Notes

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Conversational entrainment or alignment-the convergence of conversation partners over the course of a conversation in a variety of linguistic features-is a well-attested conversational phenomenon. The research on prosodic entrainment has shown correlations between prosodic entrainment and several social dimensions of rapport between conversation partners. However, little is known about how skill-level in the entrainment domain affects the ability to converge during a conversation. The goal of the current study was to investigate whether skill-level of a speaker in receptive and expressive word, sentence, and emotional prosody is correlated with the amount of prosodic entrainment contributed at the conversational level. Twenty native speakers of American English were paired into ten dyads of seven female/female and three female/male conversation pairs. Conversations for each pair were recorded and analyzed. Test scores measuring word, sentence, and emotional prosody were correlated with the amount of fundamental frequency entrainment during conversations. The results indicate that a negative correlation exists between expressive prosody skill and the amount of f0 entrainment contributed by a speaker. This suggests that speakers with better expressive prosodic skills at the word and sentence level entrain less to their conversation partners. Receptive prosody ability was not correlated with conversational prosodic entrainment.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), at the origin of the worldwide COVID-19 pandemic, is characterized by a dramatic cytokine storm in some critical patients with COVID-19. This storm is due to the release of high levels of pro-inflammatory cytokines such as interleukin (IL)-1 β, IL-6, tumor necrosis factor (TNF), and chemokines by respiratory epithelial and dendritic cells, and macrophages. We hypothesize that this cytokine storm and the worsening of patients' health status can be dampened or even prevented by specifically targeting the vagal-driven cholinergic anti-inflammatory pathway (CAP). The CAP is a concept that involves an anti-inflammatory effect of vagal efferents by the release of acetylcholine (ACh). Nicotinic acetylcholine receptor alpha7 subunit (α7nAChRs) is required for ACh inhibition of macrophage-TNF release and cytokine modulation. Hence, targeting the α7nAChRs through vagus nerve stimulation (VNS) could be of interest in the management of patients with SARS-CoV-2 infection. Indeed, through the wide innervation of the organism by the vagus nerve, especially the lungs and gastrointestinal tract, VNS appears as a serious candidate for a few side effect treatment that could dampen or prevent the cytokine storm observed in COVID-19 patients with severe symptoms. Finally, a continuous vagal tone monitoring in patients with COVID-19 could be used as a predictive marker of COVID-19 illness course but also as a predictive marker of response to COVID-19 treatment such as VNS or others.To increase situational awareness and support evidence-based policy-making, we formulated two types of mathematical models for COVID-19 transmission within a regional population. One is a fitting function that can be calibrated to reproduce an epidemic curve with two timescales (e.g., fast growth and slow decay). The other is a compartmental model that accounts for quarantine, self-isolation, social distancing, a non-exponentially distributed incubation period, asymptomatic individuals, and mild and severe forms of symptomatic disease. Using Bayesian inference, we have been calibrating our models daily for consistency with new reports of confirmed cases from the 15 most populous metropolitan statistical areas in the United States and quantifying uncertainty in parameter estimates and predictions of future case reports. Smad inhibitor This online learning approach allows for early identification of new trends despite considerable variability in case reporting. We infer new significant upward trends for five of the metropolitan areas starting between 19-April-2020 and 12-June-2020.While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.Recent advances in the interdisciplinary scientific field of machine perception, computer vision, and biomedical engineering underpin a collection of machine learning algorithms with a remarkable ability to decipher the contents of microscope and nanoscope images. Machine learning algorithms are transforming the interpretation and analysis of microscope and nanoscope imaging data through use in conjunction with biological imaging modalities. These advances are enabling researchers to carry out real-time experiments that were previously thought to be computationally impossible. Here we adapt the theory of survival of the fittest in the field of computer vision and machine perception to introduce a new framework of multi-class instance segmentation deep learning, Darwin's Neural Network (DNN), to carry out morphometric analysis and classification of COVID19 and MERS-CoV collected in vivo and of multiple mammalian cell types in vitro.
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