Description: This paper presents the technological developments and the policy contexts for the project “Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring” (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a new product for autonomous and integrated ecological deep-sea monitoring. Traditional monitoring is often vessel-based and thus resource demanding. It is economically unviable to fulfill the current policy for ecosystem monitoring with traditional approaches. Thus, this project developed platforms for bentho-pelagic monitoring using an arrangement of crawler and stationary platforms at the Lofoten-Vesterålen (LoVe) observatory network (Norway). Visual and acoustic imaging along with standard oceanographic sensors have been combined to support advanced and continuous spatial-temporal monitoring near cold water coral mounds. Just as important is the automatic processing techniques under development that have been implemented to allow species (or categories of species) quantification (i.e., tracking and classification). At the same time, real-time outboard processed three-dimensional (3D) laser scanning has been implemented to increase mission autonomy capability, delivering quantifiable information on habitat features (i.e., for seascape approaches). The first version of platform autonomy has already been tested under controlled conditions with a tethered crawler exploring the vicinity of a cabled stationary instrumented garage. Our vision is that elimination of the tether in combination with inductive battery recharge trough fuel cell technology will facilitate self-sustained long-term autonomous operations over large areas, serving not only the needs of science, but also sub-sea industries like subsea oil and gas, and mining.

Description: Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.

Description: Thioredoxin-1 (Trx1) is an endogenous dithiol reductant and antioxidant that was shown to be decreased in Alzheimer's disease (AD) neurons. A truncated form of Trx1, thioredoxin 80 (Trx80), was reported to be secreted from monocytes having cytokine activity. Here, we show that Trx80 is present in human brain in an aggregated form. Trx80 localizes mainly to neurons and is dramatically decreased in AD brains. Trx80 levels in cerebrospinal fluid (CSF) correlate with those of the classical AD biomarkers amyloid-β (Aβ) 1–42 and total tau. Moreover, Trx80 measurements in CSF discriminate between patients with stable mild cognitive impairment, prodomal AD and mild AD. We report that ADAM10 and 17, two α-secretases processing the Aβ precursor protein, are responsible for Trx80 generation. In contrast to the periphery, Trx80 has no pro-inflammatory effects in glia, either by itself or in combination with Aβ or apolipoprotein E. Instead, Trx80 inhibits Aβ(1–42) aggregation and protects against its toxicity. Thus, a reduction in Trx80 production would result in increased Aβ polymerization and enhanced neuronal vulnerability. Our data suggest that a deficit in Trx80 could participate in AD pathogenesis.