Reconciling unevenly sampled paleoclimate proxies: a Gaussian kernel correlation multiproxy reconstruction

Reconstructing past hydroclimatic variability using climate-sensitive paleoclimate proxies provides context to our relatively short instrumental climate records and a baseline from which to assess the impacts of human-induced climate change. However, many approaches to reconstructing climate are limited in their ability to address sampling variability inherent in different climate proxies. We iteratively optimise an ensemble of possible reconstruction data series to maximise the Gaussian kernel correlation of Rehfeld et al. (2011) which reconciles differences in the temporal resolution of both the target variable and proxies or covariates. The reconstruction method is evaluated using synthetic data with different degrees of sampling variability and noise. Two examples using paleoclimate proxy records and a third using instrumental rainfall data with missing values are used to demonstrate the utility of the method. While the Gaussian kernel correlation method is relatively computationally expensive, it is shown to be robust under a range of data characteristics and will therefore be valuable in analyses seeking to employ multiple input proxies or covariates

Computational Models of HIV-1 Resistance to Gene Therapy Elucidate Therapy Design Principles

Gene therapy is an emerging alternative to conventional anti-HIV-1 drugs, and can potentially control the virus while alleviating major limitations of current approaches. Yet, HIV-1's ability to rapidly acquire mutations and escape therapy presents a critical challenge to any novel treatment paradigm. Viral escape is thus a key consideration in the design of any gene-based technique. We develop a computational model of HIV's evolutionary dynamics in vivo in the presence of a genetic therapy to explore the impact of therapy parameters and strategies on the development of resistance. Our model is generic and captures the properties of a broad class of gene-based agents that inhibit early stages of the viral life cycle. We highlight the differences in viral resistance dynamics between gene and standard antiretroviral therapies, and identify key factors that impact long-term viral suppression. In particular, we underscore the importance of mutationally-induced viral fitness losses in cells that are not genetically modified, as these can severely constrain the replication of resistant virus. We also propose and investigate a novel treatment strategy that leverages upon gene therapy's unique capacity to deliver different genes to distinct cell populations, and we find that such a strategy can dramatically improve efficacy when used judiciously within a certain parametric regime. Finally, we revisit a previously-suggested idea of improving clinical outcomes by boosting the proliferation of the genetically-modified cells, but we find that such an approach has mixed effects on resistance dynamics. Our results provide insights into the short- and long-term effects of gene therapy and the role of its key properties in the evolution of resistance, which can serve as guidelines for the choice and optimization of effective therapeutic agents

International AIDS Society global scientific strategy: towards an HIV cure 2016

Antiretroviral therapy is not curative. Given the challenges in providing lifelong therapy to a global population of more than 35 million people living with HIV, there is intense interest in developing a cure for HIV infection. The International AIDS Society convened a group of international experts to develop a scientific strategy for research towards an HIV cure. This Perspective summarizes the group's strategy

Large-scale ocean-atmospheric processes and seasonal rainfall variability in South Australia: accounting for non-linearity and establishing the hierarchy of influence

An important step in developing climate forecasts is the identification of an appropriate set of predictors. Seasonal rainfall forecasts for South Australia (SA) currently have low predictive skill. It is hypothesised that this is because the large-scale climate processes influencing SA's rainfall variability have yet to be completely identified and therefore are not adequately represented in forecast models. Here, relationships between large-scale climate influences and rainfall in SA are assessed with a focus on methods that account for non-linearities inherent in the climate system. A threshold method and also a novel method for climate predictor selection, Partial Mutual Information, are used to establish the hierarchy of importance of the key influences. From a large suite of potential predictors, variability in the Subtropical Ridge intensity, blocking and the gradient of sea surface temperatures (SSTs) in the tropical central and eastern Indian Ocean are found to be key indicators of seasonal rainfall variability in SA. Interactions between processes are shown to increase the amount of variability accounted for and therefore need to be better considered when producing seasonal forecasts. Notably, the view of which climate process(es) is(are) important changes depending on the input variables used, suggesting that it is not wise to make a priori assumptions about what is or is not important. Furthermore, even a perfect forecast of El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), the focus of forecasting efforts in Australia, will not necessarily lead to an improvement to forecast skill in SA given that these processes are not key for the region. The results of this analysis can inform the development of existing and new statistical and dynamical seasonal forecasting systems for SA, and for other regions where the impacts of climate variability are significant but not necessarily dominated by ENSO and IOD