Velocimetry of cold atoms by matter-wave interferometry

We present an elegant application of matter-wave interferometry to the velocimetry of cold atoms whereby, in analogy to Fourier transform spectroscopy, the one-dimensional velocity distribution is manifest in the frequency domain of the interferometer output. By using stimulated Raman transitions between hyperfine ground states to perform a three-pulse interferometer sequence, we have measured the velocity distributions of clouds of freely expanding 85Rb atoms with temperatures of 34 and 18μK. Quadrature measurement of the interferometer output as a function of the temporal asymmetry yields velocity distributions with excellent fidelity. Our technique, which is particularly suited to ultracold samples, compares favorably with conventional Doppler and time-of-flight techniques, and it reveals artefacts in standard Raman Doppler methods. The technique is related to, and provides a conceptual foundation of, interferometric matter-wave accelerometry, gravimetry, and rotation sensing

Disposal of NORM-Contaminated Oil Field Wastes in Salt Caverns

In 1995, the U.S. Department of Energy (DOE), Office of Fossil Energy, asked Argonne National Laboratory (Argonne) to conduct a preliminary technical and legal evaluation of disposing of nonhazardous oil field waste (NOW) into salt caverns. That study concluded that disposal of NOW into salt caverns is feasible and legal. If caverns are sited and designed well, operated carefully, closed properly, and monitored routinely, they can be a suitable means of disposing of NOW (Veil et al. 1996). Considering these findings and the increased U.S. interest in using salt caverns for NOW disposal, the Office of Fossil Energy asked Argonne to conduct further research on the cost of cavern disposal compared with the cost of more traditional NOW disposal methods and on preliminary identification and investigation of the risks associated with such disposal. The cost study (Veil 1997) found that disposal costs at the four permitted disposal caverns in the United States were comparable to or lower than the costs of other disposal facilities in the same geographic area. The risk study (Tomasko et al. 1997) estimated that both cancer and noncancer human health risks from drinking water that had been contaminated by releases of cavern contents were significantly lower than the accepted risk thresholds. Since 1992, DOE has funded Argonne to conduct a series of studies evaluating issues related to management and disposal of oil field wastes contaminated with naturally occurring radioactive material (NORM). Included among these studies were radiological dose assessments of several different NORM disposal options (Smith et al. 1996). In 1997, DOE asked Argonne to conduct additional analyses on waste disposal in salt caverns, except that this time the wastes to be evaluated would be those types of oil field wastes that are contaminated by NORM. This report describes these analyses. Throughout the remainder of this report, the term ''NORM waste'' is used to mean ''oil field waste contaminated by NORM''

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration