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Wastewater monitoring is a valuable public health tool that can track a variety of health markers. The strong correlations between trends in wastewater viral concentrations and county-level COVID-19 case counts point to the ability of wastewater data to represent changes in a community's disease burden. However, studies are lacking on whether the populations sampled through wastewater monitoring represent the characteristics of the broader community and the implications on health equity. We conducted a geospatial analysis to examine the extent to which populations contributing to wastewater collected through the North Carolina Wastewater Monitoring Network as of June 2022 represent the broader countywide and statewide populations. After intersecting sewershed boundary polygons for 38 wastewater treatment plants across 18 counties with census block and tract polygons, we compared the demographics and social vulnerability of (1) people residing in monitored sewersheds with countywide and statewide populations, and (2) sewered residents, regardless of inclusion in wastewater monitoring, with unsewered residents. We flagged as meaningful any differences greater than +/- 5 percentage points or 5 percent (for categorical and continuous variables, respectively) and noted statistically significant differences (p < 0.05). We found that residents within monitored sewersheds largely resembled the broader community on most variables analyzed, with only a few exceptions. We also observed that when multiple sewersheds were monitored within a county, their combined service populations resembled the county population, although individual sewershed and county populations sometimes differed. When we contrasted sewered and unsewered populations within a given county, we found that sewered populations were more vulnerable than unsewered populations, suggesting that wastewater monitoring may fill in the data gaps needed to improve health equity. The approach we present here can be used to characterize sewershed populations nationwide to ensure that wastewater monitoring is implemented in a manner that informs equitable public health decision-making. [ABSTRACT FROM AUTHOR]

EnvironHealthPerspect.129(4):045002(2021). https://doi.org/10.1289/EHP8572 The correspondence address provided for the corresponding author was not accurate. This address has been modified to reflect the correct address, which is provided below. Correspondence address: Mathematica, [...]

Background: Wastewater testing offers a cost-effective strategy for measuring population disease prevalence and health behaviors. For COVID-19, wastewater surveillance addresses testing gaps and provides an early warning for outbreaks. As U.S. federal agencies build a National Wastewater Surveillance System around the pandemic, thinking through ways to develop flexible frameworks for wastewater sampling, testing, and reporting can avoid unnecessary system overhauls for future infectious disease, chronic disease, and drug epidemics. Objectives: We discuss ways to transform a historically academic exercise into a tool for epidemic response. We generalize lessons learned by a global network of wastewater researchers around validation and implementation for COVID-19 and opioids while also drawing on our experience with wastewater-based epidemiology in the United States. Discussion: Sustainable wastewater surveillance requires coordination between health and safety officials, utilities, labs, and researchers. Adapting sampling frequency, type, and location to threat level, community vulnerability, biomarker properties, and decisions that wastewater data will inform can increase the practical value of the data. Marketplace instabilities, coupled with a fragmented testing landscape due to specialization, may require officials to engage multiple labs to test for known and unknown threats. Government funding can stabilize the market, balancing commercial pressures with public good, and incentivize data sharing. When reporting results, standardizing metrics and contextualizing wastewater data with health resource data can provide insights into a community's vulnerability and identify strategies to prevent health care systems from being overwhelmed. If wastewater data will inform policy decisions for an entire community, comparing characteristics of the wastewater treatment plant's service population to those of the larger community can help determine whether the wastewater data are generalizable. Ethical protocols may be needed to protect privacy and avoid stigmatization. With data-driven approaches to sample collection, analysis, and interpretation, officials can use wastewater surveillance for adaptive resource allocation, pandemic management, and program evaluation. https://doi.org/10.1289/EHP8572, Introduction In the United States, the COVID-19 pandemic has been exacerbated by an inability to track infections in a timely and comprehensive manner (Parodi and Liu 2020). Traditional testing has [...]

