Proteomic identification of putative microRNA394 target genes in Arabidopsis thaliana identifies MLP family members critical for normal development

Expression of the F-Box protein Leaf Curling Responsiveness (LCR) is regulated by microRNA, miR394, and alterations to this interplay in Arabidopsis thaliana produce defects in leaf polarity and shoot apical meristem (SAM) organisation. Although the miR394-LCR node has been documented in Arabidopsis, the identification of proteins targeted by LCR F-box itself has proven problematic. Here, a proteomic analysis of shoot apices from plants with altered LCR levels identified a member of the Major Latex Protein (MLP) family gene as a potential LCR F-box target. Bioinformatic and molecular analyses also suggested that other MLP family members are likely to be targets for this post-translational regulation. Direct interaction between LCR F-Box and MLP423 was validated. Additional MLP members had reduction in protein accumulation, in varying degrees, mediated by LCR F-Box. Transgenic Arabidopsis lines, in which MLP28 expression was reduced through an artificial miRNA technology, displayed severe developmental defects, including changes in leaf patterning and morphology, shoot apex defects, and eventual premature death. These phenotypic characteristics resemble those of Arabidopsis plants modified to over-express LCR. Taken together, the results demonstrate that MLPs are driven to degradation by LCR, and indicate that MLP gene family is target of miR394-LCR regulatory node, representing potential targets for directly post-translational regulation mediated by LCR F-Box. In addition, MLP28 family member is associated with the LCR regulation that is critical for normal Arabidopsis development.

A Connection Between GRBF and MLP

Both multilayer perceptrons (MLP) and Generalized Radial Basis Functions (GRBF) have good approximation properties, theoretically and experimentally. Are they related? The main point of this paper is to show that for normalized inputs, multilayer perceptron networks are radial function networks (albeit with a non-standard radial function). This provides an interpretation of the weights w as centers t of the radial function network, and therefore as equivalent to templates. This insight may be useful for practical applications, including better initialization procedures for MLP. In the remainder of the paper, we discuss the relation between the radial functions that correspond to the sigmoid for normalized inputs and well-behaved radial basis functions, such as the Gaussian. In particular, we observe that the radial function associated with the sigmoid is an activation function that is good approximation to Gaussian basis functions for a range of values of the bias parameter. The implication is that a MLP network can always simulate a Gaussian GRBF network (with the same number of units but less parameters); the converse is true only for certain values of the bias parameter. Numerical experiments indicate that this constraint is not always satisfied in practice by MLP networks trained with backpropagation. Multiscale GRBF networks, on the other hand, can approximate MLP networks with a similar number of parameters.

Artificial keys for botanical identification using a multilayer perceptron neural network (MLP)

In this paper, practical generation of identification keys for biological taxa using a multilayer perceptron neural network is described. Unlike conventional expert systems, this method does not require an expert for key generation, but is merely based on recordings of observed character states. Like a human taxonomist, its judgement is based on experience, and it is therefore capable of generalized identification of taxa. An initial study involving identification of three species of Iris with greater than 90% confidence is presented here. In addition, the horticulturally significant genus Lithops (Aizoaceae/Mesembryanthemaceae), popular with enthusiasts of succulent plants, is used as a more practical example, because of the difficulty of generation of a conventional key to species, and the existence of a relatively recent monograph. It is demonstrated that such an Artificial Neural Network Key (ANNKEY) can identify more than half (52.9%) of the species in this genus, after training with representative data, even though data for one character is completely missing.

Detection of faults in a high speed packaging machine using a multilayer perceptron (MLP)

In high speed manufacturing systems, continuous operation is desirable, with minimal disruption for repairs and service. An intelligent diagnostic monitoring system, designed to detect developing faults before catastrophic failure, or prior to undesirable reduction in output quality, is a good means of achieving this. Artificial neural networks have already been found to be of value in fault diagnosis of machinery. The aim here is to provide a system capable of detecting a number of faults, in order that maintenance can be scheduled in advance of sudden failure, and to reduce the necessity to replace parts at intervals based on mean time between failures. Instead, parts will need to be replaced only when necessary. Analysis of control information in the form of position error data from two servomotors is described.