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© 2018 Inter- and intra-subject variability pose a major challenge to decoding human brain activity in brain-computer interfaces (BCIs) based on non-invasive electroencephalogram (EEG). Conventionally, a time-consuming and laborious training procedure is performed on each new user to collect sufficient individualized data, hindering the applications of BCIs on monitoring brain states (e.g. drowsiness) in real-world settings. This study proposes applying hierarchical clustering to assess the inter- and intra-subject variability within a large-scale dataset of EEG collected in a simulated driving task, and validates the feasibility of transferring EEG-based drowsiness-detection models across subjects. A subject-transfer framework is thus developed for detecting drowsiness based on a large-scale model pool from other subjects and a small amount of alert baseline calibration data from a new user. The model pool ensures the availability of positive model transferring, whereas the alert baseline data serve as a selector of decoding models in the pool. Compared with the conventional within-subject approach, the proposed framework remarkably reduced the required calibration time for a new user by 90% (18.00 min–1.72 ± 0.36 min) without compromising performance (p = 0.0910) when sufficient existing data are available. These findings suggest a practical pathway toward plug-and-play drowsiness detection and can ignite numerous real-world BCI applications.

© 2001-2011 IEEE. Drowsy driving is one of the major causes that lead to fatal accidents worldwide. For the past two decades, many studies have explored the feasibility and practicality of drowsiness detection using electroencephalogram (EEG)-based brain-computer interface (BCI) systems. However, on the pathway of transitioning laboratory-oriented BCI into real-world environments, one chief challenge is to obtain high-quality EEG with convenience and long-term wearing comfort. Recently, acquiring EEG from non-hair-bearing (NHB) scalp areas has been proposed as an alternative solution to avoid many of the technical limitations resulted from the interference of hair between electrodes and the skin. Furthermore, our pilot study has shown that informative drowsiness-related EEG features are accessible from the NHB areas. This study extends the previous work to quantitatively evaluate the performance of drowsiness detection using cross-session validation with widely studied machine-learning classifiers. The offline results showed no significant difference between the accuracy of drowsiness detection using the NHB EEG and the whole-scalp EEG across all subjects ( ${p} = \textsf {0.31}$ ). The findings of this study demonstrate the efficacy and practicality of the NHB EEG for drowsiness detection and could catalyze explorations and developments of many other real-world BCI applications.

© 2001-2011 IEEE. Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain-computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications.

© 2016 World Scientific Publishing Company. Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual's internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant's EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha-and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject's cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.

© 2012 IEEE. This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

© 2015 IEEE. Recent advances in mobile electroencephalogram (EEG) acquisition based on dry electrodes have started moving Brain-Computer Interface (BCI) applications from well-controlled laboratory settings to real-world environments. However, the application mechanisms and high impedance of dry electrodes over the hair-covered areas remain challenging for everyday use of BCI. In addition, whole-scalp recordings are not always necessary or applicable due to various practical constrains. Therefore, alternative montages for EEG recordings to meet the everyday needs are in-demand. Inspired by our previous work on measuring non-hair-bearing steady state visual evoked potentials for BCI applications, this study explores the feasibility and efficacy of detecting cognitive lapses of participants based on EEG signals collected from the non-hair-bearing areas. Study results suggest that informative EEG features associated with lapses could be assessed from non-hair-bearing areas with comparable accuracy obtained from the whole-scalp EEG. The design principles, validation processes and promising findings reported in this study may enable and/or facilitate numerous BCI applications in real-world environments.

A number of patient-specific and leukemia-associated factors are related to the poor outcome in older patients with acute myeloid leukemia (AML). However, comprehensive studies regarding the impact of genetic alterations in this group of patients are limited. In this study, we compared relevant mutations in 21 genes between AML patients aged 60 years or older and those younger and exposed their prognostic implications. Compared with the younger patients, the elderly had significantly higher incidences of PTPN11, NPM1, RUNX1, ASXL1, TET2, DNMT3A and TP53 mutations but a lower frequency of WT1 mutations. The older patients more frequently harbored one or more adverse genetic alterations. Multivariate analysis showed that DNMT3A and TP53 mutations were independent poor prognostic factors among the elderly, while NPM1 mutation in the absence of FLT3/ITD was an independent favorable prognostic factor. Furthermore, the status of mutations could well stratify older patients with intermediate-risk cytogenetics into three risk groups. In conclusion, older AML patients showed distinct genetic alterations from the younger group. Integration of cytogenetics and molecular mutations can better risk-stratify older AML patients. Development of novel therapies is needed to improve the outcome of older patients with poor prognosis under current treatment modalities.

