The lower cloud layer of Venus (47.5–50.5 km) is an exceptional target for exploration due to the favorable conditions for microbial life, including moderate temperatures and pressures (∼60°C and 1 atm), and the presence of micron-sized sulfuric acid aerosols. Nearly a century after the ultraviolet (UV) contrasts of Venus' cloud layer were discovered with Earth-based photographs, the substances and mechanisms responsible for the changes in Venus' contrasts and albedo are still unknown. While current models include sulfur dioxide and iron chloride as the UV absorbers, the temporal and spatial changes in contrasts, and albedo, between 330 and 500 nm, remain to be fully explained. Within this context, we present a discussion regarding the potential for microorganisms to survive in Venus' lower clouds and contribute to the observed bulk spectra. In this article, we provide an overview of relevant Venus observations, compare the spectral and physical properties of Venus' clouds to terrestrial biological materials, review the potential for an iron- and sulfur-centered metabolism in the clouds, discuss conceivable mechanisms of transport from the surface toward a more habitable zone in the clouds, and identify spectral and biological experiments that could measure the habitability of Venus' clouds and terrestrial analogues. Together, our lines of reasoning suggest that particles in Venus' lower clouds contain sufficient mass balance to harbor microorganisms, water, and solutes, and potentially sufficient biomass to be detected by optical methods. As such, the comparisons presented in this article warrant further investigations into the prospect of biosignatures in Venus' clouds.

When multiple people talk simultaneously, the healthy human auditory system is able to attend to one particular speaker of interest. Recently, it has been demonstrated that it is possible to infer to which speaker someone is attending by relating the neural activity, recorded by electroencephalography (EEG), with the speech signals. This is relevant for an effective noise suppression in hearing devices, in order to detect the target speaker in a multi-speaker scenario. Most auditory attention detection algorithms use a linear EEG decoder to reconstruct the attended stimulus envelope, which is then compared to the original stimuli envelopes to determine the attended speaker. Classifying attention within a short time interval remains the main challenge. We present two different convolutional neural network (CNN)-based approaches to solve this problem. One aims to select the attended speaker from a given set of individual speaker envelopes, and the other extracts the locus of auditory attention (left or right), without knowledge of the speech envelopes. Our results show that it is possible to decode attention within 1-2 seconds, with a median accuracy around 80%, without access to the speech envelopes. This is promising for neuro-steered noise suppression in hearing aids, which requires fast and accurate attention detection. Furthermore, the possibility of detecting the locus of auditory attention without access to the speech envelopes is promising for the scenarios in which per-speaker envelopes are unavailable. It will also enable establishing a fast and objective attention measure in future studies. Index TermsConvolutional neural networks (CNN), auditory attention detection (AAD), electroencephalography (EEG), neurosteered auditory prosthesis, brain-computer interface (BCI)

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Our findings suggest that CNN is a suitable and fast approach to classify neonatal sleep stages in preterm infants.

A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infantsAccepted for publication in Journal of neural engineering, 2019.

Solute chemistry and stable isotope tracers of NO3 - were used to assess bacterial NO3 - production and denitrification in a High Arctic glacial ecosystem during 2009. Changes in the NO3 - concentration and the d18O–NO3 in all the proglacial streams revealed that up to 95 % of total NO3 - was most likely bacterially-derived during low flow conditions towards the end of the summer (day of year 250). However, overlapping ranges of d15N values for snow NH4 +, soil organic matter, cryoconite debris and geological nitrogen in host rocks mean that neither the preferred substrate(s), nor the pathway (i.e. nitrification or simple mineralisation) can be discerned. The most plausible explanation for the bacterial production of NO3 - is nitrification in snowmelt-fed flowpaths through avalanche fans that flank the glacier and along subglacial drainage pathways at the glacier bed. Interestingly, there was no evidence for denitrification in subglacial outflow, which is contrary to earlier research at this site. Instead, increases in the d15N–NO3 of up to 20 ‰ downstream of the glacier margin, suggests that denitrification in the glacier forefield and/or the sediments that flank it was most discernable during 2009. Our observations therefore suggest that poorly understood temporal variations in the mixing ratio of nitrifying and denitrifying flowpaths occur in this glacial ecosystem

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In a multi-speaker scenario, the human auditory system is able to attend to one particular speaker of interest and ignore the others. It has been demonstrated that it is possible to use electroencephalography (EEG) signals to infer to which speaker someone is attending by relating the neural activity to the speech signals. However, classifying auditory attention within a short time interval remains the main challenge. We present a convolutional neural network-based approach to extract the locus of auditory attention (left/right) without knowledge of the speech envelopes. Our results show that it is possible to decode the locus of attention within 1–2 s, with a median accuracy of around 81%. These results are promising for neuro-steered noise suppression in hearing aids, in particular in scenarios where per-speaker envelopes are unavailable.