Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer’s Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer’s disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine

Towards an optimal design for ecosystem-level ocean observatories

27 pagesFour operational factors, together with high development cost, currently limit the use of ocean observatories in ecological and fisheries applications: 1) limited spatial coverage, 2) limited integration of multiple types of technologies, 3) limitations in the experimental design for in situ studies, and 4) potential unpredicted bias in monitoring outcomes due to the infrastructure's presence and functioning footprint. To address these limitations, we propose a novel concept of a standardised ‘ecosystem observatory module’ structure composed of a central node and three tethered satellite pods together with permanent mobile platforms. The module would be designed with a rigid spatial configuration to optimise overlap among multiple observation technologies, each providing 360° coverage of a cylindrical or hemi-spherical volume around the module, including permanent stereo video cameras, acoustic imaging sonar cameras, horizontal multibeam echosounders, and a passive acoustic array. The incorporation of multiple integrated observation technologies would enable unprecedented quantification of macrofaunal composition, abundance, and density surrounding the module, as well as the ability to track the movements of individual fishes and macroinvertebrates. Such a standardised modular design would allow for the hierarchical spatial connection of observatory modules into local module clusters and larger geographic module networks providing synoptic data within and across linked ecosystems suitable for fisheries and ecosystem-level monitoring on multiple scalesPeer reviewe

High-tech networks of robotic platforms for the monitoring of deep-sea crustacean populations

The Crustacean Society Mid-Year Meeting (TSC 2019), 26-30 May 2019, Hong Kong.-- 1 pageOur perception of deep-sea megafaunal benthic and pelagic biodiversity depends upon the development of remote and high-frequency monitoring technologies, capable of autonomous operation and during long-term deployments. In fact, deep-sea megafauna displays large inertial, internal-tidal and even diel synchronized spatial displacements, which affect biodiversity estimations usually based on temporally scattered sampling methodologies (e.g., ship-time dependent). To address this problem, underwater observatories (cabled, or in delay-mode), autonomous underwater vehicles (AUVs), which include fast improving seafloor robots (crawlers), are being assembled into highly interconnected monitoring networks that can be used for data collection via high-bandwidth telecommunications. A time-coordinated optoacoustic (i.e., HD, laser scattering and multi-beam) imaging and passive acoustic characterization (i.e., for animal soundscapes) is being implemented to deliver time-series of counted individuals (as proxy of populations¿ rhythmic benthopelagic, nektobenthic and endobenthic movements), to be linked with the surrounding habitat forcing (i.e., via a concomitant acquisition of different oceanographic, chemical, and geological data). This multi-parametric monitoring is revealing important life traits for deep-sea species including crustaceans: it allows the scaling of behavioral states of individuals up to changes in perceived species composition (e.g., richness), their relative abundances (e.g., evenness), as well as predator-prey relationships (e.g., food web structure), with a reduced ecological footprint compared to more invasive deep-sea monitoring methods (e.g. trawling). Examples of such monitoring for crustaceans will be presented and discussedThe present work was supported by the following projects: ARIM (MartTERA ERA-Net Cofound); RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades); MERCES (Grant Agreement N. 689518), and IDEM (Grant Agreement N. 11.0661/2017/750680/SUB/ENV.C2

The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment

15 pages, 4 figures, supplementary material https://doi.org/10.1093/icesjms/fsaa169.-- There are no new data associated with this article. No new datawere generated or analysed in support of this researchSeafloor multiparametric fibre-optic-cabled video observatories are emerging tools for standardized monitoring programmes, dedicated to the production of real-time fishery-independent stock assessment data. Here, we propose that a network of cabled cameras can be set up and optimized to ensure representative long-term monitoring of target commercial species and their surrounding habitats. We highlight the importance of adding the spatial dimension to fixed-point-cabled monitoring networks, and the need for close integration with Artificial Intelligence pipelines, that are necessary for fast and reliable biological data processing. We then describe two pilot studies, exemplary of using video imagery and environmental monitoring to derive robust data as a foundation for future ecosystem-based fish-stock and biodiversity management. The first example is from the NE Pacific Ocean where the deep-water sablefish (Anoplopoma fimbria) has been monitored since 2010 by the NEPTUNE cabled observatory operated by Ocean Networks Canada. The second example is from the NE Atlantic Ocean where the Norway lobster (Nephrops norvegicus) is being monitored using the SmartBay observatory developed for the European Multidisciplinary Seafloor and water column Observatories. Drawing from these two examples, we provide insights into the technological challenges and future steps required to develop full-scale fishery-independent stock assessmentsThis work was funded by the following project activities: ARIM (Autonomous Robotic sea-floor Infrastructure for benthopelagic Monitoring; MartTERA ERA-Net Cofound), ARCHES (Autonomous Robotic Networks to Help Modern Societies; German Helmholtz Association), RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades, Spanish Government), RESNEP (CTM2017-82991-C2-1-R; Ministerio de Ciencia, Innovación y Universidades, Spanish Government), and SmartLobster (EMSO-LINK Trans National Access-TNA). The EMSO_SmartBay cabled observatory was funded by Science Foundation Ireland (SFI) as part of a SFI Research Infrastructure Award Grant No. 12/RI/2331With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI