Abstract: Experiments on human and on animal excised specimens as well as in vivo animal preparations are so far the most realistic approaches to simulate the in vivo process of human phonation. These experiments do not have the disadvantage of limited space within the neck and enable studies of the actual organ necessary for phonation, i.e., the larynx. The studies additionally allow the analysis of flow, vocal fold dynamics, and resulting acoustics in relation to well-defined laryngeal alterations. Purpose of Review: This paper provides an overview of the applications and usefulness of excised (human/animal) specimen and in vivo animal experiments in voice research. These experiments have enabled visualization and analysis of dehydration effects, vocal fold scarring, bifurcation and chaotic vibrations, three-dimensional vibrations, aerodynamic effects, and mucosal wave propagation along the medial surface. Quantitative data will be shown to give an overview of measured laryngeal parameter values. As yet, a full understanding of all existing interactions in voice production has not been achieved, and thus, where possible, we try to indicate areas needing further study. Recent Findings: A further motivation behind this review is to highlight recent findings and technologies related to the study of vocal fold dynamics and its applications. For example, studies of interactions between vocal tract airflow and generation of acoustics have recently shown that airflow superior to the glottis is governed by not only vocal fold dynamics but also by subglottal and supraglottal structures. In addition, promising new methods to investigate kinematics and dynamics have been reported recently, including dynamic optical coherence tomography, X-ray stroboscopy and three-dimensional reconstruction with laser projection systems. Finally, we touch on the relevance of vocal fold dynamics to clinical laryngology and to clinically-oriented research.

Abstract: With the avalanche of protein sequences generated in the post-genomic age, many typical topics in bioinformatics, proteomics and system biology are relevant to identification of various attributes of uncharacterized proteins or need this kind of knowledge. Unfortunately, it is both time-consuming and costly to acquire the desired information by purely conducting biochemical experiments. Therefore, it is highly desirable to develop automated methods for fast and accurately identifying various attributes of proteins based on their sequences information alone. This is the convergence between bioinformatics and artificial intelligence techniques (AI). To establish powerful computational methods in this regard, one of the key procedures is to find an effective mathematical expression for the protein samples that can truly reflect their intrinsic correlation with the target to be predicted. To realize this, the pseudo amino acid (PseAA) composition or PseAAC was proposed. Stimulated by the concept of PseAAC, a series of different modes of PseAAC were developed to deal with proteins or proteins-related systems. The current review is mainly focused on those PseAAC modes that were formulated via cellular automata. By using some optimal space-time evolvement rules of cellular automata, a protein sequence can be represented by a unique image, the so-called cellular automata (CA) image or CAI. Many important features, which are deeply hidden in piles of long and complicated amino acid sequences, can be clearly revealed through their CAIs. It is anticipated that, owing to its impressive power, intuitiveness and relative simplicity, the CAI approach holds a great potential in bioinformatics and other related areas.

Abstract: Graph and Complex Network theories are applied to different levels of matter organization such as genome networks, protein-protein networks, sexual disease transmission networks, linguistic networks, law and social networks, power electric networks or Internet. A very important fact is that we can use the numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks despite the nature of the object they represent. The main reason for this success of TIs is the high flexibility of this theory to solve in a fast but rigorous way many apparently unrelated problems in all these disciplines. Another important reason for the success of TIs is that using these parameters as inputs we can find Quantitative Structure-Property Relationships (QSPR) models for any kind of bio-systems, at least in principle. In any case, there is a lack of manuscripts or issues focused on QSAR-like models with TIs and of networks more focused on Bioinformatics. In this sense, the present issue provides state-of-the-art reviews of some of these new computational approaches in this rapidly expanding area of Bioinformatics. Taking into account all the above-mentioned aspects, the present work intends to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of classic TIs and Databases of Biology, Parasitology, Technology, and Social-Legal Networks. After that, we report a definition of a new class of TIs, coined here as Markov-Harary invariants. We also present the calculation of this class of TIs for different classes of networks. Next, we carry out a comparative study of these networks using the values of the new Markov-Harary TIs. Finally, we compare these new indices with another new class of TIs called Markov entropy values, which has been previously developed. © 2011 Bentham Science Publishers Ltd.

