Harnessing naturally randomized transcription to infer regulatory relationships among genes

An approach is developed that utilizes randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level. The approach is applied to an experiment in yeast, yielding new insights into the topology of the yeast transcriptional regulatory network

Magnetic structure of free cobalt clusters studied with Stern-Gerlach deflection experiments

We have studied the magnetic properties of free cobalt clusters in two semi-independent Stern-Gerlach deflection experiments at temperatures between 60 and 307 K. We find that clusters consisting of 13 to 200 cobalt atoms exhibit behavior that is entirely consistent with superparamagnetism, though complicated by finite-system fluctuations in cluster temperature. By fitting the data to the Langevin function, we report magnetic moments per atom for each cobalt cluster size and compare the results of our two measurements and all those performed previously. In addition to a gradual decrease in moment per atom with increasing size, there are oscillations that appear to be caused by geometrical shell structure. We discuss our observations in light of the two competing models for Langevin-like magnetization behavior in free clusters, superparamagnetism and adiabatic magnetization, and conclude that the evidence strongly supports the superparamagnetic model

A Heterosynaptic Learning Rule for Neural Networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre- and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean learning time increases with the number of patterns to be learned polynomially, indicating efficient learning.Comment: 19 page

DWM07 global empirical model of upper thermospheric storm-induced disturbance winds

We present a global empirical disturbance wind model (DWM07) that represents average geospace-storm-induced perturbations of upper thermospheric (200-600 km altitude) neutral winds. DWM07 depends on the following three parameters: magnetic latitude, magnetic local time, and the 3-h Kp geomagnetic activity index. The latitude and local time dependences are represented by vector spherical harmonic functions ( up to degree 10 in latitude and order 3 in local time), and the Kp dependence is represented by quadratic B-splines. DWM07 is the storm time thermospheric component of the new Horizontal Wind Model (HWM07), which is described in a companion paper. DWM07 is based on data from the Wind Imaging Interferometer on board the Upper Atmosphere Research Satellite, the Wind and Temperature Spectrometer on board Dynamics Explorer 2, and seven ground-based Fabry-Perot interferometers. The perturbation winds derived from the three data sets are in good mutual agreement under most conditions, and the model captures most of the climatological variations evident in the data

Evidence for stratospheric sudden warming effects on the upper thermosphere derived from satellite orbital decay data during 1967–2013

We investigate possible impact of stratospheric sudden warmings (SSWs) on the thermosphere by using long-term data of the global average thermospheric total mass density derived from satellite orbital drag during 1967–2013. Residuals are analyzed between the data and empirical Global Average Mass Density Model (GAMDM) that takes into account density variability due to solar activity, season, geomagnetic activity, and long-term trend. A superposed epoch analysis of 37 SSW events reveals a density reduction of 3–7% at 250–575 km around the time of maximum polar vortex weakening. The relative density perturbation is found to be greater at higher altitudes. The temperature perturbation is estimated to be −7.0 K at 400 km. We show that the density reduction can arise from enhanced wave forcing from the lower atmosphere