A Trillion Coral Reef Colors: Deeply Annotated Underwater Hyperspectral Images for Automated Classification and Habitat Mapping

This paper describes a large dataset of underwater hyperspectral imagery that can be used by researchers in the domains of computer vision, machine learning, remote sensing, and coral reef ecology. We present the details of underwater data acquisition, processing and curation to create this large dataset of coral reef imagery annotated for habitat mapping. A diver-operated hyperspectral imaging system (HyperDiver) was used to survey 147 transects at 8 coral reef sites around the Caribbean island of Curacao. The underwater proximal sensing approach produced fine-scale images of the seafloor, with more than 2.2 billion points of detailed optical spectra. Of these, more than 10 million data points have been annotated for habitat descriptors or taxonomic identity with a total of 47 class labels up to genus- and species-levels. In addition to HyperDiver survey data, we also include images and annotations from traditional (color photo) quadrat surveys conducted along 23 of the 147 transects, which enables comparative reef description between two types of reef survey methods. This dataset promises benefits for efforts in classification algorithms, hyperspectral image segmentation and automated habitat mapping. Dataset: https://doi.org/10.1594/PANGAEA.911300 Dataset License: CC-BY-N

Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness.

As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes. While participants performed an auditory semantic classification task, we allowed them to become drowsy and unresponsive. As they stopped responding to the stimuli, we report the breakdown of alpha networks and the emergence of theta connectivity. Further, we show that the temporal dynamics of all canonical EEG microstates slow down during unresponsiveness. We identify a specific microstate (D) whose occurrence and duration are prominently increased during this period. Employing machine learning, we show that the temporal properties of microstate D, particularly its prolonged duration, predicts the response likelihood to individual stimuli. Finally, we find a novel relationship between microstates and brain networks as we show that microstate D uniquely indexes significantly stronger theta connectivity during unresponsiveness. Our findings demonstrate that the transition to unconsciousness is not linear, but rather consists of an interplay between transient brain networks reflecting different degrees of sleep depth

High-Resolution Dynamics of Hydrogen Peroxide on the Surface of Scleractinian Corals in Relation to Photosynthesis and Feeding

We developed and used a microsensor to measure fast (<1 s) dynamics of hydrogen peroxide (H2O2) on the polyp tissue of two scleractinian coral species (Stylophora pistillata and Pocillopora damicornis) under manipulations of illumination, photosynthesis, and feeding activity. Our real-time tracking of H2O2 concentrations on the coral tissue revealed rapid changes with peaks of up to 60 mu M. We observed bursts of H2O2 release, lasting seconds to minutes, with rapid increase and decrease of surficial H2O2 levels at rates up to 15 mu M s(-1). We found that the H2O2 levels on the polyp surface are enhanced by oxygenic photosynthesis and feeding, whereas H2O2 bursts occurred randomly, independently from photosynthesis. Feeding resulted in a threefold increase of baseline H2O2 levels and was accompanied by H2O2 bursts, suggesting that the coral host is the source of the bursts. Our study reveals that H2O2 levels at the surface of coral polyps are much higher and more dynamic than previously reported, and that bursts are a regular feature of the H2O2 dynamics in the coral holobiont

Arsenic speciation analysis in porewater by a novel colorimetric assay

Arsenic is common toxic contaminant, but tracking its mobility through submerged soils is difficult because microscale processes dictate its speciation and affinity to minerals. Analyses on environmental dissolved arsenic (As) species such as arsenate and arsenite currently require highly specialized equipment and large sample volumes. In an effort to unravel arsenic dynamics in sedimentary porewater, a novel, highly sensitive, and field-usable colorimetric assay requiring 100 mu L of sample was developed. Two complementary protocols are presented, suitable for sub-micromolar and micromolar ranges. Phosphate is a main interfering substance, but can be separated by measuring phosphate and arsenate under two different acidities. Arsenite is assessed by oxidation of arsenite to arsenate in the low-acidity reagent. Optimization of the protocol and spectral analyses resulted in elimination of various interferences (silicate, iron, sulfide, sulfate), and the assay is applicable across a wide range of salinities and porewater compositions. The new assay was used to study As mobilization processes through the soil of a contaminated brook. Water column sources of arsenic were limited to a modest input by a groundwater source along the flow path. In one of the sites, the arsenite and arsenate porewater profiles showed active iron-driven As redox cycling in the soil, which may play a role in arsenic mobilization and releases arsenite and arsenate into the brook water column. Low arsenic concentrations downstream from the source sites indicated arsenic retention by soil and dilution with additional sources of water. Arsenic is thus retained by the Bossegraben before it merges with larger rivers

