MEMCARE publication

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.

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.

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.

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.

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.

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.

In this paper, we consider asymptotically exact support recovery in the context of high dimensional and sparse Canonical Correlation Analysis (CCA). Our main results describe four regimes of interest based on information theoretic and computational considerations. In regimes of "low" sparsity we describe a simple, general, and computationally easy method for support recovery, whereas in a regime of "high" sparsity, it turns out that support recovery is information theoretically impossible. For the sake of information theoretic lower bounds, our results also demonstrate a non-trivial requirement on the "minimal" size of the nonzero elements of the canonical vectors that is required for asymptotically consistent support recovery. Subsequently, the regime of "moderate" sparsity is further divided into two subregimes. In the lower of the two sparsity regimes, we show that polynomial time support recovery is possible by using a sharp analysis of a co-ordinate thresholding [1] type method. In contrast, in the higher end of the moderate sparsity regime, appealing to the "Low Degree Polynomial" Conjecture [2], we provide evidence that polynomial time support recovery methods are inconsistent. Finally, we carry out numerical experiments to compare the efficacy of various methods discussed.

BACKGROUND: Aging-related cognitive decline is an early symptom of Alzheimer's disease and other dementias, and on its own can have substantial consequences on an individual's ability to perform important everyday functions. Despite increasing interest in the potential roles of extracellular microRNAs (miRNAs) in central nervous system (CNS) pathologies, there has been little research on extracellular miRNAs in early stages of cognitive decline. We leverage the longitudinal Normative Aging Study (NAS) cohort to investigate associations between plasma miRNAs and cognitive function among cognitively normal men.

METHODS: This study includes data from up to 530 NAS participants (median age: 71.0 years) collected from 1996 to 2013, with a total of 1,331 person-visits (equal to 2,471 years of follow up). Global cognitive function was assessed using the Mini-Mental State Examination (MMSE). Plasma miRNAs were profiled using small RNA sequencing. Associations of expression of 381 miRNAs with current cognitive function and rate of change in cognitive function were assessed using linear regression ( = 457) and linear mixed models ( = 530), respectively.

RESULTS: In adjusted models, levels of 2 plasma miRNAs were associated with higher MMSE scores ( < 0.05). Expression of 33 plasma miRNAs was associated with rate of change in MMSE scores over time ( < 0.05). Enriched KEGG pathways for miRNAs associated with concurrent MMSE and MMSE trajectory included Hippo signaling and extracellular matrix-receptor interactions. Gene targets of miRNAs associated with MMSE trajectory were additionally associated with prion diseases and fatty acid biosynthesis.

CONCLUSIONS: Circulating miRNAs were associated with both cross-sectional cognitive function and rate of change in cognitive function among cognitively normal men. Further research is needed to elucidate the potential functions of these miRNAs in the CNS and investigate relationships with other neurological outcomes.

In genome-wide epigenetic studies, it is of great scientific interest to assess whether the effect of an exposure on a clinical outcome is mediated through DNA methylations. However, statistical inference for causal mediation effects is challenged by the fact that one needs to test a large number of composite null hypotheses across the whole epigenome. Two popular tests, the Wald-type Sobel's test and the joint significant test using the traditional null distribution are underpowered and thus can miss important scientific discoveries. In this paper, we show that the null distribution of Sobel's test is not the standard normal distribution and the null distribution of the joint significant test is not uniform under the composite null of no mediation effect, especially in finite samples and under the singular point null case that the exposure has no effect on the mediator and the mediator has no effect on the outcome. Our results explain why these two tests are underpowered, and more importantly motivate us to develop a more powerful Divide-Aggregate Composite-null Test (DACT) for the composite null hypothesis of no mediation effect by leveraging epigenome-wide data. We adopted Efron's empirical null framework for assessing statistical significance of the DACT test. We showed analytically that the proposed DACT method had improved power, and could well control type I error rate. Our extensive simulation studies showed that, in finite samples, the DACT method properly controlled the type I error rate and outperformed Sobel's test and the joint significance test for detecting mediation effects. We applied the DACT method to the US Department of Veterans Affairs Normative Aging Study, an ongoing prospective cohort study which included men who were aged 21 to 80 years at entry. We identified multiple DNA methylation CpG sites that might mediate the effect of smoking on lung function with effect sizes ranging from -0.18 to -0.79 and false discovery rate controlled at level 0.05, including the CpG sites in the genes AHRR and F2RL3. Our sensitivity analysis found small residual correlations (less than 0.01) of the error terms between the outcome and mediator regressions, suggesting that our results are robust to unmeasured confounding factors.

Lithium (Li) is listed in the fifth Unregulated Contaminant Monitoring Rule (UCMR 5) because insufficient exposure data exists for lithium in drinking water. To help fill this data gap, lithium occurrence in source waters across the United States was assessed in 21 drinking water utilities. From the 369 samples collected from drinking water treatment plants (DWTPs), lithium ranged from 0.9 to 161 μg/L (median = 13.9 μg/L) in groundwater, and from <0.5 to 130 μg/L (median = 3.9 μg/L) in surface water. Lithium in 56% of the groundwater and 13% of the surface water samples were above non-regulatory Health-Based Screening Level (HBSL) of 10 μg/L. Sodium and lithium concentrations were strongly correlated: Kendall's τ > 0.6 (p < 0.001). As sodium is regularly monitored, this result shows that sodium can serve as an indicator to identify water sources at higher risk for elevated lithium. Lithium concentrations in the paired samples collected in source water and treated drinking water were almost identical showing lithium was not removed by conventional drinking water treatment processes. Additional sampling in wastewater effluents detected lithium at 0.8-98.2 μg/L (median = 9.9 μg/L), which suggests more research on impacts of lithium in direct and indirect potable reuse may be warranted, as the median was close to the HBSL. For comparison with the study samples collected from DWTPs, lithium concentrations from the national water quality portal (WQP) database were also investigated. Over 35,000 measurements were collected from waters that could potentially be used as drinking water sources (Cl < 250 mg/L). Data from WQP had comparable median lithium concentrations: 18 and 20 μg/L for surface water and groundwater, respectively. Overall, this study provides a comprehensive occurrence potential for lithium in US drinking water sources and can inform the data collection effort in UCMR 5.

Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics and estimates the joint posterior functional probabilities of each genomic position. This estimate offers more comprehensive and interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping and heritability enrichment analysis by using the lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.

Chronic exposure to inorganic pollutants adversely affects human health. Inductively coupled plasma mass spectrometry (ICP-MS) is the most common method used for trace metal(loid) analysis of human biomarkers. However, it leads to sample destruction, generation of secondary waste, and significant recurring costs. Portable X-ray fluorescence (XRF) instruments can rapidly and nondestructively determine low concentrations of metal(loid)s. In this work, we evaluated the applicability of portable XRF as a rapid method for analyzing trace metal(loid)s in toenail samples from three populations (n = 97) near the city of Chennai, India. A Passing-Bablok regression analysis of results from both methods revealed that there was no proportional bias among the two methods for nickel (measurement range ∼25 to 420 mg/kg), zinc (10 to 890 mg/kg), and lead (0.29 to 4.47 mg/kg). There was a small absolute bias between the two methods. There was a strong proportional bias (slope = 0.253, 95% CI: 0.027, 0.614) between the two methods for arsenic (below detection to 3.8 mg/kg) and for selenium when the concentrations were lower than 2 mg/kg. Limits of agreement between the two methods using Bland-Altman analysis were derived for nickel, zinc, and lead. Overall, a suitably calibrated and evaluated portable XRF shows promise in making high-throughput assessments at population scales.

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.