Regulation of stem cell function and neuronal differentiation by HERV-K via mTOR pathway.

Stem cells are capable of unlimited proliferation but can be induced to form brain cells. Factors that specifically regulate human development are poorly understood. We found that human stem cells expressed high levels of the envelope protein of an endogenized human-specific retrovirus (HERV-K, HML-2) from loci in chromosomes 12 and 19. The envelope protein was expressed on the cell membrane of the stem cells and was critical in maintaining the stemness via interactions with CD98HC, leading to triggering of human-specific signaling pathways involving mammalian target of rapamycin (mTOR) and lysophosphatidylcholine acyltransferase (LPCAT1)-mediated epigenetic changes. Down-regulation or epigenetic silencing of HML-2 env resulted in dissociation of the stem cell colonies and enhanced differentiation along neuronal pathways. Thus HML-2 regulation is critical for human embryonic and neurodevelopment, while it's dysregulation may play a role in tumorigenesis and neurodegeneration.

What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.

Genetic data have become increasingly complex within the past decade, leading researchers to pursue increasingly complex questions, such as those involving epistatic interactions and protein prediction. Traditional methods are ill-suited to answer these questions, but machine learning (ML) techniques offer an alternative solution. ML algorithms are commonly used in genetics to predict or classify subjects, but some methods evaluate which features (variables) are responsible for creating a good prediction; this is called feature importance. This is critical in genetics, as researchers are often interested in which features (e.g., SNP genotype or environmental exposure) are responsible for a good prediction. This allows for the deeper analysis beyond simple prediction, including the determination of risk factors associated with a given phenotype. Feature importance further permits the researcher to peer inside the black box of many ML algorithms to see how they work and which features are critical in informing a good prediction. This review focuses on ML methods that provide feature importance metrics for the analysis of genetic data. Five major categories of ML algorithms: k nearest neighbors, artificial neural networks, deep learning, support vector machines, and random forests are described. The review ends with a discussion of how to choose the best machine for a data set. This review will be particularly useful for genetic researchers looking to use ML methods to answer questions beyond basic prediction and classification.

A New Standard for Advance Care Planning (ACP) Conversations in the Hospital: Results from a Delphi Panel.

BACKGROUND: Fewer than half of the US population has an advance healthcare directive. Hospitalizations offer a key opportunity for clinicians to engage patients in advance care planning (ACP) conversations. Guidelines suggest screening for the presence of "serious illness" but do not further specify how to prioritize the 12.4 million patients hospitalized each year. OBJECTIVE: To establish a normative standard for prioritizing hospitalized patients for ACP conversations. DESIGN AND SETTING: A modified Delphi study, with three iterative rounds of online surveys. PARTICIPANTS: Multi-disciplinary group of US-based clinicians with research and practical expertise in ACP. MAIN MEASURES: Indirect and direct elicitation of short-term and 1-year risk of mortality that prompt experts to prioritize ACP conversations for hospitalized adults. MAIN RESULTS: Fifty-seven of 108 (52%) candidate panelists completed round 1, and 47 completed rounds 2 and 3. Panelists were primarily physicians (84%), with significant experience (mean years 23 [SD 9.8]), who either taught (55%) and/or performed research about ACP (55%). In round 1, > 70% of panelists agreed that all hospitalized adults ≥ 65 years should have an ACP conversation before discharge, but disagreed about the timing and content of the conversation. By round 3, > 70% of participants agreed that patients with either high (> 10%) short-term or high (≥ 34%) 1-year risk of mortality should have a goals of care conversation (i.e., focused on preferences for near-term treatment), while patients with low (≤ 10%) short-term and low (< 19%) 1-year risk of mortality warranted an ACP conversation (i.e., focused on preferences for future care) before discharge. LIMITATIONS: Use of case vignettes to elicit clinician judgment; response rate. CONCLUSIONS: Panelists agreed that clinicians should have an ACP conversation with all hospitalized adults over 65 years in an ACP conversation, adjusting the content and timing of the conversation conditional on the patient's risk of short-term and 1-year mortality.

High-resolution analysis with novel cell-surface markers identifies routes to iPS cells.

