EEG for good outcome prediction after cardiac arrest: a multicentre cohort study.

AIM Assess the prognostic ability of a non-highly malignant and reactive EEG to predict good outcome after cardiac arrest (CA). METHODS Prospective observational multicentre substudy of the "Targeted Hypothermia versus Targeted Normothermia after Out-of-hospital Cardiac Arrest Trial", also known as the TTM2-trial. Presence or absence of highly malignant EEG patterns and EEG reactivity to external stimuli were prospectively assessed and reported by the trial sites. Highly malignant patterns were defined as burst-suppression or suppression with or without superimposed periodic discharges. Multimodal prognostication was performed 96 hours after CA. Good outcome at 6 months was defined as a modified Rankin Scale score of 0-3. RESULTS 873 comatose patients at 59 sites had an EEG assessment during the hospital stay. Of these, 283 (32%) had good outcome. EEG was recorded at a median of 69 hours (IQR 47-91) after CA. Absence of highly malignant EEG patterns was seen in 543 patients of whom 255 (29% of the cohort) had preserved EEG reactivity. A non-highly malignant and reactive EEG had 56% (CI 50-61) sensitivity and 83% (CI 80-86) specificity to predict good outcome. Presence of EEG reactivity contributed (p<0.001) to the specificity of EEG to predict good outcome compared to only assessing background pattern without taking reactivity into account. CONCLUSION Nearly one-third of comatose patients resuscitated after CA had a non-highly malignant and reactive EEG that was associated with a good long-term outcome. Reactivity testing should be routinely performed since preserved EEG reactivity contributed to prognostic performance

Lack of privileged access to awareness for rewarding social scenes in Autism Spectrum Disorder

Reduced social motivation is hypothesised to underlie social behavioural symptoms of Autism Spectrum Disorder (ASD). The extent to which rewarding social stimuli are granted privileged access to awareness in ASD is currently unknown. We use continuous flash suppression to investigate whether individuals with and without ASD show privileged access to awareness for social over nonsocial rewarding scenes that are closely matched for stimulus features. Strong evidence for a privileged access to awareness for rewarding social over nonsocial scenes was observed in neurotypical adults. No such privileged access was seen in ASD individuals, and moderate support for the null model was noted. These results suggest that the purported deficits in social motivation in ASD may extend to early processing mechanisms

Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States

Increasing fire severity and warmer, drier postfire conditions are making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here, we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a dataset of postfire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Postfire regeneration is sensitive to high-severity fire, which limits seed availability, and postfire climate, which influences seedling establishment. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected climate change impacts for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in postfire regeneration. Across 40 to 42% of the study area, we project postfire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031 to 2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981 to 2000 to 26 to 31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support postfire conifer regeneration. © 2023 the Author(s)

The effect of anonymous computer-mediated communication on state anxiety: An experimental study

This study examined the effect of anonymous computer mediated communication (CMC) on state anxiety, specifically focusing on whether the valence of the interaction affected state anxiety prior to completing an anxiety-inducing task. To investigate this, 62 female participants aged 18-25 were randomly assigned to one of three conditions: positive CMC, negative CMC and writing a blog. Self-report measures of state anxiety were taken at: baseline; after participants had been given instructions about the anxiety-inducing task; after ten minutes of CMC/blog writing; and after the anxiety-inducing task had been completed. Results showed that participants in the positive CMC condition showed a significant and moderate decrease in anxiety following the CMC whereas those in the negative CMC condition showed a non-significant but moderate increase in anxiety following the CMC. Anxiety remained relatively unaffected by the blog condition. After completing the anxiety-inducing task there were no differences in anxiety scores between groups. The findings show that CMC can be beneficial for relieving state anxiety but that the valence of the communication is crucial. This has implications for advice and training given to those participating in and supporting CMC where mental health issues might be discussed

A decision support framework for proactive maintenance of water and wastewater systems

Proactive maintenance of assets is a much sought after goal in the water and wastewater industry, where substantial savings could be made by identifying impending failures in pumps and other essential components of the system. A detailed analysis of the operational behaviour of the monitored assets can be used as the foundation to generate estimations on the likelihood of a failure or malfunction in a particular component based on knowledge of previous behavioural patterns. Preventative maintenance or component replacement can then be optimally scheduled based on need, as opposed to traditional reactive maintenance strategies. In most current condition monitoring software, an alarm is normally raised once a fault has occurred, therefore often requiring immediate action. On the other hand, combining the condition monitoring and fault log data that is normally acquired with expert knowledge of the meaning and causes of faults embedded in the software allows predictive maintenance to be implemented. The paper reports on a number of advanced machine learning techniques that have been applied to operational data acquired over a significant period of water pump operation. Results from a representative site within Scottish Water's water network will be presented that demonstrate the application of such software techniques can indeed surface changes in parameters, for example flow and pump power drawn, forming the basis to infer the state of components and the onset of changes in the health of the asset