© 2015 Elsevier Inc. Studies on spatial navigation reliably demonstrate that the retrosplenial complex (RSC) plays a pivotal role for allocentric spatial information processing by transforming egocentric and allocentric spatial information into the respective other spatial reference frame (SRF). While more and more imaging studies investigate the role of the RSC in spatial tasks, high temporal resolution measures such as electroencephalography (EEG) are missing. To investigate the function of the RSC in spatial navigation with high temporal resolution we used EEG to analyze spectral perturbations during navigation based on allocentric and egocentric SRF. Participants performed a path integration task in a clearly structured virtual environment providing allothetic information. Continuous EEG recordings were decomposed by independent component analysis (ICA) with subsequent source reconstruction of independent time source series using equivalent dipole modeling. Time-frequency transformation was used to investigate reference frame-specific orientation processes during navigation as compared to a control condition with identical visual input but no orientation task. Our results demonstrate that navigation based on an egocentric reference frame recruited a network including the parietal, motor, and occipital cortices with dominant perturbations in the alpha band and theta modulation in frontal cortex. Allocentric navigation was accompanied by performance-related desynchronization of the 8-13. Hz frequency band and synchronization in the 12-14. Hz band in the RSC. The results support the claim that the retrosplenial complex is central to translating egocentric spatial information into allocentric reference frames. Modulations in different frequencies with different time courses in the RSC further provide first evidence of two distinct neural processes reflecting translation of spatial information based on distinct reference frames and the computation of heading changes.

This study investigated the effects of kinesthetic stimuli on brain activities during a sustained-attention task in an immersive driving simulator. Tonic and phasic brain responses on multiple timescales were analyzed using time-frequency analysis of electroencephalographic (EEG) sources identified by independent component analysis (ICA). Sorting EEG spectra with respect to reaction times (RT) to randomly introduced lane-departure events revealed distinct effects of kinesthetic stimuli on the brain under different performance levels. Experimental results indicated that EEG spectral dynamics highly correlated with performance lapses when driving involved kinesthetic feedback. Furthermore, in the realistic environment involving both visual and kinesthetic feedback, a transitive relationship of power spectra between optimal-, suboptimal-, and poor-performance groups was found predominately across most of the independent components. In contrast to the static environment with visual input only, kinesthetic feedback reduced theta-power augmentation in the central and frontal components when preparing for action and error monitoring, while strengthening alpha suppression in the central component while steering the wheel. In terms of behavior, subjects tended to have a short response time to process unexpected events with the assistance of kinesthesia, yet only when their performance was optimal. Decrease in attentional demand, facilitated by kinesthetic feedback, eventually significantly increased the reaction time in the suboptimal-performance state. Neurophysiological evidence of mutual relationships between behavioral performance and neurocognition in complex task paradigms and experimental environments, presented in this study, might elucidate our understanding of distributed brain dynamics, supporting natural human cognition and complex coordinated, multi-joint naturalistic behavior, and lead to improved understanding of brain-behavior relations in operating environments. © 2014 Elsevier Inc.

Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver's vigilance in real time. © 2014 IEEE.

This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK-type consequent of fuzzy rule is a linear combination of exogenous input variables. Given an initially empty the rule-base, all rules are generated with on-line type-2 fuzzy clustering. Instead of the time-consuming K-M iterative procedure, the design factors ql and qr are learned to adaptively adjust the upper and lower positions on the left and right limit outputs, using the parameter update rule based on a gradient descent algorithm. Simulation results demonstrate that our approach yields fewer test errors and less computational complexity than other type-2 FNNs. © 2012 IEEE.

