HANFORD REGULATORY EXPERIENCE REGULATION AT HANFORD A CASE STUDY

Hanford has played a pivotal role in the United States' defense for more than 60 years, beginning with the Manhattan Project in the 1940s. During its history, the Hanford Site has had nine reactors producing plutonium for the United States' nuclear weapons program. All the reactors were located next to the Columbia River and all had associated low-level radioactive and hazardous waste releases. Site cleanup, which formally began in 1989 with the signing of the Hanford Federal Facility Agreement and Consent Order, also known as the Tri-Party Agreement, involves more than 1,600 waste sites and burial grounds, and the demolition of more than 1,500buildings and structures, Cleanup is scheduled to be complete by 2035. Regulatory oversight of the cleanup is being performed by the U.S. Environmental Protection Agency (EPA) and the Washington State Department of Ecology(Ecology) under the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) and the Revised Code of Washington, 'Hazardous Waste Management.' Cleanup of the waste sites and demolition of the many buildings and structures generates large volumes of contaminated soil, equipment, demolition debris, and other wastes that must be disposed of in a secure manner to prevent further environmental degradation. From a risk perspective, it is essential the cleanup waste be moved to a disposal facility located well away from the Columbia River. The solution was to construct very large engineered landfill that meets all technical regulatory requirements, on the Hanford Site Central Plateau approximately 10kilometers from the river and 100metersabovegroundwater. This landfill, called the Environmental Restoration Disposal Facility or ERDF is a series of cells, each 150x 300 meters wide at the bottom and 20 meters deep. This paper looks at the substantive environmental regulations applied to ERDF, and how the facility is designed to protect the environment and meet regulatory requirements. The paper describes how the U.S. Department of Energy(DOE),EPA, and Ecology interact in its regulation. In addition, the response to a recent $1 million regulatory fine is described to show actual interactions and options in this aspect of the regulatory process. The author acknowledges the significant contributions by Messrs. Clifford Clark and Owen Robertson. Ms. Nancy Williams provided graphics support and Ms. Laurie Kraemer edited the report

Stellar twins determine the distance of the Pleiades

© 2016 ESO.Since the release of the Hipparcos catalogue in 1997, the distance to the Pleiades open cluster has been heavily debated. The distance obtained from Hipparcos and those by alternative methods differ by 10 to 15%. As accurate stellar distances are key to understanding stellar structure and evolution, this dilemma puts the validity of some stellar evolution models into question. Using our model-independent method to determine parallaxes based on twin stars, we report individual parallaxes of 15 FGK type stars in the Pleiades in anticipation of the astrometric mission Gaia. These parallaxes give a mean cluster parallax of 7.42 ± 0.09 mas,which corresponds to a mean cluster distance of 134.8 ± 1.7 pc. This value agrees with the current results obtained from stellar evolution models

Cerebral blood flow predicts differential neurotransmitter activity

Application of metabolic magnetic resonance imaging measures such as cerebral blood flow in translational medicine is limited by the unknown link of observed alterations to specific neurophysiological processes. In particular, the sensitivity of cerebral blood flow to activity changes in specific neurotransmitter systems remains unclear. We address this question by probing cerebral blood flow in healthy volunteers using seven established drugs with known dopaminergic, serotonergic, glutamatergic and GABAergic mechanisms of action. We use a novel framework aimed at disentangling the observed effects to contribution from underlying neurotransmitter systems. We find for all evaluated compounds a reliable spatial link of respective cerebral blood flow changes with underlying neurotransmitter receptor densities corresponding to their primary mechanisms of action. The strength of these associations with receptor density is mediated by respective drug affinities. These findings suggest that cerebral blood flow is a sensitive brain-wide in-vivo assay of metabolic demands across a variety of neurotransmitter systems in humans

Model of Yield Response of Corn to Plant Population and Absorption of Solar Energy

Biomass yield of agronomic crops is influenced by a number of factors, including crop species, soil type, applied nutrients, water availability, and plant population. This article is focused on dependence of biomass yield (Mg ha−1 and g plant−1) on plant population (plants m−2). Analysis includes data from the literature for three independent studies with the warm-season annual corn (Zea mays L.) grown in the United States. Data are analyzed with a simple exponential mathematical model which contains two parameters, viz. Ym (Mg ha−1) for maximum yield at high plant population and c (m2 plant−1) for the population response coefficient. This analysis leads to a new parameter called characteristic plant population, xc = 1/c (plants m−2). The model is shown to describe the data rather well for the three field studies. In one study measurements were made of solar radiation at different positions in the plant canopy. The coefficient of absorption of solar energy was assumed to be the same as c and provided a physical basis for the exponential model. The three studies showed no definitive peak in yield with plant population, but generally exhibited asymptotic approach to maximum yield with increased plant population. Values of xc were very similar for the three field studies with the same crop species

3D time series analysis of cell shape using Laplacian approaches

Background: Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes. Results: We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells. Conclusions: The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations

Recognizing Speech in a Novel Accent: The Motor Theory of Speech Perception Reframed

The motor theory of speech perception holds that we perceive the speech of another in terms of a motor representation of that speech. However, when we have learned to recognize a foreign accent, it seems plausible that recognition of a word rarely involves reconstruction of the speech gestures of the speaker rather than the listener. To better assess the motor theory and this observation, we proceed in three stages. Part 1 places the motor theory of speech perception in a larger framework based on our earlier models of the adaptive formation of mirror neurons for grasping, and for viewing extensions of that mirror system as part of a larger system for neuro-linguistic processing, augmented by the present consideration of recognizing speech in a novel accent. Part 2 then offers a novel computational model of how a listener comes to understand the speech of someone speaking the listener's native language with a foreign accent. The core tenet of the model is that the listener uses hypotheses about the word the speaker is currently uttering to update probabilities linking the sound produced by the speaker to phonemes in the native language repertoire of the listener. This, on average, improves the recognition of later words. This model is neutral regarding the nature of the representations it uses (motor vs. auditory). It serve as a reference point for the discussion in Part 3, which proposes a dual-stream neuro-linguistic architecture to revisits claims for and against the motor theory of speech perception and the relevance of mirror neurons, and extracts some implications for the reframing of the motor theory

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed