What we Talk about when we Talk about Earth on Earth Day?

April 22, 2021, marked the 51st anniversary of Earth Day. With the growing imperativeness of environmental protection and sustainability, we want to study people's collective attention and conversations on this themed day. What are the top-of-mind discourses and central topics about the earth? How do people feel about them, hopeful or pessimistic? How do they change over time, especially after the COVID-19 pandemic? To answer these, we extracted and quantified top frequent features, co-occurring hashtags, sentiment words, and latent sub-topics from about 300K tweets posted on the Earth Day of 2009, 2013, 2017, and 2021. The results demonstrated the longitudinal dynamics of people's rhetoric and focus regarding protecting the earth - from resources conservation to climate changes, as well as the plummeted optimism toward environmental topics after the pandemic. The findings of our paper can help decision-makers to better assess the "voices of the people"and inform evidence-based decision-making. © 2021 ACM.

Longitudinal Dynamics of the Neutralizing Antibody Response to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a global pandemic with no licensed vaccine or specific antiviral agents for therapy. Little is known about the longitudinal dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific neutralizing antibodies (NAbs) in patients with COVID-19. METHODS: Blood samples (n = 173) were collected from 30 patients with COVID-19 over a 3-month period after symptom onset and analyzed for SARS-CoV-2-specific NAbs using the lentiviral pseudotype assay, coincident with the levels of IgG and proinflammatory cytokines. RESULTS: SARS-CoV-2-specific NAb titers were low for the first 7-10 days after symptom onset and increased after 2-3 weeks. The median peak time for NAbs was 33 days (interquartile range [IQR], 24-59 days) after symptom onset. NAb titers in 93.3% (28/30) of the patients declined gradually over the 3-month study period, with a median decrease of 34.8% (IQR, 19.6-42.4%). NAb titers increased over time in parallel with the rise in immunoglobulin G (IgG) antibody levels, correlating well at week 3 (r = 0.41, P < .05). The NAb titers also demonstrated a significant positive correlation with levels of plasma proinflammatory cytokines, including stem cell factor (SCF), TNF-related apoptosis-inducing ligand (TRAIL), and macrophage colony-stimulating factor (M-CSF). CONCLUSIONS: These data provide useful information regarding dynamic changes in NAbs in patients with COVID-19 during the acute and convalescent phases.

Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements.

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort, resulting in a data matrix containing 3,065 readings for 124 types of measurements over 52 days. A machine learning model was established to predict the disease progression based on the cohort consisting of training, validation, and internal test sets. A panel of eleven routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 98% in the discovery set. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.70, 0.99, 0.93, and 0.93, respectively. Our model captured predictive dynamics of lactate dehydrogenase (LDH) and creatine kinase (CK) while their levels were in the normal range. This model is accessible at https://www.guomics.com/covidAI/ for research purpose.