IEEE Transactions on NanoBioscience (IEEE T NANOBIOSCI)

Publisher: IEEE Engineering in Medicine and Biology Society, Institute of Electrical and Electronics Engineers

Journal description

This transaction reports on original, innovative and interdisciplinary work on all aspects of molecular systems, cellular systems, and tissues - including molecular electronics.

Current impact factor: 2.31

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 2.309
2013 Impact Factor 1.768
2012 Impact Factor 1.286
2011 Impact Factor 1.28
2010 Impact Factor 1.712
2009 Impact Factor 1.705
2008 Impact Factor 1.341
2007 Impact Factor 1.899
2006 Impact Factor 2.592
2005 Impact Factor 1.392

Impact factor over time

Impact factor
Year

Additional details

5-year impact 2.08
Cited half-life 6.00
Immediacy index 0.41
Eigenfactor 0.00
Article influence 0.68
Website IEEE Transactions on NanoBioscience website
Other titles IEEE transactions on nanobioscience
ISSN 1536-1241
OCLC 47360509
Material type Periodical, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publisher details

Institute of Electrical and Electronics Engineers

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on Author's personal website, employers website or publicly accessible server
    • Author's post-print on Author's server or Institutional server
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    • Author's pre-print must be accompanied with set-phrase, once submitted to IEEE for publication ("This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible")
    • Author's pre-print must be accompanied with set-phrase, when accepted by IEEE for publication ("(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")
    • IEEE must be informed as to the electronic address of the pre-print
    • If funding rules apply authors may post Author's post-print version in funder's designated repository
    • Author's Post-print - Publisher copyright and source must be acknowledged with citation (see above set statement)
    • Author's Post-print - Must link to publisher version with DOI
    • Publisher's version/PDF cannot be used
    • Publisher copyright and source must be acknowledged
  • Classification
    green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Precious information on protein function can be extracted from a detailed characterization of protein equilibrium dynamics. This remains elusive in wet and dry laboratories, as function-modulating transitions of a protein between functionally-relevant, thermodynamically-stable and meta-stable structural states often span disparate time scales. In this paper we propose a novel, robotics-inspired algorithm that circumvents time-scale challenges by drawing analogies between protein motion and robot motion. The algorithm adapts the popular roadmap-based framework in robot motion computation to handle the more complex protein conformation space and its underlying rugged energy surface. Given known structures representing stable and meta-stable states of a protein, the algorithm yields a time- and energy-prioritized list of transition paths between the structures, with each path represented as a series of conformations. The algorithm balances computational resources between a global search aimed at obtaining a global view of the network of protein conformations and their connectivity and a detailed local search focused on realizing such connections with physically-realistic models. Promising results are presented on a variety of proteins that demonstrate the general utility of the algorithm and its capability to improve the state of the art without employing system-specific insight.
    No preview · Article · Feb 2016 · IEEE Transactions on NanoBioscience
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    ABSTRACT: Machine learning algorithms are widely used to annotate biological sequences. Low-dimensional informative feature vectors can be crucial for the performance of the algorithms. In prior work, we have proposed the use of a community detection approach to construct low dimensional feature sets for nucleotide sequence classification. Our approach used the Hamming distance between short nucleotide subsequences, called k- mers, to construct a network, and subsequently used community detection to identify groups of k-mers that appear frequently in a set of sequences. Whereas this approach worked well for nucleotide sequence classification, it could not be directly used for protein sequences, as the Hamming distance is not a good measure for comparing short protein k-mers. To address this limitation, we extended our prior approach by replacing the Hamming distance with substitution scores. Experimental results in different learning scenarios show that the features generated with the new approach are more informative than k-mers.
    No preview · Article · Feb 2016 · IEEE Transactions on NanoBioscience
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    ABSTRACT: Supervised classifiers are highly dependent on abundant labeled training data. Alternatives for addressing the lack of labeled data include: labeling data (but this is costly and time consuming); training classifiers with abundant data from another domain (however, the classification accuracy usually decreases as the distance between domains increases); or complementing the limited labeled data with abundant unlabeled data from the same domain and learning semi-supervised classifiers (but the unlabeled data can mislead the classifier). A better alternative is to use both the abundant labeled data from a source domain, the limited labeled data and optionally the unlabeled data from the target domain to train classifiers in a domain adaptation setting. We propose two such classifiers, based on logistic regression, and evaluate them for the task of splice site prediction - a difficult and essential step in gene prediction. Our classifiers achieved high accuracy, with highest areas under the precision-recall curve between 50.83% and 82.61%.
    No preview · Article · Feb 2016 · IEEE Transactions on NanoBioscience
