New insights into grocery store visits among east Los Angeles residents using mobility data.

In this study, we employed spatially aggregated population mobility data, generated from mobile phone locations in 2021, to investigate patterns of grocery store visits among residents east and northeast of Downtown Los Angeles, in which 60% of the census tracts had previously been designated as "food deserts". Further, we examined whether the store visits varied with neighborhood sociodemographics and grocery store accessibility. We found that residents averaged 0.

Effect of mobile food environments on fast food visits.

Poor diets are a leading cause of morbidity and mortality. Exposure to low-quality food environments saturated with fast food outlets is hypothesized to negatively impact diet. However, food environment research has predominantly focused on static food environments around home neighborhoods and generated mixed findings.

What's On the Menu? Towards Predicting Nutritional Quality of Food Environments.

Unhealthy diets are a leading cause of major chronic diseases including obesity, diabetes, cancer, and heart disease. Food environments-the physical spaces in which people access and consume food-have the potential to profoundly impact diet and related diseases. We take a step towards better understanding the nutritional quality of food environments by developing MINT: Menu Item to NutrienT model.

Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations.

The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people's homes. As smartphone ownership increases, large-scale data on human mobility (i.

Research gaps and opportunities in precision nutrition: an NIH workshop report.

Precision nutrition is an emerging concept that aims to develop nutrition recommendations tailored to different people's circumstances and biological characteristics. Responses to dietary change and the resulting health outcomes from consuming different diets may vary significantly between people based on interactions between their genetic backgrounds, physiology, microbiome, underlying health status, behaviors, social influences, and environmental exposures. On 11-12 January 2021, the National Institutes of Health convened a workshop entitled "Precision Nutrition: Research Gaps and Opportunities" to bring together experts to discuss the issues involved in better understanding and addressing precision nutrition.

An integrated risk and epidemiological model to estimate risk-stratified COVID-19 outcomes for Los Angeles County: March 1, 2020-March 1, 2021.

The objective of this study was to use available data on the prevalence of COVID-19 risk factors in subpopulations and epidemic dynamics at the population level to estimate probabilities of severe illness and the case and infection fatality rates (CFR and IFR) stratified across subgroups representing all combinations of the risk factors age, comorbidities, obesity, and smoking status. We focus on the first year of the epidemic in Los Angeles County (LAC) (March 1, 2020-March 1, 2021), spanning three epidemic waves. A relative risk modeling approach was developed to estimate conditional effects from available marginal data.

Emerging Applications of Machine Learning in Food Safety.

Food safety continues to threaten public health. Machine learning holds potential in leveraging large, emerging data sets to improve the safety of the food supply and mitigate the impact of food safety incidents. Foodborne pathogen genomes and novel data streams, including text, transactional, and trade data, have seen emerging applications enabled by a machine learning approach, such as prediction of antibiotic resistance, source attribution of pathogens, and foodborne outbreak detection and risk assessment.

Estimation of COVID-19 risk-stratified epidemiological parameters and policy implications for Los Angeles County through an integrated risk and stochastic epidemiological model.

Background: Health disparities have emerged with the COVID-19 epidemic because the risk of exposure to infection and the prevalence of risk factors for severe outcomes given infection vary within and between populations. However, estimated epidemic quantities such as rates of severe illness and death, the case fatality rate (CFR), and infection fatality rate (IFR), are often expressed in terms of aggregated population-level estimates due to the lack of epidemiological data at the refined subpopulation level. For public health policy makers to better address the pandemic, stratified estimates are necessary to investigate the potential outcomes of policy scenarios targeting specific subpopulations.

A Gravity-Based Food Flow Model to Identify the Source of Foodborne Disease Outbreaks.

Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in the area adjacent to their home location. This paper aims to fill this gap by introducing a gravity-based approach to model food-flows from supermarkets to consumers and demonstrating how models of consumer shopping behavior can be used to improve computational methodologies to infer the source of an outbreak of foodborne disease.

Locating the source of large-scale outbreaks of foodborne disease.

In today's globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. We study the problem of identifying the source of emerging large-scale outbreaks of foodborne disease; a crucial step in mitigating their proliferation. To solve the source identification problem, we formulate a probabilistic model of the contamination diffusion process as a random walk on a network and derive the maximum-likelihood estimator for the source location.

The HMGB1 C-Terminal Tail Regulates DNA Bending.

High mobility group box protein 1 (HMGB1) is an architectural protein that facilitates the formation of protein-DNA assemblies involved in transcription, recombination, DNA repair, and chromatin remodeling. Important to its function is the ability of HMGB1 to bend DNA non-sequence specifically. HMGB1 contains two HMG boxes that bind and bend DNA (the A box and the B box) and a C-terminal acidic tail.

Single molecule microscopy reveals mechanistic insight into RNA polymerase II preinitiation complex assembly and transcriptional activity.

Transcription by RNA polymerase II (Pol II) is a complex process that requires general transcription factors and Pol II to assemble on DNA into preinitiation complexes that can begin RNA synthesis upon binding of NTPs (nucleoside triphosphate). The pathways by which preinitiation complexes form, and how this impacts transcriptional activity are not completely clear. To address these issues, we developed a single molecule system using TIRF (total internal reflection fluorescence) microscopy and purified human transcription factors, which allows us to visualize transcriptional activity at individual template molecules.

Single molecule studies of RNA polymerase II transcription in vitro.

Eukaryotic mRNA transcription by RNA polymerase II (RNAP II) is the first step in gene expression and a key determinant of cellular regulation. Elucidating the mechanism by which RNAP II synthesizes RNA is therefore vital to determining how genes are controlled under diverse biological conditions. Significant advances in understanding RNAP II transcription have been achieved using classical biochemical and structural techniques; however, aspects of the transcription mechanism cannot be assessed using these approaches.