The effect of sexual behavior on HIV-1 seroconversion is mediated by the gut microbiome and proinflammatory cytokines.

The association between HIV-1 seroconversion and gut dysbiosis is well documented, and its association with sexual activity is also widely recognized. However, it is not known whether the gut dysbiosis mediates the effects of high-risk sexual behavior on HIV-1 seroconversion. In this report we focused on men who engaged in high-risk sexual behavior where they had receptive anal intercourse with multiple men. We demonstrate that proinflammatory cytokines, sCD14 and sCD163, and gut microbiota mediate the effects of this high-risk sexual behavior on subsequent HIV seroconversion. We discovered changes in the gut microbial ecology, prior to seroconversion, both in terms of the composition as well as inter-relationships among the commensal species. Furthermore, these changes correlate with future HIV seroconversion. Specifically, as the number of sexual partners increased, we discovered in a "dose-response" manner, a decrease in the abundance of commensal and short-chain fatty acid-producing species, A. muciniphila, B. caccae, B. fragilis, B. uniformis, Bacteroides spp., Butyricimonas spp., and Odoribacter spp, and an increase in proinflammatory species Dehalobacterium spp. and Methanobrevibacter spp. These changes were also observed among subsequent HIV seroconverters. Interestingly, we also discovered a reduction in correlations among these commensal and short-chain fatty acid producing bacteria in a "dose-response" manner with the number of sexual partners. Our mediation analysis not only provides a conceptual model for the disease process but also provides clues for future clinical interventions that will manipulate the gut microbiota to treat high-risk subjects to prevent HIV seroconversion.

Mediation Analysis using Semi-parametric Shape-Restricted Regression with Applications.

Often linear regression is used to perform mediation analysis. However, in many instances, the underlying relationships may not be linear, as in the case of placentalfetal hormones and fetal development. Although, the exact functional form of the relationship may be unknown, one may hypothesize the general shape of the relationship. For these reasons, we develop a novel shape-restricted inference-based methodology for conducting mediation analysis. This work is motivated by an application in fetal endocrinology where researchers are interested in understanding the effects of pesticide application on birth weight, with human chorionic gonadotropin (hCG) as the mediator. We assume a practically plausible set of nonlinear effects of hCG on the birth weight and a linear relationship between pesticide exposure and hCG, with both exposure-outcome and exposure-mediator models being linear in the confounding factors. Using the proposed methodology on a population-level prenatal screening program data, with hCG as the mediator, we discovered that, while the natural direct effects suggest a positive association between pesticide application and birth weight, the natural indirect effects were negative.

The Vaginal Microbiota of Pregnant Women Varies with Gestational Age, Maternal Age, and Parity.

The composition of the vaginal microbiota is heavily influenced by pregnancy and may factor into pregnancy complications, including spontaneous preterm birth. However, results among studies have been inconsistent due, in part, to variation in sample sizes and ethnicity. Thus, an association between the vaginal microbiota and preterm labor continues to be debated. Yet, before assessing associations between the composition of the vaginal microbiota and preterm labor, a robust and in-depth characterization of the vaginal microbiota throughout pregnancy in the specific study population under investigation is required. Here, we report a large longitudinal study (n = 474 women, 1,862 vaginal samples) of a predominantly African-American cohort-a population that experiences a relatively high rate of pregnancy complications-evaluating associations between individual identity, gestational age, and other maternal characteristics with the composition of the vaginal microbiota throughout gestation resulting in term delivery. The principal factors influencing the composition of the vaginal microbiota in pregnancy are individual identity and gestational age at sampling. Other factors are maternal age, parity, obesity, and self-reported Cannabis use. The general pattern across gestation is for the vaginal microbiota to remain or transition to a state of Lactobacillus dominance. This pattern can be modified by maternal parity and obesity. Regardless, network analyses reveal dynamic associations among specific bacterial taxa within the vaginal ecosystem, which shift throughout the course of pregnancy. This study provides a robust foundational understanding of the vaginal microbiota in pregnancy and sets the stage for further investigation of this microbiota in obstetrical disease. IMPORTANCE There is debate regarding links between the vaginal microbiota and pregnancy complications, especially spontaneous preterm birth. Inconsistencies in results among studies are likely due to differences in sample sizes and cohort ethnicity. Ethnicity is a complicating factor because, although all bacterial taxa commonly inhabiting the vagina are present among all ethnicities, the frequencies of these taxa vary among ethnicities. Therefore, an in-depth characterization of the vaginal microbiota throughout pregnancy in the specific study population under investigation is required prior to evaluating associations between the vaginal microbiota and obstetrical disease. This initial investigation is a large longitudinal study of the vaginal microbiota throughout gestation resulting in a term delivery in a predominantly African-American cohort, a population that experiences disproportionally negative maternal-fetal health outcomes. It establishes the magnitude of associations between maternal characteristics, such as age, parity, body mass index, and self-reported Cannabis use, on the vaginal microbiota in pregnancy.

