Investigating the enhancement of air pollutant predictions and understanding air quality disparities across racial, ethnic, and economic lines at US public schools

2022 Spring.Includes bibliographical references.Ambient air pollution has significant health and economic impacts worldwide. Even in the most developed countries, monitoring networks often lack the spatiotemporal density to resolve air pollution gradients. Though air pollution affects the entire population, it can disproportionately affect the disadvantaged and vulnerable communities in society. Pollutants such as fine particulate matter (PM2.5), nitrogen oxides (NO and NO2), and ozone, which have a variety of anthropogenic and natural sources, have garnered substantial research attention over the last few decades. Over half the world and over 80% of Americans live in urban areas, and yet many cities only have one or several air quality monitors, which limits our ability to capture differences in exposure within cities and estimate the resulting health impacts. Improving sub-city air pollution estimates could improve epidemiological and health-impact studies in cities with heterogeneous distributions of PM2.5, providing a better understanding of communities at-risk to urban air pollution. Biomass burning is a source of PM2.5 air pollution that can impact both urban and rural areas, but quantifying the health impacts of PM2.5 from biomass burning can be even more difficult than from urban sources. Monitoring networks generally lack the spatial density needed to capture the heterogeneity of biomass burning smoke, especially near the source fires. Due to limitations of both urban and rural monitoring networks several techniques have been developed to supplement and enhance air pollution estimates. For example, satellite aerosol optical depth (AOD) can be used to fill spatial gaps in PM monitoring networks, but AOD can be disconnected from time-resolved surface-level PM in a multitude of ways, including the limited overpass times of most satellites, daytime-only measurements, cloud cover, surface reflectivity, and lack of vertical-profile information. Observations of smoke plume height (PH) may provide constraints on the vertical distribution of smoke and its impact on surface concentrations. Low-cost sensor networks have been rapidly expanding to provide higher density air pollution monitoring. Finally, both geophysical modeling, statistical techniques such as machine learning and data mining, and combinations of all of the aforementioned datasets have been increasingly used to enhance surface observations. In this dissertation, we explore several of these different data sources and techniques for estimating air pollution and determining community exposure concentrations. In the first chapter of this dissertation, we assess PH characteristics from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and evaluate its correlation with co-located PM2.5 and AOD measurements. PH is generally highest over the western US. The ratio PM2.5:AOD generally decreases with increasing PH:PBLH (planetary boundary layer height), showing that PH has the potential to refine surface PM2.5 estimates for collections of smoke events. Next, to estimate spatiotemporal variability in PM2.5, we use machine learning (Random Forests; RFs) and concurrent PM2.5 and AOD measurements from the Citizen Enabled Aerosol Measurements for Satellites (CEAMS) low-cost sensor network as well as PM2.5 measurements from the Environmental Protection Agency's (EPA) reference monitors during wintertime in Denver, CO, USA. The RFs predicted PM2.5 in a 5-fold cross validation (CV) with relatively high skill (95% confidence interval R2=0.74-0.84 for CEAMS; R2=0.68-0.75 for EPA) though the models were aided by the spatiotemporal autocorrelation of the PM22.5 measurements. We find that the most important predictors of PM2.5 are factors associated with pooling of pollution in wintertime, such as low planetary boundary layer heights (PBLH), stagnant wind conditions, and, to a lesser degree, elevation. In general, spatial predictors are less important than spatiotemporal predictors because temporal variability exceeds spatial variability in our dataset. Finally, although concurrent AOD is an important predictor in our RF model for hourly PM2.5, it does not improve model performance during our campaign period in Denver. Regardless, we find that low-cost PM2.5 measurements incorporated into an RF model were useful in interpreting meteorological and geographic drivers of PM2.5 over wintertime Denver. We also explore how the RF model performance and interpretation changes based on different model configurations and data processing. Finally, we use high resolution PM2.5 and nitrogen dioxide (NO2) estimates to investigate socioeconomic disparities in air quality at public schools in the contiguous US. We find that Black and African American, Hispanic, and Asian or Pacific Islander students are more likely to attend schools in locations where the ambient concentrations of NO2 and PM2.5 are above the World Health Organization's (WHO) guidelines for annual-average air quality. Specifically, we find that ~95% of students that identified as Asian or Pacific Islander, 94% of students that identified as Hispanic, and 89% of students that identified as Black or African American, attended schools in locations where the 2019 ambient concentrations were above the WHO guideline for NO2 (10 μg m-3 or ~5.2 ppbv). Conversely, only 83% of students that identified as white and 82% of those that identified as Native American attended schools in 2019 where the ambient NO2 concentrations were above the WHO guideline. Similar disparities are found in annually averaged ambient PM2.5 across racial and ethnic groups, where students that identified as white (95%) and Native American (83%) had a smallest percentage of students above the WHO guideline (5 μg m-3), compared to students that identified with minoritized groups (98-99%). Furthermore, the disparity between white students and other minoritized groups, other than Native Americans, is larger at higher PM2.5 concentrations. Students that attend schools where a higher percentage of students are eligible for free or reduced meals, which we use as a proxy for poverty, are also more likely to attend schools where the ambient air pollutant concentrations exceed WHO guidelines. These disparities also tend to increase in magnitude at higher concentrations of NO2 and PM2.5. We investigate the intersectionality of disparities across racial/ethnic and poverty lines by quantifying the mean difference between the lowest and highest poverty schools, and the most and least white schools in each state, finding that most states have disparities above 1 ppbv of NO2 and 0.5 μg m-3 of PM2.5 across both. We also identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we also highlight that disparities do not only exist across an urban and non-urban divide, but also within urban areas

