Research Experience and Training Coordination Core (RETCC)

Exposure to environmental heavy metals such as cadmium (Cd) is often linked to neurotoxicity but the underlying mechanisms remain poorly understood. Here we show that Arrestin domain-containing protein 1 (ARRDC1)-mediated microvesicles (ARMMs)–an important class of extracellular vesicles (EVs) whose biogenesis occurs at the plasma membrane–protect against Cd-induced neurotoxicity. Cd increased the production of EVs, including ARMMs, in a human neural progenitor cell line, ReNcell CX (ReN) cells. ReN cells that lack ARMMs production as a result of CRISPR-mediated ARRDC1 knockout were more susceptible to Cd toxicity as evidenced by increased LDH production as well as elevated level of oxidative stress markers. Importantly, adding ARMMs back to the ARRDC1-knockout ReN cells significantly reduced Cd-induced toxicity. Consistent with this finding, proteomics data showed that anti-oxidative stress proteins are enriched in ARMMs secreted from ReN cells. Together our study reveals a novel protective role of ARMMs in Cd neurotoxicity and suggests that ARMMs may be used therapeutically to reduce neurotoxicity caused by exposure to Cd and potentially other metal toxicants.

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.

Adsorption is a promising under-the-sink selenate remediation technique for distributed water systems. Recently it was shown that adsorption induced water network rearrangement control adsorption energetics on the α-Al₂O₃ (012) surface. Here, we aim to elucidate the relative importance of the water network effects and surface cation identity on controlling selenate and sulfate adsorption energy using density functional theory calculations. Density functional theory (DFT) calculations predicted the adsorption energies of selenate and sulfate on nine transition metal cations (Sc–Cu) and two alkali metal cations (Ga and In) in the α-Al₂O₃ (012) surface under simulated acidic and neutral pH conditions. We find that the water network effects had a larger impact on the adsorption energy than the cationic identity. However, cation identity secondarily controlled adsorption. Most cations decreased the adsorption energy, weakening the overall performance, the larger Sc and In cations enabled inner-sphere adsorption in acidic conditions because they relaxed outward from the surface, providing more space for adsorption. Additionally, only Ti induced Se selectivity over S by reducing the adsorbing selenate to selenite but not reducing the sulfate. Overall, this study indicates that tuning water network structure will likely have a larger impact than tuning cation–selenate interactions for increasing adsorbate effectiveness.

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.

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.

Millions of households still rely on drinking water from private wells or municipal systems with arsenic levels approaching or exceeding regulatory limits. Arsenic is a potent carcinogen, and there is no safe level of it in drinking water. Point-of-use (POU) treatment systems are a promising option to mitigate arsenic exposure. However, the most commonly used POU technology, an activated carbon block filter, is ineffective at removing arsenic. Our study aimed to explore the potential of impregnating carbon blocks with amorphous titanium (hydr)oxide (THO) to improve arsenic removal without introducing titanium (Ti) into the treated water. Four synthesis methods achieved 8–16 wt % Ti-loading within the carbon block with a 58–97% amorphous THO content. The THO-modified carbon block could adsorb both oxidation states of arsenic (arsenate and arsenite) in batch or column tests. Modified carbon block with higher Ti and amorphous content always led to better arsenate removal, achieving arsenic loadings up to 31 mg As/mg Ti after 70,000 bed volumes in continuous-flow tests. Impregnating carbon block with amorphous THO consistently outperformed impregnation using crystalline TiO2. The best-performing system (TTIP-EtOH carbon block) was an amorphous THO derived using titanium isopropoxide, ethanol, and acetic acid via the sol–gel technique, aged at 80 °C for 18 h and dried overnight at 60 °C. Comparable pore-size distribution and surface area of the impregnated carbon blocks suggested that chemical properties play a more crucial role than physical and textural properties in removing arsenate via the amorphous Ti-impregnated carbon block. Freundlich isotherms indicated energetically favorable adsorption for amorphous chemically synthesized adsorbents. The mass transport coefficients for the amorphous TTIP-EtOH carbon block were fitted using a pore-surface diffusion model, resulting in Dsurface = 3.1 × 10–12 and Dpore = 3.2 × 10–6 cm2/s. Impregnating the carbon block with THO enabled effective arsenic removal from water without adversely affecting the pressure drop across the unit or the carbon block’s ability to remove polar organic chemical pollutants efficiently.Millions of households still rely on drinking water from private wells or municipal systems with arsenic levels approaching or exceeding regulatory limits. Arsenic is a potent carcinogen, and there is no safe level of it in drinking water. Point-of-use (POU) treatment systems are a promising option to mitigate arsenic exposure. However, the most commonly used POU technology, an activated carbon block filter, is ineffective at removing arsenic. Our study aimed to explore the potential of impregnating carbon blocks with amorphous titanium (hydr)oxide (THO) to improve arsenic removal without introducing titanium (Ti) into the treated water. Four synthesis methods achieved 8–16 wt % Ti-loading within the carbon block with a 58–97% amorphous THO content. The THO-modified carbon block could adsorb both oxidation states of arsenic (arsenate and arsenite) in batch or column tests. Modified carbon block with higher Ti and amorphous content always led to better arsenate removal, achieving arsenic loadings up to 31 mg As/mg Ti after 70,000 bed volumes in continuous-flow tests. Impregnating carbon block with amorphous THO consistently outperformed impregnation using crystalline TiO2. The best-performing system (TTIP-EtOH carbon block) was an amorphous THO derived using titanium isopropoxide, ethanol, and acetic acid via the sol–gel technique, aged at 80 °C for 18 h and dried overnight at 60 °C. Comparable pore-size distribution and surface area of the impregnated carbon blocks suggested that chemical properties play a more crucial role than physical and textural properties in removing arsenate via the amorphous Ti-impregnated carbon block. Freundlich isotherms indicated energetically favorable adsorption for amorphous chemically synthesized adsorbents. The mass transport coefficients for the amorphous TTIP-EtOH carbon block were fitted using a pore-surface diffusion model, resulting in Dsurface = 3.1 × 10–12 and Dpore = 3.2 × 10–6 cm2/s. Impregnating the carbon block with THO enabled effective arsenic removal from water without adversely affecting the pressure drop across the unit or the carbon block’s ability to remove polar organic chemical pollutants efficiently.