Possible link between Earth's rotation rate and oxygenation

The biotic and abiotic controls on major shifts in atmospheric oxygen and the persistence of low-oxygen periods over a majority of Earth’s history remain under debate. Explanations of Earth’s stepwise pattern of oxygenation have mostly neglected the effect of changing diel illumination dynamics linked to daylength, which has increased through geological time due to Earth’s rotational deceleration caused by tidal friction. Here we used microsensor measurements and dynamic modelling of interfacial solute fluxes in cyanobacterial mats to investigate the effect of changing daylength on Precambrian benthic ecosystems. Simulated increases in daylength across Earth’s historical range boosted the diel benthic oxygen export, even when the gross photosynthetic production remained constant. This fundamental relationship between net productivity and daylength emerges from the interaction of diffusive mass transfer and diel illumination dynamics, and is amplified by metabolic regulation and microbial behaviour. We found that the resultant daylength-driven surplus organic carbon burial could have shaped the increase in atmospheric oxygen that occurred during the Great and Neoproterozoic Oxidation Events. Our suggested mechanism, which links the coinciding increases in daylength and atmospheric oxygen via enhanced net productivity, reveals a possible contribution of planetary mechanics to the evolution of Earth’s biology and geochemistry

A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats

We developed a novel integrated technology for diver-operated surveying of shallow marine ecosystems. The HyperDiver system captures rich multifaceted data in each transect: hyperspectral and color imagery, topographic profiles, incident irradiance and water chemistry at a rate of 15-30 m(2) per minute. From surveys in a coral reef following standard diver protocols, we show how the rich optical detail can be leveraged to generate photopigment abundance and benthic composition maps. We applied machine learning techniques, with a minor annotation effort (<2% of pixels), to automatically generate cm-scale benthic habitat maps of high taxonomic resolution and accuracy (93-97%). The ability to efficiently map benthic composition, photopigment densities and rugosity at reef scales is a compelling contribution to modernize reef monitoring. Seafloor-level hyperspectral images can be used for automated mapping, avoiding operator bias in the analysis and deliver the degree of detail necessary for standardized environmental monitoring. The technique can deliver fast, objective and economic reef survey results, making it a valuable tool for coastal managers and reef ecologists. Underwater hyperspectral surveying shares the vantage point of the high spatial and taxonomic resolution restricted to field surveys, with analytical techniques of remote sensing and provides targeted validation for aerial monitoring

Seeing the Forest for the Trees: Mapping Cover and Counting Trees from Aerial Images of a Mangrove Forest Using Artificial Intelligence

Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utria National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks

Arousal modulates auditory attention and awareness: insights from sleep, sedation, and disorders of consciousness.

The interplay between attention and consciousness is frequently tested in altered states of consciousness, including transitions between stages of sleep and sedation, and in pathological disorders of consciousness (DoC; the vegetative and minimally conscious states; VS and MCS). One of the most widely used tasks to assess cognitive processing in this context is the auditory oddball paradigm, where an infrequent change in a sequence of sounds elicits, in awake subjects, a characteristic EEG event-related potential called the mismatch negativity, followed by the classic P300 wave. The latter is further separable into the slightly earlier, anterior P3a and the later, posterior P3b, thought to be linked to task-irrelevant "bottom-up" and task-oriented "top-down" attention, respectively. We discuss here the putative dissociations between attention and awareness in DoC, sedation and sleep, bearing in mind the recently emerging evidence from healthy volunteers and patients. These findings highlight the neurophysiological and cognitive parallels (and differences) across these three distinct variations in levels of consciousness, and inform the theoretical framework for interpreting the role of attention therein.This research was supported by generous funding from the Medical Research Council (U.1055.01.002.00001.01), the James S. McDonnell Foundation, and the Wellcome Trust Biomedical Research Fellowship awarded to Tristan A. Bekinschtein

Brain Connectivity Dissociates Responsiveness from Drug Exposure during Propofol-Induced Transitions of Consciousness.

