132,025 research outputs found
Statistical Indicators of Collective Behavior and Functional Clusters in Gene Networks of Yeast
We analyze gene expression time-series data of yeast S. cerevisiae measured
along two full cell-cycles. We quantify these data by using q-exponentials,
gene expression ranking and a temporal mean-variance analysis. We construct
gene interaction networks based on correlation coefficients and study the
formation of the corresponding giant components and minimum spanning trees. By
coloring genes according to their cell function we find functional clusters in
the correlation networks and functional branches in the associated trees. Our
results suggest that a percolation point of functional clusters can be
identified on these gene expression correlation networks.Comment: 8 pages, 4 figure
Variable neural networks for adaptive control of nonlinear systems
This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated example
Elucidation of Directionality for Co-Expressed Genes: Predicting Intra-Operon Termination Sites
We present a novel framework for inferring regulatory and sequence-level
information from gene co-expression networks. The key idea of our methodology
is the systematic integration of network inference and network topological
analysis approaches for uncovering biological insights. We determine the gene
co-expression network of Bacillus subtilis using Affymetrix GeneChip time
series data and show how the inferred network topology can be linked to
sequence-level information hard-wired in the organism's genome. We propose a
systematic way for determining the correlation threshold at which two genes are
assessed to be co-expressed by using the clustering coefficient and we expand
the scope of the gene co-expression network by proposing the slope ratio metric
as a means for incorporating directionality on the edges. We show through
specific examples for B. subtilis that by incorporating expression level
information in addition to the temporal expression patterns, we can uncover
sequence-level biological insights. In particular, we are able to identify a
number of cases where (i) the co-expressed genes are part of a single
transcriptional unit or operon and (ii) the inferred directionality arises due
to the presence of intra-operon transcription termination sites.Comment: 7 pages, 8 figures, accepted in Bioinformatic
Digital IP Protection Using Threshold Voltage Control
This paper proposes a method to completely hide the functionality of a
digital standard cell. This is accomplished by a differential threshold logic
gate (TLG). A TLG with inputs implements a subset of Boolean functions of
variables that are linear threshold functions. The output of such a gate is
one if and only if an integer weighted linear arithmetic sum of the inputs
equals or exceeds a given integer threshold. We present a novel architecture of
a TLG that not only allows a single TLG to implement a large number of complex
logic functions, which would require multiple levels of logic when implemented
using conventional logic primitives, but also allows the selection of that
subset of functions by assignment of the transistor threshold voltages to the
input transistors. To obfuscate the functionality of the TLG, weights of some
inputs are set to zero by setting their device threshold to be a high .
The threshold voltage of the remaining transistors is set to low to
increase their transconductance. The function of a TLG is not determined by the
cell itself but rather the signals that are connected to its inputs. This makes
it possible to hide the support set of the function by essentially removing
some variable from the support set of the function by selective assignment of
high and low to the input transistors. We describe how a standard cell
library of TLGs can be mixed with conventional standard cells to realize
complex logic circuits, whose function can never be discovered by reverse
engineering. A 32-bit Wallace tree multiplier and a 28-bit 4-tap filter were
synthesized on an ST 65nm process, placed and routed, then simulated including
extracted parastics with and without obfuscation. Both obfuscated designs had
much lower area (25%) and much lower dynamic power (30%) than their
nonobfuscated CMOS counterparts, operating at the same frequency
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