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Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain–computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain–computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III–IV(A) and BCI competition IV–I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques. [ABSTRACT FROM AUTHOR]

Background Understanding the burden of influenza is necessary to optimize recommendations for influenza vaccination. We describe the epidemiology of severe influenza in 50- to 64-year-old residents of metropolitan Toronto and Peel region, Canada, over 7 influenza seasons. Methods Prospective population-based surveillance for hospitalization associated with laboratory-confirmed influenza was conducted from September 2010 to August 2017. Conditions increasing risk of influenza complications were as defined by Canada's National Advisory Committee on Immunization. Age-specific prevalence of medical conditions was estimated using Ontario health administrative data. Population rates were estimated using Statistics Canada data. Results Over 7 seasons, 1228 hospitalizations occurred in patients aged 50–64 years: 40% due to A(H3N2), 30% A(H1N1), and 22% influenza B. The average annual hospitalization rate was 15.6, 20.9, and 33.2 per 100 000 in patients aged 50–54, 55–59, and 60–64 years, respectively; average annual mortality was 0.9/100 000. Overall, 33% of patients had received current season influenza vaccine; 963 (86%) had ≥1 underlying condition increasing influenza complication risk. The most common underlying medical conditions were chronic lung disease (38%) and diabetes mellitus (31%); 25% of patients were immunocompromised. The average annual hospitalization rate was 6.1/100 000 in those without and 41/100 000 in those with any underlying condition, and highest in those with renal disease or immunocompromise (138 and 281 per 100 000, respectively). The case fatality rate in hospitalized patients was 4.4%; median length of stay was 4 days (interquartile range, 2–8 days). Conclusions The burden of severe influenza in 50- to 64-year-olds remains significant despite our universal publicly funded vaccination program. These data may assist in improving estimates of the cost-effectiveness of new strategies to reduce this burden. [ABSTRACT FROM AUTHOR]

This study investigates the interaction of natural organic matter with iron oxide (goethite) on chlorine decay, disinfection by-product (DBP) formation, and DBP compound speciation [total trihalomethanes (TTHM4) and haloacetic acids (HAA5)]. Batch experiments were conducted with goethite, multiple finished drinking waters, variable chlorine dose, and fixed pH 8. The overall objective was to assess natural organic matter (NOM) adsorption onto goethite and its effect on chlorine decay and DBP formation. Chlorine consumption always increased in the presence of goethite and is attributed to an increase in the reactivity and/or modification of adsorbed NOM. Adsorbed NOM also led to an overall increase in TTHM4, however, HAA5 formation was suppressed during the first 2 h. Chloroform was identified as the increasing species and dichloracetic acid was identified as the suppressed species. This study clearly shows that goethite, which is the predominant iron oxide of pipe deposits, alters both chlorine decay and DBP formation and should be considered when assessing water treatment plant operations and DBP monitoring site selection. CE Database subject headings: Halogen organic compounds; Water distribution systems; Disinfection; Chlorine; Iron.

The purpose of this study is to develop a kinetic model that links [0.sub.3] decomposition reactions from the TFG ozone decay model with recognized [Br.sup.-] oxidation reactions, secondary * OH reactions, and [H.sub.2][0.sub.2] reactions in order to improve [0.sub.3] decay and bromate formation prediction capabilities under multiple water quality conditions. The model was compared with experimentally measured ozone decomposition and final bromate concentration data sets provided by two researchers. The data sets included varying pH (6.5-8.5), initial hydrogen peroxide (0-1 mM), and initial bromide concentration (0.1-1 mM). Model verification was carried out by sensitivity analysis of the rate constants and then optimization of the most sensitive rate constants using the method of least squares. Model predicted ozone decay data was analyzed and compared with measured ozone decay data using R-squared statistic for linear regression model. The model predicted final bromate concentration is analyzed by comparing it with the residual [DELTA](%) between experimental and model results. The TFG model was effectively tested for multiple data sets and it was found that model prediction was a success both for ozone decay (regression coefficients >0.95 for all experimental conditions but one) and bromate prediction with residual of less than 100% for all experimental conditions except low peroxide dose ( CE Database subject headings: Ozonization; Models; Water treatment.

