Friends in need: how chaperonins recognize and remodel proteins that require folding assistance

Chaperonins are biological nanomachines that help newly translated proteins to fold by rescuing them from kinetically trapped misfolded states. Protein folding assistance by the chaperonin machinery is obligatory in vivo for a subset of proteins in the bacterial proteome. Chaperonins are large oligomeric complexes, with unusual seven fold symmetry (group I) or eight/nine fold symmetry (group II), that form double-ring constructs, enclosing a central folding chamber. Dramatic large-scale conformational changes, that take place during ATP-driven cycles, allow chaperonins to bind misfolded proteins, encapsulate them into the expanded cavity and release them back into the cellular environment, regardless of whether they are folded or not. The theory associated with the iterative annealing mechanism, which incorporated the conformational free energy landscape description of protein folding, \textit{quantitatively} explains most, if not all, the available data. Misfolded conformations are associated with low energy minima in a rugged energy landscape. Random disruptions of these low energy conformations result in higher free energy, less folded, conformations that can stochastically partition into the native state. Group I chaperonins (GroEL homologues in eubacteria and endosymbiotic organelles), recognize a large number of misfolded proteins non-specifically and operate through highly coordinated cooperative motions. By contrast, the less well understood group II chaperonins (CCT in Eukarya and thermosome/TF55 in Archaea), assist a selected set of substrate proteins. Chaperonins are implicated in bacterial infection, autoimmune disease, as well as protein aggregation and degradation diseases. Understanding the chaperonin mechanism and their substrates is important not only for the fundamental aspect of cellular protein folding, but also for effective therapeutic strategies.Comment: 26 pages, 4 figures, to be published in Frontiers in Molecular Bioscience

Fog computing for sustainable smart cities: a survey

The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities

Novel bio-inspired 3D porous scaffold intended for bone-tissue engineering: Design and in silico characterisation of histomorphometric, mechanical and mass-transport properties

The design of novel biomimetic bone tissue scaffolds (BTS) using computer aided design (CAD) technology is challenging additive manufacturing technologies. At the microstructure level, BTS should mimic bone histomorphometry and assure optimum mass transport and mechanical properties. In this study, a novel BTS has been designed, by using a parametric and variational CAD method, to model a bio-inspired and interconnected porous structure. The mechanical (elastic modulus) and the fluid mass transport (permeability) properties have been computed and compared to other implicit surfaces modelling scaffolds. The results showed that the new BTS could be tuned during the design stage to match the microstructure and the histomorphometry properties of trabecular bone. Those with porosities between 0.7 and 0.9 and highly smooth curvatures were the most appropriates. The new BTS, once appropriately designed, could be made/manufactured by 3D printing technology with an internal microstructure mimicking the local bone properties of the selected bone volumes of interest, for example, those coming from computed tomography medical images.Peer ReviewedPostprint (published version

