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Crystallization and preliminary X-ray analysis of chicken-liver glutathione S-transferase CL 3-3 (1996)

Lin, S. C. ; Yu, H. H. ; Liu, L. F. ; [et al.]

Copenhagen : International Union of Crystallography (IUCr)

Acta crystallographica 52 (1996), S. 601-603

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ISSN: 1399-0047

Source: Crystallography Journals Online : IUCR Backfile Archive 1948-2001

Topics: Chemistry and Pharmacology , Geosciences , Physics

Notes: Five different crystal forms of recombinant chicken-liver glutathione S-transferase CL 3-3 have been obtained by the vapor-diffusion method. The form A crystals are monoclinic C2, a = 125.56, b = 85.81, c = 52.71 Å and β = 114.64°, and diffract to 4 Å resolution. The form B crystals are monoclinic P21, a = 105.13, b = 118.54, c = 62.62 Å and β = 124.74°, and diffract to 2.8 Å resolution. The form C crystals are orthorhombic C222l, a = 101.69, b = 115.46, c = 95.40 Å, and diffract to 2.8 Å resolution. The form D crystals are tetragonal, P41212 or P43212, a = b = 115.31, c = 171.20 Å and diffract to 3.5 Å resolution. The form E crystals are hexagonal, P61 or P65, a = b = 104.23, c = 114.35 Å, diffract to 3.5 Å resolution. Forms A, C and E have one dimer of molecular weight 50 kDa, while forms B and D have two dimers per asymmetric unit, respectively.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1107/S0907444995016866

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Prediction of pile capacity from stress-wave measurements: A neural network approach (1995)

Chow, Y. K. ; Chan, W. T. ; Liu, L. F. ; [et al.]

New York, NY [u.a.] : Wiley-Blackwell

International Journal for Numerical and Analytical Methods in Geomechanics 19 (1995), S. 107-126

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ISSN: 0363-9061

Keywords: Engineering ; Engineering General

Source: Wiley InterScience Backfile Collection 1832-2000

Topics: Architecture, Civil Engineering, Surveying , Geosciences

Notes: A neural network approach for the prediction of pile bearing capacity by the stress-wave matching technique is presented. The main advantage of this approach over the traditional manual or automated matching approach is that it avoids the time-consuming process of iterative adjustment. This makes it feasible to determine the static pile capacity in real time in the field. Another benefit of this approach is that as more case histories become available, the neural network can be improved by learning from these new examples.A three-layer back-propagation network is set up to illustrate the capability of the proposed approach for 70 dynamically tested concrete bored piles. A wave equation model developed at the National University of Singapore and coded in the NUSWAP computer program is used to formulate the problem. Up to 14 of the 70 piles (20 percent) are used in training the network. The NUSWAP program is used to generate simulation training examples based on the manually fitted training examples for further training of the network. Different network configurations are examined. The trained network produces results exhibiting good stress-wave matching qualities compared to those obtained by manual fitting. The pile bearing capacities predicted by the two approaches agree very closely. The load-settlement curve and axial load distribution in the pile computed using the network-predicted soil parameters are in good agreement with the field measurements obtained from a maintained load test.

Additional Material: 10 Ill.