Phylogenetic analyses demonstrate an evolutionary trade-off between the amount of harm inflicted by a broad host-range virus and how effectively the virus positions itself within plants to enable onward transmission.
Fitting a mechanistic model to data from SARS-CoV-2 source-recipient pairs generates improved estimates of changes in infectiousness during infection, indicating substantial transmission shortly before symptom onset.
M2 cortex-dorsolateral striatum circuit is functionally altered in Huntington's disease and, by boosting its activity, we reverse symptoms at behavioral, physiological, and morphological level in symptomatic mice.
Applying deep learning technology for the large-scale curation of symptoms from unstructured EHR clinical notes accurately predicts the differential signals of COVID-19 diagnosis over the week preceding typical PCR testing.
Text mining of complete EHRs for 14,017 diabetes patients and subsequent clustering led to phenotypically deep clusters, showing distinct glycemic profiles, comorbidities, and SNP association patterns.
Using data for 2566 COVID-19 patients from five hospitals, models are developed to predict for each patient hospitalization and critical care needs, based on demographics, comorbidities, medications, and laboratory findings.
Hierarchical modeling of internalizing symptoms and task performance reveals that difficulty adapting probabilistic learning to second-order uncertainty is common to anxiety and depression and holds across rewarding and punishing outcomes.