Asymmetric cross-coupled differential pair configuration to realize neuron activation function and its derivative

In this brief, we present a new circuit technique to generate the sigmoid neuron activation function (NAF) and its derivative (DNAF). The circuit makes use of transistor asymmetry in cross-coupled differential pair to obtain the derivative. The asymmetry is introduced through external control signal, as and when required. This results in the efficient utilization of the hard-ware by realizing NAF and DNAF using the same building blocks. The operation of the circuit is presented in the subthreshold region for ultra low-power applications. The proposed circuit has been experimentally prototyped and characterized as a proof of concept on the 1.5-mum AMI technology.

Activation function study for wavelet network

The main purpose of this paper is to investigate theoretically and experimentally the use of family of Polynomial Powers of the Sigmoid (PPS) Function Networks applied in speech signal representation and function approximation. This paper carries out practical investigations in terms of approximation fitness (LSE), time consuming (CPU Time), computational complexity (FLOP) and representation power (Number of Activation Function) for different PPS activation functions. We expected that different activation functions can provide performance variations and further investigations will guide us towards a class of mappings associating the best activation function to solve a class of problems under certain criteria.

Approximation of hyperbolic tangent activation function using hybrid methods

Artificial Neural Networks are widely used in various applications in engineering, as such solutions of nonlinear problems. The implementation of this technique in reconfigurable devices is a great challenge to researchers by several factors, such as floating point precision, nonlinear activation function, performance and area used in FPGA. The contribution of this work is the approximation of a nonlinear function used in ANN, the popular hyperbolic tangent activation function. The system architecture is composed of several scenarios that provide a tradeoff of performance, precision and area used in FPGA. The results are compared in different scenarios and with current literature on error analysis, area and system performance. © 2013 IEEE.

Mouse Eya genes are expressed during limb tendon development and encode a transcriptional activation function

Vertebrate limb tendons are derived from connective cells of the lateral plate mesoderm. Some of the developmental steps leading to the formation of vertebrate limb tendons have been previously identified; however, the molecular mechanisms responsible for tendinous patterning and maintenance during embryogenesis are largely unknown. The eyes absent (eya) gene of Drosophila encodes a novel nuclear protein of unknown molecular function. Here we show that Eya1 and Eya2, two mouse homologues of Drosophila eya, are expressed initially during limb development in connective tissue precursor cells. Later in limb development, Eya1 and Eya2 expression is associated with cell condensations that form different sets of limb tendons. Eya1 expression is largely restricted to flexor tendons, while Eya2 is expressed in the extensor tendons and ligaments of the phalangeal elements of the limb. These data suggest that Eya genes participate in the patterning of the distal tendons of the limb. To investigate the molecular functions of the Eya gene products, we have analyzed whether the highly divergent PST (proline-serine-threonine)-rich N-terminal regions of Eya1–3 function as transactivation domains. Our results demonstrate that Eya gene products can act as transcriptional activators, and they support a role for this molecular function in connective tissue patterning.

Estradiol repression of tumor necrosis factor-α transcription requires estrogen receptor activation function-2 and is enhanced by coactivators

The tumor necrosis factor-α (TNF-α) promoter was used to explore the molecular mechanisms of estradiol (E2)-dependent repression of gene transcription. E2 inhibited basal activity and abolished TNF-α activation of the TNF-α promoter. The E2-inhibitory element was mapped to the −125 to −82 region of the TNF-α promoter, known as the TNF-responsive element (TNF-RE). An AP-1-like site in the TNF-RE is essential for repression activity. Estrogen receptor (ER) β is more potent than ERα at repressing the −1044 TNF-α promoter and the TNF-RE upstream of the herpes simplex virus thymidine kinase promoter, but weaker at activating transcription through an estrogen response element. The activation function-2 (AF-2) surface in the ligand-binding domain is required for repression, because anti-estrogens and AF-2 mutations impair repression. The requirement of the AF-2 surface for repression is probably due to its capacity to recruit p160 coactivators or related coregulators, because overexpressing the coactivator glucocorticoid receptor interacting protein-1 enhances repression, whereas a glucocorticoid receptor interacting protein-1 mutant unable to interact with the AF-2 surface is ineffective. Furthermore, receptor interacting protein 140 prevents repression by ERβ, probably by interacting with the AF-2 surface and blocking the binding of endogenous coactivators. These studies demonstrate that E2-mediated repression requires the AF-2 surface and the participation of coactivators or other coregulatory proteins.

