A Speech Production Model for Synthesis-by-rule

Sponsored in part by the National Science Foundation through Grant GN-534.1 from the Office of Science Information Service to the Information Sciences Research Center, The Ohio State University

Sepsis biomarkers and diagnostic tools with a focus on machine learning.

Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers

Processing of affective faces varying in valence and intensity in shy adults: an event-related fMRI study

Recent behavioral and electrocortical studies have found that shy and socially anxious adults are hypersensitive to the processing of negative and ambiguous facial emotions. We attempted to extend these findings by examining the neural correlates of affective face processing in shy adults using an event-related fMRI design. We presented pairs of faces that varied in affective valence and intensity. The faces were morphed to alter the degree of intensity of the emotional expressive faces. Twenty-four (12 shy and 12 non-shy) young adult participants then made same/different judgments to these faces while in an MR scanner. We found that shy adults exhibited greater neural activation across a distinct range of brain regions to pairs of faces expressing negative emotions, moderate levels of emotional intensity, and emotional faces that were incongruent with one another. In contrast, non-shy individuals exhibited greater neural activation across a distinct range of brain regions to pairs of faces expressing positive emotions, low levels of emotional intensity, and emotional faces that were congruent with one another. Findings suggest that there are differences in neural responses between shy and non-shy adults when viewing affective faces that vary in valence, intensity, and discrepancy