Machine learning of brain-specific biomarkers from EEG.

Background: Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS).

Pollen identification through convolutional neural networks: First application on a full fossil pollen sequence.

The automation of pollen identification has seen vast improvements in the past years, with Convolutional Neural Networks coming out as the preferred tool to train models. Still, only a small portion of works published on the matter address the identification of fossil pollen. Fossil pollen is commonly extracted from organic sediment cores and are used by paleoecologists to reconstruct past environments, flora, vegetation, and their evolution through time.

Data augmentation for learning predictive models on EEG: a systematic comparison.

The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation, which consists in artificially increasing the size of the dataset during training, can be employed to alleviate this problem. While a few augmentation transformations for EEG data have been proposed in the literature, their positive impact on performance is often evaluated on a single dataset and compared to one or two competing augmentation methods.

Palaeo-trajectories of forest savannization in the southern Congo.

Tropical savannah and forest are thought to represent alternative stable states in ecosystem structure in some climates. The implication is that biomes are maintained by positive feedbacks, e.g.

Aryliodonium Ylides as Novel and Efficient Additives for Radical Chemistry: Example in Camphorquinone (CQ)/Amine Based Photoinitiating Systems.

Diaryliodonium salts are well-established compounds in free radical chemistry and are already used as photoinitiators (free radical or cationic polymerization), but the presence of counter anions is a strong drawback. Indeed, a counter anion is always required (e.g.