bluuebunny/crossref_metadata_2025_split huggingface dataset:
The involvement of neuronal nitric oxide synthase in the anti-epileptic action of curcumin on pentylenetetrazol-kindled rats
Authors: Zhu Wenting , Su Jing , Liu Jing , Jiang Changbin 
Published: August 2015

Abstract In this study, it was investigated whether a NO signaling pathway is involved in the anti-epileptic effect of curcumin on pentylenetetrazol (PTZ)-kindled rats. PTZ-kindled rats received different doses of curcumin that were administered intraperitoneally for 24 days. Either a non-selective inhibitor of nitric oxide synthase (NOS) (N-nitro-L-arginine methyl ester (L-NAME)), a selective inhibitor of neuronal NOS (7-Nitroindazole (7-NI)), a selective inhibitor of inducible NOS (aminoguanidine (AG)), or a NO precursor (L-arginine (L-ARG)) was administered chronically to evaluate the role of NO in curcumin’s anti-seizure effect. A chronic administration of curcumin (200 mg/kg) was most effective for decreasing the mean frequency of epileptiform discharge. Furthermore, a pretreatment with L-NAME or 7-NI augmented the anti-epileptic effect of curcumin. In contrast, AG failed to significantly alter the anti-epileptic effect of curcumin. A pretreatment with L-ARG temporally reversed the anti-epileptic effect of curcumin in the early stage, but in the late stage, it potentiated curcumin’s anti-epileptic effect. These findings suggest that the L-arginine–nitric oxide pathway may be involved in the anti-epileptic properties of curcumin, and that the role of nNOS (and not iNOS) is prominent in this neuroprotective feature.
Quantitative evaluation of contrast-induced-nephropathy in vascular post-angiography patients: Feasibility study of a semi-empirical model
Authors: Pan Lung Fa , Davaa Otgonbaatar , Chen Chien Yi , Pan Lung Kwang 
Published: August 2015

Abstract In this study, the contrast-induced-nephropathy (CIN) of vascular patients who had undergone angiography was quantitatively evaluated using a semi-empirical model. The model compiled six essential serum readings and biological data from 70 patients in order to develop a 1st-order nonlinear equation with 16 defined terms. The expectation value of the model was used to predict the serum creatinine reading of patients that had been determined to be at high risk of CIN after contrast media (CM) administration. The other five variables included body surface area (BSA), administrated CM, serum creatinine level before CM administration, blood urea nitrogen (BUN), and systolic blood pressure level. A loss function was used to define the difference between the observed and predicted serum creatinine readings after CM administration. The dominant variables were proven to be systolic blood pressure, serum creatinine level before CM administration, and BSA. The cross interaction between the serum creatinine level before CM administration and systolic blood pressure was the decisive term of the model’s performance, indicating that both should be specially considered in order to prevent CIN. The BSA, which was usually ignored by medical staff, was also proven to be a significant variable, whereas the BUN reading and amount of injected contrast media were negligible in the semi-empirical model.
Mental workload prediction based on attentional resource allocation and information processing
Authors: Xiao Xu , Wanyan Xiaoru , Zhuang Damin 
Published: August 2015

Abstract Mental workload is an important component in complex human-machine systems. The limited applicability of empirical workload measures produces the need for workload modeling and prediction methods. In the present study, a mental workload prediction model is built on the basis of attentional resource allocation and information processing to ensure pilots’ accuracy and speed in understanding large amounts of flight information on the cockpit display interface. Validation with an empirical study of an abnormal attitude recovery task showed that this model’s prediction of mental workload highly correlated with experimental results. This mental workload prediction model provides a new tool for optimizing human factors interface design and reducing human errors.
Fuzzy linguistic prediction model for sinoatrial node field potential analysis in acute hyperglycemia environment
Authors: Feng Yu , Cao Hui , Wang Yanxia , Zhang Yanbin 
Published: August 2015

Abstract The objective of this study is to build a fuzzy linguistic prediction model (FLPM) for analyzing the actuation duration of acute hyperglycemia to sinoatrial node field potential. The field potential was recorded using microelectrode arrays (MEA). The experimental data were analyzed using partial least squares (PLS), support vector machine (SVM), back propagation neural network (BPNN) and the proposed method. The experimental results showed that the fuzzy linguistic prediction model could be adopted for predicting the actuation duration of high glucose to the sinoatrial node field potential. Compared with the other aforementioned models, the proposed model had higher prediction accuracy.
Novel DiR and SPIO nanoparticles embedded PEG-PLGA nanobubbles as a multimodalimaging contrast agent
Authors: Luo Binhua , Zhang Huajie , Liu Xuhan , Rao Rong , Wu Yun , Liu Wei 
Published: August 2015