Chronic Inhalation Exposure to Antimony Trioxide Exacerbates the MAPK Signaling in Alveolar Bronchiolar Carcinomas in B6C3F1/N Mice.

Antimony trioxide (AT) is used as a flame retardant in fabrics and plastics. Occupational exposure in miners and smelters is mainly through inhalation and dermal contact. Chronic inhalation exposure to AT particulates in B6C3F1/N mice and Wistar Han rats resulted in increased incidences and tumor multiplicities of alveolar/bronchiolar carcinomas (ABCs). In this study, we demonstrated Kras (43%) and Egfr (46%) hotspot mutations in mouse lung tumors (n = 80) and only Egfr (50%) mutations in rat lung tumors (n = 26). Interestingly, there were no differences in the incidences of these mutations in ABCs from rats and mice at exposure concentrations that did and did not exceed the pulmonary overload threshold. There was increased expression of p44/42 mitogen-activated protein kinase (MAPK) (Erk1/2) protein in ABCs harboring mutations in Kras and/or Egfr, confirming the activation of MAPK signaling. Transcriptomic analysis indicated significant alterations in MAPK signaling such as ephrin receptor signaling and signaling by Rho-family GTPases in AT-exposed ABCs. In addition, there was significant overlap between transcriptomic data from mouse ABCs due to AT exposure and human pulmonary adenocarcinoma data. Collectively, these data suggest chronic AT exposure exacerbates MAPK signaling in ABCs and, thus, may be translationally relevant to human lung cancers.

Relation Between Dietary Protein Intake and Gut Microbiome Composition in Community-Dwelling Older Men: Findings from the Osteoporotic Fractures in Men Study (MrOS).

BACKGROUND: Little is known about the association of specific nutrients, especially proteins, on age-related gut dysbiosis. OBJECTIVES: To determine the associations between the quantity and sources (vegetable and animal) of dietary protein intake and gut microbiome composition in community-dwelling older men. METHODS: We performed a cross-sectional analysis on 775 older men from the Osteoporotic Fractures in Men Study (MrOS) (age 84.2 ± 4.0 y) with available dietary information and stool samples at visit 4 (2014-2016). Protein intake was estimated from a brief FFQ and adjusted to total energy intake. The gut microbiome composition was determined by 16S (v4) sequencing (processed by DADA2 and SILVA). A total of 11,534 amplicon sequence variants (ASVs) were identified and assigned to 21 phyla with dominance of Firmicutes (45%) and Bacteroidetes (43%). We performed α-diversity, ß-diversity, and taxa abundance (by Analysis of Compositions of Microbiomes with Bias Correction [ANCOM-BC]) to determine the associations between protein intake and the gut microbiome. RESULTS: Median protein intake was 0.7 g/(kg body weight · d). Participants with higher energy-adjusted protein intakes had higher Shannon and Chao1 α-diversity indices (P < 0.05). For ß-diversity analysis, participants with higher protein intakes had a different center in weighted and unweighted UniFrac Principal Co-ordinates Analysis (PCoA) compared with those with lower intake (P < 0.05), adjusted for age, race, education, clinical center, batch number, fiber and energy intake, weight, height, and medications. Similarly, higher protein consumptions from either animal or vegetable sources were associated with higher gut microbiome diversity. Several genus-level ASVs, including Christensenellaceae, Veillonella, Haemophilus, and Klebsiella were more abundant in participants with higher protein intakes, whereas Clostridiales bacterium DTU089 and Desulfovibrio were more abundant in participants with lower protein intake (Bonferroni corrected P < 0.05). CONCLUSIONS: We observed significant associations between protein intake and gut microbiome diversity in community-living older men. Further studies are needed to elucidate the mediation role of the gut microbiome on the relation between protein intake and health outcomes in older adults.

