EEG-based effective connectivity distinguishes between unresponsive states with and without report of conscious experience and correlates with brain complexity

Objective methods for distinguishing conscious from unconscious states in humans are of key importance for clinical evaluation of general anesthesia and patients with disorders or consciousness. Here, we test the generalizability of a DTF-based algorithm - a measure of effective connectivity - as an objective measure of conscious experience during anesthesia and correlate it with a well-tested index of consciousness: the Perturbational Complexity Index (PCI). We reanalyzed EEG data from an experimental study in which 18 healthy volunteers were randomly assigned to one of three types of general anesthesia: propofol, xenon, and ketamine. EEG was recorded before and during anesthesia, and DTF was calculated from every 1-second segment of the EEG data to quantify the effective connectivity between channel pairs. This was used to classify the state of each participant as either conscious or unconscious, and the classifications were compared with the participant’s delayed report of experience, and the PCI. The algorithm was more likely to classify participants as conscious in the awake state than during propofol and xenon anesthesia (p0.05). Furthermore, the DTF-based confidence of being classified as conscious was highly correlated with PCI (r2=0.48, p<0.05). These results provide further support for the notion that effective connectivity measured between EEG electrodes can be used to distinguish between conscious and unconscious states in humans

Herschel observations of deuterated water towards Sgr B2(M)

Observations of HDO are an important complement for studies of water, because they give strong constraints on the formation processes – grain surfaces versus energetic process in the gas phase, e.g. in shocks. The HIFI observations of multiple transitions of HDO in Sgr B2(M) presented here allow the determination of the HDO abundance throughout the envelope, which has not been possible before with ground-based observations only. The abundance structure has been modeled with the spherical Monte Carlo radiative transfer code RATRAN, which also takes radiative pumping by continuum emission from dust into account. The modeling reveals that the abundance of HDO rises steeply with temperature from a low abundance (2.5 × 10−11) in the outer envelope at temperatures below 100 K through a medium abundance (1.5 × 10−9) in the inner envelope/outer core at temperatures between 100 and 200 K, and finally a high abundance ( 3.5 × 10−9) at temperatures above 200 K in the hot core

Dust in Supernovae and Supernova Remnants I : Formation Scenarios

Supernovae are considered as prime sources of dust in space. Observations of local supernovae over the past couple of decades have detected the presence of dust in supernova ejecta. The reddening of the high redshift quasars also indicate the presence of large masses of dust in early galaxies. Considering the top heavy IMF in the early galaxies, supernovae are assumed to be the major contributor to these large amounts of dust. However, the composition and morphology of dust grains formed in a supernova ejecta is yet to be understood with clarity. Moreover, the dust masses inferred from observations in mid-infrared and submillimeter wavelength regimes differ by two orders of magnitude or more. Therefore, the mechanism responsible for the synthesis of molecules and dust in such environments plays a crucial role in studying the evolution of cosmic dust in galaxies. This review summarises our current knowledge of dust formation in supernova ejecta and tries to quantify the role of supernovae as dust producers in a galaxy.Peer reviewe

Setting a baseline for global urban virome surveillance in sewage

The rapid development of megacities, and their growing connectedness across the world is becoming a distinct driver for emerging disease outbreaks. Early detection of unusual disease emergence and spread should therefore include such cities as part of risk-based surveillance. A catch-all metagenomic sequencing approach of urban sewage could potentially provide an unbiased insight into the dynamics of viral pathogens circulating in a community irrespective of access to care, a potential which already has been proven for the surveillance of poliovirus. Here, we present a detailed characterization of sewage viromes from a snapshot of 81 high density urban areas across the globe, including in-depth assessment of potential biases, as a proof of concept for catch-all viral pathogen surveillance. We show the ability to detect a wide range of viruses and geographical and seasonal differences for specific viral groups. Our findings offer a cross-sectional baseline for further research in viral surveillance from urban sewage samples and place previous studies in a global perspective

EEG-based effective connectivity distinguishes between unresponsive states with and without report of conscious experience and correlates with brain complexity

Objective methods for distinguishing conscious from unconscious states in humans are of key importance for clinical evaluation of general anesthesia and patients with disorders or consciousness. Here, we test the generalizability of a DTF-based algorithm - a measure of effective connectivity - as an objective measure of conscious experience during anesthesia and correlate it with a well-tested index of consciousness: the Perturbational Complexity Index (PCI). We reanalyzed EEG data from an experimental study in which 18 healthy volunteers were randomly assigned to one of three types of general anesthesia: propofol, xenon, and ketamine. EEG was recorded before and during anesthesia, and DTF was calculated from every 1-second segment of the EEG data to quantify the effective connectivity between channel pairs. This was used to classify the state of each participant as either conscious or unconscious, and the classifications were compared with the participant’s delayed report of experience, and the PCI. The algorithm was more likely to classify participants as conscious in the awake state than during propofol and xenon anesthesia (p0.05). Furthermore, the DTF-based confidence of being classified as conscious was highly correlated with PCI (r2=0.48, p<0.05). These results provide further support for the notion that effective connectivity measured between EEG electrodes can be used to distinguish between conscious and unconscious states in humans