Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

How to Conduct Power Analysis for Structural Equation Models: A Practical Primer

Structural equation modeling (SEM) is popular, but planning for studies that use SEM for data analysis can be difficult. As power analysis becomes standard practice in many fields of psychology, researchers who use SEM for data analysis can benefit from knowing how to conduct power analysis for their studies. With this article, I offer a gentle, practical introduction to power analysis for SEM. First, I connect two goals that researchers often have when using SEM—to interpret the overall model and to detect target effects within the model—to power analysis. Then, I conceptually describe power to detect target effects and power to detect model misfit, summarizing what determines each and common approaches to conducting each type of power analysis. Finally, I provide an illustrative example of conducting power analysis for SEM with a concrete research scenario. Throughout the article, I prioritize plain language and practical guidance over technical depth, with the hope that it makes power analysis for SEM less daunting

Expanding access to tools and education for power analysis in structural equation modeling

This project is funded by a grant-in-aid from the Society for the Improvement of Psychological Science (SIPS)