Genomic organisation analysis of novel immunoglobulin-like transcripts in Atlantic salmon (Salmo salar) reveals a tightly clustered and multigene family

Copyright 2011 Elsevier B.V., All rights reserved.Peer reviewedPublisher PD

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

Predictive power control and multiple-description coding for wireless sensor networks

We study state estimation via wireless sensor networks over fading channels affected by random packet loss. In the configuration examined, the sensors send their measurements to a single gateway, which decides upon the source coding scheme and the sensor transmitter power levels. The decision process is carried out on-line and adapts to changing channel conditions to achieve an optimal trade-off between estimation quality and sensor energy expenditure. In particular, if some channel conditions are poor, then the gateway commands the corresponding sensors to increase power levels and use multiple-description coding. Simulations based on measured channel data illustrate that the proposed scheme gives excellent results

Predictive power control for dynamic state estimation over wireless sensor networks with relays

We present a predictive power controller for state estimation of a stationary ARMA process over a wireless sensor network (WSN), consisting of sensor nodes, relays, and a single gateway (GW). The state estimate is formed centrally at the GW by using packets received from sensors and relays. The latter perform network coding of sensor measurements. Communication from sensors and relays to the GW is over a fading channel. Packet loss probabilities depend upon the timevarying channel gains and the transmission powers used. To achieve an optimal trade-off between state estimation quality and energy expenditure, in our approach the GW decides upon the in general time-varying transmission powers of sensors and relays. This decision process is carried out on-line and adapts to changing channel conditions by using elements of stochastic model predictive control. Simulations on measured channel data illustrate the performance achieved by the proposed controller

Energy efficient state estimation with wireless sensors through the use of predictive power control and coding

We study state estimation via wireless sensors over fading channels. Packet loss probabilities depend upon time-varying channel gains, packet lengths and transmission power levels of the sensors. Measurements are coded into packets by using either independent coding or distributed zero-error coding. At the gateway, a time-varying Kalman filter uses the received packets to provide the state estimates. To trade sensor energy expenditure for state estimation accuracy, we develop a predictive control algorithm which, in an online fashion, determines the transmission power levels and codebooks to be used by the sensors. To further conserve sensor energy, the controller is located at the gateway and sends coarsely quantized power increment commands, only whenever deemed necessary. Simulations based on real channel measurements illustrate that the proposed method gives excellent results

Innovations-based state estimation with wireless sensor networks

We study a state estimation architecture for sensor networks, where several sensors transmit quantized innovations to a central estimator. Transmission is via a wireless channel, which is prone to fading leading to random packet loss. State estimation is carried out at the gateway via a time-varying Kalman filter which accounts for packet loss and quantization effects. To form the innovations at the sensors, the estimator transmits information regarding its current state estimate to the sensors. This information could be dedicated to each sensor or broadcast to all sensors. In addition, the gateway also decides upon power levels and quantization step-sizes to be used by each sensor node. Here, we adopt elements of predictive control to trade off estimation performance versus energy use