Optimizing sample size for supervised machine learning with bulk transcriptomic sequencing: a learning curve approach.

Accurate sample classification using transcriptomics data is crucial for advancing personalized medicine. Achieving this goal necessitates determining a suitable sample size that ensures adequate classification accuracy without undue resource allocation. Current sample size calculation methods rely on assumptions and algorithms that may not align with supervised machine learning techniques for sample classification.

Elevated CO alters soybean physiology and defense responses, and has disparate effects on susceptibility to diverse microbial pathogens.

Increasing atmospheric CO levels have a variety of effects that can influence plant responses to microbial pathogens. However, these responses are varied, and it is challenging to predict how elevated CO (eCO) will affect a particular plant-pathogen interaction. We investigated how eCO may influence disease development and responses to diverse pathogens in the major oilseed crop, soybean.

Optimizing Sample Size for Supervised Machine Learning with Bulk Transcriptomic Sequencing: A Learning Curve Approach.

Accurate sample classification using transcriptomics data is crucial for advancing personalized medicine. Achieving this goal necessitates determining a suitable sample size that ensures adequate statistical power without undue resource allocation. Current sample size calculation methods rely on assumptions and algorithms that may not align with supervised machine learning techniques for sample classification.

Root-associated bacterial communities and root metabolite composition are linked to nitrogen use efficiency in sorghum.

The development of cereal crops with high nitrogen use efficiency (NUE) is a priority for worldwide agriculture. In addition to conventional plant breeding and genetic engineering, the use of the plant microbiome offers another approach to improving crop NUE. To gain insight into the bacterial communities associated with sorghum lines that differ in NUE, a field experiment was designed comparing 24 diverse lines under sufficient and deficient nitrogen (N).

Design Strategies for Large Current Density Hydrogen Evolution Reaction.

Hydrogen energy is considered one of the cleanest and most promising alternatives to fossil fuel because the only combustion product is water. The development of water splitting electrocatalysts with Earth abundance, cost-efficiency, and high performance for large current density industrial applications is vital for H production. However, most of the reported catalysts are usually tested within relatively small current densities (< 100 mA cm), which is far from satisfactory for industrial applications.