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Biology subjects

He, C. X.

Publications and source records attributed to He, C. X..

2 recordsLinked to original sources

Stratified Random Sampling Methodology for Observing Community Mask Use within Indoor Settings: Results from Louisville, Kentucky during the COVID-19 Pandemic

Wearing a facial mask can limit COVID-19 transmission. Measurements of communities mask use behavior have mostly relied on self-report. This studys objective was to devise a method for measuring the prevalence of mask-wearing and proper mask use in indoor public areas without relying on self-report. A stratified random sample of retail trade stores (public areas) in Louisville, Kentucky, USA, was selected and targeted for observation by trained surveyors during December 14-20, 2020. The stratification allowed for investigating mask use behavior by city district, retail trade group, and public area size. The average mask use prevalence among observed visitors of the 382 visited public areas was 96%, while the average prevalence of proper use was 86%. In 17% of the public areas, at least one unmasked visitor was among the observed visitors; in 48%, at least one improperly masked visitor was observed. The average mask use among staff was 92%, but unmasked staff were observed in fewer public areas, as an unmasked staff member was observed in 11% of the visited public areas. The average prevalence of proper make use among staff was 87%, similar to the average among visitors. However, the percentage of public areas where at least one improperly masked staff was observed was 33. Significant disparities in mask use and its proper use were observed among both visitors and staff by public area size, retail trade type, and geographical area. Observing unmasked and incorrectly masked visitors was more common in small (less than 1500 square feet) public areas than larger ones, also in food and grocery stores than other retail stores. Also, the majority of the observed unmasked persons were male and middle age adults.

scientific communication and education↗

EZcalcium: Open Source Toolbox for Analysis of Calcium Imaging Data

Fluorescence calcium imaging using a range of microscopy approaches, such as 2-photon excitation or head-mounted miniscopes, is one of the preferred methods to record neuronal activity and glial signals in various experimental settings, including acute brain slices, brain organoids, and behaving animals. Because changes in the fluorescence intensity of genetically encoded or chemical calcium indicators correlate with action potential firing in neurons, data analysis is based on inferring such spiking from changes in pixel intensity values across time within different regions of interest. However, the algorithms necessary to extract biologically relevant information from these fluorescent signals are complex and require significant expertise in programming to develop robust analysis pipelines. For decades, the only way to perform these analyses was for individual laboratories to write their own custom code. These routines were typically not well annotated and lacked intuitive graphical user interfaces (GUIs), which made it difficult for scientists in other laboratories to adopt them. Although the panorama is changing with recent tools like CaImAn, Suite2P and others, there is still a barrier for many laboratories to adopt these packages, especially for potential users without sophisticated programming skills. As 2-photon microscopes are becoming increasingly affordable, the bottleneck is no longer the hardware, but the software used to analyze the calcium data in an optimal manner and consistently across different groups. We addressed this unmet need by incorporating recent software solutions for motion correction, segmentation, signal extraction and deconvolution of calcium imaging data into an open-source, easy to use, GUI-based, intuitive and automated data analysis software, which we named EZcalcium.

neuroscience↗