To determine the role of early acquisition of blood oxygen level-dependent (BOLD) signals and diffusion tensor imaging (DTI) for analysis of the connectivity of the ascending arousal network (AAN) in predicting neurological outcomes after acute traumatic brain injury (TBI), cardiopulmonary arrest (CPA), or stroke. A prospective analysis of 50 comatose patients was performed during their ICU stay. Image processing was conducted to assess structural and functional connectivity of the AAN. Outcomes were evaluated after 3 and 6 months. Nineteen patients (38%) had stroke, 18 (36%) CPA, and 13 (26%) TBI. Twenty-three patients were comatose (44%), 11 were in a minimally conscious state (20%), and 16 had unresponsive wakefulness syndrome (32%). Univariate analysis demonstrated that measurements of diffusivity, functional connectivity, and numbers of fibers in the gray matter, white matter, whole brain, midbrain reticular formation, and pontis oralis nucleus may serve as predictive biomarkers of outcome depending on the diagnosis. Multivariate analysis demonstrated a correlation of the predicted value and the real outcome for each separate diagnosis and for all the etiologies together. Findings suggest that the above imaging biomarkers may have a predictive role for the outcome of comatose patients after acute TBI, CPA, or stroke.
The perception of security refers to the subjective evaluation of risks related to security events and the magnitude of their consequences. Negative perceptions have severe implications in society. These feelings are commonly quantified by citizen's surveys, which are time-consuming and do not adapt well to the changing dynamics linked to security. Recently, Twitter social network emerged as an alternative to quantify some of these feelings dynamically. However, most of these approaches focused on counting the amount of content related to crime, which is only one of the relevant factors determining the perception of security. In addition, these models do not account for the polarity and tone underlying the social network content. This work introduces a model for quantification of Perception of Security on Twitter based on sentiment analysis and studies its relationship with actual crime. The model relies on an automatic strategy for filtering content related to security based on a support vector machine classifier and quantifying the sentiment in the posts using a multinomial naive Bayes classifier. These measures of sentiment allow computing estimates of perception of security at a daily scale. This model studied more than 1.700.000 tweets in Bogotá (Colombia), collected during more than one year (March 18 of 2019 -April 28 of 2020). Results suggest that machine learning-based approaches may outperform previous security content filtering strategies by 21% in F1-score and provide quantifications on the sentiments of about 40% on accuracy. Trained models were used to provide daily estimates of perception of security, which are different from those captured by counting the number of posts related to security. Furthermore, the associations between perceptions and actual crimes exhibited monthly variations and showed that in some months, perceptions were more related to robbery, a crime with high incidence in the city. These results may help decisionmakers devise strategies to reduce the impact of the negative perception on citizens.
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