Previously, we found that elastin-like polypeptide (ELP), when dried above the lower critical solution temperature on top of a hydrophilic fused silica disk, exhibited a dynamic coalescence behavior. The ELP initially wet the silica, but over the next 12 h, dewett the surface and formed aggregates of precise sizes and shapes. Using Fourier-transform infrared (FT-IR) spectroscopy, the present study explores the role of secondary structures present in ELP during this progressive desiccation and their effect on aggregate size. The amide I peak (1600–1700 cm–1) in the ELP’s FT-IR spectrum was deconvoluted using the second derivative method into eight subpeaks (1616, 1624, 1635, 1647, 1657, 1666, 1680, 1695 cm–1). These peaks were identified to represent extended strands, β-turns, 3(10)-helix, polyproline I, and polyproline II using previous studies on ELP and molecules similar in peptide composition. Positive correlations were established between the various subpeaks, water content, and aggregate size to understand the contributions of the secondary structures in particle formation. The positive correlations suggest that type II β-turns, independent of the water content, contributed to the growth of the aggregates at earlier time points (1–3.5 h). At later time points (6–12 h), the aggregate growth was attributed to the formation of 3(10)-helices that relied on a decrease in water content. Understanding these relationships gives greater control in creating precisely sized aggregates and surface coatings with varying roughness.

Objectives: The goal of the present work is to investigate trends among US counties and coronavirus disease 2019 growth rates in relation to the existence of shelter-in-place (SIP) orders in that county.Study design: This is a prospective cohort study. Methods: Compound growth rates were calculated using cumulative confirmed COVID-19 cases from January 21, 2020, to March 31, 2020, in all 3139 US counties. Compound growth was chosen as it gives a single number that can be used in machine learning to represent the speed of virus spread during defined time intervals. Statistical analyses and a random forest machine learning model were used to analyze the data for differences in counties with and without SIP orders. Results: Statistical analyses revealed that the March 16 presidential recommendation (limiting gatherings to 10 people) lowered the compound growth rate of COVID-19 for all counties in the US by 6.6%, and the counties that implemented SIP after March 16 had a further reduction of 7.8% compared with the counties that did not implement SIP after March 16. A random forest machine learning model was built to predict compound growth rate after a SIP order and was found to have an accuracy of 92.3%. The random forest found that population, longitude, and population per square mile were the most important features when predicting the effect of SIP. Conclusions: SIP orders were found to be effective at reducing the growth rate of COVID-19 cases in the US. Counties with a large population or a high population density were found to benefit the most from a SIP order.

This paper reports the synthesis of catechol-functionalized thiol–ene networks as photocurable adhesives, where adhesive interactions are derived from 4-allylpyrocatechol – an alkene readily obtained from Syzygium aromaticum flower buds (clove oil).