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

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Recent advances in functional neuroimaging have demonstrated novel potential for informing diagnosis and prognosis in the unresponsive wakeful syndrome and minimally conscious states. However, these technologies come with considerable expense and difficulty, limiting the possibility of wider clinical application in patients. Here, we show that high density electroencephalography, collected from 104 patients measured at rest, can provide valuable information about brain connectivity that correlates with behaviour and functional neuroimaging. Using graph theory, we visualize and quantify spectral connectivity estimated from electroencephalography as a dense brain network. Our findings demonstrate that key quantitative metrics of these networks correlate with the continuum of behavioural recovery in patients, ranging from those diagnosed as unresponsive, through those who have emerged from minimally conscious, to the fully conscious locked-in syndrome. In particular, a network metric indexing the presence of densely interconnected central hubs of connectivity discriminated behavioural consciousness with accuracy comparable to that achieved by expert assessment with positron emission tomography. We also show that this metric correlates strongly with brain metabolism. Further, with classification analysis, we predict the behavioural diagnosis, brain metabolism and 1-year clinical outcome of individual patients. Finally, we demonstrate that assessments of brain networks show robust connectivity in patients diagnosed as unresponsive by clinical consensus, but later rediagnosed as minimally conscious with the Coma Recovery Scale-Revised. Classification analysis of their brain network identified each of these misdiagnosed patients as minimally conscious, corroborating their behavioural diagnoses. If deployed at the bedside in the clinical context, such network measurements could complement systematic behavioural assessment and help reduce the high misdiagnosis rate reported in these patients. These metrics could also identify patients in whom further assessment is warranted using neuroimaging or conventional clinical evaluation. Finally, by providing objective characterization of states of consciousness, repeated assessments of network metrics could help track individual patients longitudinally, and also assess their neural responses to therapeutic and pharmacological interventions.The authors received funding from the UK Engineering and Physical Sciences Research Council (EP/P033199/1), Evelyn Trust (Cambridge, UK), the UK National Institute for Health Research (NIHR) as part of the Acute Brain Injury and Repair Theme of the Cambridge Biomedical Research Centre, the NIHR Brain Injury Healthcare Technology Cooperative, the NIHR Senior Investigator award, the James S. McDonnell Foundation, the Belgian National Fund for Scientific Research (FNRS), the European Commission, the Human Brain Project (EUH2020-fetflagship-hbp-sga1-ga720270), the Luminous project (EU-H2020-fetopen-ga686764), the French Speaking Community Concerted Research Action, the Belgian American Educational Foundation, the WallonieBruxelles Federation, the European Space Agency, the University and University Hospital of Liege (Belgium)

Feedback information transfer in the human brain reflects bistable perception in the absence of report

In the search for the neural basis of conscious experience, perception and the cognitive processes associated with reporting perception are typically confounded as neural activity is recorded while participants explicitly report what they experience. Here, we present a novel way to disentangle perception from report using eye movement analysis techniques based on convolutional neural networks and neurodynamical analyses based on information theory. We use a bistable visual stimulus that instantiates two well-known properties of conscious perception: integration and differentiation. At any given moment, observers either perceive the stimulus as one integrated unitary object or as two differentiated objects that are clearly distinct from each other. Using electroencephalography, we show that measures of integration and differentiation based on information theory closely follow participants' perceptual experience of those contents when switches were reported. We observed increased information integration between anterior to posterior electrodes (front to back) prior to a switch to the integrated percept, and higher information differentiation of anterior signals leading up to reporting the differentiated percept. Crucially, information integration was closely linked to perception and even observed in a no-report condition when perceptual transitions were inferred from eye movements alone. In contrast, the link between neural differentiation and perception was observed solely in the active report condition. Our results, therefore, suggest that perception and the processes associated with report require distinct amounts of anterior-posterior network communication and anterior information differentiation. While front-to-back directed information is associated with changes in the content of perception when viewing bistable visual stimuli, regardless of report, frontal information differentiation was absent in the no-report condition and therefore is not directly linked to perception per se.</p

Enhancing Hairfall Prediction: A Comparative Analysis of Individual Algorithms and An Ensemble Method

Hair fall, a prevalent issue affecting many individuals globally, necessitates early detection for preventive measures and hair health maintenance. Machine learning algorithms have gained attention in predicting hair fall by analysing genetic predisposition, lifestyle habits, and environmental factors. However, the performance of individual algorithms can be improved through ensemble models that combine their strengths. This research paper proposes an ensemble machine learning approach tailored for hair fall prediction. Comparative evaluations with individual algorithms reveal the ensemble models consistently outperform in accuracy, precision, and recall. Leveraging diverse algorithms, the ensemble approach captures a wider range of hair fall patterns, enhancing prediction accuracy. The ensemble models also exhibit higher precision and recall rates, correctly identifying both hair fall and non-hair fall instances. The ensemble models' superiority stems from mitigating the limitations of individual algorithms, resulting in a comprehensive and robust prediction framework. Overall, this research showcases the efficacy of ensemble machine learning models in hair fall prediction, enabling early detection and intervention for hair loss prevention. These findings provide valuable insights for researchers, practitioners, and individuals concerned about hair health

