IEEE Trans NanoBioScience

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Front cover

Mon, 08/31/2015 - 23:00
Presents the front cover for this issue of the publication.

IEEE Transactions on NanoBioscience publication information

Mon, 08/31/2015 - 23:00
Provides a listing of the editors, board members, and current staff for this issue of the publication.

Table of Contents

Mon, 08/31/2015 - 23:00
Presents the table of contents for this issue of the publication.

Employing TDMA Protocol in Neural Nanonetworks in Case of Neuron Specific Faults

Mon, 08/31/2015 - 23:00
Many neurodegenerative diseases arise from the malfunctioning neurons in the pathway where the signal is carried. In this paper, we propose neuron specific TDMA/multiplexing and demultiplexing mechanisms to convey the spikes of a receptor neuron over a neighboring path in case of an irreversible path fault existing in its original path. The multiplexing mechanism depends on neural delay box (NDB) which is composed of a relay unit and a buffering unit. The relay unit can be realized as a nanoelectronic device. The buffering unit can be implemented either via neural delay lines as employed in optical switching systems or via nanoelectronic delay lines, i.e., delay flip flops. Demultiplexing is realized by a demultiplexer unit according to the time slot assignment information. Besides, we propose the use of neural interfaces in the NDBs and the demultiplexer unit for detecting and stimulating the generation of spikes. The objective of the proposed mechanisms is to substitute a malfunctioning path, increase the number of spikes delivered and correctly deliver the spikes to the intended part of the somatosensory cortex. The results demonstrate that significant performance improvement on the successively delivered number of spikes is achievable when delay lines are employed as neural buffers in NDBs.

Investigation of Various Types of Nanorods as Sensitive Surface-Enhanced Raman Scattering Substrates

Mon, 08/31/2015 - 23:00
Core-shell-isolated nanorods can be used to amplify the signals of target cancer antigen molecules. Recent research has suggested that these nanorods feature surface-enhanced Raman scattering (SERS) signals superior to those of nanoparticles. In this study, nanorod geometrical models based on core-shell-isolated nanocapsule morphology were employed to analyze the scattered power density in three-dimensional spaces. Superior to the conventional cross-section field analysis method, the average scattered power density based method in this presentation could verify the enhancement effects from all possible positions on the nanorod surface. The numerical results in this study were also compared with the experimental results described in the literature. The resonance scattering power reached the maximal value when the radius of the Au/SiO2 and Ag/SiO2 nanorods was 20 nm. At an incident wavelength of 751 nm, the Au/SiO2 and Au/Al2O3 nanorods achieved maximal scattered power density when spacing d=30 nm. Conversely, the Au/TiO2 nanorods achieved maximum scattered power density when spacing d=40 nm. When the core was Au, nanorods with shell thickness h of 1 nm produced a resonant scattering intensity same as it by the nanorods without shells. The numerical results also indicated a stronger resonance peak when the incident ray illuminated the major-axis plane of the Au/SiO2 nanorods. When the incident ray illuminated the curvature plane of the nanorods, the resonance wavelength clearly shifted toward the UV wavelength range. The four Au/SiO2 nanorods with symmetric arrangement achieved the highest resonance peak when the nanorod spacing was 30 nm. This presentation can serve as a key reference for the design of core-shell-isolated nanorods as highly sensitive SERS substrates.

A PSO-Based Approach for Pathway Marker Identification From Gene Expression Data

Mon, 08/31/2015 - 23:00
In this article, a new and robust pathway activity inference scheme is proposed from gene expression data using Particle Swarm Optimization (PSO). From microarray gene expression data, the corresponding pathway information of the genes are collected from a public database. For identifying the pathway markers, the expression values of each pathway consisting of genes, termed as pathway activity, are summarized. To measure the goodness of a pathway activity vector, t-score is widely used in the existing literature. The weakness of existing techniques for inferring pathway activity is that they intend to consider all the member genes of a pathway. But in reality, all the member genes may not be significant to the corresponding pathway. Therefore, those genes, which are responsible in the corresponding pathway, should be included only. Motivated by this, in the proposed method, using PSO, important genes with respect to each pathway are identified. The objective is to maximize the average t-score. For the pathway activities inferred from different percentage of significant pathways, the average absolute t-scores are plotted. In addition, the top 50% pathway markers are evaluated using 10-fold cross validation and its performance is compared with that of other existing techniques. Biological relevance of the results is also studied.

An Acoustic Communication Technique of Nanorobot Swarms for Nanomedicine Applications

Mon, 08/31/2015 - 23:00
In this contribution, we present a communication paradigm among nanodevices, based on acoustic vibrations for medical applications. We consider a swarm of nanorobots able to communicate in a distributed and decentralized fashion, propelled in a biological environment (i.e., the human brain). Each nanorobot is intended to i) recognize a cancer cell, ii) destroy it, and then iii) forward information about the presence of cancer formation to other nanorobots, through acoustic signals. The choice of acoustic waves as communication mean is related to the application context, where it is not advisable either to use indiscriminate chemical substances or electromagnetic waves. The effectiveness of the proposed approach is assessed in terms of achievement of the objective (i.e., to destroy the majority of tumor cells), and the velocity of detection and destruction of cancer cells, through a comparison with other related techniques.

