Three algorithms for learning artificial neural network: A comparison for induction motor flux estimation

This paper presents a comparative study of three algorithms for learning artificial neural network. As neural estimator, back-propagation (BP) algorithm, uncorrelated real time recurrent learning (URTRL) algorithm and correlated real time recurrent learning (CRTRL) algorithm are used in the present work to learn the artificial neural network (ANN). The approach proposed here is based on the flux estimation of high performance induction motor drives. Simulation of the drive system was carried out to study the performance of the motor drive. It is observed that the proposed CRTRL algorithm based methodology provides better performance than the BP and URTRL algorithm based technique. The proposed method can be used for accurate measurement of the rotor flux.

MicroRNA profiling in brown adipose tissue and muscle-derived CD34+ cells

 The major findings established a mouse brown adipose tissue (BAT)-enriched miRNA profile conserved in human BAT and predicted to target genes potentially involved in growth and development. The present results also identified a human skeletal muscle-derived CD34+ cell population with the capacity to differentiate into brown adipocytes in vitro. These CD34+ expressed common miRNAs to mouse and human BAT. Finally these findings show an up-regulation of 4 miRNAs in human adult skeletal muscle following cold exposure. These miRNAs were also present in mouse and human BAT as well as in CD34+ brown adipocytes.

Characterisation of RNA in milk and prediction of microRNA functions

 Milk is considered on of the world’s most ‘complete’ food. To characterise milk composition, Amit investigated RNA present of milk form 8 different species ranging from platypus to human. By applying latest RNA sequencing and bioinformatic techniques, his work led to uncover hundreds of novel milk RNAs.

Validation of a multiplex reverse transcription and pre-amplification method using TaqMan(®) MicroRNA assays.

Since the discovery of microRNAs (miRNAs), different approaches have been developed to label, amplify and quantify miRNAs. The TaqMan(®) technology, provided by Applied Biosystems (ABIs), uses a stem-loop reverse transcription primer system to reverse transcribe the RNA and amplify the cDNA. This method is widely used to identify global differences between the expression of 100s of miRNAs across comparative samples. This technique also allows the quantification of the expression of targeted miRNAs to validate observations determined by whole-genome screening or to analyze few specific miRNAs on a large number of samples. Here, we describe the validation of a method published by ABIs on their web site allowing to reverse transcribe and pre-amplify multiple miRNAs and snoRNAs simultaneously. The validation of this protocol was performed on human muscle and plasma samples. Fast and cost efficient, this method achieves an easy and convenient way to screen a relatively large number of miRNAs in parallel.

Formations of robotic swarm: an artificial force based approach

Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm) into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding intermember collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc. Also the performance of the proposed distributed swarm control architecture was investigated in the presence of realistic implementation issues such as localization errors, communication range limitations, boundedness of forces etc.

Rigs-to-reefs: Will the deep sea benefit from artificial habitat?

As a peak in the global number of offshore oil rigs requiring decommissioning approaches, there is growing pressure for the implementation of a "rigs-to-reefs" program in the deep sea, whereby obsolete rigs are converted into artificial reefs. Such decommissioned rigs could enhance biological productivity, improve ecological connectivity, and facilitate conservation/restoration of deep-sea benthos (eg cold-water corals) by restricting access to fishing trawlers. Preliminary evidence indicates that decommissioned rigs in shallower waters can also help rebuild declining fish stocks. Conversely, potential negative impacts include physical damage to existing benthic habitats within the "drop zone", undesired changes in marine food webs, facilitation of the spread of invasive species, and release of contaminants as rigs corrode. We discuss key areas for future research and suggest alternatives to offset or minimize negative impacts. Overall, a rigs-to-reefs program may be a valid option for deep-sea benthic conservation. © The Ecological Society of America.

MicroRNA pathways : an emerging role in identification of therapeutic strategies

For years researchers have exerted every effort to improve the influential roles of microRNA (miRNA) in regulating genes that direct mammalian cell development and function. In spite of numerous advancements, many facets of miRNA generation remain unresolved due to the perplexing regulatory networks. The biogenesis of miRNA, eminently endures as a mystery as no universal pathway defines or explicates the variegation in the rise of miRNAs. Early evidence in biogenesis ignited specific steps of being omitted or replaced that eventuate in the individual miRNAs of different mechanisms. Understanding the basic foundation concerning how miRNAs are generated and function will help with diagnostic tools and therapeutic strategies. This review encompasses the canonical and the non-canonical pathways involved in miRNA biogenesis, while elucidating how miRNAs regulate genes at the nuclear level and also the mechanism that lies behind circulating miRNAs.