This study proposes a new framework, independent component ensemble, to leverage the acquired knowledge into a truly automatic and on-line EEG-based brain-computer interfacing (BCI). The envisioned design includes: (1) independent source recover using independent component analysis (ICA) (2) automatic selection of the independent components of interest (ICi) associated with human behaviors; (3) multiple classifiers with a parallel constructing and processing structure; and (4) a simple fusion scheme to combine the decisions from multiple classifiers. Its implications in BCI are demonstrated through a sample application: cognitive-state monitoring of participants performing a realistic sustained-attention driving task. Empirical results showed the proposed ensemble design could provide an improvement of 7%∼15% in overall accuracy for the classification of the arousal state and the driving performance. In summary, constructing ICi-ensemble classifiers and combining their outputs demonstrates a practical option for ICA-based BCIs to reduce the risk of not obtaining any desired independent source or selecting an inadequate component. Most importantly, the ensemble design for integrating information across multiple brain areas creates potentials for developing more complicated BCIs for real world applications. © 2013 IEEE.

Objective. This study explores the neurophysiological changes, measured using an electroencephalogram (EEG), in response to an arousing warning signal delivered to drowsy drivers, and predicts the efficacy of the feedback based on changes in the EEG. Approach. Eleven healthy subjects participated in sustained-attention driving experiments. The driving task required participants to maintain their cruising position and compensate for randomly induced lane deviations using the steering wheel, while their EEG and driving performance were continuously monitored. The arousing warning signal was delivered to participants who experienced momentary behavioral lapses, failing to respond rapidly to lane-departure events (specifically the reaction time exceeded three times the alert reaction time). Main results. The results of our previous studies revealed that arousing feedback immediately reversed deteriorating driving performance, which was accompanied by concurrent EEG theta- and alpha-power suppression in the bilateral occipital areas. This study further proposes a feedback efficacy assessment system to accurately estimate the efficacy of arousing warning signals delivered to drowsy participants by monitoring the changes in their EEG power spectra immediately thereafter. The classification accuracy was up 77.8% for determining the need for triggering additional warning signals. Significance. The findings of this study, in conjunction with previous studies on EEG correlates of behavioral lapses, might lead to a practical closed-loop system to predict, monitor and rectify behavioral lapses of human operators in attention-critical settings. © 2013 IOP Publishing Ltd.

Motion sickness is a common experience for many people. Several previous researches indicated that motion sickness has a negative effect on driving performance and sometimes leads to serious traffic accidents because of a decline in a person's ability to maintain self-control. This safety issue has motivated us to find a way to prevent vehicle accidents. Our target was to determine a set of valid motion sickness indicators that would predict the occurrence of a person's motion sickness as soon as possible. A successful method for the early detection of motion sickness will help us to construct a cognitive monitoring system. Such a monitoring system can alert people before they become sick and prevent them from being distracted by various motion sickness symptoms while driving or riding in a car. In our past researches, we investigated the physiological changes that occur during the transition of a passenger's cognitive state using electroencephalography (EEG) power spectrum analysis, and we found that the EEG power responses in the left and right motors, parietal, lateral occipital, and occipital midline brain areas were more highly correlated to subjective sickness levels than other brain areas. In this paper, we propose the use of a self-organizing neural fuzzy inference network (SONFIN) to estimate a driver's/passenger's sickness level based on EEG features that have been extracted online from five motion sickness-related brain areas, while either in real or virtual vehicle environments. The results show that our proposed learning system is capable of extracting a set of valid motion sickness indicators that originated from EEG dynamics, and through SONFIN, a neuro-fuzzy prediction model, we successfully translated the set of motion sickness indicators into motion sickness levels. The overall performance of this proposed EEG-based learning system can achieve an average prediction accuracy of ∼ 82%. © 2013 IEEE.

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future. © 1964-2012 IEEE.

