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.
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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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Arman Alam Siddique;
Mireille E. Schnitzer;
Asma Bahamyirou;
Guanbo Wang;
Timothy H. Holtz;
Giovanni B. Migliori;
Giovanni Sotgiu;
Neel Gandhi;
Mario H. Vargas;
Dick Menzies;
Andrea Benedetti
This paper investigates different approaches for causal estimation under multiple concurrent medications. Our parameter of interest is the marginal mean counterfactual outcome under different combinations of medications. We explore parametric and non-parametric methods to estimate the generalized propensity score. We then apply three causal estimation approaches (inverse probability of treatment weighting, propensity score adjustment, and targeted maximum likelihood estimation) to estimate the causal parameter of interest. Focusing on the estimation of the expected outcome under the most prevalent regimens, we compare the results obtained using these methods in a simulation study with four potentially concurrent medications. We perform a second simulation study in which some combinations of medications may occur rarely or not occur at all in the dataset. Finally, we apply the methods explored to contrast the probability of patient treatment success for the most prevalent regimens of antimicrobial agents for patients with multidrug-resistant pulmonary tuberculosis.
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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.
Quantile regression has demonstrated promising utility in longitudinal data analysis. Existing work is primarily focused on modeling cross-sectional outcomes, while outcome trajectories often carry more substantive information in practice. In this work, we develop a trajectory quantile regression framework that is designed to robustly and flexibly investigate how latent individual trajectory features are related to observed subject characteristics. The proposed models are built under multilevel modeling with usual parametric assumptions lifted or relaxed. We derive our estimation procedure by novelly transforming the problem at hand to quantile regression with perturbed responses and adapting the bias correction technique for handling covariate measurement errors. We establish desirable asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulation studies confirm the validity of the proposed method as well as its robustness. An application to the DURABLE trial uncovers sensible scientific findings and illustrates the practical value of our proposals.
There is a growing movement to encourage reproducibility and transparency practices in the scientific community, including public access to raw data and protocols, the conduct of replication studies, systematic integration of evidence in systematic reviews, and the documentation of funding and potential conflicts of interest. In this survey, we assessed the current status of reproducibility and transparency addressing these indicators in a random sample of 441 biomedical journal articles published in 2000–2014. Only one study provided a full protocol and none made all raw data directly available. Replication studies were rare (n = 4), and only 16 studies had their data included in a subsequent systematic review or meta-analysis. The majority of studies did not mention anything about funding or conflicts of interest. The percentage of articles with no statement of conflict decreased substantially between 2000 and 2014 (94.4% in 2000 to 34.6% in 2014); the percentage of articles reporting statements of conflicts (0% in 2000, 15.4% in 2014) or no conflicts (5.6% in 2000, 50.0% in 2014) increased. Articles published in journals in the clinical medicine category versus other fields were almost twice as likely to not include any information on funding and to have private funding. This study provides baseline data to compare future progress in improving these indicators in the scientific literature.