Loading of Amino Acids onto RNA in a Putative RNA-Peptide World.

RNA is a molecule that can both store genetic information and perform catalytic reactions. This observed dualism places RNA into the limelight of concepts about the origin of life. The RNA world concept argues that life started from self-replicating RNA molecules, which evolved toward increasingly complex structures. Recently, we demonstrated that RNA, with the help of conserved non-canonical nucleosides, which are also putative relics of an early RNA world, had the ability to grow peptides covalently connected to RNA nucleobases, creating RNA-peptide chimeras. It is conceivable that such molecules, which combined the information-coding properties of RNA with the catalytic potential of amino acid side chains, were once the structures from which life emerged. Herein, we report prebiotic chemistry that enabled the loading of both nucleosides and RNAs with amino acids as the first step toward RNA-based peptide synthesis in a putative RNA-peptide world.

How do Gambling Providers Use the Social Network Twitter in Germany? An Explorative Mixed-Methods Topic Modeling Approach.

This study examines the social media activities of gambling providers in Germany, focusing on the platform Twitter. A collection of 34.151 tweets from 13 Twitter accounts was made, representing casinos, sports betting, state lotteries, social lotteries and lottery brokers. We apply an explorative mixed-methods approach, integrating a summative content analysis together with a semi-supervised guided topic modeling approach, to analyse frequency, number of followers, interaction and content of Twitter messages, and work out differences among the individual providers. The results show that Twitter does not seem to be particularly important for gambling providers who are active in Germany. Regarding outreach, frequency of tweets and interaction, Twitter plays a much smaller role than, for example, in the UK. The potential for Twitter to be an advertising, marketing and interaction channel has not yet been fully exploited, which would make it a perfect moment for developing an appropriate regulatory framework. Overall, the results of semi-supervised topic modeling show that providers most often use informative content (news) and combine it with other, usually less neutral content. It is alarming that many providers make little or no use of Responsible Gambling messages. Even though the activities are presently on a low level, they contribute to the normalization of gambling, setting incentives for increasing gambling participation. Children and youth form a large segment of Twitter users. Potential harm can arise especially for this group because of the lack of enforcement of any age limit.

The Spatiotemporal Neural Dynamics of Object Recognition for Natural Images and Line Drawings.

Drawings offer a simple and efficient way to communicate meaning. While line drawings capture only coarsely how objects look in reality, we still perceive them as resembling real-world objects. Previous work has shown that this perceived similarity is mirrored by shared neural representations for drawings and natural images, which suggests that similar mechanisms underlie the recognition of both. However, other work has proposed that representations of drawings and natural images become similar only after substantial processing has taken place, suggesting distinct mechanisms. To arbitrate between those alternatives, we measured brain responses resolved in space and time using fMRI and MEG, respectively, while human participants (female and male) viewed images of objects depicted as photographs, line drawings, or sketch-like drawings. Using multivariate decoding, we demonstrate that object category information emerged similarly fast and across overlapping regions in occipital, ventral-temporal, and posterior parietal cortex for all types of depiction, yet with smaller effects at higher levels of visual abstraction. In addition, cross-decoding between depiction types revealed strong generalization of object category information from early processing stages on. Finally, by combining fMRI and MEG data using representational similarity analysis, we found that visual information traversed similar processing stages for all types of depiction, yet with an overall stronger representation for photographs. Together, our results demonstrate broad commonalities in the neural dynamics of object recognition across types of depiction, thus providing clear evidence for shared neural mechanisms underlying recognition of natural object images and abstract drawings.SIGNIFICANCE STATEMENT When we see a line drawing, we effortlessly recognize it as an object in the world despite its simple and abstract style. Here we asked to what extent this correspondence in perception is reflected in the brain. To answer this question, we measured how neural processing of objects depicted as photographs and line drawings with varying levels of detail (from natural images to abstract line drawings) evolves over space and time. We find broad commonalities in the spatiotemporal dynamics and the neural representations underlying the perception of photographs and even abstract drawings. These results indicate a shared basic mechanism supporting recognition of drawings and natural images.