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    ABSTRACT: Carbon nanomaterials have become increasingly popular microelectrode materials for neuroscience applications. Here we study how the scale of carbon nanotubes and carbon nanofibers affect neural viability, outgrowth, and adhesion. Carbon nanotubes were deposited on glass coverslips via a layer-by-layer method with polyethylenimine (PEI). Carbonized nanofibers were fabricated by electrospinning SU-8 and pyrolyzing the nanofiber depositions. Additional substrates tested were carbonized and SU-8 thin films and SU-8 nanofibers. Surfaces were O2-plasma treated, coated with varying concentrations of PEI, seeded with E18 rat cortical cells, and examined at 3, 4, and 7 days in vitro (DIV). Neural adhesion was examined at 4 DIV utilizing a parallel plate flow chamber. At 3 DIV, neural viability was lower on the nanofiber and thin film depositions treated with higher PEI concentrations which corresponded with significantly higher zeta potentials (surface charge); this significance was drastically higher on the nanofibers suggesting that the nanostructure may collect more PEI molecules, causing increased toxicity. At 7 DIV, significantly higher neurite outgrowth was observed on SU-8 nanofiber substrates with nanofibers a significant fraction of a neuron's size. No differences were detected for carbonized nanofibers or carbon nanotubes. Both carbonized and SU-8 nanofibers had significantly higher cellular adhesion post-flow in comparison to controls whereas the carbon nanotubes were statistically similar to control substrates. These data suggest a neural cell preference for larger-scale nanomaterials with specific surface treatments. These characteristics could be taken advantage of in the future design and fabrication of neural microelectrodes.
    No preview · Article · Feb 2016 · IEEE Transactions on NanoBioscience
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    ABSTRACT: —Innovative diagnostic approaches and therapies are more and more based on the use of injections or oral delivery of nanoparticle sized substances. For a better understanding of the overall phenomena, aiming to facilitate a safe application at large scale, the development of accurate models and analysis techniques are required. These techniques take into consideration different aspects of the overall process: accurate numerical modeling of the different phases of the nanoparticles in the body, and knowledge of the local environment , that can be varying very fast within a short-range in the body itself. Such aspects should be taken into account to correctly predict the amount of drug and its timely release for the specific disease. Deep and accurate analysis of the interaction between the nanoparticles and the biological fluid where the nanoparticles are immersed is mandatory for an efficient correlation of all these aspects. Because of their biocompatibility, in this paper, we focus our attention on systems of Titanium (Ti), and its oxide (e.g., TiO2), given their specific features in terms of density, lack of cytotoxic effects, etc. Specifically, we present the study and design of an in-body system by characterizing each of the emission, diffusion, and reception processes with a proper realistic model. The theoretical investigation is further supported by experimental study of the morphology and other important characteristics (e.g., the pH of the particles, and thermal stability) of TiO2 systems when immersed in a Ringer solution, in order to derive important information related to their potential toxicity inside the human body.
    No preview · Article · Feb 2016 · IEEE Transactions on NanoBioscience
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    ABSTRACT: This paper studies detection algorithms for diffusion-based molecular communication systems, where molecules freely diffuse as information carrier from a transmitter to a receiver in a fluid medium. The main limitations are strong intersymbol interference due to the random propagation of the molecules, and the low-energy/low-complexity assumption regarding future implementations in so-called nanomachines. In this contribution, a new biologically inspired detection algorithm suitable for binary signaling, named adaptive threshold detection, is proposed, which deals with these limitations. The proposed detector is of low complexity, does not require explicit channel knowledge, and seems to be biologically reasonable. Numerical results demonstrate that the proposed detector can outperform the common low-complexity fixed threshold detector under certain conditions. As a benchmark, maximum-likelihood sequence estimation (MLSE) and reduced-state sequence estimation (RSSE) are also analyzed by means of numerical simulations. In addition, the effect of molecular denaturation on the detection performances is studied. It is shown that denaturation generally improves the detection performances, while RSSE is able to outperform MLSE in the case of no denaturation.
    No preview · Article · Jan 2016 · IEEE Transactions on NanoBioscience
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    ABSTRACT: Extensive research has been conducted for the computational analysis of mass spectrometry based proteomics data. However, there are still remaining challenges, among which, one particular challenge is the low identification rate of the collected spectral data. A specific contributing factor is the existence of mixture spectra in the collected MS/MS spectra which are generated by the concurrent fragmentation of multiple precursors in one sequencing attempt. The quite frequently observed mixture spectra necessitates the development of effective computational approaches to characterize those non-conventional spectral data. In this research, we proposed an approach for matching the query mixture spectra with a pair of peptide sequences acquired from the protein database by incorporating a special de novo assisted filtration strategy. The experiment results on two different datasets of MS/MS spectra containing mixed ion fragments from multiple peptides demonstrated the efficiency of the integrated filtration strategy in reducing examination space and verified the effectiveness of the proposed matching scheme as well.
    No preview · Article · Jan 2016 · IEEE Transactions on NanoBioscience
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    ABSTRACT: Mass spectrometry has become a widely used analytical technique for proteomics study because of its high throughput and sensitivity. Among those applications, a specific one is to characterize glycan structure. Glycosylation is a frequently occurred post-translational modification of proteins which is relevant to humans' health. Therefore it is significant to develop effective computational methods to automate the identification of glycan structures from mass spectral data. In our research, we mathematically formulated the glycan de novo sequencing problem and proposed a heuristic algorithm for glycan de novo sequencing from HCD MS/MS spectra of N-linked glycopeptides. The algorithm proceeds in a carefully designate pathway to construct the best matched tree structure from MS/MS spectrum. Experimental results showed that our proposed approach can effectively identify glycan structures from HCD MS/MS spectra.
    No preview · Article · Jan 2016 · IEEE Transactions on NanoBioscience
  • Qiwen Hu · Catharina Merchante · Anna Stepanova · Jose Alonso · Steffen Heber

