The optical resolution of various racemic compounds are evaluated by high-performance liquid chromatography (HPLC) using a new series of immobilized amylose derivatives as the chiral stationary phases (CSPs), which regioselectively carry two different substituents at 2-position and 3-, 6-positions. Each derivative exhibited its own characteristic recognition ability depending on the arrangement of side chains at different positions, indicating an attractive potential for the development of chiral drug industry. Some racemates can be efficiently separated on these derivatives as well as on the immobilized amylose tris(3,5-dimethylphenylcarbamate)s, which is commercially available as Chiralpak IA and one of the most powerful CSPs.
In recent years, the trend toward the nuclear family and the phenomenon of under-population in rural areas has increased the number of elder people who live alone. Therefore, elder people have to drive themselves to go shopping or to a hospital. However, the elderly person also has a tendency to display reduced abilities of judgment and cognition and, in severe cases, displays dementia. In this paper, the brief results of an international investigation of traffic accidents among elder people based on databases published by public institutions are discussed. The aging rate and the number of dementia patients increase with the average life expectancy when it is over 70 years. Currently, the number of traffic accidents among elder people is increasing. Policies preventing the renewal of driver licenses for elder people are implemented in several countries. However, communication with family and neighbors is effective in preventing elder people from being involved in traffic accidents while walking.
In this paper, a method is developed for anatomical structures segmentation based on CT head images. The segmented structure can be used for image-guided surgery navigations. In our method, intensity rescaling, region growing, fuzzy c-means and mathematical morphology are combined and used systematically. Due to the low contrast of the CT images, intensity rescaling is applied to the images to enhance the contrast. Region growing is used to extract the intracranial area from the enhanced images. Then, fuzzy c-means is adopted to segment the intracranial images of brain matter and cerebrospinal fluid (CSF). Mathematical morphology is used to correct the pre-calculated images and obtain accurate brain matter and CSF segmentations. The experiments show that the algorithm can obtain good brain matter and CSF segmentations from CT head images.
The effects of attention on audiovisual integration at an incongruent location was investigated using behavioral measures in humans. A stream of unimodal visual (V), unimodal auditory (A), spatially congruent bimodal audiovisual (AV_Con) and spatially incongruent bimodal audiovisual (AV_Inc) stimuli were randomly presented to the left or right side; subjects covertly attended to all stimuli on one side (left or right) and promptly responded to all target stimuli on that side. The results showed that responses to the bimodal target stimuli were faster and more accurate than those to unimodal visual or atuditory target stimuli when audiovisual stimuli were presented on spatially congruent location. When audiovisual stimuli were presented on spatially incongruent location, responses to visual stimuli that were accompanied by simultaneous task-relevant, but spatially incongruent auditory stimuli were facilitated. However responses to auditory stimuli that were accompanied by simultaneous task-relevant, but spatially incongruent visual stimuli were not facilitated. The results suggested that vision was high preferential processing when auditory and visual information were simultaneously presented on spatially incongruent location.
In this article, we introduced some topics relevant to neuromedical engineering and used several measurement methods. In the future, we will continue to develop new technologies and search for new ideas for diagnosing neuropathies.
The default-mode network (DMN), which is suggested to have important functions related to internal modes of cognition and increasingly implicated in brain disorders, has attracted much attention in the past few years. Effective connectivity, defined as the influence one neuronal system exerts over another, can provide deep understanding of directed influence between brain regions in the network from the view of functional integration. Granger causality analysis is one of the conventional approaches to explore the effective connectivity in brain imaging researches. In this study, we applied Granger causality analysis to resting-state functional Magnetic Resonance Imaging (fMRI) data from 12 young subjects to explore the effective connectivity pattern of the DMN. The results demonstrated that posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC) and inferior parietal cortex (IPC) were the only three regions had significant causal relationship with all other regions in more than 50% subjects and PCC was the only brain area influenced by all others while had no directed influence to others. The strong effective connectivity pattern demonstrated that PCC, MPFC and IPC were the three key regions and PCC was the convergence hub in the network. These results provide further understanding of physiological mechanism of DMN underlying internal modes of cognition.
