A genome-wide library of MADM mice for single-cell genetic mosaic analysis

Mosaic analysis with double markers (MADM) offers one approach to visualize and concomitantly manipulate genetically defined cells in mice with single-cell resolution. MADM applications include the analysis of lineage, single-cell morphology and physiology, genomic imprinting phenotypes, and dissection of cell-autonomous gene functions in vivo in health and disease. Yet, MADM can only be applied to 96% of the entire mouse genome can now be subjected to single-cell genetic mosaic analysis. Beyond a proof of principle, we apply our MADM library to systematically trace sister chromatid segregation in distinct mitotic cell lineages. We find striking chromosome-specific biases in segregation patterns, reflecting a putative mechanism for the asymmetric segregation of genetic determinants in somatic stem cell division

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

Spelling Correction of User Search Queries through Statistical Machine Translation

We use character-based statistical machine translation in order to correct user search queries in the e-commerce domain. The training data is automatically extracted from event logs where users re-issue their search queries with potentially corrected spelling within the same session. We show results on a test set which was annotated by humans and compare against online autocorrection capabilities of three addi-tional web sites. Overall, the methods presented in this paper outperform fully productized spellchecking and autocorrec-tion services in terms of accuracy and F1 score. We also propose novel evaluation steps based on retrieved search results of the corrected queries in terms of quantity and relevance.

Redesigning the market for volunteers: A donor registry

This paper addresses volunteer labor markets where the lack of price signals, nonpecuniary motivations to supply labor, and limited fungibility of supply lead to market failure. To address the causes of the market failure, we conduct a field experiment with volunteer whole blood donors where we introduce a market-clearing mechanism (henceforth: the Registry). Our intention-to-treat estimates suggest that subjects invited to the Registry, regardless of joining, are 66% more responsive to critical shortage appeals than control subjects. While the Registry increases supply during a critical shortage episode, it does not increase supply when there is no shortage; thus, the Registry significantly improves coordination between volunteer donors and collection centers, thereby improving market outcomes. We find evidence that the Registry’s effectiveness stems from crowding-in volunteers with purely altruistic motives and volunteers with a preference for commitment