Targeting androgen receptor activation function-1 with EPI to overcome resistance mechanisms in castration-resistant prostate cancer

Financial support: This research was supported by grants to MDS from the NCI (2R01CA105304), the Canadian Institutes of Health Research (MOP79308) and the US Army Medical Research and Materiel Command Prostate Cancer Research Program (E81XWH-11-1-0551). Research by IJM’s group was supported by the Chief Scientist’s Office of the Scottish Government (ETM-258 and -382). We are grateful to Country Meadows Senior Men’s Golf Charity Classic for financial support of this research.

Activation of Androgen Receptor Function by a Novel Nuclear Protein Kinase

Androgen receptor (AR) belongs to the nuclear receptor superfamily and mediates the biological actions of male sex steroids. In this work, we have characterized a novel 130-kDa Ser/Thr protein kinase ANPK that interacts with the zinc finger region of AR in vivo and in vitro. The catalytic kinase domain of ANPK shares considerable sequence similarity with the minibrain gene product, a protein kinase suggested to contribute to learning defects associated with Down syndrome. However, the rest of ANPK sequence, including the AR-interacting interface, exhibits no apparent homology with other proteins. ANPK is a nuclear protein that is widely expressed in mammalian tissues. Its overexpression enhances AR-dependent transcription in various cell lines. In addition to the zinc finger region, ligand-binding domain and activation function AF1 of AR are needed, as the activity of AR mutants devoid of these domains was not influenced by ANPK. The receptor protein does not appear to be a substrate for ANPK in vitro, and overexpression of ANPK does not increase the extent of AR phosphorylation in vivo. In view of this, it is likely that ANPK-mediated activation of AR function is exerted through modification of AR-associated proteins, such as coregulatory factors, and/or through stabilization of the receptor protein against degradation.

Activation of peroxisome proliferator-activated receptors (PPARs) by their ligands and protein kinase A activators.

The nuclear peroxisome proliferator-activated receptors (PPARs) alpha, beta, and gamma activate the transcription of multiple genes involved in lipid metabolism. Several natural and synthetic ligands have been identified for each PPAR isotype but little is known about the phosphorylation state of these receptors. We show here that activators of protein kinase A (PKA) can enhance mouse PPAR activity in the absence and the presence of exogenous ligands in transient transfection experiments. Activation function 1 (AF-1) of PPARs was dispensable for transcriptional enhancement, whereas activation function 2 (AF-2) was required for this effect. We also show that several domains of PPAR can be phosphorylated by PKA in vitro. Moreover, gel retardation experiments suggest that PKA stabilizes binding of the liganded PPAR to DNA. PKA inhibitors decreased not only the kinase-dependent induction of PPARs but also their ligand-dependent induction, suggesting an interaction between both pathways that leads to maximal transcriptional induction by PPARs. Moreover, comparing PPAR alpha knockout (KO) with PPAR alpha WT mice, we show that the expression of the acyl CoA oxidase (ACO) gene can be regulated by PKA-activated PPAR alpha in liver. These data demonstrate that the PKA pathway is an important modulator of PPAR activity, and we propose a model associating this pathway in the control of fatty acid beta-oxidation under conditions of fasting, stress, and exercise.

Knowledge extraction from a mixed transfer function artificial neural network

One of the main problems with Artificial Neural Networks (ANNs) is that their results are not intuitively clear. For example, commonly used hidden neurons with sigmoid activation function can approximate any continuous function, including linear functions, but the coefficients (weights) of this approximation are rather meaningless. To address this problem, current paper presents a novel kind of a neural network that uses transfer functions of various complexities in contrast to mono-transfer functions used in sigmoid and hyperbolic tangent networks. The presence of transfer functions of various complexities in a Mixed Transfer Functions Artificial Neural Network (MTFANN) allow easy conversion of the full model into user-friendly equation format (similar to that of linear regression) without any pruning or simplification of the model. At the same time, MTFANN maintains similar generalization ability to mono-transfer function networks in a global optimization context. The performance and knowledge extraction of MTFANN were evaluated on a realistic simulation of the Puma 560 robot arm and compared to sigmoid, hyperbolic tangent, linear and sinusoidal networks.

