4 resultados para Soga, Sukenari, 1172-1192

em Digital Commons - Michigan Tech


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In 2009 and 2010 a study was conducted on the Hiawatha National Forest (HNF) to determine if whole-tree harvest (WTH) of jack pine would deplete the soil nutrients in the very coarse-textured Rubicon soil. WTH is restricted on Rubicon sand in order to preserve the soil fertility, but the increasing construction of biomass-fueled power plants is expected to increase the demand for forest biomass. The specific objectives of this study were to estimate biomass and nutrient content of above- and below-ground tree components in mature jack pine (Pinus banksiana) stands growing on a coarse-textured, low-productivity soil, determine pools of total C and N and exchangeable soil cations in Rubicon sand, and to compare the possible impacts of conventional stem-only harvest (CH) and WTH on soil nutrient pools and the implications for productivity of subsequent rotations. Four even-aged jack pine stands on Rubicon soil were studied. Allometric equations were used to estimate above-ground biomass and nutrients, and soil samples from each stand were taken for physical and chemical analysis. Results indicate that WTH will result in cation deficits in all stands, with exceptionally large Ca deficits occurring in two stands. Where a deficit does not occur, the cation surplus is small and, chemical weathering and atmospheric deposition is not anticipated to replace the removed cations. CH will result in a surplus of cations, and will likely not result in productivity declines during the first rotation. However even under CH, the surplus is small, and chemical weathering and atmospheric deposition will not supply enough cations for the second rotation.

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Phenylketonuria, an autosomal recessive Mendelian disorder, is one of the most common inborn errors of metabolism. Although currently treated by diet, many suboptimal outcomes occur for patients. Neuropathological outcomes include cognitive loss, white matter abnormalities, and hypo- or demyelination, resulting from high concentrations and/or fluctuating levels of phenylalanine. High phenylalanine can also result in competitive exclusion of other large neutral amino acids from the brain, including tyrosine and tryptophan (essential precursors of dopamine and serotonin). This competition occurs at the blood brain barrier, where the L-type amino acid transporter, LAT1, selectively facilitates entry of large neutral amino acids. The hypothesis of these studies is that certain non-physiological amino acids (NPAA; DL-norleucine (NL), 2-aminonorbornane (NB; 2-aminobicyclo-(2,1,1)-heptane-2-carboxylic acid), α-aminoisobutyrate (AIB), and α-methyl-aminoisobutyrate (MAIB)) would competitively inhibit LAT1 transport of phenylalanine (Phe) at the blood-brain barrier interface. To test this hypothesis, Pah-/- mice (n=5, mixed gender; Pah+/-(n=5) as controls) were fed either 5% NL, 0.5% NB, 5% AIB or 3% MAIB (w/w 18% protein mouse chow) for 3 weeks. Outcome measurements included food intake, body weight, brain LNAAs, and brain monoamines measured via LCMS/MS or HPLC. Brain Phe values at sacrifice were significantly reduced for NL, NB, and MAIB, verifying the hypothesis that these NPAAs could inhibit Phe trafficking into the brain. However, concomitant reductions in tyrosine and methionine occurred at the concentrations employed. Blood Phe levels were not altered indicating no effect of NPAA competitors in the gut. Brain NL and NB levels, measured with HPLC, verified both uptake and transport of NPAAs. Although believed predominantly unmetabolized, NL feeding significantly increased blood urea nitrogen. Pah-/-disturbances of monoamine metabolism were exacerbated by NPAA intervention, primarily with NB (the prototypical LAT inhibitor). To achieve the overarching goal of using NPAAs to stabilize Phe transport levels into the brain, a specific Phe-reducing combination and concentration of NPAAs must be found. Our studies represent the first in vivo use of NL, NB and MAIB in Pah-/- mice, and provide proof-of-principle for further characterization of these LAT inhibitors. Our data is the first to document an effect of MAIB, a specific system A transport inhibitor, on large neutral amino acid transport.

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In this report we will investigate the effect of negative energy density in a classic Friedmann cosmology. Although never measured and possibly unphysical, the evolution of a Universe containing a significant cosmological abundance of any of a number of hypothetical stable negative energy components is explored. These negative energy (Ω < 0) forms include negative phantom energy (w<-1), negative cosmological constant (w=-1), negative domain walls (w=-2/3), negative cosmic strings (w= -1/3), negative mass (w=0), negative radiation (w=1/3), and negative ultra-light (w > 1/3). Assuming that such universe components generate pressures as perfect fluids, the attractive or repulsive nature of each negative energy component is reviewed. The Friedmann equations can only be balanced when negative energies are coupled to a greater magnitude of positive energy or positive curvature, and minimal cases of both of these are reviewed. The future and fate of such universes in terms of curvature, temperature, acceleration, and energy density are reviewed including endings categorized as a Big Crunch, Big Void, or Big Rip and further qualified as "Warped", "Curved", or "Flat", "Hot" versus "Cold", "Accelerating" versus" Decelerating" versus "Coasting". A universe that ends by contracting to zero energy density is termed a Big Poof. Which contracting universes ``bounce" in expansion and which expanding universes ``turnover" into contraction are also reviewed. The name by which the ending of the Universe is mentioned is our own nomenclature.

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Neuromorphic computing has become an emerging field in wide range of applications. Its challenge lies in developing a brain-inspired architecture that can emulate human brain and can work for real time applications. In this report a flexible neural architecture is presented which consists of 128 X 128 SRAM crossbar memory and 128 spiking neurons. For Neuron, digital integrate and fire model is used. All components are designed in 45nm technology node. The core can be configured for certain Neuron parameters, Axon types and synapses states and are fully digitally implemented. Learning for this architecture is done offline. To train this circuit a well-known algorithm Restricted Boltzmann Machine (RBM) is used and linear classifiers are trained at the output of RBM. Finally, circuit was tested for handwritten digit recognition application. Future prospects for this architecture are also discussed.