Recently introduced in analyzing data from functional MRI (fMRI) and other neuroimaging techniques, Bayesian networks (BN) is a method to characterize effective connectivity patterns among multiple brain regions. So far, interests of using BN have been primarily on learning the connectivity pattern for each single group with well investigated computational algorithms. Examination of the connectivity pattern differences between groups, on the other hand, lacks rigorous statistical inference procedure. In this study, we propose using random permutation, a type of non-parametric statistical significance test in which a reference distribution is obtained by calculating all possible values of the test statistic under re-arrangements of the group labels on the observed data points, to infer whether the difference is significant. Two different approaches to perform the permutation test are introduced, compared to each other and both compared to the routinely used parametric t-test. Permutation approach 1 permutes the group labels first followed by learning BN pattern for each of the newly formed groups. Approach 2 learns BN pattern for each individual and connection parameters are then subjected to the group label permutations. Synthetic data generated under varying signal-to-noise ratios are used to investigate the performances of the proposed methods. Our results demonstrated that permutation approach 1 in detecting the effective connectivity pattern difference between two groups is superior to permutation approach 2 and to the common-sense two sample t-test.
Real time functional magnetic resonance imaging (rtfMRI) allows capturing and analyzing the image of brain activity instantly by measuring the blood oxygen level-dependent (BOLD) signal. Based on rtfMRI, it could provide on-line feedback of human subjects to learn self-regulation of physiological processes, which had shown significant and potential applications in many conducted researches. With the recent advances in computational power and algorithms, constructing rtfMRI system with high efficiency was available which had not been limited by time-consuming data analysis. In this study, we developed the rtfMRI system utilizing custom-made computer and integrated commercial on-line analysis software: Turbo-BrainVoyager as the critical part of the software system. We chose motor imagery task with real time feedback by instructing subjects to regulate his BOLD activity of supplement motor area (SMA) as confirmatory experiment. This online feedback results and the post processed activation results showed that the subject had a good ability of self-control of SMA. It demonstrated that the rtfMRI system could operate effectively.
In humans, In humans, functional imaging studies have demonstrated a homologue of the macaque motion complex, MT+ suggested to contain both middle temporal (MT) and medial superior temporal (MST), in the ascending limb of the inferior temporal sulcus. Two of the most well studied areas are MT and MST. Macaque area MST has been shown to have considerably larger receptive fields than area MT. The receptive fields of MT cells typically extend only a few degrees into the ipsilateral visual field, while area MST neurons have receptive fields that extend well into the ipsilateral visual field. However, for human most studies thus far have only concentrated on the center and/or peri-center of the visual field. We used functional magnetic resonance imaging (fMRI) distinguish putative human areas MST from MT+ by wide-view stimuli. Random dots stimuli placed in the four steps (0~8°, 8~16°, 16~32°, 32~64° eccentricity) field, produced a large cluster of functional activation in our subjects consistent with previous reports of human area MT. Wide-field random dots stimuli limited to the peripheral retina produced activation only in an anterior subsection of the MT+ complex, likely corresponding to putative MST [Fig. 1A, B]. We also investigated the retinotopy characteristic of MT+ [Fig. 1C, D]. The retinotopy stimulus was a 60° diameter circular aperture filled with white dots on a black background. At any given time, the dots within a 45° wedge of the aperture moved inward/outward from fixation as in the MT+ localizer stimulus.
Mosaic panorama technique is an active area of research. Image mosaic is brought for the limitation of camera visual angle, which is impossible to shoot a panorama image once. If a medical video sequence is recorded with a rotational camera, it is possible to reconstruct a panorama of the scene background. Generally, the significances of this synthetic panorama can be explained as follows: Firstly, increasing the angle of the optics system view, for example, the medical panorama enables an easy segmentation algorithm that detects the foreground objects based on the differences to this background image, such as the analysis of common injuries of sportsmen. Secondly, the panorama can be used for image examining, editing, analysis and understanding, for example, the panorama can be used together with the MPEG-4 sprite coding tools to transmit the background content of a medical video scene with a very low bit-rate, such as remote diagnosis. Thirdly, the virtual reality system can use the panorama to appear the virtual medical 3-D environment. So the research of such an efficient video mosaic panorama technique is necessary and possible. The technique employs the feature-based motion estimator to estimate the video motion model parameters. Thus, we can use the result to compose the panorama. After that, for the purpose of removing the foreground objects, we use the panorama estimation process. The test results showed that the proposed technique is efficient.
