Dynamics of bed bug infestations and control under disclosure policies

Bed bugs have reemerged in the United States and worldwide over recent decades, presenting a major challenge to both public health practitioners and housing authorities. A number of municipalities have proposed or initiated policies to stem the bed bug epidemic, but little guidance is available to evaluate them. One contentious policy is disclosure, whereby landlords are obligated to notify potential tenants of current or prior bed bug infestations. Aimed to protect tenants from leasing an infested rental unit, disclosure also creates a kind of quarantine, partially and temporarily removing infested units from the market. Here, we develop a mathematical model for the spread of bed bugs in a generalized rental market, calibrate it to parameters of bed bug dispersion and housing turnover, and use it to evaluate the costs and benefits of disclosure policies to landlords. We find disclosure to be an effective control policy to curb infestation prevalence. Over the short term (within 5 years), disclosure policies result in modest increases in cost to landlords, while over the long term, reductions of infestation prevalence lead, on average, to savings. These results are insensitive to different assumptions regarding the prevalence of infestation, rate of introduction of bed bugs from other municipalities, and the strength of the quarantine effect created by disclosure. Beyond its application to bed bugs, our model offers a framework to evaluate policies to curtail the spread of household pests and is appropriate for systems in which spillover effects result in highly nonlinear cost–benefit relationships

Enhancing Electronic Health Record Data For Population Health Studies

Electronic health records (EHRs) offer convenient and low-cost access to large volumes of longitudinal data for diverse, real-world populations, which have made them an invaluable resource for biomedical research. However, information is collected in EHRs for clinical and administrative uses and repurposing this data for research comes with unique challenges, including the limited scope of exposure-related variables. The objective of this dissertation was to examine methods to augment the scope of EHR data by integrating it with external data sources, including publicly available data on social and environmental factors, as well as data from personal sensing. First, we studied how linking EHR data with area-based measures of socioeconomic status (SES) can impact the results of epidemiologic studies. We showed that because individual-level SES measures do not always strongly correlate with area-based measures, the use of area-based measures can result in residual confounding by individual SES on the exposure-outcome association under study. Second, we examined whether the integration of geospatial features with EHR data can improve the prediction of asthma and chronic obstructive pulmonary disease exacerbations beyond the use of EHR data alone and determined that geospatial features have predictive value when linked to patient data. Third, we linked geospatially varying data on neighborhood SES and residential greenness to EHR data for encounters in which an animal-related disease condition was documented to identify risk factors for animal-related illness and injury and found that residential greenness was associated with an increased risk of Lyme disease and tick bite and decreased risk of allergic rhinitis due to animal dander. In addition, we illustrated how spatial regression methods, such as autologistic regression, that model spatial autocorrelation explicitly may be better suited for the study of spatially correlated exposure variables than nonspatial methods. Finally, we determined through qualitative interviews that the use of portable pollution sensors was generally acceptable to adults with asthma and demonstrated through trials deploying sensors their utility for capturing personalized exposure information at high spatiotemporal resolution. Pending improvements to make devices more amenable for general use, portable sensors could greatly improve the capture of exposure information that can be linked to EHR data

Dynamics of bed bug infestations and control under disclosure policies

Bed bugs have reemerged in the United States and worldwide over recent decades, presenting a major challenge to both public health practitioners and housing authorities. A number of municipalities have proposed or initiated policies to stem the bed bug epidemic, but little guidance is available to evaluate them. One contentious policy is disclosure, whereby landlords are obligated to notify potential tenants of current or prior bed bug infestations. Aimed to protect tenants from leasing an infested rental unit, disclosure also creates a kind of quarantine, partially and temporarily removing infested units from the market. Here, we develop a mathematical model for the spread of bed bugs in a generalized rental market, calibrate it to parameters of bed bug dispersion and housing turnover, and use it to evaluate the costs and benefits of disclosure policies to landlords. We find disclosure to be an effective control policy to curb infestation prevalence. Over the short term (within 5 years), disclosure policies result in modest increases in cost to landlords, while over the long term, reductions of infestation prevalence lead, on average, to savings. These results are insensitive to different assumptions regarding the prevalence of infestation, rate of introduction of bed bugs from other municipalities, and the strength of the quarantine effect created by disclosure. Beyond its application to bed bugs, our model offers a framework to evaluate policies to curtail the spread of household pests and is appropriate for systems in which spillover effects result in highly nonlinear cost–benefit relationships.This article is published as Xie, Sherrie, Alison L. Hill, Chris R. Rehmann, and Michael Z. Levy. "Dynamics of bed bug infestations and control under disclosure policies." Proceedings of the National Academy of Sciences (2019): 201814647. DOI: 10.1073/pnas.1814647116 . Posted with permission.</p

Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery

Accurate automated segmentation of remote sensing data could benefit applications from land cover mapping and agricultural monitoring to urban development surveyal and disaster damage assessment. While convolutional neural networks (CNNs) achieve state-of-the-art accuracy when segmenting natural images with huge labeled datasets, their successful translation to remote sensing tasks has been limited by low quantities of ground truth labels, especially fully segmented ones, in the remote sensing domain. In this work, we perform cropland segmentation using two types of labels commonly found in remote sensing datasets that can be considered sources of &ldquo;weak supervision&rdquo;: (1) labels comprised of single geotagged points and (2) image-level labels. We demonstrate that (1) a U-Net trained on a single labeled pixel per image and (2) a U-Net image classifier transferred to segmentation can outperform pixel-level algorithms such as logistic regression, support vector machine, and random forest. While the high performance of neural networks is well-established for large datasets, our experiments indicate that U-Nets trained on weak labels outperform baseline methods with as few as 100 labels. Neural networks, therefore, can combine superior classification performance with efficient label usage, and allow pixel-level labels to be obtained from image labels

Quorum-sensing agr mediates bacterial oxidation response via an intramolecular disulfide redox switch in the response regulator AgrA

Oxidation sensing and quorum sensing significantly affect bacterial physiology and host–pathogen interactions. However, little attention has been paid to the cross-talk between these two seemingly orthogonal signaling pathways. Here we show that the quorum-sensing agr system has a built-in oxidation-sensing mechanism through an intramolecular disulfide switch possessed by the DNA-binding domain of the response regulator AgrA. Biochemical and mass spectrometric analysis revealed that oxidation induces the intracellular disulfide bond formation between Cys-199 and Cys-228, thus leading to dissociation of AgrA from DNA. Molecular dynamics (MD) simulations suggest that the disulfide bond formation generates a steric clash responsible for the abolished DNA binding of the oxidized AgrA. Mutagenesis studies further established that Cys-199 is crucial for oxidation sensing. The oxidation-sensing role of Cys-199 is further supported by the observation that the mutant Staphylococcus aureus strain expressing AgrAC199S is more susceptible to H2O2 owing to repression of the antioxidant bsaA gene under oxidative stress. Together, our results show that oxidation sensing is a component of the quorum-sensing agr signaling system, which serves as an intrinsic checkpoint to ameliorate the oxidation burden caused by intense metabolic activity and potential host immune response