The generation of induced pluripotent stem (iPS) cells presents a challenge to normal developmental processes. The low efficiency and heterogeneity of most methods have hindered understanding of the precise molecular mechanisms promoting, and roadblocks preventing, efficient reprogramming. Although several intermediate populations have been described, it has proved difficult to characterize the rare, asynchronous transition from these intermediate stages to iPS cells. The rapid expansion of minor reprogrammed cells in the heterogeneous population can also obscure investigation of relevant transition processes. Understanding the biological mechanisms essential for successful iPS cell generation requires both accurate capture of cells undergoing the reprogramming process and identification of the associated global gene expression changes. Here we demonstrate that in mouse embryonic fibroblasts, reprogramming follows an orderly sequence of stage transitions, marked by changes in the cell-surface markers CD44 and ICAM1, and a Nanog-enhanced green fluorescent protein (Nanog-eGFP) reporter. RNA-sequencing analysis of these populations demonstrates two waves of pluripotency gene upregulation, and unexpectedly, transient upregulation of several epidermis-related genes, demonstrating that reprogramming is not simply the reversal of the normal developmental processes. This novel high-resolution analysis enables the construction of a detailed reprogramming route map, and the improved understanding of the reprogramming process will lead to new reprogramming strategies.

Comparative validation of the D. melanogaster modENCODE transcriptome annotation.

Accurate gene model annotation of reference genomes is critical for making them useful. The modENCODE project has improved the D. melanogaster genome annotation by using deep and diverse high-throughput data. Since transcriptional activity that has been evolutionarily conserved is likely to have an advantageous function, we have performed large-scale interspecific comparisons to increase confidence in predicted annotations. To support comparative genomics, we filled in divergence gaps in the Drosophila phylogeny by generating draft genomes for eight new species. For comparative transcriptome analysis, we generated mRNA expression profiles on 81 samples from multiple tissues and developmental stages of 15 Drosophila species, and we performed cap analysis of gene expression in D. melanogaster and D. pseudoobscura. We also describe conservation of four distinct core promoter structures composed of combinations of elements at three positions. Overall, each type of genomic feature shows a characteristic divergence rate relative to neutral models, highlighting the value of multispecies alignment in annotating a target genome that should prove useful in the annotation of other high priority genomes, especially human and other mammalian genomes that are rich in noncoding sequences. We report that the vast majority of elements in the annotation are evolutionarily conserved, indicating that the annotation will be an important springboard for functional genetic testing by the Drosophila community.

A system-level pathway-phenotype association analysis using synthetic feature random forest.

As the cost of genome-wide genotyping decreases, the number of genome-wide association studies (GWAS) has increased considerably. However, the transition from GWAS findings to the underlying biology of various phenotypes remains challenging. As a result, due to its system-level interpretability, pathway analysis has become a popular tool for gaining insights on the underlying biology from high-throughput genetic association data. In pathway analyses, gene sets representing particular biological processes are tested for significant associations with a given phenotype. Most existing pathway analysis approaches rely on single-marker statistics and assume that pathways are independent of each other. As biological systems are driven by complex biomolecular interactions, embracing the complex relationships between single-nucleotide polymorphisms (SNPs) and pathways needs to be addressed. To incorporate the complexity of gene-gene interactions and pathway-pathway relationships, we propose a system-level pathway analysis approach, synthetic feature random forest (SF-RF), which is designed to detect pathway-phenotype associations without making assumptions about the relationships among SNPs or pathways. In our approach, the genotypes of SNPs in a particular pathway are aggregated into a synthetic feature representing that pathway via Random Forest (RF). Multiple synthetic features are analyzed using RF simultaneously and the significance of a synthetic feature indicates the significance of the corresponding pathway. We further complement SF-RF with pathway-based Statistical Epistasis Network (SEN) analysis that evaluates interactions among pathways. By investigating the pathway SEN, we hope to gain additional insights into the genetic mechanisms contributing to the pathway-phenotype association. We apply SF-RF to a population-based genetic study of bladder cancer and further investigate the mechanisms that help explain the pathway-phenotype associations using SEN. The bladder cancer associated pathways we found are both consistent with existing biological knowledge and reveal novel and plausible hypotheses for future biological validations.

SCORHE: a novel and practical approach to video monitoring of laboratory mice housed in vivarium cage racks.

