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We present a Deep Learning approach to predict 3D folding structures of RNAs from their nucleic acid sequence. Our approach combines an autoregressive Deep Generative Model, Monte Carlo Tree Search, and a score model to find and rank the most likely folding structures for a given RNA sequence. We show that RNA de novo structure prediction by deep learning is possible at atom resolution, despite the low number of experimentally measured structures that can be used for training. We confirm the predictive power of our approach by achieving competitive results in a retrospective evaluation of the RNA-Puzzles prediction challenges, without using structural contact information from multiple sequence alignments or additional data from chemical probing experiments. Blind predictions for recent RNA-Puzzle challenges under the name "Dfold" further support the competitive performance of our approach.

Social media platforms are often accused of disproportionally exposing their users to like-minded opinions, thereby fueling political polarization. However, empirical evidence of this causal relationship is inconsistent at best. One reason could be that many previous studies were unable to separate the effects caused by individual exposure to like-minded content from the effects caused by the algorithms themselves. This study presents results from two quasi-experiments in which participants were exposed either to algorithmically selected or randomly selected arguments that were either in line or in contrast with their attitudes on two different topics. The results reveal that exposure to like-minded arguments increased participants’ attitude polarization and affective polarization more intensely than exposure to opposing arguments. Yet, contrary to popular expectations, these effects were not amplified by algorithmic selection. Still, for one topic, exposure to algorithmically selected arguments led to slightly stronger attitude polarization than randomly selected arguments.

Human brain atlases provide spatial reference systems for data characterizing brain organization at different levels, coming from different brains. Cytoarchitecture is a basic principle of the microstructural organization of the brain, as regional differences in the arrangement and composition of neuronal cells are indicators of changes in connectivity and function. Automated scanning procedures and observer-independent methods are prerequisites to reliably identify cytoarchitectonic areas, and to achieve reproducible models of brain segregation. Time becomes a key factor when moving from the analysis of single regions of interest towards high-throughput scanning of large series of whole-brain sections. Here we present a new workflow for mapping cytoarchitectonic areas in large series of cell-body stained histological sections of human postmortem brains. It is based on a Deep Convolutional Neural Network (CNN), which is trained on a pair of section images with annotations, with a large number of un-annotated sections in between. The model learns to create all missing annotations in between with high accuracy, and faster than our previous workflow based on observer-independent mapping. The new workflow does not require preceding 3D-reconstruction of sections, and is robust against histological artefacts. It processes large data sets with sizes in the order of multiple Terabytes efficiently. The workflow was integrated into a web interface, to allow access without expertise in deep learning and batch computing. Applying deep neural networks for cytoarchitectonic mapping opens new perspectives to enable high-resolution models of brain areas, introducing CNNs to identify borders of brain areas.

Understanding the impacts of climate extremes on the carbon cycle is important for quantifying the carbon-cycle climate feedback and highly relevant to climate change assessments. Climate extremes and fires can have severe regional effects, but a spatially explicit global impact assessment is still lacking. Here, we directly quantify spatiotemporal contiguous extreme anomalies in four global data sets of gross primary production (GPP) over the last 30 years. We find that positive and negative GPP extremes occurring on 7% of the spatiotemporal domain explain 78% of the global interannual variation in GPP and a significant fraction of variation in the net carbon flux. The largest thousand negative GPP extremes during 1982–2011 (4.3% of the data) account for a decrease in photosynthetic carbon uptake of about 3.5 Pg C yr ^−1 , with most events being attributable to water scarcity. The results imply that it is essential to understand the nature and causes of extremes to understand current and future GPP variability.