Read by QxMD icon Read

IEEE Transactions on Nanobioscience

Qianyi Zhan, Sherry Emery, Philip Yu, Chongjun Wang, Yuan Liu
E-cigarettes(vape) are now the most commonly used tobacco product among youth in the United States. Ads are claiming e-cigarettes help smokers quit, but most of them contain nicotine, which can cause addiction and harm the developing adolescent brain. Therefore national, state and local health organizations have proposed anti-vaping campaigns to warn the potential risks of e-cigarettes. However, there is some evidence that these products may reduce harm for adult users who reduce or quit combustible cigarette smoking, and little evidence that e-cigarettes cause long-term harm, pro-vaping advocates have used this equivocal evidence base to oppose the anti-vaping media campaign messaging, generating a very high volume of oppositional messages on social media...
July 12, 2018: IEEE Transactions on Nanobioscience
Omar I Al-Surkhi, Reem Y Naser
Bioimpedance spectroscopy is a promising method for tissue ischemia monitoring; it is used in this research to study ischemia and cell morphology using bioimpedance measurements of rabbit liver tissue. The current paper presents the evolution of Cole parameters and cell parameters with time, and establishes a relation between cellular morphological changes during cell ischemia with bioimpedance measurements at different frequencies. The general behavior of ischemic liver tissue has been analyzed. Evolution of Cole parameters were extrapolated from BIS measurements and their behavior was studied with respect to time...
July 5, 2018: IEEE Transactions on Nanobioscience
Shreya Ghosh, Debopam Datta, Shreya Chaudhry, Mitra Dutta, Michael A Stroscio
This study reports an optical 'turn off' aptasensor, which is comprised of a deoxyribonucleic acid aptamer attached to a quantum dot on the 5' terminus and gold nanoparticle on the 3' terminus. The photoluminescence intensity is observed to decrease upon progressive addition of the target protein tumor necrosis factor-alpha to the sensor. For PBS based TNF-alpha samples, the beacon exhibited 19 - 20% quenching at around 22 nM concentration. The photoluminescence intensity and the quenching efficiency showed a linear decrease and a linear increase respectively between 0 to 22...
July 2, 2018: IEEE Transactions on Nanobioscience
Arjun P Athreya, Alan J Gaglio, Junmei Cairns, Krishna R Kalari, Richard M Weinshilboum, Liewei Wang, Zbigniew T Kalbarczyk, Ravishankar K Iyer
This paper demonstrates the ability of machine learning approaches to identify a few genes among the 23; 398 genes of the human genome to experiment on in the laboratory to establish new drug mechanisms. As a case study, this work uses MDA-MB-231 breast cancer single-cells treated with the antidiabetic drug metformin. We show that mixture-model based unsupervised methods with validation from hierarchical clustering can identify single-cell subpopulations (clusters). These clusters are characterized by a small set of genes (1% of the genome) that have significant differential expression across the clusters and are also highly correlated with pathways with anticancer effects driven by metformin...
July 2, 2018: IEEE Transactions on Nanobioscience
Tatsuya Suda, Tadashi Nakano
The authors of this paper have been involved in molecular communication since its conception. The authors describe their decade-and-a-half long personal journey of the molecular communication research and share their observations and thoughts on how the molecular communication research started and expanded to flourish. They also share their thoughts on research challenges that they hope the molecular communication research community addresses in the coming decade.
July 2, 2018: IEEE Transactions on Nanobioscience
Shirin Salehi, Naghmeh S Moayedian, Shaghayegh Haghjooy Javanmard, Eduard Alarcon
In this paper, a multiple transmitter local drug delivery system associated with encapsulated drug transmitters is investigated. One of the limitations of drug delivery systems is the reservoir capacity. In order to improve the lifetime of drug transmitting nanomachines and hence the longevity of drug delivery scenario, the system is associated with encapsulated drug transmitters. Encapsulated drugs are incapable of reaction with the environment unless they are unpacked in a drug transmitter nanomachine. Therefore, far-reaching transmitters do not have harmful effects on the healthy parts of the body...
June 25, 2018: IEEE Transactions on Nanobioscience
Yan Xu, Yingxi Yang, Jun Ding, Chunhui Li
As one of the new posttranslational modification (PTM), lysine glutarylation has been identified in both prokaryotic and eukaryotic cells. These glutarylated proteins are involved in various cellular functions such as translation, metabolism and exhibited diverse subcellular localizations. Experimental identification of lysine glutarylation sites was in 2014 and also found its deglutarylase sirturn 5(SIRT 5). Computational prediction of lysine glutarylation could be a complementary way to the experimental technique...
