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Altered eIF4A1 activity promotes translation of highly structured, eIF4A1-dependent oncogene mRNAs at root of oncogenic translational programmes. It remains unclear how these mRNAs recruit and activate eIF4A1 unwinding specifically to facilitate their preferential translation. Here, we show that single-stranded RNA sequence motifs specifically activate eIF4A1 unwinding allowing local RNA structural rearrangement and translation of eIF4A1-dependent mRNAs in cells. Our data demonstrate that eIF4A1-dependent mRNAs contain AG-rich motifs within their 5'UTR which specifically activate eIF4A1 unwinding of local RNA structure to facilitate translation. This mode of eIF4A1 regulation is used by mRNAs encoding components of mTORC-signalling and cell cycle progression, and renders these mRNAs particularly sensitive to eIF4A1-inhibition. Mechanistically, we show that binding of eIF4A1 to AG-rich sequences leads to multimerization of eIF4A1 with eIF4A1 subunits performing distinct enzymatic activities. Our structural data suggest that RNA-binding of multimeric eIF4A1 induces conformational changes in the RNA resulting in an optimal positioning of eIF4A1 proximal to the RNA duplex enabling efficient unwinding. Our data proposes a model in which AG-motifs in the 5'UTR of eIF4A1-dependent mRNAs specifically activate eIF4A1, enabling assembly of the helicase-competent multimeric eIF4A1 complex, and positioning these complexes proximal to stable localised RNA structure allowing ribosomal subunit scanning.

This paper addressed a fractal evolution of 11 one-month lasting MHz electromagnetic disturbances, recorded in Greece prior to nine significant earthquakes of 2009. Time-space wavelet-based power spectral techniques were employed in the analysis. All investigated signals evolved naturally to epochs of fractal organization in space and time. Continuous organization was detected in seven signals. Significant number of successive () power-law-values were observed lying between 1.5 and 3.0 or above. The majority of fractal segments exhibited anti-persistent () or persistent () behaviour. Switching between persistency and anti-persistency was also found. Locality and sensitivity were traced. Findings were considered indicative of self-organized critical states of the last stages of preparation of the investigated earthquakes. Results implied fractional Brownian modelling. Explanations were proposed in view of the asperity model. Persistent–anti-persistent MHz anomalies were due to self-organized micro-cracking of the heterogeneous medium of the earth's crust which may have led the system's evolution towards global failure. The precursory value of the signals was discussed. [ABSTRACT FROM PUBLISHER]

The aim of the present work is to confirm the effective time of use of the brace, for an accurate assessment of treatment from the therapist. Experiments were performed on a Dynamic Derotation Brace (DDB). Two devices have been designed, a data holder and a reader which give to the therapist the opportunity to certify the achievement of the brace's effective time. The accuracy of the recorded time is immunized by a system of switches, which are placed at appropriate points without giving direct access to the patient. The DDB together with the embedded data holder applied in 50 patients. After six months from the first application of the DDB, the therapist using the data reader is able to read and verify the exact time of use of the brace. Forty four patients had fully followed the treatment for the time period set by the therapist (23 hours every day), three followed the treatment to a satisfactory degree (18-20 hours every day) and the rest applied the treatment for a shorter period of time than prescribed by the therapist (less than 18 hours every day). Both devices offer great potential in the way of making more effective the treatment of scoliosis and may be to achieve control of pressure in the desired points of the body. [ABSTRACT FROM AUTHOR]

In cloud storage systems, users must be able to shut down the application when not in use and restart it from the last consistent state when required. BlobSeer is a data storage application, specially designed for distributed systems, that was built as an alternative solution for the existing popular open-source storage system-Hadoop Distributed File System (HDFS). In a cloud model, all the components need to stop and restart from a consistent state when the user requires it. One of the limitations of BlobSeer DFS is the possibility of data loss when the system restarts. As such, it is important to provide a consistent start and stop state to BlobSeer components when used in a Cloud environment to prevent any data loss. In this paper, we investigate the possibility of BlobSeer providing a consistent state distributed data storage system with the integration of checkpointing restart functionality. To demonstrate the availability of a consistent state, we set up a cluster with multiple machines and deploy BlobSeer entities with checkpointing functionality on various machines. We consider uncoordinated checkpoint algorithms for their associated benefits over other alternatives while integrating the functionality to various BlobSeer components such as the Version Manager (VM) and the Data Provider. The experimental results show that with the integration of the checkpointing functionality, a consistent state can be ensured for a distributed storage system even when the system restarts, preventing any possible data loss after the system has encountered various system errors and failures. [ABSTRACT FROM AUTHOR]

Modern achievements accomplished in both cognitive neuroscience and human–machine interaction technologies have enhanced the ability to control devices with the human brain by using Brain–Computer Interface systems. Particularly, the development of brain-controlled mobile robots is very important because systems of this kind can assist people, suffering from devastating neuromuscular disorders, move and thus improve their quality of life. The research work presented in this paper, concerns the development of a system which performs motion control in a mobile robot in accordance to the eyes' blinking of a human operator via a synchronous and endogenous Electroencephalography-based Brain–Computer Interface, which uses alpha brain waveforms. The received signals are filtered in order to extract suitable features. These features are fed as inputs to a neural network, which is properly trained in order to properly guide the robotic vehicle. Experimental tests executed on 12 healthy subjects of various gender and age, proved that the system developed is able to perform movements of the robotic vehicle, under control, in forward, left, backward, and right direction according to the alpha brainwaves of its operator, with an overall accuracy equal to 92.1%. [ABSTRACT FROM AUTHOR]

This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness.