Efficacy of Functional Magnetic Stimulation in Neurogenic Bowel Dysfunction after Spinal Cord Injury

[[abstract]]Objective: The aims of this study were to assess the usefulness of functional magnetic stimulation in controlling neurogenic bowel dysfunction in spinal cord injured patients with supraconal and conal/caudal lesions, and to investigate the efficacy of this regimen with a 3-month follow-up. Design: A longitudinal, prospective before-after trial. Subjects: A total of 22 patients with chronic spinal cord injured and intractable neurogenic bowel dysfunction. They were divided into group 1 (supraconal lesion) and group 2 (conal/caudal lesion). Methods: The colonic transit time assessment and Knowles-Eccersley-Scott Symptom Questionnaire were carried out for each patient before they received a 3-week functional magnetic stimulation protocol and on the day following the treatment. Results and conclusion: Following functional magnetic stimulation, the mean colonic transit time for all patients decreased from 62.6 to 50.4 h (p < 0.001). The patients’ Knowles-Eccersley-Scott Symptom scores decreased from 24.5 to 19.2 points (p < 0.001). The colonic transit time decrement in both group 1 (p = 0.003) and group 2 (p = 0.043) showed significant differences, as did the Knowles-Eccersley-Scott Symptom score in both groups following stimulation and in the 3-month follow-up results (p < 0.01). The improvements in bowel function indicate that functional magnetic stimulation, featuring broad-spectrum application, can be incorporated successfully into other therapies as an optimal adjuvant treatment for neurogenic bowel dysfunction resulting from spinal cord injury.[[journaltype]]國外[[incitationindex]]SCI[[booktype]]紙本[[countrycodes]]SW

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

An EEG-Based Fatigue Detection and Mitigation System

© 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

The prevalence of giant cell arteritis and polymyalgia rheumatica in a UK primary care population

Background: To update community-based prevalence values for Polymyalgia Rheumatic (PMR) and Giant Cell Arteritis (GCA) using case record review supplemented by population survey and subsequent clinical review. Methods: Clinical data were obtained from case records of a large primary care practice in Norfolk, UK and reviewed for diagnoses of GCA and PMR. In addition postal survey was carried out to capture potentially undiagnosed cases within the practice population. Those screening positive for potential diagnoses of GCA and PMR were invited for clinical review. A cumulative prevalence estimate was subsequently calculated on those diagnosed within the GP practice and subsequently on those fulfilling the various published classification criteria sets. The date of the database lock and mail merge was March 2013. Results: Through detailed systematic review of 5,159 GP case records, 21 patients had a recorded diagnosis of GCA and 117 had PMR . No new cases were identified among 2,227 completed questionnaires returned from the population survey of a sample of 4,728. The resulting cumulative prevalence estimate in those aged ≥55 years meeting the ACR classification criteria set for GCA was 0.25% (95% CI 0.11% to 0.39%) and for five published criteria sets for PMR ranged from 0.91% to 1.53% (95% CI ranges 0.65%, 1.87%). The prevalence of both conditions was higher in women than in men and in older age groups. Conclusion: This study provides the first UK prevalence estimate of GCA and PMR in over 30 years and is the first to apply classification criteria sets

Fuzzy Integral with Particle Swarm Optimization for a Motor-Imagery-Based Brain-Computer Interface

© 2016 IEEE. A brain-computer interface (BCI) system using electroencephalography signals provides a convenient means of communication between the human brain and a computer. Motor imagery (MI), in which motor actions are mentally rehearsed without engaging in actual physical execution, has been widely used as a major BCI approach. One robust algorithm that can successfully cope with the individual differences in MI-related rhythmic patterns is to create diverse ensemble classifiers using the subband common spatial pattern (SBCSP) method. To aggregate outputs of ensemble members, this study uses fuzzy integral with particle swarm optimization (PSO), which can regulate subject-specific parameters for the assignment of optimal confidence levels for classifiers. The proposed system combining SBCSP, fuzzy integral, and PSO exhibits robust performance for offline single-trial classification of MI and real-time control of a robotic arm using MI. This paper represents the first attempt to utilize fuzzy fusion technique to attack the individual differences problem of MI applications in real-world noisy environments. The results of this study demonstrate the practical feasibility of implementing the proposed method for real-world applications

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

Bullous pemphigoid and comorbidities: a case-control study in Portuguese patients

BACKGROUND: Although rare, bullous pemphigoid (BP) is the most common autoimmune blistering disease. Recent studies have shown that patients with bullous pemphigoid are more likely to have neurological and psychiatric diseases, particularly prior to the diagnosis of bullous pemphigoid. OBJECTIVE: The aims were: (i) to evaluate the demographic and clinical features of bullous pemphigoid from a database of patients at a Portuguese university hospital and (ii) to compare the prevalence of comorbid conditions before the diagnosis of bullous pemphigoid with a control group. METHODS: Seventy-seven patients with bullous pemphigoid were enrolled in the study. They were compared with 176 age- and gender-matched controls, which also had the same inpatient to outpatient ratio, but no history of bullous or cutaneous malignant disease. Univariate and multivariate analyses were used to calculate odds ratios for specific comorbid diseases. RESULTS: At least one neurologic diagnosis was present in 55.8% of BP patients compared with 20.5% controls (p<0.001). Comparing cases to controls, stroke was seen in 35.1 vs. 6.8%, OR 8.10 (3.80-17.25); dementia in 37.7 vs. 11.9%, OR 5.25 (2.71-10.16); and Parkinson's disease in 5.2 vs. 1.1%, OR 4.91 (0.88-27.44). Using multivariate analysis, all diseases except Parkinson's retained their association with BP. Patients under systemic treatment were eight times more likely to have complications than those treated with topical steroids (p< 0.017). CONCLUSIONS: The results of this study substantiate the association between BP and neurological diseases. In addition, they highlight the potential complications associated with the treatment of BP

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Synthetic biology: Understanding biological design from synthetic circuits

An important aim of synthetic biology is to uncover the design principles of natural biological systems through the rational design of gene and protein circuits. Here, we highlight how the process of engineering biological systems — from synthetic promoters to the control of cell–cell interactions — has contributed to our understanding of how endogenous systems are put together and function. Synthetic biological devices allow us to grasp intuitively the ranges of behaviour generated by simple biological circuits, such as linear cascades and interlocking feedback loops, as well as to exert control over natural processes, such as gene expression and population dynamics