The System for Continuous Observation of Rodents in Home-cage Environment (SCORHE) was developed to demonstrate the viability of compact and scalable designs for quantifying activity levels and behavior patterns for mice housed within a commercial ventilated cage rack. The SCORHE in-rack design provides day- and night-time monitoring with the consistency and convenience of the home-cage environment. The dual-video camera custom hardware design makes efficient use of space, does not require home-cage modification, and is animal-facility user-friendly. Given the system's low cost and suitability for use in existing vivariums without modification to the animal husbandry procedures or housing setup, SCORHE opens up the potential for the wider use of automated video monitoring in animal facilities. SCORHE's potential uses include day-to-day health monitoring, as well as advanced behavioral screening and ethology experiments, ranging from the assessment of the short- and long-term effects of experimental cancer treatments to the evaluation of mouse models. When used for phenotyping and animal model studies, SCORHE aims to eliminate the concerns often associated with many mouse-monitoring methods, such as circadian rhythm disruption, acclimation periods, lack of night-time measurements, and short monitoring periods. Custom software integrates two video streams to extract several mouse activity and behavior measures. Studies comparing the activity levels of ABCB5 knockout and HMGN1 overexpresser mice with their respective C57BL parental strains demonstrate SCORHE's efficacy in characterizing the activity profiles for singly- and doubly-housed mice. Another study was conducted to demonstrate the ability of SCORHE to detect a change in activity resulting from administering a sedative.

Increased use of the emergency department after health care reform in Massachusetts.

STUDY OBJECTIVE: With implementation of the Patient Protection and Affordable Care Act, 30 million individuals are predicted to gain access to health insurance. The experience in Massachusetts, which implemented a similar reform beginning in 2006, should provide important lessons about the effect of health care reform on emergency department (ED) utilization. Our objective is to understand the extent to which Massachusetts health care reform was associated with changes in ED utilization. METHODS: We compared changes in ED utilization at the population level for individuals from areas of the state that were affected minimally by health care reform with those from areas that were affected the most, as well as for those younger than 65 years and aged 65 years or older. We used a difference-in-differences identification strategy to compare rates of ED visits in the prereform period, during the reform, and in the postreform period. Because we did not have population-level data on insurance status, we estimated area-level insurance rates by using the percentage of actual visits made during each period by individuals with insurance. RESULTS: We studied 13.3 million ED visits during 2004 to 2009. Increasing insurance coverage in Massachusetts was associated with increasing use of the ED; these results were consistent across all specifications, including the younger than 65 years versus aged 65 years or older comparison. Depending on the model used, the implementation of health care reform was estimated to result in an increase in ED visits per year of between 0.2% and 1.2% within reform and 0.2% and 2.2% postreform compared with the prereform period. CONCLUSION: The implementation of health care reform in Massachusetts was associated with a small but consistent increase in the use of the ED across the state. Whether this was due to the elimination of financial barriers to seeking care in the ED, a persistent shortage in access to primary care for those with insurance, or some other cause is not entirely clear and will need to be addressed in future research.

A Simplified Basis for Bell-Kochen-Specker Theorems.

We show that a reduced form of the structural requirements for deterministic hidden variables used in Bell-Kochen-Specker theorems is already sufficient for the no-go results. Those requirements are captured by the following principle: an observable takes a spectral value x if and only if the spectral projector associated with x takes the value 1. We show that the "only if" part of this condition suffices. The proof identifies an important structural feature behind the no-go results; namely, if at least one projector is assigned the value 1 in any resolution of the identity, then at most one is.

Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory.

Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077).

New genes involved in cancer identified by retroviral tagging.

Retroviral insertional mutagenesis in BXH2 and AKXD mice induces a high incidence of myeloid leukemia and B- and T-cell lymphoma, respectively. The retroviral integration sites (RISs) in these tumors thus provide powerful genetic tags for the discovery of genes involved in cancer. Here we report the first large-scale use of retroviral tagging for cancer gene discovery in the post-genome era. Using high throughput inverse PCR, we cloned and analyzed the sequences of 884 RISs from a tumor panel composed primarily of B-cell lymphomas. We then compared these sequences, and another 415 RIS sequences previously cloned from BXH2 myeloid leukemias and from a few AKXD lymphomas, against the recently assembled mouse genome sequence. These studies identified 152 loci that are targets of retroviral integration in more than one tumor (common retroviral integration sites, CISs) and therefore likely to encode a cancer gene. Thirty-six CISs encode genes that are known or predicted to be genes involved in human cancer or their homologs, whereas others encode candidate genes that have not yet been examined for a role in human cancer. Our studies demonstrate the power of retroviral tagging for cancer gene discovery in the post-genome era and indicate a largely unrecognized complexity in mouse and presumably human cancer.