June 18, 2018: IEEE Transactions on Nanobioscience
Hamideh Ramezani, Tooba Khan, Ozgur B Akan
Communication among neurons, known as neurospike communication, is the most promising technique for realization of a bio-inspired nanoscale communication paradigm to achieve biocompatible nanonetworks. In neuro-spike communication, the information, encoded into spike trains, is communicated to various brain regions through neuronal network. An output neuron needs to receive signal from multiple input neurons to generate a spike. Hence, in this paper, we aim to quantify the information transmitted through the multiple-input single-output (MISO) neuro-spike communication channel by taking into account models for axonal propagation, synaptic transmission and spike generation...
June 14, 2018: IEEE Transactions on Nanobioscience
Antonio Sze-To, Andrew K C Wong
Functional region identification is of fundamental importance for protein sequences analysis. Such knowledge provides better scientific understanding and could assist drug discovery. Up-to-date, domain annotation is one approach but it needs to leverage existing databases. For de novo discovery, motif discovery locates and aligns locally homologous sub-sequences to obtain a position-weight matrix (PWM) which is a fixedlength representation model whereas protein functional region size varies. It thus requires computational expensive exhaustive search to obtain a PWM with width of optimal range...
June 8, 2018: IEEE Transactions on Nanobioscience
Sudheer Kumar Sharma, Sanjeev Kumar, Karmeshu
A theoretical model based on the generalized neuronal model with distributed delay (GNMDD) is proposed to generate multimodal/ bimodal inter-spike interval (ISI) distribution. The distributed delay is assumed to be general in the sense that it is a linear combination of gamma distributed weak and strong memory kernels. It is found that the expected membrane potential in the subthreshold regime exhibits damped oscillatory behaviour. This causes the ISI pattern to become bimodal/multimodal. Further, the effect of external damped oscillatory current in the GNMDD model is investigated...
June 8, 2018: IEEE Transactions on Nanobioscience
Florian Pein, Inder Tecuapetla-Gomez, Ole Mathis Schutte, Claudia Steinem, Axel Munk
We propose a new model-free segmentation method, JULES, which combines recent statistical multiresolution techniques with local deconvolution for idealization of ion channel recordings. The multiresolution criterion takes into account scales down to the sampling rate enabling the detection of flickering events, i.e., events on small temporal scales, even below the filter frequency. For such small scales the deconvolution step allows for a precise determination of dwell times and, in particular, of amplitude levels, a task which is not possible with common thresholding methods...
June 7, 2018: IEEE Transactions on Nanobioscience
Xiaofei Zhang, Yi Zhang, Erik Y Han, Nathan Jacobs, Qiong Han, Xiaoqin Wang, Jinze Liu
Mammography is the most popular technology used for the early detection of breast cancer. Manual classification of mammogram images is a hard task because of the variability of the tumor. It yields a noteworthy number of patients being called back to perform biopsies, ensuring no missing diagnosis. The convolutional neural network (CNNs) has succeeded in a lot of image classification challenges during the recent years. In this study, we proposed an approach of mammogram and tomosynthesis classification based on convolutional neural networks...
June 7, 2018: IEEE Transactions on Nanobioscience
Aysenur Topsakal, Muhammet Uzun, Gaye Ugar, Aslihan Ozcan, Esra Altun, F Nuzhet Oktar, Fakhera Ikram, Ozan Ozkan, Hilal Turkoglu Sasmazel, Oguzhan Gunduz
-Biocompatible nanocomposite electrospun fibers containing Polyurethane(PU)/Chitosan(Ch)/β-Tri calcium phosphate (β-TCP) with diverse concentrations were designed and produced through electrospinning process for bone tissue engineering applications. After the production process, density measurement, viscosity, electrical conductivity and tensile strength measurement tests were carried out as physical analyses of blended solutions. The chemical structural characterization was scrutinized using Fourier Transform Infrared spectrometer (FTIR) and Scanning Electron Microscopy (SEM) was used to observe morphological details of developed electrospun scaffolds...
June 7, 2018: IEEE Transactions on Nanobioscience
Cheng-Hong Yang, Yi-Kai Kao, Li-Yeh Chuang, Yu-Da Lin
Single-nucleotide polymorphism (SNP)-SNP interactions are crucial for understanding the as-sociation between disease-related multifactorials for disease analysis. Existing statistical methods for de-termining such interactions are limited by the consid-erable computation required for evaluating all poten-tial associations between disease-related multifactori-als. Identifying SNP-SNP interactions is thus a major challenge in genetic association studies. This study proposes a catfish Taguchi-based binary differential evolution (CT-BDE) algorithm for identifying SNP-SNP interactions...
