Transformation of E. coli cells treated with CaCJ2 to multiple antibiotic resistance by purified R-factor DNA is reported. Drug resistance is expressed in a small fraction of the recipient bacterial population almost immediately after uptake of DNA, but full genetic expression of resistance requires subsequent incubation in drugfree medium before antibiotic challenge. MATERIALS AND METHODSBacterial Strains and R Factors. The I-like R factor, R64-11 (16), which specifies resistance to tetracycline (Tc) and streptomycin (Sm), was obtained from R. Curtiss. R6 (17), an F-like R factor that carries resistance to kanamycin (Km), neomycin (Nm), chloramphenicol (Cm), sulphonamide (Su), streptomycin, and tetracycline, was obtained from T. Watanabe. R6-5, a spontaneous variant of R6 that lacks tetracycline resistance, was isolated in our laboratory (18). The bacterial strains used in these experiments have been described (7,22). DNA Preparations. In certain instances, covalently-closed R-factor DNA was isolated and purified from E. coli as described (6, 7). Alternatively, a Brij-lysis procedure (19) was used for initial R-factor DNA isolation, and preparations obtained by this method were subsequently purified by centrifugation in cesium chloride-ethidium bromide gradients. The catenated, closed circular, and noncircular forms of R-factor DNA used in experiments comparing the relative transforming ability of the various R-factor DNA species were isolated from E. coli minicells. R-factor DNA was denatured by heating it at 980 for 5 min in 15 mM NaCl-1.5 mM Na citrate followed by rapid cooling at 0°. Sonication of R-factor DNA to about 9S fragments was done for 15 see at 00 by a Branson model W185 D sonicator, and the size of the R-factor DNA fragments was confirmed by sucrose gradient centrifugation (7).Transformation Reaction Mixture. Transformation was done by a variation of the procedure of Mandel and Higa (15), as modified by Lobban, Masuda, and Kaiser (personal communication). E. coli strain C600 was grown at 370 in H1 medium (20) to an optical density of 0.85 at 590 nm. At this point, the cells 2110 Abbreviation: R factor, antibiotic resistance factor. * The previous paper in this series is ref. 18.

[1] We simulate the oceanic and atmospheric distribution of methyl iodide (CH 3 I) with a global 3-D model driven by assimilated meteorological observations from the Goddard Earth Observing System of the NASA Data Assimilation Office and coupled to an oceanic mixed layer model. A global compilation of atmospheric and oceanic observations is used to constrain and evaluate the simulation. Seawater CH 3 I(aq) in the model is produced photochemically from dissolved organic carbon, and is removed by reaction with Cl À and emission to the atmosphere. The net oceanic emission to the atmosphere is 214 Gg yr À1 . Small terrestrial emissions from rice paddies, wetlands, and biomass burning are also included in the model. The model captures 40% of the variance in the observed seawater CH 3 I(aq) concentrations. Simulated concentrations at midlatitudes in summer are too high, perhaps because of a missing biological sink of CH 3 I(aq). We define a marine convection index (MCI) as the ratio of upper tropospheric (8-12 km) to lower tropospheric (0-2.5 km) CH 3 I concentrations averaged over coherent oceanic regions. The MCI in the observations ranges from 0.11 over strongly subsiding regions (southeastern subtropical Pacific) to 0.40 over strongly upwelling regions (western equatorial Pacific). The model reproduces the observed MCI with no significant global bias (offset of only +11%) but accounts for only 15% of its spatial and seasonal variance. The MCI can be used to test marine convection in global models, complementing the use of radon-222 as a test of continental convection.

[1] Field studies suggest that bedrock incision by granular flows may be the primary process cutting valleys in steep, unglaciated landscapes. An expression has been proposed for debris flow incision into bedrock which posits that erosion rate depends on stresses due to granular interactions at the snout of debris flows. Here, we explore this idea by conducting laboratory experiments to test the hypothesis that bedrock erosion is related to grain collisional stresses which scale with shear rate and particle size. We placed granular material in a 56-cm-diameter rotating drum to explore the relationship between erosion of a synthetic bedrock sample and variables such as grain size, shear rate, water content, and bed strength. Grain collisional stresses are estimated as the inertial stress using the product of the squares of particle size and vertical shear rate. Our uniform granular material consisted of 1-mm sand and quartzite river gravel with means of 4, 6, or 10 mm. In 67 experimental runs, the eroded depth of the bed sample varied with inertial stresses in the granular flow to a power less than 1.0 and inversely with the bed strength. The flows tended to slip on smooth boundaries, resulting in higher erosion rates than no-slip cases. We found that lateral wall resistance generated shear across the channel, producing two cells whose widths depended on wall roughness. While the hypothesized inertial stress dependency is supported with these data, wear mechanics needs to account for grain dynamics specifically at the snout and possibly to include lateral shear effects.

A flowing granular mass generates forces on the boundary that drive near-bed grain dynamics, bed surface erosion, and energy dissipation. Few quantitative analyses exist of the controls on the dynamically fluctuating force caused by granular flows with wide-grain-size distributions and a liquid phase in the pores. To study the mechanisms controlling the boundary forces, we used a 225 cm 2 load plate to measure the bed-normal force from a suite of granular flows in a 4 m diameter, 80 cm wide vertically rotating drum. We analyzed the time series of bed forces generated in flows composed of granular material for both narrow (gravel-water) and wide (muddy, sand-gravel-cobble) grain-size distributions. The tail of the force distribution was captured more closely by a generalized Pareto distribution than an exponential distribution, suggesting a way to predict empirically the force distribution. We show that the impulse on the bed, related to kinetic energy transferred to the bed from the granular collisions, is quantified by the standard deviation of the force. The mean bulk force equaled the static weight of the flow, whereas the force fluctuations, represented by the standard deviation and the averaged top 1% of force, were a near-linear function of effective grain diameter and flow velocity, and a ∼0.5 power function of an inertial stress scaling term. The force fluctuations depend on both Savage and Bagnold numbers. The correlations revealed in this study suggest that it may be possible to estimate dynamic forces on the bed from gross properties of the flows.

[1] Seasonal patterns in high frequency seismic waves near rivers can record energy transmitted to the river bed from particle impacts during bedload transport. Here we show that single storm events in a river can also be observed seismically. We analyzed the high frequency seismic noise in a reach of the Cho-Shui (Zhuóshuǐ) River in central Taiwan and made detailed observations during individual storm events. Discharge, derived from a water level gage 4.25 km from the seismometer, is highly variable due to typhoons. We found a correlation between seismic amplitude and discharge that differs on the rising and falling limbs of three storms. During each storm, for a given discharge the amplitude of seismic waves are on average two times greater on the rising limb of the storm than on the falling limb, in both aggradational and erosional events. Clockwise hysteresis in both aggradational and erosional events implies that water turbulence, alone, is not the source of the seismic waves. If seismic wave amplitude correlates linearly with the flux of bedload, this implies a roughly two-fold decrease in transport efficiency over the time-scale of days during individual storms. The observed change in transport efficiency can plausibly be explained by the disturbance of bed armor during storms and subsequent reformation during the waning stages. This data highlights the potential for fluvial seismology to reveal the dynamics of bedload transport. Citation: Hsu, L., N. J. Finnegan, and E. E. Brodsky (2011), A seismic signature of river bedload transport during storm events, Geophys. Res. Lett., 38, L13407,

Data on the internal velocity distribution of flowing sediment–fluid mixtures such as debris flows are rare, but necessary for model development and testing. A probe to measure the mean particle velocity at different depths and different locations within experimental debris flows in a 4 m diameter rotating drum was developed. In addition, the flow depth, basal normal stress and basal pore fluid pressure were also measured. Results show that for a given sediment–fluid mixture the velocity profiles collapse to distinct non-dimensional profiles. Macroscopic flow behaviour shows great similarity, with mean surface slopes weakly dependent on the shear rate for water-saturated gravel, but strongly shear-rate-dependent when pores are filled with mud. Poorly sorted material with a high content of fines produced fluid pressures close to normal stress and sidewall friction had a strong effect on the flow pattern. Our results reveal variability in profile characteristics for flows displaying similar macro-dynamics and provide data for model testing.

A research agenda for intelligent systems that will result in fundamental new capabilities for understanding the Earth system.

[1] Field data and laboratory experiments suggest that bedrock wear from debris flows is largely due to particle-bed impacts, rather than solely due to abrasion by sliding, and that the associated bedrock erosion rates are dependent on the particle size distribution in the debris flow. Here we use Discrete Element Method (DEM) simulations with an established contact mechanics model to explore grain-size influences on contact forces associated with particle-bed impacts in sheared granular mixtures. We first compare DEM simulations with experimental observations obtained from shallow granular flows in rotating drums of diameters 0.56 m and 4.0 m. Our simulations reproduce, without parameter tuning, experimentally measured segregation, boundary pressures, and height profiles. We perform additional simulations systematically varying particle size distributions in binary mixtures. We show that local time-averaged boundary pressures in thin flows are essentially the normal component of the weight of the flow, independent of particle size distribution. However, other statistical measures of boundary forces scale with mass-averaged particle size. We demonstrate that this is because individual particle-bed impacts, rather than impacts from multiple particle collisions, dominate the largest contact forces. We show that these largest impact forces vary as the square of grain size and the 1.2 power of impact velocity as predicted from the contact mechanics model underlying the DEM. These results support the particle size dependence of a recently proposed bedrock incision model and suggest that next steps for a predictive bedrock incision model require the statistics of the largest impact velocities.