Join Up, Scale Up: How Integration Can Defeat Disease and Poverty

This report addresses three of the core areas: primary healthcare, clean water and sanitation, and nutrition -- that are essential to achieving the MDGs. It highlights examples across 17 countries of how bringing different development approaches together (ie. integration) is working to help tackle poverty and disease and calls on the international community, including donor and developing country governments, to prioritize and invest in these joined-up programs. The experiences and lessons learned from the case studies described in this report show real world examples of how to make integration work and why it's so important to do so

Scale-up of a system for hydrocarbon production by electrochemical reduction of CO2

This work addresses the scaling up of a system for electrochemical reduction of CO2 to produce hydrocarbons that can be used as fuel for a regenerative energy storage cycle. Challenges involved in such a task are mentioned. Scalingup results of a system based on electrodes of high surface area with modified copper deposits are described. Current densities around 100 mA/cm2 were obtained. This corresponds to the current density threshold that enables technological applications. At potentials as negative as -1.6 V it was observed that CO2 reduction still dominated over hydrogen evolution reaction

Estimating population size using the network scale up method

We develop methods for estimating the size of hard-to-reach populations from data collected using network-based questions on standard surveys. Such data arise by asking respondents how many people they know in a specific group (e.g., people named Michael, intravenous drug users). The Network Scale up Method (NSUM) is a tool for producing population size estimates using these indirect measures of respondents' networks. Killworth et al. [Soc. Netw. 20 (1998a) 23-50, Evaluation Review 22 (1998b) 289-308] proposed maximum likelihood estimators of population size for a fixed effects model in which respondents' degrees or personal network sizes are treated as fixed. We extend this by treating personal network sizes as random effects, yielding principled statements of uncertainty. This allows us to generalize the model to account for variation in people's propensity to know people in particular subgroups (barrier effects), such as their tendency to know people like themselves, as well as their lack of awareness of or reluctance to acknowledge their contacts' group memberships (transmission bias). NSUM estimates also suffer from recall bias, in which respondents tend to underestimate the number of members of larger groups that they know, and conversely for smaller groups. We propose a data-driven adjustment method to deal with this. Our methods perform well in simulation studies, generating improved estimates and calibrated uncertainty intervals, as well as in back estimates of real sample data. We apply them to data from a study of HIV/AIDS prevalence in Curitiba, Brazil. Our results show that when transmission bias is present, external information about its likely extent can greatly improve the estimates. The methods are implemented in the NSUM R package.Comment: Published at http://dx.doi.org/10.1214/15-AOAS827 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Preferred antiretroviral drugs for the next decade of scale up

Global commitments aim to provide antiretroviral therapy (ART) to 15 million people living with HIV by 2015, and recent studies have demonstrated the potential for widespread ART to prevent HIV transmission. Increasingly, countries are adapting their national guidelines to start ART earlier, for both clinical and preventive benefits. To maximize the benefits of ART in resource-limited settings, six key principles need to guide ART choice: simplicity, tolerability and safety, durability, universal applicability, affordability and heat stability. Currently available drugs, combined with those in late-stage clinical development, hold great promise to simplify treatment in the short term. Over the longer-term, newer technologies, such as long-acting formulations and nanotechnology, could radically alter the treatment paradigm. This commentary reviews recommendations made in an expert consultation on treatment scale up in resource-limited settings

Models of LHC Diphoton Excesses Valid up to the Planck scale

We discuss a possibility to explain the LHC diphoton excesses at $750$GeV by the new scalar $X$ that couples to the gauge bosons through the loop of new massive particles with Standard Model charges. We assume that the new particles decay into the Standard Model particles at the tree level. We systematically examine the models that preserve the vacuum stability and the perturbativity up to the Planck scale. When we take scalars for the new particles, we find that only a few diquark and dilepton models can explain the observed diphoton cross section without conflicting the experimental mass bounds. When we take vector-like fermions for the new particles, we find rather different situations depending on whether their couplings to $X$ are scalar or pseudoscalar type. In the former case, a few models are allowed if we introduce only one species of fermions. The more fermions we introduce, the more models are allowed. In the later case, the most of the models are allowed because of the large coupling between $X$ and photon. It is interesting that the allowed mass regions of the scalar particles might be reached by the next lepton colliders.Comment: 35 pages, 4 figures; the maximum values of $\sigma_{\gamma\gamma}$ for the scalar extensions are recalculated, typos corrected, references added (v2); references added, typos corrected, version to appear in PRD (v3