June 6, 2018: IEEE Transactions on Nanobioscience
Jiancheng Zhong, Yusui Sun, Wei Peng, Minzhu Xie, Jiahong Yang, Xiwei Tang
Essential proteins as a vital part of maintaining the cells' life, play an important role in the study of biology and drug design. With the generation of large amounts of biological data related to essential proteins, an increasing number of computational methods have been proposed. Different from the methods which adopt single machine learning method or ensemble machine learning method, this study proposes a predicting framework named by XGBFEMF for identifying essential proteins, which includes a SUB-EXPAND-SHRINK method for constructing the composite features with original features and obtaining the better subset of features for essential protein prediction, and also includes a model fusion method for getting a more effective prediction model...
May 31, 2018: IEEE Transactions on Nanobioscience
Dingcheng Li, Ming Huang, Xiaodi Li, Yaoping Ruan, Lixia Yao
Data mapping plays an important role in data integration and exchanges among institutions and organizations with different data standards. However, traditional rule-based approaches and machine learning methods fail to achieve satisfactory results for the data mapping problem. In this paper, we propose a novel and sophisticated deep learning framework for data mapping called mixture feature embedding convolutional neural network (MfeCNN). The MfeCNN model converts the data mapping task to a multiple classification problem...
May 28, 2018: IEEE Transactions on Nanobioscience
Wen Cao, Juan Shan, Nicholas Czarnek, Lin Li
Diabetic retinopathy (DR) is an eye abnormality caused by long term diabetes and it is the most common cause of blindness before the age of 50. Microaneurysms (MAs), resulting from leakage from retinal blood vessels, are early indicators of DR, yielding a large body of diagnostic work focused on automatic detection of MA. However, automated detection of MAs is difficult because (1) the small size of MA lesions and low contrast between the lesion and its retinal background, (2) the large variations in color, brightness and contrast of fundus images, and (3) the high prevalence of false positives in regions with similar intensity values such as blood vessels, noises and non-homogenous background...
May 24, 2018: IEEE Transactions on Nanobioscience
Yaodong Du, Rania Almajalid, Juan Shan, Ming Zhang
This study explored the hidden biomedical information from knee MR images for osteoarthritis (OA) prediction. We have computed the Cartilage Damage Index (CDI) information from 36 informative locations on tibiofemoral cartilage compartment from 3D MR imaging and used PCA analysis to process the feature set. Four machine learning methods (artificial neural network (ANN), support vector machine (SVM), random forest and naïve Bayes) were employed to predict the progression of OA, which was measured by change of Kellgren and Lawrence (KL) grade, Joint Space Narrowing on Medial compartment (JSM) grade and Joint Space Narrowing on Lateral compartment (JSL) grade...
May 24, 2018: IEEE Transactions on Nanobioscience
Hong Peng, Jinlong Chao, Sirui Wang, Jie Dang, Fengqi Jiang, Bin Hu, Dennis Majoe
As a promising non-invasive technique,functional near-infrared spectroscopy(fNIRS) can easily detect the hemodynamic responses of cortical brain activities.This paper investigated the multiclass classification of motor imagery(MI)based on fNIRS. 10 healthy individuals were recruited to move an object using their imagination.A multi-channel continuous-wave fNIRS equipment was applied to obtain the signals from the prefrontal cortex(PFC).A combination of Ensemble Empirical Mode Decomposition (EEMD) and Independent Component Analysis(ICA) method was used to solve the signal-noise frequency spectrum aliasing issues caused by Mayer wave(0...
May 23, 2018: IEEE Transactions on Nanobioscience
Muhammad Waseem Tahir, Nayyer Abbas Zaidi, Adeel Akhtar Rao, Roland Blank, Michael J Vellekoop, Walter Lang
Fungus is an enormously notorious for food, human health and archives. Fungus sign and symptoms in medical science are non-specific and asymmetrical for extremely large areas resulting into a challenging task of fungal detection. Various traditional and computer vision techniques were applied to meet the challenge of early fungus detection. On the other hand, features learned through the convolutional neural network (CNN) provided state of the art results in many other applications of object detection and classification...
May 22, 2018: IEEE Transactions on Nanobioscience
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"