Every Day I Write the Book: Transforming EHR Data from Daily Practice into New Knowledge.

Despite producing mountains of data in the daily course of care, the documentation labors of frontline clinicians currently return very little value to them or to the health system. The potential of these painstakingly collected data are enormous and clinical registries can extract the extraordinary capacity of these data and transform them into research-ready datasets while protecting the confidentiality of the patients and clinicians. Clinical registries represent transformative tools for primary care research, bringing together the dimensions of clinical practice, research, quality improvement, and policy impact from a large, nationally reflective, diverse sample of practices and patients.

Deciphering regulatory architectures of bacterial promoters from synthetic expression patterns.

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs).

A circumpolar review of the breeding distribution and habitat use of the snow petrel (), the world's most southerly breeding vertebrate.

Knowledge of the spatial distribution of many polar seabird species is incomplete due to the remoteness of their breeding locations. Here, we compiled a new database of published and unpublished records of all known snow petrel breeding sites. We quantified local environmental conditions at sites by appending indices of climate and substrate, and regional-scale conditions by appending 30 year mean (1992-2021) sea-ice conditions within accessible foraging areas.

Accurate sample deconvolution of pooled snRNA-seq using sex-dependent gene expression patterns.

Single nucleus RNA sequencing (snRNA-seq) technology offers unprecedented resolution for studying cell type-specific gene expression patterns. However, snRNA-seq poses high costs and technical limitations, often requiring the pooling of independent biological samples and loss of individual sample-level data. Deconvolution of sample identity using inherent features would enable the incorporation of pooled barcoding and sequencing protocols, thereby increasing data throughput and analytical sample size without requiring increases in experimental sample size and sequencing costs.