Climate-dependent CO2 emissions from lakes

Inland waters, just as the world's oceans, play an important role in the global carbon cycle. While lakes and reservoirs typically emit CO2, they also bury carbon in their sediment. The net CO2 emission is largely the result of the decomposition or preservation of terrestrially supplied carbon. What regulates the balance between CO2 emission and carbon burial is not known, but climate change and temperature have been hypothesized to influence both processes. We analyzed patterns in carbon dioxide partial pressure (pCO2) in 83 shallow lakes over a large climatic gradient in South America and found a strong, positive correlation with temperature. The higher pCO2 in warmer lakes may be caused by a higher, temperature-dependent mineralization of organic carbon. This pattern suggests that cool lakes may start to emit more CO2 when they warm up because of climate ch

US Black Women and Human Immunodeficiency Virus Prevention: Time for New Approaches to Clinical Trials

Black women bear the highest burden of human immunodeficiency virus (HIV) infection among US women. Tenofovir/emtricitabine HIV prevention trials among women in Africa have yielded varying results. Ideally, a randomized controlled trial (RCT) among US women would provide data for guidelines for US women's HIV preexposure prophylaxis use. However, even among US black women at high risk for HIV infection, sample size requirements for an RCT with HIV incidence as its outcome are prohibitively high. We propose to circumvent this large sample size requirement by evaluating relationships between HIV incidence and drug concentrations measured among participants in traditional phase 3 trials in high-incidence settings and then applying these observations to drug concentrations measured among at-risk individuals in lower-incidence settings, such as US black women. This strategy could strengthen the evidence base to enable black women to fully benefit from prevention research advances and decrease racial disparities in HIV rates

Galactic cannibalism in the galaxy cluster C0337-2522 at z=0.59

According to the galactic cannibalism model, cD galaxies are formed in the center of galaxy clusters by merging of massive galaxies and accretion of smaller stellar systems: however, observational examples of the initial phases of this process are lacking. We have identified a strong candidate for this early stage of cD galaxy formation: a group of five elliptical galaxies in the core of the X-ray cluster C0337-2522 at redshift z=0.59. With the aid of numerical simulations, in which the galaxies are represented by N-body systems, we study their dynamical evolution up to z=0; the cluster dark matter distribution is also described as a N-body system. We find that a multiple merging event in the considered group of galaxies will take place before z=0 and that the merger remnant preserves the Fundamental Plane and the Faber-Jackson relations, while its behavior with respect to the Mbh-sigma relation is quite sensitive to the details of black hole merging [abridged].Comment: 30 pages, 7 figures, MNRAS (accepted

A framework for digital sunken relief generation based on 3D geometric models

Sunken relief is a special art form of sculpture whereby the depicted shapes are sunk into a given surface. This is traditionally created by laboriously carving materials such as stone. Sunken reliefs often utilize the engraved lines or strokes to strengthen the impressions of a 3D presence and to highlight the features which otherwise are unrevealed. In other types of reliefs, smooth surfaces and their shadows convey such information in a coherent manner. Existing methods for relief generation are focused on forming a smooth surface with a shallow depth which provides the presence of 3D figures. Such methods unfortunately do not help the art form of sunken reliefs as they omit the presence of feature lines. We propose a framework to produce sunken reliefs from a known 3D geometry, which transforms the 3D objects into three layers of input to incorporate the contour lines seamlessly with the smooth surfaces. The three input layers take the advantages of the geometric information and the visual cues to assist the relief generation. This framework alters existing techniques in line drawings and relief generation, and then combines them organically for this particular purpose

Estimating Human Immunodeficiency Virus (HIV) Prevention Effects in Low-incidence Settings

Background: Randomized controlled trials (RCTs) for determining efficacy of preexposure prophylaxis (PrEP) in preventing human immunodeficiency virus (HIV) infection have not been conducted among US women because their lower HIV incidence requires impractically large studies. Results from higher-incidence settings, like Sub-Saharan Africa, may not apply to US women owing to differences in age, sexual behavior, coinfections, and adherence. Methods: We propose a novel strategy for evaluating PrEP efficacy in the United States using data from both settings to obtain four parameters: (1) intention-to-treat (ITT) and (2) per-protocol effects in the higher-incidence setting, (3) per-protocol effect generalized to the lower-incidence setting, and (4) back-calculated ITT effect using adherence data from the lower-incidence setting. To illustrate, we simulated two RCTs comparing PrEP against placebo: one in 4000 African women and another in 500 US women. We estimated all parameters using g-computation and report risk ratios averaged over 2000 simulations, alongside the 2.5th and 97.5th percentiles of the simulation results. Results: Twelve months after randomization, the African ITT and per-protocol risk ratios were 0.65 (0.47, 0.88) and 0.20 (0.08, 0.34), respectively. The US ITT and per-protocol risk ratios were 0.42 (0.20, 0.62) and 0.17 (0.03, 0.38), respectively. These results matched well the simulated true effects. Conclusions: Our simple demonstration informs the design of future studies seeking to estimate the effectiveness of a treatment (like PrEP) in lower-incidence settings where a traditional RCT would not be feasible. See video abstract at, http://links.lww.com/EDE/B506

Summer CO2 evasion from streams and rivers in the Kolyma River basin, north-east Siberia