Hazardous air pollutants emitted by United States (U.S) coal-fired power plants have been controlled by the Mercury and Air Toxics Standards (MATS) since 2012. Sociodemographic disparities in traditional air pollutant exposures from U.S. power plants are known to occur but have not been evaluated for mercury (Hg), a neurotoxicant that bioaccumulates in food webs. Atmospheric Hg deposition from domestic power plants decreased by 91% across the contiguous U.S. from 6.4 Mg in 2010 to 0.55 Mg in 2020. Prior to MATS, populations living within 5 km of power plants ( = 507) included greater proportions of frequent fish consumers, individuals with low annual income and less than a high school education, and limited English-proficiency households compared to the US general population. These results reinforce a lack of distributional justice in plant siting found in prior work. Significantly greater proportions of low-income individuals lived within 5 km of active facilities in 2020 ( = 277) compared to plants that retired after 2010, suggesting that socioeconomic status may have played a role in retirement. Despite large deposition declines, an end-member scenario for remaining exposures from the largest active power plants for individuals consuming self-caught fish suggests they could still exceed the U.S. Environmental Protection Agency reference dose for methylmercury.

Single atom catalysts have been found to exhibit superior selectivity over nanoparticulate catalysts for catalytic reactions such as hydrogenation due to their single-site nature. However, improved selectively is often accompanied by loss of activity and slow kinetics. Here we demonstrate that neighboring Pd single atom catalysts retain the high selectivity merit of sparsely isolated single atom catalysts, while the cooperative interactions between neighboring atoms greatly enhance the activity for hydrogenation of carbon-halogen bonds. Experimental results and computational calculations suggest that neighboring Pd atoms work in synergy to lower the energy of key meta-stable reactions steps, i.e., initial water desorption and final hydrogenated product desorption. The placement of neighboring Pd atoms also contribute to nearly exclusive hydrogenation of carbon-chlorine bond without altering any other bonds in organohalogens. The promising hydrogenation performance achieved by neighboring single atoms sheds light on a new approach for manipulating the activity and selectivity of single atom catalysts that are increasingly studied in multiple applications.

Development of novel adsorbents often neglects the competitive adsorption between co-occurring oxo-anions, overestimating realistic pollutant removal potentials, and overlooking the need to improve selectivity of materials. This critical review focuses on adsorptive competition between commonly co-occurring oxo-anions in water and mechanistic approaches for the design and development of selective adsorbents. Six "target" oxo-anion pollutants (arsenate, arsenite, selenate, selenite, chromate, and perchlorate) were selected for study. Five "competing" co-occurring oxo-anions (phosphate, sulfate, bicarbonate, silicate, and nitrate) were selected due to their potential to compete with target oxo-anions for sorption sites resulting in decreased removal of the target oxo-anions. First, a comprehensive review of competition between target and competitor oxo-anions to sorb on commonly used, nonselective, metal (hydr)oxide materials is presented, and the strength of competition between each target and competitive oxo-anion pair is classified. This is followed by a critical discussion of the different equations and models used to quantify selectivity. Next, four mechanisms that have been successfully utilized in the development of selective adsorbents are reviewed: variation in surface complexation, Lewis acid/base hardness, steric hindrance, and electrostatic interactions. For each mechanism, the oxo-anions, both target and competitors, are ranked in terms of adsorptive attraction and technologies that exploit this mechanism are reviewed. Third, given the significant effort to evaluate these systems empirically, the potential to use computational quantum techniques, such as density functional theory (DFT), for modeling and prediction is explored. Finally, areas within the field of selective adsorption requiring further research are detailed with guidance on priorities for screening and defining selective adsorbents.

Nanoscale metal oxides (NMOs) have found wide-scale applicability in a variety of environmental fields, particularly catalysis, gas sensing, and sorption. Facet engineering, or controlled exposure of a particular crystal plane, has been established as an advantageous approach to enabling enhanced functionality of NMOs. However, the underlying mechanisms that give rise to this improved performance are often not systematically examined, leading to an insufficient understanding of NMO facet reactivity. This critical review details the unique electronic and structural characteristics of commonly studied NMO facets and further correlates these characteristics to the principal mechanisms that govern performance in various catalytic, gas sensing, and contaminant removal applications. General trends of facet-dependent behavior are established for each of the NMO compositions, and selected case studies for extensions of facet-dependent behavior, such as mixed metals, mixed-metal oxides, and mixed facets, are discussed. Key conclusions about facet reactivity, confounding variables that tend to obfuscate them, and opportunities to deepen structure-property-function understanding are detailed to encourage rational, informed design of NMOs for the intended application.