This study investigates the relationship between heart rate variability (HRV) and the level of motion sickness (MS) induced by simulated tunnel driving. The HRV indices, normalized low frequency (NLF, 0.04-0.15 Hz), normalized high frequency (NHF, 0.15-0.4 Hz), and LF/HF ratio were correlated with the subjectively and continuously rated MS levels of 20 participants. The experimental results showed that for 13 of the subjects, the MS levels positively correlated with the NLF and the LF/HF ratio and negatively correlated with the NHF. The remaining seven subjects had negative correlations between the MS levels and the NLF and the LF/HF ratio and a positive correlation between the MS levels and the NHF. To clarify this contradiction, this study also inspected the effects of subjects' self-adjustments on the correlations between the MS levels and the HRV indices and showed that the variations in the relationship might be attributed to the subjects' self-adjustments, which they used to relieve the discomfort of MS. © 2012 Elsevier B.V.

The specificity of the hemodynamic response function (HRF) is determined spatially by the vascular architecture and temporally by the evolution of hemodynamic changes. Here, we used functional photoacoustic microscopy (fPAM) to investigate single cerebral blood vessels of rats after left forepaw stimulation. In this system, we analyzed the spatiotemporal evolution of the HRFs of the total hemoglobin concentration (HbT), cerebral blood volume (CBV), and hemoglobin oxygen saturation (SO2). Changes in specific cerebral vessels corresponding to various electrical stimulation intensities and durations were bilaterally imaged with 36 × 65-μm2 spatial resolution. Stimulation intensities of 1, 2, 6, and 10 mA were applied for periods of 5 or 15 s. Our results show that the relative functional changes in HbT, CBV, and SO2 are highly dependent not only on the intensity of the stimulation, but also on its duration. Additionally, the duration of the stimulation has a strong influence on the spatiotemporal characteristics of the HRF as shorter stimuli elicit responses only in the local vasculature (smaller arterioles), whereas longer stimuli lead to greater vascular supply and drainage. This study suggests that the current fPAM system is reliable for studying relative cerebral hemodynamic changes, as well as for offering new insights into the dynamics of functional cerebral hemodynamic changes in small animals. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE).

This study investigates the independent modulators that mediate the power spectra of electrophysiological processes, measured by electroencephalogram (EEG), in a sustained-attention experiment. EEG and behavioral data were collected during 1-2. hour virtual-reality based driving experiments in which subjects were instructed to maintain their cruising position and compensate for randomly induced drift using the steering wheel. Independent component analysis (ICA) applied to 30-channel EEG data separated the recorded EEG signals into a sum of maximally temporally independent components (ICs) for each of 30 subjects. Logarithmic spectra of resultant IC activities were then decomposed by principal component analysis, followed by ICA, to find spectrally fixed and temporally independent modulators (IM). Across subjects, the spectral ICA consistently found four performance-related independent modulators: delta, delta-theta, alpha, and beta modulators that multiplicatively affected the spectra of spatially distinct IC processes when the participants experienced waves of alternating alertness and drowsiness during long-hour simulated driving. The activation of the delta-theta modulator increased monotonically as subjects' task performances decreased. Furthermore, the time courses of the theta-beta modulator were highly correlated with concurrent changes in driving errors across subjects (r = 0.77 ± 0.13). © 2012 Elsevier Inc.

This study investigates the temporal brain dynamics associated with haptic feedback in a visuomotor tracking task. Haptic feedback with deviation-related forces was used throughout tracking experiments in which subjects' behavioral responses and electroencephalogram (EEG) data were simultaneously measured. Independent component analysis was employed to decompose the acquired EEG signals into temporally independent time courses arising from distinct brain sources. Clustering analysis was used to extract independent components that were comparable across participants. The resultant independent brain processes were further analyzed via time-frequency analysis (event-related spectral perturbation) and event-related coherence (ERCOH) to contrast brain activity during tracking experiments with or without haptic feedback. Across subjects, in epochs with haptic feedback, components with equivalent dipoles in or near the right motor region exhibited greater alpha band power suppression. Components with equivalent dipoles in or near the left frontal, central, left motor, right motor, and parietal regions exhibited greater beta-band power suppression, while components with equivalent dipoles in or near the left frontal, left motor, and right motor regions showed greater gamma-band power suppression relative to non-haptic conditions. In contrast, the right occipital component cluster exhibited less beta-band power suppression in epochs with haptic feedback compared to non-haptic conditions. The results of ERCOH analysis of the six component clusters showed that there were significant increases in coherence between different brain networks in response to haptic feedback relative to the coherence observed when haptic feedback was not present. The results of this study provide novel insight into the effects of haptic feedback on the brain and may aid the development of new tools to facilitate the learning of motor skills. © 2012 Elsevier Inc..