Wastewater monitoring has provided health officials with early warnings for new COVID-19 outbreaks, but to date, no approach has been validated to distinguish signal (sustained surges) from noise (background variability) in wastewater data to alert officials to the need for heightened public health response. We analyzed 62 wk of data from 19 sites participating in the North Carolina Wastewater Monitoring Network to characterize wastewater metrics around the Delta and Omicron surges. We found that wastewater data identified outbreaks 4 to 5 d before case data (reported on the earlier of the symptom start date or test collection date), on average. At most sites, correlations between wastewater and case data were similar regardless of how wastewater concentrations were normalized and whether calculated with county-level or sewershed-level cases, suggesting that officials may not need to geospatially align case data with sewershed boundaries to gain insights into disease transmission. Although wastewater trend lines captured clear differences in the Delta versus Omicron surge trajectories, no single wastewater metric (detectability, percent change, or flow-population normalized viral concentrations) reliably signaled when these surges started. After iteratively examining different combinations of these three metrics, we developed the Covid-SURGE (Signaling Unprecedented Rises in Groupwide Exposure) algorithm, which identifies unprecedented signals in the wastewater data. With a true positive rate of 82%, a false positive rate of 7%, and strong performance during both surges and in small and large sites, our algorithm provides public health officials with an automated way to flag community-level COVID-19 surges in real time. [ABSTRACT FROM AUTHOR]

Background: Between 2013 and 2015, concentrations of poly- and perfluoroalkyl substances (PFAS) in public drinking water supplies serving at least six million individuals exceeded the level set forth in the health advisory established by the U.S. Environmental Protection Agency. Other than data reported for contaminated sites, no systematic or prospective data exist on the relative source contribution (RSC) of drinking water to human PFAS exposures. Objectives: This study estimates the RSC of tap water to overall PFAS exposure among members of the general U.S. population. Methods: We measured concentrations of 15 PFAS in home tap water samples collected in 1989-1990 from 225 participants in a nationwide prospective cohort of U.S. women: the Nurses' Health Study (NHS). We used a one-compartment toxicokinetic model to estimate plasma concentrations corresponding to tap water intake of PFAS. We compared modeled results with measured plasma PFAS concentrations among a subset of 110 NHS participants. Results: Tap water perfluorooctanoic acid (PFOA) and perfluorononanoic acid (PFNA) were statistically significant predictors of plasma concentrations among individuals who consumed [greater than or equal to] 8 cups of tap water per day. Modeled median contributions of tap water to measured plasma concentrations were: PFOA 12% (95% probability interval 11%--14%), PFNA 13% (8.7%-21%), linear perfluorooctanesulfonic acid (nPFOS) 2.2% (2.0%-2.5%), branched perfluorooctanesulfonic acid (brPFOS) 3.0% (2.5%-3.2%), and perfluorohexanesulfonic acid (PFHxS) 34% (29%- 39%). In five locations, comparisons of PFASs in community tap water collected in the period 2013-2016 with samples from 1989-1990 indicated increases in quantifiable PFAS and extractable organic fluorine (a proxy for unquantified PFAS). Conclusions: Our results for 1989-1990 compare well with the default RSC of 20% used in risk assessments for legacy PFAS by many agencies. Future evaluation of drinking water exposures should incorporate emerging PFAS. https://doi.org/10.1289/EHP4093, Introduction Poly- and perfluoroalkyl substances (PFAS) are a broad class of fluorinated aliphatic compounds that are widely used by industry and for commercial applications and have been detected in the [...]

