Informing policy via dynamic models: Cholera in Haiti.

Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.

A high-throughput screening method for evolving a demethylase enzyme with improved and new functionalities.

AlkB is a DNA/RNA repair enzyme that removes base alkylations such as N1-methyladenosine (m1A) or N3-methylcytosine (m3C) from DNA and RNA. The AlkB enzyme has been used as a critical tool to facilitate tRNA sequencing and identification of mRNA modifications. As a tool, AlkB mutants with better reactivity and new functionalities are highly desired; however, previous identification of such AlkB mutants was based on the classical approach of targeted mutagenesis. Here, we introduce a high-throughput screening method to evaluate libraries of AlkB variants for demethylation activity on RNA and DNA substrates. This method is based on a fluorogenic RNA aptamer with an internal modified RNA/DNA residue which can block reverse transcription or introduce mutations leading to loss of fluorescence inherent in the cDNA product. Demethylation by an AlkB variant eliminates the blockage or mutation thereby restores the fluorescence signals. We applied our screening method to sites D135 and R210 in the Escherichia coli AlkB protein and identified a variant with improved activity beyond a previously known hyperactive mutant toward N1-methylguanosine (m1G) in RNA. We also applied our method to O6-methylguanosine (O6mG) modified DNA substrates and identified candidate AlkB variants with demethylating activity. Our study provides a high-throughput screening method for in vitro evolution of any demethylase enzyme.

Nanowire-Seeded Growth of Single-Crystalline (010) ß-Ga2 O3 Nanosheets with High Field-Effect Electron Mobility and On/Off Current Ratio.

2D ß-Ga2 O3 nanosheets, as fundamental materials, have great potential in next generations of ultraviolet transparent electrodes, high-temperature gas sensors, solar-blind photodetectors, and power devices, while their synthesis and growth with high crystalline quality and well-controlled orientation have not been reported yet. The present study demonstrates how to grow single-crystalline ultrathin quasi-hexagonal ß-Ga2 O3 nanosheets with nanowire seeds and proposes a hierarchy-oriented growth mechanism. The hierarchy-oriented growth is initiated by epitaxial growth of a single-crystalline ( 2 - 01 ) ß-Ga2 O3 nanowire on a GaN nanocrystal and followed by homoepitaxial growth of quasi-hexagonal (010) ß-Ga2 O3 nanosheets. The undoped 2D (010) ß-Ga2 O3 nanosheet field effect transistor has a field-effect electron mobility of 38 cm2 V-1 s-1 and an on/off current ratio of 107 with an average subthreshold swing of 150 mV dec-1 . The from-nanowires-to-nanosheets technique paves a novel way to fabricate nanosheets, which has great impact on the field of nanomaterial synthesis and growth and the area of nanoelectronics as well.

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci.

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

Genetic regulatory variation in populations informs transcriptome analysis in rare disease.

Transcriptome data can facilitate the interpretation of the effects of rare genetic variants. Here, we introduce ANEVA (analysis of expression variation) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application of ANEVA to the Genotype-Tissues Expression (GTEx) data showed that this variance estimate is robust and correlated with selective constraint in a gene. Using these variance estimates in a dosage outlier test (ANEVA-DOT) applied to AE data from 70 Mendelian muscular disease patients showed accuracy in detecting genes with pathogenic variants in previously resolved cases and led to one confirmed and several potential new diagnoses. Using our reference estimates from GTEx data, ANEVA-DOT can be incorporated in rare disease diagnostic pipelines to use RNA-sequencing data more effectively.