Mathematical programming tools for randomization purposes in small two-arm clinical trials: A case study with real data.

Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate-adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.

Superinfection exclusion factors drive a history-dependent switch from vertical to horizontal phage transmission.

Temperate bacterial viruses are commonly thought to favor vertical (lysogenic) transmission over horizontal (lytic) transmission when the virion-to-host-cell ratio is high and available host cells become scarce. In P22-infected Salmonella Typhimurium populations, however, we find that host subpopulations become lytically consumed despite high phage-to-host ratios that would normally favor lysogeny. These subpopulations originate from the proliferation of P22-free siblings that spawn off from P22-carrier cells from which they cytoplasmically inherit P22-borne superinfection exclusion factors (SEFs). In fact, we demonstrate that the gradual dilution of these SEFs in the growing subpopulation of P22-free siblings restricts the number of incoming phages, thereby imposing the perception of a low phage-to-host ratio that favors lytic development. Although their role has so far been neglected, our data indicate that phage-borne SEFs can spur complex infection dynamics and a history-dependent switch from vertical to horizontal transmission in the face of host-cell scarcity.

Model Selection Reveals the Butyrate-Producing Gut Bacterium Coprococcus eutactus as Predictor for Language Development in 3-Year-Old Rural Ugandan Children.

INTRODUCTION: The metabolic activity of the gut microbiota plays a pivotal role in the gut-brain axis through the effects of bacterial metabolites on brain function and development. In this study we investigated the association of gut microbiota composition with language development of 3-year-old rural Ugandan children. METHODS: We studied the language ability in 139 children of 36 months in our controlled maternal education intervention trial to stimulate children's growth and development. The dataset includes 1170 potential predictors, including anthropometric and cognitive parameters at 24 months, 542 composition parameters of the children's gut microbiota at 24 months and 621 of these parameters at 36 months. We applied a novel computationally efficient version of the all-subsets regression methodology and identified predictors of language ability of 36-months-old children scored according to the Bayley Scales of Infant and Toddler Development (BSID-III). RESULTS: The best three-term model, selected from more than 266 million models, includes the predictors Coprococcus eutactus at 24 months of age, Bifidobacterium at 36 months of age, and language development at 24 months. The top 20 four-term models, selected from more than 77 billion models, consistently include C. eutactus abundance at 24 months, while 14 of these models include the other two predictors as well. Mann-Whitney U tests suggest that the abundance of gut bacteria in language non-impaired children (n = 78) differs from that in language impaired children (n = 61). While anaerobic butyrate-producers, including C. eutactus, Faecalibacterium prausnitzii, Holdemanella biformis, Roseburia hominis are less abundant, facultative anaerobic bacteria, including Granulicatella elegans, Escherichia/Shigella and Campylobacter coli, are more abundant in language impaired children. The overall predominance of oxygen tolerant species in the gut microbiota was slightly higher in the language impaired group than in the non-impaired group (P = 0.09). CONCLUSION: Application of the all-subsets regression methodology to microbiota data established a correlation between the relative abundance of the anaerobic butyrate-producing gut bacterium C. eutactus and language development in Ugandan children. We propose that the gut redox potential and the overall bacterial butyrate-producing capacity in the gut are important factors for language development.