The present study investigated the brain dynamics accompanying spatial navigation based on distinct reference frames. Participants preferentially using an allocentric or an egocentric reference frame navigated through virtual tunnels and reported their homing direction at the end of each trial based on their spatial representation of the passage. Task-related electroencephalographic (EEG) dynamics were analyzed based on independent component analysis (ICA) and subsequent clustering of independent components. Parietal alpha desynchronization during encoding of spatial information predicted homing performance for participants using an egocentric reference frame. In contrast, retrosplenial and occipital alpha desynchronization during retrieval covaried with homing performance of participants using an allocentric reference frame. These results support the assumption of distinct neural networks underlying the computation of distinct reference frames and reveal a direct relationship of alpha modulation in parietal and retrosplenial areas with encoding and retrieval of spatial information for homing behavior. © 2011 Society for Psychophysiological Research.

Most research based on Fitts' law define a log-linear relationship between temporal and spatial accuracy in goal-directed aiming tasks using stationary targets. Whether this relationship holds or not when the targets have varying velocities, and how the behavioral strategies and physical activities may change accordingly are of interest. The aim of this study was to investigate the relationship between temporal and spatial accuracy in goal-directed aiming tasks with moving targets. Participants were asked to aim at two target widths using a joystick. Results demonstrated that in a goal-directed aiming task there was a negative effect on performance when target velocity was increased or target width was decreased. Participants moved faster and then made more systematic errors in a high-velocity target condition. Results may be applicable to the complex perceptual-motor behavior of people who perform tasks using computers.© Perceptual and Motor Skills 2011.

A novel dry foam-based electrode for long-term EEG measurement was proposed in this study. In general, the conventional wet electrodes are most frequently used for EEG measurement. However, they require skin preparation and conduction gels to reduce the skinelectrode contact impedance. The aforementioned procedures when wet electrodes were used usually make trouble to users easily. In order to overcome the aforesaid issues, a novel dry foam electrode, fabricated by electrically conductive polymer foam covered by a conductive fabric, was proposed. By using conductive fabric, which provides partly polarizable electric characteristic, our dry foam electrode exhibits both polarization and conductivity, and can be used to measure biopotentials without skin preparation and conduction gel. In addition, the foam substrate of our dry electrode allows a high geometric conformity between the electrode and irregular scalp surface to maintain low skinelectrode interface impedance, even under motion. The experimental results presented that the dry foam electrode performs better for long-term EEG measurement, and is practicable for daily life applications. © 2006 IEEE.

Although the health benefits of vegetarian diets have been well documented among Western population, there are geographic differences of vegetarian diets and the health benefits of the Taiwanese vegetarian diet have not been studied extensively. In addition to conventional risk factors, homocysteine and high-sensitivity C-reactive protein (hs-CRP) levels have been found to predict first atherothrombotic events. We undertook this study to examine the total risk profile of Taiwanese vegetarians.A total of 198 healthy subjects (99 vegetarians and 99 omnivores) were recruited. Fasting blood samples were analyzed for glucose, cholesterol, triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), white blood cell count, hs-CRP and homocysteine.There was no significant difference in age, body mass index, blood glucose, white blood cell count, triglyceride and HDL-C between the two groups. The vegetarian group had significantly more females (65.7 vs 46.5%); lower body weight (58.66+/-11.13 vs 62.88+/-12.24 kg); shorter height (159.14+/-7.88 vs 162.53 +/-8.14 cm); lower total cholesterol (184.74+/-33.23 vs 202.01+/-41.05 mg/dl); and lower LDL-C (119.63+/-31.59 vs 135.89+/-39.50 mg/dl). Hs-CRP was significantly lower (0.14+/-0.23 vs 0.23+/-0.44 mg/dl, P=0.025), whereas homocysteine was significantly higher (10.97+/-6.69 vs 8.44+/-2.50 micromol/l, P=0.001) in vegetarians than omnivores.Taiwanese vegetarians have lower total cholesterol, LDL-C and hs-CRP levels, and higher homocysteine levels than omnivores. Owing to different predictive value of each risk factor, the Taiwanese vegetarians had a better cardiovascular risk profile than omnivores. Whether the Taiwanese vegetarian diet should be supplemented with vitamin B(12) to lower serum homocysteine level remains to be addressed.

