A prediction approach for multichannel EEG signals modeling using local wavelet SVM

Accurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal. © 2006 IEEE.published_or_final_versio

Synchrony of clinical and laboratory surveillance for influenza in Hong Kong

Background: Consultation rates of influenza-like illness (ILI) in an outpatient setting have been regarded as a good indicator of influenza virus activity in the community. As ILI-like symptoms may be caused by etiologies other than influenza, and influenza virus activity in the tropics and subtropics is less predictable than in temperate regions, the correlation between of ILI and influenza virus activity in tropical and subtropical regions is less well defined. Methodology and Principal Findings: In this study, we used wavelet analysis to investigate the relationship between seasonality of influenza virus activity and consultation rates of ILI reported separately by General Out-patient Clinics (GOPC) and General Practitioners (GP). During the periods 1998-2000 and 2002-2003, influenza virus activity exhibited both annual and semiannual cycles, with one peak in the winter and another in late spring or early summer. But during 2001 and 2004-2006, only annual cycles could be clearly identified. ILI consultation rates in both GOPC and GP settings share a similar non-stationary seasonal pattern. We found high coherence between ILI in GOPC and influenza virus activity for the annual cycle but this was only significant (P<0.05) during the periods 1998-1999 and 2002-2006. For the semiannual cycle high coherence (p<0.05) was also found significant during the period 1998-1999 and year 2003 when two peaks of influenza were evident. Similarly, ILI in GP setting is also associated with influenza virus activity for both the annual and semiannual cycles. On average, oscillation of ILI in GP and of ILI in GOPC preceded influenza virus isolation by approximately four and two weeks, respectively. Conclusions: Our findings suggest that consultation rates of ILI precede the oscillations of laboratory surveillance by at least two weeks and can be used as a predictor for influenza epidemics in Hong Kong. The validity of our model for other tropical regions needs to be explored. © 2008 Yang et al.published_or_final_versio

Smoking cessation and carotid atherosclerosis: The guangzhou biobank cohort studydCVD

Introduction Smoking has been shown to be associated with carotid atherosclerosis in cross-sectional and prospective studies in Western populations. However, few studies have examined the reversal of risk resulting from quitting smoking, and the results are conflicting. Methods 959 men aged 50e85 years were randomly selected from phase III (2006e2007) of the Guangzhou Biobank Cohort Study into this cross-sectional study. Common carotid artery intima-media thickness (CCAIMT) was measured by B-mode ultrasonography, and carotid artery plaques were identified. Major cardiovascular risk factors, including fasting triglyceride, low-density and high-density lipoprotein (LDL and HDL) cholesterol and glucose, and systolic and diastolic blood pressure, were assessed. Results CCA-IMT and the number of carotid plaque increased from never to former to current smokers (both p≤0.001). Among former smokers compared to current smokers, after adjustment for cigarette pack-years and other potential confounders, the adjusted ORs (95% CI) for quitting for 1-9, 10-19 and 20+ years were 0.77 (0.47 to 1.26), 0.45 (0.26 to 0.79) and 0.37 (0.17 to 0.77) for the presence of CCA atherosclerosis, and 0.69 (0.43 to 1.12), 0.47 (0.27 to 0.82) and 0.45 (0.23 to 0.96) for the presence of carotid plaques, respectively. Longer duration of quitting smoking was also significantly associated with decreasing risk of the severity of CCA atherosclerosis and carotid plaques (all p≤0.001). Conclusion Smoking cessation was beneficial in attenuating the risk of carotid atherosclerosis associated with cigarette smoking. The short duration of cessation in earlier studies is a likely explanation for the inconsistent results.published_or_final_versio