Abstract Fluorescence dye DiR and superparamagnetic iron oxide nanoparticles (SPIONs) embedded in PEG-PLGA nanobubbles (DiR-SPIO-NBs) were produced using double emulsion method on a membrane of Shirasu porous glass (SPG). The nanobubbles encapsulated with DiR and SPIONs had a liquid core (perfluoropentane) and a PEG-PLGA shell. DiR-SPIO-NBs showed biocompatibility based on MTT cytotoxicity and hemolysis studies. The PFP encapsulated in the nanobubbles experienced phase transition under ultrasonic irradation. Nanobubbles dispersed well in saline over 3 months, and the relaxivity was 127.9 mM−1s−1, suggesting that it could be used as a contrast agent in MRI. The MR and fluorescence images in vivo demonstrated that the signal intensity in the spleen and liver was significantly enhanced with the treatment of nanobubbles. In addition, results of ultrasound images suggested that the nanobubbles had persistent contrast ability. In conclusion, nanobubbles could be utilized as an US/MRI/fluorescence contrast agent.
Analog front-end measuring biopotential signal with effective offset rejection loop
Authors: Lim Seunghyun , Kim Hyunho , Song Haryong , Cho Dong-il “Dan” , Ko Hyoungho 
Published: August 2015

Abstract This paper presents an analog front-end (AFE) IC design for recording biopotential signals. The AFE employs a capacitively coupled instrumentation amplifier to achieve a low-noise and high-common mode rejection ratio (CMRR) system. A ripple reduction loop is proposed to reduce the ripple generated by the up-modulating chopper. The low frequency noise is attenuated by an input AC coupling capacitor, and is attenuated again by a DC servo loop. The proposed AFE features a differential gain of 71 dB, and a CMRR of 89 dB, at 50 Hz. Furthermore, the proposed AFE can robustly acquire biopotential signals even in the presence of an input offset and ripples.
A radiation emission shielding method for high intensity focus ultrasound probes
Authors: Wu Hao , Shen Guofeng , Chen Yazhu 
Published: August 2015

Abstract Electromagnetic compatibility (EMC) is a key issue in the design and development of safe and effective medical instruments. The treatment probes of high intensity focused ultrasound (HIFU) systems not only receive and transmit electromagnetic waves, but also radiate ultrasound waves, resulting in electromagnetic coupling. In this paper, an electromagnetic shielding method involving the enclosure of the probe in a copper wire mesh was introduced. First, sound pressure distribution simulations and measurements were performed using a hydrophone in order to evaluate the effects of the wire mesh on the acoustic performance of the HIFU system. The results indicated that the wire mesh did not disturb the normalized sound pressure field. In addition, the attenuation of the maximum pressure in the focal plane was equal to 6.2%. Then, the electronic emission level was tested in a chamber. After the implementation of the wire mesh, the 10-100 MHz frequency band radiation was suppressed, and the HIFU system satisfied the national EMC standards.
Brain white matter fiber estimation and tractography using Q-ball imaging and Bayesian MODEL
Authors: Lu Meng 
Published: August 2015

Abstract Diffusion tensor imaging allows for the non-invasive in vivo mapping of the brain tractography. However, fiber bundles have complex structures such as fiber crossings, fiber branchings and fibers with large curvatures that tensor imaging (DTI) cannot accurately handle. This study presents a novel brain white matter tractography method using Q-ball imaging as the data source instead of DTI, because QBI can provide accurate information about multiple fiber crossings and branchings in a single voxel using an orientation distribution function (ODF). The presented method also uses graph theory to construct the Bayesian model-based graph, so that the fiber tracking between two voxels can be represented as the shortest path in a graph. Our experiment showed that our new method can accurately handle brain white matter fiber crossings and branchings, and reconstruct brain tractograhpy both in phantom data and real brain data.
Iterative reconstruction with segmentation penalty for PET
Authors: Teng Yueyang , Zhang Yaonan , Kang Yan 
Published: August 2015

Abstract Segmentation technique is widely accepted to reduce noise propagation from transmission scanning for positron emission tomography. The conventional routine is to sequentially perform reconstruction and segmentation. A smoothness penalty is also usually used to reduce noise, which can be imposed to both the ML and WLS estimators. In this paper we replace the smoothness penalty by a segmentation penalty that biases the object toward piecewise-homogeneous reconstruction. Two updating algorithms are developed to solve the penalized ML and WLS estimates, which monotonically decrease the cost functions. Experimental results on simulated phantom and real clinical data were both given to demonstrate the effectiveness and efficiency of the algorithms which were proposed.
Deformable image registration of CT images for automatic contour propagation in radiation therapy
Authors: Wu Qian , Cao Ruifen , Pei Xi , Jia Jing , Hu Liqin 
Published: August 2015