Microcantilever biosensors produce cantilever bending due to differential surface stress between upper and lower surfaces of the cantilever. The bending is associated with concentration of ligands and adsorbed ligand-receptor intermolecular forces. Sample volume sizes in clinical diagnostic applications are usually very minute requiring a highly sensitive microcantilever for disease detection. This paper investigates a number of parameters that influence the sensitivity of microcantilever biosensors. The parameters include length, thickness, shape, and material of the cantilever beam. Biosensors of varying parameters are modeled and simulated. The results show that increasing the length of the cantilever beam enhances its sensitivity. However, increasing the thickness of the cantilever beam reduces its sensitivity. In static analysis, the shape of the cantilever beam does not notably impact upon its sensitivity. Also, using materials with lower Young's modulus improves the sensitivity.
The technology of invasive Brain-Computer Interfaces (BCIs) has been developed in last decades, for its possibility to restore motor function of the disabilities. The main task of BCI system is to translate the cortical neural activities into commands of direct brain-controlled prosthetic devices. To study how cortical signals simultaneously recorded from primary motor cortex (M1) neurons were used for external devices control, invasive brain-machine interfaces in rat were investigated. In these experiments, rats were trained to control a relay to obtain water by pressing a lever over a pressure threshold. Microwire array was implanted in rat's motor cortex to record neural activities, and pressure was recorded by a pressure sensor. After spike detecting and sorting, totally 22-58 neurons were found in all 15 channels per rat (except the reference electrode in the array). To compute the firing rate of individual unit, the numbers of spikes in a time bin (Δt =100ms) were counted. Meanwhile, the pressure signal was also computed into bin size (Δt =100ms), by averaging pressure value in the bin period. After that some decoders were designed to make mapping between neural activities and pressure, such as Optimum Linear Estimation(OLE), Kalman filter (KF), and Kernel-Based Membership combined with Curve Fitting algorithm (KMCF). These decoders can be easily learned using a few minutes of training data and provides real-time estimates of hand position every 100ms given the firing rates of neurons in motor cortex. The performances of these decoders were evaluated by Correlation Coefficient (CC) between real and predictive pressure value. The results showed that the KMCF decoder performed best in these experiment, which had higher CC than others (CC=0.94). The higher CC indicated that the activity of motor cortex (M1) neurons can be used for detection of the corresponding movement states and estimation of continuous kinematic parameters. The rats could use their neural activities to directly control the relay and successfully get rewards after about one week of training. In further, with the development of the motor-related BCIs technology, BCIs will be possibly used to motor function restoration of paralytics and greatly rehabilitate their capacity of life. Furthermore, the experiment results provide insights into the nature of the neural coding of movement.
A hybrid 3D segmentation approach for extracting vascular structures of CTA (computed tomographic angiography) scans is presented. In order to remove large amount of granular noises which are commonly exist in CTA images, repetition median filters are employed to smooth the 3D data. Compared to the well performed Vessel Enhancing Diffusion method, the adopted method is more fast, robust, easy to operate, and reproducible. Then, the vascular structure is enhanced by sigmoid filter which converts the vascular intensity distribution to a new level. After that, 3D region growing method is selected to get raw segmentation results. Based on the obtained rough vasculatures, Geodesic Active Contour Level Set algorithm is adopted to refine the segmentation results for its good performance of solving segmentation leakage. Finally, the vascular surface is reconstructed by Marching Cubes and smoothed by Laplacian filter. Experiments show that the developed method can obtain good segmentation vasculature from CTA images.
In order to enhance the reliability of electroencephalogram (EEG) signal source analyses, utilizing EEG lead field matrices obtained by field analyses in custom-made real head models is effective technique. Custom-made models are usually constructed from voxel models derived from magnetic resonance (MR) images using a variety of image-processing techniques. We have improved one of the techniques that select threshold levels dividing signals and noises based on the EM (expectation-maximization) algorithm. This technique contributes to rapid and high-quality voxel model acquisition. We demonstrate the following operations: (a) voxel model construction, (b) lead field calculation, and (c) simulation of EEG electrode voltage measurement induced by an equivalent current dipole (ECD), which is set in the primary motor cortex by considering application of brain-machine interfaces. The proposed technique is compared with other techniques based on the voltage differences caused by the constructed model-shape differences.
The high dimensionality of microarray data, the expressions of thousands of genes in a much smaller number of samples, presents challenges that affect the applicability of the analytical results. In principle, it would be better to describe the data in terms of a small number of metagenes, derived as a result of matrix factorization, which could reduce noise while still capturing the essential features of the data. Our system represents a two-step procedure. Firstly, using a gradient-based matrix factorization (GMF) proposed in our previous study, we reduce a given microarray to a few metagenes. Secondly, we demonstrate the sensitivity of the system using a linear support vector machine (SVM). We conducted experiments in this paper on three real datasets. The standard leave-one-out (LOO) scheme was employed in order to evaluate the quality of the system. The evaluation with LOO misclassification rates (LMR) demonstrates that metagenes acquired as an outcome of our method can capture the important biological features of the data. In addition, we considered links between our model and the gene ontology GO taking into account the pathway records of the Kyoto Encyclopedia of Genes and Genomes (KEGG) extracted from candidate gene sets according to absolute value of their correlations with the metagenes. This knowledge may be particularly useful in order to improve interpretability of the results presented to biologists.
