Publications

Lead (Pb) and arsenic (As) are prevalent metal contaminants in the environment. Exposures to these metals are associated with impaired neuronal functions and adverse effects on neurodevelopment in children. However, the molecular mechanisms by which Pb and As impair neuronal functions remain poorly understood. Here, we identified F2RL2, TRIM16L, and PANX2 as novel targets of Nuclear factor erythroid 2-related factor 2 (NRF2)-the master transcriptional factor for the oxidative stress response-that are commonly upregulated with both Pb and As in human neural progenitor cells (NPCs). Using a ChIP (Chromatin immunoprecipitation)-qPCR assay, we showed that NRF2 directly binds to the promoter region of F2RL2, TRIM16L, and PANX2 to regulate expression of these genes. We demonstrated that F2RL2, PANX2, and TRIM16L have differential effects on cell death, proliferation, and differentiation of NPCs in both the presence and absence of metal exposures, highlighting their roles in regulating NPC function. Furthermore, the analyses of the transcriptomic data on NPCs derived from autism spectrum disorder (ASD) patients revealed that dysregulation of F2RL2, TRIM16L, and PANX2 was associated with ASD genetic backgrounds and ASD risk genes. Our findings revealed that Pb and As induce a shared NRF2-dependent transcriptional response in NPCs and identified novel genes regulating NPC function. While further in vivo studies are warranted, this study provides a novel mechanism linking metal exposures to NPC function and identifies potential genes of interest in the context of neurodevelopment.

Fe-cross-linked chitosan exhibits the potential for selectively adsorbing arsenic (As) over competing species, such as phosphate, for water remediation. However, the effective binding mechanisms, bond nature, and controlling factor(s) of the selectivity are poorly understood. This study employs ab initio calculations to examine the competitive binding of As(V), P(V), and As(III) to neat chitosan and Fe-chitosan. Neat chitosan fails to selectively bind As oxyanions, as all three oxyanions bind similarly via weak hydrogen bonds with preferences of P(V) = As(V) > As(III). Conversely, Fe-chitosan selectively binds As(V) over As(III) and P(V) with binding energies of -1.9, -1, and -1.8 eV for As(V), As(III), and P(V), respectively. The preferences are due to varying Fe-oxyanion donor-acceptor characteristics, forming covalent bonds with distinct strengths (Fe-O bond ICOHP values: - 4.9 eV/bond for As(V), - 4.7 eV/bond for P(V), and -3.5 eV/bond for As(III)). Differences in p between As(V)/P(V) and As(III) preclude any preference for As(III) under typical environmental pH conditions. Furthermore, our calculations suggest that the binding selectivity of Fe-chitosan exhibits a pH dependence. These findings enhance our understanding of the Fe-oxyanion interaction crucial for preferential oxyanion binding using Fe-chitosan and provide a lens for further exploration into alternative transition-metal-chitosan combinations and coordination chemistries for applications in selective separations.

BACKGROUND: Spatial elemental analysis of deciduous tooth dentin combined with odontochronological estimates can provide an early life (in utero to ~2 years of age) history of inorganic element exposure and status.

OBJECTIVE: To demonstrate the importance of data normalization to a certified reference material to enable between-study comparisons, using populations with assumed contrasting elemental exposures.

METHODS: We used laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) of dentin to derive a history of elemental composition from three distinct cohort studies: a present day rural cohort, (the New Hampshire Birth Cohort Study (NHBCS; N = 154)), an historical cohort from an urban area (1958-1970), (the St. Louis Baby Tooth Study (SLBT; N = 78)), and a present-day Nigerian cohort established to study maternal HIV transmission (Dental caries and its association with Oral Microbiomes and HIV in young children-Nigeria (DOMHaIN; N = 31)).

RESULTS: We report Li, Al, Mn, Cu, Zn, Sr, Ba and Pb concentrations (µg/g) and qualitatively examine As, Cd and Hg across all three cohorts. Rates of detection were highest, both overall and for each cohort individually, for Zn, Sr, Ba and Li. Zinc was detected in 100% of samples and was stably present in teeth at a concentration range of 64 - 86 µg/g. Mercury, As and Cd detection rates were the lowest, and had high variability within individual ablated spots. We found the highest concentrations of Pb in the pre- and postnatal dentin of the SLBT cohort, consistent with the prevalent use of Pb as an additive to gasoline prior to 1975. The characteristic decline in Mn after the second trimester was observed in all cohorts.

