Correction: Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model.

[This corrects the article DOI: 10.1371/journal.pone.

Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model.

During the COVID-19 pandemic, many public schools across the United States shifted from fully in-person learning to alternative learning modalities such as hybrid and fully remote learning. In this study, data from 14,688 unique school districts from August 2020 to June 2021 were collected to track changes in the proportion of schools offering fully in-person, hybrid and fully remote learning over time. These data were provided by Burbio, MCH Strategic Data, the American Enterprise Institute's Return to Learn Tracker and individual state dashboards.

Gecko: A time-series model for COVID-19 hospital admission forecasting.

During the COVID-19 pandemic, concerns about hospital capacity in the United States led to a demand for models that forecast COVID-19 hospital admissions. These short-term forecasts were needed to support planning efforts by providing decision-makers with insight about future demands for health care capacity and resources. We present a SARIMA time-series model called Gecko developed for this purpose.

Promotion of Myogenic Maturation by Timely Application of Electric Field Along the Topographical Alignment.

Engineered muscular substitutes can restore the impaired muscle functions when integrated properly into the host tissue. To generate functional muscles with sufficient contractility at the site of transplant, the in vitro construction of fully differentiated muscle fibers would be desired. Many previous reports have identified either topographical alignment or electrical stimulation as an effective tool to promote myogenic differentiation.