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NanoBioscience, IEEE Transactions on

Issue 4 • Date Dec. 2012

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Displaying Results 1 - 17 of 17
  • Table of Contents

    Publication Year: 2012 , Page(s): C1
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  • IEEE Transactions on NanoBioscience publication information

    Publication Year: 2012 , Page(s): C2
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  • Micro-Emulsion Synthesis, Surface Modification, and Photophysical Properties of {\rm Zn}_{1-x}~{\rm Mn}_{\rm x} {\rm S} Nanocrystals for Biomolecular Recognition

    Publication Year: 2012 , Page(s): 317 - 323
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1717 KB) |  | HTML iconHTML  

    In this research, we mainly focused on the micro-emulsion synthesis of biotinylated ZnS (zinc sulfide) nanocrystals for avidin recognition. Various samples of Zn1-xMnx S, with x = 0.0001, 0.007, 0.02, 0.03, 0.055, 0.09 and 0.13, prepared by quaternary W/O (water-in-oil) microemulsion system. Cyclohexane was used as oil, Triton X-100 as surfactant, n-hexanol as a co-surfactant and mercaptoethanol and thioglycolic acid as linking agents. The obtained products were evaluated by commonly techniques such as: scanning electron microscopy (SEM), transmission electron microscopy (TEM), zeta meter for measurement ZP (zeta potential) and fluorescence spectroscopy analyses. The above-experimental results indicated that the optimum doping concentration of Mn was ~ 5.5% . The fluorescence spectra of the doped crystals consist of orange-red emissions. Eventually, this research showed with increasing more than 18 μl biotin to nanocrystals, no changes were observed in the emission intensity spectra. View full abstract»

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  • Identifying Protein Complexes From Interactome Based on Essential Proteins and Local Fitness Method

    Publication Year: 2012 , Page(s): 324 - 335
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1910 KB) |  | HTML iconHTML  

    High-throughput experimental technologies, along with computational predictions, have promoted the emergence of large-scale interactome for numerous organisms. Identification of protein complexes from these interactome networks is crucial to understand principles of cellular organization and predict protein functions. Protein complexes are generally considered as dense subgraphs. However, the real protein complexes do not always have highly connected topologies. In this paper, a novel protein complex identifying method, named EPOF, is proposed, using essential proteins and the local metric of vertex fitness. In EPOF, cliques in the subnetwork which is consisted by the essential proteins are firstly considered as seeds, which are ordered according to their size and the number of their neighbors. A protein complex is extended from a seed based on the evaluation of its neighbors' fitness value. Then, the similar procedure is applied to the cliques identified in the subnetwork which is consisted by the proteins which is not clustered in the first step. When EPOF identifies complexes by expanding essential protein cliques, the essential proteins have higher priority and lower threshold. When it identifies complexes by expanding nonessential protein cliques, the nonessential proteins have higher priority and lower threshold. Finally, the last step, we output the identified complexes set. The proposed algorithm EPOF is applied to the unweighted and weighted interaction networks of S. cerevisiae and detects many well known protein complexes. We compare the performances of EPOF to other ten previous algorithms, including EAGLE, NFC, MCODE, DPClus, IPCA, CPM, MCL, CMC, SPICi, and Core-Attachment. Experimental results show that EPOF outperforms other previous competing algorithms in terms of matching with known complexes, sensitivity, specificity, f-measure, function enrichment and accuracy. The program and related files available on https://github.com/gangchen/epof. View full abstract»

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  • Effect of Particle Size of Hydroxyapatite Nanoparticles on its Biocompatibility

    Publication Year: 2012 , Page(s): 336 - 340
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (555 KB) |  | HTML iconHTML  