Objectives: To determine the association between printing works and nasopharygeal carcinoma as well as other diseases. Methods: Demographic data were obtained for those who had worked in a particular newspaper company since its establishment in 1950. Through access to the data bank of the hospital records of the Labor Insurance Bureau for 1985‐94, all workers were identified who had been admitted to hospital during their employment in the newspaper company. Multiple logistic regressions were performed to estimate the adjusted morbidity odds ratio (OR) for various diseases among the printing workers with cardiovascular diseases as the reference diseases. Biopsy specimens from patients with nasopharyngeal carcinoma were all subjected to in situ hybridisation of Epstein-Barr virus (EBV), and colocalisation of EBV and secretor component protein. Results: Of the 1564 people who had worked in this company, 579 of them were admitted to hospital at least once. Five out of 144 printing workers admitted to hospital were diagnosed with nasopharyngeal carcinoma compared with none of the other 435 non-printing workers admitted to hospital. The morbidity OR for nasopharyngeal carcinoma in printing workers was 57.0 (95% confidence interval (95% CI) 2.8 to 1155.3). The morbidity OR for benign skin tumours was 28.0 (95% CI 2.7 to 293.1). Chronic pharyngitis or sinusitis also showed significant relations with printing works with a morbidity OR 29.4 (95% CI 1.7 to 514.7). Using all other diseases as the reference diseases for calculation of morbidity ORs still showed a similar trend. In situ hybridisation of EBV encoded small nuclear RNA-1 (EBER-1) showed tumour cells free of the EBV in each biopsy specimen. Colocalisation of EBER-1 and secretor component showed that some tumour cells contained both secretor component and EBV signal in each case. Conclusion: Printing works are associated with an increased risk of nasopharyngeal carcinoma, benign skin tumours, chronic pharyngitis or sinusitis, chronic liver diseases, and mechanical injuries. Induction of the development of nasopharyngeal carcinoma is probably not related to EBV infection in these patients.

Chyluria is invariably associated with lymphatic abnormalities. As part of our study, 15 patients who manifested chyluria were examined by means of lymphography.In Table 1 are summarized the roentgenographic changes found on the lymphograms of these 15 patients and 17 others which were reported in the English literature. There was a moderate to marked increase in number, size and diameter of pelvic and retroperitoneal lymph vessels. The vessels were tortuous. When chyluria was associated with elephantiasis, similar appearing vessels could be found in the swollen extremities and/or genitalia. Many abnormal communications between the retroperitoneal and renal lymph vessels exist. Intrarenally, these vessels follow the collecting system and not infrequently a fine halo of lymph vessels can be seen surrounding the minor calyces. An important, fairly consistent finding was the marked decrease in the number of visualized lymph nodes in the pelvis and retroperitoneum. These lymph nodes fail to visualize because ...

The purpose of this study is to examine brain activities of participants solving mental math problems. The research investigated how problem difficulty affected the subjects' responses and electroencephalogram (EEG) in different brain regions. In general, it was found that solution latencies (SL) to the math problems increased with difficulty. The EEG results showed that across subjects, the right-central beta, left-parietal theta, left-occipital theta and alpha, right-parietal alpha and beta, medial-frontal beta and medial central theta power decreased as task difficulty increased. This study further explored the effects of problem-solving performance on the EEG. Slow solvers exhibited greater frontal theta activities in the right hemisphere, whereas an inverse pattern of hemispheric asymmetry was found in fast solvers. Furthermore, analyses of spatio-temporal brain dynamics during problem solving show progressively stronger alpha- and beta-power suppression and theta-power augmentation as subjects were reaching a solution. These findings provide a better understanding of cortical activities mediating math-based problem solving and knowledge acquisition that can ultimately benefit math learning and education. © 2012 IEEE.