The ultrasound (US) screening of the infant hip is vital for the early diagnosis of developmental dysplasia of the hip (DDH). The US diagnosis of DDH refers to measuring alpha and beta angles that quantify hip joint development. These two angles are calculated from key anatomical landmarks and structures of the hip. However, this measurement process is not trivial for sonographers and usually requires a thorough understanding of complex anatomical structures. In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH. Our multi-task networks are equipped with three novel modules. Firstly, we adopt Mask R-CNN as the basic framework to detect and segment key anatomical structures and add one landmark detection branch to form a new multi-task framework. Secondly, we propose a novel shape similarity loss to refine the incomplete anatomical structure prediction robustly and accurately. Thirdly, we further incorporate the landmark-structure consistent prior to ensure the consistency of the bony rim estimated from the segmented structure and the detected landmark. In our experiments, 1231 US images of the infant hip from 632 patients are collected, of which 247 images from 126 patients are tested. The average errors in alpha and beta angles are 2.221 ${}^{\circ }$ and 2.899 ${}^{\circ }$. About 93% and 85% estimates of alpha and beta angles have errors less than 5 degrees, respectively. Experimental results demonstrate that the proposed method can accurately and robustly realize the automatic evaluation of DDH, showing great potential for clinical application. [ABSTRACT FROM AUTHOR]

We synthesize current understanding of the magnitudes and methods for assessing human and wildlife exposures to poly‐ and perfluoroalkyl substances (PFAS). Most human exposure assessments have focused on 2 to 5 legacy PFAS, and wildlife assessments are typically limited to targeted PFAS (up to ~30 substances). However, shifts in chemical production are occurring rapidly, and targeted methods for detecting PFAS have not kept pace with these changes. Total fluorine measurements complemented by suspect screening using high‐resolution mass spectrometry are thus emerging as essential tools for PFAS exposure assessment. Such methods enable researchers to better understand contributions from precursor compounds that degrade into terminal perfluoroalkyl acids. Available data suggest that diet is the major human exposure pathway for some PFAS, but there is large variability across populations and PFAS compounds. Additional data on total fluorine in exposure media and the fraction of unidentified organofluorine are needed. Drinking water has been established as the major exposure source in contaminated communities. As water supplies are remediated, for the general population, exposures from dust, personal care products, indoor environments, and other sources may be more important. A major challenge for exposure assessments is the lack of statistically representative population surveys. For wildlife, bioaccumulation processes differ substantially between PFAS and neutral lipophilic organic compounds, prompting a reevaluation of traditional bioaccumulation metrics. There is evidence that both phospholipids and proteins are important for the tissue partitioning and accumulation of PFAS. New mechanistic models for PFAS bioaccumulation are being developed that will assist in wildlife risk evaluations. Environ Toxicol Chem 2021;40:631–657. © 2020 SETAC [ABSTRACT FROM AUTHOR]

Background: Humans are exposed to poly- and perfluoroalkyl substances (PFASs) from diverse sources and this has been associated with negative health impacts. Advances in analytical methods have enabled routine detection of more than 15 PFASs in human sera, allowing better profiling of PFAS exposures. The composition of PFASs in human sera reflects the complexity of exposure sources but source identification can be confounded by differences in toxicokinetics affecting uptake, distribution, and elimination. Common PFASs, such as perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS) and their precursors are ubiquitous in multiple exposure sources. However, their composition varies among sources, which may impact associated adverse health effects.Methods: We use available PFAS concentrations from several demographic groups in a North Atlantic seafood consuming population (Faroe Islands) to explore whether chemical fingerprints in human sera provide insights into predominant exposure sources. We compare serum PFAS profiles from Faroese individuals to other North American populations to investigate commonalities in potential exposure sources. We compare individuals with similar demographic and physiological characteristics and samples from the same years to reduce confounding by toxicokinetic differences and changing environmental releases.Results: Using principal components analysis (PCA) confirmed by hierarchical clustering, we assess variability in serum PFAS concentrations across three Faroese groups. The first principal component (PC)/cluster consists of C9-C12 perfluoroalkyl carboxylates (PFCAs) and is consistent with measured PFAS profiles in consumed seafood. The second PC/cluster includes perfluorohexanesulfonic acid (PFHxS) and the PFOS precursor N-ethyl perfluorooctane sulfonamidoacetate (N-EtFOSAA), which are directly used or metabolized from fluorochemicals in consumer products such as carpet and food packaging. We find that the same compounds are associated with the same exposure sources in two North American populations, suggesting generalizability of results from the Faroese population.Conclusions: We conclude that PFAS homologue profiles in serum provide valuable information on major exposure sources. It is essential to compare samples collected at similar time periods and to correct for demographic groups that are highly affected by differences in physiological processes (e.g., pregnancy). Information on PFAS homologue profiles is crucial for attributing adverse health effects to the proper mixtures or individual PFASs. [ABSTRACT FROM AUTHOR]