Congestion and medium access control in 6LoWPAN WSN

In computer networks, congestion is a condition in which one or more egressinterfaces are offered more packets than are forwarded at any given instant [1]. In wireless sensor networks, congestion can cause a number of problems including packet loss, lower throughput and poor energy efficiency. These problems can potentially result in a reduced deployment lifetime and underperforming applications. Moreover, idle radio listening is a major source of energy consumption therefore low-power wireless devices must keep their radio transceivers off to maximise their battery lifetime. In order to minimise energy consumption and thus maximise the lifetime of wireless sensor networks, the research community has made significant efforts towards power saving medium access control protocols with Radio Duty Cycling. However, careful study of previous work reveals that radio duty cycle schemes are often neglected during the design and evaluation of congestion control algorithms. This thesis argues that the presence (or lack) of radio duty cycle can drastically influence the performance of congestion control mechanisms. To investigate if previous findings regarding congestion control are still applicable in IPv6 over low power wireless personal area and duty cycling networks; some of the most commonly used congestion detection algorithms are evaluated through simulations. The research aims to develop duty cycle aware congestion control schemes for IPv6 over low power wireless personal area networks. The proposed schemes must be able to maximise the networks goodput, while minimising packet loss, energy consumption and packet delay. Two congestion control schemes, namely DCCC6 (Duty Cycle-Aware Congestion Control for 6LoWPAN Networks) and CADC (Congestion Aware Duty Cycle MAC) are proposed to realise this claim. DCCC6 performs congestion detection based on a dynamic buffer. When congestion occurs, parent nodes will inform the nodes contributing to congestion and rates will be readjusted based on a new rate adaptation scheme aiming for local fairness. The child notification procedure is decided by DCCC6 and will be different when the network is duty cycling. When the network is duty cycling the child notification will be made through unicast frames. On the contrary broadcast frames will be used for congestion notification when the network is not duty cycling. Simulation and test-bed experiments have shown that DCCC6 achieved higher goodput and lower packet loss than previous works. Moreover, simulations show that DCCC6 maintained low energy consumption, with average delay times while it achieved a high degree of fairness. CADC, uses a new mechanism for duty cycle adaptation that reacts quickly to changing traffic loads and patterns. CADC is the first dynamic duty cycle pro- tocol implemented in Contiki Operating system (OS) as well as one of the first schemes designed based on the arbitrary traffic characteristics of IPv6 wireless sensor networks. Furthermore, CADC is designed as a stand alone medium access control scheme and thus it can easily be transfered to any wireless sensor network architecture. Additionally, CADC does not require any time synchronisation algorithms to operate at the nodes and does not use any additional packets for the exchange of information between the nodes (For example no overhead). In this research, 10000 simulation experiments and 700 test-bed experiments have been conducted for the evaluation of CADC. These experiments demonstrate that CADC can successfully adapt its cycle based on traffic patterns in every traffic scenario. Moreover, CADC consistently achieved the lowest energy consumption, very low packet delay times and packet loss, while its goodput performance was better than other dynamic duty cycle protocols and similar to the highest goodput observed among static duty cycle configurations

New Perspectives on Modelling and Control for Next Generation Intelligent Transport Systems

This PhD thesis contains 3 major application areas all within an Intelligent Transportation System context. The first problem we discuss considers models that make beneficial use of the large amounts of data generated in the context of traffic systems. We use a Markov chain model to do this, where important data can be taken into account in an aggregate form. The Markovian model is simple and allows for fast computation, even on low end computers, while at the same time allowing meaningful insight into a variety of traffic system related issues. This allows us to both model and enable the control of aggregate, macroscopic features of traffic networks. We then discuss three application areas for this model: the modelling of congestion, emissions, and the dissipation of energy in electric vehicles. The second problem we discuss is the control of pollution emissions in eets of hybrid vehicles. We consider parallel hybrids that have two power units, an internal combustion engine and an electric motor. We propose a scheme in which we can in uence the mix of the two engines in each car based on simple broadcast signals from a central infrastructure. The infrastructure monitors pollution levels and can thus make the vehicles react to its changes. This leads to a context aware system that can be used to avoid pollution peaks, yet does not restrict drivers unnecessarily. In this context we also discuss technical constraints that have to be taken into account in the design of traffic control algorithms that are of a microscopic nature, i.e. they affect the operation of individual vehicles. We also investigate ideas on decentralised trading of emissions. The goal here is to allocate the rights to pollute fairly among the eet's vehicles. Lastly we discuss the usage of decentralised stochastic assignment strategies in traffic applications. Systems are considered in which reservation schemes can not reliably be provided or enforced and there is a signifficant delay between decisions and their effect. In particular, our approach facilitates taking into account the feedback induced into traffic systems by providing forecasts to large groups of users. This feedback can invalidate the predictions if not modelled carefully. At the same time our proposed strategies are simple rules that are easy to follow, easy to accept, and significantly improve the performance of the systems under study. We apply this approach to three application areas, the assignment of electric vehicles to charging stations, the assignment of vehicles to parking facilities, and the assignment of customers to bike sharing stations. All discussed approaches are analysed using mathematical tools and validated through extensive simulations

Implementation of second-life batteries as energy storage systems enhancing the interoperability and flexibility of the energy infrastructure in tertiary buildings