IMPACT: Spatially resolved elemental analysis of deciduous teeth combined with methods for estimating crown formation times can be used to reconstruct an early-life history of elemental exposure inaccessible via other biomarkers. Quantification of data into absolute values using an external standard reference material has not been conducted since 2012, preventing comparison between studies, a common and highly informative component of epidemiology. We demonstrate, with three contrasting populations, that absolute quantification produces data with the lowest variability, compares well with available data and recommends that future tooth biomarker studies report data in this way.

We consider asymptotically exact inference on the leading canonical correlation directions and strengths between two high dimensional vectors under sparsity restrictions. In this regard, our main contribution is the development of a loss function, based on which, one can operationalize a one-step bias-correction on reasonable initial estimators. Our analytic results in this regard are adaptive over suitable structural restrictions of the high dimensional nuisance parameters, which, in this set-up, correspond to the covariance matrices of the variables of interest. We further supplement the theoretical guarantees behind our procedures with extensive numerical studies.

PURPOSE OF REVIEW: This review aims to better understand the utility of machine learning algorithms for predicting spatial patterns of contaminants in the United States (U.S.) drinking water.

RECENT FINDINGS: We found 27 U.S. drinking water studies in the past ten years that used machine learning algorithms to predict water quality. Most studies (42%) developed random forest classification models for groundwater. Continuous models show low predictive power, suggesting that larger datasets and additional predictors are needed. Categorical/classification models for arsenic and nitrate that predict exceedances of pollution thresholds are most common in the literature because of good national scale data coverage and priority as environmental health concerns. Most groundwater data used to develop models were obtained from the United States Geological Survey (USGS) National Water Information System (NWIS). Predictors were similar across contaminants but challenges are posed by the lack of a standard methodology for imputation, pre-processing, and differing availability of data across regions. We reviewed 27 articles that focused on seven drinking water contaminants. Good performance metrics were reported for binary models that classified chemical concentrations above a threshold value by finding significant predictors. Classification models are especially useful for assisting in the design of sampling efforts by identifying high-risk areas. Only a few studies have developed continuous models and obtaining good predictive performance for such models is still challenging. Improving continuous models is important for potential future use in epidemiological studies to supplement data gaps in exposure assessments for drinking water contaminants. While significant progress has been made over the past decade, methodological advances are still needed for selecting appropriate model performance metrics and accounting for spatial autocorrelations in data. Finally, improved infrastructure for code and data sharing would spearhead more rapid advances in machine-learning models for drinking water quality.

Set-based association tests are widely popular in genetic association settings for their ability to aggregate weak signals and reduce multiple testing burdens. In particular, a class of set-based tests including the Higher Criticism, Berk-Jones, and other statistics have recently been popularized for reaching a so-called detection boundary when signals are rare and weak. Such tests have been applied in two subtly different settings: (a) associating a genetic variant set with a single phenotype and (b) associating a single genetic variant with a phenotype set. A significant issue in practice is the choice of test, especially when deciding between innovated and generalized type methods for detection boundary tests. Conflicting guidance is present in the literature. This work describes how correlation structures generate marked differences in relative operating characteristics for settings (a) and (b). The implications for study design are significant. We also develop novel power bounds that facilitate the aforementioned calculations and allow for analysis of individual testing settings. In more concrete terms, our investigation is motivated by translational expression quantitative trait loci (eQTL) studies in lung cancer. These studies involve both testing for groups of variants associated with a single gene expression (multiple explanatory factors) and testing whether a single variant is associated with a group of gene expressions (multiple outcomes). Results are supported by a collection of simulation studies and illustrated through lung cancer eQTL examples.

Centralized water infrastructure has, over the last century, brought safe and reliable drinking water to much of the world. But climate change, combined with aging and underfunding, is increasingly testing the limits of-and reversing gains made by-these large-scale water systems. To address these growing strains and gaps, we must assess and advance alternatives to centralized water provision and sanitation. The water literature is rife with examples of systems that are neither centralized nor networked, but still meet water needs of local communities in important ways, including: informal and hybrid water systems, decentralized water provision, community-based water management, small drinking water systems, point-of-use treatment, small-scale water vendors, and packaged water. Our work builds on these literatures by proposing a convergence approach that can integrate and explore the benefits and challenges of modular, adaptive, and decentralized ("MAD") water provision and sanitation, often foregrounding important advances in engineering technology. We further provide frameworks to evaluate justice, economic feasibility, governance, human health, and environmental sustainability as key parameters of MAD water system performance.

Adsorption and ion exchange technologies are two of the most widely used approaches to separate pollutants from water; however, their intrinsic diffusion limitations continue to be a challenge. Pore functionalized membranes are a promising technology that can help overcome these challenges, but the extents of their competitive benefits and broad applicability have not been systematically evaluated. Herein, three types of adsorptive/ion exchange (IX) polymers containing strong/weak acid, strong base, and iron-chitosan complex groups were synthesized in the pores and partially on the surface of microfiltration (MF) membranes and tested for the removal of organic and inorganic cations and anions from water, including arsenic, per- and polyfluoroalkyl substances (PFAS), and calcium (hardness). When directly compared with beads (0.5-6 mm) and crushed resins (0.05 mm), adsorptive/IX pore-functionalized membranes demonstrated an increased relative sorption capacity, up to 2 orders of magnitude faster kinetics and the ability to regenerate up to 70-100% of their capacity while concentrating the initial solution concentration up to 12 times. The simple and versatile synthesis approach used to functionalize membranes, notably independent of the polymer type of the MF membrane, utilized pores throughout the entire cross section of the membrane to immobilize the polymers that contain the functional groups. Utilizing the pore volume of commercial membranes (6-112 mL/m), the scientific weight capacity of the polymer (3.1-11.5 mequiv/g), and the synthesis conditions (e.g., monomer concentration), the theoretical adsorption/IX capacities per area of the membranes were calculated to be as high as 550 mequiv/m, substantially higher than the 175 mequiv/m value needed to compete with commercially available IX resins. This work therefore shows that pore functionalized membranes are a promising path to tackle water contamination challenges, lowering separation diffusion limitations.

We consider asymptotically exact inference on the leading canonical correlation directions and strengths between two high-dimensional vectors under sparsity restrictions. In this regard, our main contribution is developing a novel representation of the Canonical Correlation Analysis problem, based on which one can operationalize a one-step bias correction on reasonable initial estimators. Our analytic results in this regard are adaptive over suitable structural restrictions of the high-dimensional nuisance parameters, which, in this set-up, correspond to the covariance matrices of the variables of interest. We further supplement the theoretical guarantees behind our procedures with extensive numerical studies.

Distributed lag models (DLMs) are often used to estimate lagged associations and identify critical exposure windows. In a simulation study of prenatal nitrogen dioxide (NO2) exposure and birth weight, we demonstrate that bias amplification and variance inflation can manifest under certain combinations of DLM estimation approaches and time-trend adjustment methods when using low-spatial-resolution exposures with extended lags. Our simulations showed that when using high-spatial-resolution exposure data, any time-trend adjustment method produced low bias and nominal coverage for the distributed lag estimator. When using either low- or no-spatial-resolution exposures, bias due to time trends was amplified for all adjustment methods. Variance inflation was higher in low- or no-spatial-resolution DLMs when using a long-term spline to adjust for seasonality and long-term trends due to concurvity between a distributed lag function and secular function of time. NO2-birth weight analyses in a Massachusetts-based cohort showed that associations were negative for exposures experienced in gestational weeks 15-30 when using high-spatial-resolution DLMs; however, associations were null and positive for DLMs with low- and no-spatial-resolution exposures, respectively, which is likely due to bias amplification. DLM analyses should jointly consider the spatial resolution of exposure data and the parameterizations of the time trend adjustment and lag constraints.

Membrane proteins expressed on the surface of enveloped viruses are conformational antigens readily recognized by B cells of the immune system. An effective vaccine would require the synthesis and delivery of these native conformational antigens in lipid membranes that preserve specific epitope structures. We have created an extracellular vesicle-based technology that allows viral membrane antigens to be selectively recruited onto the surface of WW domain-activated extracellular vesicles (WAEVs). Budding of WAEVs requires secretory carrier-associated membrane protein 3, which through its proline-proline-alanine-tyrosine motif interacts with WW domains to recruit fused viral membrane antigens onto WAEVs. Immunization with influenza and HIV viral membrane proteins displayed on WAEVs elicits production of virus-specific neutralizing antibodies and, in the case of influenza antigens, protects mice from the lethal viral infection. WAEVs thus represent a versatile platform for presenting and delivering membrane antigens as vaccines against influenza, HIV, and potentially many other viral pathogens.

BACKGROUND: Early-life stressful experiences are associated with increased risk of adverse psychological outcomes in later life. However, much less is known about associations between early-life positive experiences, such as participation in cognitively stimulating activities, and late-life mental health. We investigated whether greater engagement in cognitively stimulating activities in early life is associated with lower risk of depression and anxiety in late life.

METHODS: We surveyed former participants of the St. Louis Baby Tooth study, between 22 June 2021 and 25 March 2022 to collect information on participants' current depression/anxiety symptoms and their early-life activities ( = 2187 responded). A composite activity score was created to represent the early-life activity level by averaging the frequency of self-reported participation in common cognitively stimulating activities in participants' early life (age 6, 12, 18), each rated on a 1 (least frequent) to 5 (most frequent) point scale. Depression/anxiety symptoms were measured by Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder Screener (GAD-7). We used logistic regressions to estimate odds ratios (OR) and 95% confidence intervals (CI) of outcome risk associated with frequency of early-life activity.

RESULTS: Each one-point increase in the early-life composite cognitive activity score was associated with an OR of 0.54 (95% CI 0.38-0.77) for late-life depression and an OR of 0.94 (95% CI 0.61-1.43) for late-life anxiety, adjusting for age, sex, race, parental education, childhood family structure, and socioeconomic status.

CONCLUSIONS: More frequent participation in cognitively stimulating activities during early life was associated with reduced risk of late-life depression.

Organic capping agents are a ubiquitous and crucial part of preparing reproducible and homogeneous batches of nanomaterials, particularly nanocrystals with well-defined facets. Despite studies reporting surface ligands (e.g., capping agents) having a non-negligible role in catalytic behavior, their impact is less understood in contaminant adsorption, an important consideration given their potential to obfuscate facet-dependent trends in performance. To ascribe observed behaviors to the facet or the ligand, this report evaluates the impact of poly(-vinyl-2-pyrrolidone) (PVP), a commonly utilized capping agent, on the adsorption performance of nanohematite particles of varying prevailing facet in the removal of selenite (Se(IV)) as a model system. The PVP capping agent reduces the available surface area for contaminant binding, thus resulting in a reduction in overall Se(IV) adsorbed. However, accounting for the effects of surface area, {012}-faceted nanohematite demonstrates a significantly higher sorption capacity for Se(IV) compared with that of {001}-faceted nanohematite. Notably, chemical treatment is minimally effective in removing strongly bound PVP, indicating that complete removal of surface ligands remains challenging.

Large biobank-scale whole genome sequencing (WGS) studies are rapidly identifying a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries and functionally annotate the genotype data of large biobank-scale WGS studies. We develop the Functional Annotation of Variants Online Resources (FAVOR) to meet these pressing needs. FAVOR provides a comprehensive multi-faceted variant functional annotation online portal that summarizes and visualizes findings of all possible nine billion single nucleotide variants (SNVs) across the genome. It allows for rapid variant-, gene- and region-level queries of variant functional annotations. FAVOR integrates variant functional information from multiple sources to describe the functional characteristics of variants and facilitates prioritizing plausible causal variants influencing human phenotypes. Furthermore, we provide a scalable annotation tool, FAVORannotator, to functionally annotate large-scale WGS studies and efficiently store the genotype and their variant functional annotation data in a single file using the annotated Genomic Data Structure (aGDS) format, making downstream analysis more convenient. FAVOR and FAVORannotator are available at https://favor.genohub.org.

Chlorine-based disinfection for drinking water treatment (DWT) was one of the 20th century's great public health achievements, as it substantially reduced the risk of acute microbial waterborne disease. However, today's chlorinated drinking water is not unambiguously safe; trace levels of regulated and unregulated disinfection byproducts (DBPs), and other known, unknown, and emerging contaminants (KUECs), present chronic risks that make them essential removal targets. Because conventional chemical-based DWT processes do little to remove DBPs or KUECs, alternative approaches are needed to minimize risks by removing DBP precursors and KUECs that are ubiquitous in water supplies. We present the "Minus Approach" as a toolbox of practices and technologies to mitigate KUECs and DBPs without compromising microbiological safety. The Minus Approach reduces problem-causing chemical addition treatment (i.e., the conventional "Plus Approach") by producing biologically stable water containing pathogens at levels having negligible human health risk and substantially lower concentrations of KUECs and DBPs. Aside from ozonation, the Minus Approach avoids primary chemical-based coagulants, disinfectants, and advanced oxidation processes. The Minus Approach focuses on bank filtration, biofiltration, adsorption, and membranes to biologically and physically remove DBP precursors, KUECs, and pathogens; consequently, water purveyors can use ultraviolet light at key locations in conjunction with smaller dosages of secondary chemical disinfectants to minimize microbial regrowth in distribution systems. We describe how the Minus Approach contrasts with the conventional Plus Approach, integrates with artificial intelligence, and can ultimately improve the sustainability performance of water treatment. Finally, we consider barriers to adoption of the Minus Approach.