Exposure to Antibacterial Chemicals Is Associated With Altered Composition of Oral Microbiome.

Antimicrobial chemicals are used as preservatives in cosmetics, pharmaceuticals, and food to prevent the growth of bacteria and fungi in the products. Unintentional exposure in humans to such chemicals is well documented, but whether they also interfere with human oral microbiome composition is largely unexplored. In this study, we explored whether the oral bacterial composition is affected by exposure to antibacterial and environmental chemicals. Gingival fluid, urine, and interview data were collected from 477 adults (18-47 years) from the RHINESSA study in Bergen, Norway. Urine biomarkers of triclosan, triclocarban, parabens, benzophenone-3, bisphenols, and 2,4- and 2,5-dichlorophenols (DCPs) were quantified (by mass spectrometry). Microbiome analysis was based on 16S amplicon sequencing. Diversity and differential abundance analyses were performed to identify how microbial communities may change when comparing groups of different chemical exposure. We identified that high urine levels (>75th percentile) of propyl parabens were associated with a lower abundance of bacteria genera TM7 [G-3], Helicobacter, Megasphaera, Mitsuokella, Tannerella, Propionibacteriaceae [G-2], and Dermabacter, as compared with low propylparaben levels (<25th percentile). High exposure to ethylparaben was associated with a higher abundance of Paracoccus. High urine levels of bisphenol A were associated with a lower abundance of Streptococcus and exposure to another environmental chemical, 2,4-DCP, was associated with a lower abundance of Treponema, Fretibacterium, and Bacteroidales [G-2]. High exposure to antibacterial and environmental chemicals was associated with an altered composition of gingiva bacteria; mostly commensal bacteria in the oral cavity. Our results highlight a need for a better understanding of how antimicrobial chemical exposure influences the human microbiome.

Temporal and Spatial Changes in the Microbiome Following Pediatric Severe Traumatic Brain Injury.

OBJECTIVES: The microbiome may be affected by trauma and critical illness. Many studies of the microbiome in critical illness are restricted to a single body site or time point and confounded by preexisting conditions. We report temporal and spatial alterations in the microbiome of previously healthy children with severe traumatic brain injury (TBI). DESIGN: We collected oral, rectal, and skin swabs within 72 hours of admission and then twice weekly until ICU discharge. Samples were analyzed by 16S rRNA gene amplicon sequencing. Children undergoing elective outpatient surgery served as controls. Alpha and beta diversity comparisons were performed with Phyloseq, and differentially abundant taxa were predicted using Analysis of Composition of Microbiomes. SETTING: Five quaternary-care PICUs. PATIENTS: Patients less than 18 years with severe TBI requiring placement of an intracranial pressure monitor. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Three hundred twenty-seven samples were analyzed from 23 children with severe TBI and 35 controls. The community composition of initial oral (F = 3.2756, R2 = 0.0535, p = 0.012) and rectal (F = 3.0702, R2 = 0.0649, p = 0.007) samples differed between TBI and control patients. Rectal samples were depleted of commensal bacteria from Ruminococcaceae, Bacteroidaceae, and Lachnospiraceae families and enriched in Staphylococcaceae after TBI (p < 0.05). In exploratory analyses, antibiotic exposure, presence of an endotracheal tube, and occurrence of an infection were associated with greater differences of the rectal and oral microbiomes between TBI patients and healthy controls, whereas enteral nutrition was associated with smaller differences (p < 0.05). CONCLUSIONS: The microbiome of children with severe TBI is characterized by early depletion of commensal bacteria, loss of site specificity, and an enrichment of potential pathogens. Additional studies are needed to determine the impact of these changes on clinical outcomes.

