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Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC's), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.

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Stated choice (SC) experiments are the most popular method to estimate the value of travel time changes (VTTC) of a population. In the simplest VTTC experiment, the SC design variables are time changes and cost changes. The levels of these variables create a particular setting from which preferences are inferred. This paper tries to answer the question "do preferences vary with SC settings?". For this, we investigate the role of the variables used in the SC experiment on the estimation of the set of VTTC (i.e. mean and covariates). Ideally, one would like to observe the same individuals completing different SC experiments. Since that option is not available, an alternative approach is to use a large dataset of responses, and split it according to different levels of the variable of interest. We refer to this as partial data analysis. The estimation of the same model on each sub-sample provides insights into potential effects of the variable of interest. This approach is applied in relation to three design variables on the data for the last national VTTC study in the UK, using state-of-the-art model specifications. The results show several ways in which the estimated set of VTTC can be affected by the levels of SC design variables. We conclude that model estimates (including the VTTC and covariates) are different in different settings. Hence by focussing the survey on specific settings, sample level results will be affected accordingly. Our findings have implications for appraisal and can inform the construction of future SC experiments.

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BACKGROUND: Cancer screening has contributed to downward trends in cancer mortality, but is also associated with adverse effects, which highlights the importance of promoting the participation based on informed decisions. OBJECTIVES: We aimed to describe the use of cancer screening (either in organized programmes or as opportunistic screening), awareness of organized programmes and perception of its potential benefits and adverse effects, depicting possible sex differences. DESIGN AND METHODS: We evaluated 1624 Portuguese-speaking dwellers, aged between 16 and 79 years, through face-to-face interviews. To quantify sex differences, adjusted prevalence ratios and respective 95% confidence intervals were computed using Poisson regression. RESULTS: Among eligible age groups, the lifetime prevalence of screening for breast and cervical cancers was 89.8 and 71.9%, respectively. The prevalence was 23.7% for colorectal cancer and no significant sex differences were observed. Prostate cancer screening was reported by 63.8% of men. Over half of the participants referred that cancers such as prostate, skin, lung and stomach should be screened for, in addition to those for which organized programmes are recommended. Reassurance by negative results was identified as the main potential benefit of screening by nearly one-third of men and women. Anxiety while waiting for results was the most mentioned potential adverse effect (60.4%); men refer less often this and financial costs, although statistical significance of these results was borderline. CONCLUSIONS: This study provides a benchmark to plan and monitor the effects of awareness-raising interventions, as well as for international comparisons across countries with different cancer prevention and control structures.

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The increasing number of new cases of cancer highlights the relevance of primary prevention for cancer control, which is influenced, among other factors, by the population's health-related knowledge. Therefore, we aimed to describe cancer-related knowledge in Portugal, including perception of risk, awareness of cancer causes and preventive behaviours. We evaluated 1624 Portuguese-speaking dwellers, aged between 16 and 79 years, through face-to-face interviews conducted using a structured questionnaire. We computed adjusted (sex, age, education) regression coefficients and prevalence ratios, using linear and Poisson regression, respectively, to quantify associations with cancer-specific knowledge. The proportions of nonresponse ranged from 13.4 to 63.5% for the most frequent cancer in Portugal and the leading cause of cancer, respectively. The mean of the estimated lifetime risk of cancer in the Portuguese population was 37.0%. A total of 47.5% of the respondents identified breast cancer as the most frequent in Portugal, 72.0% named lifestyles as the leading cause of cancer and 40.2% selected not smoking as the most important preventive behaviour. Lower levels of education were associated with higher proportions of nonresponse, but not consistently with inaccurate knowledge. Men provided lower estimates of the lifetime risk of cancer, indicated breast cancer less frequently and more often lung cancer as the most frequent, and were more likely to select not smoking as the most important preventive behaviour. The present study provides relevant data on knowledge of cancer prevention, which may be used for the planning and evaluation of awareness-raising and primary prevention interventions in Portugal.

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