Poly- and perfluoroalkyl substances (PFASs) are persistent, bioaccumulative anthropogenic compounds associated with adverse health impacts on humans and wildlife. PFAS production changed in North America and Europe around the year 2000, but impacts on wildlife appear to vary across species and location. Unlike other mammal species, cetaceans lack the enzyme for transforming an important intermediate precursor (perfluorooctane sulfonamide: FOSA), into a prevalent compound in most wildlife (perfluorooctanesulfonate: PFOS). Thus, their tissue burden differentiates these two compounds while other mammals contain PFOS from both direct exposure and precursor degradation. Here we report temporal trends in 15 PFASs measured in muscle from juvenile male North Atlantic pilot whales (Globicephala melas) harvested between 1986 and 2013. FOSA accounted for a peak of 84% of the 15 PFASs around 2000 but declined to 34% in recent years. PFOS and long-chained PFCAs (C9--C13) increased significantly over the whole period (2.8% yr-1 to 8.3% yr-1), but FOSA declined by 13% yr-1 after 2006. Results from FOSA partitioning and bioaccumulation modeling forced by changes in atmospheric inputs reasonably capture magnitudes and temporal patterns in FOSA concentrations measured in pilot whales. Rapid changes in atmospheric FOSA in polar and subpolar regions around 2000 helps to explain large declines in PFOS exposure for species that metabolize FOSA, including seafood consuming human populations. This work reinforces the importance of accounting for biological exposures to PFAS precursors. [ABSTRACT FROM AUTHOR]

The fate and transfer of mercury in urban river is an important environmental concern. In this study, QWASI (Quantitative Water–Air–Sediment Interaction) model was selected to estimate the levels of total mercury and three mercury species in water and sediment, and was used to quantify the fluxes of mercury at water/air and sediment/water interfaces of an urban river. The predicted mercury levels in water and sediments were closed to the measured values. Water inflow, re-suspension of sediment and diffusion from sediment to water are major input sources of mercury in water. The net mercury transfer flux from water to air was 0.16 ng/(m2 h). At the sediment/water interface, a net total mercury transfer of 1.32 ng/(m2 h) from water to sediment was seen. In addition to the existing dynamic flux chambers measurement, this model method could provide a new perspective to identify the distribution and transfer of mercury in the urban river. [Copyright &y& Elsevier]

Drinking water contamination with poly- and perfluoroalkyl substances (PFASs) poses risks to the developmental, immune, metabolic, and endocrine health of consumers. We present a spatial analysis of 2013–2015 national drinking water PFAS concentrations from the U.S. Environmental Protection Agency’s (US EPA) third Unregulated Contaminant Monitoring Rule (UCMR3) program. The number of industrial sites that manufacture or use these compounds, the number of military fire training areas, and the number of wastewater treatment plants are all significant predictors of PFAS detection frequencies and concentrations in public water supplies. Among samples with detectable PFAS levels, each additional military site within a watershed’s eight-digit hydrologic unit is associated with a 20% increase in PFHxS, a 10% increase in both PFHpA and PFOA, and a 35% increase in PFOS. The number of civilian airports with personnel trained in the use of aqueous film-forming foams is significantly associated with the detection of PFASs above the minimal reporting level. We find drinking water supplies for 6 million U.S. residents exceed US EPA’s lifetime health advisory (70 ng/L) for PFOS and PFOA. Lower analytical reporting limits and additional sampling of smaller utilities serving <10000 individuals and private wells would greatly assist in further identifying PFAS contamination sources.