    Nano-particulate biomaterials have been used in clinical diagnosis and treatment, as drug carrier or in cosmetics because of their excellent performance properties. The toxicity and biocompatibility of nanoparticles (NPs), however, are always a focused concern for a doctor or a scientist. At present, there is almost no systemic evaluation standard or testing methods of safety for nanoparticles. In this study, two kinds of hydroxylapatite, (HAP) NPs with different particle sizes were selected. A number of biocompatibility tests in vivo or in vitro were conducted. They were cytotoxicity (MTT assay), genotoxicity (Ames, Mouse Lymphoma Mutagenesis Assay), and systemic toxicity (Acute and Subacute). The results indicated that, under the concentration of 100 mg/L, both HAP NPs could cause significant inhibition of cell growth. The size of NPs might have close tie with cell response. The mutagenic test in vitro was negative in this study. Histopathological findings showed that both kinds of HAP NPs could induce pseudotubercles in lung. Moreover, smaller size of nanoparticles resulted in a vacuolar degeneration of nephric tubule epithelium at 7 days post-intraveneous injection. The results implied that the size of NPs might play an important role in the biocompatibility of the materials. The kidney might be the main organ of discharge of nanoparticles from body. View full abstract»

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  • Representative Distance: A New Similarity Measure for Class Discovery From Gene Expression Data

    Publication Year: 2012 , Page(s): 341 - 351
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2195 KB) |  | HTML iconHTML  

    Similarity measurement is one of the most important stages in the process of cancer discovery from gene expression data. Traditional distance functions, such as the Euclidean distance, the correlation coefficient measure, the cosine distance, and so on, are selected to quantify the similarity between two cancer samples. However, these measures do not take into account the properties of cancer samples and do not consider the relationships among the genes in gene expression data. In order to explore the properties of cancer samples and the relationships among genes, we design a new similarity measure called representative distance (RD) to identify cancer samples in gene expression data. Specifically, RD does not compute the distance between two cancer samples using all the genes, but only calculates the similarity using representative genes selected by the affinity propagation algorithm. Then, a similarity matrix is constructed based on the representative distance. Finally, the spectral clustering algorithm is adopted to partition the similarity matrix, and discover the biological meaningful samples. To our knowledge, this is the first time in which the representative distance is applied to class discovery for gene expression data. Experiments on real cancer datasets indicate that our similarity measure can (i) outperform most of the traditional distance measures, (ii) identify cancer samples correctly in most of the datasets. View full abstract»

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  • Normal Forms of Spiking Neural P Systems With Anti-Spikes

    Publication Year: 2012 , Page(s): 352 - 359
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1847 KB) |  | HTML iconHTML  

    Spiking neural P systems with anti-spikes (ASN P systems, for short) are a variant of spiking neural P systems, which were inspired by inhibitory impulses/spikes or inhibitory synapses. In this work, we consider normal forms of ASN P systems. Specifically, we prove that ASN P systems with pure spiking rules of categories (a, a) and (a, a̅) without forgetting rules are universal as number generating devices. In an ASN P system with spiking rules of categories (a, a̅) and (a̅, a) without forgetting rules, the neurons change spikes to anti-spikes or change anti-spikes to spikes; such systems are proved to be universal. We also prove that ASN P systems with inhibitory synapses using pure spiking rules of category (a, a) and forgetting rules are universal. These results answer an open problem and improve a corresponding result from [IJCCC, IV(3), 2009, 273-282]. View full abstract»

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  • Hybrid Nanomaterial for Stabilizing the Antibiofilm Activity of Eugenia carryophyllata Essential Oil

    Publication Year: 2012 , Page(s): 360 - 365
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1699 KB) |  | HTML iconHTML  

    The aim of the present study was to demonstrate that Fe3O4/oleic acid core/shell nanostructures could be used as systems for stabilizing the Eugenia carryophyllata essential oil (EO) on catheter surface pellicles, in order to improve their resistance to fungal colonization. EO microwave assisted extraction was performed in a Neo-Clevenger (related) device and its chemical composition was settled by GC-MS analysis. Fe3O4/oleic acid-core/shell nanoparticles (NP) were obtained by a precipitation method under microwave condition. High resolution transmission electron microscopy (HR-TEM) was used as a primary characterization method. The NPs were processed to achieve a core/shell/EO coated-shell nanosystem further used for coating the inner surface of central venous catheter samples. The tested fungal strains have been recently isolated from different clinical specimens. The biofilm architecture was assessed by confocal laser scanning microscopy (CLSM). Our results claim the usage of hybrid nanomaterial (core/shell/coated-shell) for the stabilization of E. carryophyllata EO, which prevented or inhibited the fungal biofilm development on the functionalized catheter, highlighting the opportunity of using these nanosystems to obtain improved, anti-biofilm coatings for biomedical applications. View full abstract»