Shape Detection using Semi-parametric Shape-Restricted Mixed Effects Regression Spline with Applications.

Linear models are widely used in the field of epidemiology to model the relationship between placental-fetal hormone and fetal/infant outcome. When researchers suspect curvilinear relationship exists, some nonparametric techniques, including regression splines, smoothing splines and penalized regression splines, can be used to model the relationship (Korevaar et al. 2016; Wu and Zhang 2006). By applying these nonparametric techniques, researchers can relax the linearity assumption and capture scientifically meaningful or appropriate shapes. In this paper, we focus on the regression spline technique and develop a method to help researchers select the most suitable shape to describe their data among increasing, decreasing, convex and concave shapes. Specifically, we develop a mixed effects regression spline to model hormonal data described in this paper. The proposed methodology is general enough to be applied to other similar problems. We illustrate the method using a state-wide prenatal screening program data set.

House dust microbiota in relation to adult asthma and atopy in a US farming population.

BACKGROUND: Bacterial exposure from house dust has been associated with asthma and atopy in children but whether these relationships are present in adults remains unclear. OBJECTIVE: We sought to examine associations of house dust microbiota with adult asthma, atopy, and hay fever. METHODS: Vacuumed bedroom dust samples from the homes of 879 participants (average age, 62 years) in the Agricultural Lung Health Study, a case-control study of asthma nested within a farming cohort, were subjected to 16S rRNA amplicon sequencing to characterize bacterial communities. We defined current asthma and hay fever using questionnaires and current atopy by blood specific IgE level > 0.70 IU/mL to 1 or more of 10 common allergens. We used linear regression to examine whether overall within-sample bacterial diversity differed by outcome, microbiome regression-based kernel association test to evaluate whether between-sample bacterial community compositions differed by outcome, and analysis of composition of microbiomes to identify differentially abundant bacterial taxa. RESULTS: Overall diversity of bacterial communities in house dust was similar by asthma status but was lower (P < .05) with atopy or hay fever. Many individual bacterial taxa were differentially abundant (false-discovery rate, <0.05) by asthma, atopy, or hay fever. Several taxa from Cyanobacteria, Bacteroidetes, and Fusobacteria were more abundant with asthma, atopy, or hay fever. In contrast, several taxa from Firmicutes were more abundant in homes of individuals with adequately controlled asthma (vs inadequately controlled asthma), individuals without atopy, or individuals without hay fever. CONCLUSIONS: Microbial composition of house dust may influence allergic outcomes in adults.

Unique microRNA alterations in hepatocellular carcinomas arising either spontaneously or due to chronic exposure to Ginkgo biloba extract (GBE) in B6C3F1/N mice.

Ginkgo biloba extract (GBE) is used in traditional Chinese medicine as a herbal supplement for improving memory. Exposure of B6C3F1/N mice to GBE in a 2-year National Toxicology Program (NTP) bioassay resulted in a dose-dependent increase in hepatocellular carcinomas (HCC). To identify key microRNAs that modulate GBE-induced hepatocarcinogenesis, we compared the global miRNA expression profiles in GBE-exposed HCC (GBE-HCC) and spontaneous HCC (SPNT-HCC) with age-matched vehicle control normal livers (CNTL) from B6C3F1/N mice. The number of differentially altered miRNAs in GBE-HCC and SPNT-HCC was 74 (52 up and 22 down) and 33 (15 up and 18 down), respectively. Among the uniquely differentially altered miRNAs in GBE-HCC, miR-31 and one of its predicted targets, Cdk1 were selected for functional validation. A potential miRNA response element (MRE) in the 3'-untranslated regions (3'-UTR) of Cdk1 mRNA was revealed by in silico analysis and confirmed by luciferase assays. In mouse hepatoma cell line HEPA-1 cells, we demonstrated an inverse correlation between miR-31 and CDK1 protein levels, but no change in Cdk1 mRNA levels, suggesting a post-transcriptional effect. Additionally, a set of miRNAs (miRs-411, 300, 127, 134, 409-3p, and 433-3p) that were altered in the GBE-HCCs were also altered in non-tumor liver samples from the 90-day GBE-exposed group compared to the vehicle control group, suggesting that some of these miRNAs could serve as potential biomarkers for GBE exposure or hepatocellular carcinogenesis. These data increase our understanding of miRNA-mediated epigenetic regulation of GBE-mediated hepatocellular carcinogenesis in B6C3F1/N mice.