A marine food web in Bohai Bay, China, was selected to study methylmercury (MeHg) bioaccumulation, and an aquivalence-based mass balance model was established to explore the possibility of predicting the MeHg concentrations and quantifying MeHg bioaccumulation in the food web. Results showed that both total mercury (THg) and MeHg were biomagnified in the food web. The calculated MeHg concentrations in the selected species agreed well with the measured values, which shows the model could be a useful tool in MeHg concentration prediction in food web. Model outputs also showed that metabolism and growth dilution could be the dominant mechanisms for the reduction of MeHg levels in aquatic organisms. With the increase of trophic level, the contribution of food as a MeHg source for organism is increasing, and MeHg from prey was the dominant source. [Copyright &y& Elsevier]

Despite the well-known acknowledgement of both the toxicity of cadmium (Cd) and the ameliorative effect of selenium (Se), the mechanism of the protective effect of selenium on cadmium-induced Mouse Leydig (TM3) cell apoptosis remains unknown. In this study, we hypothesized that the reactive oxygen species (ROS)-mediated c-jun N-terminal kinase (JNK) signaling pathway is involved in anti-apoptosis of selenium against cadmium in TM3 cells. We found that exposure to cadmium caused evident cytotoxicity, in which cell viability was inhibited, followed by inducement of apoptosis. Moreover, the level of ROS generation was elevated, leading to the phosphorylation of JNK. In addition, following cadmium exposure, the nuclear transcription factor c-jun was significantly activated, which led to increased expression of downstream gene c-jun, resulting in downstream activation of the apoptosis-related protein Caspase3 and upregulation of Cleaved-PARP, as well as inhibition of the anti-apoptosis protein Bcl-2. However, pretreatment with selenium remarkably suppressed cadmium-induced TM3 cell apoptosis. Furthermore, the level of ROS declined, and the JNK signaling pathway was blocked. Following this, the gene expression of c-jun decreased while Bcl-2 increased, which was consistent with the effects on proteins, that Caspase3 activity and Cleaved-PARP were inhibited while Bcl-2 level was restored. In order to explain the relationship between molecules of the signaling pathway, N -acetyl-L-cysteine (NAC), the ROS inhibitor, and JNK1/2 siRNA were administered, which further indicated the mediatory role of the ROS/JNK/c-jun signaling pathway in regulating anti-apoptosis of selenium against cadmium-induced TM3 cell apoptosis. • Selenium ameliorated cadmium-induced the apoptosis of TM3 cells. • Protection of selenium from cadmium-induced TM3 cell apoptosis was regulated by JNK signaling pathway. • Blockage of JNK signaling pathway by NAC and JNK1/2 siRNA suppressed cadmium-induced TM3 cell apoptosis. [ABSTRACT FROM AUTHOR]

Abstract Cadmium (Cd) is a heavy metal that widely exists in the environment and industry, and which causes serious damages to reproductive system. Recent studies have reported that cadmium induces apoptosis of various germ cells in testes, resulting in male infertility. However, the exact mechanism of cadmium-induced apoptosis remains unclear. In this study, we hypothesized that reactive oxygen species (ROS)-mediated c-jun N-terminal kinase (JNK) signaling pathway was involved in cadmium-induced apoptosis in TM3 cells, a model of mouse Leydig cells. TM3 cells were exposed for various times to a range of cadmium concentrations. We found that cadmium reduced TM3 cell viability and increased apoptosis in a time- and dose- dependent manner. Moreover, the levels of ROS generation and the phosphorylation of JNK were elevated by cadmium treatment. In addition, the nuclear transcription factor c-jun was significantly activated, which led to increased expression of downstream c-jun targets and Bcl-2 was decreased, accompanied with downstream activation of apoptosis-related proteins such as Cleaved-Caspase3 and Cleaved-PARP. However, pretreatment with the ROS inhibitor N -acetyl-L-cysteine (NAC) and JNK inhibitor JNK-IN-8, ROS, JNK and cadmium-induced TM3 cell apoptosis were remarkably suppressed. Based on above-mentioned results, this study provides a mechanistic understanding of cadmium induced TM3 cell apoptosis through the ROS/JNK signaling pathways. Highlights • Cadmium induced the apoptosis of TM3 cells in a dose- and time- dependent manner. • TM3 cell apoptosis induced by cadmium was regulated by JNK signaling pathway. • NAC suppressed cadmium-induced TM3 cell apoptosis through JNK signaling pathway. [ABSTRACT FROM AUTHOR]

