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Significance Self-assembling RNA molecules play critical roles throughout biology and bioengineering. To accelerate progress in RNA design, we present EteRNA, the first internet-scale citizen science “game” scored by high-throughput experiments. A community of 37,000 nonexperts leveraged continuous remote laboratory feedback to learn new design rules that substantially improve the experimental accuracy of RNA structure designs. These rules, distilled by machine learning into a new automated algorithm EteRNABot, also significantly outperform prior algorithms in a gauntlet of independent tests. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.

We propose a computational method called high-throughput robust analysis for capillary electrophoresis (HiTRACE) to automate the key tasks in large-scale nucleic acid CE analysis, including the profile alignment that has heretofore been a rate-limiting step in the highest throughput experiments. We illustrate the application of HiTRACE on 13 datasets representing 4 different RNAs, 3 chemical modification strategies and up to 480 single mutant variants; the largest datasets each include 87 360 bands. By applying a series of robust dynamic programming algorithms, HiTRACE outperforms prior tools in terms of alignment and fitting quality, as assessed by measures including the correlation between quantified band intensities between replicate datasets. Furthermore, while the smallest of these datasets required 7-10 h of manual intervention using prior approaches, HiTRACE quantitation of even the largest datasets herein was achieved in 3-12 min. The HiTRACE method, therefore, resolves a critical barrier to the efficient and accurate analysis of nucleic acid structure in experiments involving tens of thousands of electrophoretic bands.

Chemical mapping experiments offer powerful information about RNA structure but currently involve ad hoc assumptions in data processing. We show that simple dilutions, referencing standards (GAGUA hairpins), and HiTRACE/MAPseeker analysis allow rigorous overmodification correction, background subtraction, and normalization for electrophoretic data and a ligation bias correction needed for accurate deep sequencing data. Comparisons across six noncoding RNAs stringently test the proposed standardization of dimethyl sulfate (DMS), 2′-OH acylation (SHAPE), and carbodiimide measurements. Identification of new signatures for extrahelical bulges and DMS “hot spot” pockets (including tRNA A58, methylated in vivo) illustrates the utility and necessity of standardization for quantitative RNA mapping.

The method of generalized estimating equations (GEE) models the association between the repeated observations on a subject with a patterned correlation matrix. Correct specification of the underlying structure is a potentially beneficial goal, in terms of improving efficiency and enhancing scientific understanding. We consider two sets of criteria that have previously been suggested, respectively, for selecting an appropriate working correlation structure, and for ruling out a particular structure(s), in the GEE analysis of longitudinal studies with binary outcomes. The first selection criterion chooses the structure for which the model-based and the sandwich-based estimator of the covariance matrix of the regression parameter estimator are closest, while the second selection criterion chooses the structure that minimizes the weighted error sum of squares. The rule out criterion deselects structures for which the estimated correlation parameter violates standard constraints for binary data that depend on the marginal means. In addition, we remove structures from consideration if their estimated parameter values yield an estimated correlation structure that is not positive definite. We investigate the performance of the two sets of criteria using both simulated and real data, in the context of a longitudinal trial that compares two treatments for major depressive episode. Practical recommendations are also given on using these criteria to aid in the efficient selection of a working correlation structure in GEE analysis of longitudinal binary data.

The current data available describing the relationship of obesity and abdominal wall hernias is sparse. The objective of this study was to investigate the current prevalence of noninguinal abdominal wall hernias and their correlation with body mass index (BMI) and other demographic risk factors. Patients with umbilical, incisional, ventral, epigastric, or Spigelian hernias with or without incarceration were identified using the regional database for 14 hospitals over a 3-year period. Patients were stratified based on their BMI. Univariate and multivariate analyses were performed to distinguish other significant risk factors associated with the hernias. Of 2,807,414 patients, 26,268 (0.9%) had one of the specified diagnoses. Average age of the patients was 52 years and 61 per cent were male. The majority of patients had nonincarcerated umbilical hernias (74%). Average BMI was 32 kg/m2. Compared with patients with a normal BMI, the odds of having a hernia increased with BMI: BMI of 25 to 29.9 kg/m2 odds ratio (OR) 1.63, BMI of 30 to 39.9 kg/m2 OR 2.62, BMI 40 to 49.9 kg/m2 OR 3.91, BMI 50 to 59.9 kg/m2 OR 4.85, and BMI greater than 60 kg/m2 OR 5.17 ( P < 0.0001). Age older than 50 years was associated with a higher risk for having a hernia (OR, 2.12; 95% [CI], 2.07 to 2.17), whereas female gender was associated with a lower risk (OR, 0.53; 95% CI, 0.52 to 0.55). Those with incarcerated hernias had a higher average BMI (32 kg/m2 vs 35 kg/m2; P < 0.0001). Overall, BMI greater than 40 kg/m2 showed an increased chance of incarceration, and a BMI greater than 60 kg/m2 had the highest chance of incarceration, OR 12.7 ( P < 0.0001). Age older than 50 years and female gender were also associated with a higher risk of incarceration (OR, 1.28; 95% CI, 1.02 to 1.59 and OR, 1.80; CI, 1.45 to 2.24). Increasing BMI and increasing age are associated with a higher prevalence and an increased risk of incarceration of noninguinal abdominal wall hernias.

Since the global outbreak of SARS-CoV-2 (COVID-19), infections of diverse human organs along with multiple symptoms continue to be reported. However, the susceptibility of the brain to SARS-CoV-2, and the mechanisms underlying neurological infection are still elusive. Here, we utilized human embryonic stem cell-derived brain organoids and monolayer cortical neurons to investigate infection of brain with pseudotyped SARS-CoV-2 viral particles. Spike-containing SARS-CoV-2 pseudovirus infected neural layers within brain organoids. The expression of ACE2, a host cell receptor for SARS-CoV-2, was sustained during the development of brain organoids, especially in the somas of mature neurons, while remaining rare in neural stem cells. However, pseudotyped SARS-CoV-2 was observed in the axon of neurons, which lack ACE2. Neural infectivity of SARS-CoV-2 pseudovirus did not increase in proportion to viral load, but only 10% of neurons were infected. Our findings demonstrate that brain organoids provide a useful model for investigating SARS-CoV-2 entry into the human brain and elucidating the susceptibility of the brain to SARS-CoV-2.

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