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  • Performing Four Basic Arithmetic Operations With Spiking Neural P Systems

    Publication Year: 2012 , Page(s): 366 - 374
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2591 KB) |  | HTML iconHTML  

    Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bio-inspired computing devices-spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information is encoded as the time interval between spikes). In this work, four SN P systems are constructed as adder, subtracter, multiplier, and divider, respectively. In these systems, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes. View full abstract»

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  • Enhancing Membrane Protein Subcellular Localization Prediction by Parallel Fusion of Multi-View Features

    Publication Year: 2012 , Page(s): 375 - 385
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2082 KB) |  | HTML iconHTML  

    Membrane proteins are encoded by ~ 30% in the genome and function importantly in the living organisms. Previous studies have revealed that membrane proteins' structures and functions show obvious cell organelle-specific properties. Hence, it is highly desired to predict membrane protein's subcellular location from the primary sequence considering the extreme difficulties of membrane protein wet-lab studies. Although many models have been developed for predicting protein subcellular locations, only a few are specific to membrane proteins. Existing prediction approaches were constructed based on statistical machine learning algorithms with serial combination of multi-view features, i.e., different feature vectors are simply serially combined to form a super feature vector. However, such simple combination of features will simultaneously increase the information redundancy that could, in turn, deteriorate the final prediction accuracy. That's why it was often found that prediction success rates in the serial super space were even lower than those in a single-view space. The purpose of this paper is investigation of a proper method for fusing multiple multi-view protein sequential features for subcellular location predictions. Instead of serial strategy, we propose a novel parallel framework for fusing multiple membrane protein multi-view attributes that will represent protein samples in complex spaces. We also proposed generalized principle component analysis (GPCA) for feature reduction purpose in the complex geometry. All the experimental results through different machine learning algorithms on benchmark membrane protein subcellular localization datasets demonstrate that the newly proposed parallel strategy outperforms the traditional serial approach. We also demonstrate the efficacy of the parallel strategy on a soluble protein subcellular localization dataset indicating the parallel technique is flexible to suite for other computational biology problems. The so- tware and datasets are available at: http://www.csbio.sjtu.edu.cn/bioinf/mpsp. View full abstract»

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  • Identification of Hierarchical and Overlapping Functional Modules in PPI Networks

    Publication Year: 2012 , Page(s): 386 - 393
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1367 KB) |  | HTML iconHTML  

    Various evidences have demonstrated that functional modules are overlapping and hierarchically organized in protein-protein interaction (PPI) networks. Up to now, few methods are able to identify both overlapping and hierarchical functional modules in PPI networks. In this paper, a new hierarchical clustering algorithm, called OH-PIN, is proposed based on the overlapping M_clusters, λ-module, and a new concept of clustering coefficient between two clusters. By recursively merging two clusters with the maximum clustering coefficient, OH-PIN finally assembles all M_clusters into λ -modules. Since M_clusters are overlapping, λ -modules based on them are also overlapping. Thus, OH-PIN can detect a hierarchical organization of overlapping modules by tuning the value of λ. The hierarchical organization is similar to the hierarchical organization of GO annotations and that of the known complexes in MIPS. To compare the performance of OH-PIN and other existing competing algorithms, we apply them to the yeast PPI network. The experimental results show that OH-PIN outperforms the existing algorithms in terms of the functional enrichment and matching with known protein complexes. View full abstract»

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  • Control of Low-Density Lipoprotein Concentration in the Arterial Wall by Proportional Drug-Encapsulated Nanoparticles

    Publication Year: 2012 , Page(s): 394 - 401
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1480 KB) |  | HTML iconHTML  

