Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines.

The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. The visual assessment of every imaging scan is a necessary quality control step, yet arduous and time-consuming. A sizeable body of evidence shows that images of low quality are a source of variability that may be comparable to the effect size under study. We present the MRIQC Web-API, an open crowdsourced database that collects image quality metrics extracted from MR images and corresponding manual assessments by experts. The database is rapidly growing, and currently contains over 100,000 records of image quality metrics of functional and anatomical MRIs of the human brain, and over 200 expert ratings. The resource is designed for researchers to share image quality metrics and annotations that can readily be reused in training human experts and machine learning algorithms. The ultimate goal of the database is to allow the development of fully automated quality control tools that outperform expert ratings in identifying subpar images.

A neutralization assay for respiratory syncytial virus using a quantitative PCR-based endpoint assessment.

BACKGROUND: Few studies have used quantitative polymerase chain reaction (qPCR) as an approach to measure virus neutralization assay endpoints. Its lack of use may not be surprising considering that sample nucleic acid extraction and purification can be expensive, labor-intensive, and rate-limiting. METHODS: Virus/antibody mixtures were incubated for one hour at 37°C and then transferred to Vero cell monolayers in a 96-well plate format. At 24 (or 48) hours post-infection, we used a commercially available reagent to prepare cell lysates amenable to direct analysis by one-step SYBR Green quantitative reverse transcription PCR using primers specific for the RSV-N gene, thereby obviating the need for cumbersome RNA extraction and purification. The neutralization titer was defined as the reciprocal of the highest dilution needed to inhibit the PCR signal by 90% when compared with the mean value observed in virus control wells in the absence of neutralizing antibodies. RESULTS: We have developed a qPCR-based neutralization assay for human respiratory syncytial virus. Due to the sensitivity of qPCR in detecting virus replication, endpoints may be assessed as early as 24 hours post-infection. In addition, the dynamic range of qPCR provides a basis for the assay to be relatively robust to perturbations in input virus dose (i.e., the assay is in compliance with the Percentage Law). CONCLUSIONS: This qPCR-based neutralization assay is suitable for automated high-throughput applications. In addition, our experimental approach may be generalizable for the rapid development of neutralization assays for other virus families.

On an unbiased and consistent estimator for mutation rates.

Spontaneous mutations are stochastic events. The mutation rate, defined as mutations per genome per replication, is generally very low, and it is widely accepted that spontaneous mutations occur at defined, but different, rates in bacteriophage and in bacterial, insect, and mammalian cells. The calculation of mutation rates has proved to be a significant problem. Mutation rates can be calculated by following mutant accumulation during growth or from the distribution of mutants obtained in parallel cultures. As Luria and Delbrück described in 1943, the number of mutants in parallel populations of bacterial cells varies widely depending on when a spontaneous mutation occurs during growth of the culture. Since 1943, many mathematical refinements to estimating rates, called estimators, have been described to facilitate determination of the mutation rate from the distribution or frequency of mutants detected following growth of parallel cultures. We present a rigorous mathematical solution to the mutation rate problem using an unbiased and consistent estimator. Using this estimator we demonstrate experimentally that mutation rates can be easily calculated by determining mutant accumulation, that is, from the number of mutants measured in two successive generations. Moreover, to verify the consistency of our estimator we conduct a series of simulation trials that show a surprisingly rapid convergence to the targeted mutation rate (reached between 25th and 30th generations).