Sensitivity analysis for the assessment of causal vaccine effects on viral load in HIV vaccine trials

Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002, Biometrics 58, 21-29), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias.     

Mutagenicity in lung of big Blue((R)) mice and induction of tandem-base substitutions in Salmonella by the air pollutant peroxyacetyl nitrate (PAN): predicted formation of intrastrand cross-links

Peroxyacetyl nitrate (PAN) is a ubiquitous air pollutant formed from NO(2) reacting with acetoxy radicals generated from ambient aldehydes in the presence of sunlight and ozone. It contributes to eye irritation associated with photochemical smog and is present in most urban air. PAN was generated in a chamber containing open petri dishes of Salmonella TA100 (gas-phase exposure). After subtraction of the background mutation spectrum, the spectrum of PAN-induced mutants selected at 3.1-fold above the background mutant yield was 59% GC-->TA, 29% GC-->AT, 2% GC-->CG, and 10% multiple mutations - primarily GG-->TT tandem-base substitutions. Using computational molecular modeling methods, a mechanism was developed for producing this unusual tandem-base substitution. The mechanism depends on the protonation of PAN near the polyanionic DNA to release NO(2)(+) resulting in intrastrand dimer formation. Insertion of AA opposite the dimerized GG would account for the tandem GG-->TT transversions. Nose-only exposure of Big Blue((R)) mice to PAN at 78ppm (near the MTD) was mutagenic at the lacI gene in the lung (mutant frequency +/-S.E. of 6.16+/-0.58/10(5) for controls versus 8.24+/-0.30/10(5) for PAN, P=0.016). No tandem-base mutations were detected among the 40 lacI mutants sequenced. Dosimetry with 3H-PAN showed that 24h after exposure, 3.9% of the radiolabel was in the nasal tissue, and only 0.3% was in the lung. However, based on the molecular modeling considerations, the labeled portion of the molecule would not have been expected to have been bound covalently to DNA. Our results indicate that PAN is weakly mutagenic in the lungs of mice and in Salmonella and that PAN produces a unique signature mutation (a tandem GG-->TT transversion) in Salmonella that is likely due to a GG intrastrand cross-link. Thus, PAN may pose a mutagenic and possible carcinogenic risk to humans, especially at the high concentrations at which it is present in some urban environments.     

Comparison of group testing algorithms for case identification in the presence of test error

We derive and compare the operating characteristics of hierarchical and square array-based testing algorithms for case identification in the presence of testing error. The operating characteristics investigated include efficiency (i.e., expected number of tests per specimen) and error rates (i.e., sensitivity, specificity, positive and negative predictive values, per-family error rate, and per-comparison error rate). The methodology is illustrated by comparing different pooling algorithms for the detection of individuals recently infected with HIV in North Carolina and Malawi.     

Incorporating Cold-Air Pooling into Downscaled Climate Models Increases Potential Refugia for Snow-Dependent Species within the Sierra Nevada Ecoregion,CA

We present a unique water-balance approach for modeling snowpack under historic, current and future climates throughout the Sierra Nevada Ecoregion. Our methodology uses a finer scale (270 m) than previous regional studies and incorporates cold-air pooling, an atmospheric process that sustains cooler temperatures in topographic depressions thereby mitigating snowmelt. Our results are intended to support management and conservation of snow-dependent species, which requires characterization of suitable habitat under current and future climates. We use the wolverine (Gulo gulo) as an example species and investigate potential habitat based on the depth and extent of spring snowpack within four National Park units with proposed wolverine reintroduction programs. Our estimates of change in spring snowpack conditions under current and future climates are consistent with recent studies that generally predict declining snowpack. However, model development at a finer scale and incorporation of cold-air pooling increased the persistence of April 1^st snowpack. More specifically, incorporation of cold-air pooling into future climate projections increased April 1^st snowpack by 6.5% when spatially averaged over the study region and the trajectory of declining April 1^st snowpack reverses at mid-elevations where snow pack losses are mitigated by topographic shading and cold-air pooling. Under future climates with sustained or increased precipitation, our results indicate a high likelihood for the persistence of late spring snowpack at elevations above approximately 2,800 m and identify potential climate refugia sites for snow-dependent species at mid-elevations, where significant topographic shading and cold-air pooling potential exist.     

Assessing intervention effects in a randomized trial within a social network

Studies of social networks provide unique opportunities to assess the causal effects of interventions that may impact more of the population than just those intervened on directly. Such effects are sometimes called peer or spillover effects, and may exist in the presence of interference, that is, when one individual's treatment affects another individual's outcome. Randomization-based inference (RI) methods provide a theoretical basis for causal inference in randomized studies, even in the presence of interference. In this article, we consider RI of the intervention effect in the eX-FLU trial, a randomized study designed to assess the effect of a social distancing intervention on influenza-like-illness transmission in a connected network of college students. The approach considered enables inference about the effect of the social distancing intervention on the per-contact probability of influenza-like-illness transmission in the observed network. The methods allow for interference between connected individuals and for heterogeneous treatment effects. The proposed methods are evaluated empirically via simulation studies, and then applied to data from the eX-FLU trial.     

Nonparametric inference for competing risks current status data with continuous, discrete or grouped observation times

New methods and theory have recently been developed to nonparametrically estimate cumulative incidence functions for competing risks survival data subject to current status censoring. In particular, the limiting distribution of the nonparametric maximum likelihood estimator and a simplified naive estimator have been established under certain smoothness conditions. In this paper, we establish the large-sample behaviour of these estimators in two additional models, namely when the observation time distribution has discrete support and when the observation times are grouped. These asymptotic results are applied to the construction of confidence intervals in the three different models. The methods are illustrated on two datasets regarding the cumulative incidence of different types of menopause from a cross-sectional sample of women in the United States and subtype-specific HIV infection from a sero-prevalence study in injecting drug users in Thailand.     

Phylogeny and expression of carbonic anhydrase-related proteins

Background  

Carbonic anhydrases (CAs) are found in many organisms, in which they contribute to several important biological processes. The vertebrate α-CA family consists of 16 subfamilies, three of which (VIII, X and XI) consist of acatalytic proteins. These are named carbonic anhydrase related proteins (CARPs), and their inactivity is due to absence of one or more Zn-binding histidine residues. In this study, we analyzed and evaluated the distribution of genes encoding CARPs in different organisms using bioinformatic methods, and studied their expression in mouse tissues using immunohistochemistry and real-time quantitative PCR.