systems. The authors assumed a minimum task in which a CA has to change its global state drastically after every occurrence of a perturbation period. By conducting evolutionary searches for rules of CAs, they obtained interesting behaviors of CAs in which their global state cyclically transited among different stable states in either ascending or descending order. They analyze the emergent behavior in detail and also introduce applications of the evolved CA for controlling pervasive robots and an interactive art.

A Benchmark of Structural Variant Analysis Tools for Next Generation Sequencing Data

In language of genetics and biochemistry, sequencing is the determination of an unbranched biopolymer's primary structure. A sequence is a symbolic linear depiction, result of sequencing. This sequence is a succinct summary of the most of the sequenced molecule's atomic-level structure. (Most known is DNA-sequencing, RNA-sequencing, Protein-sequencing and Next-Generation-sequencing)

Neuroscience and Symptoms Related to the CADASIL Disease

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) disease belongs to the group of rare diseases. It is well established that Notch3 protein is primarily responsible for the development of the CADASIL syndrome. Herein, we attempt to shed light to the actual molecular mechanism underlying CADASIL syndrome via insights that we have from preliminary in silico and proteomics studies on the Notch3 protein, which is involved in many cancers and in particular lung and ovarian cancer. In this disease we always see accumulation of Granular Osmiophilic Material (GOM), which has been a hallmark for the final diagnosis based on electron microcopy (EM). Consequently, we present the regions of the brain that get affected by the disease and their functions. Finally, the symptoms of CADASIL are examined with reference to the neurological analysis that has preceded.

State-of-the-Art Neural Networks Applications in Biology

Artificial neural networks (ANNs) are a well-established computational method inspired by the structure and function of biological central nervous systems. Since their conception, ANNs have been utilized in a vast variety of applications due to their impressive information processing abilities. A vibrant field, ANNs have been utilized in bioinformatics, a general term for describing the combination of informatics, biology and medicine. This article is an effort to investigate recent advances in the area of bioinformatical applications of ANNs, with emphasis in disease diagnosis, genetics, proteomics, and chemoinformatics. The combination of neural networks and game theory in some of these application is also discussed.

An Up-To-Date Review of Piglet Isosporosis

Piglet isosporosis caused by Isospora suis represents a considerable problem worldwide with great economic losses and veterinary importance in pig production. So the control of this parasite is a great need. However, little is known about porcine coccidiosis concerning dynamics, pathophysiology and immunology of this disease, as well as host-parasite interactions. In addition, only few studies deal with experimental modelling of this illness with parameters such as the excretion patterns and the age-related susceptibility. However, besides natural I. suis infections occurring in pig farms, there are some experimental infections described that allow investigating accurately the course of infection. Experimental infections could contribute to a more effective control of these infections. In addition, managerial practices of farrowing facilities and piglet manipulations can contribute to this purpose. So, the description of hygiene measures, the appropriate management of farrowing facilities and piglet manipulations, as well as appropriate farm-specific environment, comprising appropriate design and materials of the farrowing pen and enough room, could diminish the occurrence and transmission of this parasite. However, unfortunately there are only very few reports documenting all this subjects that are so important for the effective control of this disease.

Computer-Aided Drug Design and Biological Evaluation of Novel Anti-Greek Goat Encephalitis Agents

The Flaviviridae family of viruses infects vertebrates and is primarily spread through arthropod vectors. The Greek Goat Encephalitis (GGE) flavivirus belongs to the Flaviviridae family and specifically to the genus Flavivirus. GGE virus, which is endemic in Greece, is the causative agent of tick-borne encephalitis (TBE), an infection of the central nervous system that can be transmitted from animals to humans by ticks. Although, there are very limited data regarding the GGE virus and its epidemiology in Greece, there have been few reported cases of GGE viral infection of goats in northern Greece. However, despite the severity of Flaviviridae causing diseases (e.g. Hepatitis C, Dengue fever, Yellow fever, Classical swine fever, Japanese encephalitis), currently there is not any available anti-flaviviridae therapy. Thus, there is a need for the development of effective anti-GGE viral pharmaceutical strategies. It has been shown that RNA helicases, which are involved in duplex unwinding during viral RNA replication, represent promising antiviral targets. Therefore, we suggest that inhibition of the GGE viral helicase would be an effective approach of interrupting the life cycle of the GGE virus. Our proposed research will be directed towards the computer-aided development of a series of drug-like low molecular weight compounds capable of inhibiting the helicase enzyme of GGE virus. Results derived from a repertoire of multi-disciplinary bioinformatics and statistical methods would enhance our understanding of the mechanism of action of the GGE viral helicase enzyme. Our ultimate goal is to design a series of novel anti-helicase compounds as drug candidates against the endemic GGE virus while the inhibitory activity of our novel compounds will be evaluated biologically.

State-of-the-Art GPGPU Applications in Bioinformatics

The exponential growth of available biological data in recent years coupled with their increasing complexity has made their analysis a computationally challenging process. Traditional central processing unist (CPUs) are reaching their limit in processing power and are not designed primarily for multithreaded applications. Graphics processing units (GPUs) on the other hand are affordable, scalable computer powerhouses that, thanks to the ever increasing demand for higher quality graphics, have yet to reach their limit. Typically high-end CPUs have 8-16 cores, whereas GPUs can have more than 2,500 cores. GPUs are also, by design, highly parallel, multicore and multithreaded, able of handling thousands of threads doing the same calculation on different subsets of a large data set. This ability is what makes them perfectly suited for biological analysis tasks. Lately this potential has been realized by many bioinformatics researches and a huge variety of tools and algorithms have been ported to GPUs, or designed from the ground up to maximize the usage of available cores. Here, we present a comprehensive review of available bioinformatics tools ranging from sequence and image analysis to protein structure prediction and systems biology that use NVIDIA Compute Unified Device Architecture (CUDA) general-purpose computing on graphics processing units (GPGPU) programming language.