Capacity building efforts and perceptions for wildlife surveillance to detect zoonotic pathogens: comparing stakeholder perspectives.

BackgroundThe capacity to conduct zoonotic pathogen surveillance in wildlife is critical for the recognition and identification of emerging health threats. The PREDICT project, a component of United States Agency for International Development's Emerging Pandemic Threats program, has introduced capacity building efforts to increase zoonotic pathogen surveillance in wildlife in global 'hot spot' regions where zoonotic disease emergence is likely to occur. Understanding priorities, challenges, and opportunities from the perspectives of the stakeholders is a key component of any successful capacity building program.MethodsA survey was administered to wildlife officials and to PREDICT-implementing in-country project scientists in 16 participating countries in order to identify similarities and differences in perspectives between the groups regarding capacity needs for zoonotic pathogen surveillance in wildlife.ResultsBoth stakeholder groups identified some human-animal interfaces (i.e. areas of high contact between wildlife and humans with the potential risk for disease transmission), such as hunting and markets, as important for ongoing targeting of wildlife surveillance. Similarly, findings regarding challenges across stakeholder groups showed some agreement in that a lack of sustainable funding across regions was the greatest challenge for conducting wildlife surveillance for zoonotic pathogens (wildlife officials: 96% and project scientists: 81%). However, the opportunity for improving zoonotic pathogen surveillance capacity identified most frequently by wildlife officials as important was increasing communication or coordination among agencies, sectors, or regions (100% of wildlife officials), whereas the most frequent opportunities identified as important by project scientists were increasing human capacity, increasing laboratory capacity, and the growing interest or awareness regarding wildlife disease or surveillance programs (all identified by 69% of project scientists).ConclusionsA One Health approach to capacity building applied at local and global scales will have the greatest impact on improving zoonotic pathogen surveillance in wildlife. This approach will involve increasing communication and cooperation across ministries and sectors so that experts and stakeholders work together to identify and mitigate surveillance gaps. Over time, this transdisciplinary approach to capacity building will help overcome existing challenges and promote efficient targeting of high risk interfaces for zoonotic pathogen transmission

What does inflation really predict?

If the inflaton potential has multiple minima, as may be expected in, e.g., the string theory "landscape", inflation predicts a probability distribution for the cosmological parameters describing spatial curvature (Omega_tot), dark energy (rho_Lambda, w, etc.), the primordial density fluctuations (Omega_tot, dark energy (rho_Lambda, w, etc.). We compute this multivariate probability distribution for various classes of single-field slow-roll models, exploring its dependence on the characteristic inflationary energy scales, the shape of the potential V and and the choice of measure underlying the calculation. We find that unless the characteristic scale Delta-phi on which V varies happens to be near the Planck scale, the only aspect of V that matters observationally is the statistical distribution of its peaks and troughs. For all energy scales and plausible measures considered, we obtain the predictions Omega_tot ~ 1+-0.00001, w=-1 and rho_Lambda in the observed ballpark but uncomfortably high. The high energy limit predicts n_s ~ 0.96, dn_s/dlnk ~ -0.0006, r ~ 0.15 and n_t ~ -0.02, consistent with observational data and indistinguishable from eternal phi^2-inflation. The low-energy limit predicts 5 parameters but prefers larger Q and redder n_s than observed. We discuss the coolness problem, the smoothness problem and the pothole paradox, which severely limit the viable class of models and measures. Our findings bode well for detecting an inflationary gravitational wave signature with future CMB polarization experiments, with the arguably best-motivated single-field models favoring the detectable level r ~ 0.03. (Abridged)Comment: Replaced to match accepted JCAP version. Improved discussion, references. 42 pages, 17 fig

Learning Features that Predict Cue Usage

Our goal is to identify the features that predict the occurrence and placement of discourse cues in tutorial explanations in order to aid in the automatic generation of explanations. Previous attempts to devise rules for text generation were based on intuition or small numbers of constructed examples. We apply a machine learning program, C4.5, to induce decision trees for cue occurrence and placement from a corpus of data coded for a variety of features previously thought to affect cue usage. Our experiments enable us to identify the features with most predictive power, and show that machine learning can be used to induce decision trees useful for text generation.Comment: 10 pages, 2 Postscript figures, uses aclap.sty, psfig.te

Why didn't scientists predict the earthquakes?

The earthquakes that caused devastation in Haiti on 12 January 2010 and in Chile on 27 February 2010 have reminded us once more of the tremendous destructive power of nature. The magnitude 7 Haiti earthquake is believed to have claimed 230,000 lives; in contrast current estimates of fatalities caused by the magnitude 8.8 Chilean earthquake stand at less than 1,000, even though the earthquake released 500 times the energy of the Haiti event. However, in both countries there has been destruction of homes, businesses and infrastructure on a huge scale, creating a human and economic catastrophe that will take years to recover from

Can Neuroscience Help Predict Future Antisocial Behavior?

Part I of this Article reviews the tools currently available to predict antisocial behavior. Part II discusses legal precedent regarding the use of, and challenges to, various prediction methods. Part III introduces recent neuroscience work in this area and reviews two studies that have successfully used neuroimaging techniques to predict recidivism. Part IV discusses some criticisms that are commonly levied against the various prediction methods and highlights the disparity between the attitudes of the scientific and legal communities toward risk assessment generally and neuroscience specifically. Lastly, Part V explains why neuroscience methods will likely continue to help inform and, ideally, improve the tools we use to help assess, understand, and predict human behavior