The United States's Clean Water Act stipulates in section 303(d) that states must identify impaired water bodies for which total maximum daily loads (TMDLs) of pollution inputs into water bodies are developed. Decision-making procedures about how to list, or delist, water bodies as impaired, or not, per Clean Water Act 303(d) differ across states. In states such as California, whether or not a particular monitoring sample suggests that water quality is impaired can be regarded as a binary outcome variable, and California's current regulatory framework invokes a version of the exact binomial test to consolidate evidence across samples and assess whether the overall water body complies with the Clean Water Act. Here, we contrast the performance of California's exact binomial test with one potential alternative, the Sequential Probability Ratio Test (SPRT). The SPRT uses a sequential testing framework, testing samples as they become available and evaluating evidence as it emerges, rather than measuring all the samples and calculating a test statistic at the end of the data collection process. Through simulations and theoretical derivations, we demonstrate that the SPRT on average requires fewer samples to be measured to have comparable Type I an d Type II error rates as the current fixed-sample binomial test. Policymakers might consider efficient alternatives such as SPRT to current procedure.
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Michael Zwick;
Sandeep J. Joseph;
Xavier Didelot;
Peter E. Chen;
Kimberly A. Bishop-Lilly;
Andrew C. Stewart;
Kristin Willner;
Nichole Nolan;
Shannon Lentz;
Maureen K. Thomason;
Shanmuga Sozhamannan;
Alfred J. Mateczun;
Lei Du;
Timothy D Read
The key genes required for Bacillus anthracis to cause anthrax have been acquired recently by horizontal gene transfer. To understand the genetic background for the evolution of B. anthracis virulence, we obtained high-redundancy genome sequences of 45 strains of the Bacillus cereus sensu lato (s.l.) species that were chosen for their genetic diversity within the species based on the existing multilocus sequence typing scheme. From the resulting data, we called more than 324,000 new genes representing more than 12,333 new gene families for this group. The core genome size for the B. cereus s.l. group was ∼1750 genes, with another 2150 genes found in almost every genome constituting the extended core. There was a paucity of genes specific and conserved in any clade. We found no evidence of recent large-scale gene loss in B. anthracis or for unusual accumulation of nonsynonymous DNA substitutions in the chromosome; however, several B. cereus genomes isolated from soil and not previously associated with human disease were degraded to various degrees. Although B. anthracis has undergone an ecological shift within the species, its chromosome does not appear to be exceptional on a macroscopic scale compared with close relatives.
Genome-Wide association studies (GWAS) routinely apply principal component analysis (PCA) to infer population structure within a sample to correct for confounding due to ancestry. GWAS implementation of PCA uses tens of thousands of single-nucleotide polymorphisms (SNPs) to infer structure, despite the fact that only a small fraction of such SNPs provides useful information on ancestry. The identification of this reduced set of Ancestry-Informative markers (AIMs) from a GWAS has practical value; for example, researchers can genotype the AIM set to correct for potential confounding due to ancestry in follow-up studies that utilize custom SNP or sequencing technology. We propose a novel technique to identify AIMs from Genome-Wide SNP data using sparse PCA. The procedure uses penalized regression methods to identify those SNPs in a Genome-Wide panel that significantly contribute to the principal components while encouraging SNPs that provide negligible loadings to vanish from the analysis. We found that sparse PCA leads to negligible loss of ancestry information compared to traditional PCA analysis of Genome-Wide SNP data. We further demonstrate the value of sparse PCA for AIM selection using real data from the International HapMap Project and a Genome-Wide study of inflammatory bowel disease. We have implemented our approach in open-source R software for public use.
