Current Issue : April - June Volume : 2012 Issue Number : 2 Articles : 6 Articles
A modified BioBrickââ??¢ assembly method was developed with higher fidelity than current protocols. The method\r\nutilizes a PCR reaction with a standard primer set to amplify the inserted part. Background colonies are reduced by\r\na combination of dephosphorylation and digestion with DpnI restriction endonuclease to reduce vector and insert\r\nbackground respectively. The molar ratio of the insert to vector in the ligation was also optimized, with the\r\naccuracy of the transformed construct approaching 100%....
Background: The extraction of brain tissue from magnetic resonance head images,\r\nis an important image processing step for the analyses of neuroimage data. The\r\nauthors have developed an automated and simple brain extraction method using an\r\nimproved geometric active contour model.\r\nMethods: The method uses an improved geometric active contour model which\r\ncan not only solve the boundary leakage problem but also is less sensitive to\r\nintensity inhomogeneity. The method defines the initial function as a binary level set\r\nfunction to improve computational efficiency. The method is applied to both our\r\ndata and Internet brain MR data provided by the Internet Brain Segmentation\r\nRepository.\r\nResults: The results obtained from our method are compared with manual\r\nsegmentation results using multiple indices. In addition, the method is compared to\r\ntwo popular methods, Brain extraction tool and Model-based Level Set.\r\nConclusions: The proposed method can provide automated and accurate brain\r\nextraction result with high efficiency...
Background: The detection of T-wave end points on electrocardiogram (ECG) is a\r\nbasic procedure for ECG processing and analysis. Several methods have been\r\nproposed and tested, featuring high accuracy and percentages of correct detection.\r\nNevertheless, their performance in noisy conditions remains an open problem.\r\nMethods: A new approach and algorithm for T-wave end location based on the\r\ncomputation of Trapezium�s areas is proposed and validated (in terms of accuracy\r\nand repeatability), using signals from the Physionet QT Database. The performance of\r\nthe proposed algorithm in noisy conditions has been tested and compared with one\r\nof the most used approaches for estimating the T-wave end point: the method\r\nbased on the threshold on the first derivative.\r\nResults: The results indicated that the proposed approach based on Trapezium�s\r\nareas outperformed the baseline method with respect to accuracy and repeatability.\r\nAlso, the proposed method is more robust to wideband noise.\r\nConclusions: The trapezium-based approach has a good performance in noisy\r\nconditions and does not rely on any empirical threshold. It is very adequate for use\r\nin scenarios where the levels of broadband noise are significant....
CD-ELISA uses the microfluidic ranking method and centrifugal force to control the testing solution as it flows into\r\nthe reaction region. The most challenging part of CD-ELISA is controlling the flow process for different biological\r\ntesting solutions, i.e. the controlling sequence for the microfluidic channel valves. The microfluidic channel valve is\r\ntherefore the most important fluid channel structure for CD-ELISA. In this study, we propose a valve design suitable\r\nfor a wide range rotational speeds which can be applied for mass production (molding). Together with supporting\r\nexperiments, simulation based on two-phase flow theory is used in this study, and the feasibility of this novel valve\r\ndesign is confirmed. Influencing design factors for the microfluidic channel valves in CD-ELISA are investigated,\r\nincluding various shapes of the arc, distance d, radius r, the location of the center of the circle, and the contact\r\nangle. From both the experimental results and the simulated results, it is evident that the narrowest channel width\r\nand the contact angle are the primary factors influencing valve burst frequency. These can be used as the main\r\ncontrolling factors during the design....
Background: The electroencephalography (EEG) signals are known to involve the\r\nfirings of neurons in the brain. The P300 wave is a high potential caused by an\r\nevent-related stimulus. The detection of P300s included in the measured EEG signals\r\nis widely investigated. The difficulties in detecting them are that they are mixed with\r\nother signals generated over a large brain area and their amplitudes are very small\r\ndue to the distance and resistivity differences in their transmittance.\r\nMethods: A novel real-time feature extraction method for detecting P300 waves by\r\ncombining an adaptive nonlinear principal component analysis (ANPCA) and a\r\nmultilayer neural network is proposed. The measured EEG signals are first filtered\r\nusing a sixth-order band-pass filter with cut-off frequencies of 1 Hz and 12 Hz. The\r\nproposed ANPCA scheme consists of four steps: pre-separation, whitening,\r\nseparation, and estimation. In the experiment, four different inter-stimulus intervals\r\n(ISIs) are utilized: 325 ms, 350 ms, 375 ms, and 400 ms.\r\nResults: The developed multi-stage principal component analysis method applied at\r\nthe pre-separation step has reduced the external noises and artifacts significantly.\r\nThe introduced adaptive law in the whitening step has made the subsequent\r\nalgorithm in the separation step to converge fast. The separation performance index\r\nhas varied from -20 dB to -33 dB due to randomness of source signals. The\r\nrobustness of the ANPCA against background noises has been evaluated by\r\ncomparing the separation performance indices of the ANPCA with four algorithms\r\n(NPCA, NSS-JD, JADE, and SOBI), in which the ANPCA algorithm demonstrated the\r\nshortest iteration time with performance index about 0.03. Upon this, it is asserted\r\nthat the ANPCA algorithm successfully separates mixed source signals.\r\nConclusions: The independent components produced from the observed data using\r\nthe proposed method illustrated that the extracted signals were clearly the P300\r\ncomponents elicited by task-related stimuli. The experiment using 350 ms ISI showed\r\nthe best performance. Since the proposed method does not use down-sampling and\r\naveraging, it can be used as a viable tool for real-time clinical applications....
Background: Parkinson�s disease (PD) is a neurodegenerative disorder resulting in\r\nmotor disturbances that can impact normal gait. Although PD initially responds well\r\nto pharmacological treatment, as the disease progresses efficacy often fluctuates over\r\nthe course of the day, and clinical management would benefit from long-term\r\nobjective measures of gait. We have previously described a small device worn on the\r\nshank that uses acceleration and angular velocity sensors to calculate stride length\r\nand identify freezing of gait in PD patients. In this study we extend validation of the\r\ngait monitor to 24-h using simultaneous video observation of PD patients.\r\nMethods: A sleep laboratory was adapted to perform 24-hr video monitoring of\r\npatients while wearing the device. Continuous video monitoring of a sleep lab,\r\nhallway, kitchen and conference room was performed using a 4-camera security\r\nsystem and recorded to hard disk. Subjects (3) wore the gait monitor on the left\r\nshank (just above the ankle) for a 24-h period beginning around 5 pm in the\r\nevening. Accuracy of stride length measures were assessed at the beginning and\r\nend of the 24-h epoch. Two independent observers rated the video logs to identify\r\nwhen subjects were walking or lying down.\r\nResults: The mean error in stride length at the start of recording was 0.05 m (SD 0)\r\nand at the conclusion of the 24 h epoch was 0.06 m (SD 0.026). There was full\r\nagreement between observer coding of the video logs and the output from the gait\r\nmonitor software; that is, for every video observation of the subject walking there\r\nwas a corresponding pulse in the monitor data that indicated gait.\r\nConclusions: The accuracy of ambulatory stride length measurement was\r\nmaintained over the 24-h period, and there was 100% agreement between the\r\nautonomous detection of locomotion by the gait monitor and video observation....
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