Sensing centromere tension: Aurora B and the regulation of kinetochore function

Maintaining genome integrity during cell division requires regulated interactions between chromosomes and spindle microtubules. To ensure that daughter cells inherit the correct chromosomes, the sister kinetochores must attach to opposite spindle poles. Tension across the centromere stabilizes correct attachments, whereas phosphorylation of kinetochore substrates by the conserved Ipl1/Aurora B kinase selectively eliminates incorrect attachments. Here, we review our current understanding of how mechanical forces acting on the kinetochore are linked to biochemical changes to control chromosome segregation. We discuss models for tension sensing and regulation of kinetochore function downstream of Aurora B, and mechanisms that specify Aurora B localization to the inner centromere and determine its interactions with substrates at distinct locations.National Institutes of Health (U.S.) (Grant GM088313)Kinship Foundation. Searle Scholars Progra

Coastal Shelf Model of Northern European waters to inform tidal power industry decisions

Water Qualit

Cytokine Expression in Chicken Peripheral Blood Mononuclear Cells after In Vitro Exposure to Salmonella enterica serovar Enteritidis

Cytokines are secreted proteins involved with cell recruitment and regulation of both innate and adaptive immune responses. They are essential for an effective host immune response to pathogens. The objective of this study was to determine the effect of Salmonella enterica serovar Enteritidis (S. Enteritidis) exposure and genetic line on cytokine mRNA expression level of cultured chicken peripheral blood mononuclear cells (PBMC). Interleukin-2, interleukin-6 (IL-6), CXCLi2, and transforming growth factor-β4 (TGF-B4) messenger ribonucleic acid expression was measured by quantitative reverse transcription-PCR assays in PBMC from 3 chicken lines (broiler, Leghorn, Fayoumi) after in vitro exposure to S. Enteritidis. The PBMC were isolated from uninfected birds and cultured overnight. The next day, live pathogenic S. Enteritidis was added to half of the cultures. All cultures were harvested after 2 or 4 h of exposure. Exposure to S. Enteritidis downregulated IL-6, CXCLi2, and TGF-β4 but not interleukin-2 mRNA expression. No significant genetic line or exposure time effects were detected. These findings demonstrate that exposure of chicken PBMC to S. Enteritidis can induce a rapid change in both proinflammatory (IL-6, CXCLi2) and antiinflammatory (TGF-β4) cytokine gene expression

Aurora B phosphorylates spatially distinct targets to differentially regulate the kinetochore-microtubule interface

