The Effect of Exposure to Neighborhood Violence on Glucocorticoid Receptor Signaling in Lung Tumors.

Despite lower rates and intensity of smoking, Black men experience a higher incidence of lung cancer compared to white men. The racial disparity in lung cancer is particularly pronounced in Chicago, a highly segregated urban city. Neighborhood conditions, particularly social stress, may play a role in lung tumorigenesis. Preliminary studies indicate that Black men residing in neighborhoods with higher rates of violent crime have significantly higher levels of hair cortisol, an indicator of stress response. To examine the relationship between social stress exposure and gene expression in lung tumors, we investigated glucocorticoid receptor (GR) binding in 15 lung tumor samples in relation to GR target gene expression levels and zip code level residential violent crime rates. Spatial transcriptomics and a version of ChIP sequencing known as CUT&RUN were used. Heatmap of genes, pathway analysis, and motif analysis were conducted at the statistical significance of P < 0.05. GR recruitment to chromatin was correlated with zip code level residential violent crime rate and overall GR binding increased with higher violent crime rates. Our findings suggest that exposure to residential violent crime may influence tumor biology via reprogramming GR recruitment. Prioritizing lung cancer screening in neighborhoods with increased social stress, such as high levels of violent crime, may reduce racial disparities in lung cancer. SIGNIFICANCE: Exposure to neighborhood violent crime is correlated with glucocorticoid signaling and lung tumor gene expression changes associated with increased tumor aggressiveness, suggesting social conditions have downstream biophysical consequences that contribute to lung cancer disparities.

Epidemic spread on patch networks with community structure.

Predicting and preparing for the trajectory of disease epidemics relies on a knowledge of environmental and socioeconomic factors that affect transmission rates on local and global spatial scales. This article discusses the simulation of epidemic outbreaks on human metapopulation networks with community structure, such as cities within national boundaries, for which infection rates vary both within and between communities. We demonstrate mathematically, through next-generation matrices, that the structures of these communities, setting aside all other considerations such as disease virulence and human decision-making, have a profound effect on the reproduction rate of the disease throughout the network. In high modularity networks, with high levels of separation between neighboring communities, disease epidemics tend to spread rapidly in high-risk communities and very slowly in others, whereas in low modularity networks, the epidemic spreads throughout the entire network as a steady pace, with little regard for variations in infection rate. The correlation between network modularity and effective reproduction number is stronger in population with high rates of human movement. This implies that the community structure, human diffusion rate, and disease reproduction number are all intertwined, and the relationships between them can be affected by mitigation strategies such as restricting movement between and within high-risk communities. We then test through numerical simulation the effectiveness of movement restriction and vaccination strategies in reducing the peak prevalence and spread area of outbreaks. Our results show that the effectiveness of these strategies depends on the structure of the network and the properties of the disease. For example, vaccination strategies are most effective in networks with high rates of diffusion, whereas movement restriction strategies are most effective in networks with high modularity and high infection rates. Finally, we offer guidance to epidemic modelers as to the ideal spatial resolution to balance accuracy and data collection costs.

Social sensing of urban land use based on analysis of Twitter users' mobility patterns.

A number of recent studies showed that digital footprints around built environments, such as geo-located tweets, are promising data sources for characterizing urban land use. However, challenges for achieving this purpose exist due to the volume and unstructured nature of geo-located social media. Previous studies focused on analyzing Twitter data collectively resulting in coarse resolution maps of urban land use. We argue that the complex spatial structure of a large collection of tweets, when viewed through the lens of individual-level human mobility patterns, can be simplified to a series of key locations for each user, which could be used to characterize urban land use at a higher spatial resolution. Contingent issues that could affect our approach, such as Twitter users' biases and tendencies at locations where they tweet the most, were systematically investigated using 39 million geo-located Tweets and two independent datasets of the City of Chicago: 1) travel survey and 2) parcel-level land use map. Our results support that the majority of Twitter users show a preferential return, where their digital traces are clustered around a few key locations. However, we did not find a general relation among users between the ranks of locations for an individual-based on the density of tweets-and their land use types. On the contrary, temporal patterns of tweeting at key locations were found to be coherent among the majority of users and significantly associated with land use types of these locations. Furthermore, we used these temporal patterns to classify key locations into generic land use types with an overall classification accuracy of 0.78. The contribution of our research is twofold: a novel approach to resolving land use types at a higher resolution, and in-depth understanding of Twitter users' location-related and temporal biases, promising to benefit human mobility and urban studies in general.