Impacts of Surface Characteristics and Dew Point on the Blue-Light (BL405) Inactivation of Viruses.

The increased prevalence of multidrug-resistant organisms (MDROs), healthcare associated infections (HAIs), and the recent COVID-19 pandemic has caused the photoinactivation industry to explore alternative wavelengths. Blue light (BL405) has gained significant interest as it is much less harmful to the skin and eyes than traditional germicidal wavelengths; therefore, in theory, it can be used continuously with human exposure. At present, the viricidal effects of BL405 are largely unknown as the literature predominately addresses bacterial disinfection performed with this wavelength. This work provides novel findings to the industry, reporting on the virucidal effects of BL405 on surfaces. This research utilizes three surfaces: ceramic, PTFE, and stainless steel. The efficacy of BL405 inactivation varied by surface type, which was due to surface characteristics, such as the contact angle, porosity, zeta potential, and reflectivity. Additionally, the effect of the dew point on BL405 inactivation efficacy was determined. This research is the first to study the effects of the dew point on the virucidal effectiveness of BL405 surface inactivation. The effects of the dew point were significant for all surfaces and the control experiments. The high-dew-point conditions (18 °C) yielded higher levels of BL405 inactivation and viral degradation for the experiments and controls, respectively.

Virus Behavior after UV254 Treatment of Materials with Different Surface Properties.

The COVID-19 pandemic highlighted the limitations in scientific and engineering understanding of applying germicidal UV to surfaces. This study combines surface characterization, viral retention, and the related UV dose response to evaluate the effectiveness of UV254 as a viral inactivation technology on five surfaces: aluminum, ceramic, Formica laminate, PTFE and stainless steel. Images of each surface were determined using SEM (Scanning Electron Microscopy), which produced a detailed characterization of the surfaces at a nanometer scale. From the SEM images, the surface porosity of each material was calculated. Through further analysis, it was determined that surface porosity, surface roughness, contact angle, and zeta potential correlate to viral retention on the material. The imaging revealed that the aluminum surface, after repeated treatment, is highly oxidized, increasing surface area and surface porosity. These interactions are important as they prevent the recovery of MS-2 without exposure to UV254. The dose response curve for PTFE was steeper than ceramic, Formica laminate and stainless steel, as inactivation to the detection limit was achieved at 25 mJ/cm2. These findings are consistent with well-established literature indicating UV reflectivity of PTFE is maximized. Statistical testing reinforced that the efficacy of UV254 for surface inactivation varies by surface type.

Brief review of regression-based and machine learning methods in genetic epidemiology: the Genetic Analysis Workshop 17 experience.

Genetics Analysis Workshop 17 provided common and rare genetic variants from exome sequencing data and simulated binary and quantitative traits in 200 replicates. We provide a brief review of the machine learning and regression-based methods used in the analyses of these data. Several regression and machine learning methods were used to address different problems inherent in the analyses of these data, which are high-dimension, low-sample-size data typical of many genetic association studies. Unsupervised methods, such as cluster analysis, were used for data segmentation and, subset selection. Supervised learning methods, which include regression-based methods (e.g., generalized linear models, logic regression, and regularized regression) and tree-based methods (e.g., decision trees and random forests), were used for variable selection (selecting genetic and clinical features most associated or predictive of outcome) and prediction (developing models using common and rare genetic variants to accurately predict outcome), with the outcome being case-control status or quantitative trait value. We include a discussion of cross-validation for model selection and assessment, and a description of available software resources for these methods.

Physician patient-sharing networks and the cost and intensity of care in US hospitals.