Abstract Radiotherapy treatment plan may be replanned due the changes of tumors and organs at risk (OARs) during the treatment. Deformable image registration (DIR) based Computed Tomography (CT) contour propagation in the routine clinical setting is expected to reduce time needed for necessary manual tumors and OARs delineations and increase the efficiency of replanning. In this study, a DIR method was developed for CT contour propagation. Prior structure delineations were incorporated into Demons DIR, which was represented by adding an intensity matching term of the delineated tissues pairs to the energy function of Demons. The performance of our DIR was evaluated with five clinical head-and-neck and five lung cancer cases. The experimental results verified the improved accuracy of the proposed registration method compared with conventional registration and Demons DIR.
A novel approach for arrhythmia diagnosis: Self-adaptive and distribution-free mode
Authors: Li Fenghuan , Zheng Dequan , Zhao Tiejun 
Published: August 2015

Abstract Arrhythmia diagnosis is very significant to ensure human health. In this paper, a new model is developed for arrhythmia diagnosis. A salient feature of the algorithm is a synergistic combination of statistical and fuzzy set-based techniques. It is distribution-free and is realized in an unsupervised mode. Arrhythmia diagnosis is viewed as a certain statistical hypothesis testing. ‘Abnormal’ is typically a much complex concept, so it can be described with the technology of fuzzy sets which bring a facet of robustness to the overall scheme and play an important role in the successive step of hypothesis testing. Intensive fuzzification is engaged in parameters determination which is self-adaptive and no parameter needs to be specified by the user. The algorithm is validated with a number of experiments, which prove its effectiveness for arrhythmia diagnosis.
Analysis of the sleep quality of elderly people using biomedical signals
Authors: Moreno-Alsasua L. , Garcia-Zapirain B. , Mendez-Zorrilla A. 
Published: August 2015

Abstract This paper presents a technical solution that analyses sleep signals captured by biomedical sensors to find possible disorders during rest. Specifically, the method evaluates electrooculogram (EOG) signals, skin conductance (GSR), air flow (AS), and body temperature. Next, a quantitative sleep quality analysis determines significant changes in the biological signals, and any similarities between them in a given time period. Filtering techniques such as the Fourier transform method and IIR filters process the signal and identify significant variations. Once these changes have been identified, all significant data is compared and a quantitative and statistical analysis is carried out to determine the level of a person’s rest. To evaluate the correlation and significant differences, a statistical analysis has been calculated showing correlation between EOG and AS signals (p=0,005), EOG, and GSR signals (p=0,037) and, finally, the EOG and Body temperature (p=0,04). Doctors could use this information to monitor changes within a patient.
The detection of long-range correlations of operation force and sEMG with multifractal detrended fluctuation analysis
Authors: Li Fan , Li Dongxu , Wang Chunhui , Chen Shanguang , Lv Ming , Wang Miao 
Published: August 2015

Abstract This paper explores the application of multifractal detrended fluctuation analysis (MF-DFA) on the nonlinear characteristics of correlation between operation force and surface electromyography (sEMG), which is an applied frontier of human neuromuscular system activity. We established cross-correlation functions between the signal of force and four typical sEMG time-frequency domain index sequences (force-sEMG cross-correlation sequences), and dealt with the sequences with MF-DFA. In addition, we demonstrated that the force-sEMG cross-correlation sequences have strong statistical self-similarity and the fractal characteristic of the signal spectrum is similar to 1/f noise or fractional Brownian motion.
A study on locating the sonic source of sinusoidal magneto-acoustic signals using a vector method
Authors: Zhang Shunqi , Zhou Xiaoqing , Ma Ren , Yin Tao , Liu Zhipeng 
Published: August 2015

Abstract Methods based on the magnetic-acoustic effect are of great significance in studying the electrical imaging properties of biological tissues and currents. The continuous wave method, which is commonly used, can only detect the current amplitude without the sound source position. Although the pulse mode adopted in magneto-acoustic imaging can locate the sonic source, the low measuring accuracy and low SNR has limited its application. In this study, a vector method was used to solve and analyze the magnetic-acoustic signal based on the continuous sine wave mode. This study includes theory modeling of the vector method, simulations to the line model, and experiments with wire samples to analyze magneto-acoustic (MA) signal characteristics. The results showed that the amplitude and phase of the MA signal contained the location information of the sonic source. The amplitude and phase obeyed the vector theory in the complex plane. This study sets a foundation for a new technique to locate sonic sources for biomedical imaging of tissue conductivity. It also aids in studying biological current detecting and reconstruction based on the magneto-acoustic effect.
Anticorrelated networks in resting-state fMRI-BOLD data
Authors: Liu Yadong , Huang Liangming , Li Ming , Zhou Zongtan , Hu Dewen 
Published: August 2015