Spatio-temporal dynamics of oscillatory brain activity during the observation of actions and interactions between point-light agents.

Predicting actions from non-verbal cues and using them to optimise one's response behaviour (i.e. interpersonal predictive coding) is essential in everyday social interactions. We aimed to investigate the neural correlates of different cognitive processes evolving over time during interpersonal predictive coding. Thirty-nine participants watched two agents depicted by moving point-light stimuli while an electroencephalogram (EEG) was recorded. One well-recognizable agent performed either a 'communicative' or an 'individual' action. The second agent either was blended into a cluster of noise dots (i.e. present) or was entirely replaced by noise dots (i.e. absent), which participants had to differentiate. EEG amplitude and coherence analyses for theta, alpha and beta frequency bands revealed a dynamic pattern unfolding over time: Watching communicative actions was associated with enhanced coupling within medial anterior regions involved in social and mentalising processes and with dorsolateral prefrontal activation indicating a higher deployment of cognitive resources. Trying to detect the agent in the cluster of noise dots without having seen communicative cues was related to enhanced coupling in posterior regions for social perception and visual processing. Observing an expected outcome was modulated by motor system activation. Finally, when the agent was detected correctly, activation in posterior areas for visual processing of socially relevant features was increased. Taken together, our results demonstrate that it is crucial to consider the temporal dynamics of social interactions and of their neural correlates to better understand interpersonal predictive coding. This could lead to optimised treatment approaches for individuals with problems in social interactions.

Seeing a Bayesian ghost: Sensorimotor activation leads to an illusory social perception.

Based on our prior experiences we form social expectations and anticipate another person's response. Under certain conditions, these expectations can be so strong that they lead to illusory perception of another person who is actually not there (i.e., seeing a Bayesian ghost). We used EEG to investigate the neural correlates of such illusory social perception. Our results showed that activation of the premotor cortex predicted the occurrence of the Bayesian ghost, whereas its actual appearance was later accompanied by activation in sensorimotor and adjacent parietal regions. These findings confirm that our perception of others is so strongly affected by prior expectations, in such a way they can prompt illusory social perceptions associated with activity change in brain regions relevant for action perception. They also contribute to a better understanding of social interaction in healthy individuals as well as persons with mental illnesses, which can be characterized by illusory perception and social interaction difficulties.

From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction.

Line drawings convey meaning with just a few strokes. Despite strong simplifications, humans can recognize objects depicted in such abstracted images without effort. To what degree do deep convolutional neural networks (CNNs) mirror this human ability to generalize to abstracted object images? While CNNs trained on natural images have been shown to exhibit poor classification performance on drawings, other work has demonstrated highly similar latent representations in the networks for abstracted and natural images. Here, we address these seemingly conflicting findings by analyzing the activation patterns of a CNN trained on natural images across a set of photographs, drawings, and sketches of the same objects and comparing them to human behavior. We find a highly similar representational structure across levels of visual abstraction in early and intermediate layers of the network. This similarity, however, does not translate to later stages in the network, resulting in low classification performance for drawings and sketches. We identified that texture bias in CNNs contributes to the dissimilar representational structure in late layers and the poor performance on drawings. Finally, by fine-tuning late network layers with object drawings, we show that performance can be largely restored, demonstrating the general utility of features learned on natural images in early and intermediate layers for the recognition of drawings. In conclusion, generalization to abstracted images, such as drawings, seems to be an emergent property of CNNs trained on natural images, which is, however, suppressed by domain-related biases that arise during later processing stages in the network.

Iron-Mediated Electrophilic Amination of Organozinc Halides using Organic Azides.

A wide range of alkyl-, aryl- and heteroarylzinc halides were aminated with highly functionalized alkyl, aryl, and heterocyclic azides. The reaction proceeds smoothly at 50 °C within 1 h in the presence of FeCl3 (0.5 equiv) to furnish the corresponding secondary amines in good yields. This method was extended to peptidic azides and provided the arylated substrates with full retention of configuration. To demonstrate the utility of this reaction, we prepared two amine derivatives of pharmaceutical relevance using this iron-mediated electrophilic amination as the key step.