Knowledge extraction from a mixed transfer function artificial neural network

One of the big problems with Artificial Neural Networks (ANN) is that their results are not intuitively clear. For example, if we use the traditional neurons, with a sigmoid activation function, we can approximate any function, including linear functions, but the coefficients (weights) in this approximation will be rather meaningless. To resolve this problem, this paper presents a novel kind of ANN with different transfer functions mixed together. The aim of such a network is to i) obtain a better generalization than current networks ii) to obtain knowledge from the networks without a sophisticated knowledge extraction algorithm iii) to increase the understanding and acceptance of ANNs. Transfer Complexity Ratio is defined to make a sense of the weights associated with the network. The paper begins with a review of the knowledge extraction from ANNs and then presents a Mixed Transfer Function Artificial Neural Network (MTFANN). A MTFANN contains different transfer functions mixed together rather than mono-transfer functions. This mixed presence has helped to obtain high level knowledge and similar generalization comparatively to monotransfer function nets in a global optimization context.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

Feedforward neural networks based on PPS-wavelet activation functions

Function approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. Neural networks and wavenets have been recently seen as attractive tools for developing efficient solutions for many real world problems in function approximation. In this paper, it is shown how feedforward neural networks can be built using a different type of activation function referred to as the PPS-wavelet. An algorithm is presented to generate a family of PPS-wavelets that can be used to efficiently construct feedforward networks for function approximation.

Activation of the orphan nuclear receptor steroidogenic factor 1 by oxysterols

Steroidogenic factor 1 (SF-1), an orphan member of the intracellular receptor superfamily, plays an essential role in the development and function of multiple endocrine organs. It is expressed in all steroidogenic tissues where it regulates the P450 steroidogenic genes to generate physiologically active steroids. Although many of the functions of SF-1 in vivo have been defined, an unresolved question is whether a ligand modulates its transcriptional activity. Here, we show that 25-, 26-, or 27-hydroxycholesterol, known suppressors of cholesterol biosynthesis, enhance SF-1-dependent transcriptional activity. This activation is dependent upon the SF-1 activation function domain, and, is specific for SF-1 as several other receptors do not respond to these molecules. The oxysterols activate at concentrations comparable to those previously shown to inhibit cholesterol biosynthesis, and, can be derived from cholesterol by P450c27, an enzyme expressed within steroidogenic tissues. Recent studies have shown that the nuclear receptor LXR also is activated by oxysterols. We demonstrate that different oxysterols differ in their rank order potency for these two receptors, with 25-hydroxycholesterol preferentially activating SF-1 and 22(R)-hydroxycholesterol preferentially activating LXR. These results suggest that specific oxysterols may mediate transcriptional activation via different intracellular receptors. Finally, ligand-dependent transactivation of SF-1 by oxysterols may play an important role in enhancing steroidogenesis in vivo.

The N-terminal region (A/B) of rat thyroid hormone receptors alpha 1, beta 1, but not beta 2 contains a strong thyroid hormone-dependent transactivation function.

In this study we have investigated the role of the N-terminal region of thyroid hormone receptors (TRs) in thyroid hormone (TH)-dependent transactivation of a thymidine kinase promoter containing TH response elements composed either of a direct repeat or an inverted palindrome. Comparison of rat TR beta 1 with TR beta 2 provides an excellent model since they share identical sequences except for their N termini. Our results show that TR beta 2 is an inefficient TH-dependent transcriptional activator. The degree of transactivation corresponds to that observed for the mutant TR delta N beta 1/2, which contains only those sequences common to TR beta 1 and TR beta 2. Thus, TH-dependent activation appears to be associated with two separate domains. The more important region, however, is embedded in the N-terminal domain. Furthermore, the transactivating property of TR alpha 1 was also localized to the N-terminal domain between amino acids 19 and 30. Using a coimmunoprecipitation assay, we show that the differential interaction of the N terminus of TR beta 1 and TR beta 2 with transcription factor IIB correlates with the TR beta 1 activation function. Hence, our results underscore the importance of the N-terminal region of TRs in TH-dependent transactivation and suggest that a transactivating signal is transmitted to the general transcriptional machinery via a direct interaction of the receptor N-terminal region with transcription factor IIB.

Double-Wavelet Neuron Based on Analytical Activation Functions

In this paper a new double-wavelet neuron architecture obtained by modification of standard wavelet neuron, and its learning algorithm are proposed. The offered architecture allows to improve the approximation properties of wavelet neuron. Double-wavelet neuron and its learning algorithm are examined for forecasting non-stationary chaotic time series.