Abstract In this work, abundant anticorrelated networks were successfully detected in rest-stating fMRI-BOLD data from 20 subjects. Spatial independent component analysis (sICA) method was applied at both individual and group levels. At the individual level, for each subject, 30 independent components (IC) were estimated, and each IC was transformed using Z-score mapping. The voxels with >5 and < -5 Z-score were denoted as positive signals (PS) and negative signals (NS) respectively. The correlation coefficients between the mean time series of the PS and NS voxels were computed; if the calculated coefficients were <-0.3, the PS and NS voxels were considered to form an anticorrelated PS-NS network. It was found that 36.5% of the ICs contained an anticorrelated PS-NS network. The spatiotemporal patterns of most PS-NS networks varied from subject to subject, but three networks displaying spatial patterns were comparably consistent among different subjects. For group-level analysis, no anticorrelated PS-NS networks were detected. Our results suggest that future investigations adopt a broader approach for negative BOLD signal characterization. Combined consideration of PS and NS systems help to better elucidate hemodynamic and neuronal brain behavior and further develop understanding of neural mechanisms of brain information processing.
An improved cerebral vessel extraction method for MRA images
Authors: Zou Hua , Zhang Wen , Wang Qian 
Published: August 2015

Abstract To improve the speed and accuracy of cerebral vessel extraction, a fast and robust method is proposed in this paper. First, volume data are divided into sub-volumes by using octree, and at the same time invalid volume data are eliminated. Second, fuzzy connectedness is introduced to achieve fast cerebral vessel segmentation from 3D MRA Images. The values of gradient and Laplacian transformation are then calculated to improve the accuracy of the distance field. Last, the center of gravity is utilized to refine the initial centerline to make it closer to the actual centerline of the vessel cavity. The experiment demonstrates that the proposed method can effectively improve the speed and precision of centerline extraction.
Automatic detection of regions of interest in breast ultrasound images based on local phase information
Authors: Wang Xin , Guo Yi , Wang Yuanyuan 
Published: August 2015

Abstract Due to the inherent speckling and low contrast of ultrasonic images, the accurate and efficient location of regions of interest (ROIs) is still a challenging task for breast ultrasound (BUS) computer-aided diagnosis (CAD) systems. In this paper, a fully automatic and efficient ROI generation approach is proposed. First, a BUS image is preprocessed to improve image quality. Second, a phase of max-energy orientation (PMO) image of the preprocessed image is calculated. Otsu’s threshold selection method is then used to binarize the preprocessed image, the phase image and the composed image obtained by adding and normalizing the set of two images. Finally, a region selection algorithm is developed to select the true tumor region from these three binary images before generating a final ROI. The method was validated on a BUS database with 168 cases (81 benign and 87 malignant); the accuracy, average precision rate and average recall rate are calculated and compared with conventional method. The results indicate that the proposed method is more accurate and efficient in locating ROIs.
Automatic brain MR image denoising based on texture feature-based artificial neural networks
Authors: Chang Yu-Ning , Chang Herng-Hua 
Published: August 2015

Abstract Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.
Pathological brain detection based on wavelet entropy and Hu moment invariants
Authors: Zhang Yudong , Wang Shuihua , Sun Ping , Phillips Preetha 
Published: August 2015

Abstract With the aim of developing an accurate pathological brain detection system, we proposed a novel automatic computer-aided diagnosis (CAD) to detect pathological brains from normal brains obtained by magnetic resonance imaging (MRI) scanning. The problem still remained a challenge for technicians and clinicians, since MR imaging generated an exceptionally large information dataset. A new two-step approach was proposed in this study. We used wavelet entropy (WE) and Hu moment invariants (HMI) for feature extraction, and the generalized eigenvalue proximal support vector machine (GEPSVM) for classification. To further enhance classification accuracy, the popular radial basis function (RBF) kernel was employed. The 10 runs of k-fold stratified cross validation result showed that the proposed “WE + HMI + GEPSVM + RBF” method was superior to existing methods w.r.t. classification accuracy. It obtained the average classification accuracies of 100%, 100%, and 99.45% over Dataset-66, Dataset-160, and Dataset-255, respectively. The proposed method is effective and can be applied to realistic use.
An image-enhancement method based on variable-order fractional differential operators
Authors: Xu Mengjia , Yang Jinzhu , Zhao Dazhe , Zhao Hong 
Published: August 2015

Abstract In this study, we develop a new algorithm based on fractional operators of variable-order in order to enhance image quality. First, three kinds of popular high-order discrete formulas are adopted to obtain the coefficients, and subsequently, a mask optimization method for selecting the fractional order adaptively is applied to construct a variable-order fractional differential mask along with the coefficients generated from the first step. We carry out experiments on OCT thoracic aorta images and some nature images with low contrast and noise, demonstrating that the high-order discrete method leads to significantly better performance in enhancing the edge information nonlinearly compared to the standard first-order discrete method. Moreover, the optimized mask with variable-order of the fractional derivative not only can preserve the edge information of the processed images adequately, but it also effectively suppresses the noise in the smooth area.