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Abstract ADP-ribosylation factor 1 (Arf1) interacts with multiple cellular partners and membranes to regulate intracellular traffic, organelle structure and actin dynamics. Defining the dynamic conformational landscape of Arf1 in its active form, when bound to the membrane, is of high functional relevance and key to understanding how Arf1 can alter diverse cellular processes. Through concerted application of nuclear magnetic resonance (NMR), neutron reflectometry (NR) and molecular dynamics (MD) simulations, we show that, while Arf1 is anchored to the membrane through its N-terminal myristoylated amphipathic helix, the G domain explores a large conformational space, existing in a dynamic equilibrium between membrane-associated and membrane-distal conformations. These configurational dynamics expose different interfaces for interaction with effectors. Interaction with the Pleckstrin homology domain of ASAP1, an Arf-GTPase activating protein (ArfGAP), restricts motions of the G domain to lock it in what seems to be a conformation exposing functionally relevant regions.

Pant et al. describe the mechanism by which PIP2 might regulate homomeric Kv7.2 channels. They identify sites important in the binding of the PIP2 lipid to Kv7.2 channels and propose that the PIP2 binding to a specific site results in the coupling between the voltage sensor domain (VSD) and pore domain (PD), which stabilizes the open state of the channel.

Glutamate is the predominant excitatory neurotransmitter in the mammalian central nervous system (CNS). Excitatory amino acid transporters (EAATs) regulate extracellular glutamate by transporting it into cells, mostly glia, to terminate neurotransmission and to avoid neurotoxicity. EAATs are also chloride (Cl–) channels, but the physiological role of Cl– conductance through EAATs is poorly understood. Mutations of human EAAT1 (hEAAT1) have been identified in patients with episodic ataxia type 6 (EA6). One mutation showed increased Cl– channel activity and decreased glutamate transport, but the relative contributions of each function of hEAAT1 to mechanisms underlying the pathology of EA6 remain unclear. Here we investigated the effects of 5 additional EA6-related mutations on hEAAT1 function in Xenopus laevis oocytes, and on CNS function in a Drosophila melanogaster model of locomotor behavior. Our results indicate that mutations resulting in decreased hEAAT1 Cl– channel activity but with functional glutamate transport can also contribute to the pathology of EA6, highlighting the importance of Cl– homeostasis in glial cells for proper CNS function. We also identified what we believe is a novel mechanism involving an ectopic sodium (Na+) leak conductance in glial cells. Together, these results strongly support the idea that EA6 is primarily an ion channelopathy of CNS glia.

XylE is a bacterial xylose transporter and homologue of human glucose transporters GLUTs 1-4. HDX-MS, mutagenesis and MD simulations suggest that protonation of a conserved aspartate triggers conformational transition from outward- to inward facing state only in the presence of substrate xylose. In contrast, inhibitor glucose locks the transporter in the outward facing state.

Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented.

Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.

Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem of detecting rotary drones and distinguishing between single-propeller and multi-propeller drones using a micro-Doppler analysis. Two different radars were used, an ultra wideband (UWB) continuous wave (CW) C-band radar and an automotive frequency modulated continuous wave (FMCW) W-band radar, to collect micro-Doppler signatures of the drones. By taking a closer look at HElicopter Rotor Modulation (HERM) lines, the spool and chopping lines are identified for the first time in the context of drones to determine the number of propeller blades. Furthermore, a new multi-frequency analysis method using HERM lines is developed, which allows the detection of propeller rotation rates (spool and chopping frequencies) of single and multi-propeller drones. Therefore, the presented method is a promising technique to aid in the classification of drones.