Using bacterial biomarkers to identify early indicators of cystic fibrosis pulmonary exacerbation onset

Acute periods of pulmonary exacerbation are the single most important cause of morbidity in cystic fibrosis patients, and may be associated with a loss of lung function. Intervening prior to the onset of a substantially increased inflammatory response may limit the associated damage to the airways. While a number of biomarker assays based on inflammatory markers have been developed, providing useful and important measures of disease during these periods, such factors are typically only elevated once the process of exacerbation has been initiated. Identifying biomarkers that can predict the onset of pulmonary exacerbation at an early stage would provide an opportunity to intervene before the establishment of a substantial immune response, with major implications for the advancement of cystic fibrosis care. The precise triggers of pulmonary exacerbation remain to be determined; however, the majority of models relate to the activity of microbes present in the patient's lower airways of cystic fibrosis. Advances in diagnostic microbiology now allow for the examination of these complex systems at a level likely to identify factors on which biomarker assays can be based. In this article, we discuss key considerations in the design and testing of assays that could predict pulmonary exacerbations

The NASA CSTI high capacity power project

The SP-100 Space Nuclear Power Program was established in 1983 by DOD, DOE, and NASA as a joint program to develop technology for military and civil applications. Starting in 1986, NASA has funded a technology program to maintain the momentum of promising aerospace technology advancement started during Phase 1 of SP-100 and to strengthen, in key areas, the chances for successful development and growth capability of space nuclear reactor power systems for a wide range of future space applications. The elements of the Civilian Space Technology Initiative (CSTI) High Capacity Power Project include Systems Analysis, Stirling Power Conversion, Thermoelectric Power Conversion, Thermal Management, Power Management, Systems Diagnostics, Environmental Interactions, and Material/Structural Development. Technology advancement in all elements is required to provide the growth capability, high reliability and 7 to 10 year lifetime demanded for future space nuclear power systems. The overall project will develop and demonstrate the technology base required to provide a wide range of modular power systems compatible with the SP-100 reactor which facilitates operation during lunar and planetary day/night cycles as well as allowing spacecraft operation at any attitude or distance from the sun. Significant accomplishments in all of the project elements will be presented, along with revised goals and project timelines recently developed

Learning from Minimum Entropy Queries in a Large Committee Machine

In supervised learning, the redundancy contained in random examples can be avoided by learning from queries. Using statistical mechanics, we study learning from minimum entropy queries in a large tree-committee machine. The generalization error decreases exponentially with the number of training examples, providing a significant improvement over the algebraic decay for random examples. The connection between entropy and generalization error in multi-layer networks is discussed, and a computationally cheap algorithm for constructing queries is suggested and analysed.Comment: 4 pages, REVTeX, multicol, epsf, two postscript figures. To appear in Physical Review E (Rapid Communications

Statistical Mechanics of Learning in the Presence of Outliers

Using methods of statistical mechanics, we analyse the effect of outliers on the supervised learning of a classification problem. The learning strategy aims at selecting informative examples and discarding outliers. We compare two algorithms which perform the selection either in a soft or a hard way. When the fraction of outliers grows large, the estimation errors undergo a first order phase transition.Comment: 24 pages, 7 figures (minor extensions added

Release of copper-amended particles from micronized copper-pressure-treated wood during mechanical abrasion

Background: We investigated the particles released due to abrasion of wood surfaces pressure-treated with micronized copper azole (MCA) wood preservative and we gathered preliminary data on its in vitro cytotoxicity for lung cells. The data were compared with particles released after abrasion of untreated, water (0% MCA)-pressure-treated, chromated copper (CC)-pressure-treated wood, and varnished wood. Size, morphology, and composition of the released particles were analyzed. Results: Our results indicate that the abrasion of MCA-pressure-treated wood does not cause an additional release of nanoparticles from the unreacted copper (Cu) carbonate nanoparticles from of the MCA formulation. However, a small amount of released Cu was detected in the nanosized fraction of wood dust, which could penetrate the deep lungs. The acute cytotoxicity studies were performed on a human lung epithelial cell line and human macrophages derived from a monocytic cell line. These cell types are likely to encounter the released wood particles after inhalation. Conclusions: Our findings indicate that under the experimental conditions chosen, MCA does not pose a specific additional nano-risk, i.e. there is no additional release of nanoparticles and no specific nano-toxicity for lung epithelial cells and macrophages

Transparent model concrete with tunable rheology for investigating flow and particle-migration during transport in pipes

The article describes the adaption and properties of a model concrete for detailed flow studies. To adapt the yield stress and plastic viscosity of the model concrete to the corresponding rheological properties of real concrete, the model concrete is made of a mixture of glass beads and a non-Newtonian fluid. The refractive index of the non-Newtonian fluid is adjusted to the refractive index of the glass beads by the addition of a further constituent. The rheological properties of the model concrete are characterised by measurements in concrete rheometers. Finally, the first exemplary results from experiments with the model concrete are presented, which give incipient impressions of the complex internal dynamics in flowing concrete

Retarded Learning: Rigorous Results from Statistical Mechanics

We study learning of probability distributions characterized by an unknown symmetry direction. Based on an entropic performance measure and the variational method of statistical mechanics we develop exact upper and lower bounds on the scaled critical number of examples below which learning of the direction is impossible. The asymptotic tightness of the bounds suggests an asymptotically optimal method for learning nonsmooth distributions.Comment: 8 pages, 1 figur