BACKGROUND: There is substantial variation in the cost and intensity of care delivered by US hospitals. We assessed how the structure of patient-sharing networks of physicians affiliated with hospitals might contribute to this variation. METHODS: We constructed hospital-based professional networks based on patient-sharing ties among 61,461 physicians affiliated with 528 hospitals in 51 hospital referral regions in the US using Medicare data on clinical encounters during 2006. We estimated linear regression models to assess the relationship between measures of hospital network structure and hospital measures of spending and care intensity in the last 2 years of life. RESULTS: The typical physician in an average-sized urban hospital was connected to 187 other doctors for every 100 Medicare patients shared with other doctors. For the average-sized urban hospital an increase of 1 standard deviation (SD) in the median number of connections per physician was associated with a 17.8% increase in total spending, in addition to 17.4% more hospital days, and 23.8% more physician visits (all P<0.001). In addition, higher "centrality" of primary care providers within these hospital networks was associated with 14.7% fewer medical specialist visits (P<0.001) and lower spending on imaging and tests (-9.2% and -12.9% for 1 SD increase in centrality, P<0.001). CONCLUSIONS: Hospital-based physician network structure has a significant relationship with an institution's care patterns for their patients. Hospitals with doctors who have higher numbers of connections have higher costs and more intensive care, and hospitals with primary care-centered networks have lower costs and care intensity.

Quantifying the population burden of musculoskeletal disorders, including impact on sickness absence: analysis of national Scottish data.

Objectives: Musculoskeletal disorders (MSDs) account for the greatest burden of years lived with disability globally. To prevent disability, good-quality services need to be commissioned, appropriate for local need. We analysed data collected systematically from a new musculoskeletal service serving 70% of the population of Scotland to evaluate: age- and sex-specific occurrence; anatomical distribution; and impact and effect on work ability. Methods: A new centralized telephone-based triage for people with musculoskeletal disorders was set up in Scotland in 2015. Available to most of the population aged >16 years (>3 million people), data were collected systematically into a database detailing: anatomical site, nature of onset, duration, impact/risk (modified STarT score), deprivation level and, for those in employment, sickness absence. Results: Data were available from 219 314 new callers, 2015-18. Calls were more frequently from women (60%), increased with age until the eighth decade, and 66% reported symptoms that had been present for >6 weeks. Callers were more likely to be living in more deprived areas in each age band between 20 and 64 years and tended to have higher-impact symptoms. The majority (53%) of callers were in employment, and 19% of these were off sick because of their symptoms. Sickness absence was more common among those with highest impact/risk scores from deprived areas with more acute symptoms. Discussion: Large-scale systematic data collection for MSDs emphasizes the size and impact of the burden among adults aged >16 years. A socio-economic gradient is evident in terms of prevalence and impact of MSDs, particularly for sickness absence.

MarZIC: A Marginal Mediation Model for Zero-Inflated Compositional Mediators with Applications to Microbiome Data.

BACKGROUND: The human microbiome can contribute to pathogeneses of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis methods are not adequate to analyze the microbiome as a mediator due to the excessive number of zero-valued sequencing reads in the data and that the relative abundances have to sum to one. The two main challenges raised by the zero-inflated data structure are: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are not zero (i.e., false zeros). METHODS: We develop a novel marginal mediation analysis method under the potential-outcomes framework to address the issues. We also show that the marginal model can account for the compositional structure of microbiome data. RESULTS: The mediation effect can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the application in a real microbiome study showcase our approach in comparison with existing approaches. CONCLUSIONS: When analyzing the zero-inflated microbiome composition as the mediators, MarZIC approach has better performance than standard causal mediation analysis approaches and existing competing approach.

Increased DNA methylation, cellular senescence and premature epigenetic aging in guinea pigs and humans with tuberculosis.

BACKGROUND: Tuberculosis (TB) is the archetypical chronic infection, with patients having months of symptoms before diagnosis. In the two years after successful therapy, survivors of TB have a three-fold increased risk of death. METHODS: Guinea pigs were infected with Mycobacterium tuberculosis (Mtb) for 45 days, followed by RRBS DNA methylation analysis. In humans, network analysis of differentially expressed genes across three TB cohorts were visualized at the pathway-level. Serum levels of inflammation were measured by ELISA. Horvath (DNA methylation) and RNA-seq biological clocks were used to investigate shifts in chronological age among humans with TB. RESULTS: Guinea pigs with TB demonstrated DNA hypermethylation and showed system-level similarity to humans with TB (p-value = 0.002). The transcriptome in TB in multiple cohorts was enriched for DNA methylation and cellular senescence. Senescence associated proteins CXCL9, CXCL10, and TNF were elevated in TB patients compared to healthy controls. Humans with TB demonstrate 12.7 years (95% CI: 7.5, 21.9) and 14.38 years (95% CI: 10.23-18.53) of cellular aging as measured by epigenetic and gene expression based cellular clocks, respectively. CONCLUSIONS: In both guinea pigs and humans, TB perturbs epigenetic processes, promoting premature cellular aging and inflammation, a plausible means to explain the long-term detrimental health outcomes after TB.