Multi-Layer and Recursive Neural Networks for Metagenomic Classification

Mon, 08/31/2015 - 23:00
Recent advances in machine learning, specifically in deep learning with neural networks, has made a profound impact on fields such as natural language processing, image classification, and language modeling; however, feasibility and potential benefits of the approaches to metagenomic data analysis has been largely under-explored. Deep learning exploits many layers of learning nonlinear feature representations, typically in an unsupervised fashion, and recent results have shown outstanding generalization performance on previously unseen data. Furthermore, some deep learning methods can also represent the structure in a data set. Consequently, deep learning and neural networks may prove to be an appropriate approach for metagenomic data. To determine whether such approaches are indeed appropriate for metagenomics, we experiment with two deep learning methods: i) a deep belief network, and ii) a recursive neural network, the latter of which provides a tree representing the structure of the data. We compare these approaches to the standard multi-layer perceptron, which has been well-established in the machine learning community as a powerful prediction algorithm, though its presence is largely missing in metagenomics literature. We find that traditional neural networks can be quite powerful classifiers on metagenomic data compared to baseline methods, such as random forests. On the other hand, while the deep learning approaches did not result in improvements to the classification accuracy, they do provide the ability to learn hierarchical representations of a data set that standard classification methods do not allow. Our goal in this effort is not to determine the best algorithm in terms accuracy-as that depends on the specific application-but rather to highlight the benefits and drawbacks of each of the approach we discuss and provide insight on how they can be improved for predictive metagenomic analysis.

Implementation of Arithmetic Operations With Time-Free Spiking Neural P Systems

Mon, 08/31/2015 - 23:00
Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In most applications of SN P systems, synchronization plays a key role which means the execution of a rule is completed in exactly one time unit (one step). However, such synchronization does not coincide with the biological fact: in biological nervous systems, the execution times of spiking rules cannot be known exactly. Therefore, a “realistic” system called time-free SN P systems were proposed, where the precise execution time of rules is removed. In this paper, we consider building arithmetical operation systems based on time-free SN P systems. Specifically, adder, subtracter, multiplier, and divider are constructed by using time-free SN P systems. The obtained systems always produce the same computation result independently from the execution time of the rules.

Nanoscale Surface Characterization of Human Erythrocytes by Atomic Force Microscopy: A Critical Review

Mon, 08/31/2015 - 23:00
Erythrocytes (red blood cells, RBCs), the most common type of blood cells in humans are well known for their ability in transporting oxygen to the whole body through hemoglobin. Alterations in their membrane skeletal proteins modify shape and mechanical properties resulting in several diseases. Atomic force microscopy (AFM), a new emerging technique allows non-invasive imaging of cell, its membrane and characterization of surface roughness at micrometer/nanometer resolution with minimal sample preparation. AFM imaging provides direct measurement of single cell morphology, its alteration and quantitative data on surface properties. Hence, AFM studies of human RBCs have picked up pace in the last decade. The aim of this paper is to review the various applications of AFM for characterization of human RBCs topology. AFM has been used for studying surface characteristics like nanostructure of membranes, cytoskeleton, microstructure, fluidity, vascular endothelium, etc., of human RBCs. Various modes of AFM imaging has been used to measure surface properties like stiffness, roughness, and elasticity. Topological alterations of erythrocytes in response to different pathological conditions have also been investigated by AFM. Thus, AFM-based studies and application of image processing techniques can effectively provide detailed insights about the morphology and membrane properties of human erythrocytes at nanoscale.

Ultra-Low Level Detection of L-Histidine Using Solution-Processed ZnO Nanorod on Flexible Substrate

Mon, 08/31/2015 - 23:00
This work demonstrates a novel label free and sensitive approach for the detection of L-histidine. This is a simple and reliable method for ultra-low level detection of L-histidine. All solution processed synthesizing technique was utilized to develop such type of detection scheme. Silicon substrate was replaced by normal transparent sheet to make it more facile and cost-effective detection technique. Fabricated device for L-histidine detection works upon the variation of current through the ZnO nanorod with L-histidine concentration. Operation principle strongly depends upon the electron charge transfer between metal cation and L-histidine inside the chelating complex. Morphological, structural and optical characterization of solution processed synthesized ZnO nanorod (ZnO NR) was carried out prior to sensor device fabrication. Our sensor device exhibits the sensitivity around 0.86 nA/fM and lower limit of detection (LOD) ~ 0.1 fM(S/N=3).