• A GAN-based framework for synthesizing realistic, high-resolution and editable US images. • Sketch guidance enhances structural details of generated images. • We adopt a progressive training strategy to stabilize training. • We propose a novel feature loss to suppress the artifacts in generated images. • Our method is solidly validated on three large US datasets. [Display omitted] Ultrasound (US) imaging is widely used for anatomical structure inspection in clinical diagnosis. The training of new sonographers and deep learning based algorithms for US image analysis usually requires a large amount of data. However, obtaining and labeling large-scale US imaging data are not easy tasks, especially for diseases with low incidence. Realistic US image synthesis can alleviate this problem to a great extent. In this paper, we propose a generative adversarial network (GAN) based image synthesis framework. Our main contributions include: (1) we present the first work that can synthesize realistic B-mode US images with high-resolution and customized texture editing features; (2) to enhance structural details of generated images, we propose to introduce auxiliary sketch guidance into a conditional GAN. We superpose the edge sketch onto the object mask and use the composite mask as the network input; (3) to generate high-resolution US images, we adopt a progressive training strategy to gradually generate high-resolution images from low-resolution images. In addition, a feature loss is proposed to minimize the difference of high-level features between the generated and real images, which further improves the quality of generated images; (4) the proposed US image synthesis method is quite universal and can also be generalized to the US images of other anatomical structures besides the three ones tested in our study (lung, hip joint, and ovary); (5) extensive experiments on three large US image datasets are conducted to validate our method. Ablation studies, customized texture editing, user studies, and segmentation tests demonstrate promising results of our method in synthesizing realistic US images. [ABSTRACT FROM AUTHOR]

In this work, an angiotensin converting enzyme (ACE) inhibitory peptide from loach, Ala-His-Leu-Leu (AHLL), was incorporated into horse spleen apoferritin (HSF) nanoparticles by a disassembly/reconstitute approach. AHLL-loaded HSF nanoparticles displayed a hydrodynamic diameter of about 20 nm and encapsulation efficiency (EE) of about 55%. Sodium deoxycholate (NaDC) of 0.2 mM caused the aggregation of HSF nanoparticles and improved the EE of AHLL. Compared with HSF nanoparticles, NaDC-coated HSF nanoparticles (HSF@NaDC) were more efficient in reducing AHLL degradation in accelerated models and simulated digestion tract owing to their more compact structure. In this regard, the bioactivities of AHLL were better maintained in HSF@NaDC nanoparticles. HSF nanoparticle encapsulation improved the apparent permeability coefficients (P app) of AHLL due to the caveolae-related endocytosis. On the other hand, two additional pathways, macropinocytosis-mediated and clathrin- and caveolae-independent routes, were involved in internalized of HSF@NaDC nanoparticles, resulting in an even higher P app value of the loaded AHLL. [Display omitted] • The disassembly/reconstitute process of ferritin enabled the incorporation of AHLL. • Sodium deoxycholate of 0.2 mM caused the aggregation of ferritin nanoparticles. • Sodium deoxycholate decoration contributed to the stability of the loaded AHLL. • Nanoencapsulation enhanced the intestinal permeability of AHLL. • Multiple endocytosis mechanisms were involved in coated nanoparticle absorption. [ABSTRACT FROM AUTHOR]

