Low-dose versus high-dose fish oil for pain reduction and function improvement in patients with knee osteoarthritis

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Numerical modelling of a dual electrolyte membraneless electrolytic cell for CO<font size=-1>₂</font> to fuel conversion

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A switchable pH-differential reactor with high reactivity and efficiency for CO<font size=-1>₂</font> utilization

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Recent development of VUV-based processes for air pollutant degradation

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Lumbar intervertebral disc allograft transplantation: long-term mobility and impact on the adjacent segments

Purpose: Fresh-frozen intervertebral disc (IVD) allograft transplantation has been successfully performed in the human cervical spine. Whether this non-fusion technology could truly decrease adjacent segment disease is still unknown. This study evaluated the long-term mobility of the IVD-transplanted segment and the impact on the adjacent spinal segments in a goat model. Methods: Twelve goats were used. IVD allograft transplantation was performed at lumbar L4/L5 in 5 goats; the other 7 goats were used as the untreated control (5) and for the supply of allografts (2). Post-operation lateral radiographs of the lumbar spine in the neutral, full-flexion and full-extension positions were taken at 1, 3, 6, 9 and 12 months. Disc height (DH) of the allograft and the adjacent levels was calculated and range of motion (ROM) was measured using the Cobb’s method. The anatomy of the adjacent discs was observed histologically. Results: DH of the transplanted segment was decreased significantly after 3 months but no further reduction was recorded until the final follow-up. No obvious alteration was seen in the ROM of the transplanted segment at different time points with the ROM at 12 months being comparable to that of the untreated control. The DH and ROM in the adjacent segments were well maintained during the whole observation period. At post-operative 12 months, the ROM of the adjacent levels was similar to that of the untreated control and the anatomical morphology was well preserved. Conclusions: Lumbar IVD allograft transplantation in goats could restore the segmental mobility and did not negatively affect the adjacent segments after 12 months.postprin

Single channel wireless EEG device for real-time fatigue level detection

© 2015 IEEE. Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real-time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth-enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments

Monetary reward and punishment to response inhibition modulate activation and synchronization within the inhibitory brain network

© 2018 Chikara, Chang, Lu, Lin, Lin and Ko. A reward or punishment can modulate motivation and emotions, which in turn affect cognitive processing. The present simultaneous functional magnetic resonance imaging-electroencephalography study examines neural mechanisms of response inhibition under the influence of a monetary reward or punishment by implementing a modified stop-signal task in a virtual battlefield scenario. The participants were instructed to play as snipers who open fire at a terrorist target but withhold shooting in the presence of a hostage. The participants performed the task under three different feedback conditions in counterbalanced order: a reward condition where each successfully withheld response added a bonus (i.e., positive feedback) to the startup credit, a punishment condition where each failure in stopping deduced a penalty (i.e., negative feedback), and a no-feedback condition where response outcome had no consequences and served as a control setting. Behaviorally both reward and punishment conditions led to significantly down-regulated inhibitory function in terms of the critical stop-signal delay. As for the neuroimaging results, increased activities were found for the no-feedback condition in regions previously reported to be associated with response inhibition, including the right inferior frontal gyrus and the pre-supplementary motor area. Moreover, higher activation of the lingual gyrus, posterior cingulate gyrus (PCG) and inferior parietal lobule were found in the reward condition, while stronger activation of the precuneus gyrus was found in the punishment condition. The positive feedback was also associated with stronger changes of delta, theta, and alpha synchronization in the PCG than were the negative or no-feedback conditions. These findings depicted the intertwining relationship between response inhibition and motivation networks

Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface

© 2005-2012 IEEE. Brain-computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery-based brain-computer interfaces are popular because they avoid unnecessary external stimuli. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain-computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain-computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain-computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain-computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain-computer interface commands

Optimization of fermentation medium for nisin production from Lactococcus lactis subsp. lactis using response surface methodology (RSM) combined with artificial neural network-genetic algorithm (ANN-GA)

Nisin is a bacteriocin approved in more than 50 countries as a safe natural food preservative. Response surface methodology (RSM) combined with artificial neural network-genetic algorithm (ANN-GA) was employed to optimize the fermentation medium for nisin production. Plackett-Burman design (PBD) was used for identifying the significant components in the fermentation medium. After that, the path of steepest ascent method (PSA) was employed to approach their optimal concentrations. Sequentially, Box-Behnken design experiments were implemented for further optimization. RSM combined with ANNGA were used for analysis of data. Specially, a RSM model was used for determining the individual effect and mutual interaction effect of tested variables on nisin titer (NT), an ANN model was used for NT prediction, and GA was employed to search for the optimum solutions based on the ANN model. As the optimal medium obtained by ANN-GA was located at the verge of the test region, a further Box- Behnken design based on the RSM statistical analysis results was implemented. ANN-GA was implemented using the further Box-Behnken design data to locate the optimum solution which was as follow (g/l): Glucose (GLU) 15.92, peptone (PEP) 30.57, yeast extraction powder (YEP) 39.07, NaCl 5.25, KH2PO4 10.00, and MgSO4·7H2O 0.20, with expected NT of 22216 IU/ml. The validation experiments with the optimum solution were implemented in triplicate and the average NT was 21423 IU/ml, which was 2.13 times higher than that without ANN-GA methods and 8.34 times higher than that without optimization.Key words: Response surface methodology, artificial neural network, genetic algorithm, nisin titer

Density functional theory studies of hydrogen bonding vibrations in sI gas hydrates

Abstract To analyze the vibrational modes of water and methane in structure I gas hydrates, we constructed a 178-atom supercell with two small cages of type 512 and six large cages of type 51262. We applied the density functional theory method to simulate the vibrational spectrum and normal modes of methane hydrates. In accord with our previous studies, we confirmed that two groups of hydrogen bond (H-bond) peaks (at around 291 and 210 cm−1) in the translational bands come from two kinds of intermolecular H-bond vibrational modes. This is the first investigation of H-bond vibrations in methane hydrates. The partial modes of CH4 were extracted. We found that the CH4 phonons in the translational region are below 180 cm−1 so that the influence of methane on the H-bond is insignificant. We proposed a new method to decompose gas hydrates via direct application of terahertz radiation to the H-bonds. Herein, we confirmed that CH4 molecules do not absorb this energy.</jats:p