Accurate chromosome segregation requires carefully regulated interactions between kinetochores and microtubules, but how plasticity is achieved to correct diverse attachment defects remains unclear. Here we demonstrate that Aurora B kinase phosphorylates three spatially distinct targets within the conserved outer kinetochore KNL1/Mis12 complex/Ndc80 complex (KMN) network, the key player in kinetochore-microtubule attachments. The combinatorial phosphorylation of the KMN network generates graded levels of microtubule-binding activity, with full phosphorylation severely compromising microtubule binding. Altering the phosphorylation state of each protein causes corresponding chromosome segregation defects. Importantly, the spatial distribution of these targets along the kinetochore axis leads to their differential phosphorylation in response to changes in tension and attachment state. In total, rather than generating exclusively binary changes in microtubule binding, our results suggest a mechanism for the tension-dependent fine-tuning of kinetochore-microtubule interactions.Smith Family FoundationMassachusetts Life Sciences CenterKinship Foundation. Searle Scholars ProgramNational Institute of General Medical Sciences (U.S.) (Grant number GM088313

Ectodysplasin signalling deficiency in mouse models of Hypohidrotic Ectodermal Dysplasia leads to middle ear and nasal pathology

Hypohidrotic ectodermal dysplasia (HED) results from mutation of the EDA, EDAR or EDARADD genes and is characterized by reduced or absent eccrine sweat glands, hair follicles and teeth, and defective formation of salivary, mammary and craniofacial glands. Mouse models with HED also carry Eda, Edar or Edaradd mutations and have defects that map to the same structures. Patients with HED have ear, nose and throat disease, but this has not been investigated in mice bearing comparable genetic mutations. We report that otitis media, rhinitis and nasopharyngitis occur at high frequency in Eda and Edar mutant mice and explore the pathogenic mechanisms related to glandular function, microbial and immune parameters in these lines. Nasopharynx auditory tube glands fail to develop in HED mutant mice and the functional implications include loss of lysozyme secretion, reduced mucociliary clearance and overgrowth of nasal commensal bacteria accompanied by neutrophil exudation. Heavy nasopharynx foreign body load and loss of gland protection alters the auditory tube gating function and the auditory tubes can become pathologically dilated. Accumulation of large foreign body particles in the bulla stimulates granuloma formation. Analysis of immune cell populations and myeloid cell function shows no evidence of overt immune deficiency in HED mutant mice. Our findings using HED mutant mice as a model for the human condition support the idea that ear and nose pathology in HED patients arises as a result of nasal and nasopharyngeal gland deficits, reduced mucociliary clearance and impaired auditory tube gating function underlies the pathological sequelae in the bulla

Loss of the deubiquitylase BAP1 alters class I histone deacetylase expression and sensitivity of mesothelioma cells to HDAC inhibitors

Histone deacetylases are important targets for cancer therapeutics, but their regulation is poorly understood. Our data show coordinated transcription of HDAC1 and HDAC2 in lung cancer cell lines, but suggest HDAC2 protein expression is cell-context specific. Through an unbiased siRNA screen we found that BRCA1-associated protein 1 (BAP1) regulates their expression, with HDAC2 reduced and HDAC1 increased in BAP1 depleted cells. BAP1 loss-of-function is increasingly reported in cancers including thoracic malignancies, with frequent mutation in malignant pleural mesothelioma. Endogenous HDAC2 directly correlates with BAP1 across a panel of lung cancer cell lines, and is downregulated in mesothelioma cell lines with genetic BAP1 inactivation. We find that BAP1 regulates HDAC2 by increasing transcript abundance, rather than opposing its ubiquitylation. Importantly, although total cellular HDAC activity is unaffected by transient depletion of HDAC2 or of BAP1 due to HDAC1 compensation, this isoenzyme imbalance sensitizes MSTO-211H cells to HDAC inhibitors. However, other established mesothelioma cell lines with low endogenous HDAC2 have adapted to become more resistant to HDAC inhibition. Our work establishes a mechanism by which BAP1 loss alters sensitivity of cancer cells to HDAC inhibitors. Assessment of BAP1 and HDAC expression may ultimately help identify patients likely to respond to HDAC inhibitors