Eigenvector alignment : assessing functional network changes in amnestic mild cognitive impairment and Alzheimer's disease

Eigenvector alignment, introduced herein to investigate human brain functional networks, is adapted from methods developed to detect influential nodes and communities in networked systems. It is used to identify differences in the brain networks of subjects with Alzheimer’s disease (AD), amnestic Mild Cognitive Impairment (aMCI) and healthy controls (HC). Well-established methods exist for analysing connectivity networks composed of brain regions, including the widespread use of centrality metrics such as eigenvector centrality. However, these metrics provide only limited information on the relationship between regions, with this understanding often sought by comparing the strength of pairwise functional connectivity. Our holistic approach, eigenvector alignment, considers the impact of all functional connectivity changes before assessing the strength of the functional relationship, i.e. alignment, between any two regions. This is achieved by comparing the placement of regions in a Euclidean space defined by the network's dominant eigenvectors. Eigenvector alignment recognises the strength of bilateral connectivity in cortical areas of healthy control subjects, but also reveals degradation of this commissural system in those with AD. Surprisingly little structural change is detected for key regions in the Default Mode Network, despite significant declines in the functional connectivity of these regions. In contrast, regions in the auditory cortex display significant alignment changes that begin in aMCI and are the most prominent structural changes for those with AD. Alignment differences between aMCI and AD subjects are detected, including notable changes to the hippocampal regions. These findings suggest eigenvector alignment can play a complementary role, alongside established network analytic approaches, to capture how the brain's functional networks develop and adapt when challenged by disease processes such as AD

Dynamical influence driven space system design

Complex networks are emerging in low-Earth-orbit, with many thousands of satellites set for launch over the coming decade. These data transfer networks vary based on spacecraft interactions with targets, ground stations, and other spacecraft. While constellations of a few, large, and precisely deployed satellites often produce simple, grid-like, networks. New small-satellite constellations are being deployed on an ad-hoc basis into various orbits, resulting in complex network topologies. By modelling these space systems as flow networks, the dominant eigenvectors of the adjacency matrix identify influential communities of ground stations. This approach provides space system designers with much needed insight into how differing station locations can better achieve alternative mission priorities and how inter-satellite links are set to impact upon constellation design. Maximum flow and consensus-based optimisation methods are used to define system architectures that support the findings of eigenvector-based community detection

Identifying effective sink node combinations in spacecraft data transfer networks

Complex networks are emerging in low-Earth-orbit as the communication architectures of inter-linked space systems. These data transfer networks vary based on spacecraft interaction with targets and ground stations, which respectively represent source and sink nodes for data flowing through the network. We demonstrate how networks can be used to identify effective sink node selections that, in combination, provide source coverage, high data throughput, and low latency connections for intermittently connected, store-and-forward space systems. The challenge in this work is to account for the changing data transfer network that varies significantly depending on the ground stations selected -- given a system where data is downlinked by spacecraft at the first opportunity. Therefore, passed-on networks are created to capture the redistribution of data following a sink node's removal from the system, a problem of relevance to traffic management in a variety of flow network applications. Modelling the system using consensus dynamics, enables sink node selections to be evaluated in terms of their source coverage and data throughput. While restrictions in the depth of propagation when defining passed-on networks, ensures the optimisation implicitly rewards lower latency connections. This is a beneficial by-product for both space system design and store-and-forward data networks in general. The passed-on networks also provide an insight into the relationship between sink nodes, with eigenvector embedding-based communities identifying sink node divisions that correspond with differences in source node coverage

Analysis of responsive satellite manoeuvres using graph theoretical techniques

Manoeuvrable, responsive satellite constellations that can respond to real-time events could provide critical data on-demand to support, for example, disaster monitoring and relief efforts. The authors demonstrate the feasibil-ity of such a system by expanding on a fully-analytical method for designing responsive spacecraft manoeuvres using low-thrust propulsion. This method enables responsive manoeuvre planning to provide coverage of targets on the Earth, with each manoeuvre option having a different target look angle, and requiring a different manoeuvre time and propellant cost. The trade-space for this analysis rapidly expands when considering multiple space-craft, targets and manoeuvres. To explore the trade-space efficiently, it is perceived as a graph in which connections are rapidly traversed to identify favourable routes to achieve the mission goals. The case study presented considers four satellites required to provide flyovers of two targets, with an associated graph of possible manoeuvres comprising 10726 nodes. The min-imum time solution is 2.59 days to complete both flyovers with 7.037 m/s change in velocity. Investigation of the graph highlights that selecting a good but not minimum time solution can allow the system to perform well but also have alternate options available to deal with possible errors in the manoeuvre execution, or changes in mission priorities. Restricting the prob-lem to consider only two satellites, with a smaller swath and less available propellant, reduces the graph to 510 nodes. In this case, the minimum time solution requires 9.04 m/s velocity change and takes approximately 2.59 days. The analysis also provides non-intuitive solutions, for example, that it is faster for one satellite to perform two targeting manoeuvres than for two satellites to manoeuvre simultaneously

Mapping of shifting tidal estuaries to support inshore rescue

Across the world, many coastal tidal regions are unsafe to navigate due to shifting mud and sand pushed by water currents. Ability to regularly map the current location of a channel will aid safe passage for commercial, leisure and rescue craft. This work investigates the use of synthetic aperture radar data derived from satellites to provide accurate mapping of moving channels in coastal regions. As images must be collected at low tide, data availability is assessed considering the relationship between the orbital motion of the satellites and the tides. Change detection methods are applied to suitable images to map changes in the location of navigable channels. Pixels that undergo similar changes over time (e.g. from water covered to exposed sand) are grouped together by examining the principal component of the covariance matrix, for a vector composed of pixel values from the same location at different times. The Solway Firth in Great Britain is selected as a trial site as it is exposed to some of Europe's fastest tidal movements and ranges, and hence is one of Great Britain's most treacherous stretches of coastline

Small satellite operations planning for agile disaster response using graph theoretical techniques

Agile, manoeuvrable, satellite constellations have the potential to fundamentally change space mission design by moving away from traditional missions, designed to address predicted demand, and instead providing responsive systems that can react to real-time events, such as natural disasters. The unique advantages of responsive constellations are enhanced by the use of small satellites, whose short development times and low cost can offset the increased risk and shorter mission life inherent in the use of manoeuvrable spacecraft. In addition, newly developed, highly efficient propulsion systems can provide small satellites with agile manoeuvrability. This could enable agile satellite systems where efficient, low-thrust, responsive manoeuvres can be used to ensure rapid flyover of targets on Earth. The authors have previously developed a fully analytical method of designing such manoeuvres, which allows consideration of multiple targeting options, each with different flyover times, view angles, and propellant requirements. However, a long-term, holistic understanding of the concept of operations is required to effectively implement an agile satellite system. To facilitate this, the existing analytical methodology has been combined with graph theoretical techniques to allow the complex trade-space to be perceived as a graph. The connections in the graph represent possible manoeuvres and are rapidly traversed to identify favourable routes to achieve the desired goal. The effect of changes in mission priorities can be assessed by reweighting the graph, avoiding the need to recalculate the manoeuvre options. This work demonstrates that the proposed method can be successfully used to plan sequential flyovers of a moving target; in this case, a tropical storm. For the small spacecraft and low-thrust propulsion system considered, the possible changes in flyover time for each target are small, however, these small adjustments can be used to significantly improve the quality of the obtained data compared to a non-manoeuvring spacecraft

Finding navigable paths through tidal flats with Synthetic Aperture Radar

Tidal flats are some of the most dynamic coastal environments in the world, where traditional coastal mapping and monitoring provide insufficient temporal resolution to reliably map channels and sand flats. Satellite-based Synthetic Aperture Radar (SAR) enables regular cloud-penetrating detection of water flowing through channels within the tidal flats, referred to as tidal channels. This paper presents a method for detecting a path through tidal channels, using satellite imagery, that supports our understanding and safe exploitation of this valuable coastal environment. This approach is the first proposed to identify navigable paths in all conditions, with SAR images susceptible to variation due to weather and tidal conditions. Tidal channels are known to vary in SAR presentation, and we find that tidal flat presentation is also influenced by conditions. The most influential factor is the wind, with high winds causing an inversion in how both tidal flats and tidal channels present in SAR images. The presented method for the automatic detection of tidal channels accounts for this variability by using previous channel paths as a reference to reliably correct imagery and detect the latest path. The final algorithm produces paths with minor errors in 17.6% of images; the error rate increases to 71.7%, with an almost tenfold increase in errors, when the SAR image and paths are not adjusted to account for conditions. This capability has been used to support the Nith Inshore Rescue in attending call-outs from their base in Glencaple, UK, while the insights from monitoring tidal channels for a year demonstrate how periods of high river flow preceded major changes in the channel path

