How important is personality in neurological patient' quality of life perception?

In the last decades, the value of research on neurological patients’ quality of life (QOL) has become unquestionable. In this context, most studies focus on the relationship between patients’ QOL and their sociodemographic and/or clinical and/or modifiable psychosocial characteristics. They give us information regarding the sociodemographic and clinical profile most prone to low QOL reports and also on ways to improve patients’ QOL (e.g., targeting their selfesteem). Nevertheless, little is known about the role nonmodifiable psychosocial variables can have on patients’ QOL perception. Consequently, the aim of the present study is to explore the relationship between QOL and personality in neurological patients.

Theoretical Analysis of Multimodal Four-Wave Mixing in Optical Microwires

Optical fiber microwires (OFMs) are nonlinear optical waveguides that support several spatial modes. The multimodal generalized nonlinear Schrodinger equation (MM-GNLSE) is deduced taking into account the linear and nonlinear modal coupling. A detailed theoretical description of four-wave mixing (FWM) considering the modal coupling is developed. Both, the intramode and the intermode phase-matching conditions is calculated for an optical microwire in a strong guiding regime. Finally, the FWM dynamics is studied and the amplitude evolution of the pump beams, the signal and the idler are analyzed.

Implications of the Intervention Program for Physical Activity (IPPA) in the perception of illness and wellbeing in people with Multiple Sclerosis (MS)

The aim of this study is to examine the implications of the IPPA in the perception of illness and wellbeing in MS patients. Methods - This is a quasi experimental study non-randomized study with 24 MS patients diagnosed at least 1 year before, and with an EDSS score of under 7. We used the IPPA in 3 groups of eight people in 3 Portuguese hospitals (Lisbon, Coimbra, and Porto). The sessions were held once a week for 90 minutes, over a period of 7 weeks. The instruments used were: We asked the subjects the question “Please classify the severity of your disease?” and used the Personal Wellbeing Scale (PWS) at the beginning (time A) and end (time B) of the IPPA. We used the SPSS version 20. A non-parametric statistical hypothesis test (Wilcoxon test) was used for the variable analysis. The intervention followed the recommendations of the Helsinki Declaration. Results – The results suggest that there are differences between time A and B, the perception of illness decreased (p<0.08), while wellbeing increased (p<0.01). Conclusions: The IPPA can play an important role in modifying the perception of disease severity and personal wellbeing.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Psychological well-being and perception of disease severity in people with multiple sclerosis, who underwent a program of self-regulation to promote physical activity

Rehabilitation is very important for in the results of treatment in individuals with multiple sclerosis. Rehabilitation processes occur through gradual changes. These changes integrate intrinsic and extrinsic mechanisms of the individual, promoting adaptations to the needs and activities of daily living according to individual goals. Recommendations for exercise in multiple sclerosis: these recommendations apply only to patients with EDSS less than 7; moderate intensity aerobic exercise for a total of 20 to 30 minutes, twice or three times for week; the resistance training with low or moderate intensity is well tolerated by patients with MS; associated with these exercises were recommended flexibility exercises of moderate intensity, as well as strengthening exercises. The aim of this study is to examine the implications of the program of self-regulation in the perception of illness and mental health (psychological well-being domain) in multiple sclerosis patients.

Enhancing Multimodal Silent Speech Interfaces With Feature Selection

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.