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Jian-Xin Lin;
Peng Li;
Hyun Tak Jin;
Jianping He;
Mohammed Rasheed;
Yrina Rochman;
Lu Wang;
Kairong Cui;
Chengyu Liu;
Brian L. Kelsall;
Rafi Ahmed;
Warren J. Leonard
Cytokine-activated STAT proteins dimerize and bind to high-affinity motifs, and N-terminal domain-mediated oligomerization of dimers allows tetramer formation and binding to low-affinity tandem motifs, but the functions of dimers versus tetramers are unknown. We generated Stat5a-Stat5b double knockin (DKI) N-domain mutant mice in which STAT5 proteins form dimers but not tetramers, identified cytokine-regulated genes whose expression required STAT5 tetramers, and defined dimer versus tetramer consensus motifs. Whereas Stat5-deficient mice exhibited perinatal lethality, DKI mice were viable; thus, STAT5 dimers were sufficient for survival. Nevertheless, STAT5 DKI mice had fewer CD4+CD25+T cells, NK cells, and CD8+T cells, with impaired cytokine-induced and homeostatic proliferation of CD8+T cells. Moreover, DKI CD8+T cell proliferation after viral infection was diminished and DKI Treg cells did not efficiently control colitis. Thus, tetramerization of STAT5 is critical for cytokine responses and normal immune function, establishing a critical role for STAT5 tetramerization in vivo.
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Seetha Shankaran;
Edward F. Bell;
Abbot R. Laptook;
Shampa Saha;
Nancy S. Newman;
S. Nadya J. Kazzi;
John Barks;
Barbara Stoll;
Rebecca Bara;
Jenna Gabrio;
Kirsten Childs;
Abhik Das;
Rosemary D. Higgins;
Waldemar A. Carlo;
Pablo J. Sanchez;
David Carlton;
Lara Pavageau;
William F. Malcolm;
Carl T. D'Angio;
Robin K. Ohls;
Brenda B. Poindexter;
Gregory M. Sokol;
Krisa P. Van Meurs;
Tarah T. Colaizy;
Ayman Khmour;
Karen M. Puopolo;
Meena Garg;
Michele C. Walsh
Objective: To assess whether length of hospital stay is decreased among moderately preterm infants weaned from incubator to crib at a lower vs higher weight. Study design: This trial was conducted in the Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network. Infants with gestational ages 29-33 weeks, birthweight <1600 g, and in an incubator were randomly assigned to a weaning weight of 1600 or 1800 g. Within 60 to 100 g of weaning weight, the incubator temperature was decreased by 1.0°C to 1.5°C every 24 hours until 28.0°C. The infants were weaned to the crib following stable temperature at 36.5°C to 37.4°C for 8 to 12 hours. Clothing and bedcoverings were standardized. The primary outcome was length of hospital stay from birth to discharge; secondary outcomes included length of stay and growth velocity from weaning to discharge. Adverse events were monitored. Results: Of 1565 infants screened, 885 were eligible, and 366 enrolled—187 to the 1600-g and 179 to the 1800-g group. Maternal and neonatal characteristics did not differ among weight groups. Length of hospital stay was a median of 43 days in the lower and 41 days in the higher weight group (P =.12). Growth velocity from completion of weaning to discharge was higher in the lower weight group, 13.7 g/kg/day vs 12.8 g/kg/day (P =.005). Groups did not differ in adverse events. Conclusions: Among moderately preterm neonates, weaning from incubator to crib at a lower weight did not decrease length of stay, but was safe and was accompanied by higher weight gain after weaning. Trial registration: ClinicalTrials.gov: NCT02160002.
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Subrata Sarkar;
Seetha Shankaran;
John Barks;
Barbara T. Do;
Abbot R. Laptook;
Abhik Das;
Namasivayam Ambalavanan;
Krisa P. Van Meurs;
Edward F. Bell;
Pablo J. Sanchez;
Susan R. Hintz;
Myra H. Wyckoff;
Barbara Stoll;
Waldemar A. Carlo;
David Carlton;
Ira Adams-Chapman
Objective: To determine the outcome of preterm infants whose cystic periventricular leukomalacia “disappeared” on serial screening cranial imaging studies. Study design: Infants ≤26 weeks of gestation born between 2002 and 2012 who had cranial imaging studies at least twice, the most abnormal study at <28 days of age and another closest to 36 weeks, were reviewed. The outcome of late death (after 36 weeks postmenstrual age) or neurodevelopmental impairment (NDI) in surviving infants at 18-26 months corrected age was compared between the infants with no cystic periventricular leukomalacia on both studies and cystic periventricular leukomalacia that disappeared (cystic periventricular leukomalacia at <28 days but not at 36 weeks), persisted (cystic periventricular leukomalacia on both studies), or appeared late (cystic periventricular leukomalacia only at 36 weeks). Predictors of NDI were evaluated by logistic regression. Results: Of 7063 eligible infants, 433 (6.1%) had cystic periventricular leukomalacia. Among the 433 infants with cystic periventricular leukomalacia, cystic periventricular leukomalacia disappeared in 76 (18%), persisted in 87 (20%), and 270 (62%) had late cystic periventricular leukomalacia. Loss to follow-up ranged between 3% and 13%. Death or NDI was more common in infants with disappeared cystic periventricular leukomalacia compared with those with no cystic periventricular leukomalacia (38 of 72 [53%] vs 1776 of 6376 [28%]; OR [95% CI] 2.8 [1.8-4.6]). Disappeared, persistent, and late cystic periventricular leukomalacia were all also independently associated with NDI (OR 1.17, 1.21, and 1.16, respectively). Conclusions: Infants with “disappeared” cystic periventricular leukomalacia are at increased risk of adverse outcome similar to infants with persistent or late cystic periventricular leukomalacia.
BACKGROUND
Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy. This report describes the survival of children with ALL in the United States using the most comprehensive and up‐to‐date cancer registry data.
METHODS
Data from 37 state cancer registries that cover approximately 80% of the US population were used. Age‐standardized survival up to 5 years was estimated for children aged 0‐14 years who were diagnosed with ALL during 2 periods (2001‐2003 and 2004‐2009).
RESULTS
In total, 17,500 children with ALL were included. The pooled age‐standardized net survival estimates for all US registries combined were 95% at 1 year, 90% at 3 years, and 86% at 5 years for children diagnosed during 2001‐2003, and 96%, 91%, and 88%, respectively, for those diagnosed during 2004‐2009. Black children who were diagnosed during 2001‐2003 had lower 5‐year survival (84%) than white children (87%) and had less improvement in survival by 2004‐2009. For those diagnosed during 2004‐2009, the 1‐year and 5‐year survival estimates were 96% and 89%, respectively, for white children and 96% and 84%, respectively, for black children. During 2004‐2009, survival was highest among children aged 1 to 4 years (95%) and lowest among children aged <1 year (60%).
CONCLUSIONS
The current results indicate that overall net survival from childhood ALL in the United States is high, but disparities by race still exist, especially beyond the first year after diagnosis. Clinical and public health strategies are needed to improve health care access, clinical trial enrollment, treatment, and survivorship care for children with ALL.
BACKGROUND: Three decades of rapid economic development is causing severe and widespread PM2.5 (particulate matter ≤ 2.5 μm) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data.
OBJECTIVES: We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1° resolution using the most recent satellite data and evaluated model performance with available ground observations.
METHODS: We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model-predicted PM2.5 concentrations from 2004 to early 2014 using ground observations.
RESULTS: The overall model cross-validation R2 and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM2.5 concentrations with little bias at the monthly (R2 = 0.73, regression slope = 0.91) and seasonal (R2 = 0.79, regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 μg/m3 between 2004 and 2007 and a decrease of 0.46 μg/m3 between 2008 and 2013.
CONCLUSIONS: Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.
Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically prespecified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high dimensional setting. We employ adaptive L1 penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quantile levels to avoid some of the potential problems with model selection at individual quantile levels. Our proposed approach achieves consistent shrinkage of regression quantile estimates across a continuous range of quantiles levels, enhancing the flexibility and robustness of the existing penalized quantile regression methods. Our theoretical results include the oracle rate of uniform convergence and weak convergence of the parameter estimators. We also use numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposal.
Estimation of longitudinal models of relationship status between all pairs of individuals (dyads) in social networks is challenging due to the complex inter-dependencies among observations and lengthy computation times. To reduce the computational burden of model estimation, a method is developed that subsamples the "always-null" dyads in which no relationships develop throughout the period of observation. The informative sampling process is accounted for by weighting the likelihood contributions of the observations by the inverses of the sampling probabilities. This weighted-likelihood estimation method is implemented using Bayesian computation and evaluated in terms of its bias, efficiency, and speed of computation under various settings. Comparisons are also made to a full information likelihood-based procedure that is only feasible to compute when limited follow-up observations are available. Calculations are performed on two real social networks of very different sizes. The easily computed weighted-likelihood procedure closely approximates the corresponding estimates for the full network, even when using low sub-sampling fractions. The fast computation times make the weighted-likelihood approach practical and able to be applied to networks of any size.