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

Nitrate Absorption and Desorption by Biochar

Biochar is a potential solution for addressing environmental problems related to excessive nitrogen (N). However, there is still some debate about the absorption and desorption of nitrate nitrogen (NO3−-N). Therefore, this study investigated the NO3−-N adsorption and desorption performance onto biochar and biochar-soil mixture to address this gap. The results showed that the biochar produced from apple branches had the ability to absorb NO3−-N with an absorption capacity of 3.51 mg·g−1. The absorption data fitted well with the pseudo-second-order kinetic model and Langmuir model. The application of biochar significantly improved soil absorption capacity and slow release of NO3−-N. While higher NO3−-N concentrations had better NO3−-N supply capacity and poorer slow-release effect. Integrating nutrient supply and slow-release effect, it is recommended to control the application ratio of biochar to NO3−-N at 34–42.75 g·g−1. Although the unoptimized biochar application rate cannot be directly applied to the soil as a slow-release fertilizer carrier to meet commercial standards, biochar modification provides new possibilities for this purpose. Moreover, compared with traditional slow-release fertilizer, biochar had good stability and regeneration performance, alleviating the high cost due to the biochar price. In general, biochar still has potential and prospects as a slow-release material. This study provides support for biochar in mitigating environmental problems associated with excess N

A systematic evaluation of nucleotide properties for CRISPR sgRNA design

Abstract Background CRISPR is a versatile gene editing tool which has revolutionized genetic research in the past few years. Optimizing sgRNA design to improve the efficiency of target/DNA cleavage is critical to ensure the success of CRISPR screens. Results By borrowing knowledge from oligonucleotide design and nucleosome occupancy models, we systematically evaluated candidate features computed from a number of nucleic acid, thermodynamic and secondary structure models on real CRISPR datasets. Our results showed that taking into account position-dependent dinucleotide features improved the design of effective sgRNAs with area under the receiver operating characteristic curve (AUC) >0.8, and the inclusion of additional features offered marginal improvement (∼2% increase in AUC). Conclusion Using a machine-learning approach, we proposed an accurate prediction model for sgRNA design efficiency. An R package predictSGRNA implementing the predictive model is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#predictsgrna

Additional file 1 of A systematic evaluation of nucleotide properties for CRISPR sgRNA design

Supplementary Information. The pdf document that contains all supplementary notes, figures and tables. Figures S1-S2 plot the top 10 most informative features ranked by BIC and variable importance scores, respectively. Tables S1-S3 contain the results from randomforest in binary outcome model. Tables S4-S6 contain the results from gbm in binary outcome model. Tables S7-S9 contain the results from elastic net in continuous outcome model. Tables S10-S12 contain the results from randomforest in continuous outcome model. Tables S13-S15 contain the results from gbm in continuous outcome model. Tables S16-S18 contain the results comparing 30bp and 40bp sequences. Tables S19-S20 contain the results from leave-one-gene out prediction. (PDF 151 kb