CryoDRGN-ET: deep reconstructing generative networks for visualizing dynamic biomolecules inside cells.

Advances in cryo-electron tomography (cryo-ET) have produced new opportunities to visualize the structures of dynamic macromolecules in native cellular environments. While cryo-ET can reveal structures at molecular resolution, image processing algorithms remain a bottleneck in resolving the heterogeneity of biomolecular structures in situ. Here, we introduce cryoDRGN-ET for heterogeneous reconstruction of cryo-ET subtomograms.

SIMBA: single-cell embedding along with features.

Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space.

Extreme plasticity of reproductive state in a female rodent.

Successful sexual reproduction relies on the coordination of multiple biological systems, yet traditional concepts of biological sex often ignore the natural plasticity in morphology and physiology underlying sex. Most female mammals develop a patent (i.e.

Microwave Electrodynamic Study on Antiferroelectric Materials in a Wide Temperature Range.

The electrodynamic properties of lead zirconate titanate ceramic solid solutions, exhibiting ferro-antiferroelectric phase transition, are investigated at microwave frequencies in a wide temperature range. Significant changes in the electrodynamic response are found, presumably associated with structural rearrangements accompanying the sequence of phase transitions between para-, ferro-, and antiferroelectric states. The phenomena observed in the experiments are considered under conditions of changing temperature and concentrations of the components; several independent measurement techniques were used for their unambiguous identification.

Human-level play in the game of by combining language models with strategic reasoning.

Despite much progress in training artificial intelligence (AI) systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge. We introduce Cicero, the first AI agent to achieve human-level performance in , a strategy game involving both cooperation and competition that emphasizes natural language negotiation and tactical coordination between seven players. Cicero integrates a language model with planning and reinforcement learning algorithms by inferring players' beliefs and intentions from its conversations and generating dialogue in pursuit of its plans.