How Psychological Stress Affects Emotional Prosody

We explored how experimentally induced psychological stress affects the production and recognition of vocal emotions. In Study 1a, we demonstrate that sentences spoken by stressed speakers are judged by naive listeners as sounding more stressed than sentences uttered by non-stressed speakers. In Study 1b, negative emotions produced by stressed speakers are generally less well recognized than the same emotions produced by non-stressed speakers. Multiple mediation analyses suggest this poorer recognition of negative stimuli was due to a mismatch between the variation of volume voiced by speakers and the range of volume expected by listeners. Together, this suggests that the stress level of the speaker affects judgments made by the receiver. In Study 2, we demonstrate that participants who were induced with a feeling of stress before carrying out an emotional prosody recognition task performed worse than non-stressed participants. Overall, findings suggest detrimental effects of induced stress on interpersonal sensitivity

A systematic review of the diagnostic accuracy of physical examination for the detection of cirrhosis

BACKGROUND: We conducted a review of the diagnostic accuracy of clinical examination for the diagnosis of cirrhosis. The objectives were: to identify studies assessing the accuracy of clinical examination in the detection of cirrhosis; to summarize the diagnostic accuracy of reported physical examination findings; and to define the effects of study characteristics on estimates of diagnostic accuracy. METHODS: Studies were identified through electronic literature search of MEDLINE (1966 to 2000), search of bibliographic references, and contact with authors. Studies that evaluated indicants from physical examination of patients with known or suspected liver disease undergoing liver biopsy were included. Qualitative data on study characteristics were extracted. Two-by-two tables of presence or absence of physical findings for patients with and without cirrhosis were created from study data. Data for physical findings reported in each study were combined using Summary Receiver Operating Characteristic (SROC) curves or random effects modeling, as appropriate. RESULTS: Twelve studies met inclusion criteria, including a total of 1895 patients, ranging in age from 3 to 90 years. Most studies were conducted in referral populations with elevated aminotransferase levels. Ten physical signs were reported in three or more studies and ten signs in only a single study. Signs for which there was more study data were associated with high specificity (range 75–98%), but low sensitivity (range 15–68%) for histologically-proven cirrhosis. CONCLUSIONS: Physical findings are generally of low sensitivity for the diagnosis of cirrhosis, and signs with higher specificity represent decompensated disease. Most studies have been undertaken in highly selected populations

Tutorial: Multivariate Classification for Vibrational Spectroscopy in Biological Samples

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental