Benitez, L. | bioRxiv Science

bioRxiv2023

Using a decision tree to predict COVID case numbers: a tutorial for beginners

This manuscript describes the development of a module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on machine learning and decision tree concepts in an interactive format that uses appropriate cloud resources for data access and analyses. Machine learning (ML) is an important tool in biomedical research and can lead to improvements in diagnosis, treatment, and prevention of diseases. During the COVID pandemic ML was used for predictions at the patient and community levels. Given its ubiquity, it is important that future doctors, researchers and teachers get acquainted with ML and its contributions to research. Our goal is to make it easier for everyone to learn about machine learning. The learning module we present here is based on a small COVID dataset, videos, annotated code and the use of Google Colab or the Google Cloud Platform (GCP). The benefit of these platforms is that students do not have to set up a programming environment on their computer which saves time and is also an important democratization factor. The module focuses on learning the basics of decision trees by applying them to COVID data. It introduces basic terminology used in supervised machine learning and its relevance to research. Our experience with biology students at San Francisco State University suggests that the material increases interest in ML.

scientific communication and education↗

bioRxiv2021

Invasive grass litter suppresses native species and promotes disease

Plant litter can alter ecosystems and promote plant invasions by changing resource acquisition, depositing toxins, and transmitting microorganisms to living plants. Transmission of microorganisms from invasive litter to live plants may gain importance as invasive plants accumulate pathogens over time since introduction. It is unclear, however, if invasive plant litter affects native plant communities by promoting disease. Microstegium vimineum is an invasive grass that suppresses native populations, in part through litter production, and has accumulated leaf spot diseases since its introduction to the U.S. In a greenhouse experiment, we evaluated how M. vimineum litter and accumulated pathogens mediated resource competition with the native grass Elymus virginicus. Resource competition reduced biomass of both species and live M. vimineum increased disease incidence on the native species. Microstegium vimineum litter also promoted disease on the native species, suppressed establishment of both species, and reduced biomass of M. vimineum. Nonetheless, interference competition from litter had a stronger negative effect on the native species, increasing the relative abundance of M. vimineum. Altogether, invasive grass litter suppressed both species, ultimately favoring the invasive species in competition, and increased disease incidence on the native species.

ecology↗