Potential impact of the COVID-19 pandemic on financial toxicity in cancer survivors.

BACKGROUND: In the context of COVID-19, cancer survivors represent a particularly vulnerable population that may be "doubly hit" by both costs of cancer treatment and financial strain imposed by the pandemic. METHODS: We performed a review of the literature pertaining to cancer, financial toxicity, and economic challenges. RESULTS: Multiple societies have put forth recommendations to modify delivery of cancer care in order to minimize patient exposure to the virus. Cancer survivors, especially patients with head and neck cancer, have been disproportionately affected by rising unemployment levels and economic recessions in the past, both of which are linked to higher cancer mortality. Patients who rely on employer-provided insurance and do not qualify for Medicaid may lose access to life-saving treatments. CONCLUSIONS: It is essential to implement interventions and policy changes in order to mitigate the effects of this pandemic but also to ensure this becomes a nonissue during the next one.

Associations of Thigh and Abdominal Adipose Tissue Radiodensity with Glucose and Insulin in Nondiabetic African-Ancestry Men.

OBJECTIVE: Decreased radiodensity of adipose tissue (AT) located in the visceral AT (VAT), subcutaneous AT (SAT), and intermuscular AT (IMAT) abdominal depots is associated with hyperglycemia, hyperinsulinemia, and insulin resistance independent of AT volumes. These associations were sought in African-ancestry men, who have higher risk for type 2 diabetes and have been underrepresented in previous studies. METHODS: This cross-sectional analysis included 505 nondiabetic men of African-Caribbean ancestry (median age: 61 years; median BMI: 26.8 kg/m2 ) from the Tobago Health Study. AT volumes and radiodensities were assessed using computed tomography, including abdominal (VAT and SAT) and thigh (IMAT) depots. Associations between AT radiodensities were assessed with fasting serum glucose and insulin and with insulin resistance (updated homeostatic model assessment of insulin resistance, HOMA2-IR). RESULTS: Higher radiodensity in any AT depot was associated with lower log-insulin and log-HOMA2-IR (ß range: -0.16 to -0.18 for each; all P < 0.0001). No AT radiodensity was associated with glucose. Thigh IMAT radiodensity associations were independent of, and similar in magnitude to, VAT radiodensities. Model fit statistics suggested that AT radiodensities were a better predictor for insulin and insulin resistance compared with AT volumes in individuals with overweight and obesity. CONCLUSIONS: AT radiodensities at multiple depots are significantly associated with insulin and insulin resistance in African-ancestry men.

Order restricted inference in chronobiology.

This paper is motivated by applications in oscillatory systems where researchers are typically interested in discovering components of those systems that display rhythmic temporal patterns. The contributions of this paper are twofold. First, a methodology is developed based on a circular signal plus error model that is defined using order restrictions. This mathematical formulation of rhythmicity is simple, easily interpretable and very flexible, with the latter property derived from the nonparametric formulation of the signal. Second, we address various commonly encountered problems in the analysis of oscillatory systems data. Specifically, we propose a methodology for (a) detecting rhythmic signals in an oscillatory system and (b) estimating the unknown sampling time that occurs when tissues are obtained from subjects whose time of death is unknown. The proposed methodology is computationally efficient, outperforms the existing methods, and is broadly applicable to address a wide range of questions related to oscillatory systems.

Frequency Modulated Möbius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences.