Resident and fellow experiences after the introduction of endovascular aneurysm repair for abdominal aortic aneurysm.

OBJECTIVES: This study assessed trends in open and endovascular repair (EVAR) of intact and ruptured abdominal aortic aneurysm (AAA) in the Medicare population and evaluated recent trends in AAA repair at vascular fellowship training programs. METHODS: We identified all Medicare beneficiaries with a diagnosis of AAA who underwent repair or had a primary diagnosis of rupture (1995-2008). Cohorts were compared by type of repair (open vs EVAR) and presentation (intact vs ruptured AAA). Demographics of age, sex, and race were evaluated. We used unique hospital identifier codes to compare trends and 30-day mortality between hospitals that participate in vascular surgery fellowship training and those that do not. American Council on Graduate Medical Education data, only available for the years 1999 to 2008, were further used to better understand the changes in number of EVAR and open repairs of AAA performed each year for vascular fellows and general surgery residents, over time. RESULTS: We identified 449,122 patients (76% men), with 376,355 intact AAAs (84%) and 72,767 ruptured AAAs (16%). Mean age was 75.1 years. Use of EVAR for intact AAA rose to from 35% in 2001 to 63% in 2005 and comprised 78% of repairs by 2008. During the same period, the number of ruptured AAAs decreased by 40% overall, with nonoperative ruptured AAAs decreasing by 29% and EVAR increasing to 31% of rupture repairs. Hospitals training vascular fellows were quicker to adopt EVAR (2-year lag time) for intact AAA and had higher rates of EVAR for ruptured AAA (41.1% vs 29.2%; P = .001) than did hospitals without fellows. Mortality rates for open repairs of intact (4.0% vs 5.0%; P = .01) and ruptured AAA (34.1% vs 41.0%; P = .031) were lower at fellowship hospitals. The average number of open AAA repairs performed by vascular fellows dropped 50% (44.1 to 21.6/year) from 1999 to 2008. CONCLUSIONS: Contrary to the expectation of a plateau, use of EVAR for intact AAA continues to rise at fellowship and nonfellowship hospitals. Use of EVAR for rupture is being used more often at fellowship programs. The decline in open repairs performed by vascular fellows, and at fellowship and non-fellowship hospitals, may have important implications for future attending experience.

Distribution and fate of per- and polyfluoroalkyl substances (PFAS) in wastewater treatment facilities.

Anthropogenic compounds known as per- and polyfluoroalkyl substances (PFAS) represent a major class of contaminants of emerging concern composed of nearly 5000 chemicals. Many PFAS are persistent, bioaccumulative and toxic, and their widespread use makes their environmental distribution a growing concern. Wastewater treatment facilities (WWTFs) are a conduit of PFAS to the environment, integrating common household products from municipal sewage, industrial wastewater sources, septic materials, and firefighting wastewaters in effluent and sludge. This study investigated the distribution and fate of twenty-four PFAS within six New Hampshire municipal WWTFs applying a range of biological and disinfection unit processes. PFAS quantification was conducted using two approaches: (1) liquid chromatography with tandem mass spectrometry (LC-MS/MS) of 24 known compounds and (2) a total oxidizable precursor assay (TOP assay) followed by LC-MS/MS to determine the total oxidizable PFAS concentration. Of the 24 PFAS analyzed, up to 7 and 12 constituents were detected in influent and effluent of WWTFs, respectively, with concentrations ranging from 30 to 128 ng L-1 in March. Effluent ΣPFAS concentration increased during July, with concentrations between 70 and 198 ng L-1 for the same detected constituents. Short-chain PFAS were dominant in both influent and effluent, while long-chain compounds dominated in WWTF sludge. The increase in terminal end-products after oxidation by the TOP assay indicates the presence of unquantified PFAS precursors in both influent and effluent. A significantly lower proportion of oxidizable PFAS precursors were detected in July influent and effluent relative to March, indicating a possible role of season or temperature on microbial transformation of these compounds prior to reaching WWTFs and during treatment. These results provide new insight into PFAS distribution and fate during two seasons in New England municipal WWTFs.