Accurately measuring the neural correlates of consciousness is a grand challenge for neuroscience. Despite theoretical advances, developing reliable brain measures to track the loss of reportable consciousness during sedation is hampered by significant individual variability in susceptibility to anaesthetics. We addressed this challenge using high-density electroencephalography to characterise changes in brain networks during propofol sedation. Assessments of spectral connectivity networks before, during and after sedation were combined with measurements of behavioural responsiveness and drug concentrations in blood. Strikingly, we found that participants who had weaker alpha band networks at baseline were more likely to become unresponsive during sedation, despite registering similar levels of drug in blood. In contrast, phase-amplitude coupling between slow and alpha oscillations correlated with drug concentrations in blood. Our findings highlight novel markers that prognosticate individual differences in susceptibility to propofol and track drug exposure. These advances could inform accurate drug titration and brain state monitoring during anaesthesia.This work was supported by grants from the James S. McDonnell Foundation, the Wellcome Trust [WT093811MA to TAB], and the British Oxygen Professorship from the Royal College of Anaesthetists [to DKM]. The research was also supported by the NIHR Brain Injury Healthcare Technology Co-operative based at Cambridge University Hospitals NHS Foundation Trust and University of Cambridge. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR or the UK Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It was first available from PLOS via http://dx.doi.org/10.1371/journal.pcbi.100466

Silent Expectations: Dynamic Causal Modeling of Cortical Prediction and Attention to Sounds That Weren't.

UNLABELLED: There is increasing evidence that human perception is realized by a hierarchy of neural processes in which predictions sent backward from higher levels result in prediction errors that are fed forward from lower levels, to update the current model of the environment. Moreover, the precision of prediction errors is thought to be modulated by attention. Much of this evidence comes from paradigms in which a stimulus differs from that predicted by the recent history of other stimuli (generating a so-called "mismatch response"). There is less evidence from situations where a prediction is not fulfilled by any sensory input (an "omission" response). This situation arguably provides a more direct measure of "top-down" predictions in the absence of confounding "bottom-up" input. We applied Dynamic Causal Modeling of evoked electromagnetic responses recorded by EEG and MEG to an auditory paradigm in which we factorially crossed the presence versus absence of "bottom-up" stimuli with the presence versus absence of "top-down" attention. Model comparison revealed that both mismatch and omission responses were mediated by increased forward and backward connections, differing primarily in the driving input. In both responses, modeling results suggested that the presence of attention selectively modulated backward "prediction" connections. Our results provide new model-driven evidence of the pure top-down prediction signal posited in theories of hierarchical perception, and highlight the role of attentional precision in strengthening this prediction. SIGNIFICANCE STATEMENT: Human auditory perception is thought to be realized by a network of neurons that maintain a model of and predict future stimuli. Much of the evidence for this comes from experiments where a stimulus unexpectedly differs from previous ones, which generates a well-known "mismatch response." But what happens when a stimulus is unexpectedly omitted altogether? By measuring the brain's electromagnetic activity, we show that it also generates an "omission response" that is contingent on the presence of attention. We model these responses computationally, revealing that mismatch and omission responses only differ in the location of inputs into the same underlying neuronal network. In both cases, we show that attention selectively strengthens the brain's prediction of the future.This work was supported by the UK Medical Research Council Programme [MC-A060-5PR10 to RH], in addition to grants from the Wellcome Trust [WT093811MA to TAB], the James S. McDonnell Foundation, and the Evelyn Trust [15/07 to SC].This is the final version of the article. It first appeared from the Society for Neuroscience via http://dx.doi.org/10.1523/JNEUROSCI.1125-16.201