Inland water systems are generally supersaturated in carbon dioxide (CO2) and are increasingly recognized as playing an important role in the global carbon cycle. The Arctic may be particularly important in this respect, given the abundance of inland waters and carbon contained in Arctic soils; however, a lack of trace gas measurements from small streams in the Arctic currently limits this understanding.We investigated the spatial variability of CO2 evasion during the summer low-flow period from streams and rivers in the northern portion of the Kolyma River basin in north-eastern Siberia. To this end, partial pressure of carbon dioxide (pCO2) and gas exchange velocities (k) were measured at a diverse set of streams and rivers to calculate CO2 evasion fluxes. We combined these CO2 evasion estimates with satellite remote sensing and geographic information system techniques to calculate total areal CO2 emissions. Our results show that small streams are substantial sources of atmospheric CO2 owing to high pCO2 and k, despite being a small portion of total inland water surface area. In contrast, large rivers were generally near equilibrium with atmospheric CO2. Extrapolating our findings across the Panteleikha-Ambolikha sub-watersheds demonstrated that small streams play a major role in CO2 evasion, accounting for 86% of the total summer CO2 emissions from inland waters within these two sub-watersheds. Further expansion of these regional CO2 emission estimates across time and space will be critical to accurately quantify and understand the role of Arctic streams and rivers in the global carbon budget

Fusion designs and estimators for treatment effects

While randomized trials remain the best evidence for treatment effectiveness, lack of generalizability often remains an important concern. Additionally, when new treatments are compared against existing standards of care, the potentially small benefit of the new treatment may be difficult to detect in a trial without extremely large sample sizes and long follow-up times. Recent advances in “data fusion” provide a framework to combine results across studies that are applicable to a given population of interest and allow treatment comparisons that may not be feasible with traditional study designs. We propose a data fusion-based estimator that can be used to combine information from two studies: (1) a study comparing a new treatment to the standard of care in the local population of interest, and (2) a study comparing the standard of care to placebo in a separate, distal population. We provide conditions under which the parameter of interest can be identified from the two studies described and explore properties of the estimator through simulation. Finally, we apply the estimator to estimate the effect of triple- vs monotherapy for the treatment of HIV using data from two randomized trials. The proposed estimator can account for underlying population structures that induce differences in case mix, adherence, and outcome prevalence between the local and distal populations, and the estimator can also account for potentially informative loss to follow-up. Approaches like those detailed here are increasingly important to speed the approval and adoption of effective new therapies by leveraging multiple sources of information

Sensitivity analyses for misclassification of cause of death in the parametric G-formula

Cause-specific mortality is an important outcome in studies of interventions to improve survival, yet causes of death can be misclassified. Here, we present an approach to performing sensitivity analyses formisclassification of cause of death in the parametric g-formula. The g-formula is a useful method to estimate effects of interventions in epidemiologic research because it appropriately accounts for time-varying confounding affected by prior treatment and can estimate risk under dynamic treatment plans.We illustrate our approach using an example comparing acquired immune deficiency syndrome (AIDS)-related mortality under immediate and delayed treatment strategies in a cohort of therapy-naive adults entering care for human immunodeficiency virus infection in the United States. In the standard g-formula approach, 10-year risk of AIDSrelatedmortality under delayed treatment was 1.73 (95% CI: 1.17, 2.54) times the risk under immediate treatment. In a sensitivity analysis assuming that AIDS-related death was measured with sensitivity of 95% and specificity of 90%, the 10-year risk ratio comparing AIDS-related mortality between treatment plans was 1.89 (95% CI: 1.13, 3.14). When sensitivity and specificity are unknown, this approach can be used to estimate the effects of dynamic treatment plans under a range of plausible values of sensitivity and specificity of the recorded event type

Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial: Assumptions, models, effect scales, data scenarios, and implementation details

Background inform policy and practice for broad populations. The average treatment effect (ATE) for a target population, however, may be different from the ATE observed in a trial if there are effect modifiers whose distribution in the target population is different that from that in the trial. Methods exist to use trial data to estimate the target population ATE, provided the distributions of treatment effect modifiers are observed in both the trial and target population—an assumption that may not hold in practice. Methods The proposed sensitivity analyses address the situation where a treatment effect modifier is observed in the trial but not the target population. These methods are based on an outcome model or the combination of such a model and weighting adjustment for observed differences between the trial sample and target population. They accommodate several types of outcome models: linear models (including single time outcome and pre- and post-treatment outcomes) for additive effects, and models with log or logit link for multiplicative effects. We clarify the methods’ assumptions and provide detailed implementation instructions. Illustration We illustrate the methods using an example generalizing the effects of an HIV treatment regimen from a randomized trial to a relevant target population. Conclusion These methods allow researchers and decision-makers to have more appropriate confidence when drawing conclusions about target population effects

Directed polymers in high dimensions

We study directed polymers subject to a quenched random potential in d transversal dimensions. This system is closely related to the Kardar-Parisi-Zhang equation of nonlinear stochastic growth. By a careful analysis of the perturbation theory we show that physical quantities develop singular behavior for d to 4. For example, the universal finite size amplitude of the free energy at the roughening transition is proportional to (4-d)^(1/2). This shows that the dimension d=4 plays a special role for this system and points towards d=4 as the upper critical dimension of the Kardar-Parisi-Zhang problem.Comment: 37 pages REVTEX including 4 PostScript figure