Abstract: For understanding the phonatory process in human voice production, physical as well as numerical models have been suggested. Material properties within these models are crucial for achieving vocal fold dynamics being close to in vivo human laryngeal dynamics. Hence, different approaches have been suggested to gain insight into human laryngeal tissue, evaluate clinical treatment, as well as to analyze and verify parameters within synthetically built vocal folds. Purpose of Review: The authors want to give an overview of approaches on receiving material parameters being important in voice research. For the different devices and methods being applied for different set-ups, we will present the functionality and applicability. Hence, for future work, this review shall give an indication, what kind of measurement techniques are suitable for the intended study, advantages or disadvantages of the approaches, and what parameters can be

Abstract: The study of the nervous system of human beings is an arduous task. The reasons are that it is very complex and it is internal to the organism. The nervous system is comprised not only of neuronal networks but also of different types of cells that constitute the glial system. Astrocytes, a type of glial cells, have traditionally been considered as passive, supportive cells. However, through the use of neuroscientific techniques, it has recently been demonstrated that astrocytes are actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. Also in recent studies employing artificial intelligence (AI) techniques, it has been shown that adding artificial astrocytes to Artificial Neural Networks (ANNs), the effectiveness of such networks in classification tasks is markedly improved. At present, the actual impact of astrocytes in neural network function is largely unknown. Therefore, our group is placing increasing emphasis on the study of the influence that astrocytes may have on brain information processing using a rather different perspective based on the use of multielectrode arrays (MEAs). This represents a hybrid approach given that it combines a biological component (cultured cells), hardware technology (MEAs), and AI (computer simulations based on AI techniques to control the system). With this in mind, the objective of this paper is to present a review of the state of the art in the use of MEAs containing nerve cells. This review is intended as a preliminary theoretical analysis on the suitability of these devices to achieve the aforementioned future goal of fusing bioinformatics, micro/nano-technologies, and AI techniques to study these complex systems.

Abstract: Sensitivity improvement in molecular genetic analysis has led to increased detection of novel sequence variants of unknown clinical significance in disease related genes. These unclassified variants (UVs) can often induce pathogenesis by mutating the protein product of the gene. However, they can also manifest non-pathogenic or neutral effects, coding for amino acid changes which do not significantly affect the protein product. Diagnostic laboratories have great difficulty to identify whether an UV is pathogenic or not. Significant characterization of such variants represents a major challenge for medical management of patients in whom they are identified. Functional assays may help to prove whether an UV cause pathogenicity, but these analyses are tedious and laborious. Conversely, in silico prediction tools are very useful to perform a fast bioinformatics analysis which can predict the pathogenicity of a variant based on the change to an amino acid. Despite the amount of in silico tools, only a small number of these are regularly used by genetic testing laboratories. Practice guidelines at the Clinical Molecular Genetics Society for analysis of UVs (UK CMGS UV guidelines) recommend the use of AGVGD, SIFT and Polyphen, but it is unknown whether these are the most useful methods. The aim of the present study was the ability assessment of several in silico bioinformatics tools to accurately predict both pathogenic and neutral missense variants. © 2011 Bentham Science Publishers Ltd.

Abstract: The specificity of drug targets is a great challenge in the pharma-proteomics field of cancer biology. In particular, bioinformatics methods based on complex networks and Gene Ontologies (GOs) are very useful in this area. This work reviews some bioinformatics concepts and previous studies related to Colorectal Cancer (CRC) research using GOs, complex networks, and related methods. Also, we report new results mapping natural compounds on potential drug target CRC network using GOs. The literature mining along with OMIM database gives the details of diseased genes which are further subjected to design a well connected gene regulatory network for cancer. The resultant network is then extrapolated to proteomics level to sort out the genes only expressed in the specific cancer types. The network is statistically analyzed and represented by the graphical interpretation to encounter the hub nodes and their locally parsed neighbors, ligands multi-receptor docking, and the propensity of drug targets in hub nodes and related sub-networks.