Fetal pose estimation in 3D ultrasound (US) involves identifying a set of associated fetal anatomical landmarks. Its primary objective is to provide comprehensive information about the fetus through landmark connections, thus benefiting various critical applications, such as biometric measurements, plane localization, and fetal movement monitoring. However, accurately estimating the 3D fetal pose in US volume has several challenges, including poor image quality, limited GPU memory for tackling high dimensional data, symmetrical or ambiguous anatomical structures, and considerable variations in fetal poses. In this study, we propose a novel 3D fetal pose estimation framework (called FetusMapV2) to overcome the above challenges. Our contribution is three-fold. First, we propose a heuristic scheme that explores the complementary network structure-unconstrained and activation-unreserved GPU memory management approaches, which can enlarge the input image resolution for better results under limited GPU memory. Second, we design a novel Pair Loss to mitigate confusion caused by symmetrical and similar anatomical structures. It separates the hidden classification task from the landmark localization task and thus progressively eases model learning. Last, we propose a shape priors-based self-supervised learning by selecting the relatively stable landmarks to refine the pose online. Extensive experiments and diverse applications on a large-scale fetal US dataset including 1000 volumes with 22 landmarks per volume demonstrate that our method outperforms other strong competitors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Qualitative research remains underused, in part due to the time and cost of annotating qualitative data (coding). Artificial intelligence (AI) has been suggested as a means to reduce those burdens, and has been used in exploratory studies to reduce the burden of coding. However, methods to date use AI analytical techniques that lack transparency, potentially limiting acceptance of results. We developed an automated qu alitative assistant (AQUA) using a semiclassical approach, replacing Latent Semantic Indexing/Latent Dirichlet Allocation with a more transparent graph-theoretic topic extraction and clustering method. Applied to a large dataset of free-text survey responses, AQUA generated unsupervised topic categories and circle hierarchical representations of free-text responses, enabling rapid interpretation of data. When tasked with coding a subset of free-text data into user-defined qualitative categories, AQUA demonstrated intercoder reliability in several multicategory combinations with a Cohen's kappa comparable to human coders (0.62-0.72), enabling researchers to automate coding on those categories for the entire dataset. The aim of this manuscript is to describe pertinent components of best practices of AI/machine learning (ML)-assisted qualitative methods, illustrating how primary care researchers may use AQUA to rapidly and accurately code large text datasets. The contribution of this article is providing guidance that should increase AI/ML transparency and reproducibility., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Drinking water concentrations of per- and polyfluoroalkyl substances (PFAS) exceed provisional guidelines for millions of Americans. Data on private well PFAS concentrations are limited in many regions and monitoring initiatives are costly and time-consuming. Here we examine modeling approaches for predicting private wells likely to have detectable PFAS concentrations that could be used to prioritize monitoring initiatives. We used nationally available data on PFAS sources, and geologic, hydrologic and soil properties that affect PFAS transport as predictors and trained and evaluated models using PFAS data ( n ~2300 wells) collected by the state of New Hampshire between 2014 and 2017. Models were developed for the five most frequently detected PFAS: perfluoropentanoate, perfluorohexanoate, perfluoroheptanoate, perfluorooctanoate, and perfluorooctane sulfonate. Classification random forest models that allow non-linearity in interactions among predictors performed the best (area under the receiver operating characteristics curve: 0.74 - 0.86). Point sources such as the plastics/rubber and textile industries accounted for the highest contribution to accuracy. Groundwater recharge, precipitation, soil sand content, and hydraulic conductivity were secondary predictors. Our study demonstrates the utility of machine learning models for predicting PFAS in private wells and the classification random forest model based on nationally available predictors is readily extendable to other regions., Competing Interests: Conflicts of interest The authors declare they have no actual or potential competing financial interests.