Functional alignments in brain connectivity networks

Alzheimer’s disease (AD) is a brain disconnection syndrome, where functional connectivity analysis can detect changes in neural activity in pre-dementia stages [8]. Functional connectivity networks from functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) are susceptible to signal noise from biologic artefacts (e.g. cardiac artefacts) and environmental sources (e.g. electrical interference). A particular challenge for EEG is volume conduction, whereby a signal from a single source propagates through biological tissue to be detected simultaneously by multiple sensors (channels). The imaginary part of coherency (iCOH) provides a measure for connectivity that avoids this signal contamination, by ignoring correlation between signals with zero or π-phase lag. This removes false instantaneous activity with connectivity denoting synchronised signals at a given time lag, but it does come at the cost of erasing true instantaneous activity. We propose eigenvector alignment (EA) as a method for evaluating pairwise relationships from network eigenvectors; revealing noise robust, structural, insights from functional connectivity networks

Autonomous and scalable control for remote inspection with multiple aerial vehicles

© 2016 Elsevier B.V.A novel approach to the autonomous generation of trajectories for multiple aerial vehicles is presented, whereby an artificial kinematic field provides autonomous control in a distributed and highly scalable manner. The kinematic field is generated relative to a central target and is modified when a vehicle is in close proximity of another to avoid collisions. This control scheme is then applied to the mock visual inspection of a nuclear intermediate level waste storage drum. The inspection is completed using two commercially available quadcopters, in a laboratory environment, with the acquired visual inspection data processed and photogrammetrically meshed to generate a three-dimensional surface-meshed model of the drum. This paper contributes to the field of multi-agent coverage path planning for structural inspection and provides experimental validation of the control and inspection results

Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK.

BACKGROUND: A safe and efficacious vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), if deployed with high coverage, could contribute to the control of the COVID-19 pandemic. We evaluated the safety and efficacy of the ChAdOx1 nCoV-19 vaccine in a pooled interim analysis of four trials. METHODS: This analysis includes data from four ongoing blinded, randomised, controlled trials done across the UK, Brazil, and South Africa. Participants aged 18 years and older were randomly assigned (1:1) to ChAdOx1 nCoV-19 vaccine or control (meningococcal group A, C, W, and Y conjugate vaccine or saline). Participants in the ChAdOx1 nCoV-19 group received two doses containing 5 × 1010 viral particles (standard dose; SD/SD cohort); a subset in the UK trial received a half dose as their first dose (low dose) and a standard dose as their second dose (LD/SD cohort). The primary efficacy analysis included symptomatic COVID-19 in seronegative participants with a nucleic acid amplification test-positive swab more than 14 days after a second dose of vaccine. Participants were analysed according to treatment received, with data cutoff on Nov 4, 2020. Vaccine efficacy was calculated as 1 - relative risk derived from a robust Poisson regression model adjusted for age. Studies are registered at ISRCTN89951424 and ClinicalTrials.gov, NCT04324606, NCT04400838, and NCT04444674. FINDINGS: Between April 23 and Nov 4, 2020, 23 848 participants were enrolled and 11 636 participants (7548 in the UK, 4088 in Brazil) were included in the interim primary efficacy analysis. In participants who received two standard doses, vaccine efficacy was 62·1% (95% CI 41·0-75·7; 27 [0·6%] of 4440 in the ChAdOx1 nCoV-19 group vs71 [1·6%] of 4455 in the control group) and in participants who received a low dose followed by a standard dose, efficacy was 90·0% (67·4-97·0; three [0·2%] of 1367 vs 30 [2·2%] of 1374; pinteraction=0·010). Overall vaccine efficacy across both groups was 70·4% (95·8% CI 54·8-80·6; 30 [0·5%] of 5807 vs 101 [1·7%] of 5829). From 21 days after the first dose, there were ten cases hospitalised for COVID-19, all in the control arm; two were classified as severe COVID-19, including one death. There were 74 341 person-months of safety follow-up (median 3·4 months, IQR 1·3-4·8): 175 severe adverse events occurred in 168 participants, 84 events in the ChAdOx1 nCoV-19 group and 91 in the control group. Three events were classified as possibly related to a vaccine: one in the ChAdOx1 nCoV-19 group, one in the control group, and one in a participant who remains masked to group allocation. INTERPRETATION: ChAdOx1 nCoV-19 has an acceptable safety profile and has been found to be efficacious against symptomatic COVID-19 in this interim analysis of ongoing clinical trials. FUNDING: UK Research and Innovation, National Institutes for Health Research (NIHR), Coalition for Epidemic Preparedness Innovations, Bill & Melinda Gates Foundation, Lemann Foundation, Rede D'Or, Brava and Telles Foundation, NIHR Oxford Biomedical Research Centre, Thames Valley and South Midland's NIHR Clinical Research Network, and AstraZeneca