Measuring the Long-Term Regional Benefits of Salinity Reduction

Approaches for evaluating salinity management benefits are generalized and extended to incorporate consideration of desalination and long-term changes in salinity concentration and water use patterns. Previous research indicates urban users incur the vast majority of salinity-related damages in affected regions, suggesting municipalities may benefit by considering mitigating actions independent of agriculture. However, previous studies have included no consideration of desalination. Earlier studies have also considered stepped increases in salinity, assuming a single future concentration when estimating the long-term benefits of salinity reduction, an approach inconsistent with the incremental nature of these increases. Long-term changes in water use patterns (urban vs. agricultural), when considered at all, have often been treated in the same stepwise fashion. For this analysis, a suitable region is selected and the benefits of a hypothetical salinity management program are estimated using the approach described. These results are then compared with those obtained through the use of several previous methods. Findings suggest that consideration of desalination and incremental variations in salinity and water use patterns can substantially lower the estimated benefits of regional salinity management programs.benefits, regional water resource modeling, salinity, water quality management, Resource /Energy Economics and Policy,

The Ætiology of Sigma Model Anomalies

Certain nonlinear sigma models with fermions are ill-defined due to an anomaly which exhibits characteristics of both the nonabelian gauge theory anomaly and the SU(2) anomaly. The simplest way to diagnose the anomaly involves consideration of the global topology of the theory. We review the mathematical methods needed for this analysis and apply them to several supersymmetric sigma models. Some of these are found to be anomalous

A Bayesian Periodogram Finds Evidence for Three Planets in 47 Ursae Majoris

A Bayesian analysis of 47 Ursae Majoris (47 UMa) radial velocity data confirms and refines the properties of two previously reported planets with periods of 1079 and 2325 days and finds evidence for an additional long period planet with a period of approximately 10000 days. The three planet model is found to be 10^5 times more probable than the next most probable model which is a two planet model. The nonlinear model fitting is accomplished with a new hybrid Markov chain Monte Carlo (HMCMC) algorithm which incorporates parallel tempering, simulated annealing and genetic crossover operations. Each of these features facilitate the detection of a global minimum in chi-squared. By combining all three, the HMCMC greatly increases the probability of realizing this goal. When applied to the Kepler problem it acts as a powerful multi-planet Kepler periodogram. The measured periods are 1078 \pm 2, 2391{+100}{-87}, and 14002{+4018}{-5095}d, and the corresponding eccentricities are 0.032 \pm 0.014, 0.098{+.047}{-.096}, and 0.16{+.09}{-.16}. The results favor low eccentricity orbits for all three. Assuming the three signals (each one consistent with a Keplerian orbit) are caused by planets, the corresponding limits on planetary mass (M sin i) and semi-major axis are (2.53{+.07}{-.06}MJ, 2.10\pm0.02au), (0.54\pm0.07MJ, 3.6\pm0.1au), and (1.6{+0.3}{-0.5}MJ, 11.6{+2.1}{-2.9}au), respectively. We have also characterized a noise induced eccentricity bias and designed a correction filter that can be used as an alternate prior for eccentricity, to enhance the detection of planetary orbits of low or moderate eccentricity

Strings and Supermoduli

Polyakov\u27s prescription for fermionic closed string amplitudes requires that we integrate over gauge-inequivalent geometries on a 2D supermanifold. These inequivalent geometries are parametrized by a finite-dimensional superspace of moduli. This space is described and an integration measure on it is proposed which comes from gauge-fixing the heterotic string. The measure thus obtained is free of conformal and Lorentz anomalies and so can be used to compute invariant string amplitudes

A Comment on Sigma Model Anomalies

Some non-linear sigma models with fermions are known to be ill-defined because of a global obstruction to any consistent quantization. Sigma models relevant to phenomenological theories of dynamical symmetry breaking must satisfy the additional constraint of appropriately realizing the flavor symmetries of the underlying theory at the one-loop level. This is possible if and only if \u27t Hooft\u27s anomaly condition is satisfied. In particular, we show that there always exists a Wess-Zumino term which correctly reproduces the flavor anomalies, and the global obstruction vanishes, whenever \u27t Hooft\u27s condition is satisfied

Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin.

Water is often found to mediate interactions between a ligand and a protein. It can play a significant role in orientating the ligand within a binding pocket and contribute to the free energy of binding. It would thus be extremely useful to be able to accurately predict the position and orientation of water molecules within a binding pocket. Recently, we developed the WaterDock protocol that was able to predict 97% of the water molecules in a test set. However, this approach generated false positives at a rate of over 20% in most cases and whilst this might be acceptable for some applications, in high throughput scenarios this is not desirable. Here we tackle this problem via the inclusion of knowledge regarding the solvation structure of ligand functional groups. We call this new protocol WaterDock2 and demonstrate that this protocol maintains a similar true positive rate to the original implementation but is capable of reducing the false-positive rate by over 50%. To improve the usability of the method, we have also developed a plugin for the popular graphics program PyMOL. The plugin also contains an implementation of the original WaterDock.GAR is supported by the Memorial Sloan Kettering Cancer Center, NIH grant P30 CA008748

Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin.

Water is often found to mediate interactions between a ligand and a protein. It can play a significant role in orientating the ligand within a binding pocket and contribute to the free energy of binding. It would thus be extremely useful to be able to accurately predict the position and orientation of water molecules within a binding pocket. Recently, we developed the WaterDock protocol that was able to predict 97% of the water molecules in a test set. However, this approach generated false positives at a rate of over 20% in most cases and whilst this might be acceptable for some applications, in high throughput scenarios this is not desirable. Here we tackle this problem via the inclusion of knowledge regarding the solvation structure of ligand functional groups. We call this new protocol WaterDock2 and demonstrate that this protocol maintains a similar true positive rate to the original implementation but is capable of reducing the false-positive rate by over 50%. To improve the usability of the method, we have also developed a plugin for the popular graphics program PyMOL. The plugin also contains an implementation of the original WaterDock.GAR is supported by the Memorial Sloan Kettering Cancer Center, NIH grant P30 CA008748