Constraints on a special running vacuum model

Funder: Physics Division, National Center for Theoretical Sciences; doi: http://dx.doi.org/10.13039/100009638AbstractWe study a special running vacuum model (RVM) with $$\Lambda = 3 \alpha H^2+3\beta H_0^4 H^{-2}+\Lambda _0$$Λ=3αH2+3βH04H-2+Λ0, where $$\alpha$$α, $$\beta$$β and $$\Lambda _0$$Λ0 are the model parameters and H is the Hubble one. This RVM has non-analytic background solutions for the energy densities of matter and radiation, which can only be evaluated numerically. From the analysis of the CMB power spectrum and baryon acoustic oscillation along with the prior of \alpha >0α&gt;0 to avoid having a negative dark energy density, we find that \alpha <2.83\times 10^{-4}α&lt;2.83×10-4 and $$\beta =(-0.2^{+3.9}_{-4.5})\times 10^{-4}$$β=(-0.2-4.5+3.9)×10-4 (95$$\%$$% C.L.). We show that the RVM fits the cosmological data comparably to the $$\Lambda \hbox {CDM}$$ΛCDM. In addition, we relate the fluctuation amplitude $$\sigma _8$$σ8 to the neutrino mass sum $$\Sigma m_\nu$$Σmν.</jats:p

Effect of alcohol and aldehyde dehydrogenase gene polymorphisms on alcohol-associated hypertension: the Guangzhou Biobank Cohort Study

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Legislative behaviour absent re‐election incentives: findings from a natural experiment in the Arkansas Senate

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141545/1/rssa12293.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141545/2/rssa12293-sup-0001-SupInfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141545/3/rssa12293_am.pd

Is exercise protective against influenza-associated mortality?

Background: Little is known about the effect of physical exercise on influenza-associated mortality. Methods and Findings: We collected information about exercise habits and other lifestyle, and socioeconomic and demographic status, the underlying cause of death of 24,656 adults (21% aged 30-64, 79% aged 65 or above who died in 1998 in Hong Kong, and the weekly proportion of specimens positive for influenza A (H3N1 and H1N1) and B isolation during the same period. We assessed the excess risks (ER) of influenza-associated mortality due to all-natural causes, cardiovascular diseases, or respiratory disease among different levels of exercise: never/seldom (less than once per month), low/moderate (once per month to three times per week), and frequent (four times or more per week) by Poisson regression. We also assessed the differences in ER between exercise groups by case-only logistic regression. For all the mortality outcomes under study in relation to each 10% increase in weekly proportion of specimens positive for influenza A+B, never/seldom exercise (as reference) was associated with 5.8% to 8.5% excess risks (ER) of mortality (P<0.0001), while low/moderate exercise was associated with ER which were 4.2% to 6.4% lower than those of the reference (P<0.001 for all-natural causes; P=0.001 for cardiovascular; and P=0.07 for respiratory mortality). Frequent exercise was not different from the reference (change in ER -0.8% to 1.7%, P=0.30 to 0.73). Conclusion: When compared with never or seldom exercise, exercising at low to moderate frequency is beneficial with lower influenza-associated mortality. © 2008 Wong et al.published_or_final_versio

Development of Building Thermal Load and Discomfort Degree Hour Prediction Models Using Data Mining Approaches

Thermal load and indoor comfort level are two important building performance indicators, rapid predictions of which can help significantly reduce the computation time during design optimization. In this paper, a three-step approach is used to develop and evaluate prediction models. Firstly, the Latin Hypercube Sampling Method (LHSM) is used to generate a representative 19-dimensional design database and DesignBuilder is then used to obtain the thermal load and discomfort degree hours through simulation. Secondly, samples from the database are used to develop and validate seven prediction models, using data mining approaches including multilinear regression (MLR), chi-square automatic interaction detector (CHAID), exhaustive CHAID (ECHAID), back-propagation neural network (BPNN), radial basis function network (RBFN), classification and regression trees (CART), and support vector machines (SVM). It is found that the MLR and BPNN models outperform the others in the prediction of thermal load with average absolute error of less than 1.19%, and the BPNN model is the best at predicting discomfort degree hour with 0.62% average absolute error. Finally, two hybrid models&mdash;MLR (MLR + BPNN) and MLR-BPNN&mdash;are developed. The MLR-BPNN models are found to be the best prediction models, with average absolute error of 0.82% in thermal load and 0.59% in discomfort degree hour