Motivated by applications in physical and biological sciences, we developed a Frequency Modulated Möbius (FMM) model to describe rhythmic patterns in oscillatory systems. Unlike standard symmetric sinusoidal models, FMM is a flexible parametric model that allows deformations to sinusoidal shape to accommodate commonly seen asymmetries in applications. FMM model parameters are easy to estimate and the model is easy to interpret complex rhythmic data. We illustrate FMM model in three disparate applications, namely, circadian clock gene expression, corticoptropin levels in depressed patients and the temporal light intensity patterns of distant stars. In each case, FMM model is demonstrated to be flexible, scientifically plausible and easy to interpret. Analysis of synthetic data derived from patterns of real data, suggest that FMM model fits the data very well both visually as well as in terms of the goodness of fit measure total mean squared error. An R language based software for implementing FMM model is available.

Understanding financial toxicity in head and neck cancer survivors.

OBJECTIVES: (1) Describe financial toxicity (FT) in head and neck cancer (HNC) survivors and assess its association with personal/health characteristics and health-related quality of life (HRQOL); (2) examine financial coping mechanisms (savings/loans); (3) assess relationship between COmprehensive Score for financial Toxicity (COST) and Financial Distress Questionnaire (FDQ). PATIENTS AND METHODS: Cross-sectional survey from January - April 2018 of insured patients at a tertiary multidisciplinary HNC survivorship clinic who completed primary treatment for squamous cell carcinoma of the oral cavity, oropharynx, or larynx/hypopharynx. RESULTS: Of 104 survivors, 30 (40.5%) demonstrated high FT. Patients with worse FT were more likely (1) not married (COST, 25.33 ±â€¯1.87 vs. 30.61 ±â€¯1.34, p = 0.008); (2) of lower education levels (COST, 26.12 ±â€¯1.47 vs. 34.14 ±â€¯1.47, p < 0.001); and (3) with larynx/hypopharynx primaries (COST, 22.86 ±â€¯2.28 vs. 30.27 ±â€¯1.50 vs. 32.72 ±â€¯1.98, p = 0.005). Younger age (4.23, 95%CI 2.20 to 6.26, p < 0.001), lower earnings at diagnosis (1.17, 95%CI 0.76 to 1.58, p < 0.001), and loss in earnings (-1.80, 95%CI -2.43 to -1.16, p < 0.001) were associated with worse FT. COST was associated with HRQOL (0.08, p = 0.03). Most survivors (63/102, 60%) reported using savings and/or loans. Worse FT was associated with increased likelihood of using more mechanisms (COST, OR1.06, 95%CI 1.02 to 1.10, p = 0.004). Similar results were found with FDQ. CONCLUSIONS: We found differences in FT by primary site, with worst FT in larynx/hypopharynx patients. This finding illuminates potential site-specific factors, e.g. workplace discrimination or inability to return to work, that may contribute to increased risk. FDQ correlates strongly with COST, encouraging further exploration as a clinically-meaningful screening tool.

Microarray Data Normalization and Robust Detection of Rhythmic Features.

Data derived from microarray technologies are generally subject to various sources of noise and accordingly the raw data are pre-processed before formally analysed. Data normalization is a key pre-processing step when dealing with microarray experiments, such as circadian gene-expressions, since it removes systematic variations across arrays. A wide variety of normalization methods are available in the literature. However, from our experience in the study of rhythmic expression patterns in oscillatory systems (e.g. cell-cycle, circadian clock), the choice of the normalization method may substantially impair the identification of rhythmic genes. Hence, the identification of a gene as rhythmic could be just as an artefact of how the data were normalized. Yet, gene rhythmicity detection is crucial in modern toxicological and pharmacological studies, thus a procedure to truly identify rhythmic genes that are robust to the choice of a normalization method is required.To perform the task of detecting rhythmic features, we propose a rhythmicity measure based on bootstrap methodology to robustly identify rhythmic genes in oscillatory systems. Although our methodology can be extended to any high-throughput experiment, in this chapter, we illustrate how to apply it to a publicly available circadian clock microarray gene-expression data and give full details (both statistical and computational) so that the methodology can be used in an easy way. We will show that the choice of normalization method has very little effect on the proposed methodology since the results derived from the bootstrap-based rhythmicity measure are highly rank correlated for any pair of normalization methods considered. This suggests, on the one hand, that the rhythmicity measure proposed is robust to the choice of the normalization method, and on the other hand, that gene rhythmicity detected using this measure is potentially not a mere artefact of the normalization method used. In this way the researcher using this methodology will be protected against the possible effect of different normalizations, as the conclusions obtained will not depend so strongly on them. Additionally, the described bootstrap methodology can also be employed as a tool to simulate gene-expression participating in an oscillatory system from a reference data set.

