Trafficking activity of myosin XXI is required in assembly of Leishmania flagellum

Actin-based myosin motors have a pivotal role in intracellular trafficking in eukaryotic cells. The parasitic protozoan organism Leishmania expresses a novel class of myosin, myosin XXI (Myo21), which is preferentially localized at the proximal region of the flagellum. However, its function in this organism remains largely unknown. Here, we show that Myo21 interacts with actin, and its expression is dependent of the growth stage. We further reveal that depletion of Myo21 levels results in impairment of the flagellar assembly and intracellular trafficking. These defects are, however, reversed by episomal complementation. Additionally, it is shown that deletion of the Myo21 gene leads to generation of ploidy, suggesting an essential role of Myo21 in survival of Leishmania cells. Together, these results indicate that actin-dependent trafficking activity of Myo21 is essentially required during assembly of the Leishmania flagellum

Removal of the endocrine disrupter butyl benzyl phthalate from the environment

Butyl benzyl phthalate (BBP), an aryl alkyl ester of 1,2-benzene dicarboxylic acid, is extensively used in vinyl tiles and as a plasticizer in PVC in many commonly used products. BBP, which readily leaches from these products, is one of the most important environmental contaminants, and the increased awareness of its adverse effects on human health has led to a dramatic increase in research aimed at removing BBP from the environment via bioremediation. This review highlights recent progress in the degradation of BBP by pure and mixed bacterial cultures, fungi, and in sludge, sediment, and wastewater. Sonochemical degradation, a unique abiotic remediation technique, and photocatalytic degradation are also discussed. The degradation pathways for BBP are described, and future research directions are considered

One axis artificial feel system (pilot proprioceptive cue forces on aircraft joy stick)

Joy stick feel forces, called proprioceptive cues, provide an important sensory feedback to the pilot. Ideally the stick would possess feel forces governed by maneuvers, aerodynamic and structural forces and manual control requirements. Such ideal systems are not normally incorporated in aircraft, since these forces vary continuously and are difficult to produce artificially. This paper discusses a mechanization technique by which such a goal can be achieved. The technique consists of combining an electro-hydraulic position and load closed loop system which can accept any load-position-time command from an electronic function generator. The closed loop dynamic analysis is made and the system is synthesized. Such a feel system is being used in a ground-based motion simulator

A two channel data processing system for the fixed base research simulator

The memo gives a detailed text of a two channel data processing scheme of the fixed base research simulator for manual control studies. The data processing system for various quantities round the manual control loop consists of a two channel on-line recorder and on off-line digitisation set up. The modifications of the available instruments and the performance tests conducted to enhance the confidence in the data processing system are also detailed

Memory neuron networks for identification and control of dynamical systems

Abstract- This paper discusses Memory Neuron Networks as models for identification and adaptive control of nonlinear dy-namical systems. These are a class of recurrent networks obtained by adding trainable temporal elements to feed-forward networks that makes the output history-sensitive. By virtue of this capa-bility, these networks can identify dynamical systems without having to be explicitly fed with past inputs and outputs. Thus, they can identify systems whose order is unknown or systems with unknown delay. It is argued that for satisfactory modeling of dynamical systems, neural networks should be endowed with such internal memory. The paper presents a preliminary analysis of the learning algorithm, providing theoretical justification for the identification method. Methods for adaptive control of nonlinear systems using these networks are presented. Through extensive simulations, these models are shown to be effective both for identification and model reference adaptive control of nonlinear systems. I