Somatosensory attention identifies both overt and covert awareness in disorders of consciousness

Objective Some patients diagnosed with disorders of consciousness retain sensory and cognitive abilities beyond those apparent from their overt behavior. Characterizing these covert abilities is crucial for diagnosis, prognosis, and medical ethics. This multimodal study investigates the relationship between electroencephalographic evidence for perceptual/cognitive preservation and both overt and covert markers of awareness. Methods Fourteen patients with severe brain injuries were evaluated with an electroencephalographic vibrotactile attention task designed to identify a hierarchy of residual somatosensory and cognitive abilities: (1) somatosensory steady-state evoked responses, (2) bottom-up attention orienting (P3a event-related potential), and (3) top-down attention (P3b event-related potential). Each patient was also assessed with a clinical behavioral scale and 2 functional magnetic resonance imaging assessments of covert command following. Results Six patients produced only sensory responses, with no evidence of cognitive event-related potentials. A further 8 patients demonstrated reliable bottom-up attention-orienting responses (P3a). No patient showed evidence of top-down attention (P3b). Only those patients who followed commands, whether overtly with behavior or covertly with functional neuroimaging, also demonstrated event-related potential evidence of attentional orienting. Interpretation Somatosensory attention-orienting event-related potentials differentiated patients who could follow commands from those who could not. Crucially, this differentiation was irrespective of whether command following was evident through overt external behavior, or through covert functional neuroimaging methods. Bedside electroencephalographic methods may corroborate more expensive and challenging methods such as functional neuroimaging, and thereby assist in the accurate diagnosis of awareness

Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness

Despite the common use of anesthetics to modulate consciousness in the clinic, brain-based monitoring of consciousness is uncommon. We com-bined electroencephalographic measurement of brain activity with deep neural networks to automatically discriminate anesthetic states induced by propofol. Our results with leave-one-participant-out-cross-validation show that convolutional neural networks significantly outperform multilayer perceptrons in discrimination accuracy when working with raw time series. Perceptrons achieved comparable accuracy when provided with power spec-tral densities. These findings highlight the potential of deep convolutional networks for completely automatic extraction of useful spatio-temporo-spectral features from human EEG

Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia

Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions). In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation. Here we investigated complexity of spontaneous high-density EEG data during propofol-induced general anaesthesia. We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels. After some simulations on Kuramoto oscillator models which demonstrate that these measures capture distinct ‘flavours’ of complexity, we show that there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia

Sedation Modulates Frontotemporal Predictive Coding Circuits and the Double Surprise Acceleration Effect

Two important theories in cognitive neuroscience are predictive coding (PC) and the global workspace (GW) theory. A key research task is to understand how these two theories relate to one another, and particularly, how the brain transitions from a predictive early state to the eventual engagement of a brain-scale state (the GW). To address this question, we present a source-localization of EEG responses evoked by the local-global task—an experimental paradigm that engages a predictive hierarchy, which encompasses the GW. The results of our source reconstruction suggest three phases of processing. The first phase involves the sensory (here auditory) regions of the superior temporal lobe and predicts sensory regularities over a short timeframe (as per the local effect). The third phase is brain-scale, involving inferior frontal, as well as inferior and superior parietal regions, consistent with a global neuronal workspace (GNW; as per the global effect). Crucially, our analysis suggests that there is an intermediate (second) phase, involving modulatory interactions between inferior frontal and superior temporal regions. Furthermore, sedation with propofol reduces modulatory interactions in the second phase. This selective effect is consistent with a PC explanation of sedation, with propofol acting on descending predictions of the precision of prediction errors; thereby constraining access to the GNW

Clam feeding plasticity reduces herbivore vulnerability to ocean warming and acidification

Ocean warming and acidification affect species populations, but how interactions within communities are affected and how this translates into ecosystem functioning and resilience remain poorly understood. Here we demonstrate that experimental ocean warming and acidification significantly alters the interaction network among porewater nutrients, primary producers, herbivores and burrowing invertebrates in a seafloor sediment community, and is linked to behavioural plasticity in the clam Scrobicularia plana. Warming and acidification induced a shift in the clam's feeding mode from predominantly suspension feeding under ambient conditions to deposit feeding with cascading effects on nutrient supply to primary producers. Surface-dwelling invertebrates were more tolerant to warming and acidification in the presence of S. plana, most probably due to the stimulatory effect of the clam on their microalgal food resources. This study demonstrates that predictions of population resilience to climate change require consideration of non-lethal effects such as behavioural changes of key species. Changes in ocean temperature and pH will impact on species, as well as impacting on community interactions. Here warming and acidification cause a clam species to change their feeding mode, with cascading effects for the marine sedimentary food web