An improved Gbest guided artificial bee colony (IGGABC) algorithm for classification and prediction tasks

Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Many existing works used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based technique for training Multilayer Perceptron (MLP). Normally Gbest Guided Artificial Bee Colony (GGABC) algorithm has strong exploitation process for solving mathematical problems, however the poor exploration creates problems like slow convergence and trapping in local minima. In this paper, the Improved Gbest Guided Artificial Bee Colony (IGGABC) algorithm is proposed for finding global optima. The proposed IGGABC algorithm has strong exploitation and exploration processes. The experimental results show that IGGABC algorithm performs better than that standard GGABC, BP and ABC algorithms for Boolean data classification and time-series prediction tasks.

Artificial neural network modeling of flow stress in hot rolling

In this study, an artificial neural network model is proposed to predict the flow stress variations during the hot rolling process. Optimization of the proposed neural network with respect to number of neurons within the hidden layer, different training methods and transfer functions of the neural network is performed. The results of the optimal network were compared with those of the conventional analytic method and it is shown that using an optimal neural network the mean calculated error is drastically reduced.

MicroRNA-23a has minimal effect on endurance exercise-induced adaptation of mouse skeletal muscle.

Skeletal muscles contain several subtypes of myofibers that differ in contractile and metabolic properties. Transcriptional control of fiber-type specification and adaptation has been intensively investigated over the past several decades. Recently, microRNA (miRNA)-mediated posttranscriptional gene regulation has attracted increasing attention. MiR-23a targets key molecules regulating contractile and metabolic properties of skeletal muscle, such as myosin heavy-chains and peroxisome proliferator-activated receptor gamma, coactivator 1 alpha (PGC-1α). In the present study, we analyzed the skeletal muscle phenotype of miR-23a transgenic (miR-23a Tg) mice to explore whether forced expression of miR-23a affects markers of mitochondrial content, muscle fiber composition, and muscle adaptations induced by 4 weeks of voluntary wheel running. When compared with wild-type mice, protein markers of mitochondrial content, including PGC-1α, and cytochrome c oxidase complex IV (COX IV), were significantly decreased in the slow soleus muscle, but not the fast plantaris muscle of miR-23a Tg mice. There was a decrease in type IId/x fibers only in the soleus muscle of the Tg mice. Following 4 weeks of voluntary wheel running, there was no difference in the endurance exercise capacity as well as in several muscle adaptive responses including an increase in muscle mass, capillary density, or the protein content of myosin heavy-chain IIa, PGC-1α, COX IV, and cytochrome c. These results show that miR-23a targets PGC-1α and regulates basal metabolic properties of slow but not fast twitch muscles. Elevated levels of miR-23a did not impact on whole body endurance capacity or exercise-induced muscle adaptations in the fast plantaris muscle.

Teaching a digital performing agent: artificial neural network and hidden Markov Model for recognising and performing dance movement

For a Digital Performing Agent to be able to perform live with a human dancer, it would be useful for the agent to be able to contextualize the movement the dancer is performing and to have a suitable movement vocabulary with which to contribute to the performance. In this paper we will discuss our research into the use of Artificial Neural Networks (ANN) as a means of allowing a software agent to learn a shared vocabulary of movement from a dancer. The agent is able to use the learnt movements to form an internal representation of what the dancer is performing, allowing it to follow the dancer, generate movement sequences based on the dancer's current movement and dance independently of the dancer using a shared movement vocabulary. By combining the ANN with a Hidden Markov Model (HMM) the agent is able to recognize short full body movement phrases and respond when the dancer performs these phrases. We consider the relationship between the dancer and agent as a means of supporting the agent's learning and performance, rather than developing the agent's capability in a self-contained fashion.

Development of a novel soft parallel robot equipped with polymeric artificial muscles

This paper presents the design, analysis and fabrication of a novel low-cost soft parallel robot for biomedical applications, including bio-micromanipulation devices. The robot consists of two active flexible polymer actuator-based links, which are connected to two rigid links by means of flexible joints. A mathematical model is established between the input voltage to the polymer actuators and the robot's end effector position. The robot has two degrees-of-freedom, making it suitable for handling planar micromanipulation tasks. Moreover, a number of robots can be configured to operate in a cooperative manner for increasing micromanipulation dexterity. Finally, the experimental results demonstrate two main motion modes of the robot.