Radiation-induced nickel deposits

Low cost, photographic process uses surface coating of nickel hypophosphite sensitive to X-rays and electron radiation. Exposed coated surface can be amplified to produce permanent visible image of wide tonal gradation in grays. Coating may be sodium, ammonium, or lithium hypophosphite or sodium phosphite, with nickel supplied in developer

Production of metals and compounds by radiation chemistry

Preparation of metals and compounds by radiation induced chemical reactions involves irradiation of metal salt solutions with high energy electrons. This technique offers a method for the preparation of high purity metals with minimum contamination from the container material or the cover gas

Dynamics of Cell Shape and Forces on Micropatterned Substrates Predicted by a Cellular Potts Model

Micropatterned substrates are often used to standardize cell experiments and to quantitatively study the relation between cell shape and function. Moreover, they are increasingly used in combination with traction force microscopy on soft elastic substrates. To predict the dynamics and steady states of cell shape and forces without any a priori knowledge of how the cell will spread on a given micropattern, here we extend earlier formulations of the two-dimensional cellular Potts model. The third dimension is treated as an area reservoir for spreading. To account for local contour reinforcement by peripheral bundles, we augment the cellular Potts model by elements of the tension-elasticity model. We first parameterize our model and show that it accounts for momentum conservation. We then demonstrate that it is in good agreement with experimental data for shape, spreading dynamics, and traction force patterns of cells on micropatterned substrates. We finally predict shapes and forces for micropatterns that have not yet been experimentally studied.Comment: Revtex, 32 pages, 11 PDF figures, to appear in Biophysical Journa

Standing on Shaky Ground: Americans' Experiences With Economic Insecurity

Based on 2009 Surveys of Economic Risk Perceptions and Insecurity, examines Americans' experience of economic insecurity, such as frequency and duration, buffers against hardship, and concerns by income, family structure, race/ethnicity, and education

Dynamics of a Semiflexible Polymer or Polymer Ring in Shear Flow

Polymers exposed to shear flow exhibit a rich tumbling dynamics. While rigid rods rotate on Jeffery orbits, flexible polymers stretch and coil up during tumbling. Theoretical results show that in both of these asymptotic regimes the tumbling frequency f_c in a linear shear flow of strength \gamma scales as a power law Wi^(2/3) in the Weissenberg number Wi=\gamma \tau, where \tau is a characteristic time of the polymer's relaxational dynamics. For flexible polymers these theoretical results are well confirmed by experimental single molecule studies. However, for the intermediate semiflexible regime the situation is less clear. Here we perform extensive Brownian dynamics simulations to explore the tumbling dynamics of semiflexible polymers over a broad range of shear strength and the polymer's persistence length l_p. We find that the Weissenberg number alone does not suffice to fully characterize the tumbling dynamics, and the classical scaling law breaks down. Instead, both the polymer's stiffness and the shear rate are relevant control parameters. Based on our Brownian dynamics simulations we postulate that in the parameter range most relevant for cytoskeletal filaments there is a distinct scaling behavior with f_c \tau*=Wi^(3/4) f_c (x) with Wi=\gamma \tau* and the scaling variable x=(l_p/L)(Wi)^(-1/3); here \tau* is the time the polymer's center of mass requires to diffuse its own contour length L. Comparing these results with experimental data on F-actin we find that the Wi^(3/4) scaling law agrees quantitatively significantly better with the data than the classical Wi^(2/3) law. Finally, we extend our results to single ring polymers in shear flow, and find similar results as for linear polymers with slightly different power laws.Comment: 17 pages, 14 figure

The effect of noise correlations in populations of diversely tuned neurons

The amount of information encoded by networks of neurons critically depends on the correlation structure of their activity. Neurons with similar stimulus preferences tend to have higher noise correlations than others. In homogeneous populations of neurons this limited range correlation structure is highly detrimental to the accuracy of a population code. Therefore, reduced spike count correlations under attention, after adaptation or after learning have been interpreted as evidence for a more efficient population code. Here we analyze the role of limited range correlations in more realistic, heterogeneous population models. We use Fisher information and maximum likelihood decoding to show that reduced correlations do not necessarily improve encoding accuracy. In fact, in populations with more than a few hundred neurons, increasing the level of limited range correlations can substantially improve encoding accuracy. We found that this improvement results from a decrease in noise entropy that is associated with increasing correlations if the marginal distributions are unchanged. Surprisingly, for constant noise entropy and in the limit of large populations the encoding accuracy is independent of both structure and magnitude of noise correlations

Capturing Regular Human Activity through a Learning Context Memory

A learning context memory consisting of two main parts is presented. The first part performs lossy data compression, keeping the amount of stored data at a minimum by combining similar context attributes — the compression rate for the presented GPS data is 150:1 on average. The resulting data is stored in an appropriate data structure highlighting the level of compression. Elements with a high level of compression are used in the second part to form the start and end points of episodes capturing common activity consisting of consecutive events. The context memory is used to investigate how little context data can be stored containing still enough information to capture regular human activity