Simulating an Actomyosin in Vitro Motility Assay: Toward the Rational Design of Actomyosin-Based Microtransporters

Mon, 08/31/2015 - 23:00
We present a simulation study of an actomyosin in vitro motility assay. In vitro motility assays have served as an essential element facilitating the application of actomyosin in nanotechnology; such applications include biosensors and biocomputation. Although actomyosin in vitro motility assays have been extensively investigated, some ambiguities remain, as a result of the limited spatio-temporal resolution and unavoidable uncertainties associated with the experimental process. These ambiguities hamper the rational design of nanodevices for practical applications. Here, with the aim of moving toward a rational design process, we developed a 3D computer simulation method of an actomyosin in vitro motility assay, based on a Brownian dynamics simulation. The simulation explicitly included the ATP hydrolysis cycle of myosin. The simulation was validated by the reproduction of previous experimental results. More importantly, the simulation provided new insights that are difficult to obtain experimentally, including data on the number of myosin motors actually binding to actin filaments, the mechanism responsible for the guiding of actin filaments by chemical edges, and the effect of the processivity of motor proteins on the guiding probabilities. The simulations presented here will be useful in interpreting experimental results, and also in designing future nanodevices integrated with myosin motors.

Enhanced Protein Fold Prediction Method Through a Novel Feature Extraction Technique

Mon, 08/31/2015 - 23:00
Information of protein 3-dimensional (3D) structures plays an essential role in molecular biology, cell biology, biomedicine, and drug design. Protein fold prediction is considered as an immediate step for deciphering the protein 3D structures. Therefore, protein fold prediction is one of fundamental problems in structural bioinformatics. Recently, numerous taxonomic methods have been developed for protein fold prediction. Unfortunately, the overall prediction accuracies achieved by existing taxonomic methods are not satisfactory although much progress has been made. To address this problem, we propose a novel taxonomic method, called PFPA, which is featured by combining a novel feature set through an ensemble classifier. Particularly, the sequential evolution information from the profiles of PSI-BLAST and the local and global secondary structure information from the profiles of PSI-PRED are combined to construct a comprehensive feature set. Experimental results demonstrate that PFPA outperforms the state-of-the-art predictors. To be specific, when tested on the independent testing set of a benchmark dataset, PFPA achieves an overall accuracy of 73.6%, which is the leading accuracy ever reported. Moreover, PFPA performs well without significant performance degradation on three updated large-scale datasets, indicating the robustness and generalization of PFPA. Currently, a webserver that implements PFPA is freely available on http://121.192.180.204:8080/PFPA/Index.html.

Numerical Study of Pillar Shapes in Deterministic Lateral Displacement Microfluidic Arrays for Spherical Particle Separation

Mon, 08/31/2015 - 23:00
Deterministic lateral displacement (DLD) arrays containing shaped pillars have been found to be more effective in biomedical sample separation. This study aims to numerically investigate the interplay between particles and microfluidic arrays, and to find out the key factors in determining the critical size of a DLD device with shaped pillars. A new formula is thus proposed to estimate the critical size for spherical particle separation in this kind of new DLD microfluidic arrays. The simulation results show that both rectangular and I-shaped arrays have considerably smaller critical sizes. The ratio of sub-channel widths is also found to play an important role in reducing the critical sizes. This paves a valuable way toward designing high-performance DLD microfluidic arrays.

Influence of Metallic Nanoparticles on Blood Flow Through Arteries Having Both Stenosis and Aneurysm

Mon, 08/31/2015 - 23:00
The main objective of the present paper is to discuss the blood flow analysis through inclined arteries by treating its nature as viscous fluid. The effects of both dilatation and constriction are considered to investigate the behavior of the both abnormal wall segments with variable nanofluid viscosity. The nonlinear momentum equation for proposed model is simplified by considering the nondimensionless parameters to find the exact solutions of the formulated problem. The main hemodynamic effects of stenosis and aneurysm are discussed for different values of the interest by plotting the graphs of wall shear stress and resistance impedance to flow and opposite behavior is observed for both cases. The results also reveal that the nanoparticles with high concentration are important to reduce the resistance impedance to blood flow. The graphs of stream lines show the formation of bolus appears in the aneurysm segment but no formation is observed or seen in the stenotic segment.

D-MoSK Modulation in Molecular Communications

Mon, 08/31/2015 - 23:00
Molecular communication in nanonetworks is an emerging communication paradigm that uses molecules as information carriers. In molecule shift keying (MoSK), where different types of molecules are used for encoding, transmitter and receiver complexities increase as the modulation order increases. We propose a modulation technique called depleted MoSK (D-MoSK) in which, molecules are released if the information bit is 1 and no molecule is released for 0. The proposed scheme enjoys reduced number of the types of molecules for encoding. Numerical results show that the achievable rate is considerably higher and symbol error rate (SER) performance is better in the proposed technique.

Open Access

Mon, 08/31/2015 - 23:00
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IEEE Transactions on NanoBioscience information for authors

Mon, 08/31/2015 - 23:00
Provides a listing of board members, committee members and society officers.

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