Objectives: The objective of this study was to assess the incidence of hypoglycemia in patients with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) in Bangladeshi cohort of the International Operations-Hypoglycemia Assessment Tool study. Materials and Methods: Patients diagnosed with either T1DM or T2DM, aged ≥18 years, treated with insulin (any regimen) for >12 months, and completed self-assessment questionnaires (SAQs) to record demography, treatment information, and hypoglycemia during the 6-month retrospective and 4-week prospective periods (a total of 7 months) were enrolled in the study. Results: A total of 1179 patients were enrolled and completed the SAQ1 (T1DM, n = 25; T2DM, n = 1154). Almost all patients (T1DM: 100.0% [95% confidence interval (CI): 86.3%, 100.0%] and T2DM: 97.0% [95% CI: 95.9%, 97.9%]) experienced at least 1 hypoglycemic event prospectively. The estimated rates of any and severe hypoglycemia were 26.6 (95% CI: 19.8, 35.0) and 14.1 (95% CI: 9.3, 20.4) events per patient-per year (PPY), respectively, for patients with T1DM and 18.3 (95% CI: 17.4, 19.2) and 12.1 (95% CI: 11.4, 12.9) events PPY, respectively, for patients with T2DM during the prospective period. At baseline, mean glycated hemoglobin (HbA1c) (±standard deviation) was 8.1 (±1.8%) for T1DM and 8.8 (±1.8%) for T2DM. Hypoglycemic rate was independent of HbA1c levels and types of insulin. Conclusions: This is the first patient dataset of self-reported hypoglycemia in Bangladesh; results confirm that hypoglycemia is underreported. [ABSTRACT FROM AUTHOR]

Background Marjolin's ulcer (MU) is a rare malignancy arising from various forms of scars. This potentially fatal complication typically occurs after a certain latent period. This article attempts to reveal the importance of the latency period in the prevention and early treatment of the malignancy. Methods A retrospective review of 17 MU patients who underwent surgical procedures between June of 2005 and December 2011 was conducted. Etiology of injuries, latency period, repeated ulceration, and outcomes were recorded. This observational report reveals characteristics of patients who develop MU. Results An incidence of 0.7% of MU was found amongst patients complaining of existing scars in our study; burns and trauma were the most common etiology of MU. The mean latency period was 29 years (SD = 19) and the mean post-ulceration period was 7 years (SD = 9). Statistical analysis revealed a negative correlation between the age of patients at injury and the length of latency period (r = -0.8, P <0.01), as well as the lengths of pre-ulceration and post-ulceration periods (r = -0.7, P <0.01). Conclusions Patients experience different lengths of pre- and post-ulceration periods during the latency period. Younger patients tend to have a longer latency period. Skin breakdown on chronic scars and chronic unhealed ulcers are two main sources of MU. MU may be preventable with a close surveillance of the ulcer during the latency period. [ABSTRACT FROM AUTHOR]

Background: The demand for inpatient medical services increases during influenza season. A scoring system capable of identifying influenza patients at low risk death or ICU admission could help clinicians make hospital admission decisions. Methods: Hospitalized patients with laboratory confirmed influenza were identified over 3 influenza seasons at 25 Ontario hospitals. Each patient was assigned a score for 6 pneumonia severity and 2 sepsis scores using the first data available following their registration in the emergency room. In-hospital mortality and ICU admission were the outcomes. Score performance was assessed using the area under the receiver operating characteristic curve (AUC) and the sensitivity and specificity for identifying low risk patients (risk of outcome <5%). Results: The cohort consisted of 607 adult patients. Mean age was 76 years, 12% of patients died (71/607) and 9% required ICU care (55/607). None of the scores examined demonstrated good discriminatory ability (AUC≥0.80). The Pneumonia Severity Index (AUC 0.78, 95% CI 0.72-0.83) and the Mortality in Emergency Department Sepsis score (AUC 0.77, 95% 0.71-0.83) demonstrated fair predictive ability (AUC≥0.70) for in-hospital mortality. The best predictor of ICU admission was SMART-COP (AUC 0.73, 95% CI 0.67-0.79). All other scores were poor predictors (AUC <0.70) of either outcome. If patients classified as low risk for in-hospital mortality using the PSI were discharged, 35% of admissions would have been avoided. Conclusions: None of the scores studied were good predictors of in-hospital mortality or ICU admission. The PSI and MEDS score were fair predictors of death and if these results are validated, their use could reduce influenza admission rates significantly. [ABSTRACT FROM AUTHOR]