Water supplies for millions of U.S. individuals exceed maximum contaminant levels for per- and polyfluoroalkyl substances (PFAS). Contemporary and legacy use of aqueous film forming foams (AFFF) is a major contamination source. However, diverse PFAS sources are present within watersheds, making it difficult to isolate their predominant origins. Here we examine PFAS source signatures among six adjacent coastal watersheds on Cape Cod, MA, U.S.A. using multivariate clustering techniques. A distinct signature of AFFF contamination enriched in precursors with six perfluorinated carbons (C6) was identified in watersheds with an AFFF source, while others were enriched in C4 precursors. Principal component analysis of PFAS composition in impacted watersheds showed a decline in precursor composition relative to AFFF stocks and a corresponding increase in terminal perfluoroalkyl sulfonates with < C6 but not those with ≥ C6. Prior work shows that in AFFF stocks, all extractable organofluorine (EOF) can be explained by targeted PFAS and precursors inferred using Bayesian inference on the total oxidizable precursor assay. Using the same techniques for the first time in impacted watersheds, we find that only 24%-63% of the EOF can be explained by targeted PFAS and oxidizable precursors. Our work thus indicates the presence of large non-AFFF organofluorine sources in these coastal watersheds.

Here, we review present understanding of sources and trends in human exposure to poly- and perfluoroalkyl substances (PFASs) and epidemiologic evidence for impacts on cancer, immune function, metabolic outcomes, and neurodevelopment. More than 4000 PFASs have been manufactured by humans and hundreds have been detected in environmental samples. Direct exposures due to use in products can be quickly phased out by shifts in chemical production but exposures driven by PFAS accumulation in the ocean and marine food chains and contamination of groundwater persist over long timescales. Serum concentrations of legacy PFASs in humans are declining globally but total exposures to newer PFASs and precursor compounds have not been well characterized. Human exposures to legacy PFASs from seafood and drinking water are stable or increasing in many regions, suggesting observed declines reflect phase-outs in legacy PFAS use in consumer products. Many regions globally are continuing to discover PFAS contaminated sites from aqueous film forming foam (AFFF) use, particularly next to airports and military bases. Exposures from food packaging and indoor environments are uncertain due to a rapidly changing chemical landscape where legacy PFASs have been replaced by diverse precursors and custom molecules that are difficult to detect. Multiple studies find significant associations between PFAS exposure and adverse immune outcomes in children. Dyslipidemia is the strongest metabolic outcome associated with PFAS exposure. Evidence for cancer is limited to manufacturing locations with extremely high exposures and insufficient data are available to characterize impacts of PFAS exposures on neurodevelopment. Preliminary evidence suggests significant health effects associated with exposures to emerging PFASs. Lessons learned from legacy PFASs indicate that limited data should not be used as a justification to delay risk mitigation actions for replacement PFASs.

Exposure to poly and perfluoroalkyl substances (PFASs) has been associated with adverse health effects in humans and wildlife. Understanding pollution sources is essential for environmental regulation but source attribution for PFASs has been confounded by limited information on industrial releases and rapid changes in chemical production. Here we use principal component analysis (PCA), hierarchical clustering, and geospatial analysis to understand source contributions to 14 PFASs measured across 37 sites in the Northeastern United States in 2014. PFASs are significantly elevated in urban areas compared to rural sites except for perfluorobutane sulfonate (PFBS), N-methyl perfluorooctanesulfonamidoacetic acid (N-MeFOSAA), perfluoroundecanate (PFUnDA) and perfluorododecanate (PFDoDA). The highest PFAS concentrations across sites were for perfluorooctanate (PFOA, 56 ng L -1 ) and perfluorohexane sulfonate (PFOS, 43 ng L -1 ) and PFOS levels are lower than earlier measurements of U.S. surface waters. PCA and cluster analysis indicates three main statistical groupings of PFASs. Geospatial analysis of watersheds reveals the first component/cluster originates from a mixture of contemporary point sources such as airports and textile mills. Atmospheric sources from the waste sector are consistent with the second component, and the metal smelting industry plausibly explains the third component. We find this source-attribution technique is effective for better understanding PFAS sources in urban areas.