The main focus of this project is to evaluate the implementation of second-life batteries for a building stock enabling the energy flexibility schemes like Demand Response (DR). This project will focus particularly on how the building stock and its energy infrastructure (energy storage systems, legacy-assets, communication devices and grid architecture, among others) can participate as innovative energy solutions of the next generation of smart-grids, acting as virtual power plants (VPP) in order to deploy the distributed generation (DG) concept in the actual energy field and paving the way to unlock the demand response (DR) market in the distribution energy network. In addition, the implementation of these technologies will led to plan different business models and the scalability of them in the tertiary building sector. Battery energy storage systems (BESSs) are already being deployed for several stationary applications in a techno-economical feasible way. This project focuses in the study to obtain potential revenues from BESSs built from EVs lithium-ion batteries with varying states of health (SoH). For this analysis, a stationary BESS sizing model is done, using the parameters of a 14 kWh new battery, but also doing a comparison with parameters if the same battery would be 11.2 kWh second-life battery. The comprehensive sizing model consists of several detailed sub-models, considering battery specifications, aging and an operational strategy plan, which allow a technical assessment through a determined time frame. Therefore, battery depreciation and energy losses are considered in this techno-economic analysis. Potential economical feasible applications of new and second-life batteries, such the integration of a Building Integrated Photovoltaics (BIPV), self-consumption schemes, feed-in-tariff schemes and frequency regulation as well as their combined operation are compared. The research includes different electricity price scenarios mostly from the current Spanish energy market. The operation and integration of ICT-IoT technology upgrades is found to have the highest economic viability for this specific case study. A detailed study for this project will enhance the relevant importance of these topics in the energy field and how it will be a disruptive solution for the initial problem statement. A general context is given in order to introduce the main and specific objectives thus to trace an adequate way to follow and achieve them. The development of this master thesis will be coupled with the Demand Response Integration technologies (DRIvE) [10] H2020 EU funded project, currently on-going, considering some of the energy consumption data and initial parameters from the selected case study at COMSA Corporación office building in Barcelona, Spain

Architectures for the Future Networks and the Next Generation Internet: A Survey

Networking research funding agencies in the USA, Europe, Japan, and other countries are encouraging research on revolutionary networking architectures that may or may not be bound by the restrictions of the current TCP/IP based Internet. We present a comprehensive survey of such research projects and activities. The topics covered include various testbeds for experimentations for new architectures, new security mechanisms, content delivery mechanisms, management and control frameworks, service architectures, and routing mechanisms. Delay/Disruption tolerant networks, which allow communications even when complete end-to-end path is not available, are also discussed

A Privacy-Preserving Method with Flexible Charging Schedules for Electric Vehicles in the Smart Grid

The Smart Grid (SG) is an emerging modernized electrical power system with advanced monitoring and control mechanism, and improved faulttolerance. The SG converges traditional power grid with a bidirectional communication and information system into the same infrastructure. Electric Vehicles (EVs), with their energy storage capacity and bidirectional communication capability, are envisioned to be an essential component of the SG. EVs can play the role of distributed energy resources by storing energy in off-peak hours and providing energy to the grid during peak hours or system contingencies. The energy stored by an EV is equivalent to the average energy drawn by multiple residential houses. As a result, simultaneous charging by a large number of EVs can create sudden energy imbalance in the grid. The mismatch between the energy generation and demand can create cascading faults resulting in load shedding. To prevent such situation, EVs are required to pre-schedule charging events at a Charging Station (CS). To efficiently manage a scheduled event, an EV is required to transmit information such as a valid ID, state-of-charge, distance from a CS, location, speed, etc. However, the data transmitted by an EV can be used to reveal information such as the movement of the vehicle, visits to a hospital, time to arrive at office, etc. The transmitted information can be used to create profiles of the owners of the EVs, breaching their location privacy. In the existing literature, it is recommended to use pseudonyms for different transactions by an EV to achieve location privacy. The majority of the works in the literature are based on anonymous authentication mechanism, where missing a charging event by an EV is considered as malicious and the corresponding EV is penalized (e.g., blacklisted). However, missing a charging event may happen due to many valid reasons and flexibility of scheduling can encourage consumer participation. On the other hand, missing charging events results in monetary loss to the CSs. In this thesis, an authentication method is developed to provide anonymity to EVs. The proposed method also addresses the cost-effectiveness of flexibility in charging events for the EVs and the CSs. A network setup that sub-divides a regional area into smaller zones to achieve better privacy, is proposed. A MATLAB simulation is designed to demonstrate the Degree of Anonymity (DoA) achieved in different stages of the proposed method and the optimal number of missed charging events. Additionally, a method to determine sub-division of zones from the simulation results, is studied

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs