IUCN's encounter with 007: safeguarding consensus for conservation

A controversy at the 2016 IUCN World Conservation Congress on the topic of closing domestic ivory markets (the 007, or so-called James Bond, motion) has given rise to a debate on IUCN's value proposition. A cross-section of authors who are engaged in IUCN but not employed by the organization, and with diverse perspectives and opinions, here argue for the importance of safeguarding and strengthening the unique technical and convening roles of IUCN, providing examples of what has and has not worked. Recommendations for protecting and enhancing IUCN's contribution to global conservation debates and policy formulation are given

Lawson criterion for ignition exceeded in an inertial fusion experiment

For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition as a proof of principle of various fusion concepts. Following the Lawson criterion, an ignited plasma is one where the fusion heating power is high enough to overcome all the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop with rapidly increasing temperature. In inertially confined fusion, ignition is a state where the fusion plasma can begin "burn propagation" into surrounding cold fuel, enabling the possibility of high energy gain. While "scientific breakeven" (i.e., unity target gain) has not yet been achieved (here target gain is 0.72, 1.37 MJ of fusion for 1.92 MJ of laser energy), this Letter reports the first controlled fusion experiment, using laser indirect drive, on the National Ignition Facility to produce capsule gain (here 5.8) and reach ignition by nine different formulations of the Lawson criterion

Identification of pre-clinical Alzheimer's disease in a population of elderly cognitively normal participants

Alzheimer's disease (AD) has a long pathological process, with an approximate lead-time of 20 years. During the early stages of the disease process, little evidence of the building pathology is identifiable without cerebrospinal fluid and/or imaging analyses. Clinical manifestations of AD do not present until irreversible pathological changes have occurred. Given an opportunity to provide treatment prior to irreversible pathological change, this study aims to identify a subgroup of cognitively normal (CN) participants from the Australian Imaging, Biomarker &amp; Lifestyle Flagship Study of Ageing (AIBL), where subtle changes in cognition are indicative of early AD-related pathology. Using a Bayesian method for unsupervised clustering via mixture models, we define an aggregate measure of posterior probabilities (AMPP score) establishing the likelihood of pre-clinical AD. From Baseline through to 54 months, visuo-spatial function had the greatest contribution to the AMPP score, followed by attention and processing speed and visual memory. Participants with the highest AMPP scores had both increasing neo-cortical amyloid burden and decreasing hippocampus volume over 54 months, compared to those in the lowest category with stable amyloid burden and hippocampus volume. The identification of a possible pre-clinical stage in CN participants via this method, without the aid of disease specific biomarkers, represents an important step in utilizing the strength of cognitive composite scores for the early detection of AD pathology.</p

Bayesian spike sorting : Parametric and nonparametric multivariate Gaussian mixture models

The analysis of action potentials is an important task in neuroscience research, which aims to characterise neural activity under different subject conditions. The classification of action potentials, or “spike sorting”, can be formulated as an unsupervised clustering problem, and latent variable models such as mixture models are often used. In this chapter, we compare the performance of two mixture-based approaches when applied to spike sorting: the Overfitted Finite Mixture model (OFM) and the Dirichlet Process Mixture model (DPM). Both of these models can be used to cluster multivariate data when the number of clusters is unknown, however differences in model specification and assumptions may affect resulting statistical inference. Using real datasets obtained from extracellular recordings of the brain, model outputs are compared with respect to the number of identified clusters and classification uncertainty, with the intent of providing guidance on their application in practice.</p