Monitoring of metabolic compounds, such as glucose and lactate, is extensively reported in literature, especially for clinical purposes. Instead, the application of such technologies for monitoring metabolites in cell cultures has not been explored. From one side, such devices can provide information to the current state-of-the-art of cell lines, particularly those which are not fully known, as stem and embryonic cells. On the other hand, those systems can pave the way to fully automation for growing cell cultures, when coupled with robots for feeding. Among different presented strategies to develop biosensors, carbon nanotubes exhibit great properties, particularly suitable for biosensing. In this work nanostructured electrodes by using multi-walled carbon nanotubes are presented for the detection of glucose and lactate. Firstly, some results from simulations are illustrated in order to foresee the behavior of carbon nanotubes depending on their orientation, when they are dispersed onto the electrode surface. Then, such developed biosensors are characterized in terms of sensitivity and detection limit, and are compared to previously published results. Finally, monitoring of a cell culture is performed and the behavior of metabolites is analyzed as biosensors validation.
Technology development has produced motion and position sensors small in size and capable of wireless communication, yet a suitable system for easy and reliable monitoring and interpretation of movement parameters responsive to the treatment for MS patients remains elusive. We designed a study with the following specific aims: 1) to identify core quantitative kinetic and kinematic gait variables that are responsive to temporary changes induced by the treatment; 2) to determine the association of clinical and gait laboratory measures with quantitative measures of motor function in the patient's home environment and quality of life measures. We present in this paper a preliminary investigation of identifying a few selected parameters that may capture the key parameters most responsive to the treatment. Our goal for this study is to develop a suitable monitoring device to track changes in gait performance on selected parameters to provide clinicians with objective and sensitive measures that can be easily obtained in clinic or at home. These objective assessments will assist the clinicians in making more effective treatment decisions and facilitate new treatment development.
The cerebral cortex is the main target of analysis in many functional magnetic resonance imaging (fMRI) studies; statistical analysis can be restricted to the subset of the voxels obtained after cortex segmentation. We used a event-related design and contrasted the cognitive processing of Chinese character and figure in left Brodmann areas 44 and 45, which constitute Broca's region. in Chinese-speaking individual. Participants carried out a visual judgments task on a list of randomly intermixed Chinese characters and figure. The fMRI data were mapped directly onto cortical surface models. Processing of Chinese characters and figure judgments activated a comparable network of brain regions. In Broca's area, the Chinese character judgment task is also similar but more activations than figure judgment task. The result confirms a popular view that this area plays a role in grapheme-to-phoneme conversion.
This paper investigates the effects of inhomogeneity and anisotropy on the EEG forward problem using spherical head models. The role of skull and brain anisotropy along with the effects of inhomogeneity in different layers of the spherical volume conductor have been quantified, numerically and analytically, using statistical modalities such as RDM, MAG and CC. The paper also investigates the effect of mesh density, selection of element type and role of adaptive mesh refinement on the accuracy and convergence of the finite element method.
This paper presents a novel hybrid approach based on clustering technique (CT) and least square support vector machine (LS-SVM) denoted as CT-LS-SVM for classifying two-class EEG signals. The study aims to extract representative features from the original EEG data through the CT method and then to classify two-class EEG signals by the LS-SVM using these features as inputs. In order to test the effectiveness of the proposed method, the experiment is carried out on an epileptic EEG data and a mental imagery tasks EEG data. The classification accuracy of the current method is compared to the previous reported methods of the literature. The proposed approach is found to achieve an average classification accuracy of 99.19% for the mental imagery tasks EEG data and 94.18% for the epileptic EEG data. Our results show the highest classification accuracy (99.90%) for healthy subjects with eyes open (Set A) and epileptic patients during seizure activity (Set E) from the epileptic EEG data among the reported algorithms. Thus, the findings of the current research demonstrate that the CT method is efficient for extracting features representing the EEG signals and the LS-SVM classifier has the inherent ability to solve a pattern recognition task for these features.