    No preview · Article · Jan 2016 · IEEE Transactions on NanoBioscience
  • Paul Retif · Thierry Bastogne · Muriel Barberi-Heyob

    No preview · Article · Jan 2016 · IEEE Transactions on NanoBioscience

  • No preview · Article · Jan 2016 · IEEE Transactions on NanoBioscience
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    ABSTRACT: Homing of stem cells to the sites of injury is crucial for tissue regeneration. Stromal derived factor 1 (SDF-1) is among the most important chemokines recruiting these cells. Unexpectedly, our previous experimental data on mouse models of acute kidney injury showed that SDF-1 has a declining trend following ischemic kidney insult. To describe this unforeseen observation, a stochastic Petri net model of SDF-1 regulation in the hypoxia pathway was constructed based on main related components extracted from literature. Using this strategy, predictions regarding the underlying mechanisms of SDF-1 kinetics are generated and a novel incoherent feed forward loop regulating SDF-1 expression is proposed. The computational approach suggested here can be exploited to propose novel therapies for debilitating disorders such as kidney injury.
    No preview · Article · Dec 2015 · IEEE Transactions on NanoBioscience
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    ABSTRACT: In-vivo wireless nanosensor networks (iWNSNs) consist of nanosized communicating devices, which can operate inside the human body in real time. iWNSNs are at the basis of transformative healthcare techniques, ranging from intrabody health-monitoring systems to drug-delivery applications. Plasmonic nano-antennas are expected to enable the communication among nanosensors in the near infrared and optical transmission window. This result motivates the analysis of the phenomena aecting the propagation of such electromagnetic (EM) signals inside the human body. In this paper, a channel model for intra-body optical communication among nanosensors is developed. The total path loss is computed by taking into account the absorption from dierent types of molecules and the scattering by dierent types of cells. In particular, first, the impact of a single cell on the propagation of an optical wave is analytically obtained, by modeling a cell as a multilayer sphere with complex permittivity. Then, the impact of having a large number of cells with dierent properties arranged in layered tissues is analyzed. The analytical channel model is validated by means of electromagnetic simulations and extensive numerical results are provided to understand the behavior of the intra-body optical wireless channel. The result shows that, at optical frequencies, the scattering loss introduced by cells is much larger than the absorption loss from the medium. This result motivates the utilization of the lower frequencies of the near-infrared window for communication in iWNSNs.
    No preview · Article · Dec 2015 · IEEE Transactions on NanoBioscience
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    ABSTRACT: The papers in this special section were presented at the 8th International Conference on Nano/Molecular Medicine and Engineering (IEEE-NANOMED 2014).
    No preview · Article · Dec 2015 · IEEE Transactions on NanoBioscience
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    ABSTRACT: Recent applications of PDMS nanocomposite materials and nanostructures have dramatically increased in biomedical fields due to optical, mechanical and electrical properties that are controllable by nanoengineering fabrication processes. These applications include biomedical imaging, biosensing, and cellular bioengineering studies using PDMS engineered structures with nanoparticles, nanopillars and functional nanoporous membranes. This article reviews the recent progress of PDMS nanocomposite materials and nanostructures and provides descriptions of various fabrication techniques. Together with these fabrication techniques, we discuss how these nanocomposite PDMS biomedical devices are revolutionizing biomedical science and engineering fields.
    No preview · Article · Dec 2015 · IEEE Transactions on NanoBioscience