    Atherosclerosis, or hardening of the arteries, is one of the major causes of death in humans. High accumulation of Low-Density Lipoprotein (LDL) macromolecules within the arterial wall plays a critical role in initiation and development of atherosclerotic plaques. This paper proposes a proportional drug-encapsulated nanoparticle (PDENP) that utilizes a simple piecewise-proportional controller to realize swarm feedback control of LDL concentration in the interior of the arterial wall. In contrast to the competing strategies on nanorobotics, PDENPs carry simpler hardware architecture in order to be more reasonably realized technologically as well as to penetrate the interior arterial wall. Furthermore, in contrast to the existing targeted DENPs that usually target the surface proteins of atherosclerotic plaque, the proposed PDENPs directly sense the LDL level in the arterial walls. Hence, they can diagnose abnormal LDL accumulation before plaque formation, prevent critical growth of atherosclerotic plaques, while considerably reducing the unwanted drug side effects in healthy tissue. Simulation results on a well-known mathematical model of the arterial wall demonstrate that the proposed approach successfully reduces the LDL level to a desired value in the arterial wall of a patient with very high LDL level. Also, the mass of the released drug by PDENPs in a healthy wall is 11 times less than its corresponding value in an unhealthy wall. View full abstract»

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  • Extracting Gene-Gene Interactions Through Curve Fitting

    Publication Year: 2012 , Page(s): 402 - 409
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1473 KB) |  | HTML iconHTML  

    This paper presents a simple and novel curve fitting approach for generating simple gene regulatory subnetworks from time series gene expression data. Microarray experiments simultaneously generate massive data sets and help immensely in the large-scale study of gene expression patterns. Initial biclustering reduces the search space in the high-dimensional microarray data. The least-squares error between fitting of gene pairs is minimized to extract a set of gene-gene interactions, involving transcriptional regulation of genes. The higher error values are eliminated to retain only the strong interacting gene pairs in the resultant gene regulatory subnetwork. Next the algorithm is extended to a generalized framework to enhance its capability. The methodology takes care of the higher-order dependencies involving multiple genes co-regulating a single gene, while eliminating the need for user-defined parameters. It has been applied to the time-series Yeast data, and the experimental results biologically validated using standard databases and literature. View full abstract»

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  • Optoelectronic Logic Gates Based on Photovoltaic Response of Bacteriorhodopsin Polymer Composite Thin Films

    Publication Year: 2012 , Page(s): 410 - 420
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3478 KB) |  | HTML iconHTML  

    We present designs of optoelectronic OR, AND, NOR, and NAND logic gates with multiple pulsed pump laser beams based on the photovoltaic response of bacteriorhodopsin (BR) molecules embedded in a polyvinyl matrix coated on ITO. A detailed experimental study of the photovoltaic response reveals that continuous pulsed exposure to 532 nm and 405 nm laser light results in a large photocurrent/photovoltage, due to rapid reprotonation and chromophore reisomerization, taking BR to the ground state in hundreds of nanoseconds. It also helps in sustaining the photovoltage at higher frequencies and in maintaining the shape of the photovoltage. It is shown experimentally that for a pulsed laser beam at 532 nm with peak pump intensity of 1.19 W/cm2, a photovoltage of 50 mV is generated. A detailed numerical simulation of the photovoltaic response of BR has been carried out taking into account all the six states (B, K, L, M, N, and O) in the BR photocycle to ascertain the effect of various parameters such as lifetime of the M-state, the pump pulse-width, pump intensity, lifetime of excited protons, and rate constant of excited protons. Experimental results are in good agreement with theoretical simulations. The present study opens up new prospects for protein-based optoelectronic computing. View full abstract»

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  • 2012 Index IEEE Transactions on NanoBioscience Vol. 11

    Publication Year: 2012 , Page(s): 421 - 430
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  • IEEE Transactions on NanoBioscience information for authors

    Publication Year: 2012 , Page(s): C3
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    Publication Year: 2012 , Page(s): C4
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Aims & Scope

The IEEE Transactions on NanoBioscience publishes basic and applied papers dealing both with engineering, physics, chemistry, modeling and computer science and with biology and medicine with respect to molecules, cells, tissues. The content of acceptable papers ranges from practical/clinical/environmental applications to formalized mathematical theory.

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Meet Our Editors

Editor-in-Chief
Henry Hess
Department of Biomedical Engineering
Columbia University