Quality Control of Quantitative High Throughput Screening Data.

Quantitative high throughput screening (qHTS) experiments can generate 1000s of concentration-response profiles to screen compounds for potentially adverse effects. However, potency estimates for a single compound can vary considerably in study designs incorporating multiple concentration-response profiles for each compound. We introduce an automated quality control procedure based on analysis of variance (ANOVA) to identify and filter out compounds with multiple cluster response patterns and improve potency estimation in qHTS assays. Our approach, called Cluster Analysis by Subgroups using ANOVA (CASANOVA), clusters compound-specific response patterns into statistically supported subgroups. Applying CASANOVA to 43 publicly available qHTS data sets, we found that only about 20% of compounds with response values outside of the noise band have single cluster responses. The error rates for incorrectly separating true clusters and incorrectly clumping disparate clusters were both less than 5% in extensive simulation studies. Simulation studies also showed that the bias and variance of concentration at half-maximal response (AC50 ) estimates were usually within 10-fold when using a weighted average approach for potency estimation. In short, CASANOVA effectively sorts out compounds with "inconsistent" response patterns and produces trustworthy AC50 values.

Threshold Knot Selection for Large-Scale Spatial Models With Applications to the Deepwater Horizon Disaster.

Large spatial datasets are typically modeled through a small set of knot locations; often these locations are specified by the investigator by arbitrary criteria. Existing methods of estimating the locations of knots assume their number is known a priori, or are otherwise computationally intensive. We develop a computationally efficient method of estimating both the location and number of knots for spatial mixed effects models. Our proposed algorithm, Threshold Knot Selection (TKS), estimates knot locations by identifying clusters of large residuals and placing a knot in the centroid of those clusters. We conduct a simulation study showing TKS in relation to several comparable methods of estimating knot locations. Our case study utilizes data of particulate matter concentrations collected during the course of the response and clean-up effort from the 2010 Deepwater Horizon oil spill in the Gulf of Mexico.

Exposures Related to House Dust Microbiota in a U.S. Farming Population.

BACKGROUND: Environmental factors can influence the house dust microbiota, which may impact health outcomes. Little is known about how farming exposures impact the indoor microbiota. OBJECTIVE: We aimed to identify exposures related to bacterial communities in house dust in a U.S. farming population. METHODS: We used 16S rRNA amplicon sequencing to characterize bacterial communities in vacuumed dust samples from the bedrooms of a subset of 879 households of farmers and farmers' spouses enrolled in the Agricultural Lung Health Study (ALHS), a case-control study of asthma nested within the Agricultural Health Study (AHS) in North Carolina and Iowa. Information on current farming (past 12 mo), including both crop and animal farming, and other potential microbial sources was obtained via questionnaires. We used linear regression to evaluate associations between exposures and bacterial diversity within each sample, analysis of similarity (ANOSIM), and permutational multivariate analysis of variance (PERMANOVA) to identify exposures related to diversity between samples, and analysis of composition of microbiome to examine whether exposures related to diversity were also related to differential abundance of specific operational taxonomic units (OTUs). RESULTS: Current farming was positively associated with bacterial diversity in house dust, with or without adjustment for nonfarm exposures related to diversity, including presence of indoor pets, home condition, and season of dust collection. Many taxa exhibited differential abundance related to farming. Some taxa in the phyla Chloroflexi and Verrucomicrobia were associated [false discovery rate (FDR)<0.05] with farming but not with other nonfarm factors. Many taxa correlated with the concentration of house dust of endotoxin, commonly studied as a general marker of exposure to the farming environment. CONCLUSIONS: In this farming population, house dust microbiota differed by current farming status. Understanding the determinants of the indoor microbiota is the first step toward understanding potential relationships with health outcomes. https://doi.org/10.1289/EHP3145.