[1] In this study, an ecohydrological Cellular Automata Tree-Grass-Shrub Simulator (CATGraSS) is presented. CATGraSS is driven by pulses of rainfall and daily solar radiation. In the model, each cell can hold a single plant type (tree, shrub, tree seedlings, shrub seedlings, grass) or can be bare soil. Plant competition is modeled explicitly by keeping track of mortality and establishment of plants, both calculated probabilistically based on soil moisture stress. Topographic influence on incoming shortwave radiation is treated explicitly, which leads to spatial variations in potential evapotranspiration and soil moisture over storm and interstorm time scales, and plant distribution over annual time scales. The model is implemented in a small basin (3.3 km 2 ) in central New Mexico, USA, where north facing slopes are characterized by a juniper pine and grass savanna, and south facing slopes by creosotebush shrubs and grasses. Representing the current climate by a seasonal-varying Poisson rectangular pulse rainfall model, CATGraSS is calibrated against the existing plant patterns in the study catchment. The model is then used in a series of numerical experiments. CATGraSS is first run on flat terrain to examine the role of topography on plant patterns. Consistent with our observation in the region, this ''flat run'' gave a shrubland ecosystem with scattered grasses and trees. Model sensitivity to rainfall is investigated in a limited number of simulations by altering rainfall frequency-magnitude statistics, and seasonality. The sensitivity runs suggest that changes in the storm characteristics could lead to a dramatic reorganization of the plant composition on topography in central New Mexico. CATGraSS results underscore the importance of solar irradiance in determining vegetation composition over complex terrain under a waterlimited ecosystem.Citation: Zhou, X. E. Istanbulluoglu, and E. R. Vivoni (2013), Modeling the ecohydrological role of aspect-controlled radiation on tree-grass-shrub coexistence in a semiarid climate, Water Resour.

Early in its evolution, Enterococcus faecium acquired traits that allowed it to become a successful nosocomial pathogen. E. faecium inherent tenacity to build resistance to antibiotics and environmental stressors that allows the species to thrive in hospital environments. The continual wide use of antibiotics in medicine has been an important driver in the evolution of E. faecium becoming a highly proficient hospital pathogen. For successful prevention and reduction of nosocomial infections with vancomycin resistant E. faecium (VREfm), it is essential to focus on reducing VREfm carriage and spread. The aim of this review is to incorporate microbiological insights of E. faecium into practical infection control recommendations, to reduce the spread of hospital-acquired VREfm (carriage and infections). The spread of VREfm can be controlled by intensified cleaning procedures, antibiotic stewardship, rapid screening of VREfm carriage focused on high-risk populations, and identification of transmission routes through accurate detection and typing methods in outbreak situations. Further, for successful management of E. faecium, continual innovation in the fields of diagnostics, treatment, and eradication is necessary.

This paper addresses the need for surveillance of fugitive methane emissions over broad geographical regions. Most existing techniques suffer from being either extensive (but qualitative) or quantitative (but intensive with poor scalability). A total of two novel advancements are made here. First, a recursive Bayesian method is presented for probabilistically characterizing fugitive point-sources from mobile sensor data. This approach is made possible by a new cross-plume integrated dispersion formulation that overcomes much of the need for time-averaging concentration data. The method is tested here against a limited data set of controlled methane release and shown to perform well. We then present an information-theoretic approach to plan the paths of the sensor-equipped vehicle, where the path is chosen so as to maximize expected reduction in integrated target source rate uncertainty in the region, subject to given starting and ending positions and prevailing meteorological conditions. The information-driven sensor path planning algorithm is tested and shown to provide robust results across a wide range of conditions. An overall system concept is presented for optionally piggybacking of these techniques onto normal industry maintenance operations using sensor-equipped work trucks.